Imports
In [1]:
# This tells matplotlib not to try opening a new window for each plot.
%matplotlib inline
# Import a bunch of libraries.
import time
import numpy as np
import matplotlib.pyplot as plt
# Preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer
# Feature Selection
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.decomposition import PCA
from sklearn.decomposition import KernelPCA
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LassoCV
# Regression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import LinearSVC
from sklearn.linear_model import LassoLars
from sklearn.neural_network import MLPRegressor
import matplotlib.mlab as mlab
# Set the randomizer seed so results are the same each time.
np.random.seed(0)
In [2]:
# Each record in the training data loads as a tuple.
# This function take an array of tuples and turns into a 2D numpy array
def SplitTuples(data):
new_data = []
for i in range(data.shape[0]):
new_data.append(list(data[i]))
data = np.array(new_data)
return data
In [3]:
# Binarize the depth feature
def binarizeColumn(data,col,string):
for i in range(data.shape[0]):
data[i,col] = np.where(data[i,col] == string,1.0,0.0)
return data
In [4]:
class AfricanDataSplit:
# Initialize an instance of the class.
# This class enables us to do PCA on only the wavelength portion of the
# Arican soil sample data, while leaving the rest alone
def __init__(self, maxcol = 3577, pca_components=20):
self.maxcol = maxcol
self.pca_components = pca_components
self.pca = PCA(n_components=pca_components)
def fit(self, data, junk):
maxcol = self.maxcol
pca_components = self.pca_components
# Split data into two sections
left_data = data[:,0:maxcol]
right_data = data[:,maxcol:]
# Fit PCA to the left data
self.pca.fit(left_data)
# Output variance fractions
#print '\n-------------------------------------------'
#print 'Fraction of the total variance in the training explained by first k components: \n'
#for k in range(1,pca_components):
# s = sum(self.pca.explained_variance_ratio_[0:k])
# #SANITY CHECK: print np.cumsum(pca_all.explained_variance_ratio_)[k-1]
# print("%d \t %s" % (k, '{0:.2f}%'.format(s * 100)))
# Make predictions for each test example and return results.
def transform(self, data):
# Get the split point
maxcol = self.maxcol
# Split the data into two sections
left_data = data[:,0:maxcol]
right_data = data[:,maxcol:]
# Transform the left data
new_left_data = self.pca.transform(left_data)
# Concatenate into new dataset
new_data = np.concatenate((new_left_data, right_data), axis = 1)
return new_data
Loading Data
In [5]:
# Load training data
X = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=range(1, 3595)) # Load columns 1 to 3594 inclusive
X = SplitTuples(X)
X = binarizeColumn(X,3593,'Topsoil')
X = X.astype('float64')
n = np.genfromtxt('training.csv',
delimiter=',',
max_rows = 1,
names = True,
usecols=range(1, 3595)) # Load columns 1 to 3594 inclusive
# Extract feature names
feature_names = np.asarray(n.dtype.names)
PIDN = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=0) # Load the PIDN for reference
Ca = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3595) # Load Mehlich-3 extractable Calcium data
P = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3596) # Load Mehlich-3 extractable Phosphorus data
pH = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3597) # Load pH data
SOC = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3598) # Load Soil Organic Carbon data
Sand = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3599) # Load Sand Content data
In [6]:
# Shuffle the input: create a random permutation of the integers between 0 and the number of data points and apply this
# permutation to X and Y.
# NOTE: Each time you run this cell, you'll re-shuffle the data, resulting in a different ordering.
shuffle = np.random.permutation(np.arange(X.shape[0]))
X, Ca, P, pH, SOC, Sand = X[shuffle], Ca[shuffle], P[shuffle], pH[shuffle], SOC[shuffle], Sand[shuffle]
# Define the size of the evaluation data set
evalSetSize = 100
# Define the size of the dev data set
devSetSize = 100
# Total size to hold out
holdOutSize = evalSetSize + devSetSize
eval_data = X[0:evalSetSize]
eval_Ca_labels = Ca[0:evalSetSize]
eval_P_labels = P[0:evalSetSize]
eval_pH_labels = pH[0:evalSetSize]
eval_SOC_labels = SOC[0:evalSetSize]
eval_Sand_labels = Sand[0:evalSetSize]
dev_data = X[evalSetSize:holdOutSize]
dev_Ca_labels = Ca[evalSetSize:holdOutSize]
dev_P_labels = P[evalSetSize:holdOutSize]
dev_pH_labels = pH[evalSetSize:holdOutSize]
dev_SOC_labels = SOC[evalSetSize:holdOutSize]
dev_Sand_labels = Sand[evalSetSize:holdOutSize]
dev_labels = [dev_Ca_labels, dev_P_labels, dev_pH_labels, dev_SOC_labels, dev_Sand_labels]
eval_labels = [eval_Ca_labels, eval_P_labels, eval_pH_labels, eval_SOC_labels, eval_Sand_labels]
outcome_vars = ['Ca', 'P', 'pH', 'Soc', 'Sand']
train_data = X[holdOutSize:]
train_Ca_labels = Ca[holdOutSize:]
train_P_labels = P[holdOutSize:]
train_pH_labels = pH[holdOutSize:]
train_SOC_labels = SOC[holdOutSize:]
train_Sand_labels = Sand[holdOutSize:]
train_labels = [train_Ca_labels, train_P_labels, train_pH_labels, train_SOC_labels, train_Sand_labels]
print(eval_Ca_labels.shape)
print(dev_Ca_labels.shape)
print(train_Ca_labels.shape)
print('Number of features:', dev_data.shape[1])
print('Number of training examples:', train_data.shape[0])
print('Number of dev examples:', dev_data.shape[0])
print('Number of eval examples:', eval_data.shape[0])
# Load test data
test_x = np.genfromtxt('sorted_test.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=range(1, 3595)) # Load columns 0 to 3594 inclusive
test_ids = np.genfromtxt('sorted_test.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=0) # Load columns 0 to 3594 inclusive
test_x = SplitTuples(test_x)
test_x = binarizeColumn(test_x,3593,'Topsoil')
test_x = test_x.astype('float64')
print('Number of test examples:', test_x.shape[0])
scaler = Normalizer().fit(train_data)
transformedTrainData = scaler.transform(train_data)
transformedDevData = scaler.transform(dev_data)
#scaler = Normalizer().fit(train_data)
transformedEvalData = scaler.transform(eval_data)
#transformedEvalData = eval_data
#scaler = Normalizer().fit(train_data)
normalizedTestData = scaler.transform(test_x)
#normalizedTestData = test_x
(100L,)
(100L,)
(957L,)
('Number of features:', 3594L)
('Number of training examples:', 957L)
('Number of dev examples:', 100L)
('Number of eval examples:', 100L)
('Number of test examples:', 727L)
Experiment on PCA: Analyze the explaned variances for PCA over our features. We observe that the first 20 components explain increasing portions of the variance, however after 20 components, the subsequent ones don't really help.
Feature selectors
In [7]:
def getFeatureSelectors():
return [
#['lasso', SelectFromModel(LassoCV()) ], # Doesn't work
#['linearc0.01', SelectFromModel(LinearSVC(C=0.01, penalty="l1")) ],
#['linearc0.1', SelectFromModel(LinearSVC(C=0.1, penalty="l1")) ],
#['linearc11', SelectFromModel(LinearSVC(C=1, penalty="l1")) ],
#['kbest100', SelectKBest(k=100)],
#['kbest250', SelectKBest(k=250)],
#['pca5', PCA(n_components=5)],
#['pca10', PCA(n_components=10)],
['ads', AfricanDataSplit(pca_components=20)],
['pca20', PCA(n_components=20)],
['pca30', PCA(n_components=30)],
['pca30rbf', KernelPCA(n_components=30,kernel='rbf')],
['pca20kbest5', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest5", SelectKBest(k=5))])],
['pca20kbest50', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest50", SelectKBest(k=50))])],
['pca20kbest250', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest250", SelectKBest(k=250))])],
]
Classifiers
In [8]:
# Predict the mean value of an array
def PredictMean(labels):
mean = np.mean(labels)
return mean
In [9]:
# Get the means of the dev data for our reference
Ca_mean = PredictMean(dev_Ca_labels)
print('Calcium Mean: ', Ca_mean)
P_mean = PredictMean(dev_P_labels)
print('Phosphorus Mean: ', P_mean)
pH_mean = PredictMean(dev_pH_labels)
print('pH Mean: ', pH_mean)
SOC_mean = PredictMean(dev_SOC_labels)
print('SOC Mean: ', SOC_mean)
Sand_mean = PredictMean(dev_Sand_labels)
print('Sand Mean: ', Sand_mean)
('Calcium Mean: ', 0.060906193376832396)
('Phosphorus Mean: ', 0.042220159248226287)
('pH Mean: ', 0.027768020593356191)
('SOC Mean: ', 0.021567274983507304)
('Sand Mean: ', 0.058996238160944625)
In [10]:
def getClassifiers():
return [
['KNN', KNeighborsRegressor(), {'n_neighbors':[1, 2, 3, 5, 8]}],
['SVRdict', SVR(cache_size=200), {'C':[0.1,1.0,100.0,1000.0],'kernel':['linear','rbf']}],
#['SVR', SVR(cache_size=200, kernel='linear', C=1.0, epsilon=0.05, shrinking=False),{}]
['Lasso', Lasso(), {'alpha':[0.01, 0.05, 0.25, 0.9]}],
#['LassoLars', LassoLars(), {'alpha':[0.01, 0.1, 0.5, 1.0]}],
['RandomForest', RandomForestRegressor(), {'n_estimators':[1, 2, 3, 5, 8]}],
['nn', MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(2, 2)), {}]
#['RandomForest', RandomForestRegressor(), {'n_estimators':[1, 2, 3, 5, 8]}]
]
Test combinations of selectors and classifiers
In [11]:
# For each outcome variable, for each classifier and for each selector, it will obtain and print
# the best hyperparameters and inmediately print the mean squared error.
# Finally, after finishing the calculations, it will print the methods and score ordered by score (MSE)
# To see what would be the final score we'd get, you can get the best MSE score for each outcome variable,
# and calculate the average of them.
def run():
allResults = []
#scaler = Normalizer().fit(train_data)
#transformedTrainData = scaler.transform(train_data)
#transformedDevData = scaler.transform(dev_data)
for outcomeVarIndex in range(0, 5):
print('*************************************************************')
print('Outcome Variable:', outcome_vars[outcomeVarIndex])
print('*************************************************************')
results = []
scaler = Normalizer().fit(train_data)
# Get the mean value of the outcome variable
DevMean = PredictMean(dev_labels[outcomeVarIndex])
for selector in getFeatureSelectors():
#selectedTrainData = selector[1].fit(transformedTrainData, train_labels[outcomeVarIndex]).transform(transformedTrainData)
selector[1].fit(transformedTrainData, train_labels[outcomeVarIndex])
selectedTrainData = selector[1].transform(transformedTrainData)
selectedDevData = selector[1].transform(transformedDevData)
for classifier in getClassifiers():
print('-------------------------------------------------------')
print(selector[0] + ' ' + classifier[0])
grid_search = GridSearchCV(classifier[1], param_grid=classifier[2],cv=5)
grid_search.fit(selectedTrainData, train_labels[outcomeVarIndex])
print(grid_search.best_estimator_)
# Mean Squared Error: (y_true - y_pred)**2.sum()
meanSquaredError = 0.0
for i in range(len(selectedDevData)):
diff = grid_search.predict(selectedDevData[i].reshape(1, -1)) - dev_labels[outcomeVarIndex][i]
squaredDiff = diff ** 2
meanSquaredError = meanSquaredError + squaredDiff
#meanSquaredError = meanSquaredError / float(len(selectedDevData))
# Residual Sum of squares: (y_true - y_mean)**2.sum()
residualSquaredError = 0.0
for i in range(len(selectedDevData)):
diff = dev_labels[outcomeVarIndex][i] - DevMean
squaredDiff = diff ** 2
residualSquaredError = residualSquaredError + squaredDiff
myScore = 1 - meanSquaredError/residualSquaredError
print('Mean Squared Error: ', str(meanSquaredError / float(len(selectedDevData))))
print('Residual Squared Error: ', str(residualSquaredError))
print('Calculated Score: ', str(1 - meanSquaredError/residualSquaredError))
print('Score: ' + str(grid_search.score(selectedDevData, dev_labels[outcomeVarIndex])))
# Store in an array, for each combination, the following:
# [selector name, classifier name, mean squared error, selector instance, classifier instance]
results.append([selector[0], classifier[0], myScore, selector[1], grid_search])
sortedResults = sorted(results, key=lambda result: result[2], reverse=True)
for result in sortedResults:
print('Selector: ' + str(result[0]) + ', Classifier: ' + str(result[1]) + ', Score: ' + str(result[2]))
copyResults = sortedResults[:]
allResults.append(copyResults)
#TODO Calculate columnwise mean of the mean squared error
#Each item has the best result on item 0
squaredErrorSum = 0.0
bestModels = []
print('-------------------------------------------------------')
print('Best Results')
print('-------------------------------------------------------')
for i in range(len(allResults)):
columnResults = allResults[i]
bestColumnResult = columnResults[0]
squaredError = bestColumnResult[2]
print('Outcome Variable: ' + outcome_vars[i] + ', Selector: ' + str(bestColumnResult[0]) +
', Classifier: ' + str(bestColumnResult[1]) + ', Score: ' + str(bestColumnResult[2]))
squaredErrorSum = squaredErrorSum + squaredError
bestModels.append(bestColumnResult)
print('Best result obtained: ' + str(squaredErrorSum / 5.0))
return bestModels
models = run()
*************************************************************
('Outcome Variable:', 'Ca')
*************************************************************
-------------------------------------------------------
ads KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20256988]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83985216]')
Score: 0.839852161391
-------------------------------------------------------
ads SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.23236578]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.8162961]')
Score: 0.816296096016
-------------------------------------------------------
ads Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.93394729]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.26163928]')
Score: 0.261639276792
-------------------------------------------------------
ads RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.27557591]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.782135]')
Score: 0.782134999774
-------------------------------------------------------
ads nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.26859881]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[-0.00292976]')
Score: -0.00292975594083
-------------------------------------------------------
pca20 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20765774]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83582979]')
Score: 0.83582979182
-------------------------------------------------------
pca20 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.31046524]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.75455216]')
Score: 0.754552163695
-------------------------------------------------------
pca20 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94823139]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.25034655]')
Score: 0.250346547037
-------------------------------------------------------
pca20 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.54286087]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.57082466]')
Score: 0.570824660938
-------------------------------------------------------
pca20 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.21007234]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83392086]')
Score: 0.83392085855
-------------------------------------------------------
pca30 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20267739]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83976717]')
Score: 0.839767165989
-------------------------------------------------------
pca30 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.28199178]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.77706273]')
Score: 0.777062734833
-------------------------------------------------------
pca30 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94823139]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.25034655]')
Score: 0.250346547037
-------------------------------------------------------
pca30 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.47380666]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.62541759]')
Score: 0.625417593934
-------------------------------------------------------
pca30 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.16021892]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.87333401]')
Score: 0.873334014464
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20267739]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83976717]')
Score: 0.839767165989
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 1.03224929]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.18392361]')
Score: 0.183923613973
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 1.26859202]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[-0.00292439]')
Score: -0.00292439058104
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.37334295]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.70484227]')
Score: 0.704842266199
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.26859202]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[-0.00292439]')
Score: -0.00292439058104
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20765774]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83582979]')
Score: 0.83582979182
