In [1]:

    

from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
from scipy.stats import randint
from scipy.stats import uniform
from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier



forest = {"max_depth": [3, None],
              "min_samples_leaf": randint(1, 11),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"],
              "max_depth":randint(1,1000),
              "warm_start":[True ,False]
         }
mlp = {
    "activation":["identity", "logistic", "tanh", "relu"], 
    "solver":["lbfgs", "sgd", "adam"], 
    "learning_rate":["constant", "invscaling", "adaptive"], 
    "hidden_layer_sizes":randint(1,99), 
    "alpha":uniform(0.000001, .001), 
    "power_t":uniform(0.1, .9), 
    "momentum":uniform(0.0, 1.0)
}

bagging = {
    "n_estimators":randint(1,50), 
    "base_estimator":[None, RandomForestClassifier()], 
    "bootstrap":[True, False], 
    "bootstrap_features":[True, False]
}
#"multi:softprob" try running with this at another time to explore classy confidence 
xgb = {
    "learning_rate":uniform(.1, .9),
    "min_child_weight":randint(1,10), 
    "max_depth":randint(2, 15), 
    "scale_pos_weight":randint(1,10), 
    "objective":["reg:linear"], 
}
print(XGBClassifier().get_params().keys())


    

    




dict_keys(['base_score', 'colsample_bylevel', 'colsample_bytree', 'gamma', 'learning_rate', 'max_delta_step', 'max_depth', 'min_child_weight', 'missing', 'n_estimators', 'nthread', 'objective', 'reg_alpha', 'reg_lambda', 'scale_pos_weight', 'seed', 'silent', 'subsample'])






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)

In [22]:

    

classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
best_score = 0
best_params = []
aa = "S"
data = "Data/Training/clean_s_filtered.csv"
benchmark_data = "Data/Benchmarks/phos.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [1, 16, 8, 11, 11, 2, 10, 8, 8, 11, 18, 2, 18, -1.7769230769230768, 98.12307692307694, 0.0]
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.291353524345
TP 3588 FP 4615 TN 36315 FN 5573



None
Cross Validation: 0.264848357473
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0772190917854
TP 755 FP 6942 TN 55575 FN 2623



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.129701446976
TP 871 FP 1174 TN 39756 FN 8290



None
Cross Validation: 0.126574760624
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.147818607091
TP 605 FP 2423 TN 60094 FN 2773



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.153296597343
TP 734 FP 634 TN 40296 





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




FN 8427



None
Cross Validation: 0.0829424436097
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0214955532486
TP 109 FP 1175 TN 61342 FN 3269



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.167133852265
TP 947 FP 898 TN 40032 FN 8214



None
Cross Validation: 0.173109699486
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0257667681381
TP 136 FP 1411 TN 61106 FN 3242



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Loading Data
Loaded Data
Working on Data
Sample Vector [9, 12, 17, 9, 2, 3, 10, 17, 2, 20, 3, 8, 17, -0.776923076923077, 49.12307692307692, 0.0]
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.229648372418
TP 1658 FP 1471 TN 39370 FN 7592



None
Cross Validation: 0.243750795559
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0477406729787
TP 305 FP 2784 TN 59733 FN 3073



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.128177680395
TP 835 FP 1092 TN 39749 FN 8415



None
Cross Validation: 0.128008548143
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.161601242252
TP 683 FP 2630 TN 59887 FN 2695



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.140687704851
TP 





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




614 FP 514 TN 40327 FN 8636



None
Cross Validation: 0.113756655932
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0336230898305
TP 104 FP 810 TN 61707 FN 3274



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 200363
Test Data Points: 50091
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.162236138183
TP 997 FP 1034 TN 39807 FN 8253



None
Cross Validation: 0.166880131939
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0415784401606
TP 186 FP 1553 TN 60964 FN 3192



None
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 9, 11, 3, 18, 7, 10, 11, 5, 13, 11, 12, 8, -0.0384615384615385, 103.47692307692309, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 331724
Test Data Points: 82932
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.761796015736
TP 32419 FP 927 TN 39894 FN 9692



None
Cross Validation: 0.75246052344
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0202446593752
TP 123 FP 1411 TN 61106 FN 3255



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 331724
Test Data Points: 82932
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.774090596311
TP 33392 FP 1203 TN 39618 FN 8719



None
Cross Validation: 0.763856509096
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.182293117123
TP 803 FP 2924 TN 59593 FN 2575



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 331724
Test Data Points: 82932
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.579916990921
TP 30292 FP 5882 TN 34939 FN 11819



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.544670280973
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.137144294982
TP 1155 FP 7956 TN 54561 FN 2223



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 331724
Test Data Points: 82932
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.753313810952
TP 34022 FP 2449 TN 38372 FN 8089



None
Cross Validation: 0.73940679345
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.173501725902
TP 1042 FP 5044 TN 57473 FN 2336



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Loading Data
Loaded Data
Working on Data
Sample Vector [1, 19, 17, 15, 11, 1, 10, 20, 15, 20, 19, 16, 12, -1.3615384615384618, 0.592307692307692, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 331714
Test Data Points: 82929
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.745077082107
TP 36132 FP 4577 TN 36205 FN 6015



None
Cross Validation: 0.729568214093
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0810803121338
TP 809 FP 7387 TN 55130 FN 2569



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 331714
Test Data Points: 82929
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.768612534561
TP 33913 FP 1771 TN 39011 FN 8234



None
Cross Validation: 0.764537570274
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.168674122026
TP 853 FP 3682 TN 58835 FN 2525



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 331714
Test Data Points: 82929
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.570422843772
TP 29494 FP 5553 TN 35229 FN 12653



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.545604157809
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.126079384126
TP 1158 FP 8689 TN 53828 FN 2220



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 331714
Test Data Points: 82929
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.751203476145
TP 33666 FP 2215 TN 38567 FN 8481



None
Cross Validation: 0.740783008184
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.112978553151
TP 672 FP 4120 TN 58397 FN 2706



None
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 2, 10, 11, 10, 11, 10, 11, 10, 10, 10, 3, 10, -0.04615384615384621, 184.7076923076923, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 306016
Test Data Points: 76504
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.810922350619
TP 29040 FP 823 TN 39966 FN 6675



None
Cross Validation: 0.786064638887
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.00537129322004
TP 97 FP 1557 TN 60960 FN 3281



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 306016
Test Data Points: 76504
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.81566815333
TP 28806 FP 483 TN 40306 FN 6909



None
Cross Validation: 0.801394140809
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.153689264374
TP 539 FP 1843 TN 60674 FN 2839



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 306016
Test Data Points: 76504
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.393379866694
TP 25768 FP 13352 TN 27437 FN 9947



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.406111701342
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0452986325862
TP 1536 FP 22259 TN 40258 FN 1842



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 306016
Test Data Points: 76504
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.792366831305
TP 29089 FP 1489 TN 39300 FN 6626



None
Cross Validation: 0.787554432589
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0726672782559
TP 456 FP 3529 TN 58988 FN 2922



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Loading Data
Loaded Data
Working on Data
Sample Vector [18, 4, 3, 10, 17, 10, 10, 10, 2, 3, 20, 10, 20, -0.16153846153846158, 87.47692307692309, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 305615
Test Data Points: 76404
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.787149369911
TP 30259 FP 2843 TN 38028 FN 5274



None
Cross Validation: 0.752596469573
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0243401916205
TP 378 FP 5092 TN 57425 FN 3000



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 305615
Test Data Points: 76404
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.811137362449
TP 28747 FP 714 TN 40157 FN 6786



None
Cross Validation: 0.800978032084
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.114121152377
TP 513 FP 2615 TN 59902 FN 2865



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 305615
Test Data Points: 76404
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.377399535752
TP 25932 FP 14389 TN 26482 FN 9601



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.371981582392
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0363106101117
TP 1525 FP 23238 TN 39279 FN 1853



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 305615
Test Data Points: 76404
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.79079246662
TP 28983 FP 1590 TN 39281 FN 6550



None
Cross Validation: 0.789507381066
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0631598023551
TP 454 FP 3937 TN 58580 FN 2924