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.30675218]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.75748764]')
Score: 0.757487638257
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94823139]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.25034655]')
Score: 0.250346547037
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.62431814]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.50642612]')
Score: 0.506426115795
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.34712682]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.72556823]')
Score: 0.725568228045
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20924512]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83457484]')
Score: 0.8345748391
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.31322822]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.75236781]')
Score: 0.752367811816
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94823139]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.25034655]')
Score: 0.250346547037
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.40807606]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.67738293]')
Score: 0.677382933942
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.26273463]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.79228706]')
Score: 0.792287063727
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.20425014]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.83852378]')
Score: 0.838523779902
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.29763068]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.76469892]')
Score: 0.764698919685
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94823139]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.25034655]')
Score: 0.250346547037
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.40181395]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.68233364]')
Score: 0.682333635671
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.16309939]')
('Residual Squared Error: ', '126.489297838')
('Calculated Score: ', '[ 0.87105676]')
Score: 0.871056764795
Selector: pca30, Classifier: nn, Score: [ 0.87333401]
Selector: pca20kbest250, Classifier: nn, Score: [ 0.87105676]
Selector: ads, Classifier: KNN, Score: [ 0.83985216]
Selector: pca30, Classifier: KNN, Score: [ 0.83976717]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.83976717]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.83852378]
Selector: pca20, Classifier: KNN, Score: [ 0.83582979]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.83582979]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.83457484]
Selector: pca20, Classifier: nn, Score: [ 0.83392086]
Selector: ads, Classifier: SVRdict, Score: [ 0.8162961]
Selector: pca20kbest50, Classifier: nn, Score: [ 0.79228706]
Selector: ads, Classifier: RandomForest, Score: [ 0.782135]
Selector: pca30, Classifier: SVRdict, Score: [ 0.77706273]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.76469892]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.75748764]
Selector: pca20, Classifier: SVRdict, Score: [ 0.75455216]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.75236781]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.72556823]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.70484227]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.68233364]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.67738293]
Selector: pca30, Classifier: RandomForest, Score: [ 0.62541759]
Selector: pca20, Classifier: RandomForest, Score: [ 0.57082466]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.50642612]
Selector: ads, Classifier: Lasso, Score: [ 0.26163928]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.25034655]
Selector: pca20, Classifier: Lasso, Score: [ 0.25034655]
Selector: pca30, Classifier: Lasso, Score: [ 0.25034655]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.25034655]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.25034655]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.18392361]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00292439]
Selector: pca30rbf, Classifier: nn, Score: [-0.00292439]
Selector: ads, Classifier: nn, Score: [-0.00292976]
*************************************************************
('Outcome Variable:', 'P')
*************************************************************
-------------------------------------------------------
ads KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.66384576]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.214569]')
Score: 0.214568997875
-------------------------------------------------------
ads SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.15856633]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.01896759]')
Score: -0.0189675935091
-------------------------------------------------------
ads Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.11100837]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.0034825]')
Score: 0.00348250021358
-------------------------------------------------------
ads RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.70586228]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.19473478]')
Score: 0.194734782165
-------------------------------------------------------
ads nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 2.12162869]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.0015309]')
Score: -0.00153090031758
-------------------------------------------------------
pca20 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.67288982]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.21029968]')
Score: 0.210299680396
-------------------------------------------------------
pca20 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.15557036]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.01755333]')
Score: -0.0175533262412
-------------------------------------------------------
pca20 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.10970913]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.00409581]')
Score: 0.00409581499116
-------------------------------------------------------
pca20 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 2.48529491]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.1732023]')
Score: -0.173202297078
-------------------------------------------------------
pca20 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.95530397]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.07698394]')
Score: 0.0769839422033
-------------------------------------------------------
pca30 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.66412593]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.21443674]')
Score: 0.214436741802
-------------------------------------------------------
pca30 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.16607544]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.02251233]')
Score: -0.0225123262405
-------------------------------------------------------
pca30 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.10970913]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.00409581]')
Score: 0.00409581499116
-------------------------------------------------------
pca30 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 2.15487566]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.01722539]')
Score: -0.0172253883152
-------------------------------------------------------
pca30 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 2.12162942]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.00153125]')
Score: -0.00153124856806
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.66412593]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.21443674]')
Score: 0.214436741802
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.16624617]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.02259292]')
Score: -0.0225929216917
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.12162869]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.0015309]')
Score: -0.0015309003179
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.96413324]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.07281602]')
Score: 0.0728160163317
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 2.12162869]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.0015309]')
Score: -0.0015309003179
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.71064908]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.19247514]')
Score: 0.192475137353
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.1568425]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.01815385]')
Score: -0.0181538464147
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.10970913]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.00409581]')
Score: 0.00409581499116
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 2.11948701]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.00051991]')
Score: -0.000519905278733
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 2.1216287]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.00153091]')
Score: -0.0015309076696
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=8, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.86414496]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.12001624]')
Score: 0.120016243221
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.15786236]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.01863528]')
Score: -0.0186352822253
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.10970913]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.00409581]')
Score: 0.00409581499116
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.98567086]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.06264902]')
Score: 0.0626490196506
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 2.12162869]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.0015309]')
Score: -0.00153090031493
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 1.70111495]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.1969758]')
Score: 0.196975796246
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 2.14421916]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[-0.0121949]')
Score: -0.0121949033266
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 2.10970913]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.00409581]')
Score: 0.00409581499116
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.69115073]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.20167948]')
Score: 0.201679482675
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.96209778]')
('Residual Squared Error: ', '211.838564811')
('Calculated Score: ', '[ 0.07377687]')
Score: 0.0737768728705
Selector: ads, Classifier: KNN, Score: [ 0.214569]
Selector: pca30, Classifier: KNN, Score: [ 0.21443674]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.21443674]
Selector: pca20, Classifier: KNN, Score: [ 0.21029968]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.20167948]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.1969758]
Selector: ads, Classifier: RandomForest, Score: [ 0.19473478]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.19247514]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.12001624]
Selector: pca20, Classifier: nn, Score: [ 0.07698394]
Selector: pca20kbest250, Classifier: nn, Score: [ 0.07377687]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.07281602]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.06264902]
Selector: pca20, Classifier: Lasso, Score: [ 0.00409581]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.00409581]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.00409581]
Selector: pca30, Classifier: Lasso, Score: [ 0.00409581]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.00409581]
Selector: ads, Classifier: Lasso, Score: [ 0.0034825]
Selector: pca20kbest5, Classifier: RandomForest, Score: [-0.00051991]
Selector: pca20kbest50, Classifier: nn, Score: [-0.0015309]
Selector: ads, Classifier: nn, Score: [-0.0015309]
Selector: pca30rbf, Classifier: nn, Score: [-0.0015309]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.0015309]
Selector: pca20kbest5, Classifier: nn, Score: [-0.00153091]
Selector: pca30, Classifier: nn, Score: [-0.00153125]
Selector: pca20kbest250, Classifier: SVRdict, Score: [-0.0121949]
Selector: pca30, Classifier: RandomForest, Score: [-0.01722539]
Selector: pca20, Classifier: SVRdict, Score: [-0.01755333]
Selector: pca20kbest5, Classifier: SVRdict, Score: [-0.01815385]
Selector: pca20kbest50, Classifier: SVRdict, Score: [-0.01863528]
Selector: ads, Classifier: SVRdict, Score: [-0.01896759]
Selector: pca30, Classifier: SVRdict, Score: [-0.02251233]
Selector: pca30rbf, Classifier: SVRdict, Score: [-0.02259292]
Selector: pca20, Classifier: RandomForest, Score: [-0.1732023]
*************************************************************
('Outcome Variable:', 'pH')
*************************************************************
-------------------------------------------------------
ads KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.27023384]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.72271195]')
Score: 0.722711948315
-------------------------------------------------------
ads SVRdict
SVR(C=100.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.19693876]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.79792033]')
Score: 0.797920334394
-------------------------------------------------------
ads Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.82168786]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.15686272]')
Score: 0.156862722767
-------------------------------------------------------
ads RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.21170637]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.78276723]')
Score: 0.78276722805
-------------------------------------------------------
ads nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946559508
-------------------------------------------------------
pca20 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.25762927]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.73564555]')
Score: 0.73564554819
-------------------------------------------------------
pca20 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.24005544]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.75367813]')
Score: 0.753678130472
-------------------------------------------------------
pca20 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.77440273]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.20538219]')
Score: 0.205382191753
-------------------------------------------------------
pca20 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.21743656]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.77688745]')
Score: 0.776887454715
-------------------------------------------------------
pca20 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946559834
-------------------------------------------------------
pca30 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26339538]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.72972892]')
Score: 0.729728922167
-------------------------------------------------------
pca30 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.19824345]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.79658158]')
Score: 0.796581580204
-------------------------------------------------------
pca30 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.77440273]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.20538219]')
Score: 0.205382191753
-------------------------------------------------------
pca30 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.21602495]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.77833592]')
Score: 0.778335915451
-------------------------------------------------------
pca30 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.22062466]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.77361614]')
Score: 0.773616135514
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26339538]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.72972892]')
Score: 0.729728922167
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.62905788]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.35452114]')
Score: 0.354521135152
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946559834
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.22518478]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.76893698]')
Score: 0.768936979145
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946559834
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.25000917]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.74346457]')
Score: 0.743464570192
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.24098906]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.75272014]')
Score: 0.75272014152
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.77440273]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.20538219]')
Score: 0.205382191753
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.29784132]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.6943838]')
Score: 0.694383802705
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.22629042]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.76780248]')