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 11, 10, 17, 10, 11, 10, 7, 12, 9, 12, 20, 0, -0.8833333333333334, 153.01666666666665, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.396956908584
TP 6680 FP 2894 TN 6210 FN 2668



None
Cross Validation: 0.35952798234
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.100709130677
TP 2130 FP 25344 TN 37173 FN 1248



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.414924889705
TP 7024 FP 3080 TN 6024 FN 2324



None
Cross Validation: 0.418484893756
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.090985873732
TP 2136 FP 26734 TN 35783 FN 1242



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.28239181307
TP 6905 FP 4208 TN 4896 FN 2443



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.296208472544
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0923316795718
TP 2428 FP 31859 TN 30658 FN 950



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.372489268727
TP 6690 FP 3131 TN 5973 FN 2658



None
Cross Validation: 0.374282132105
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0947700289586
TP 2167 FP 26772 TN 35745 FN 1211



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Loading Data
Loaded Data
Working on Data
Sample Vector [5, 7, 15, 5, 14, 17, 10, 14, 6, 16, 6, 17, 16, -0.9692307692307693, 105.57692307692308, 0.38461538461538464]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.373057184916
TP 8298 FP 5041 TN 4063 FN 1050



None
Cross Validation: 0.373306848278
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0892535655363
TP 2884 FP 41512 TN 21005 FN 494



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.411301457857
TP 6961 FP 3048 TN 6056 FN 2387



None
Cross Validation: 0.416707708466
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.110751717641
TP 2334 FP 27565 TN 34952 FN 1044



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.320907595861
TP 7327 FP 4316 TN 4788 FN 2021



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.304394326677
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.100520662953
TP 2561 FP 33201 TN 29316 FN 817



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.37318569366
TP 6663 FP 3097 TN 6007 FN 2685



None
Cross Validation: 0.373081044336
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0680903505513
TP 2008 FP 27562 TN 34955 FN 1370



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 6, 10, 17, 1, 10, 11, 10, 20, 12, 10, 10, 0, -0.7250000000000001, 86.85833333333333, 0.08333333333333333]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 143647
Test Data Points: 35912
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.377984880143
TP 3452 FP 1843 TN 24893 FN 5724



None
Cross Validation: 0.356216574139
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0896967265287
TP 794 FP 6647 TN 55870 FN 2584



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 143647
Test Data Points: 35912
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.356859259248
TP 2703 FP 1125 TN 25611 FN 6473



None
Cross Validation: 0.364675172919
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.118900901156
TP 896 FP 6160 TN 56357 FN 2482



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 143647
Test Data Points: 35912
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.305431287279
TP 2690 FP 1703 TN 25033 FN 6486



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.273943040277
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0728068285185
TP 646 FP 5817 TN 56700 FN 2732



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 143647
Test Data Points: 35912
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.340099034355
TP 2879 FP 1540 TN 25196 FN 6297



None
Cross Validation: 0.347600707452
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0892310084884
TP 775 FP 6443 TN 56074 FN 2603



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 5, 17, 20, 6, 9, 10, 10, 1, 2, 20, 18, 18, -0.8076923076923077, 9.207692307692309, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 143747
Test Data Points: 35937
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.407285765807
TP 5522 FP 4811 TN 21963 FN 3641



None
Cross Validation: 0.354614678944
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0910804718666
TP 1426 FP 15181 TN 47336 FN 1952



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 143747
Test Data Points: 35937
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.373798734093
TP 2968 FP 1268 TN 25506 FN 6195



None
Cross Validation: 0.370185861533
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.177772514616
TP 1235 FP 6571 TN 55946 FN 2143



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 143747
Test Data Points: 35937
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.320492878425
TP 3530 FP 2810 TN 23964 FN 5633



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.284053361814
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0610049519204
TP 830 FP 9159 TN 53358 FN 2548



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 143747
Test Data Points: 35937
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.336701514227
TP 2873 FP 1580 TN 25194 FN 6290



None
Cross Validation: 0.348265361751
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.1067369633
TP 812 FP 5885 TN 56632 FN 2566



None
Loading Data
Loaded Data
Working on Data
Sample Vector [17, 19, 10, 3, 8, 14, 10, 10, 1, 15, 10, 20, 8, -1.1, 45.807692307692314, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.34418038388
TP 8978 FP 6412 TN 2692 FN 370



None
Cross Validation: 0.38266023311
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0554864300448
TP 3254 FP 55262 TN 7255 FN 124



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.355608892527
TP 6332 FP 2929 TN 6175 FN 3016



None
Cross Validation: 0.323872451129
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0595267920151
TP 2561 FP 39272 TN 23245 FN 817



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.33877404123
TP 6032 FP 2793 TN 6311 FN 3316



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.372051826274
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0268308812965
TP 2276 FP 38426 TN 24091 FN 1102



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.350171410546
TP 6368 FP 3014 TN 6090 FN 2980



None
Cross Validation: 0.346219724612
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.057997476258
TP 2436 FP 37025 TN 25492 FN 942



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 8, 3, 11, 13, 12, 10, 20, 10, 2, 10, 19, 11, 0.4000000000000001, 17.51538461538462, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.404511430687
TP 8251 FP 4659 TN 4445 FN 1097



None
Cross Validation: 0.34899257918
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0417946174817
TP 2889 FP 48566 TN 13951 FN 489



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.346543886634
TP 6275 FP 2956 TN 6148 FN 3073



None
Cross Validation: 0.332698175913
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0562630010851
TP 2524 FP 39013 TN 23504 FN 854



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.381607050894
TP 6290 FP 2653 TN 6451 FN 3058



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.369723518982
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0112191639645
TP 2210 FP 39366 TN 23151 FN 1168



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 73804
Test Data Points: 18452
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.359652435046
TP 6384 FP 2943 TN 6161 FN 2964



None
Cross Validation: 0.356343079315
Number of data points in benchmark 65895
Sample Vector [12, 20, 9, 2, 9, 18, 10, 8, 16, 8, 8, 7, 9, -2.0846153846153843, 47.14615384615385, 0.0]
Benchmark Results 
Matthews Correlation Coeff:  0.0408044571169
TP 2308 FP 37041 TN 25476 FN 1070



None
Best Score
0.182293117123
['y', 'ADASYN', ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}], RandomizedSearchCV(cv=None, error_score='raise',
          estimator=BaggingClassifier(base_estimator=None, bootstrap=True,
         bootstrap_features=False, max_features=1.0, max_samples=1.0,
         n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
         verbose=0, warm_start=False),
          fit_params={}, iid=True, n_iter=10, n_jobs=1,
          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurit...     verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]},
          pre_dispatch='2*n_jobs', random_state=None, refit=True,
          return_train_score=True, scoring=None, verbose=0)]

In [23]:

    

classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
best_score = 0
best_params = []
aa = "T"
data = "Data/Training/clean_t_filtered.csv"
benchmark_data = "Data/Benchmarks/phos.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [7, 7, 16, 20, 9, 19, 20, 13, 10, 12, 2, 10, 10, -0.8692307692307693, 36.31538461538462, 0.0]
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.13787684119
TP 698 FP 1500 TN 12070 FN 2313



None
Cross Validation: 0.156806197173
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.066542615411
TP 297 FP 5049 TN 41448 FN 936



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0932620441131
TP 184 FP 285 TN 13285 FN 2827



None
Cross Validation: 0.083486076444
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.144091609822
TP 233 FP 1312 TN 45185 FN 1000



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 13570 FN 3011



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.020262847996
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 46497 FN 1233



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.127142370042
TP 179 FP 167 TN 13403 FN 2832



None
Cross Validation: 0.112777391575
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  -0.00525632585532
TP 12 FP 630 TN 45867 FN 1221



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 2, 2, 1, 11, 11, 20, 17, 7, 7, 10, 17, 17, -1.6153846153846154, 82.92307692307693, 0.0]
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.170308185862
TP 544 FP 826 TN 12781 FN 2430



None
Cross Validation: 0.161963632679
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.155156163588
TP 376 FP 2817 TN 43680 FN 857



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0628960546283
TP 105 FP 187 TN 13420 FN 2869



None
Cross Validation: 0.0718589341485
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.078513226937
TP 102 FP 768 TN 45729 FN 1131