Score: 0.767802479431
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.27296464]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.71990987]')
Score: 0.719909866144
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.21500332]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.77938421]')
Score: 0.779384213678
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.77440273]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.20538219]')
Score: 0.205382191753
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.30451927]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.68753153]')
Score: 0.687531530717
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946610795
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=8, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26485385]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.72823238]')
Score: 0.728232382611
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.19107497]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.80393719]')
Score: 0.803937191126
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.77440273]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.20538219]')
Score: 0.205382191753
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.25591771]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[ 0.73740179]')
Score: 0.73740178786
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.97917889]')
('Residual Squared Error: ', '97.4559998173')
('Calculated Score: ', '[-0.00473947]')
Score: -0.00473946559834
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.80393719]
Selector: ads, Classifier: SVRdict, Score: [ 0.79792033]
Selector: pca30, Classifier: SVRdict, Score: [ 0.79658158]
Selector: ads, Classifier: RandomForest, Score: [ 0.78276723]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.77938421]
Selector: pca30, Classifier: RandomForest, Score: [ 0.77833592]
Selector: pca20, Classifier: RandomForest, Score: [ 0.77688745]
Selector: pca30, Classifier: nn, Score: [ 0.77361614]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.76893698]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.76780248]
Selector: pca20, Classifier: SVRdict, Score: [ 0.75367813]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.75272014]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.74346457]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.73740179]
Selector: pca20, Classifier: KNN, Score: [ 0.73564555]
Selector: pca30, Classifier: KNN, Score: [ 0.72972892]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.72972892]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.72823238]
Selector: ads, Classifier: KNN, Score: [ 0.72271195]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.71990987]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.6943838]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.68753153]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.35452114]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.20538219]
Selector: pca20, Classifier: Lasso, Score: [ 0.20538219]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.20538219]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.20538219]
Selector: pca30, Classifier: Lasso, Score: [ 0.20538219]
Selector: ads, Classifier: Lasso, Score: [ 0.15686272]
Selector: ads, Classifier: nn, Score: [-0.00473947]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00473947]
Selector: pca20, Classifier: nn, Score: [-0.00473947]
Selector: pca30rbf, Classifier: nn, Score: [-0.00473947]
Selector: pca20kbest250, Classifier: nn, Score: [-0.00473947]
Selector: pca20kbest50, Classifier: nn, Score: [-0.00473947]
*************************************************************
('Outcome Variable:', 'Soc')
*************************************************************
-------------------------------------------------------
ads KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.56638942]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.52933668]')
Score: 0.529336676938
-------------------------------------------------------
ads SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.37343748]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.68967759]')
Score: 0.689677592951
-------------------------------------------------------
ads Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.81328091]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32417258]')
Score: 0.3241725848
-------------------------------------------------------
ads RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.4209176]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.65022214]')
Score: 0.650222139299
-------------------------------------------------------
ads nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.34975832]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.70935471]')
Score: 0.709354712126
-------------------------------------------------------
pca20 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.60851558]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.4943303]')
Score: 0.494330302722
-------------------------------------------------------
pca20 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.40567364]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.66288971]')
Score: 0.662889707412
-------------------------------------------------------
pca20 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.80728475]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32915533]')
Score: 0.329155329175
-------------------------------------------------------
pca20 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.55647355]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.53757665]')
Score: 0.537576647869
-------------------------------------------------------
pca20 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.20693766]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[-0.00295181]')
Score: -0.00295180510454
-------------------------------------------------------
pca30 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.56638942]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.52933668]')
Score: 0.529336676938
-------------------------------------------------------
pca30 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.37780826]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.68604553]')
Score: 0.686045525708
-------------------------------------------------------
pca30 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.80728475]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32915533]')
Score: 0.329155329175
-------------------------------------------------------
pca30 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.50133746]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.58339413]')
Score: 0.583394130749
-------------------------------------------------------
pca30 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.20796469]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.82718365]')
Score: 0.827183646647
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.56638942]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.52933668]')
Score: 0.529336676938
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.81690815]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32115838]')
Score: 0.321158384072
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 1.20693766]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[-0.00295181]')
Score: -0.00295180511261
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.61144946]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.49189228]')
Score: 0.491892280889
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.20693803]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[-0.00295212]')
Score: -0.00295211612439
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.60786276]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.49487279]')
Score: 0.49487278648
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.40638389]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.66229949]')
Score: 0.662299494294
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.80728475]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32915533]')
Score: 0.329155329175
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.44790422]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.62779656]')
Score: 0.627796559585
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.20693766]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[-0.00295181]')
Score: -0.00295180511261
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.60810454]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.49467188]')
Score: 0.494671875965
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.39284924]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.67354664]')
Score: 0.673546638381
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.80728475]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32915533]')
Score: 0.329155329175
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.24826188]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.79369713]')
Score: 0.79369713336
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.3041211]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.74727874]')
Score: 0.747278738741
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.54619879]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.54611486]')
Score: 0.546114861201
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.37720515]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.6865467]')
Score: 0.686546704193
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.80728475]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.32915533]')
Score: 0.329155329175
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.29763815]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.752666]')
Score: 0.752665996288
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.22756733]')
('Residual Squared Error: ', '120.338549573')
('Calculated Score: ', '[ 0.81089407]')
Score: 0.810894069476
Selector: pca30, Classifier: nn, Score: [ 0.82718365]
Selector: pca20kbest250, Classifier: nn, Score: [ 0.81089407]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.79369713]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.752666]
Selector: pca20kbest50, Classifier: nn, Score: [ 0.74727874]
Selector: ads, Classifier: nn, Score: [ 0.70935471]
Selector: ads, Classifier: SVRdict, Score: [ 0.68967759]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.6865467]
Selector: pca30, Classifier: SVRdict, Score: [ 0.68604553]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.67354664]
Selector: pca20, Classifier: SVRdict, Score: [ 0.66288971]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.66229949]
Selector: ads, Classifier: RandomForest, Score: [ 0.65022214]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.62779656]
Selector: pca30, Classifier: RandomForest, Score: [ 0.58339413]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.54611486]
Selector: pca20, Classifier: RandomForest, Score: [ 0.53757665]
Selector: ads, Classifier: KNN, Score: [ 0.52933668]
Selector: pca30, Classifier: KNN, Score: [ 0.52933668]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.52933668]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.49487279]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.49467188]
Selector: pca20, Classifier: KNN, Score: [ 0.4943303]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.49189228]
Selector: pca30, Classifier: Lasso, Score: [ 0.32915533]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.32915533]
Selector: pca20, Classifier: Lasso, Score: [ 0.32915533]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.32915533]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.32915533]
Selector: ads, Classifier: Lasso, Score: [ 0.32417258]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.32115838]
Selector: pca20, Classifier: nn, Score: [-0.00295181]
Selector: pca20kbest5, Classifier: nn, Score: [-0.00295181]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00295181]
Selector: pca30rbf, Classifier: nn, Score: [-0.00295212]
*************************************************************
('Outcome Variable:', 'Sand')
*************************************************************
-------------------------------------------------------
ads KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14311392]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.87201896]')
Score: 0.872018964091
-------------------------------------------------------
ads SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.25869884]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.768656]')
Score: 0.768656002974
-------------------------------------------------------
ads Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43779198]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.60850018]')
Score: 0.608500183583
-------------------------------------------------------
ads RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.20828323]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.81374066]')
Score: 0.813740660819
-------------------------------------------------------
ads nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.12306666]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.00431348]')
Score: -0.00431348278482
-------------------------------------------------------
pca20 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14861834]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.86709657]')
Score: 0.867096574905
-------------------------------------------------------
pca20 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.30290329]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.72912573]')
Score: 0.729125732752
-------------------------------------------------------
pca20 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43480822]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.61116845]')
Score: 0.611168446516
-------------------------------------------------------
pca20 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.26969011]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.75882695]')
Score: 0.758826951691
-------------------------------------------------------
pca20 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.1230665]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.00431334]')
Score: -0.00431334005113
-------------------------------------------------------
pca30 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14048564]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.87436932]')
Score: 0.874369324731
-------------------------------------------------------
pca30 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.25685395]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.77030581]')
Score: 0.770305808556
-------------------------------------------------------
pca30 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43480822]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.61116845]')
Score: 0.611168446516
-------------------------------------------------------
pca30 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.14907936]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.8666843]')
Score: 0.866684303142
-------------------------------------------------------
pca30 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.1230654]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.00431235]')
Score: -0.00431235069103
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14048564]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.87436932]')
Score: 0.874369324731
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.38493472]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.65576832]')
Score: 0.655768315195
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 1.1230665]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.00431334]')
Score: -0.00431333992032
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.15465067]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.8617021]')
Score: 0.861702103671
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.12306648]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.00431332]')
Score: -0.00431331657091
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14861834]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.86709657]')
Score: 0.867096574905
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.30556773]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.72674304]')
Score: 0.726743036161
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43480822]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.61116845]')
Score: 0.611168446516
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.19862261]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.82237976]')
Score: 0.822379762516
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.29576952]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.73550518]')
Score: 0.735505177888
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14805613]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.86759934]')
Score: 0.867599336844