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  -0.00311313720984
TP 1 FP 7 TN 





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




13600 FN 2973



None
Cross Validation: 0.065749093478
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  -0.00210840844517
TP 0 FP 8 TN 46489 FN 1233



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 66323
Test Data Points: 16581
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.110709820499
TP 144 FP 145 TN 13462 FN 2830



None
Cross Validation: 0.114548838752
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0442917188201
TP 50 FP 500 TN 45997 FN 1183



None
Loading Data
Loaded Data
Working on Data
Sample Vector [13, 18, 10, 20, 8, 10, 20, 2, 3, 16, 12, 2, 12, 0.5461538461538461, 37.23076923076923, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 109764
Test Data Points: 27442
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.752153390335
TP 11945 FP 1517 TN 12092 FN 1888



None
Cross Validation: 0.719687592081
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.123924536354
TP 471 FP 5632 TN 40865 FN 762



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 109764
Test Data Points: 27442
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.788792954199
TP 11026 FP 280 TN 13329 FN 2807



None
Cross Validation: 0.784085777715
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.118449133041
TP 194 FP 1290 TN 45207 FN 1039



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 109764
Test Data Points: 27442
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.543220764509
TP 8392 FP 1205 TN 12404 FN 5441



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.490734777361
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.109377739228
TP 302 FP 3119 TN 43378 FN 931



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 109764
Test Data Points: 27442
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.761530391614
TP 11069 FP 638 TN 12971 FN 2764



None
Cross Validation: 0.749388756619
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.173859824127
TP 359 FP 2155 TN 44342 FN 874



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 12, 3, 20, 18, 12, 20, 12, 12, 8, 3, 17, 17, 0.8615384615384615, 50.015384615384626, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 109771
Test Data Points: 27443
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.736251622559
TP 11948 FP 1806 TN 11876 FN 1813



None
Cross Validation: 0.725744823084
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.151559537646
TP 586 FP 6398 TN 40099 FN 647



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 109771
Test Data Points: 27443
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.790233281792
TP 11082 FP 357 TN 13325 FN 2679



None
Cross Validation: 0.781188723576
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.109381380656
TP 199 FP 1524 TN 44973 FN 1034



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 109771
Test Data Points: 27443
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.545141466646
TP 7527 FP 659 TN 13023 FN 6234



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.490357469249
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0952145193294
TP 216 FP 2122 TN 44375 FN 1017



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 109771
Test Data Points: 27443
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.764489707582
TP 11083 FP 668 TN 13014 FN 2678



None
Cross Validation: 0.751488088159
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.146805994885
TP 323 FP 2327 TN 44170 FN 910



None
Loading Data
Loaded Data
Working on Data
Sample Vector [11, 17, 10, 10, 1, 1, 20, 11, 3, 16, 10, 11, 3, -0.676923076923077, 88.80769230769232, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 102940
Test Data Points: 25735
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.794511721182
TP 10235 FP 678 TN 12821 FN 2001



None
Cross Validation: 0.797441784172
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.102231543797
TP 246 FP 2409 TN 44088 FN 987



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 102940
Test Data Points: 25735
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.823148854372
TP 9999 FP 156 TN 13343 FN 2237



None
Cross Validation: 0.816700191179
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.169535445404
TP 226 FP 919 TN 45578 FN 1007



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 102940
Test Data Points: 25735
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.367124787963
TP 8371 FP 4274 TN 9225 FN 3865



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.352487803238
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0166019697598
TP 469 FP 15394 TN 31103 FN 764



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 102940
Test Data Points: 25735
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.794026573042
TP 10006 FP 491 TN 13008 FN 2230



None
Cross Validation: 0.79576506553
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0757418663142
TP 192 FP 2301 TN 44196 FN 1041



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Loading Data
Loaded Data
Working on Data
Sample Vector [13, 2, 4, 1, 10, 1, 20, 2, 12, 2, 7, 20, 2, 0.8384615384615385, 2.7, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 102962
Test Data Points: 25741
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.739301915052
TP 10490 FP 1595 TN 11905 FN 1751



None
Cross Validation: 0.777534669846
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0735333959161
TP 337 FP 5597 TN 40900 FN 896



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 102962
Test Data Points: 25741
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.812423020755
TP 9938 FP 225 TN 13275 FN 2303



None
Cross Validation: 0.817667127887
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.127714253783
TP 192 FP 1120 TN 45377 FN 1041



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 102962
Test Data Points: 25741
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.37803756428
TP 9461 FP 5385 TN 8115 FN 2780



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.363214422672
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0435418639585
TP 669 FP 18949 TN 27548 FN 564



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 102962
Test Data Points: 25741
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.790837218698
TP 10036 FP 549 TN 12951 FN 2205



None
Cross Validation: 0.797056767596
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0858856434594
TP 204 FP 2194 TN 44303 FN 1029



None
Loading Data
Loaded Data
Working on Data
Sample Vector [16, 7, 13, 7, 7, 10, 20, 7, 8, 2, 5, 2, 1, -1.023076923076923, 32.56923076923077, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.268054982746
TP 1944 FP 1105 TN 1870 FN 1096



None
Cross Validation: 0.284076651058
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0569406831781
TP 732 FP 19364 TN 27133 FN 501



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.328223034726
TP 2041 FP 1021 TN 1954 FN 999



None
Cross Validation: 0.338706870682
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.121732705566
TP 938 FP 17928 TN 28569 FN 295



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.199773822661
TP 1808 FP 1175 TN 1800 FN 1232



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.217031243355
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.114607758978
TP 914 FP 18032 TN 28465 FN 319



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.27217064331
TP 1929 FP 1078 TN 1897 FN 1111



None
Cross Validation: 0.299177477903
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0786267836604
TP 778 FP 18073 TN 28424 FN 455



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 10, 15, 20, 18, 3, 20, 8, 1, 2, 7, 9, 16, -0.8999999999999999, -26.99230769230769, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.296822913867
TP 1960 FP 1035 TN 1940 FN 1080



None
Cross Validation: 0.299366644312
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0860345581287
TP 807 FP 18099 TN 28398 FN 426



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.322207287853
TP 2044 FP 1042 TN 1933 FN 996



None
Cross Validation: 0.332791682882
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0913930783871
TP 856 FP 19070 TN 27427 FN 377



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.208057978985
TP 1894 FP 1235 TN 1740 FN 1146



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.228762314249
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.106269657333
TP 871 FP 17665 TN 28832 FN 362



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.294405409206
TP 1969 FP 1051 TN 1924 FN 1071



None
Cross Validation: 0.292068463743
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0813265545154
TP 819 FP 19128 TN 27369 FN 414



None
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 10, 1, 10, 12, 7, 20, 12, 18, 12, 11, 5, 16, 0.36153846153846164, 36.64615384615385, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 47252
Test Data Points: 11814
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.30929836362
TP 789 FP 445 TN 8437 FN 2143



None
Cross Validation: 0.28553880282
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.118139155857
TP 330 FP 3284 TN 43213 FN 903



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 47252
Test Data Points: 11814
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.282680561022
TP 543 FP 218 TN 8664 FN 2389



None
Cross Validation: 0.294640704685
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.112878874039
TP 284 FP 2700 TN 43797 FN 949



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 47252
Test Data Points: 11814
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.21343275007
TP 462 FP 313 TN 8569 FN 2470



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.209874093597
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0924230093045
TP 225 FP 2357 TN 44140 FN 1008



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 47252
Test Data Points: 11814
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.26216385094
TP 600 FP 351 TN 8531 FN 2332



None
Cross Validation: 0.266898822671
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0474074199381
TP 154 FP 2579 TN 43918 FN 1079



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [8, 8, 19, 9, 12, 3, 20, 11, 2, 9, 7, 1, 7, -1.4, 40.61538461538461, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 47234
Test Data Points: 11809
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.298026311418
TP 743 FP 420 TN 8449 FN 2197



None
Cross Validation: 0.292644412085
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.160066483755
TP 371 FP 2620 TN 43877 FN 862



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 47234
Test Data Points: 11809
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.299471447978
TP 628 FP 268 TN 8601 FN 2312



None
Cross Validation: 0.298319229854
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.167579071776
TP 373 FP 2460 TN 44037 FN 860