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.28245979]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.74740754]')
Score: 0.747407538135
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43480822]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.61116845]')
Score: 0.611168446516
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.29228753]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.73861898]')
Score: 0.738618984603
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.26627039]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.76188507]')
Score: 0.761885065042
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.14784936]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.86778425]')
Score: 0.867784247494
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.24739054]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.77876856]')
Score: 0.778768556628
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43480822]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.61116845]')
Score: 0.611168446516
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', 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=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.23306121]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[ 0.7915827]')
Score: 0.791582701024
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.1230668]')
('Residual Squared Error: ', '111.824314058')
('Calculated Score: ', '[-0.0043136]')
Score: -0.00431360125802
Selector: pca30, Classifier: KNN, Score: [ 0.87436932]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.87436932]
Selector: ads, Classifier: KNN, Score: [ 0.87201896]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.86778425]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.86759934]
Selector: pca20, Classifier: KNN, Score: [ 0.86709657]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.86709657]
Selector: pca30, Classifier: RandomForest, Score: [ 0.8666843]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.8617021]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.82237976]
Selector: ads, Classifier: RandomForest, Score: [ 0.81374066]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.7915827]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.77876856]
Selector: pca30, Classifier: SVRdict, Score: [ 0.77030581]
Selector: ads, Classifier: SVRdict, Score: [ 0.768656]
Selector: pca20kbest50, Classifier: nn, Score: [ 0.76188507]
Selector: pca20, Classifier: RandomForest, Score: [ 0.75882695]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.74740754]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.73861898]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.73550518]
Selector: pca20, Classifier: SVRdict, Score: [ 0.72912573]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.72674304]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.65576832]
Selector: pca30, Classifier: Lasso, Score: [ 0.61116845]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.61116845]
Selector: pca20, Classifier: Lasso, Score: [ 0.61116845]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.61116845]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.61116845]
Selector: ads, Classifier: Lasso, Score: [ 0.60850018]
Selector: pca30, Classifier: nn, Score: [-0.00431235]
Selector: pca30rbf, Classifier: nn, Score: [-0.00431332]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00431334]
Selector: pca20, Classifier: nn, Score: [-0.00431334]
Selector: ads, Classifier: nn, Score: [-0.00431348]
Selector: pca20kbest250, Classifier: nn, Score: [-0.0043136]
-------------------------------------------------------
Best Results
-------------------------------------------------------
Outcome Variable: Ca, Selector: pca30, Classifier: nn, Score: [ 0.87333401]
Outcome Variable: P, Selector: ads, Classifier: KNN, Score: [ 0.214569]
Outcome Variable: pH, Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.80393719]
Outcome Variable: Soc, Selector: pca30, Classifier: nn, Score: [ 0.82718365]
Outcome Variable: Sand, Selector: pca30, Classifier: KNN, Score: [ 0.87436932]
Best result obtained: [ 0.71867863]
Predictions based on our dev data
Test on the evaluation data
In [12]:
myScore = []
#scaler = Normalizer().fit(train_data)
#transformedEvalData = scaler.transform(eval_data)
#transformedEvalData = eval_data
# Use the appropriate model to estimate the 5 outcome variables
for outcomeVarIndex in range(0, 5):
# Grab selector and classifier
selector = models[outcomeVarIndex][3]
classifier = models[outcomeVarIndex][4]
# Transform the input variables
selectedSample = selector.transform(transformedEvalData)
# Predict
myScore.append(classifier.score(selectedSample,eval_labels[outcomeVarIndex]))
print(myScore)
[0.62122203585477465, 0.35467539089637223, 0.70822872540118986, 0.94364298626380738, 0.85257083079361018]
In [13]:
scaler = Normalizer().fit(train_data)
normalizedTestData = scaler.transform(test_x)
#normalizedTestData = test_x
allPredictions = []
# Iterate through test samples
for sampleIndex in range(len(test_x)):
sampleId = test_ids[sampleIndex]
sample = normalizedTestData[sampleIndex]
currentSamplePredictions = []
# Use the appropriate model to estimate the 5 outcome variables
for outcomeVarIndex in range(0, 5):
# Grab selector and classifier
selector = models[outcomeVarIndex][3]
classifier = models[outcomeVarIndex][4]
# Transform the input variables
selectedSample = selector.transform(sample.reshape(1, -1))
# Predict
predicted = classifier.predict(selectedSample.reshape(1, -1))
# Store
currentSamplePredictions.append(predicted[0])
allPredictions.append(currentSamplePredictions)
print(allPredictions)
[[-0.37661254480705231, -0.30763825587811122, -0.44294710961295936, -0.52851457155854265, 0.57477975792323166], [0.5092224628291071, -0.31317180139216938, 0.59545642469538462, -0.12506326637089127, -0.72000738362878969], [-0.30011569198230503, -0.26129481219787287, -0.032017197777520767, 0.04913013226720353, -0.90269340007351995], [0.04464046146229439, -0.31040502863514041, -0.35981053392782814, 0.80672765286021719, -0.36605322676712648], [-0.37661254480705231, -0.3041797899318246, -1.4214430310899222, -0.011121905454265502, -0.37366514411899021], [-0.37661254480705231, -0.37265741566829658, -0.53690502792164851, -0.87464705847220015, 0.28476570681722263], [-0.37661254480705231, -0.25576126668381438, -0.68133553204160124, 0.61393317248660018, -1.3160205122797199], [-0.37661254480705231, -0.25921973263010079, 0.73427756097558428, -0.11496707506914511, 0.3479446208376924], [-0.37661254480705231, 0.73889353946816994, 0.47518190361304802, -0.024353167641585238, -0.68879852248614892], [-0.17992713262070392, -0.378882654371612, -1.1442381105538577, -0.43269037238074176, 0.1218706754873394], [-0.37661254480705231, -0.062087173691773058, -0.72174191922943054, 0.04664852160875671, 0.61664530335848178], [-0.29529712192695712, -0.16099929975556559, -0.36425124560825894, -0.79209511508874786, 1.4958217574987462], [0.79787981431936861, -0.28827084657890722, 1.2812292115016812, -0.29940773881640853, -0.85017117034566159], [-0.37661254480705231, 0.045125270643106896, -1.0583453150142934, 0.038159499008742132, -0.31124742183370818], [-0.37661254480705231, 0.29344312558647478, -0.062156146073818253, -0.65203434750179423, 0.96146515939790933], [-0.37661254480705231, 0.8073711652046407, -0.22671875522644358, -0.088241037247980436, 0.32206410184135581], [-0.37661254480705231, 0.66903252735318319, -0.23033304188973647, -0.50692921819037551, 0.43776524558968355], [-0.37661254480705231, -0.080762889801719986, -0.10294690100033721, -0.51957586102946784, 0.67525706696783305], [1.0089122977908223, -0.28896253976816444, 1.3788294260302787, 0.098206591863178871, -0.38203825320604068], [-0.37661254480705231, 0.061034213996024489, 0.64375977244452498, -0.48601635729738052, 0.70798831158084696], [-0.37661254480705231, -0.25921973263010079, -0.28130589232705505, -0.88999747029061949, 1.0809722618221715], [-0.37661254480705231, -0.23086031187055198, -0.90459023263002836, 0.16991192739322411, -0.5434109010655519], [0.79730630968729899, 1.2943231704417726, 1.6459789098054212, -0.35172508632164939, 0.00084118959270581552], [-0.37661254480705231, -0.079379503423205405, 0.0646373057529952, 0.0029504889750089913, -0.40487400526163164], [-0.37661254480705231, 0.29551820515424665, -0.66455477514708194, -0.80945800930170653, -0.57005261179707534], [-0.37661254480705231, -0.135406651753046, 0.41472741378595357, -0.70498128152842487, 0.71712261240308361], [0.82602597556816137, 1.0861235204753303, 2.0658419643955876, -0.43491003929038535, -0.31733695571519832], [-0.37661254480705231, -0.084913048937263841, -0.10457445875619031, -0.4919497179298758, 0.6912420934067468], [0.31373525418573878, 0.65519866356803713, 1.4463631005905593, -0.38967925866398839, 0.43091451997300706], [-0.37661254480705231, 0.067951145888597561, -0.016204061372122158, -0.68502692016948874, 0.58315286701028168], [-0.37661254480705231, 0.099769032594432733, 0.49452239268998666, -0.55839789177369692, 0.92949510652008094], [-0.033130786469135476, -0.331155824312859, -0.044596916088295746, 0.044318614625750652, -0.84864878687528744], [-0.37661254480705231, -0.18590025456882825, -1.1232363168818158, 0.015362933986695526, 0.48495913317123901], [-0.37661254480705231, -0.36020693826166539, -0.33419641274440481, -0.38701850979579749, -0.97043946450510798], [-0.37661254480705231, -0.086296435315778394, -1.2070283681456719, 1.7695739192538655, -0.68575375554540297], [-0.37661254480705231, -0.082837969369491865, -1.0769623519423424, 3.7270122089524609, 0.019870982972365003], [-0.37661254480705231, -0.060703787313258463, -0.26522804316255177, -0.87105242678791939, 0.89219671149595003], [-0.37661254480705231, -0.3802660407501266, -0.87749628565254101, 0.08210698047431339, -0.99708117523662954], [-0.37661254480705231, -0.35329000636909241, -0.63396791451691614, -0.48126756292968648, 0.239855394441227], [-0.37661254480705231, -0.37265741566829658, -1.0497296415319401, 2.1686880856244533, -1.1660657404480061], [-0.37661254480705231, -0.081454582990977215, -0.92555669136970709, -0.075994167826889225, 0.24518373658753162], [-0.37661254480705231, -0.22394337997797925, -0.36626789132654425, -0.54338755331417077, 0.6455705892955641], [-0.37661254480705231, -0.34844815404429141, -0.92552290953329974, -0.016315040631541988, -0.65606727787313324], [-0.37661254480705231, -0.19765903878620208, -0.49745878515622666, -0.76659192185343517, 0.67145110829190124], [-0.37661254480705231, -0.27582036917227576, -1.0593061551742271, -0.51514507748358418, -0.4261873738468519], [1.157706685776476, 0.0133073839372716, 1.5278913461824, -0.339912133660278, -0.02047217899251199], [-0.37661254480705231, -0.29518777847148014, -0.68174731511123854, -0.12514026685829938, -0.22523275575764642], [-0.21206924086191009, 0.72160120973673803, 0.28677989690297023, -0.022867138172504298, -0.51905276553958735], [-0.16189300374095894, -0.27582036917227615, 0.62467997159947641, 0.12103385491502477, -0.83799210258267798], [-0.37661254480705231, -0.14163189045636151, -0.94648292411147894, -0.16772535308282777, -0.2039193871724288], [-0.37661254480705231, -0.3768075748038402, -1.3838571172413485, 1.4525684590788484, -1.2246775040573579], [-0.37661254480705231, -0.032344366553709779, -0.61726717128902964, -0.30648938454404379, 0.064020103613175089], [-0.37661254480705231, -0.169299618026653, -0.21524216935910134, -0.47120068068529236, 0.23300466882454995], [-0.37661254480705231, -0.32354719923102898, -0.65508859712622369, -0.52579547121641834, 0.53443659595835347], [0.022758833848628424, -0.32769735836657299, 0.12896316480146153, -0.1905624689940173, -0.86615619678457334], [-0.24826733617228863, 2.5532047748900419, 0.36049701991674921, 0.013301111017431144, -0.20772534584836161], [-0.28694770740644709, -0.023352355093364863, 0.061276300466999611, 0.14245141904958045, -0.2473073160780524], [-0.37661254480705231, -0.35052323361206345, -0.54359774547720052, -0.5518171257420923, 0.16449741265777618], [-0.37661254480705231, -0.36020693826166561, 0.016353862106427197, -0.6214333228410267, 0.60218266038994039], [-0.37661254480705231, -0.37334910885755385, -0.3084490121705703, -0.58924428498193127, -0.28079975242625244], [-0.37661254480705231, 0.62130569729442997, -0.56680089300209646, -0.13938198987234984, -0.59669432252859744], [-0.37661254480705231, -0.1250312539141864, -0.74547462271723974, 0.17066747318844944, -0.55482877709334766], [6.1623434763902196e-06, -0.1125807765075552, 1.1292296495307705, -0.40055350740990542, -0.46576934407654191], [0.023670548227514687, 2.0939204972232019, 0.73033683915812686, 0.132481179190333, -0.7397983687436358], [-0.24698078236320548, 1.2265372378945603, 0.34126001358771374, -0.042730580831235765, -0.68423137207502971], [-0.37661254480705231, -0.21495136851763424, -0.69257642231389172, -0.26520719567130563, -0.40867996393756323], [-0.37661254480705231, -0.35951524507240801, -0.063724883525623444, -0.46971333649629982, -0.99708117523663087], [0.043422326244408227, -0.26129481219787271, 0.47805617958502422, -0.078186348251586213, -0.56548546138595623], [-0.37661254480705231, 0.30105175066830503, -0.24136102733280085, -0.97518678377118695, -0.53656017544887491], [-0.37661254480705231, -0.36919894972201017, -1.2881800282628721, 0.70221727363638342, -0.55026162668222856], [-0.37661254480705231, -0.26544497133341638, -0.13677290460398739, -0.47122842197649489, 0.68363017605488297], [-0.37661254480705231, 0.17032173789867669, -1.1981904047741101, -0.89474219011174627, 1.4356876104190204], [0.41301985499645877, -0.33530598344840257, 0.95907084002942566, -0.3489811121839142, -0.81515635052708646], [-0.37661254480705231, 0.70015872086975928, 0.6932539366274848, -0.60448656727389549, 0.72245095454938846], [0.52760910174634401, -0.27028682365821738, 0.29797714342726023, 1.8979759687376307, -1.1538866726850237], [-0.37661254480705231, 0.056884054860480801, -0.58623294925621439, -0.028966088078508812, -0.06994964177962705], [-0.37661254480705231, 0.49403415047108856, -0.33546231887140765, -0.3441993906102791, 0.27030306384868208], [2.323012659696138, -0.32008873328474241, 1.7755707154694274, 0.41045335149345508, -1.2589311321407419], [-0.37661254480705231, -0.3622820178294372, -1.2724329537003034, 0.074674629405458853, -0.75882816212329518], [-0.37661254480705231, -0.33392259706988836, -0.79374469968246375, 0.12705456272226176, -0.7367536018028904], [-0.31983073101389137, -0.14232358364561884, 0.87078521967927669, -0.5289025814134829, 0.12263186722252561], [1.0329943958115029, 0.50302616193143357, 1.6642672308819608, -0.22365662139783227, -0.32190410612631581], [0.41411219965807855, -0.35605677912612144, 1.1644159459121108, -0.31261367512929111, -0.41096353914312261], [-0.092893252642297419, -0.36159032464017982, 0.045478652610767778, 0.22560849582138714, -0.87452930587162425], [-0.37661254480705231, 0.89037434791551517, 0.23060014897549719, -0.055055073410985644, -0.67205230431204799], [-0.37661254480705231, -0.19005041370437156, -0.060141906963921077, -0.66994848858883826, 0.56260069016025016], [-0.37661254480705231, -0.029577593796680589, -0.37925692678419454, -0.48064789735829999, 0.77192841733650241], [-0.37661254480705231, -0.3097133354458832, -0.87743638119658884, -0.42697729739796514, -0.61953007458418929], [1.7351840569469181, 0.54591113966538574, 2.2909448171504319, -0.13985608923019027, -0.53732136718405865], [-0.066153336023625942, -0.23777724376312487, 0.14110658364086204, -0.21098859826760441, 0.78562986856985761], [-0.37661254480705231, 0.41310604732798561, -0.53326961320860322, -0.42128061115816556, -0.12323306324267361], [0.21081775396402969, 0.30243513704681896, 0.18795717201459139, 0.74434194456200697, -0.71772380842323069], [-0.37661254480705231, -0.30556317631033936, -1.1788821355578141, 0.99299627484061981, -0.66215681175462582], [-0.37661254480705231, -0.30556317631033941, -0.79861331021757609, -0.78420549188178024, 1.1015244386722001], [-0.37661254480705231, 0.75480248282108731, 0.14141627999700246, -0.31886067929293532, -0.21229249625947913], [-0.27036353634410043, 0.67041591373169795, 0.43675868350226166, 0.18038580439954555, 0.023676941648296611], [-0.37661254480705231, 0.2132067156326293, 0.74652108220780589, -0.90125841755844127, 1.1243601907277938], [-0.37661254480705231, -0.31870534690622776, -0.87706609037874772, -0.013050045928019893, -0.36909799370787161], [-0.37661254480705231, 0.85163952931710818, -0.42678263588183585, 0.23032198935670961, -0.24959089128361164], [-0.37661254480705231, -0.33807275620543181, -0.79240102314659611, -0.58722587207036225, 0.08000513005208873], [-0.37661254480705231, 1.7266314137275809, 0.24946874008196529, -0.17544204117143292, -0.40944115567274963], [0.5303145534168543, 0.18484729487307969, 0.44893033962318851, -0.16591860590297236, 0.92188318916821788], [-0.37661254480705231, -0.31178841501365501, -0.24195544600888796, -0.049121872556139146, -0.38279944494122642], [0.17188553464726897, -0.24331078927718308, 0.3689706522180809, -0.051334861345258165, -0.95293205459582142], [-0.37661254480705231, -0.17690824310848319, -0.64540011226100136, -0.42408694272405595, -0.9658723140939911], [-0.37661254480705231, 