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 47234
Test Data Points: 11809
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.230746993068
TP 559 FP 396 TN 8473 FN 2381



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.227750790927
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.086990570798
TP 234 FP 2700 TN 43797 FN 999



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 47234
Test Data Points: 11809
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.28680134331
TP 728 FP 440 TN 8429 FN 2212



None
Cross Validation: 0.27235278042
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0889311388082
TP 296 FP 3834 TN 42663 FN 937



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 3, 19, 1, 12, 1, 20, 10, 18, 5, 9, 11, 8, -0.6076923076923078, 14.238461538461543, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.301766511249
TP 2374 FP 1460 TN 1515 FN 666



None
Cross Validation: 0.317889466832
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.04371717076
TP 1022 FP 32706 TN 13791 FN 211



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.328698346157
TP 1935 FP 917 TN 2058 FN 1105



None
Cross Validation: 0.314201332623
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0519964060604
TP 925 FP 27396 TN 19101 FN 308



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.343416003881
TP 1716 FP 679 TN 2296 FN 1324



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.325629774576
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0411735864289
TP 784 FP 23532 TN 22965 FN 449



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.283915533621
TP 1902 FP 1017 TN 1958 FN 1138



None
Cross Validation: 0.300576887913
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0242105656164
TP 802 FP 26738 TN 19759 FN 431



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Loading Data
Loaded Data
Working on Data
Sample Vector [3, 3, 18, 1, 7, 3, 20, 4, 10, 2, 12, 12, 7, 0.7923076923076923, 16.192307692307693, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.427433500931
TP 2109 FP 793 TN 2182 FN 931



None
Cross Validation: 0.3580512453
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0489491463966
TP 886 FP 26308 TN 20189 FN 347



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.356494576029
TP 2007 FP 904 TN 2071 FN 1033



None
Cross Validation: 0.315027939732
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0847063306408
TP 1051 FP 27457 TN 19040 FN 182



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.383497569741
TP 2044 FP 860 TN 2115 FN 996



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.314149068489
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0204068533007
TP 831 FP 28424 TN 18073 FN 402



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 24059
Test Data Points: 6015
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.329410301753
TP 2009 FP 986 TN 1989 FN 1031



None
Cross Validation: 0.281367650023
Number of data points in benchmark 47730
Sample Vector [7, 9, 13, 9, 5, 1, 20, 10, 3, 19, 9, 9, 13, -0.5923076923076924, 91.20769230769231, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0303649155855
TP 806 FP 25978 TN 20519 FN 427



None
Best Score
0.173859824127
['y', 'ADASYN', ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}], RandomizedSearchCV(cv=None, error_score='raise',
          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=4, oob_score=False, random_state=None,
            verbose=0, warm_start=False),
          fit_params={}, iid=True, n_iter=10, n_jobs=1,
          param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]},
          pre_dispatch='2*n_jobs', random_state=None, refit=True,
          return_train_score=True, scoring=None, verbose=0)]

In [24]:

    

classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
best_score = 0
best_params = []
aa = "Y"
data = "Data/Training/clean_Y_filtered.csv"
benchmark_data = "Data/Benchmarks/phos.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [19, 3, 11, 10, 18, 12, 15, 12, 14, 10, 1, 11, 19, -0.1769230769230769, 27.261538461538468, 0.07692307692307693]
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.202078468676
TP 5 FP 0 TN 2408 FN 112



None
Cross Validation: 0.174191147623
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 23514 FN 41



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Done training
Test Results
Matthews Correlation Coeff:  0.203366044184
TP 6 FP 1 TN 2407 FN 111



None
Cross Validation: 0.198316280142
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2408 FN 117



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0127162061582
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 23514 FN 41



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.203366044184
TP 6 FP 1 TN 2407 FN 111



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.194061307375
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 5, 2, 11, 17, 9, 15, 3, 14, 8, 18, 3, 9, -0.376923076923077, 12.44615384615385, 0.15384615384615385]
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.13533717896
TP 9 FP 17 TN 2377 FN 122



None
Cross Validation: 0.204869429044
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.555441103907
TP 40 FP 86 TN 23428 FN 1



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.19575116435
TP 7 FP 2 TN 2392 FN 124



None
Cross Validation: 0.198819531262
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.689198381251
TP 40 FP 42 TN 23472 FN 1



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2394 FN 131



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0173669179996
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 23514 FN 41



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 10099
Test Data Points: 2525
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.157473728362
TP 5 FP 2 TN 2392 FN 126



None
Cross Validation: 0.175141039412
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.689198381251
TP 40 FP 42 TN 23472 FN 1



None
Loading Data
Loaded Data
Working on Data
Sample Vector [13, 17, 18, 18, 5, 20, 15, 3, 14, 8, 13, 9, 3, 0.10769230769230766, 35.16153846153846, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 19360
Test Data Points: 4840
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.945528338234
TP 2293 FP 14 TN 2413 FN 120



None
Cross Validation: 0.938949549793
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 19360
Test Data Points: 4840
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.948943597226
TP 2294 FP 7 TN 2420 FN 119



None
Cross Validation: 0.946916182763
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.489457585874
TP 40 FP 122 TN 23392 FN 1



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 19360
Test Data Points: 4840
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.683014868506
TP 2109 FP 467 TN 1960 FN 304



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.316949270384
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0904400596036
TP 41 FP 4098 TN 19416 FN 0



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 19360
Test Data Points: 4840
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.938970207309
TP 2272 FP 10 TN 2417 FN 141



None
Cross Validation: 0.921619811074
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Loading Data
Loaded Data
Working on Data
Sample Vector [5, 20, 5, 7, 19, 3, 15, 5, 7, 4, 5, 19, 12, 0.3307692307692307, 30.16923076923077, 0.38461538461538464]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 19355
Test Data Points: 4839
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.959768680167
TP 2306 FP 19 TN 2435 FN 79



None
Cross Validation: 0.941706442733
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.431511068873
TP 40 FP 168 TN 23346 FN 1



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 19355
Test Data Points: 4839
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.957769527801
TP 2284 FP 3 TN 2451 FN 101



None
Cross Validation: 0.946036941695
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ADASYN
Training Data Points: 19355
Test Data Points: 4839
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.590541939636
TP 1982 FP 593 TN 1861 FN 403



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.364684777401
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0802671900736
TP 41 FP 4976 TN 18538 FN 0



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 19355
Test Data Points: 4839
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.956113640675
TP 2282 FP 5 TN 2449 FN 103



None
Cross Validation: 0.91984371941
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
Loading Data
Loaded Data
Working on Data
Sample Vector [9, 2, 3, 8, 16, 11, 15, 13, 18, 12, 6, 15, 19, -0.6153846153846154, 50.307692307692314, 0.23076923076923078]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 19224
Test Data Points: 4806
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.940036200073
TP 2229 FP 11 TN 2430 FN 136



None
Cross Validation: 0.943827352445
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.555441103907
TP 40 FP 86 TN 23428 FN 1



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 19224
Test Data Points: 4806
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.949967980945
TP 2243 FP 1 TN 2440 FN 122



None
Cross Validation: 0.961372195281
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 19224
Test Data Points: 4806
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.649425856412
TP 2016 FP 497 TN 1944 FN 349



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.679883942115
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0880379365566
TP 40 FP 4078 TN 19436 FN 1



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 19224
Test Data Points: 4806
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.938574426389
TP 2233 FP 18 TN 2423 FN 132



None
Cross Validation: 0.953436780434
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.343762787323
TP 40 FP 286 TN 23228 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Loading Data
Loaded Data
Working on Data
Sample Vector [17, 8, 9, 9, 10, 20, 15, 2, 11, 17, 16, 18, 12, -1.8923076923076925, 139.01538461538462, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 19232
Test Data Points: 4808
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.949466863658
TP 2284 FP 34 TN 2402 FN 88



None
Cross Validation: 0.946283142585
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.363504621173
TP 40 FP 252 TN 23262 FN 1



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 19232
Test Data Points: 4808
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.957937452103
TP 2271 FP 2 TN 2434 FN 101



None
Cross Validation: 0.96109060582
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.941659080926
TP 40 FP 4 TN 23510 FN 1



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] SMOTEENN
Training Data Points: 19232
Test Data Points: 4808
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.780176045967
TP 2226 FP 393 TN 2043 FN 146



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.683601517557
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0989063295038
TP 40 FP 3352 TN 20162 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 19232
Test Data Points: 4808
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.946337026846
TP 2255 FP 14 TN 2422 FN 117



None
Cross Validation: 0.950591183616
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.650519153411
TP 40 FP 52 TN 23462 FN 1



None
Loading Data
Loaded Data
Working on Data
Sample Vector [18, 20, 11, 2, 18, 7, 15, 10, 10, 1, 4, 13, 16, -0.8846153846153849, 60.03846153846155, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.223935654854
TP 93 FP 69 TN 50 FN 25



None
Cross Validation: 0.181069168985
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0330711256718
TP 40 FP 13750 TN 9764 FN 1



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.367112946874
TP 81 FP 38 TN 81 FN 37



None
Cross Validation: 0.225988546028
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0587760376827
TP 41 FP 7860 TN 15654 FN 0



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.164505572169
TP 67 FP 48 TN 71 FN 51



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.127211758678
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0481760262618
TP 40 FP 9582 TN 13932 FN 1



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.206822876388
TP 72 FP 48 TN 71 FN 46



None
Cross Validation: 0.144524446393
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0519744704045
TP 40 FP 8758 TN 14756 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Loading Data
Loaded Data
Working on Data
Sample Vector [16, 16, 12, 12, 10, 3, 15, 19, 3, 17, 15, 11, 18, -0.5307692307692308, 45.95384615384615, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.258382718995
TP 68 FP 38 TN 81 FN 50



None
Cross Validation: 0.253779530036
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0455856430758
TP 40 FP 10194 TN 13320 FN 1



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.333738357025
TP 75 FP 36 TN 83 FN 43



None
Cross Validation: 0.292808981657
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0534139972004
TP 41 FP 8894 TN 14620 FN 0



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.165929257182
TP 75 FP 56 TN 63 FN 43



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.105624170843
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0476539585367
TP 40 FP 9702 TN 13812 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.206832840221
TP 68 FP 44 TN 75 FN 50



None
Cross Validation: 0.228342795527
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.051895677485
TP 41 FP 9216 TN 14298 FN 0



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 9, 8, 1, 9, 10, 15, 9, 2, 3, 17, 2, 9, -1.376923076923077, 56.8923076923077, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 9235
Test Data Points: 2309
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.204953118944
TP 8 FP 4 TN 2190 FN 107



None
Cross Validation: 0.191853206691
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.987708594309
TP 40 FP 0 TN 23514 FN 1



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 9235
Test Data Points: 2309
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.288071242499
TP 10 FP 0 TN 2194 FN 105



None
Cross Validation: 0.22737124641
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.680913744625
TP 40 FP 44 TN 23470 FN 1



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 9235
Test Data Points: 2309
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2194 FN 115



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: -0.000682995471769
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 23514 FN 41



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 9235
Test Data Points: 2309
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.203475983461
TP 5 FP 0 TN 2194 FN 110



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.172105411528
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.697791397108
TP 40 FP 40 TN 23474 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [1, 2, 7, 13, 9, 8, 15, 3, 3, 9, 7, 10, 17, -0.8769230769230768, 53.800000000000004, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] ncl
Training Data Points: 9240
Test Data Points: 2310
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.19344807548
TP 10 FP 10 TN 2185 FN 105



None
Cross Validation: 0.169409734265
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.433620231729
TP 40 FP 166 TN 23348 FN 1



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 9240
Test Data Points: 2310
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.260332268787
TP 10 FP 2 TN 2193 FN 105



None
Cross Validation: 0.208673117983
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] ncl
Training Data Points: 9240
Test Data Points: 2310
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2195 FN 115



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 23514 FN 41



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 9240
Test Data Points: 2310
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.248849008343
TP 10 FP 3 TN 2192 FN 105



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.162239613076
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.564523864978
TP 40 FP 82 TN 23432 FN 1



None
Loading Data
Loaded Data
Working on Data
Sample Vector [8, 4, 12, 17, 5, 5, 15, 7, 9, 7, 9, 10, 12, -0.6923076923076923, 49.12384615384616, 0.23076923076923078]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.308004557755
TP 77 FP 41 TN 78 FN 41



None
Cross Validation: 0.310997025229
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0346520095521
TP 41 FP 13908 TN 9606 FN 0



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.461744188129
TP 91 FP 37 TN 82 FN 27



None
Cross Validation: 0.459301357539
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0259539514367
TP 41 FP 16950 TN 6564 FN 0



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.333499544618
TP 80 FP 41 TN 78 FN 38



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.269827820962
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0228478487009
TP 40 FP 17244 TN 6270 FN 1



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.350235009258
TP 80 FP 39 TN 80 FN 38



None
Cross Validation: 0.358256153873
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0312433029088
TP 41 FP 15060 TN 8454 FN 0



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 12, 18, 15, 9, 17, 15, 16, 3, 16, 16, 3, 12, -0.45384615384615395, 7.1230769230769235, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.460381373665
TP 83 FP 29 TN 90 FN 35



None
Cross Validation: 0.353679638872
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0340220732858
TP 41 FP 14116 TN 9398 FN 0



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb588>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.453210548661
TP 80 FP 27 TN 92 FN 38



None
Cross Validation: 0.47095392144
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0303038767439
TP 41 FP 15388 TN 8126 FN 0



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb128>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcbe10>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176112e8>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b38>}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.257747531411
TP 70 FP 40 TN 79 FN 48



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.272654685849
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0280131171204
TP 40 FP 15440 TN 8074 FN 1



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb048>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11bfcb710>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 945
Test Data Points: 237
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.40180246233
TP 78 FP 31 TN 88 FN 40



None
Cross Validation: 0.366665721449
Number of data points in benchmark 23555
Sample Vector [8, 15, 14, 8, 9, 12, 15, 11, 9, 3, 8, 13, 20, -0.5692307692307692, 54.5846153846154, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0321017340212
TP 41 FP 14764 TN 8750 FN 0



None
Best Score
0.987708594309
['y', 'ncl', ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']}], RandomizedSearchCV(cv=None, error_score='raise',
          estimator=XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,
       gamma=0, learning_rate=0.1, max_delta_step=5, max_depth=3,
       min_child_weight=1, missing=None, n_estimators=100, nthread=-1,
       objective='binary:logistic', reg_alpha=0, reg_lambda=1,
       scale_pos_weight=1, seed=0, silent=True, subsample=1),
          fit_params={}, iid=True, n_iter=10, n_jobs=1,
          param_distributions={'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611a90>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1176116d8>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611b70>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x117611d68>, 'objective': ['reg:linear']},
          pre_dispatch='2*n_jobs', random_state=None, refit=True,
          return_train_score=True, scoring=None, verbose=0)]

In [ ]:

    

classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
best_score = 0
best_params = []
aa = "K"
data = "Data/Training/k_acetylation.csv"
benchmark_data = "Data/Benchmarks/acet.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [2, 19, 3, 2, 10, 3, 7, 11, 2, 17, 1, 12, 15, 0.09230769230769227, -7.146153846153846, 0.07692307692307693]
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.00745306279842
TP 2 FP 21 TN 64146 





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




FN 1635



None
Cross Validation: 0.0224048388324
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0301803624924
TP 6 FP 4 TN 36204 FN 3208



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.042487157116
TP 5 FP 3 TN 64164 FN 1632



None
Cross Validation: 0.0435160267243
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.112502648994
TP 59 FP 16 TN 36192 FN 3155



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




 0 TN 64167 FN 1637



None
Cross Validation: -0.000121867908297
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 36208 FN 3214



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




TP 0 FP 0 TN 64167 FN 1637



None
Cross Validation: 0.00375052198289
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0447298649203
TP 7 FP 0 TN 36208 FN 3207



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 4, 10, 12, 10, 3, 7, 7, 12, 10, 1, 13, 7, 0.4538461538461539, 16.9076923076923, 0.0]
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0131242063776
TP 8 FP 97





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




 TN 64064 FN 1635



None
Cross Validation: 0.0114233947466
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0527835938268
TP 22 FP 20 TN 36188 FN 3192