0.31626900083196541, -0.47906173618019965, -0.35609169886892261, -0.69412686463245454], [-0.37661254480705231, -0.060703787313258442, -0.73511448817220115, -0.20562287043918315, 0.572496182717672], [-0.37661254480705231, -0.15546575424150721, 0.092792744781604153, -0.29197192292298352, -0.62485841673049447], [1.0535608284299562, -0.27443698279376144, 1.3687990836132538, 0.12265259912383927, -0.19402389461500605], [-0.22617275613653626, -0.37196572247903936, -0.80683009802271544, -0.45767901792569038, 0.15460192010035256], [-0.36059303286273819, -0.26613666452267387, -0.14514511397385421, 0.020544820635322147, -0.65454489440276209], [-0.37661254480705231, -0.13056479942824481, -0.1504047569668221, -0.77644399377841999, 0.64024224714926059], [-0.37661254480705231, -0.024735741471879392, -0.31716841731383782, -0.67366038274095996, -0.27471021854476141], [-0.37661254480705231, -0.3325392106913736, 0.13696517812019193, -0.29412962783722257, -0.58832121344154764], [-0.37661254480705231, 0.47189996841485538, -0.69505666438996672, -0.27295385695786684, 0.048796268909447389], [-0.37661254480705231, -0.16238268613407997, 0.31861108174646757, 0.12796201149442099, -0.08441228474816892], [1.6378264773307762, -0.22394337997797925, 1.5497004625776181, 1.0224612879650943, -1.0024095173829342], [-0.37661254480705231, -0.19350887965065816, -0.39797062671340244, -0.40833553328038813, 0.07772155484652879], [-0.37661254480705231, -0.37473249523606861, -0.68637460698755648, -0.3573278702370894, -0.59821670599897048], [0.58401335003392729, 0.88691588196922788, 1.4617764498684944, -0.51593187756185199, 0.66916753308634225], [3.0139358131266483, -0.3373810630161746, 1.8895106842091987, 0.77951571558187682, -1.30155786931118], [-0.37661254480705231, -0.18244178862254171, 0.22142493304525557, -0.47531602163165981, 0.82749541400510862], [-0.13737361859542618, 0.2028313177937692, 1.0702158963011068, -0.57565218785576278, 0.1378557019262531], [-0.37661254480705231, 0.11775305551512202, 0.072338033229626841, -0.63999589739769203, 0.018348599501991591], [-0.37661254480705231, 0.68217469794907182, 0.05765978097323865, -0.43047065954285763, 0.36164607207104582], [1.6297989083238931, 1.0287129857669741, 1.9838174048267372, -0.60194415698276571, -0.26100876731140688], [-0.37661254480705231, 0.68148300475981438, -0.39410887686108298, -0.34015353992738701, 0.7574657743679607], [1.6268454869876772, 1.1241666458844801, 2.2501703832444941, -0.14768765891995217, -0.63399271755272901], [-0.37661254480705231, -0.095980139965380379, -0.088684110412004191, -0.46011399846336504, 0.63110794632702338], [-0.37661254480705231, -0.14094019726710419, 0.52884419797297211, -0.89016005673617826, 1.208852473333482], [-0.37661254480705231, -0.25783634625158652, -0.32972711373145147, 0.13023600789610437, -0.054725807075899378], [-0.37661254480705231, 0.80875455158315612, -0.69709906065446647, 1.0863039500816263, -0.43303809946352628], [-0.37661254480705231, -0.32908074474508719, -0.86448696949580084, 0.21663145038193421, -0.53427660024331503], [-0.37661254480705231, -0.34083952896246139, -0.59516634352596087, -0.60099694110559643, -0.32038172265594456], [-0.37661254480705231, -0.21633475489614878, 0.62039774342014153, -0.62891772320217676, 0.27487021425980124], [-0.37661254480705231, -0.3325392106913736, 0.03135619789833255, 0.063322118470365479, -0.58832121344154764], [-0.18136235551624247, -0.25022772116975628, -0.29260822123930597, -0.031207157631353732, 0.35175057951362498], [-0.33284027119963983, 0.99343663311485297, -0.1522589793389959, 3.0929852931970112, -0.69336567289726792], [-0.37661254480705231, -0.13471495856378879, -0.06464403027271759, -0.34641256624474731, 0.5595559232195042], [3.2239310959158045, -0.3276973583665726, 2.023612089675026, 0.27205823606964152, -1.319826470955654], [-0.37661254480705231, -0.38095773393938398, -1.4037689123794341, 0.98616563970661542, -1.0655884314034021], [-0.37661254480705231, -0.27997052830781943, -0.25005668868971087, -0.69716072695652409, -0.041024355842544782], [0.21228835578468386, -0.293112698903708, 0.5684940486320893, 0.017982720961310261, -0.88442479842904631], [-0.37661254480705231, -0.33323090388063098, -1.1464462791606427, 0.11944305328685034, -1.0252452694385255], [0.23087965954626211, 0.69670025492347532, 1.3164345745601334, -0.17471565912774778, 0.33500436133952471], [-0.37661254480705231, 0.46567472971154017, -0.17653647852986909, -0.77657431871467519, -0.50459012257104718], [-0.27519221425687812, -0.32838905155582976, -0.89347027340880958, -0.52549847218337864, 0.36316845554141997], [1.3128107487139902, -0.128489719860473, 1.3398013418624255, -0.047633980032804224, -0.86082785463826972], [-0.37661254480705231, 0.20905655649708602, 0.59344843162511962, -0.17833727284677203, -0.92705153559948417], [-0.37661254480705231, -0.14716543597041992, -0.19874561519716893, 0.021646602551294247, 0.81303277103656657], [0.69710728586820414, -0.20042581154323127, 1.3000017435552134, 0.07718506543706119, -0.67052992084167395], [-0.37661254480705231, 0.70292549362679024, -0.014949202820389917, -0.080200303877446255, -0.46120219366542392], [-0.37661254480705231, -0.048945003095884385, -1.0520915867802505, -0.70298129406655074, 1.2339718005946325], [-0.37661254480705231, -0.16238268613407997, 0.035754391663459284, -0.0015500003358832615, -0.084412284748168948], [-0.37661254480705231, -0.19350887965065811, -0.67435932019515166, -0.80816875412528111, 0.90437577925893231], [-0.14120960644184, -0.26336989176564485, -0.058584149293921417, 0.074782080294973907, 0.17515409695038606], [-0.37661254480705231, 0.014690770315786039, 0.04835100374611967, -0.60721462911609758, 0.87240572638110192], [-0.37661254480705231, -0.31663026733845601, -0.4521284588842609, -0.41825452137202473, -0.014382645111020609], [-0.37661254480705231, -0.22325168678872159, -0.060099234937340373, -1.0583243200588737, 1.0010471296276005], [-0.048851656270940458, -0.36850725653275301, 0.21361262708614914, 1.4837680328648957, -0.57461976220819455], [-0.37661254480705231, -0.23293539143832387, -0.018218323383110135, -0.3968876495772598, 1.1829719543371449], [-0.37661254480705231, 0.011923997558757593, -0.57207044595165302, -0.36030047854775676, -0.73447002659733296], [-0.37661254480705231, -0.034419446121481588, 0.27244500920423276, -0.58979863343724359, 1.0322559907702415], [2.1516068452751287, -0.25783634625158613, 1.6122552746374477, 0.21188531758933909, -1.2855728428722659], [-0.37661254480705231, -0.34775646085503398, -0.57243647387124996, -0.36535255402470929, -0.17194933429460002], [-0.37661254480705231, -0.2529944939267853, 0.4093997934848228, -0.58655424246531207, 0.46973529846751177], [-0.37661254480705231, 1.915463654394826, -0.14132929276453909, -0.61835594561898866, 0.71483903719752528], [-0.30715679665608281, 0.13988723757135674, 1.0421405427950385, -0.60493166035088208, 0.16069145398184459], [-0.37661254480705231, -0.28412068744336338, -0.38231210079598599, 0.054369529342195494, 0.18504958950780903], [-0.37661254480705231, 0.36399583089071924, 0.62604449368842507, 0.066529535317865235, -0.55102281841741474], [-0.37661254480705231, -0.27305359641524674, -0.92933792983552665, -0.11356599723124233, -0.42999333252278171], [-0.37661254480705231, -0.18659194775808541, -1.0189313064753382, 0.11286538889590042, -0.45815742672467807], [-0.11938378965878915, -0.17621654991922581, 0.67110011593891228, -0.34087496957858804, -1.0046930925884954], [-0.37661254480705231, 0.42278975197758817, -0.63254597200202101, -0.33597967670562862, 0.23985539444122717], [0.017218140394560488, 0.48850060495703029, 0.80210399447400915, -0.2364404569075827, -0.69412686463245232], [-0.37661254480705231, 0.38889678570398162, 0.53823423080473232, -0.27105375144351257, -0.12094948803711478], [-0.37661254480705231, -0.082146276180234429, -1.0894501932591258, -0.68869369445038964, 0.96146515939791011], [1.7334941600933647, 0.056192361671223545, 2.2752154326619514, -0.31500204816750105, -0.89127552404572286], [1.7183656173273651, 0.24087444320292034, 1.6642920215251134, -0.40414989695419584, 0.49561581746384925], [-0.37661254480705231, 0.41310604732798595, -0.77251599282149686, -0.20864484805712447, -0.25720280863547507], [-0.36797234165298592, 0.70015872086975939, 0.50880174829680369, -0.59864511372279305, 0.72245095454938846], [0.047124088035559786, 1.0010452581966809, 1.3275716633065424, -0.60326659212951728, 0.67754064217339249], [-0.37661254480705231, -0.20665105024654679, -0.090044993871203349, -0.44747812412117477, 0.21778083412082244], [0.1330233565140968, -0.25991142581935811, 0.50331477985727657, -0.14844433226925047, -0.84408163646416889], [0.89468573860847633, 0.15372110135650158, 1.1878971997858225, 0.099843353314161165, -0.98718568267920637], [-0.30938389118972304, -0.03580283249999601, 0.31368781921531719, -0.44478434323557897, 0.87392810985147784], [-0.37661254480705231, -0.073154264719889672, 0.17308874156904563, -0.50990813170263238, 0.55346638933801373], [-0.37661254480705231, 0.021607702208359593, -0.54705992486822241, -0.13079917594640578, -0.54493328453592504], [-0.37661254480705231, -0.31663026733845595, -0.47423139216320509, -0.14471273357238967, -0.014382645111020631], [-0.2147947929623979, 0.74027692584668436, 0.22732897193623058, 0.016331233796492928, -0.57309737873782018], [-0.37661254480705231, -0.3622820178294372, -1.2119518608294872, 0.51938011181152821, -0.7382759852732631], [-0.37661254480705231, -0.33668936982691744, -0.30862477277648326, -0.12181201477368142, -0.73827598527326321], [-0.37661254480705231, 0.045125270643106966, -1.0691478482978316, -0.20862506712054507, -0.088979435159286219], [-0.37661254480705231, -0.24953602798049901, -1.2467144562988044, -0.00066976950331382179, -0.46120219366542303], [1.8303242634620731, 0.76517788065994685, 2.4704679470709432, -0.2333611840501752, -0.4764260283691496], [-0.37661254480705231, -0.30902164225662582, -0.69787794273969483, -0.57001091073796628, -0.70782831586580841], [-0.37661254480705231, 0.049275429778650751, -0.82251465170691107, 0.18076276195746632, -0.43303809946352773], [-0.37661254480705231, -0.21702644808540628, -0.4178424185207068, -0.57035848336256989, 0.53443659595835236], [1.9161456790470672, -0.33392259706988836, 1.7161090195239121, 0.18454217267285056, -1.1165882776608911], [2.6941694256350206, 1.7971841190318241, 2.0696543287243396, -0.0017706489444067053, -0.35768011768007602], [-0.37661254480705231, -0.046869923528112596, -0.50387602764399775, -0.60121150172622628, 0.52149633646018578], [-0.29718304101591886, -0.31178841501365478, 0.32358210359004103, -0.39326244049075093, -1.097558484281234], [-0.37661254480705231, 0.53622743501578385, -0.57306606872546739, -0.12131583233380949, 0.17896005562631739], [-0.37661254480705231, -0.14647374278116235, -0.56574688221243763, -0.76134205780069264, 0.5116008439027615], [-0.37661254480705231, -0.3104050286351403, -1.0507525064719521, -0.30863504164119432, -0.49697820521918279], [-0.37661254480705231, -0.34222291534097582, -0.48902571204575951, -0.59286678654940728, 0.25203446220420916], [-0.37661254480705231, -0.05932040093474391, -0.51964667833311795, -0.38399008234936099, -0.23893420699100201], [-0.37661254480705231, -0.22325168678872181, -0.098105891702843895, -0.66993152800023581, 0.4468995464119202], [2.4003773224978815, -0.33115582431285923, 1.8481439773583186, 0.16061770234606837, -1.1318121123646168], [-0.065082427129249387, 2.439075398662589, 0.89982859392330239, -0.081595210302560783, -0.638559867963849], [-0.37661254480705231, -0.28827084657890717, -0.7220190277301417, -0.34397713997035068, -0.28460571110218519], [-0.37661254480705231, -0.34637307447651938, -0.95456597599141491, 0.48825277682585294, -0.80602204970484936], [-0.37661254480705231, 0.33148625099562523, -0.39395563050132454, 1.0770045370654955, -0.71772380842323202], [-0.37661254480705231, -0.24123570970941127, 0.17971394784234418, -0.29039432589939657, 0.3928549332136882], [-0.37661254480705231, 0.13642877162506933, -1.3343824464045615, -0.24024148790076988, -0.40715758046719042], [-0.37661254480705231, -0.34291460853023298, -0.84869113617770653, 1.7023301099407568, -1.1653045487128197], [-0.37661254480705231, 0.063800986753053693, -0.33299096513405901, -0.39586788661422806, -0.17042695082422821], [-0.37661254480705231, -0.34360630171949058, -1.0254278798680336, -0.20579748929999153, -0.43456048293390026], [1.6116862368242848, -0.33046413112360196, 1.3413938280884277, 0.021059664550080792, -0.63779867622866182], [-0.37661254480705231, -0.19904242516471657, -0.81305485852473725, -0.30135772591065219, -0.85473832075677936], [-0.37661254480705231, -0.2066510502465469, 0.25679468896649338, -0.27570754528833702, -0.32951602347818038], [1.6426161695733525, 1.2244621583267874, 1.9691059765594896, -0.44571171325686515, -0.15900907479643273], [-0.37661254480705231, -0.3574401655046362, -1.3534468765282957, -0.081394545691929721, -0.68118660513428497], [-0.07355081814058495, -0.26544497133341671, 0.0089764164486605402, 0.06234336520071268, -0.8692009637253193], [-0.37661254480705231, -0.34775646085503398, -0.96685460425843006, -0.17559234121255479, -0.023516945933258239], [1.6817220886071689, -0.3131718013921696, 1.4786798741491505, 0.85122781555481231, -1.2429461057018281], [-0.37661254480705231, -0.16791623164813854, 0.71310377732109442, -0.95650541583908377, 0.72168976281420039], [0.021556912363043357, 2.9640705293088745, 0.63596416523981203, 1.5656149038747331, -0.89964863313277399], [0.88726223350450772, 0.68494147070610101, 1.5617314571470611, -0.16057536825248497, -0.29297882018923349], [-0.37661254480705231, -0.32977243793434463, -0.13582351183708896, -0.33892886748515155, -0.8867083736346062], [-0.37661254480705231, -0.048945003095884516, -0.55870419629501145, -0.55295709427622941, 0.90970412140523682], [-0.09929449907178356, -0.18313348181179884, 0.56701016575013297, -0.5621092820663629, -0.2952623953947936], [0.12673926561705007, 0.22358211347148799, 0.64587545593323581, -0.21173749298389077, 1.4098070914226857], [-0.016538490362903335, 0.19245591995491096, 1.2144664176599345, -0.56369818986698816, 0.27334783078942781], [-0.035090232077836236, -0.24261909608792614, 0.036512337673371587, 0.13684841504444398, -1.0465586380237455], [-0.20985672985185139, 2.468818205800654, 0.37179292031109368, 1.1757765205039525, -0.62866437540642439], [-0.37661254480705231, 0.00016521334138320799, 0.28924266560161938, -0.68502309465403322, 0.70418235290491427], [1.8709924757317031, 0.74996063049628636, 2.5058897179891058, -0.11433625531500419, -0.57081380353225986], [-0.37661254480705231, 0.80114592650132566, -0.70641426656487782, 0.18856780110782467, -0.55787354403409395], [-0.37661254480705231, -0.32008873328474241, 0.68174383396620852, -0.82269826232509735, 0.60370504386031409], [-0.37661254480705231, -0.30832994906736844, 0.10563289699791722, -0.17020937387266727, -0.28384451936699684], [-0.37661254480705231, 1.3918519101270521, 0.21120810812340052, -0.056173088788077641, -0.94760371244951425], [0.013741770065991199, 0.20006454503674079, 0.62537500525348788, 0.6871847265259301, -0.8851859901642346], [0.38647693299328933, 1.11379124804562, 0.30961735520176559, 3.0856656076894566, -1.1546478644202118], [-0.37661254480705231, -0.18520856137957098, -0.0080133986526225165, -0.56670150060403413, 0.62882437112146428], [-0.18262407192535957, -0.3325392106913736, 0.85095515902088392, -0.57261557729289136, -0.58832121344154764], [-0.37661254480705231, -0.041336378014054195, 0.23524451644138322, -0.30415753933670536, -0.57614214567856603], [-0.054572988984135873, -0.10704723099349685, 0.83333127852517874, -0.39747093389434424, 1.4737471971783405], [-0.37661254480705231, -0.35882355188315057, 0.60156704627388491, -0.48062165736778012, -0.40411281352644501], [-0.37661254480705231, -0.30487148312108214, -0.84929826480518544, -0.47114763862025627, 0.26269114649681879], [3.6514923714445509, 0.82051333580052999, 2.1505883011876104, -0.082547267086427845, -0.41705307302461342], [-0.37661254480705231, -0.3048714831210822, -1.3909603112019515, 0.89477300220131983, -0.65454489440276242], [-0.37661254480705231, -0.10082199229018138, -0.55234422487791379, -0.47946724059039297, 0.52225752819537141], [-0.084280835069573845, -0.24815264160198428, -0.64680626275034614, -0.67215847403757456, 0.36621322248216559], [-0.37661254480705231, 0.0050070656661838856, -0.95714523371913218, 0.169209677026257, 0.1599302622466581], [-0.37661254480705231, -0.37749926799309785, -1.6879052476876955, 0.016220774360116319, -0.78394748938444481], [-0.37661254480705231, -0.29172931252519363, -0.28525314600634077, -0.67121144346699801, -0.034173630225867321], [0.036252619296411104, -0.07107918515211778, 0.58069985584839356, -0.25802865007305792, -0.38279944494122736], [-0.37661254480705231, -0.35467339274760679, -0.79354272207160692, 0.25356496141541424, -1.1942298346499021], [0.69196827090145585, 1.0868152136645863, 1.3558792903499746, -0.10037688991556071, -0.51676919033402746], [-0.043621049904000397, 0.14472908989615682, 0.028196400817250389, 2.1978536507198485, -0.11029280374450567], [0.094782133963017823, -0.33253921069137382, 