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0339042409107
TP 5 FP 7 TN 64154 FN 1638



None
Cross Validation: 0.0319909191251
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.123845695411
TP 60 FP 6 TN 36202 FN 3154



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




 0 TN 64161 FN 1643



None
Cross Validation: -0.000625179238541
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 36208 FN 3214



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 263216
Test Data Points: 65804
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0339042409107
TP 5 FP 7 TN 





    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




64154 FN 1638



None
Cross Validation: 0.00420549278296
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0975921753386
TP 42 FP 9 TN 36199 FN 3172



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 15, 17, 1, 12, 20, 7, 12, 1, 5, 1, 9, 10, -0.4230769230769231, -20.884615384615387, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 512828
Test Data Points: 128208
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.973603371995
TP 62693 FP 70 TN 63804 FN 1641



None
Cross Validation: 0.965524768505
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.141152830842
TP 104 FP 41 TN 36167 FN 3110



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 512828
Test Data Points: 128208
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.9755623428
TP 62833 FP 81 TN 63793 FN 1501



None
Cross Validation: 0.969920746675
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.312346386038
TP 478 FP 162 TN 36046 FN 2736



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] ADASYN
Training Data Points: 512828
Test Data Points: 128208
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.687146264905
TP 45865 FP 2918 TN 60956 FN 18469



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.623405198515
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.00277778834994
TP 156 FP 1680 TN 34528 FN 3058



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 512828
Test Data Points: 128208
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.974143072099
TP 62741 FP 82 TN 63792 FN 1593



None
Cross Validation: 0.949405127733
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.313512820782
TP 480 FP 161 TN 36047 FN 2734



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 12, 19, 3, 3, 19, 7, 3, 18, 16, 15, 3, 9, -0.09230769230769233, 18.407692307692308, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 512854
Test Data Points: 128214
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.96858286419
TP 62837 FP 607 TN 63358 FN 1412



None
Cross Validation: 0.964178715461
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.265803171179
TP 479 FP 350 TN 35858 FN 2735



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 512854
Test Data Points: 128214
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.976372897092
TP 62800 FP 80 TN 63885 FN 1449



None
Cross Validation: 0.968085108023
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.312749828042
TP 481 FP 165 TN 36043 FN 2733



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] ADASYN
Training Data Points: 512854
Test Data Points: 128214
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.664593968666
TP 44271 FP 3048 TN 60917 FN 19978



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.621606685113
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  -0.00540510851366
TP 167 FP 2046 TN 34162 FN 3047



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 512854
Test Data Points: 128214
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.974068152988
TP 62650 FP 81 TN 63884 FN 1599



None
Cross Validation: 0.950593826585
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.207507358397
TP 221 FP 84 TN 36124 FN 2993



None
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 14, 9, 18, 17, 17, 7, 18, 17, 10, 8, 19, 2, -2.476923076923077, 141.79999999999998, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 512536
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.973562280444
TP 62611 FP 44 TN 63809 FN 1670



None
Cross Validation: 0.9756875734
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0889788107586
TP 45 FP 22 TN 36186 FN 3169



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 512536
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.976434442515
TP 62768 FP 14 TN 63839 FN 1513



None
Cross Validation: 0.978120093592
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.217117169261
TP 209 FP 47 TN 36161 FN 3005



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] SMOTEENN
Training Data Points: 512536
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.479420402795
TP 49480 FP 18598 TN 45255 FN 14801



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.415918151866
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0526740026573
TP 1227 FP 10627 TN 25581 FN 1987



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 512536
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.973776574938
TP 62603 FP 23 TN 63830 FN 1678



None
Cross Validation: 0.976523950613
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.176791892276
TP 148 FP 43 TN 36165 FN 3066



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Loading Data
Loaded Data
Working on Data
Sample Vector [12, 15, 5, 19, 20, 2, 7, 5, 2, 10, 10, 9, 7, -0.38461538461538464, 30.992307692307698, 0.23076923076923078]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 512535
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.975527436843
TP 62611 FP 23 TN 63937 FN 1563



None
Cross Validation: 0.974908605802
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.165091015538
TP 128 FP 36 TN 36172 FN 3086



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 512535
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.975754697447
TP 62628 FP 25 TN 63935 FN 1546



None
Cross Validation: 0.978136711474
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.316128042777
TP 466 FP 133 TN 36075 FN 2748



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] SMOTEENN
Training Data Points: 512535
Test Data Points: 128134
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.537029720103
TP 49399 FP 14886 TN 49074 FN 14775



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.44070062007
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0591331919316
TP 1005 FP 8033 TN 28175 FN 2209



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 512535
Test Data Points: 128134
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.974016883495
TP 62515 FP 26 TN 63934 FN 1659



None
Cross Validation: 0.9762891327
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.244681329468
TP 277 FP 75 TN 36133 FN 2937



None
Loading Data
Loaded Data
Working on Data
Sample Vector [17, 19, 7, 5, 4, 16, 7, 19, 1, 10, 12, 11, 3, -0.9692307692307696, 79.7923076923077, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.198812451227
TP 1039 FP 695 TN 899 FN 599



None
Cross Validation: 0.231460823597
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0921670313589
TP 2160 FP 18240 TN 17968 FN 1054



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.252198133419
TP 1048 FP 618 TN 976 FN 590



None
Cross Validation: 0.240217626451
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.141263175628
TP 2194 FP 15424 TN 20784 FN 1020



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.0851972576404
TP 865 FP 706 TN 888 FN 773



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.0823354719679
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0636832819403
TP 1783 FP 15896 TN 20312 FN 1431



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.214375558387
TP 979 FP 611 TN 983 FN 659



None
Cross Validation: 0.171279236037
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.114734975767
TP 2029 FP 15322 TN 20886 FN 1185



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Loading Data
Loaded Data
Working on Data
Sample Vector [9, 8, 13, 9, 9, 3, 7, 17, 8, 18, 9, 3, 17, -1.9461538461538461, 131.10000000000002, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.199286977141
TP 950 FP 607 TN 987 FN 688



None
Cross Validation: 0.235662856943
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.105870666389
TP 2009 FP 15666 TN 20542 FN 1205



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.261579478268
TP 1045 FP 600 TN 994 FN 593



None
Cross Validation: 0.241426008361
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.13790073084
TP 2184 FP 15528 TN 20680 FN 1030



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.118512827901
TP 1078 FP 864 TN 730 FN 560



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.0934298237169
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0640568964959
TP 2242 FP 21092 TN 15116 FN 972



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 12926
Test Data Points: 3232
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.188903318396
TP 936 FP 610 TN 984 FN 702



None
Cross Validation: 0.185557386425
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.10656179969
TP 2022 FP 15763 TN 20445 FN 1192



None
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 7, 3, 2, 3, 9, 7, 15, 9, 9, 4, 5, 11, -0.40769230769230774, 36.792307692307695, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] ncl
Training Data Points: 251368
Test Data Points: 62843
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.118034727075
TP 34 FP 15 TN 61215 FN 1579



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.037556330848
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.301673095648
TP 442 FP 145 TN 36063 FN 2772



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 251368
Test Data Points: 62843
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.102108652717
TP 20 FP 3 TN 61227 FN 1593



None
Cross Validation: 0.140348162531
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.294132379083
TP 417 FP 133 TN 36075 FN 2797



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a20>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118beceb8>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c044e0>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04668>}] ncl
Training Data Points: 251368
Test Data Points: 62843
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 61230 FN 1613



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 36208 FN 3214



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 251368
Test Data Points: 62843
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0777268465063
TP 10 FP 0 TN 61230 FN 1603



None
Cross Validation: 0.128038900394
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.202300398529
TP 202 FP 68 TN 36140 FN 3012



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109fc11d0>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d739b0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [13, 1, 16, 7, 12, 7, 7, 13, 14, 19, 14, 15, 16, -0.3923076923076923, 36.47692307692308, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c047f0>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04b70>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04898>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x118c04a58>, 'objective': ['reg:linear']}] ncl
Training Data Points: 251366
Test Data Points: 62842
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.10783390377
TP 40 FP 39 TN 61190 FN 1573