0.15277624131397216, -0.006064060788677661, -0.37290395238380258], [-0.37661254480705231, -0.31870534690622798, -0.76417140366638181, -0.0017352647441213342, -0.45815742672467685], [-0.37661254480705231, -0.072462571530632514, -0.35303209429289995, 0.049928646578299291, 0.25812399608570019], [-0.37661254480705231, -0.063470560070287688, -0.95260853230553311, -0.14692768981401028, 0.051079844115006566], [-0.37661254480705231, -0.086988128505035525, -0.75984084023608078, -0.6066830823534004, 0.90742054619967738], [-0.37661254480705231, -0.1845168681903134, -0.55590776302210587, -0.21342954462941749, 1.0040918965683463], [-0.37661254480705231, -0.13194818580675954, -1.0745596310249543, 0.22493449914804309, -0.092785393835218222], [-0.37661254480705231, -0.30348809674256738, -0.51518196668956806, -0.68292744143349737, -0.88518599016423527], [-0.37661254480705231, -0.152698981484478, -0.19274732987540277, -0.45913238820166896, 0.43776524558968399], [-0.37661254480705231, -0.31317180139216982, -0.3818974377719262, -0.2156687556490795, 0.58695882568621371], [-0.32478681674168713, -0.28135391468633419, 0.79980763016123957, -0.60055153574953346, -0.85017117034566125], [1.850255454281962, 1.2106282945416413, 1.8620042871744995, -0.20407466608498831, 0.54128732157503168], [0.050567041725284589, 1.4464956720783781, 0.478105338320574, 0.83385969749471145, -0.83723091084749091], [-0.37661254480705231, -0.10497215142572525, 0.36998010512203861, -0.55968958781003431, 0.76051054130870666], [1.9577065197889434, -0.013668650443762786, 1.9011124893059725, 0.54334735655559152, -0.95673801327175256], [-0.37661254480705231, 0.081785009673743345, 0.53324344786705025, -0.8631195916868688, 0.9272115313145225], [-0.37661254480705231, 0.3162690008319658, -0.57203330773828087, -0.12490794817759082, -0.34854581685784058], [-0.37661254480705231, -0.27443698279376139, -0.25911824878238621, -0.15019273546109235, 0.25964637955607384], [-0.13722890077772454, -0.11742262883235619, 0.4681387801936121, -0.096408849979516431, -0.94608132897914388], [-0.37661254480705231, -0.34913984723354879, 0.24528950588266651, -0.97958184041487439, 1.7120002102916723], [-0.37661254480705231, 0.019532622640587201, -1.377013848222902, -0.11170030001051501, 0.14090046886699797], [0.42742260332967219, -0.1845168681903134, -0.053206092776264757, -0.13303388239993746, -0.072994408720372814], [2.3974625786461354, -0.24815264160198441, 1.7161349909359269, 0.21033103344100906, -1.2292446544684741], [1.800571843811281, -0.31040502863514058, 1.5645939093704437, 0.88429666037329535, -1.3183040874852798], [-0.37661254480705231, -0.12779802667121559, -0.15513709309276225, -0.45622679868764299, 1.0071366635090917], [0.018315381118544471, -0.26613666452267387, 0.22241469650899104, 0.13695990828667254, -0.65454489440276209], [0.65596961451005642, 0.10806935086552039, 1.064458745812686, -0.015367297606089181, -0.40182923832088752], [-0.34881171838170177, 1.6816713564258567, 0.41731918803844748, 0.10801097582188338, -0.63170914234717002], [-0.37661254480705231, -0.088371514883549995, -0.96435896585165937, 0.93945049090979427, -1.1272449619535012], [2.5685009256061986, 0.27960926180132839, 2.0876017303210967, -0.12111496321776666, -0.32038172265594395], [-0.36436496456887496, -0.36504879058646639, -1.0534099443545135, -0.35420427548324745, 0.22995990188380439], [-0.37661254480705231, 0.66142390227135284, 0.17169406435266366, -0.26621387103277178, -0.51905276553958757], [-0.37661254480705231, -0.3325392106913736, -0.083956082461299264, -0.053485392090131534, -0.58832121344154764], [2.6018565468113186, -0.012285264065248191, 1.7471008971880333, -0.056307714857818936, -0.57538095394337996], [-0.37661254480705231, -0.32700566517731561, -0.46109437696310396, -0.78343703489876471, 1.69525399211757], [0.074455458279060638, -0.37265741566829658, 0.48626103701539547, 0.17629261460528522, -0.65302251093239005], [-0.37661254480705231, -0.24676925522346979, 0.052612004278358437, -0.71304043091431613, 0.51388441910832172], [-0.17008348319479405, 0.21735687476817339, 0.47874196620659815, 1.3163488212701548, -0.61344054070269771], [-0.37661254480705231, -0.35813185869389341, -1.1161358176858021, 0.049617611352521296, -0.4787096035747096], [-0.37661254480705231, -0.34498968809800479, -0.57193539277238914, -0.65140190889100014, 0.04118435155758364], [-0.37544543779200651, 0.44146546808753556, 0.90509790023241932, -0.2624752787687416, -0.80449966623447744], [-0.37661254480705231, -0.14370697002413352, -1.1740274728393563, 0.22850648758975348, 0.12491544242808499], [-0.37661254480705231, -0.2730535964152469, 0.28293479688559797, -0.14197073752906608, -0.8410368695234236], [-0.37661254480705231, -0.22671015273500825, 0.25181106173971513, -0.10274925927196654, 0.41264591832853242], [0.069759801860535764, -0.34913984723354879, -0.70542201987089515, -0.43588871476968899, 0.19113912338929936], [-0.24923345884392412, -0.28896253976816444, -0.8387582122686017, -0.7158035771961182, 0.4948546257286619], [-0.37661254480705231, -0.23985232333089704, -0.16896270414201853, -0.30204180103876094, 0.12719901763364466], [-0.37661254480705231, -0.3622820178294372, -1.2214603370109107, -0.21861268352816476, -0.75882816212329518], [-0.37661254480705231, -0.10358876504721062, -0.19035240857235225, -0.46345792776851674, 0.60370504386031398], [1.6524553669673638, 0.45668271825119522, 2.2184915921424699, -0.20671678809190153, -0.62561960846567799], [-0.37661254480705231, -0.36020693826166539, -0.30233071287932534, -0.4447326936217107, -0.97043946450510798], [-0.37661254480705231, 0.31142714850716441, -0.78705094660489339, -0.42024012103737035, -0.59897789773415755], [-0.37661254480705231, -0.079379503423205072, 0.21272365661812342, -0.20797454501597956, 0.18048243909669029], [-0.37661254480705231, -0.16722453845888122, -0.55740128875658046, -0.14374232959964478, 0.22843751841343077], [-0.37661254480705231, -0.3774992679930978, -1.2932370067999277, -0.53937985056877347, -0.13997928141677324], [2.3306460232175148, 2.1181297588472061, 1.8201482018025383, -0.2025259152652864, -0.52514229942107848], [-0.37661254480705231, -0.22740184592426577, -0.16535495903221475, 0.18031695812291254, -0.59060478864710664], [-0.37661254480705231, -0.19904242516471651, -0.69917633051983374, -0.14225419857199106, -0.308202654892962], [-0.042807294705192844, -0.27512867598301854, 0.27839872432363633, -0.27594532289664425, -0.60658981508602039], [-0.37661254480705231, -0.26060311900861538, -1.1135627772018215, -0.013711941168297603, -0.73142525965658622], [3.5562313794680147, -0.24815264160198441, 1.9639728785131179, 2.0862176937047048, -1.2292446544684741], [-0.37661254480705231, -0.23570216419535323, -0.66950241660981691, -0.64447595500390986, 0.59000359262695901], [-0.37661254480705231, -0.087679821694292656, -0.64636830395772948, -0.58246238892314328, 0.81227157930137961], [-0.37661254480705231, 0.29551820515424659, -0.7295609175825466, -0.08014389870682892, -0.57005261179707545], [-0.37661254480705231, -0.33599767663765995, -1.1493212445016359, 0.23378978010099599, -1.0907077586645542], [-0.37661254480705231, 0.51063478701326426, -0.45792942656353874, -0.25322402231117541, -0.71772380842323213], [-0.37661254480705231, 0.43039837705941802, 0.0087832311890561066, -0.37539376069983738, -0.08745705168891392], [-0.37661254480705231, -0.33738106301617476, -0.73129656494273876, -0.6252070391667317, 0.051079844115006559], [-0.37661254480705231, -0.079379503423205322, 0.13731877990898012, -0.5912643073846473, 1.0033307048331603], [-0.37661254480705231, 0.74926893730702882, 0.14357481787879989, 0.056995004421060008, -0.58603763823598864], [-0.37661254480705231, -0.33530598344840301, -0.45990559573922996, -0.43889709336892008, -0.71772380842323025], [-0.3008710627803986, 0.7001587208697595, 1.0971685601341217, -0.74728277093154083, 0.72245095454938846], [-0.37661254480705231, 0.23534089768886268, -0.31689035357892736, -0.70190825079463326, 0.99647997921648324], [-0.37661254480705231, 0.16409649919536101, -0.90294396155405865, -0.021908809631762061, -0.080606326072236181], [-0.37661254480705231, 0.17239681746644858, -0.33019654669391274, -0.26170431399574196, 0.45831742243971679], [0.077280441612068373, -0.26406158495490167, 0.25910590776749087, -0.058123752372249204, -1.0176333520866643], [-0.37661254480705231, -0.21287628894986238, -0.21272784678261347, -0.90609566587659218, 0.73691359751792862], [-0.37661254480705231, -0.33392259706988858, -0.20939280933583904, -0.52452752113335999, -0.74817147783068694], [-0.37661254480705231, 0.29690159153276097, -0.66984174777201178, -0.35676738935049529, -0.42923214078759547], [-0.13238470752195453, -0.29311269890370817, 0.76367031090597903, -0.49054537752725585, -0.92857391906985787], [-0.37661254480705231, 0.322494239535281, -0.74123089425583588, -0.3360633821694356, -0.65454489440276153], [-0.37661254480705231, 0.50786801425623462, -0.66220916167358768, -0.19326917821770345, 0.048796268909447438], [-0.37661254480705231, 0.77416989212029141, 0.049049404858294121, -0.26741145047632803, 1.469941238502408], [-0.37661254480705231, -0.22809353911352304, -0.29226774359045793, -0.61181530394868999, 0.6113169612121776], [-0.37661254480705231, -0.2951877784714802, 0.01954355871637814, -0.7833507077412305, 1.7317911954065177], [-0.37661254480705231, -0.14509035640264784, 0.64615780714640847, -0.56743096823114647, 0.79476416939209382], [-0.37661254480705231, -0.22048491403169282, -0.85989568897753133, 0.89817738571142614, 0.45298908029341139], [-0.37661254480705231, -0.034419446121481442, -0.67449574656291178, -0.30602743538843286, 0.80922681236063509], [-0.37661254480705231, -0.061395480502515767, -0.16363531900407269, -0.52661684240022555, 0.73767478925311658], [-0.32089493764416199, 0.22289042028223074, 0.39973686828062638, 0.10312100038031657, 0.41569068526927999], [-0.37661254480705231, -0.053095162231428329, 0.81233603935862631, -0.81077259448900429, 0.60218266038994062], [-0.32962918183442991, -0.066929026016574397, 0.051199601237442049, -0.74117189419084117, 0.96450992633865429], [-0.37661254480705231, -0.22048491403169285, -0.051355924854846524, -0.44105649594520885, 0.47049649020269835], [0.089537913596115071, -0.27997052830781949, -0.12294587665647372, -0.043963820947205412, 0.21930321759119722], [0.03253315798344325, -0.24331078927718314, 0.047710010645374279, -0.28880365720223039, -0.95293205459582142], [-0.37661254480705231, -0.20734274343580422, -0.61887215275658081, -0.47358671004506642, 0.20103461594672228], [1.9535685304234298, -0.26336989176564479, 1.7088861923793952, -0.21514273813806378, -0.45130670110800042], [-0.37661254480705231, -0.20042581154323119, 0.52108858145055548, -0.8157461611019905, 0.78410748509948536], [0.28186941770993124, -0.32977243793434463, 0.99841755288406331, -0.1888586426581636, -0.8867083736346062], [-0.37661254480705231, -0.197659038786202, 0.56996306698525201, -0.6053745827606889, 1.0101814304498375], [2.9528086631664983, -0.31040502863514063, 1.8582186709784589, 1.4034389679680805, -1.2566475569351838], [-0.37661254480705231, -0.33323090388063081, -1.2738245083183419, 1.9787186463800097, -1.0747227322256401], [-0.37661254480705231, -0.26821174409044596, -0.90313696831282231, 0.18332276233007394, -0.39193374576346351], [1.3679800563738613, 0.99412832630410952, 2.1522648278620689, -0.089639866403268653, -0.4239037986412898], [-0.37661254480705231, -0.21633475489614901, -1.1178829049335097, -0.27530604509275941, -0.89508148272165733], [0.014759404688978517, -0.33253921069137354, 0.25506501467737008, -0.10712559048421533, -0.58832121344154764], [-0.37661254480705231, -0.32008873328474258, -1.1636037932236438, 1.2638902989469805, -0.8014548992937327], [-0.37661254480705231, 0.04028341831830639, -0.60850392023772359, -0.33801535515668407, -0.5365601754488758], [-0.24738758600370608, -0.3325392106913736, 0.012857338358343906, -0.12666791048113357, -0.58832121344154764], [-0.37661254480705231, -0.26336989176564457, -0.17305745121700555, -0.43438200319072279, 0.44385477947117502], [-0.3561086412541003, -0.24607756203421224, -0.10561862437253966, 0.019739861216775678, -0.99175283309032669], [1.5830727146272263, -0.29587947166073741, 1.5243048787472442, 0.20743644051129717, -0.64008225143422037], [-0.37661254480705231, -0.044794843960340738, 0.065405414302005749, -0.20748694493994957, 0.44233239600080287], [-0.37661254480705231, -0.36159032464017982, -1.4094911055345851, 1.9149188812308426, -1.2277222709981039], [-0.37661254480705231, -0.18659194775808519, -0.84852587832044479, -0.90905643417047566, 1.5095232087320982], [-0.37661254480705231, -0.21495136851763422, 0.03322667531924095, -0.76470256125226421, 0.019870982972365153], [-0.37661254480705231, -0.10428045823646777, 0.74510624382523316, -0.50634027954597371, 1.0984796717314551], [-0.37661254480705231, -0.24746094841272687, -0.10783609749270839, -0.44907210293555949, 0.96831588501458721], [-0.37661254480705231, 0.31626900083196535, -0.22587740199111339, -0.65520580637945391, -0.37138156891343099], [-0.37661254480705231, -0.15477406105224997, -0.54714990781502637, -0.34528684233411183, 0.39970565883036624], [-0.37661254480705231, -0.33046413112360179, -0.46867445995775414, -0.68403749341635423, -0.92248438518836784], [1.2902458480987997, 0.43247345662718983, 1.9361593761059335, -0.32521714276892416, 0.38219824892107923], [-0.37661254480705231, -0.161690992944823, -1.0778506298753432, 0.12007893126215793, -0.064621299633322499], [-0.21565291038946593, -0.23639385738461041, -0.23897136415763542, 1.545573966397072, -1.2064089024128841], [-0.37661254480705231, -0.3622820178294372, -1.4946939770357806, 0.27620847163004808, -0.75882816212329518], [-0.37661254480705231, 0.30036005747904759, -0.46463763977607231, -0.52134094213409599, -0.45435146804874577], [-0.37661254480705231, -0.37888265437161223, -0.99211525039112347, 0.027580283964404484, -1.1439911801275999], [-0.37661254480705231, -0.20180919792174579, -0.7705472548439336, -0.84749090814720929, 1.1144646981703696], [-0.37661254480705231, 0.608855219887799, -1.1972167053234561, 0.19266020557351823, -0.83038018523081303], [-0.37661254480705231, -0.30348809674256738, -0.56584107338682132, -0.81854083947914247, -0.88518599016423527], [-0.37661254480705231, -0.35259831317983537, -0.81438506301296387, -0.27993245629591129, -0.63703748449347619], [-0.37661254480705231, -0.35813185869389363, -1.0539520484332745, 0.11944917606133404, -0.74588790262512683], [-0.37661254480705231, -0.33807275620543198, -1.0792794242284462, -0.58524071310108816, -0.0075319194943449744], [-0.21945282009092154, -0.18036670905476962, 0.16775670128990283, -0.56129626239755148, 1.1510019014593162], [1.5314710522040094, 0.2789175686120714, 1.6563942960498532, 0.084190494934113236, -0.48556032919138598], [-0.37661254480705231, -0.12641464029270097, -0.32115367678144979, -0.66530976697898381, 0.92645033957933598], [2.0496668726004525, -0.32008873328474241, 1.5274772996170891, 0.64666424096407937, -1.2589311321407419], [-0.37661254480705231, -0.22878523230278006, -0.81635634851730332, -0.17189285206285626, -0.36681441850231283], [-0.37661254480705231, -0.15062390191670622, -1.1338250206441107, -0.018567276032890989, 0.69580924381786491], [-0.37661254480705231, 0.020916009019101799, -0.25450828484301313, 0.058407786579572213, -0.65150012746201802], [0.59585147493732737, 0.59294627653488186, 1.9524826708364169, -0.28551208830103986, -0.43608286640427274], [-0.37661254480705231, 0.17931374935902139, -0.97182043309618438, -0.88153157505060387, 1.5125679756728441], [-0.37661254480705231, -0.30487148312108214, -0.71655811911495038, -0.61877535207496948, 0.5222575281953723], [2.6894115653921964, -0.3034880967425676, 1.7972670307040626, 0.55851482570067246, -1.261214707346302], [-0.37472869456349711, -0.21425967532837675, -0.34309519530559429, -0.68917247100195, 0.77268960907168915], [4.0145216534286794, -0.30556317631033958, 1.8997679668799488, 3.0474429135062433, -1.2703490081685378], [-0.37661254480705231, -0.29518777847147998, 0.2495909499531872, -0.45971216804865928, 0.41873545221002406], [-0.37661254480705231, -0.1533906746737356, -0.65303681464746477, -0.22307232565541113, 0.74528670660497931], [-0.37661254480705231, -0.15477406105225006, -0.98318332313306245, -0.23970859961044605, -0.069188450044440478], [-0.37661254480705231, -0.058628707745486383, 0.33449477339001943, -0.25601813946137103, -0.39802327964495454], [-0.37661254480705231, -0.1983507319754593, -0.5675681190723485, -0.39500758729551971, 0.39818327535999293], [-0.10963708381353743, -0.062778866881030487, 0.69766593094942619, -0.16340300614249798, 0.38295944065626442], [0.13476080492881648, -0.3325392106913736, 0.51891650426475122, -0.07230399615266897, -0.58832121344154764], [-0.37661254480705231, -0.2025008911110032, -1.4035213038473562, 0.16223699367988659, -0.53199302503775581], [-0.035325517986339428, 0.2028313177937692, 0.92521752487249342, -0.36177440715111048, 0.1378557019262531], [-0.37661254480705231, -0.22878523230278, -0.5914642124741083, -0.31057486536230045, -0.36681441850231283], [-0.37661254480705231, 0.016074156694300679, -0.88182149669310705, -0.097240026537117719, -0.12780021365379132], [-0.37661254480705231, 0.68009961838129962, -0.019522960980737469, 