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0409658705044
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.305126919772
TP 484 FP 196 TN 36012 FN 2730



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x109d73a58>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 251366
Test Data Points: 62842
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.107141297485
TP 20 FP 1 TN 61228 FN 1593



None

In [ ]:

    

classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
best_score = 0
best_params = []
aa = "K"
data = "Data/Training/k_methylation.csv"
benchmark_data = "Data/Benchmarks/methylation.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [16, 13, 7, 9, 3, 9, 7, 3, 3, 19, 12, 10, 10, -0.2307692307692307, 44.784615384615385, 0.0]
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  -0.00614250735902
TP 0 FP 9 TN 5198 FN 116



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0181336935966
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0568002222527
TP 1 FP 1 TN 2479 FN 131



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5207 FN 116



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0681653893385
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.149839265988
TP 4 FP 1 TN 2479 FN 128



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5207 FN 116



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0634992632311
TP 1 FP 1 TN 5206 FN 115



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0606919727705
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.346591838696
TP 21 FP 5 TN 2475 FN 111



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Loading Data
Loaded Data
Working on Data
Sample Vector [18, 9, 20, 19, 19, 3, 7, 17, 2, 9, 10, 4, 3, -1.2384615384615383, 42.5923076923077, 0.0]
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.123116499199
TP 3 FP 2 TN 5208 FN 110



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0195106506261
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.280493783506
TP 15 FP 5 TN 2475 FN 117



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.161244329661
TP 3 FP 0 TN 5210 FN 110



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0370774275853
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.288764453453
TP 15 FP 4 TN 2476 FN 117



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5210 FN 113



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] pass
Training Data Points: 21290
Test Data Points: 5323
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5210 FN 113



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0134873944065
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.145360931481
TP 5 FP 3 TN 2477 FN 127



None
Loading Data
Loaded Data
Working on Data
Sample Vector [20, 19, 2, 12, 14, 19, 7, 12, 20, 9, 5, 5, 3, 0.4846153846153846, 19.269230769230774, 0.15384615384615385]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 41585
Test Data Points: 10397
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.983557355423
TP 5061 FP 5 TN 5250 FN 81



None
Cross Validation: 0.966565015738
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.335996457926
TP 22 FP 8 TN 2472 FN 110



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 41585
Test Data Points: 10397
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.98300610094
TP 5055 FP 2 TN 5253 FN 87



None
Cross Validation: 0.971368741992
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.352399395054
TP 23 FP 7 TN 2473 FN 109



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] ADASYN
Training Data Points: 41585
Test Data Points: 10397
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.651917742162
TP 4098 FP 769 TN 4486 FN 1044



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.310175252145
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0291804155131
TP 26 FP 370 TN 2110 FN 106



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 41585
Test Data Points: 10397
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.981309842471
TP 5045 FP 1 TN 5254 FN 97



None
Cross Validation: 0.942967945197
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.359197311046
TP 23 FP 6 TN 2474 FN 109



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Loading Data
Loaded Data
Working on Data
Sample Vector [10, 19, 10, 16, 11, 17, 7, 7, 7, 7, 9, 7, 7, -3.1769230769230767, 16.030769230769234, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ADASYN
Training Data Points: 41577
Test Data Points: 10395
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.979950360609
TP 5054 FP 6 TN 5236 FN 99



None
Cross Validation: 0.969389914644
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.322985272868
TP 18 FP 4 TN 2476 FN 114



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ADASYN
Training Data Points: 41577
Test Data Points: 10395
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.98015303221
TP 5053 FP 4 TN 5238 FN 100



None
Cross Validation: 0.970152985333
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.325820677645
TP 19 FP 5 TN 2475 FN 113



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] ADASYN
Training Data Points: 41577
Test Data Points: 10395
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.732255292201
TP 4637 FP 886 TN 4356 FN 516



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.306348706404
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0942071731459
TP 48 FP 475 TN 2005 FN 84



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ADASYN
Training Data Points: 41577
Test Data Points: 10395
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.978634538502
TP 5046 FP 5 TN 5237 FN 107



None
Cross Validation: 0.936297401847
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.31839227874
TP 19 FP 6 TN 2474 FN 113



None
Loading Data
Loaded Data
Working on Data
Sample Vector [13, 7, 3, 11, 17, 9, 7, 1, 17, 4, 17, 5, 16, -1.3076923076923077, -7.146153846153846, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 41753
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.976094561945
TP 5125 FP 6 TN 5188 FN 120



None
Cross Validation: 0.978593702787
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.304745068004
TP 19 FP 8 TN 2472 FN 113



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 41753
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.977269592934
TP 5125 FP 0 TN 5194 FN 120



None
Cross Validation: 0.982476523781
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.325820677645
TP 19 FP 5 TN 2475 FN 113



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] SMOTEENN
Training Data Points: 41753
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.685925673473
TP 4609 FP 1012 TN 4182 FN 636



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.70646980608
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.059387195076
TP 39 FP 467 TN 2013 FN 93



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 41753
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.976137435293
TP 5120 FP 1 TN 5193 FN 125



None
Cross Validation: 0.982327898018
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.297687759603
TP 15 FP 3 TN 2477 FN 117



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Loading Data
Loaded Data
Working on Data
Sample Vector [11, 12, 13, 10, 2, 9, 7, 2, 15, 16, 9, 8, 3, -0.4, 48.353846153846156, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] SMOTEENN
Training Data Points: 41754
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.976019970587
TP 5160 FP 16 TN 5153 FN 110



None
Cross Validation: 0.97998048264
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.349370157805
TP 22 FP 6 TN 2474 FN 110



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] SMOTEENN
Training Data Points: 41754
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.979332562882
TP 5161 FP 0 TN 5169 FN 109



None
Cross Validation: 0.981672202739
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.364197787406
TP 22 FP 4 TN 2476 FN 110



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] SMOTEENN
Training Data Points: 41754
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.615110801197
TP 4318 FP 1057 TN 4112 FN 952



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.728554569173
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0500205059373
TP 37 FP 471 TN 2009 FN 95



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] SMOTEENN
Training Data Points: 41754
Test Data Points: 10439
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.97819088257
TP 5157 FP 2 TN 5167 FN 113



None
Cross Validation: 0.982137505645
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.364197787406
TP 22 FP 4 TN 2476 FN 110



None
Loading Data
Loaded Data
Working on Data
Sample Vector [5, 13, 5, 20, 3, 11, 7, 1, 5, 14, 7, 18, 19, 0.13076923076923058, 17.51538461538462, 0.23076923076923078]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.107676778508
TP 55 FP 46 TN 58 FN 45



None
Cross Validation: -0.0137415022821
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0747789341009
TP 83 FP 1137 TN 1343 FN 49



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.126003543657
TP 50 FP 39 TN 65 FN 50



None
Cross Validation: 0.0437213945147
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0844588987487
TP 83 FP 1084 TN 1396 FN 49



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.0149557626028
TP 94 FP 97 TN 7 FN 6



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.0314707446595
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  -0.0244305486862
TP 117 FP 2275 TN 205 FN 15



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0496273156928
TP 54 FP 51 TN 53 FN 46



None
Cross Validation: 0.0870259562691
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.045234049626
TP 80 FP 1247 TN 1233 FN 52



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Loading Data
Loaded Data
Working on Data
Sample Vector [1, 19, 19, 12, 13, 9, 7, 19, 10, 11, 9, 10, 11, -1.3769230769230771, 153.63076923076923, 0.0]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  -0.0392232270276
TP 48 FP 54 TN 50 FN 52



None
Cross Validation: 0.0449917147751
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0722615025347
TP 80 FP 1096 TN 1384 FN 52



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.118529940857
TP 58 FP 48 TN 56 FN 42



None
Cross Validation: 0.0655124090015
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0630968123454
TP 84 FP 1221 TN 1259 FN 48



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  -0.0488391102516
TP 48 FP 55 TN 49 FN 52



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.0409727425082
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  -0.00939116945585
TP 62 FP 1218 TN 1262 FN 70



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] random_under_sample
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0111780556639
TP 54 FP 55 TN 49 FN 46



None
Cross Validation: 0.0697471391998
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0611322837289
TP 85 FP 1251 TN 1229 FN 47



None
Loading Data
Loaded Data
Working on Data
Sample Vector [15, 15, 2, 10, 13, 10, 7, 19, 17, 17, 15, 2, 1, -1.0923076923076924, 87.6153846153846, 0.23076923076923078]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ncl
Training Data Points: 20568
Test Data Points: 5142
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  -0.00326114074911
TP 0 FP 3 TN 5047 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0358045957056
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 20568
Test Data Points: 5142
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.103330367374
TP 1 FP 0 TN 5050 FN 91



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0747962365039
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.325820677645
TP 19 FP 5 TN 2475 FN 113



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] ncl
Training Data Points: 20568
Test Data Points: 5142
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5050 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 20568
Test Data Points: 5142
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5050 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0554636711224
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.366342238823
TP 23 FP 5 TN 2475 FN 109



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [7, 1, 2, 20, 15, 1, 7, 11, 12, 16, 16, 1, 12, -0.676923076923077, -15.853846153846154, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ncl
Training Data Points: 20569
Test Data Points: 5143
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  -0.0018820848259
TP 0 FP 1 TN 5050 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0184801132782
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 20569
Test Data Points: 5143
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5051 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0693296424069
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.366342238823
TP 23 FP 5 TN 2475 FN 109



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] ncl
Training Data Points: 20569
Test Data Points: 5143
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5051 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 2480 FN 132



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 20569
Test Data Points: 5143
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 5051 FN 92



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0413171870811
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.352417657866
TP 20 FP 3 TN 2477 FN 112



None
Loading Data
Loaded Data
Working on Data
Sample Vector [3, 18, 9, 19, 9, 3, 7, 8, 8, 9, 9, 3, 13, -1.2307692307692304, 90.40769230769232, 0.0]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.345363374995
TP 71 FP 38 TN 66 FN 29



None
Cross Validation: 0.349097238446
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0564142734691
TP 115 FP 1891 TN 589 FN 17



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.425037919385
TP 76 FP 35 TN 69 FN 24



None
Cross Validation: 0.485279730035
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0530310540683
TP 119 FP 2001 TN 479 FN 13



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.359898569058
TP 55 FP 21 TN 83 FN 45



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.304090602669
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0188050103302
TP 86 FP 1512 TN 968 FN 46



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.36354894054
TP 70 FP 35 TN 69 FN 30



None
Cross Validation: 0.40416495076
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0417737940863
TP 112 FP 1906 TN 574 FN 20



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Loading Data
Loaded Data
Working on Data
Sample Vector [3, 12, 1, 1, 20, 3, 7, 19, 15, 8, 13, 7, 1, -0.13076923076923086, -13.930769230769231, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.448666663736
TP 84 FP 42 TN 62 FN 16



None
Cross Validation: 0.408415099758
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0569571398249
TP 120 FP 2003 TN 477 FN 12



None
x ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.52833602114
TP 84 FP 33 TN 71 FN 16



None
Cross Validation: 0.507809275089
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0413870605937
TP 117 FP 2017 TN 463 FN 15



None
x ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Done training
Test Results
Matthews Correlation Coeff:  0.423119711729
TP 74 FP 33 TN 71 FN 26



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)






    




Cross Validation: 0.320957867128
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.0504748268129
TP 114 FP 1902 TN 578 FN 18



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] near_miss
Training Data Points: 814
Test Data Points: 204
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.458094416396
TP 80 FP 36 TN 68 FN 20



None
Cross Validation: 0.395363118908
Number of data points in benchmark 2612
Sample Vector [9, 20, 20, 3, 3, 15, 7, 9, 13, 2, 17, 18, 12, -0.2692307692307693, 33.53846153846154, 0.07692307692307693]
Benchmark Results 
Matthews Correlation Coeff:  0.059156656178
TP 114 FP 1853 TN 627 FN 18



None
Best Score
0.366342238823
['y', 'ncl', ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}], RandomizedSearchCV(cv=None, error_score='raise',
          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=4, oob_score=False, random_state=None,
            verbose=0, warm_start=False),
          fit_params={}, iid=True, n_iter=10, n_jobs=1,
          param_distributions={'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]},
          pre_dispatch='2*n_jobs', random_state=None, refit=True,
          return_train_score=True, scoring=None, verbose=0)]

In [ ]:

    

#Had to restart due to memory issues so restarting part of the acetylation search here. 
classy = [["xgb", xgb], ["bagging", bagging], ["mlp_adam", mlp], ["forest", forest]]
par = ["ncl", "near_miss"]
best_score = 0
best_params = []
aa = "K"
data = "Data/Training/k_acetylation.csv"
benchmark_data = "Data/Benchmarks/acet.csv"
for i in par:
    y = Predictor()
    y.load_data(file=data)
    y.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=0)
    for j in classy:
        print("y",j, i)
        y.supervised_training(j[0], params=j[1])
        y.benchmark(benchmark_data, aa)
        if y.benchmark_mcc > best_score:
            best_score = y.benchmark_mcc
            best_params = ["y", i, j, y.classifier]
    del y
    print("x",j , i)
    x = Predictor()
    x.load_data(file=data)
    x.process_data(vector_function="sequence", amino_acid=aa, imbalance_function=i, random_data=1)
    for j in classy:
        print("x",j, i)
        x.supervised_training(j[0], params=j[1])
        x.benchmark(benchmark_data, aa)
        if x.benchmark_mcc > best_score:
            best_score = x.benchmark_mcc
            best_params = ["x", i, j, x.classifier]
    del x
print("Best Score")
print(best_score)
print(best_params)


    

    




Loading Data
Loaded Data
Working on Data
Sample Vector [2, 9, 4, 9, 7, 16, 7, 7, 5, 2, 9, 8, 20, -1.6384615384615382, 42.261538461538464, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
y ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ncl
Training Data Points: 251398
Test Data Points: 62850
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 61238 FN 1612



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0442873618906
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 36208 FN 3214



None
y ['bagging', {'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108347748>, 'base_estimator': [None, RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)], 'bootstrap': [True, False], 'bootstrap_features': [True, False]}] ncl
Training Data Points: 251398
Test Data Points: 62850
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.139156036563
TP 39 FP 8 TN 61230 FN 1573



None
Cross Validation: 0.143900771766
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.31575819435
TP 481 FP 155 TN 36053 FN 2733



None
y ['mlp_adam', {'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'hidden_layer_sizes': <scipy.stats._distn_infrastructure.rv_frozen object at 0x108625240>, 'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bb70>, 'power_t': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11749bf60>, 'momentum': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0470>}] ncl
Training Data Points: 251398
Test Data Points: 62850
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 61238 FN 1612



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)






    




Cross Validation: 0.0
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.0
TP 0 FP 0 TN 36208 FN 3214



None
y ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Training Data Points: 251398
Test Data Points: 62850
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.118366499424
TP 26 FP 3 TN 61235 FN 1586



None
Cross Validation: 0.126713124671
Number of data points in benchmark 39422
Sample Vector [9, 11, 20, 12, 15, 18, 7, 1, 4, 12, 1, 10, 5, -0.2307692307692307, 23.400000000000002, 0.15384615384615385]
Benchmark Results 
Matthews Correlation Coeff:  0.277403939372
TP 369 FP 115 TN 36093 FN 2845



None
x ['forest', {'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x10861f9e8>, 'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1086251d0>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy'], 'warm_start': [True, False]}] ncl
Loading Data
Loaded Data
Working on Data
Sample Vector [2, 8, 14, 13, 19, 9, 7, 14, 5, 18, 3, 17, 18, -0.6153846153846154, 27.738461538461543, 0.07692307692307693]
Balancing Data
Balanced Data
Finished working with Data
x ['xgb', {'learning_rate': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b05f8>, 'min_child_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0978>, 'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b06a0>, 'scale_pos_weight': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1174b0860>, 'objective': ['reg:linear']}] ncl
Training Data Points: 251370
Test Data Points: 62843
Starting Training
Done training
Test Results
Matthews Correlation Coeff:  0.0986484030749
TP 18 FP 2 TN 61228 FN 1595



None






    




/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)
/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:516: RuntimeWarning: invalid value encountered in double_scalars
  mcc = cov_ytyp / np.sqrt(var_yt * var_yp)

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