0.08099122551951643, -0.40487400526163164], [-0.37661254480705231, -0.20665105024654679, -0.61178316977861913, -0.13437088696207289, 0.083049896992834132], [-0.37661254480705231, -0.057937014556229363, -0.15789367226244666, -0.39467385947060679, 0.85870427514774961], [-0.37092075888714249, -0.24469417565569782, -0.22152475765881308, -0.23511274681428657, -0.011337878170276188], [-0.37661254480705231, -0.10843061737201165, 0.079504310319719718, -0.10894643727085879, 0.10207969037249351], [-0.37661254480705231, 0.0029319860984122871, -0.45763739595213382, -0.21684676169902584, -0.05091984839996759], [-0.37661254480705231, -0.34775646085503398, -0.62603349297026556, -0.20584783327431028, -0.17194933429460005], [-0.37661254480705231, -0.2025008911110032, -0.64812003371869809, -0.28137568460191281, 0.041184351557583619], [-0.37661254480705231, -0.22671015273500822, -0.76599351659426462, -0.88927596792917107, 1.1159870816407436], [-0.37661254480705231, 0.47881690030742829, -0.59232340622671176, -0.36445185513003847, 0.18428839777262124], [-0.21044235810098644, -0.3754241884253256, 0.11878192311704039, -0.48971423764976446, -0.74588790262512694], [-0.37661254480705231, -0.35536508593686439, -0.35703074018650272, -0.37682250830801028, -0.61267934896751164], [-0.37661254480705231, -0.23708555057386765, 0.22617860600806194, -0.48806464383526199, -0.81058920011596913], [-0.37661254480705231, -0.37542418842532577, -1.1293216841258418, -0.58406662872070214, -0.034934821961053832], [0.22909497269024381, 0.2028313177937692, 1.2559824367452843, -0.42249683416756151, 0.1378557019262531], [-0.36734342298841993, 1.5246570024644521, 0.31331940018753768, 0.66848467032538839, -0.76035054559366699], [-0.37661254480705231, 0.6683408341639262, -0.083109624080208722, -0.16592684676712199, -0.59517193905822552], [-0.37661254480705231, -0.27305359641524685, 0.87254540226766109, -1.0024778904735618, 0.44994431335266621], [0.26999745062088853, 1.6934301406432308, 0.65169007831926207, 0.24870440271055727, -0.20468057890761501], [1.8398115881025316, 0.13919554438209861, 1.4339730656175784, 0.14934685476471776, -1.26578185775742], [3.0246022217449666, -0.29657116484999479, 2.1798435220856329, 0.99604075766056721, -1.2962295271648738], [-0.34380834809956146, -0.38649127945344242, -1.1473007963285231, -0.2493091589345125, -0.53199302503775503], [2.585272741307477, 0.62338077686220228, 2.4979668844630676, -0.20094077149241085, -0.54188851759517775], [-0.37661254480705231, 0.29690159153276119, -0.37314861233770147, -0.61344242203380661, -0.5335154085081294], [1.584390502847981, 0.45668271825119505, 2.1823735459112181, -0.22335006864883372, -0.62561960846567799], [0.17956334478035629, -0.12157278796789987, 0.41143777521909852, 0.1599754884463887, -0.95521562980138164], [-0.37661254480705231, -0.3477564608550342, -1.5887981361771897, 0.22926861286454853, -0.59973908946934329], [-0.37661254480705231, 1.9445147683436272, 0.24595319343345023, -0.24128448126268892, -0.050158656664782364], [-0.37661254480705231, -0.070387491962860524, -0.66105608545089511, 0.054485071848673128, 0.10131849863730688], [0.20795902450244919, -0.24331078927718314, 0.38378196140200727, -0.22181560844434145, -0.95293205459582142], [2.5712600903949472, 0.46498303652228179, 1.7012871908882796, 0.33510806105135693, -0.51905276553958735], [2.0659411924657554, 0.74996063049628636, 2.3819147223097157, -0.043085629178310547, -0.57081380353225997], [-0.37661254480705231, -0.38164942712864119, -0.89798066585251535, 1.9467323664386356, -1.2079312858832558], [-0.37661254480705231, -0.20250089111100295, -0.010184451749524204, -0.8820691502980329, 0.89448028670150881], [-0.3624005666866067, 0.023682781776131034, 0.46761156984786068, -0.16148741116087217, 1.1502407097241287], [-0.37661254480705231, -0.10981400375052619, -0.24315588936674759, -0.81463101016086181, 0.59989908518438206], [-0.37661254480705231, -0.24054401652015417, -0.14214034847128731, 0.033294572244894904, -0.7390371770084504], [-0.37661254480705231, -0.29034592614667903, 0.13580876683749876, 0.05161860668946, -0.31809814745038562], [-0.37661254480705231, -0.378882654371612, -1.3590869625735711, 2.507922473514653, -1.1660657404480059], [-0.3678949196567135, -0.27305359641524685, 0.068180299655352439, 0.018934652206846536, -0.8410368695234236], [-0.37661254480705231, -0.37473249523606839, -0.37497784413562885, -0.017129553475752957, -0.89812624966240251], [-0.37661254480705231, -0.12364786753567199, -0.92148506576299427, -0.20960887908128739, -0.81515635052708679], [-0.37661254480705231, -0.3083299490673686, -0.37367535433352872, -0.10610483205026794, 0.097512539961376252], [-0.37661254480705231, -0.25645295987307198, -0.13967907582213046, -0.44427678127605291, 0.4682129149971388], [-0.37661254480705231, 0.17862205616976401, -1.3424876628519944, 0.0983636000603684, 0.1043632655780522], [-0.37661254480705231, -0.16791623164813868, -0.58910195904573515, -0.31807598598652093, 0.65927204052891963], [-0.37661254480705231, -0.33945614258394657, -0.91898530327955585, -0.53587374792485609, -0.78546987285481928], [-0.37661254480705231, -0.32908074474508719, -0.92086995142921113, -0.096859678137915128, -0.53427660024331503], [1.5661371504401402, -0.27443698279376127, 1.6769717239045572, 0.13366454948306425, -0.59441074732303767], [-0.37661254480705231, -0.35329000636909258, -1.3969461991302699, -0.068538260187760158, -0.51600799859884183], [-0.35431425418048534, -0.026810821039651406, 0.48719858675561012, -0.66269389860888039, 0.99419640401092413], [-0.37661254480705231, -0.29242100571445079, -1.029316488818353, -0.89128578516233814, -0.48327675398582981], [-0.37661254480705231, -0.26752005090118841, -0.51241348963307631, -0.78064898273358452, 1.0474798254739708], [-0.37661254480705231, 0.27684248904429964, -1.0061411533398545, 0.47342773448229392, 0.21625845065045013], [-0.37661254480705231, -0.063470560070287577, -0.99639570375737252, 1.2093319145136612, -1.140185221451669], [-0.37661254480705231, 0.67525776605649868, 0.058612385830542291, -0.0073543220132251119, -0.31505338050963871], [-0.37661254480705231, -0.34706476766577665, -1.0417799922345243, -0.15044418295011591, -1.0404691041422522], [0.57728945480088778, 0.2000645450367402, 1.1507241232031329, -0.43025371834201859, 0.2429001613819734], [-0.37661254480705231, -0.33668936982691744, -1.3583356793640751, -0.054098382291603242, -0.92324557692355247], [-0.37661254480705231, -0.13263987899601673, 0.17657351238022967, -0.41001260089337693, 0.59380955130289137], [0.010356123973908504, -0.27443698279376144, 1.2996935226076682, -0.55321337517907909, -0.17499410123534567], [-0.37661254480705231, -0.31663026733845601, -0.56040933432143492, -0.2944853763345669, -0.014382645111020609], [3.3016310471842245, -0.31870534690622798, 2.1322232511630541, 1.2523000064520107, -1.296229527164876], [-0.37661254480705231, 0.4027306494891264, -0.064321549852593751, -0.3991525967785215, -0.29450120365960675], [-0.37661254480705231, -0.18520856137957081, -0.23318942448554525, -0.44846931540638485, 0.16982575480408096], [-0.37661254480705231, -0.18935872051511457, -1.5098071935310036, -0.22427008114200392, -0.31733695571519838], [-0.030961581198732047, 0.72575136887228253, -0.29103253555709374, 0.05839345135545207, -0.82429065134932311], [-0.37661254480705231, -0.21495136851763422, -1.1969979595571378, -0.8512110329190008, 1.1365392584907756], [2.0258399491271866, 0.59986320842745466, 2.5067828345605672, -0.23005677146941506, -0.35615773420970354], [-0.15748779436972882, 0.48365875263222941, 0.50682721940589159, -0.6521074900074233, 0.69733162728823817], [-0.37661254480705231, -0.10566384461498249, 0.56633725634952681, -0.72534120877458275, 0.7445255148697939], [-0.37661254480705231, -0.123647867535672, -1.1762282666198591, -0.080353061580068774, 0.17287052174482637], [0.06905181577043551, 0.90974175721471973, 0.88841713512536513, -0.09024906811177974, -0.54645566800629641], [-0.37661254480705231, 0.22773227260703166, -1.1698272558870433, 0.63418938317740969, 0.37686990677477461], [-0.37661254480705231, -0.18382517500105619, -0.26414023163256717, -0.57910639593092528, 0.42863094476744806], [-0.26761739506221821, 0.70015872086975939, 0.42151099156970373, -0.60064606248499608, 0.72245095454938846], [-0.37661254480705231, 0.22980735217480364, 0.62456915431766857, -0.23861075568807605, 0.88230121893852598], [-0.37661254480705231, -0.17690824310848324, 0.14928151430697723, -0.50000244912626468, 0.86022665861812087], [-0.37661254480705231, -0.1416318904563616, -0.93245053795971033, -0.016347926846930871, 0.50627250175645755], [-0.37661254480705231, -0.30625486949959679, -0.47428396111467963, -0.65075341030726852, 0.49942177613978062], [-0.37661254480705231, -0.22325168678872159, -0.057802483876287614, -0.67738131890579933, 0.28019855640610603], [-0.37661254480705231, -0.10082199229018123, 0.66639108113827161, -0.67452798129548519, 0.9774501858368243], [-0.37661254480705231, -0.26821174409044563, 0.090296732829301174, -0.11466157318433817, -0.50763488951179081], [0.42899833902906637, 0.92495900737838022, 1.730002651679285, -0.34278356987459485, -0.55482877709334621], [-0.37661254480705231, -0.16238268613408019, -0.13816068081692423, -0.451911186650858, 1.0018083213627875], [-0.37661254480705231, -0.10981400375052619, -0.14436560985458913, -0.59621952879977402, 0.59989908518438206], [0.1743016533538746, 0.1357370784358132, 0.97090989962504137, -0.41789207384667643, 0.11273637466510292], [-0.37661254480705231, 0.063109293563796451, -0.65366113108723356, 0.64795679585014687, 0.20103461594672239], [-0.37661254480705231, -0.33461429025914541, -1.2596781555079137, 0.1145370876040388, -1.0747227322256403], [0.44915280684358239, -0.331155824312859, 1.2455038661438489, -0.29543703741171895, -0.84864878687528744], [-0.37661254480705231, -0.18036670905476987, -0.56016328536497939, -0.74068972356670981, 0.67906302564376531], [-0.37661254480705231, 0.61715553815888646, -0.99539470160581844, 0.32720602397164517, -0.71391784974729866], [-0.37661254480705231, -0.10981400375052619, -0.18724400753350823, -0.84045919408960601, 0.59989908518438184], [0.46748236248350428, -0.36297371101869441, 0.68184355543738162, -0.12429365165998402, -0.61420173243788445], [-0.37661254480705231, -0.34775646085503398, -0.2524672784646218, 0.05259445843789326, -0.17194933429460002], [-0.2942338808868179, -0.26682835771193131, -0.051062108048853805, 0.012240800793319295, 0.5861976339510282], [-0.37661254480705231, -0.26406158495490201, 0.13576584838537498, -0.71992854424652508, 1.3968668319245161], [-0.37661254480705231, -0.10635553780423986, 0.8267357875723742, -0.66182946060886083, 0.93558464040157219], [1.6520403378532991, 0.76517788065994685, 2.3435413423173936, -0.11845007636976501, -0.47642602836914955], [0.046761038175779146, -0.29933793760702349, 0.56453892190805233, 0.0029857490663973141, -0.84027567778823808], [-0.37661254480705231, -0.05724532136697208, 0.57212547291948068, -0.55421825423572746, 0.7445255148697939], [-0.37661254480705231, -0.28965423295742182, -0.402786987578168, 0.029768815074561111, -0.31581457224482656], [-0.22360307881844482, 2.39826550049641, 0.51852743690329772, 0.55637161398265178, -0.7763355720325803], [-0.37661254480705231, -0.078687810233947997, -0.064728835150987596, -0.39127474578765881, 0.44309358773598928], [-0.37661254480705231, 0.22842396579628899, -0.028554763695780583, -0.096110186076197535, 0.92416676437377654], [-0.37661254480705231, -0.17206639078368222, 0.37453667419597947, -0.46717174177118309, 1.0946737130555249], [-0.37661254480705231, -0.19558395921843003, 0.26406592215522506, -0.5376934180038655, 1.2073300898631083], [-0.37661254480705231, -0.12710633348195832, -0.59317348691681149, -0.20176688774807794, 0.27258663905424135], [-0.37661254480705231, 0.0029319860984122091, 0.48081839753821454, -0.87950315253193212, 0.84119686523846227], [-0.37661254480705231, 0.71191750508713503, -0.26212173994076737, -0.11442894578093399, -0.061576532692577943], [-0.37661254480705231, -0.10428045823646785, -0.65188485788451134, 0.2365813651878941, 0.031288859000160499], [1.9312642910358946, 0.74996063049628636, 2.2791216526872229, -0.18503277573381932, -0.57081380353225997], [-0.37661254480705231, -0.012976957254505423, -0.72956037924706196, -0.088310819950760566, 0.69580924381786458], [3.7293741321720066, -0.31040502863514063, 1.8794062943268268, 2.1376039837636256, -1.2566475569351838], [-0.15907593443081014, 0.19245591995491096, 1.1709190763335466, -0.47131791730878253, 0.27334783078942781], [-0.37661254480705231, -0.27858714192930523, -0.17443684621031297, -0.1833164493750436, -0.54112732585999324], [-0.37661254480705231, -0.20319258430026038, -0.69559196115017174, -0.30271511989901528, -0.1902179359390738], [-0.37661254480705231, -0.064853946448802116, -0.38857264160590477, -0.91738739841012906, 0.71712261240308384], [-0.37661254480705231, -0.12018940158938551, -0.7175985683688002, 0.027209832972017539, 0.17591528868557177], [-0.079621164710068526, -0.22325168678872184, -0.71439815923079708, -0.85659652619027393, 0.6295855628566509], [-0.32783982932342959, -0.32977243793434458, -0.27627010195271895, -0.11005615503742894, -0.8867083736346062], [-0.37661254480705231, -0.23362708462758119, -0.31604867296192696, -0.7709787711244509, 0.61436172815292323], [-0.37661254480705231, -0.20042581154323119, 0.31274408525490527, -0.66623720196349945, 0.78410748509948536], [-0.37661254480705231, -0.16514945889110949, -0.1611571387866495, -0.27190543935025674, 0.25736280435051373], [-0.12519437186373483, -0.34291460853023314, -0.39085966876979272, 0.046086373429207655, -0.51905276553958735], [0.082877764026793588, -0.36504879058646639, -1.0164130466463352, -0.49008033558613967, 0.28781047375796892], [-0.37661254480705231, -0.34568138128726239, -1.1180150232539465, 0.13531658570610078, -0.63399271755273023], [-0.37661254480705231, -0.16307437932333749, 0.40763078452846413, -0.41801629262449497, 1.047479825473969], [-0.37661254480705231, -0.10497215142572511, -0.024040085223248742, -0.31838468420164062, 0.72016737934382813], [-0.19111593133851124, -0.26336989176564457, 0.81440242806067387, -0.37617081580491157, -0.79079821500112402], [-0.37661254480705231, -0.21010951619283316, -0.3016073447496983, -0.60618587129774704, 0.69124209340674736], [0.36797981375869204, 0.26024185250212439, 1.0352818596380753, -0.2504043162625223, 1.5087620169969143], [-0.069389016416755323, -0.26406158495490184, 0.36414276065488549, 0.11021142673544346, -0.60430623988046139], [-0.37661254480705231, -0.3671238701542382, -1.1054349281040463, 0.045171545324916546, -1.0633048561978442], [-0.37661254480705231, -0.30072132398553858, 0.11013969127424117, -0.302211746732099, -0.66900753737130358], [-0.22584391145002192, -0.25091941435901349, 0.3531715807172584, -0.64672189327884388, 0.59457074303807678], [-0.37661254480705231, -0.152007288295221, -0.27055598199143605, -0.85495115102196939, 0.63719748020851441], [-0.37661254480705231, -0.21495136851763422, -0.11757985930430026, -0.61298332875997263, 0.47125768193788442], [-0.37661254480705231, -0.326313971988058, -0.30764205348700147, -0.6403157707580327, -0.070710833514813359], [-0.37661254480705231, -0.33668936982691744, -1.0973696949326954, 0.15350531222837452, -0.73827598527326321], [2.7738617640327576, -0.054478548609942987, 1.6385962434256949, 0.48443228379629677, -0.82961899349562762], [0.93527377352499474, 0.5659702421538475, 1.6586497183536149, -0.18971951205921533, -0.34854581685783975], [-0.37661254480705231, 0.57634563999270583, 0.12339964247908553, -0.33152700083184122, 0.18048243909669029], [-0.37661254480705231, -0.19973411835397378, 0.59137239953560039, -0.4441274566122963, 0.43624286211931118], [-0.37661254480705231, -0.34706476766577699, -0.16894076966113447, -0.9374534045948526, 1.5201798930247059], [-0.37661254480705231, -0.19074210689362897, -0.70004301823740933, -0.4655741346175995, 1.0071366635090917], [0.035249713199193011, 0.1357370784358132, 0.75162615861501036, -0.44563040564486006, 0.11273637466510289], [-0.37661254480705231, 0.1288201465432392, -0.18879501538975818, 0.40523260210314915, 0.19951223247634978], [-0.37661254480705231, -0.27097851684747493, -0.50174778334167103, -0.24649348640347596, -0.48327675398582776], [0.37611770826956836, -0.17690824310848347, 0.33087588179219507, -0.27654181543528605, -0.019710987257326194], [-0.33506154890840079, 0.2028313177937692, 0.62928884965722842, -0.48069976962468236, 0.1378557019262531], [-0.37661254480705231, -0.028194207418165786, 0.4249632534765051, -0.22327800989320878, -0.73066406792140026], [2.536928640125653, 0.10391919172997688, 2.551871775565473, -0.43916049815044472, -0.30059073754109844], [-0.0931069472219197, 0.21804856795743074, 1.2847376143728577, -0.61954471280760948, 0.34337747042657407], [-0.37661254480705231, -0.11811432202161357, 0.95690866345293513, -1.2715140699050504, 1.0314947990350565], [-0.37661254480705231, 0.35154535348408705, 0.34840634664575942, -0.65344782994160311, 1.3138969327892007], [-0.37661254480705231, -0.11811432202161369, -0.10484699489512694, -0.72369341677301402, 1.224837499772395], [-0.37661254480705231, 0.50994309382400682, -0.61468279505859913, -0.32599790934267953, -0.067666066574067804], [-0.37661254480705231, -0.34844815404429164, -1.1340944763064731, -0.20562362702463677, -0.20011342849649666], [-0.37661254480705231, -0.31386349458142676, -0.39491351964491672, -0.40981264875503315, 0.58086929180472269], [-0.37661254480705231, -0.2951877784714802, -0.64902276854346086, 0.035081607592664099, -0.2343670565798841], [-0.16122620775071084, 0.29482651196498993, 1.1213698823190721, -0.52167583090463188, 0.44994431335266638], [-0.37661254480705231, -0.082146276180234595, 0.092697772909246634, -0.24035949093941139, 0.41264591832853414], [-0.37661254480705231, -0.29518777847147998, -0.27719886957828566, -0.58441871099110432, 0.069348445759479557], [2.0430502955180208, 1.2548966586541073, 2.0572860218427702, -0.21821528150428698, 0.15307953662998064], [-0.37661254480705231, -0.1513155951059636, -1.2660169369319765, 0.018127872632032638, -0.72152976709916294], [-0.21809034294530671, 0.38267154700066597, 0.81747941468805108, -0.091076959489436449, -0.73066406792139915], [-0.37661254480705231, -0.34083952896246139, -0.21522110337451794, -0.48712437381704593, -0.88442479842904687], [0.74448872782589781, -0.18036670905477004, 1.1964087035549364, -0.065181609849089606, -0.69412686463245388], [-0.015937773113373632, 0.48365875263222941, 0.90376152937392318, -0.51582371508273384, 0.69733162728823817], [-0.37661254480705231, 0.091468714323345995, -0.41671475726630591, -0.60625679617207773, -0.25872519210584849], [-0.37661254480705231, 1.7072640044283769, -0.028355808837603025, -0.10238285080332898, -0.40182923832088602], [-0.37661254480705231, -0.3097133354458832, -0.71044552070199574, -0.63051386963638612, -0.61953007458418918], [0.026287517024923934, -0.33876444939468919, -0.24241453409027303, -0.94445646916898252, 1.4440607195060697], [-0.37661254480705231, 0.31696069402122257, -0.51119514722458392, -0.4746190119898, -0.5791869126193111], [-0.37661254480705231, -0.1630743793233376, -0.37625826442140964, 0.65079650297169456, 0.15003476968923496], [-0.37661254480705231, 0.34186164883448505, -1.0452361196839779, -0.62040197701158561, -0.29526239539479376], [0.21500248402461497, 0.92495900737838022, 1.5671462840395516, -0.44872124579686673, -0.55482877709334621], [-0.37661254480705231, -0.34222291534097576, -0.39583381412677499, -1.0116319663084801, -0.28536690283737082], [-0.37661254480705231, -0.11603924245384174, -0.89348587263376134, -0.48199899994790801, 0.81836111318287086], [-0.37661254480705231, -0.33738106301617482, -1.0084720130558087, -0.46538169772013521, -0.22142679708171525], [-0.37661254480705231, -0.29795455122850945, -0.504384261130014, -0.19280659632225261, 0.45451146376378465], [-0.37661254480705231, -0.33876444939468919, -1.3450383598814146, 0.29046143660131829, -1.0237228859681544], [-0.37661254480705231, -0.10428045823646781, -1.3121818161594665, 0.4908627984684838, -0.21762083840578339], [-0.37661254480705231, -0.27374528960450395, -1.6448180104133034, 0.22879985975089029, -0.72990287618621219], [-0.37661254480705231, -0.13609834494230316, -0.41033279655505384, -0.54217970238928781, 1.0117038139202106], [-0.37661254480705231, 0.058267441238995576, 0.57192935440258408, -0.64219848131282919, 1.1220766155222326], [-0.37661254480705231, -0.2481526416019845, -0.036281942284262403, 0.06399407563403664, -0.83646971911230517], [-0.37661254480705231, -0.1741414703514543, -0.18276685087695954, -0.63937402660240505, -0.46729172754691428], [-0.37661254480705231, -0.38164942712864125, -0.61878817086122284, 0.16133165133864552, -1.18205076688692], [-0.37661254480705231, -0.2204849140316926, -0.15954608683151195, -0.73172953722863543, 0.61816768682885503], [-0.043033984788779733, 0.029216327290189414, 0.41336231237320886, -0.11651051067022022, -0.068427258309255556], [-0.37661254480705231, -0.29726285803925201, -0.52009796221563009, -0.66412064505963242, 0.71864499587345509], [-0.37661254480705231, -0.2101095161928333, -0.18970433898223904, -0.386860028329782, 0.54966043066208192], [-0.37661254480705231, 0.055500668481966373, -0.013579092255725289, -0.41919759360000547, 0.85642069994219061], [-0.37661254480705231, -0.26544497133341649, -0.40058857975500972, -0.59065335204805014, 0.14318404407255794], [0.24057517209121193, 0.16409649919536101, 0.72699416872813982, 0.086452269516324343, -0.22294918055208796], [-0.37661254480705231, -0.26475327814415917, -0.71321259858694974, 0.070509047687704662, -0.7405595604788221], [-0.37661254480705231, -0.33668936982691744, -1.012415026043755, 0.21517854165609335, -0.73827598527326321], [-0.37661254480705231, -0.28204560787559141, 0.78051947542459699, -0.4044074954982832, -1.1477971388035342], [-0.37661254480705231, -0.13471495856378879, -0.30476046033308579, -0.45557820493272277, 0.5595559232195042], [0.17987589842721918, -0.29311269890370806, -0.14098638674732955, 0.41222109162561854, 0.042706735027958187], [-0.049208215860898075, -0.24400248246644068, 0.13533498614032125, 0.09121491483851038, 0.51616799431388105], [-0.37661254480705231, 0.032674793236475964, -0.12767432335434181, -0.29808551175564063, -0.58984359691192023], [0.45926951773622582, 1.3538087847179006, 1.4364116195010008, -0.22556134219737695, 0.39666089188962017], [-0.17221408159718021, 0.46498303652228296, 0.819059493914295, -0.4450607063647804, -0.70782831586580719], [-0.37661254480705231, -0.13194818580675935, -0.23454381903314969, -0.72690078007001357, 0.74528670660498031], [3.7736494441252315, 0.16893835152016201, 2.3403991591649169, -0.079006313338190248, -0.036457205431425513], [-0.027401525574517915, -0.13263987899601687, 0.53808945106462103, -0.14593185505307293, 1.0330171825054293], [0.8501988334423356, -0.30487148312108214, 1.0153913499366065, 0.05186234953652788, -0.35311296726895802], [-0.37661254480705231, -0.0019098662263887143, -0.92800490577283812, 0.044543221883528605, -0.58299287129524269], [-0.37661254480705231, -0.12918141304973013, -0.53526975171128344, -0.45159724841952359, 0.84043567350327675], [0.084263757768138103, -0.3083299490673686, 0.89481752375203838, -0.28186895022646258, 0.0069307234741957657], [0.21583247551364193, 0.34601180797002884, 0.48739046902261363, -0.36798243722333884, 0.12719901763364319], [-0.37661254480705231, -0.040644684824797106, 0.82571301748768589, -0.66311667868517321, 0.61436172815292234], [-0.37661254480705231, -0.31732196052771322, -1.1625759573273062, -0.002408213244113111, -0.54341090106555234], [-0.23262132105843852, -0.30279640355331, 0.79775020699926769, -0.48048817760795587, -0.47338126142840559], [-0.37661254480705231, 0.66280728864986771, -0.39280795947865998, -0.5242673961875961, 0.47810840755456141], [-0.37661254480705231, -0.10013029910092389, -0.49114025837252528, -1.0702105875755614, 0.10512445731323816], [-0.37661254480705231, -0.26406158495490201, 0.23861974559798638, -0.39774888143183412, 1.3968668319245161], [-0.37661254480705231, -0.14716543597041987, -0.71357869786112404, -0.33602465292667466, 0.73463002231236929], [-0.37661254480705231, -0.16238268613408019, 0.55381529212613101, -0.96996879804722269, 0.84880878259032588], [-0.37661254480705231, -0.14094019726710411, -0.32146912419177953, -0.8652029922997706, 0.71712261240308406], [-0.37661254480705231, -0.15477406105224997, 0.49688606850560246, -0.37993691356803511, 1.0885841791740352], [-0.37661254480705231, -0.24815264160198422, -1.7996635810908406, 0.01600995077909767, -0.68118660513428597], [-0.37661254480705231, -0.26959513046896022, -0.87293447957742432, -0.6747837817713942, 0.87240572638110281], [-0.37661254480705231, 0.89729127980808943, 0.30263673577084615, -0.017352442160124326, -0.35082939206339897], [0.15105240177589951, -0.26129481219787287, 0.2930721140564283, 1.6754369747672602, -0.90269340007351995], [-0.37661254480705231, 0.75272740325331533, 0.11347071135040165, -0.30974970018523179, 1.0033307048331601], [-0.37661254480705231, -0.37957434756086939, -1.2310526622741809, -0.074542767337320515, -0.61648530764344245], [-0.37661254480705231, -0.21425967532837706, -0.80186025053799925, -0.56650019507403959, 0.59989908518438084], [-0.37661254480705231, -0.069004105584345957, -0.011571292075615691, -0.5506686161917882, -0.31885933918557174], [-0.37661254480705231, -0.25576126668381438, -0.86982954989926542, 1.8881155608519546, -1.30536382798711], [0.60903704932807923, 0.095618873458889003, 1.1439365585688388, -0.2275510214495356, -0.42390379864129085], [0.24063751072820472, -0.35882355188315057, 1.0706860115171248, -0.84207992058457015, -0.27014306813364336], [2.2254410089530769, 0.82051333580052999, 2.564450803868453, -0.19318523160721091, -0.41705307302461342], [-0.37661254480705231, -0.27582036917227598, -0.36919513898450917, 0.08265862028918744, 0.091423006079885127], [-0.37661254480705231, -0.33945614258394657, -0.71108774719687107, -0.43631556651258691, -0.78546987285481928], [0.1623831871879059, -0.29311269890370834, 0.76653191759672223, -0.079031743929517451, 0.43928762906005714], [-0.37661254480705231, -0.36020693826166539, -1.0923654007591499, 2.0093570700560632, -1.1660657404480059], [-0.37661254480705231, 0.67387437967798414, 0.094809900659015067, -0.22471529333707185, -0.27090425986883082], [-0.37661254480705231, -0.15823252699853646, -0.39042514181142041, -0.62204794907716687, -0.38812778708753121], [-0.37661254480705231, -0.33392259706988836, -0.95472213862015609, 0.80541587962658667, -0.95064847939026298], [-0.16553894954346271, -0.31524688095994158, 0.70726220625570546, -0.52545894002461035, -0.97120065624029395], [-0.37661254480705231, -0.26336989176564463, 0.064898779884884572, -0.48229309331390974, 0.44385477947117502], [-0.37661254480705231, 0.36399583089071796, -0.59783970669093423, -0.35860852154002626, -0.21685964667059623], [0.23222952923652751, -0.23777724376312487, 0.76762444958339371, -0.20698360451511943, 0.78562986856985761], [2.2597199703591584, 0.76863634660623326, 2.3392129539207072, -0.031176115878497279, -0.6575896613435066], [-0.37661254480705231, -0.37473249523606839, -0.68932194542492242, 0.054533542568674709, -1.147035947068346], [-0.37661254480705231, -0.21702644808540628, -0.47838903596007798, -0.72085975184464268, 0.53443659595835236], [-0.37661254480705231, -0.27097851684747482, -1.9285031315045562, 0.10999483379837344, -0.89355909925128163], [1.1096949127266598, 0.86893185904854042, 1.7042945736563122, -0.20092706981427894, -0.49469463001362246], [-0.37661254480705231, -0.27789544874004785, -0.023742890841419229, -0.5332808542393459, 0.85642069994218917], [-0.37661254480705231, -0.33323090388063081, -1.1197319798153278, 0.22012054173111584, -1.0747227322256401], [-0.37661254480705231, -0.34913984723354885, 0.49058321928980275, -1.0974114885867801, 1.6587167888286281], [-0.37661254480705231, -0.28965423295742176, -0.60177244941209596, -0.64345919198361812, 0.20331819115228136], [-0.37661254480705231, 0.06034252080676792, -0.37237771330276859, -0.31655223190745524, -0.24959089128361117], [-0.37661254480705231, -0.15546575424150738, 0.45589723226808543, -0.69421402267645371, 0.58010810006953617], [-0.37661254480705231, -0.19005041370437178, -1.0303307547926299, 0.067674703652823492, -0.39041136229309037], [-0.37661254480705231, -0.30417978993182482, -0.67843015886202851, -0.12579484399865265, -0.55178401015260237], [3.357293709949559, 0.30036005747904626, 1.8197966397188448, 0.89420204597003838, -1.1729164660646823], [-0.37661254480705231, -0.3097133354458832, -0.98783764015544806, -0.54386514084880377, -0.61953007458418918], [-0.37661254480705231, -0.31455518777068442, 0.024208566809448495, -0.29295046101181516, -0.66063442828425389], [-0.37661254480705231, -0.093905060397608597, -1.0413948171658665, 0.4321827031287615, -0.5776645291489384], [-0.37661254480705231, -0.3463730744765196, -0.13688244451591958, -0.28788774281619328, -1.0808122661071322], [-0.37661254480705231, -0.081454582990977215, 0.19662752771966963, -0.44124860203983846, 0.96755469327940147], [-0.053280089132866748, 0.7001587208697595, 1.1916616643062805, -0.48214222925716405, 0.72245095454938846], [-0.37661254480705231, -0.37058233610052477, -0.88660238632624599, -0.52255944117285358, 0.25888518782088593], [-0.37661254480705231, -0.22601845954575123, -0.23349167242656721, -0.5492670955773078, -0.25948638384103412], [-0.37661254480705231, -0.075921037476918807, -0.69950049825436666, -0.49937512968520753, 1.2385389510057498], [-0.37661254480705231, -0.22947692549203746, -0.34387093723501083, -0.32013878340465979, -0.42999333252278182], [2.1065211076397472, 0.87239032499482683, 2.4575388286085622, -0.32890452034101098, -0.35082939206339886], [-0.37661254480705231, -0.16238268613408025, -0.62496937100556282, -0.76963071518316051, 0.83358494788659832], [2.023732849599047, 0.65450697037878025, 2.5323365210743698, -0.36653645003820651, -0.50991846471735003], [-0.11917006839367106, -0.26544497133341638, 0.84432684811261494, -0.17247516465753254, -0.42770975731722272], [0.70712796591191895, 1.7065723112391189, 0.7438621579333069, 0.31553764604972118, -0.37594871932454915], [-0.37661254480705231, -0.3622820178294372, -0.19818465768111126, -0.5165016032257399, -0.02123337072769884], [-0.37661254480705231, -0.17137469759442492, -1.1594404845092474, -0.20876336001101919, 0.47125768193788503], [-0.37661254480705231, 0.097693953026661201, -0.87125157154471866, -0.81470781228361489, 1.3877325311022786], [-0.37661254480705231, -0.34498968809800518, 0.9100002739866393, -1.1924638682664712, 1.0459574420035971], [-0.37661254480705231, -0.26821174409044563, -0.13480763971429655, -0.64261534470630499, 1.3664191625170599], [-0.37661254480705231, 0.16686327195239095, 0.098544791648549346, 0.12957777667032538, -1.1432299883924131], [-0.37661254480705231, -0.15131559510596362, -0.16073882441645582, -0.4322220312636213, 0.58315286701028135], [-0.13890127950945441, 1.935522756883282, 0.40475304941565993, 0.15528173809353266, -0.62257484152493436], [-0.22275836582736469, 0.53000219631246837, 0.44465705215586082, -0.29327858707752058, -0.6530225109323885], [-0.37661254480705231, -0.38303281350715557, -0.7884871134452287, -0.10588353562307407, -1.2193491619110519], [2.6509772727657945, -0.3276973583665726, 1.7364546164880319, 0.95442780402847993, -1.319826470955654], [-0.37661254480705231, -0.33115582431285923, 0.2118219501533356, -0.29451181175527025, -0.63703748449347608], [-0.37661254480705231, -0.23916063014163952, -0.63485341781233773, -0.59530062315481369, -0.095830160775963596], [-0.026898065040031782, -0.29726285803925201, -0.73721490362245889, -0.45738602737826034, 0.09066181434469818], [-0.37661254480705231, -0.20388427748951782, 0.40226507259899291, -0.82769973508481609, 0.79324178592172156], [-0.37661254480705231, -0.149932208727449, -0.63101440559319188, -0.3268874112459183, -0.50687369777660574], [-0.37661254480705231, -0.28481238063262077, -1.0450406256162244, 0.24093533769123421, -0.019710987257326406], [-0.37661254480705231, 0.4739750479826273, -0.421273006732378, -0.23307058082653881, 0.17591528868557121], [-0.2553929725024533, 0.2028313177937692, 0.94720689600535435, -0.50526343391600803, 0.1378557019262531], [-0.37661254480705231, 0.6973919481127322, -0.17503495455812912, -0.17385795181174218, -0.40335162179125855], [-0.37661254480705231, -0.2654449713334166, -0.58544338947584196, -0.45622685799204282, -0.26557591772252581], [-0.37661254480705231, -0.34429799490874757, -0.6630631434329648, -0.203537692803894, -0.82200707614376323], [-0.37661254480705231, -0.38856635902121417, -0.37568247818606482, -0.44042064156671096, -0.87757407281236954], [0.23629303410670777, -0.14232358364561867, 0.68487347401781973, 0.15587456602323488, -0.32571006480224857], [-0.37661254480705231, -0.34983154042280595, -0.55063842776456839, -0.014319695791171261, -0.36605322676712559], [-0.37661254480705231, 0.11360289637957881, -0.69052650179918418, 0.041624139840082314, -0.46805291928210108], [-0.37661254480705231, 0.15648787411353077, -0.98725062396218322, -0.40432313385197899, -0.29069524498367522], [-0.37661254480705231, -0.18244178862254171, 0.25690827969412977, -0.37872084431801062, 0.82749541400510862], [-0.34462267599978541, -0.3131718013921696, -0.83499713316078372, -0.60611724282661261, 0.80998800409581972], [-0.37661254480705231, 0.052042202535679857, 0.089478880551353779, -0.28316080853617415, 0.65166012317705468], [-0.37661254480705231, -0.21979322084243536, -0.023493457934251172, -0.15725932124538078, 0.10588564904842598], [-0.37661254480705231, -0.37819096118235501, -1.0614933002165121, 0.43965584882718145, -1.119633044601636], [-0.37661254480705231, -0.21080120938209065, -1.5828091403442959, 0.15448638623494365, -0.20772534584836039]]
Generate CSV file for kaggle submission
In [14]:
# Print header
header = 'PIDN,Ca,P,pH,SOC,Sand'
#np.savetxt('test.out', header, delimiter=',')
filename = 'jds1.txt'
# Clean file
open(filename, 'w').close()
with open(filename, 'w') as f:
f.write('PIDN,Ca,P,pH,SOC,Sand\n') # python will convert \n to os.linesep
# Iterate through test samples
for i in range(len(allPredictions)):
pred = allPredictions[i]
testId = test_ids[i]
text = testId + ',' + str(pred[0]) + ',' + str(pred[1]) + ',' + str(pred[2]) + ',' + str(pred[3]) + ',' + str(pred[4]) + '\n'
f.write(text)
f.close()
Investigation: Sand variable
In [ ]:
In [ ]:
Content source: carlosscastro/kaggle_africa_soil
Similar notebooks: