In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
import numpy as np
from functools import reduce
# Needed for the scikit-learn wrapper function
from sklearn.tree import irf_utils
from sklearn.ensemble import RandomForestClassifier
from math import ceil
# Import our custom utilities
from imp import reload
from utils import irf_jupyter_utils
reload(irf_jupyter_utils)
Out[1]:
<module 'utils.irf_jupyter_utils' from '/Users/shamindras/PERSONAL/LEARNING/REPOS/scikit-learn-sandbox/jupyter/utils/irf_jupyter_utils.py'>
In [2]:
load_breast_cancer = load_breast_cancer()
In [3]:
X_train, X_test, y_train, y_test, rf = irf_jupyter_utils.generate_rf_example(n_estimators=20,
feature_weight=None)
In [4]:
print("Training feature dimensions", X_train.shape, sep = ":\n")
print("\n")
print("Training outcome dimensions", y_train.shape, sep = ":\n")
print("\n")
print("Test feature dimensions", X_test.shape, sep = ":\n")
print("\n")
print("Test outcome dimensions", y_test.shape, sep = ":\n")
print("\n")
print("first 2 rows of the training set features", X_train[:2], sep = ":\n")
print("\n")
print("first 2 rows of the training set outcomes", y_train[:2], sep = ":\n")
Training feature dimensions:
(512, 30)
Training outcome dimensions:
(512,)
Test feature dimensions:
(57, 30)
Test outcome dimensions:
(57,)
first 2 rows of the training set features:
[[ 1.98900000e+01 2.02600000e+01 1.30500000e+02 1.21400000e+03
1.03700000e-01 1.31000000e-01 1.41100000e-01 9.43100000e-02
1.80200000e-01 6.18800000e-02 5.07900000e-01 8.73700000e-01
3.65400000e+00 5.97000000e+01 5.08900000e-03 2.30300000e-02
3.05200000e-02 1.17800000e-02 1.05700000e-02 3.39100000e-03
2.37300000e+01 2.52300000e+01 1.60500000e+02 1.64600000e+03
1.41700000e-01 3.30900000e-01 4.18500000e-01 1.61300000e-01
2.54900000e-01 9.13600000e-02]
[ 2.01800000e+01 1.95400000e+01 1.33800000e+02 1.25000000e+03
1.13300000e-01 1.48900000e-01 2.13300000e-01 1.25900000e-01
1.72400000e-01 6.05300000e-02 4.33100000e-01 1.00100000e+00
3.00800000e+00 5.24900000e+01 9.08700000e-03 2.71500000e-02
5.54600000e-02 1.91000000e-02 2.45100000e-02 4.00500000e-03
2.20300000e+01 2.50700000e+01 1.46000000e+02 1.47900000e+03
1.66500000e-01 2.94200000e-01 5.30800000e-01 2.17300000e-01
3.03200000e-01 8.07500000e-02]]
first 2 rows of the training set outcomes:
[0 0]
In [5]:
all_rf_tree_data = irf_utils.get_rf_tree_data(
rf=rf, X_train=X_train, X_test=X_test, y_test=y_test)
In [6]:
np.random.seed(12)
all_rit_tree_data = irf_utils.get_rit_tree_data(
all_rf_tree_data=all_rf_tree_data,
bin_class_type=1,
M=100,
max_depth=2,
noisy_split=False,
num_splits=2)
In [1]:
# Print the feature ranking
print("Feature ranking:")
feature_importances_rank_idx = all_rf_tree_data['feature_importances_rank_idx']
feature_importances = all_rf_tree_data['feature_importances']
for f in range(X_train.shape[1]):
print("%d. feature %d (%f)" % (f + 1
, feature_importances_rank_idx[f]
, feature_importances[feature_importances_rank_idx[f]]))
Feature ranking:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-10fae13b9e98> in <module>()
2 print("Feature ranking:")
3
----> 4 feature_importances_rank_idx = all_rf_tree_data['feature_importances_rank_idx']
5 feature_importances = all_rf_tree_data['feature_importances']
6
NameError: name 'all_rf_tree_data' is not defined
In [2]:
# Plot the feature importances of the forest
feature_importances_std = all_rf_tree_data['feature_importances_std']
plt.title("Feature importances")
plt.bar(range(X_train.shape[1])
, feature_importances[feature_importances_rank_idx]
, color="r"
, yerr = feature_importances_std[feature_importances_rank_idx], align="center")
plt.xticks(range(X_train.shape[1]), feature_importances_rank_idx)
plt.xlim([-1, X_train.shape[1]])
plt.show()
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-2-e8c4820509d3> in <module>()
1 # Plot the feature importances of the forest
----> 2 feature_importances_std = all_rf_tree_data['feature_importances_std']
3
4 width = 12
5 height = 12
NameError: name 'all_rf_tree_data' is not defined
In [9]:
# Now plot the trees individually
irf_jupyter_utils.draw_tree(decision_tree = all_rf_tree_data['rf_obj'].estimators_[0])
In [10]:
#irf_jupyter_utils.pretty_print_dict(inp_dict = all_rf_tree_data['dtree0'])
In [11]:
# Count the number of samples passing through the leaf nodes
sum(all_rf_tree_data['dtree0']['tot_leaf_node_values'])
Out[11]:
512
In [12]:
#irf_jupyter_utils.pretty_print_dict(inp_dict = all_rf_tree_data['dtree0']['all_leaf_paths_features'])
We will run the iRF with the following parameters
Will be useful when we build the interface later
In [13]:
all_rf_weights, all_K_iter_rf_data, \
all_rf_bootstrap_output, all_rit_bootstrap_output, \
stability_score = irf_utils.run_iRF(X_train=X_train,
X_test=X_test,
y_train=y_train,
y_test=y_test,
K=5,
n_estimators=20,
B=30,
random_state_classifier=2018,
propn_n_samples=.2,
bin_class_type=1,
M=20,
max_depth=5,
noisy_split=False,
num_splits=2,
n_estimators_bootstrap=5)
In [14]:
stability_score
Out[14]:
{'1_21_22_27': 0.03333333333333333,
'1_22': 0.03333333333333333,
'1_22_27': 0.03333333333333333,
'1_23_27': 0.1,
'1_6_21_23_27': 0.03333333333333333,
'1_7_22': 0.06666666666666667,
'1_7_22_27': 0.03333333333333333,
'20': 0.3,
'20_21': 0.03333333333333333,
'20_21_22': 0.03333333333333333,
'20_21_22_27': 0.06666666666666667,
'20_21_27': 0.03333333333333333,
'20_22': 0.13333333333333333,
'20_22_23': 0.03333333333333333,
'20_22_27': 0.06666666666666667,
'20_23': 0.03333333333333333,
'20_23_27': 0.03333333333333333,
'20_27': 0.1,
'21': 0.16666666666666666,
'21_22': 0.13333333333333333,
'21_22_27': 0.06666666666666667,
'21_23': 0.06666666666666667,
'21_23_27': 0.03333333333333333,
'21_27': 0.13333333333333333,
'22': 0.6333333333333333,
'22_23': 0.03333333333333333,
'22_23_27': 0.23333333333333334,
'22_26_27': 0.03333333333333333,
'22_27': 0.7,
'23': 0.26666666666666666,
'23_27': 0.23333333333333334,
'26_27': 0.03333333333333333,
'27': 0.6,
'6_21_23_27': 0.03333333333333333,
'6_23': 0.03333333333333333,
'7': 0.36666666666666664,
'7_20': 0.1,
'7_20_21_23_27': 0.03333333333333333,
'7_20_22': 0.06666666666666667,
'7_20_23': 0.03333333333333333,
'7_20_27': 0.06666666666666667,
'7_21': 0.2,
'7_21_22': 0.03333333333333333,
'7_21_22_23': 0.03333333333333333,
'7_21_23': 0.06666666666666667,
'7_21_23_27': 0.03333333333333333,
'7_21_27': 0.06666666666666667,
'7_22': 0.4,
'7_22_23': 0.06666666666666667,
'7_22_23_27': 0.03333333333333333,
'7_22_27': 0.3333333333333333,
'7_23': 0.1,
'7_23_27': 0.03333333333333333,
'7_27': 0.23333333333333334}
In [15]:
irf_jupyter_utils._get_histogram(stability_score, sort = True)
That's interesting - feature 22, 27, 20, 23 keep popping up!
We should probably look at the feature importances to understand if there is a useful correlation
In [16]:
for k in range(5):
iteration = "rf_iter{}".format(k)
feature_importances_std = all_K_iter_rf_data[iteration]['feature_importances_std']
feature_importances_rank_idx = all_K_iter_rf_data[iteration]['feature_importances_rank_idx']
feature_importances = all_K_iter_rf_data[iteration]['feature_importances']
plt.figure(figsize=(8, 6))
title = "Feature importances; iteration = {}".format(k)
plt.title(title)
plt.bar(range(X_train.shape[1])
, feature_importances[feature_importances_rank_idx]
, color="r"
, yerr = feature_importances_std[feature_importances_rank_idx], align="center")
plt.xticks(range(X_train.shape[1]), feature_importances_rank_idx, rotation='vertical')
plt.xlim([-1, X_train.shape[1]])
plt.show()
Let's look at the final iteration RF - the key validation metrics
In [17]:
irf_jupyter_utils.pretty_print_dict(all_K_iter_rf_data['rf_iter4']['rf_validation_metrics'])
{ 'accuracy_score': 0.96491228070175439,
'confusion_matrix': array([[12, 2],
[ 0, 43]]),
'f1_score': 0.97727272727272729,
'hamming_loss': 0.035087719298245612,
'log_loss': 1.2119149470996806,
'precision_score': 0.9555555555555556,
'recall_score': 1.0,
'zero_one_loss': 0.035087719298245612}
In [18]:
# Now plot the trees individually
irf_jupyter_utils.draw_tree(decision_tree = all_K_iter_rf_data['rf_iter4']['rf_obj'].estimators_[0])
In [19]:
irf_jupyter_utils.pretty_print_dict(
all_K_iter_rf_data['rf_iter4']['dtree0']['all_leaf_paths_features'])
[ array([ 7, 23, 7, 21, 23]),
array([ 7, 23, 7, 21, 23, 22]),
array([ 7, 23, 7, 21, 23, 22]),
array([ 7, 23, 7, 21, 27]),
array([ 7, 23, 7, 21, 27]),
array([ 7, 23, 7, 22]),
array([ 7, 23, 7, 22, 7]),
array([ 7, 23, 7, 22, 7]),
array([ 7, 23, 27]),
array([ 7, 23, 27, 21]),
array([ 7, 23, 27, 21]),
array([ 7, 27, 27]),
array([ 7, 27, 27]),
array([ 7, 27, 22]),
array([ 7, 27, 22, 21]),
array([ 7, 27, 22, 21, 26]),
array([ 7, 27, 22, 21, 26, 23, 21]),
array([ 7, 27, 22, 21, 26, 23, 21]),
array([ 7, 27, 22, 21, 26, 23])]
This checks nicely against the plotted diagram above.
In fact - we can go further and plot some interesting data from the Decision Trees
In [20]:
irf_jupyter_utils.pretty_print_dict(
all_K_iter_rf_data['rf_iter4']['dtree0']['all_leaf_node_values'])
[ array([[ 0, 252]]),
array([[2, 0]]),
array([[ 0, 32]]),
array([[ 0, 15]]),
array([[7, 0]]),
array([[0, 8]]),
array([[7, 0]]),
array([[0, 2]]),
array([[0, 1]]),
array([[0, 1]]),
array([[9, 0]]),
array([[1, 0]]),
array([[0, 6]]),
array([[0, 1]]),
array([[0, 1]]),
array([[0, 1]]),
array([[0, 1]]),
array([[6, 0]]),
array([[159, 0]])]
In [21]:
irf_jupyter_utils._hist_features(all_K_iter_rf_data['rf_iter4'], n_estimators = 20, \
title = 'Frequency of features along decision paths : iteration = 4')
The most common features that appeared were 27,22,23, and 7. This matches well with the feature importance plot above.
In [22]:
all_K_iter_rf_data.keys()
print(all_K_iter_rf_data['rf_iter0']['feature_importances'])
[ 0.0378531 0.01143301 0.00917885 0.0389645 0.00570286 0.00355542
0.04189169 0.04736217 0.00276282 0.00375928 0.00758323 0.00132309
0.00668335 0.00753545 0.0027825 0.0043194 0.00388132 0.00557761
0.00168787 0.00332904 0.26950483 0.01216281 0.12508337 0.16629184
0.01540501 0.00929946 0.03860752 0.0959075 0.01366094 0.00691016]
Compare to the original single fitted random forest
In [23]:
rf = RandomForestClassifier(n_estimators=20, random_state=2018)
rf.fit(X=X_train, y=y_train)
print(rf.feature_importances_)
[ 0.0378531 0.01143301 0.00917885 0.0389645 0.00570286 0.00355542
0.04189169 0.04736217 0.00276282 0.00375928 0.00758323 0.00132309
0.00668335 0.00753545 0.0027825 0.0043194 0.00388132 0.00557761
0.00168787 0.00332904 0.26950483 0.01216281 0.12508337 0.16629184
0.01540501 0.00929946 0.03860752 0.0959075 0.01366094 0.00691016]
And they match perfectly as expected.
In [24]:
all_rf_weights['rf_weight1']
Out[24]:
array([ 0.0378531 , 0.01143301, 0.00917885, 0.0389645 , 0.00570286,
0.00355542, 0.04189169, 0.04736217, 0.00276282, 0.00375928,
0.00758323, 0.00132309, 0.00668335, 0.00753545, 0.0027825 ,
0.0043194 , 0.00388132, 0.00557761, 0.00168787, 0.00332904,
0.26950483, 0.01216281, 0.12508337, 0.16629184, 0.01540501,
0.00929946, 0.03860752, 0.0959075 , 0.01366094, 0.00691016])
In [25]:
all_K_iter_rf_data
Out[25]:
{'rf_iter0': {'dtree0': {'all_leaf_node_classes': [1,
0,
1,
0,
1,
0,
1,
1,
0,
1,
1,
0,
0,
1,
0,
1,
1,
1,
0,
1,
0,
0],
'all_leaf_node_paths': [array([0, 1, 2, 3, 4, 5, 6]),
array([0, 1, 2, 3, 4, 5, 7, 8]),
array([0, 1, 2, 3, 4, 5, 7, 9]),
array([ 0, 1, 2, 3, 4, 10]),
array([ 0, 1, 2, 3, 11, 12]),
array([ 0, 1, 2, 3, 11, 13, 14]),
array([ 0, 1, 2, 3, 11, 13, 15]),
array([ 0, 1, 2, 16, 17, 18, 19]),
array([ 0, 1, 2, 16, 17, 18, 20, 21, 22]),
array([ 0, 1, 2, 16, 17, 18, 20, 21, 23]),
array([ 0, 1, 2, 16, 17, 18, 20, 24]),
array([ 0, 1, 2, 16, 17, 25]),
array([ 0, 1, 2, 16, 26]),
array([ 0, 1, 27, 28, 29]),
array([ 0, 1, 27, 28, 30]),
array([ 0, 1, 27, 31, 32]),
array([ 0, 1, 27, 31, 33, 34]),
array([ 0, 1, 27, 31, 33, 35, 36]),
array([ 0, 1, 27, 31, 33, 35, 37]),
array([ 0, 38, 39, 40]),
array([ 0, 38, 39, 41]),
array([ 0, 38, 42])],
'all_leaf_node_samples': [157,
1,
6,
1,
6,
3,
1,
16,
1,
1,
5,
2,
3,
5,
1,
2,
1,
1,
11,
4,
2,
99],
'all_leaf_node_samples_percent': [47.72036474164134,
0.303951367781155,
1.8237082066869301,
0.303951367781155,
1.8237082066869301,
0.91185410334346506,
0.303951367781155,
4.86322188449848,
0.303951367781155,
0.303951367781155,
1.5197568389057752,
0.60790273556231,
0.91185410334346506,
1.5197568389057752,
0.303951367781155,
0.60790273556231,
0.303951367781155,
0.303951367781155,
3.3434650455927053,
1.21580547112462,
0.60790273556231,
30.091185410334347],
'all_leaf_node_values': [array([[ 0, 239]]),
array([[1, 0]]),
array([[0, 8]]),
array([[2, 0]]),
array([[0, 8]]),
array([[7, 0]]),
array([[0, 2]]),
array([[ 0, 27]]),
array([[3, 0]]),
array([[0, 1]]),
array([[ 0, 10]]),
array([[2, 0]]),
array([[7, 0]]),
array([[0, 7]]),
array([[1, 0]]),
array([[0, 2]]),
array([[0, 2]]),
array([[0, 1]]),
array([[19, 0]]),
array([[0, 6]]),
array([[2, 0]]),
array([[155, 0]])],
'all_leaf_nodes': [6,
8,
9,
10,
12,
14,
15,
19,
22,
23,
24,
25,
26,
29,
30,
32,
34,
36,
37,
40,
41,
42],
'all_leaf_paths_features': [array([23, 26, 1, 6, 13, 5]),
array([23, 26, 1, 6, 13, 5, 9]),
array([23, 26, 1, 6, 13, 5, 9]),
array([23, 26, 1, 6, 13]),
array([23, 26, 1, 6, 22]),
array([23, 26, 1, 6, 22, 13]),
array([23, 26, 1, 6, 22, 13]),
array([23, 26, 1, 3, 27, 27]),
array([23, 26, 1, 3, 27, 27, 19, 17]),
array([23, 26, 1, 3, 27, 27, 19, 17]),
array([23, 26, 1, 3, 27, 27, 19]),
array([23, 26, 1, 3, 27]),
array([23, 26, 1, 3]),
array([23, 26, 3, 18]),
array([23, 26, 3, 18]),
array([23, 26, 3, 8]),
array([23, 26, 3, 8, 22]),
array([23, 26, 3, 8, 22, 4]),
array([23, 26, 3, 8, 22, 4]),
array([23, 26, 22]),
array([23, 26, 22]),
array([23, 26])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.46679688]]),
array([[ 0.00195312, 0. ]]),
array([[ 0. , 0.015625]]),
array([[ 0.00390625, 0. ]]),
array([[ 0. , 0.015625]]),
array([[ 0.01367188, 0. ]]),
array([[ 0. , 0.00390625]]),
array([[ 0. , 0.05273438]]),
array([[ 0.00585938, 0. ]]),
array([[ 0. , 0.00195312]]),
array([[ 0. , 0.01953125]]),
array([[ 0.00390625, 0. ]]),
array([[ 0.01367188, 0. ]]),
array([[ 0. , 0.01367188]]),
array([[ 0.00195312, 0. ]]),
array([[ 0. , 0.00390625]]),
array([[ 0. , 0.00390625]]),
array([[ 0. , 0.00195312]]),
array([[ 0.03710938, 0. ]]),
array([[ 0. , 0.01171875]]),
array([[ 0.00390625, 0. ]]),
array([[ 0.30273438, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 1, 5, 6, 13, 23, 26]),
array([ 1, 5, 6, 9, 13, 23, 26]),
array([ 1, 5, 6, 9, 13, 23, 26]),
array([ 1, 6, 13, 23, 26]),
array([ 1, 6, 22, 23, 26]),
array([ 1, 6, 13, 22, 23, 26]),
array([ 1, 6, 13, 22, 23, 26]),
array([ 1, 3, 23, 26, 27]),
array([ 1, 3, 17, 19, 23, 26, 27]),
array([ 1, 3, 17, 19, 23, 26, 27]),
array([ 1, 3, 19, 23, 26, 27]),
array([ 1, 3, 23, 26, 27]),
array([ 1, 3, 23, 26]),
array([ 3, 18, 23, 26]),
array([ 3, 18, 23, 26]),
array([ 3, 8, 23, 26]),
array([ 3, 8, 22, 23, 26]),
array([ 3, 4, 8, 22, 23, 26]),
array([ 3, 4, 8, 22, 23, 26]),
array([22, 23, 26]),
array([22, 23, 26]),
array([23, 26])],
'leaf_nodes_depths': [6,
7,
7,
5,
5,
6,
6,
6,
8,
8,
7,
5,
4,
4,
4,
4,
5,
6,
6,
3,
3,
2],
'max_node_depth': 8,
'n_nodes': 43,
'node_features_idx': array([23, 26, 1, 6, 13, 5, 28, 9, 28, 28, 28, 22, 28, 13, 28, 28, 3,
27, 27, 28, 19, 17, 28, 28, 28, 28, 28, 3, 18, 28, 28, 8, 28, 22,
28, 4, 28, 28, 26, 22, 28, 28, 28]),
'num_features_used': 16,
'tot_leaf_node_values': [239,
1,
8,
2,
8,
7,
2,
27,
3,
1,
10,
2,
7,
7,
1,
2,
2,
1,
19,
6,
2,
155],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241098,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'dtree1': {'all_leaf_node_classes': [1,
0,
1,
0,
1,
0,
1,
0,
1,
1,
0,
1,
1,
0,
1,
0,
1,
0,
1,
0,
0],
'all_leaf_node_paths': [array([0, 1, 2, 3, 4, 5]),
array([0, 1, 2, 3, 4, 6, 7, 8]),
array([ 0, 1, 2, 3, 4, 6, 7, 9, 10, 11]),
array([ 0, 1, 2, 3, 4, 6, 7, 9, 10, 12]),
array([ 0, 1, 2, 3, 4, 6, 7, 9, 13]),
array([ 0, 1, 2, 3, 4, 6, 14, 15]),
array([ 0, 1, 2, 3, 4, 6, 14, 16]),
array([ 0, 1, 2, 3, 17, 18]),
array([ 0, 1, 2, 3, 17, 19]),
array([ 0, 1, 2, 20, 21, 22, 23]),
array([ 0, 1, 2, 20, 21, 22, 24, 25]),
array([ 0, 1, 2, 20, 21, 22, 24, 26]),
array([ 0, 1, 2, 20, 21, 27]),
array([ 0, 1, 2, 20, 28]),
array([ 0, 1, 29, 30]),
array([ 0, 1, 29, 31]),
array([ 0, 32, 33, 34, 35]),
array([ 0, 32, 33, 34, 36]),
array([ 0, 32, 33, 37, 38]),
array([ 0, 32, 33, 37, 39]),
array([ 0, 32, 40])],
'all_leaf_node_samples': [114,
1,
3,
1,
67,
1,
1,
1,
3,
2,
3,
1,
3,
7,
2,
7,
1,
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array([20, 27]),
array([20, 27]),
array([20, 27]),
array([20, 22, 23, 27]),
array([20, 22, 23, 27]),
array([20, 23, 27])],
'leaf_nodes_depths': [3, 4, 5, 6, 6, 4, 5, 5, 4, 4, 5, 5, 4, 3, 4, 4, 3],
'max_node_depth': 6,
'n_nodes': 33,
'node_features_idx': array([20, 27, 23, 28, 27, 28, 7, 28, 27, 28, 28, 21, 23, 28, 21, 28, 28,
7, 28, 28, 27, 27, 27, 27, 28, 28, 28, 28, 23, 22, 28, 28, 28]),
'num_features_used': 7,
'tot_leaf_node_values': [226,
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'validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[13, 1],
[ 2, 41]]),
'f1_score': 0.96470588235294119,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178443645993385,
'precision_score': 0.97619047619047616,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.052631578947368474}},
'dtree8': {'all_leaf_node_classes': [1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8, 9]),
array([ 0, 1, 7, 8, 10]),
array([ 0, 1, 7, 11]),
array([ 0, 12, 13, 14]),
array([ 0, 12, 13, 15]),
array([ 0, 12, 16, 17, 18]),
array([ 0, 12, 16, 17, 19, 20, 21]),
array([ 0, 12, 16, 17, 19, 20, 22]),
array([ 0, 12, 16, 17, 19, 23]),
array([ 0, 12, 16, 24])],
'all_leaf_node_samples': [155, 1, 23, 1, 8, 3, 10, 2, 5, 1, 2, 14, 105],
'all_leaf_node_samples_percent': [46.969696969696969,
0.30303030303030304,
6.9696969696969697,
0.30303030303030304,
2.4242424242424243,
0.90909090909090906,
3.0303030303030303,
0.60606060606060608,
1.5151515151515151,
0.30303030303030304,
0.60606060606060608,
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31.818181818181817],
'all_leaf_node_values': [array([[ 0, 243]]),
array([[1, 0]]),
array([[ 0, 39]]),
array([[0, 1]]),
array([[13, 0]]),
array([[0, 6]]),
array([[ 0, 13]]),
array([[4, 0]]),
array([[0, 5]]),
array([[0, 1]]),
array([[4, 0]]),
array([[19, 0]]),
array([[163, 0]])],
'all_leaf_nodes': [3, 5, 6, 9, 10, 11, 14, 15, 18, 21, 22, 23, 24],
'all_leaf_paths_features': [array([ 7, 20, 7]),
array([ 7, 20, 7, 7]),
array([ 7, 20, 7, 7]),
array([ 7, 20, 7, 27]),
array([ 7, 20, 7, 27]),
array([ 7, 20, 7]),
array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([ 7, 22, 22, 21]),
array([ 7, 22, 22, 21, 23, 27]),
array([ 7, 22, 22, 21, 23, 27]),
array([ 7, 22, 22, 21, 23]),
array([ 7, 22, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.47460938]]),
array([[ 0.00195312, 0. ]]),
array([[ 0. , 0.07617188]]),
array([[ 0. , 0.00195312]]),
array([[ 0.02539062, 0. ]]),
array([[ 0. , 0.01171875]]),
array([[ 0. , 0.02539062]]),
array([[ 0.0078125, 0. ]]),
array([[ 0. , 0.00976562]]),
array([[ 0. , 0.00195312]]),
array([[ 0.0078125, 0. ]]),
array([[ 0.03710938, 0. ]]),
array([[ 0.31835938, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 20]),
array([ 7, 20]),
array([ 7, 20]),
array([ 7, 20, 27]),
array([ 7, 20, 27]),
array([ 7, 20]),
array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([ 7, 21, 22]),
array([ 7, 21, 22, 23, 27]),
array([ 7, 21, 22, 23, 27]),
array([ 7, 21, 22, 23]),
array([ 7, 22])],
'leaf_nodes_depths': [3, 4, 4, 4, 4, 3, 3, 3, 4, 6, 6, 5, 3],
'max_node_depth': 6,
'n_nodes': 25,
'node_features_idx': array([ 7, 20, 7, 28, 7, 28, 28, 7, 27, 28, 28, 28, 22, 27, 28, 28, 22,
21, 28, 23, 27, 28, 28, 28, 28]),
'num_features_used': 7,
'tot_leaf_node_values': [243, 1, 39, 1, 13, 6, 13, 4, 5, 1, 4, 19, 163],
'validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[12, 2],
[ 1, 42]]),
'f1_score': 0.96551724137931039,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178583926244296,
'precision_score': 0.95454545454545459,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.052631578947368474}},
'dtree9': {'all_leaf_node_classes': [1,
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'all_leaf_node_paths': [array([0, 1, 2, 3, 4, 5, 6]),
array([0, 1, 2, 3, 4, 5, 7, 8]),
array([0, 1, 2, 3, 4, 5, 7, 9]),
array([ 0, 1, 2, 3, 4, 10]),
array([ 0, 1, 2, 3, 11]),
array([ 0, 1, 2, 12, 13]),
array([ 0, 1, 2, 12, 14]),
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array([ 0, 18, 22, 23, 29, 30, 32]),
array([ 0, 18, 22, 23, 29, 33, 34, 35]),
array([ 0, 18, 22, 23, 29, 33, 34, 36]),
array([ 0, 18, 22, 23, 29, 33, 37]),
array([ 0, 18, 22, 38, 39, 40]),
array([ 0, 18, 22, 38, 39, 41]),
array([ 0, 18, 22, 38, 42])],
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'all_leaf_node_samples_percent': [10.443037974683545,
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30.696202531645568],
'all_leaf_node_values': [array([[ 0, 60]]),
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array([[1, 0]]),
array([[2, 0]]),
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array([[0, 6]]),
array([[0, 2]]),
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array([[2, 0]]),
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array([[3, 0]]),
array([[0, 3]]),
array([[0, 1]]),
array([[2, 0]]),
array([[12, 0]]),
array([[8, 0]]),
array([[0, 1]]),
array([[151, 0]])],
'all_leaf_nodes': [6,
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'all_leaf_paths_features': [array([27, 22, 22, 26, 7, 7]),
array([27, 22, 22, 26, 7, 7, 22]),
array([27, 22, 22, 26, 7, 7, 22]),
array([27, 22, 22, 26, 7]),
array([27, 22, 22, 26]),
array([27, 22, 22, 22]),
array([27, 22, 22, 22]),
array([27, 22, 27]),
array([27, 22, 27]),
array([27, 23, 27]),
array([27, 23, 27]),
array([27, 23, 7, 21, 22, 22]),
array([27, 23, 7, 21, 22, 22]),
array([27, 23, 7, 21, 22]),
array([27, 23, 7, 21, 7, 7]),
array([27, 23, 7, 21, 7, 7]),
array([27, 23, 7, 21, 7, 6, 23]),
array([27, 23, 7, 21, 7, 6, 23]),
array([27, 23, 7, 21, 7, 6]),
array([27, 23, 7, 20, 20]),
array([27, 23, 7, 20, 20]),
array([27, 23, 7, 20])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.1171875]]),
array([[ 0. , 0.00390625]]),
array([[ 0.00195312, 0. ]]),
array([[ 0.00390625, 0. ]]),
array([[ 0. , 0.44140625]]),
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array([[ 0. , 0.01171875]]),
array([[ 0. , 0.00390625]]),
array([[ 0.0078125, 0. ]]),
array([[ 0. , 0.02929688]]),
array([[ 0.00195312, 0. ]]),
array([[ 0. , 0.00390625]]),
array([[ 0.00390625, 0. ]]),
array([[ 0. , 0.01171875]]),
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array([[ 0. , 0.00585938]]),
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array([[ 0.00390625, 0. ]]),
array([[ 0.0234375, 0. ]]),
array([[ 0.015625, 0. ]]),
array([[ 0. , 0.00195312]]),
array([[ 0.29492188, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 22, 26, 27]),
array([ 7, 22, 26, 27]),
array([ 7, 22, 26, 27]),
array([ 7, 22, 26, 27]),
array([22, 26, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([23, 27]),
array([23, 27]),
array([ 7, 21, 22, 23, 27]),
array([ 7, 21, 22, 23, 27]),
array([ 7, 21, 22, 23, 27]),
array([ 7, 21, 23, 27]),
array([ 7, 21, 23, 27]),
array([ 6, 7, 21, 23, 27]),
array([ 6, 7, 21, 23, 27]),
array([ 6, 7, 21, 23, 27]),
array([ 7, 20, 23, 27]),
array([ 7, 20, 23, 27]),
array([ 7, 20, 23, 27])],
'leaf_nodes_depths': [6,
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3,
3,
3,
3,
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5,
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4],
'max_node_depth': 7,
'n_nodes': 43,
'node_features_idx': array([27, 22, 22, 26, 7, 7, 28, 22, 28, 28, 28, 28, 22, 28, 28, 27, 28,
28, 23, 27, 28, 28, 7, 21, 22, 22, 28, 28, 28, 7, 7, 28, 28, 6,
23, 28, 28, 28, 20, 20, 28, 28, 28]),
'num_features_used': 9,
'tot_leaf_node_values': [60,
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151],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
'feature_importances': array([ 0. , 0.01000883, 0. , 0. , 0. ,
0. , 0.00241698, 0.09720962, 0. , 0. ,
0.00126956, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.05707778, 0.03086415, 0.38451391, 0.0762436 , 0. ,
0. , 0.00246584, 0.33792972, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 27, 7, 23, 20, 21, 1, 26, 6, 10, 3, 4, 5, 11, 2, 8, 9,
29, 14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 0]),
'feature_importances_std': array([ 0. , 0.01688327, 0. , 0. , 0. ,
0. , 0.00442531, 0.2266052 , 0. , 0. ,
0.00335185, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.1621713 , 0.02349025, 0.36313971, 0.16672893, 0. ,
0. , 0.00493408, 0.31758588, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=20, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False),
'rf_validation_metrics': {'accuracy_score': 0.96491228070175439,
'confusion_matrix': array([[12, 2],
[ 0, 43]]),
'f1_score': 0.97727272727272729,
'hamming_loss': 0.035087719298245612,
'log_loss': 1.2119149470996806,
'precision_score': 0.9555555555555556,
'recall_score': 1.0,
'zero_one_loss': 0.035087719298245612}}}
In [26]:
all_rf_bootstrap_output
Out[26]:
{'rf_bootstrap0': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
'all_leaf_node_samples': [42, 1, 3, 18],
'all_leaf_node_samples_percent': [65.625, 1.5625, 4.6875, 28.125],
'all_leaf_node_values': [array([[ 0, 62]]),
array([[0, 1]]),
array([[4, 0]]),
array([[36, 0]])],
'all_leaf_nodes': [2, 4, 5, 6],
'all_leaf_paths_features': [array([27, 22]),
array([27, 22, 20]),
array([27, 22, 20]),
array([27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.60194175]]),
array([[ 0. , 0.00970874]]),
array([[ 0.03883495, 0. ]]),
array([[ 0.34951456, 0. ]])],
'all_uniq_leaf_paths_features': [array([22, 27]),
array([20, 22, 27]),
array([20, 22, 27]),
array([27])],
'leaf_nodes_depths': [2, 3, 3, 1],
'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([27, 22, 28, 20, 28, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [62, 1, 4, 36],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238158661742695,
'precision_score': 0.93333333333333335,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.070175438596491224}},
'dtree1': {'all_leaf_node_classes': [1, 1, 0],
'all_leaf_node_paths': [array([0, 1]), array([0, 2, 3]), array([0, 2, 4])],
'all_leaf_node_samples': [42, 1, 19],
'all_leaf_node_samples_percent': [67.741935483870961,
1.6129032258064515,
30.64516129032258],
'all_leaf_node_values': [array([[ 0, 75]]),
array([[0, 1]]),
array([[27, 0]])],
'all_leaf_nodes': [1, 3, 4],
'all_leaf_paths_features': [array([22]), array([22, 23]), array([22, 23])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.72815534]]),
array([[ 0. , 0.00970874]]),
array([[ 0.26213592, 0. ]])],
'all_uniq_leaf_paths_features': [array([22]),
array([22, 23]),
array([22, 23])],
'leaf_nodes_depths': [1, 2, 2],
'max_node_depth': 2,
'n_nodes': 5,
'node_features_idx': array([22, 28, 23, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [75, 1, 27],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[ 9, 5],
[ 2, 41]]),
'f1_score': 0.92134831460674171,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.2416742587986977,
'precision_score': 0.89130434782608692,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.1228070175438597}},
'dtree2': {'all_leaf_node_classes': [1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]), array([0, 1, 3]), array([0, 4])],
'all_leaf_node_samples': [48, 1, 21],
'all_leaf_node_samples_percent': [68.571428571428569,
1.4285714285714286,
30.0],
'all_leaf_node_values': [array([[ 0, 65]]),
array([[1, 0]]),
array([[37, 0]])],
'all_leaf_nodes': [2, 3, 4],
'all_leaf_paths_features': [array([22, 27]), array([22, 27]), array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.63106796]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.3592233, 0. ]])],
'all_uniq_leaf_paths_features': [array([22, 27]),
array([22, 27]),
array([22])],
'leaf_nodes_depths': [2, 2, 1],
'max_node_depth': 2,
'n_nodes': 5,
'node_features_idx': array([22, 27, 28, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [65, 1, 37],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[10, 4],
[ 2, 41]]),
'f1_score': 0.93181818181818188,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357167852488583,
'precision_score': 0.91111111111111109,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.10526315789473684}},
'dtree3': {'all_leaf_node_classes': [1, 0],
'all_leaf_node_paths': [array([0, 1]), array([0, 2])],
'all_leaf_node_samples': [45, 17],
'all_leaf_node_samples_percent': [72.58064516129032, 27.419354838709676],
'all_leaf_node_values': [array([[ 0, 75]]), array([[28, 0]])],
'all_leaf_nodes': [1, 2],
'all_leaf_paths_features': [array([20]), array([20])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.72815534]]),
array([[ 0.27184466, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]), array([20])],
'leaf_nodes_depths': [1, 1],
'max_node_depth': 1,
'n_nodes': 3,
'node_features_idx': array([20, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [75, 28],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238158661742695,
'precision_score': 0.93333333333333335,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.070175438596491224}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0],
'all_leaf_node_paths': [array([0, 1]), array([0, 2, 3]), array([0, 2, 4])],
'all_leaf_node_samples': [43, 1, 19],
'all_leaf_node_samples_percent': [68.253968253968253,
1.5873015873015872,
30.158730158730158],
'all_leaf_node_values': [array([[ 0, 70]]),
array([[0, 1]]),
array([[32, 0]])],
'all_leaf_nodes': [1, 3, 4],
'all_leaf_paths_features': [array([20]), array([20, 27]), array([20, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.67961165]]),
array([[ 0. , 0.00970874]]),
array([[ 0.31067961, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]),
array([20, 27]),
array([20, 27])],
'leaf_nodes_depths': [1, 2, 2],
'max_node_depth': 2,
'n_nodes': 5,
'node_features_idx': array([20, 28, 27, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [70, 1, 32],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[10, 4],
[ 0, 43]]),
'f1_score': 0.9555555555555556,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238298941993603,
'precision_score': 0.91489361702127658,
'recall_score': 1.0,
'zero_one_loss': 0.070175438596491224}},
'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.39774754, 0. , 0.40631242, 0.00968045, 0. ,
0. , 0. , 0.18625959, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 20, 27, 23, 29, 6, 10, 9, 8, 7, 4, 5, 12, 3, 2, 1, 11,
14, 13, 28, 15, 16, 17, 18, 19, 21, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.47414444, 0. , 0.45039711, 0.0193609 , 0. ,
0. , 0. , 0.33055306, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[ 9, 5],
[ 1, 42]]),
'f1_score': 0.93333333333333324,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357308132739496,
'precision_score': 0.8936170212765957,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.10526315789473684}},
'rf_bootstrap1': {'dtree0': {'all_leaf_node_classes': [1,
1,
0,
0,
1,
1,
0,
0],
'all_leaf_node_paths': [array([0, 1]),
array([0, 2, 3, 4, 5]),
array([0, 2, 3, 4, 6]),
array([0, 2, 3, 7, 8]),
array([0, 2, 3, 7, 9]),
array([ 0, 2, 10, 11, 12]),
array([ 0, 2, 10, 11, 13]),
array([ 0, 2, 10, 14])],
'all_leaf_node_samples': [33, 1, 6, 1, 6, 1, 1, 14],
'all_leaf_node_samples_percent': [52.38095238095238,
1.5873015873015872,
9.5238095238095237,
1.5873015873015872,
9.5238095238095237,
1.5873015873015872,
1.5873015873015872,
22.222222222222221],
'all_leaf_node_values': [array([[ 0, 59]]),
array([[0, 1]]),
array([[8, 0]]),
array([[2, 0]]),
array([[0, 8]]),
array([[0, 1]]),
array([[1, 0]]),
array([[23, 0]])],
'all_leaf_nodes': [1, 5, 6, 8, 9, 12, 13, 14],
'all_leaf_paths_features': [array([20]),
array([20, 27, 27, 27]),
array([20, 27, 27, 27]),
array([20, 27, 27, 22]),
array([20, 27, 27, 22]),
array([20, 27, 1, 22]),
array([20, 27, 1, 22]),
array([20, 27, 1])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.57281553]]),
array([[ 0. , 0.00970874]]),
array([[ 0.0776699, 0. ]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.0776699]]),
array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.22330097, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]),
array([20, 27]),
array([20, 27]),
array([20, 22, 27]),
array([20, 22, 27]),
array([ 1, 20, 22, 27]),
array([ 1, 20, 22, 27]),
array([ 1, 20, 27])],
'leaf_nodes_depths': [1, 4, 4, 4, 4, 4, 4, 3],
'max_node_depth': 4,
'n_nodes': 15,
'node_features_idx': array([20, 28, 27, 27, 27, 28, 28, 22, 28, 28, 1, 22, 28, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [59, 1, 8, 2, 8, 1, 1, 23],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241098,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'dtree1': {'all_leaf_node_classes': [1, 1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([0, 6, 8])],
'all_leaf_node_samples': [38, 6, 3, 1, 20],
'all_leaf_node_samples_percent': [55.882352941176471,
8.8235294117647065,
4.4117647058823533,
1.4705882352941178,
29.411764705882351],
'all_leaf_node_values': [array([[ 0, 59]]),
array([[0, 8]]),
array([[4, 0]]),
array([[0, 1]]),
array([[31, 0]])],
'all_leaf_nodes': [2, 4, 5, 7, 8],
'all_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 27]),
array([ 7, 21, 27]),
array([ 7, 23]),
array([ 7, 23])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.57281553]]),
array([[ 0. , 0.0776699]]),
array([[ 0.03883495, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.30097087, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 27]),
array([ 7, 21, 27]),
array([ 7, 23]),
array([ 7, 23])],
'leaf_nodes_depths': [2, 3, 3, 2, 2],
'max_node_depth': 3,
'n_nodes': 9,
'node_features_idx': array([ 7, 21, 28, 27, 28, 28, 23, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [59, 8, 4, 1, 31],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
'dtree2': {'all_leaf_node_classes': [1, 1, 0, 1, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([0, 6, 8, 9]),
array([ 0, 6, 8, 10])],
'all_leaf_node_samples': [29, 4, 3, 1, 1, 23],
'all_leaf_node_samples_percent': [47.540983606557376,
6.557377049180328,
4.918032786885246,
1.639344262295082,
1.639344262295082,
37.704918032786885],
'all_leaf_node_values': [array([[ 0, 55]]),
array([[0, 5]]),
array([[5, 0]]),
array([[0, 2]]),
array([[0, 1]]),
array([[35, 0]])],
'all_leaf_nodes': [2, 4, 5, 7, 9, 10],
'all_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([ 7, 23]),
array([ 7, 23, 1]),
array([ 7, 23, 1])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.53398058]]),
array([[ 0. , 0.04854369]]),
array([[ 0.04854369, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0. , 0.00970874]]),
array([[ 0.33980583, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([ 7, 23]),
array([ 1, 7, 23]),
array([ 1, 7, 23])],
'leaf_nodes_depths': [2, 3, 3, 2, 3, 3],
'max_node_depth': 3,
'n_nodes': 11,
'node_features_idx': array([ 7, 21, 28, 22, 28, 28, 23, 28, 1, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [55, 5, 5, 2, 1, 35],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
'dtree3': {'all_leaf_node_classes': [1, 1, 1, 0],
'all_leaf_node_paths': [array([0, 1]),
array([0, 2, 3]),
array([0, 2, 4, 5]),
array([0, 2, 4, 6])],
'all_leaf_node_samples': [32, 3, 1, 23],
'all_leaf_node_samples_percent': [54.237288135593218,
5.0847457627118642,
1.6949152542372881,
38.983050847457626],
'all_leaf_node_values': [array([[ 0, 54]]),
array([[0, 5]]),
array([[0, 1]]),
array([[43, 0]])],
'all_leaf_nodes': [1, 3, 5, 6],
'all_leaf_paths_features': [array([23]),
array([23, 1]),
array([23, 1, 27]),
array([23, 1, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.52427184]]),
array([[ 0. , 0.04854369]]),
array([[ 0. , 0.00970874]]),
array([[ 0.41747573, 0. ]])],
'all_uniq_leaf_paths_features': [array([23]),
array([ 1, 23]),
array([ 1, 23, 27]),
array([ 1, 23, 27])],
'leaf_nodes_depths': [1, 2, 3, 3],
'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([23, 28, 1, 28, 27, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [54, 5, 1, 43],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[10, 4],
[ 2, 41]]),
'f1_score': 0.93181818181818188,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357167852488583,
'precision_score': 0.91111111111111109,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.10526315789473684}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8])],
'all_leaf_node_samples': [39, 2, 2, 4, 16],
'all_leaf_node_samples_percent': [61.904761904761905,
3.1746031746031744,
3.1746031746031744,
6.3492063492063489,
25.396825396825395],
'all_leaf_node_values': [array([[ 0, 63]]),
array([[0, 7]]),
array([[3, 0]]),
array([[4, 0]]),
array([[26, 0]])],
'all_leaf_nodes': [3, 5, 6, 7, 8],
'all_leaf_paths_features': [array([20, 27, 21]),
array([20, 27, 21, 22]),
array([20, 27, 21, 22]),
array([20, 27]),
array([20])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.61165049]]),
array([[ 0. , 0.06796117]]),
array([[ 0.02912621, 0. ]]),
array([[ 0.03883495, 0. ]]),
array([[ 0.25242718, 0. ]])],
'all_uniq_leaf_paths_features': [array([20, 21, 27]),
array([20, 21, 22, 27]),
array([20, 21, 22, 27]),
array([20, 27]),
array([20])],
'leaf_nodes_depths': [3, 4, 4, 2, 1],
'max_node_depth': 4,
'n_nodes': 9,
'node_features_idx': array([20, 27, 21, 28, 22, 28, 28, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [63, 7, 3, 4, 26],
'validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[13, 1],
[ 2, 41]]),
'f1_score': 0.96470588235294119,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178443645993383,
'precision_score': 0.97619047619047616,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.052631578947368474}},
'feature_importances': array([ 0. , 0.04622453, 0. , 0. , 0. ,
0. , 0. , 0.29862704, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.27539825, 0.03380904, 0.05760368, 0.18098435, 0. ,
0. , 0. , 0.1073531 , 0. , 0. ]),
'feature_importances_rank_idx': array([ 7, 20, 23, 27, 22, 1, 21, 5, 9, 8, 6, 29, 4, 11, 3, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0.06420136, 0. , 0. , 0. ,
0. , 0. , 0.36700759, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.33774916, 0.03243181, 0.0471569 , 0.30565241, 0. ,
0. , 0. , 0.08106671, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[11, 3],
[ 0, 43]]),
'f1_score': 0.96629213483146059,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178724206495209,
'precision_score': 0.93478260869565222,
'recall_score': 1.0,
'zero_one_loss': 0.052631578947368474}},
'rf_bootstrap10': {'dtree0': {'all_leaf_node_classes': [1, 0],
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'all_leaf_node_samples': [37, 30],
'all_leaf_node_samples_percent': [55.223880597014926, 44.776119402985074],
'all_leaf_node_values': [array([[ 0, 60]]), array([[43, 0]])],
'all_leaf_nodes': [1, 2],
'all_leaf_paths_features': [array([20]), array([20])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.58252427]]),
array([[ 0.41747573, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]), array([20])],
'leaf_nodes_depths': [1, 1],
'max_node_depth': 1,
'n_nodes': 3,
'node_features_idx': array([20, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [60, 43],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238158661742695,
'precision_score': 0.93333333333333335,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.070175438596491224}},
'dtree1': {'all_leaf_node_classes': [1, 1, 0],
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'all_leaf_node_samples_percent': [59.375, 1.5625, 39.0625],
'all_leaf_node_values': [array([[ 0, 64]]),
array([[0, 1]]),
array([[38, 0]])],
'all_leaf_nodes': [1, 3, 4],
'all_leaf_paths_features': [array([20]), array([20, 27]), array([20, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.62135922]]),
array([[ 0. , 0.00970874]]),
array([[ 0.36893204, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]),
array([20, 27]),
array([20, 27])],
'leaf_nodes_depths': [1, 2, 2],
'max_node_depth': 2,
'n_nodes': 5,
'node_features_idx': array([20, 28, 27, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [64, 1, 38],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241098,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'dtree2': {'all_leaf_node_classes': [1, 0],
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'all_leaf_node_samples': [41, 27],
'all_leaf_node_samples_percent': [60.294117647058826, 39.705882352941174],
'all_leaf_node_values': [array([[ 0, 59]]), array([[44, 0]])],
'all_leaf_nodes': [1, 2],
'all_leaf_paths_features': [array([20]), array([20])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.57281553]]),
array([[ 0.42718447, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]), array([20])],
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'max_node_depth': 1,
'n_nodes': 3,
'node_features_idx': array([20, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [59, 44],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
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'log_loss': 2.4238158661742695,
'precision_score': 0.93333333333333335,
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'zero_one_loss': 0.070175438596491224}},
'dtree3': {'all_leaf_node_classes': [1, 1, 0],
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'all_leaf_node_samples_percent': [54.098360655737707,
1.639344262295082,
44.26229508196721],
'all_leaf_node_values': [array([[ 0, 57]]),
array([[0, 1]]),
array([[45, 0]])],
'all_leaf_nodes': [1, 3, 4],
'all_leaf_paths_features': [array([20]), array([20, 7]), array([20, 7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.55339806]]),
array([[ 0. , 0.00970874]]),
array([[ 0.4368932, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]),
array([ 7, 20]),
array([ 7, 20])],
'leaf_nodes_depths': [1, 2, 2],
'max_node_depth': 2,
'n_nodes': 5,
'node_features_idx': array([20, 28, 7, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [57, 1, 45],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241098,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
'all_leaf_node_samples': [41, 1, 1, 24],
'all_leaf_node_samples_percent': [61.194029850746269,
1.4925373134328359,
1.4925373134328359,
35.820895522388057],
'all_leaf_node_values': [array([[ 0, 62]]),
array([[0, 2]]),
array([[1, 0]]),
array([[38, 0]])],
'all_leaf_nodes': [2, 4, 5, 6],
'all_leaf_paths_features': [array([22, 22]),
array([22, 22, 27]),
array([22, 22, 27]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.60194175]]),
array([[ 0. , 0.01941748]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.36893204, 0. ]])],
'all_uniq_leaf_paths_features': [array([22]),
array([22, 27]),
array([22, 27]),
array([22])],
'leaf_nodes_depths': [2, 3, 3, 1],
'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([22, 22, 28, 27, 28, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [62, 2, 1, 38],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[ 9, 5],
[ 2, 41]]),
'f1_score': 0.92134831460674171,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.2416742587986977,
'precision_score': 0.89130434782608692,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.1228070175438597}},
'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.00772114, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.78415263, 0. , 0.19449786, 0. , 0. ,
0. , 0. , 0.01362837, 0. , 0. ]),
'feature_importances_rank_idx': array([20, 22, 27, 7, 29, 12, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 14,
13, 28, 15, 16, 17, 18, 19, 21, 23, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.01544228, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.39247697, 0. , 0.38899573, 0. , 0. ,
0. , 0. , 0.01719923, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241098,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'rf_bootstrap11': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8, 9]),
array([ 0, 8, 10])],
'all_leaf_node_samples': [33, 2, 1, 1, 1, 32],
'all_leaf_node_samples_percent': [47.142857142857146,
2.8571428571428572,
1.4285714285714286,
1.4285714285714286,
1.4285714285714286,
45.714285714285715],
'all_leaf_node_values': [array([[ 0, 47]]),
array([[0, 3]]),
array([[1, 0]]),
array([[1, 0]]),
array([[0, 1]]),
array([[50, 0]])],
'all_leaf_nodes': [3, 5, 6, 7, 9, 10],
'all_leaf_paths_features': [array([27, 21, 23]),
array([27, 21, 23, 7]),
array([27, 21, 23, 7]),
array([27, 21]),
array([27, 22]),
array([27, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.45631068]]),
array([[ 0. , 0.02912621]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.48543689, 0. ]])],
'all_uniq_leaf_paths_features': [array([21, 23, 27]),
array([ 7, 21, 23, 27]),
array([ 7, 21, 23, 27]),
array([21, 27]),
array([22, 27]),
array([22, 27])],
'leaf_nodes_depths': [3, 4, 4, 2, 2, 2],
'max_node_depth': 4,
'n_nodes': 11,
'node_features_idx': array([27, 21, 23, 28, 7, 28, 28, 28, 22, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [47, 3, 1, 1, 1, 50],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[13, 1],
[ 3, 40]]),
'f1_score': 0.95238095238095244,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4237878101240868,
'precision_score': 0.97560975609756095,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.070175438596491224}},
'dtree1': {'all_leaf_node_classes': [1, 0, 0, 0, 0],
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array([0, 1, 2, 3, 5]),
array([0, 1, 2, 6]),
array([0, 1, 7]),
array([0, 8])],
'all_leaf_node_samples': [34, 2, 1, 1, 22],
'all_leaf_node_samples_percent': [56.666666666666664,
3.3333333333333335,
1.6666666666666667,
1.6666666666666667,
36.666666666666664],
'all_leaf_node_values': [array([[ 0, 56]]),
array([[2, 0]]),
array([[1, 0]]),
array([[2, 0]]),
array([[42, 0]])],
'all_leaf_nodes': [4, 5, 6, 7, 8],
'all_leaf_paths_features': [array([22, 7, 23, 21]),
array([22, 7, 23, 21]),
array([22, 7, 23]),
array([22, 7]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.54368932]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.40776699, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 21, 22, 23]),
array([ 7, 21, 22, 23]),
array([ 7, 22, 23]),
array([ 7, 22]),
array([22])],
'leaf_nodes_depths': [4, 4, 3, 2, 1],
'max_node_depth': 4,
'n_nodes': 9,
'node_features_idx': array([22, 7, 23, 21, 28, 28, 28, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [56, 2, 1, 2, 42],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[13, 1],
[ 5, 38]]),
'f1_score': 0.92682926829268308,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6356747011735853,
'precision_score': 0.97435897435897434,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.10526315789473684}},
'dtree2': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8, 9]),
array([ 0, 8, 10, 11, 12]),
array([ 0, 8, 10, 11, 13]),
array([ 0, 8, 10, 14])],
'all_leaf_node_samples': [25, 1, 7, 1, 2, 2, 1, 33],
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1.3888888888888888,
9.7222222222222214,
1.3888888888888888,
2.7777777777777777,
2.7777777777777777,
1.3888888888888888,
45.833333333333336],
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array([[1, 0]]),
array([[ 0, 10]]),
array([[1, 0]]),
array([[0, 2]]),
array([[0, 2]]),
array([[1, 0]]),
array([[49, 0]])],
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array([27, 21, 7, 7]),
array([27, 21, 7, 7]),
array([27, 21]),
array([27, 20]),
array([27, 20, 1, 27]),
array([27, 20, 1, 27]),
array([27, 20, 1])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.09708738]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0. , 0.01941748]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.47572816, 0. ]])],
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array([ 7, 21, 27]),
array([ 7, 21, 27]),
array([21, 27]),
array([20, 27]),
array([ 1, 20, 27]),
array([ 1, 20, 27]),
array([ 1, 20, 27])],
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'tot_leaf_node_values': [37, 1, 10, 1, 2, 2, 1, 49],
'validation_metrics': {'accuracy_score': 0.85964912280701755,
'confusion_matrix': array([[11, 3],
[ 5, 38]]),
'f1_score': 0.90476190476190466,
'hamming_loss': 0.14035087719298245,
'log_loss': 4.8475896482732646,
'precision_score': 0.92682926829268297,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.14035087719298245}},
'dtree3': {'all_leaf_node_classes': [1, 0, 1, 1, 0, 0, 1, 0],
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array([0, 1, 2, 3, 5, 6]),
array([0, 1, 2, 3, 5, 7]),
array([0, 1, 2, 8, 9]),
array([ 0, 1, 2, 8, 10]),
array([ 0, 1, 11]),
array([ 0, 12, 13]),
array([ 0, 12, 14])],
'all_leaf_node_samples': [23, 1, 3, 4, 1, 1, 1, 30],
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1.5625,
4.6875,
6.25,
1.5625,
1.5625,
1.5625,
46.875],
'all_leaf_node_values': [array([[ 0, 41]]),
array([[2, 0]]),
array([[0, 7]]),
array([[0, 5]]),
array([[2, 0]]),
array([[1, 0]]),
array([[0, 1]]),
array([[44, 0]])],
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array([27, 22, 1, 7, 27]),
array([27, 22, 1, 7, 27]),
array([27, 22, 1, 21]),
array([27, 22, 1, 21]),
array([27, 22]),
array([27, 22]),
array([27, 22])],
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.06796117]]),
array([[ 0. , 0.04854369]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.42718447, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 1, 7, 22, 27]),
array([ 1, 7, 22, 27]),
array([ 1, 7, 22, 27]),
array([ 1, 21, 22, 27]),
array([ 1, 21, 22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])],
'leaf_nodes_depths': [4, 5, 5, 4, 4, 2, 2, 2],
'max_node_depth': 5,
'n_nodes': 15,
'node_features_idx': array([27, 22, 1, 7, 28, 27, 28, 28, 21, 28, 28, 28, 22, 28, 28]),
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'tot_leaf_node_values': [41, 2, 7, 5, 2, 1, 1, 44],
'validation_metrics': {'accuracy_score': 0.84210526315789469,
'confusion_matrix': array([[12, 2],
[ 7, 36]]),
'f1_score': 0.88888888888888884,
'hamming_loss': 0.15789473684210525,
'log_loss': 5.4535190657729222,
'precision_score': 0.94736842105263153,
'recall_score': 0.83720930232558144,
'zero_one_loss': 0.15789473684210531}},
'dtree4': {'all_leaf_node_classes': [1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
'all_leaf_node_samples': [31, 1, 1, 33],
'all_leaf_node_samples_percent': [46.969696969696969,
1.5151515151515151,
1.5151515151515151,
50.0],
'all_leaf_node_values': [array([[ 0, 50]]),
array([[2, 0]]),
array([[0, 1]]),
array([[50, 0]])],
'all_leaf_nodes': [2, 3, 5, 6],
'all_leaf_paths_features': [array([27, 21]),
array([27, 21]),
array([27, 22]),
array([27, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.48543689]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.48543689, 0. ]])],
'all_uniq_leaf_paths_features': [array([21, 27]),
array([21, 27]),
array([22, 27]),
array([22, 27])],
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'n_nodes': 7,
'node_features_idx': array([27, 21, 28, 28, 22, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [50, 2, 1, 50],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[12, 2],
[ 5, 38]]),
'f1_score': 0.91566265060240959,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.2416321747234242,
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'recall_score': 0.88372093023255816,
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'feature_importances': array([ 0. , 0.01264568, 0. , 0. , 0. ,
0. , 0. , 0.02990873, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01383141, 0.05579152, 0.19353205, 0.00896224, 0. ,
0. , 0. , 0.68532837, 0. , 0. ]),
'feature_importances_rank_idx': array([27, 22, 21, 7, 20, 1, 23, 5, 9, 8, 6, 29, 4, 11, 3, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0.01892016, 0. , 0. , 0. ,
0. , 0. , 0.02312742, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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0.02766283, 0.01725791, 0.31422709, 0.01388971, 0. ,
0. , 0. , 0.34395366, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[12, 2],
[ 5, 38]]),
'f1_score': 0.91566265060240959,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.2416321747234242,
'precision_score': 0.94999999999999996,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.1228070175438597}},
'rf_bootstrap12': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 1, 0],
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6, 7]),
array([0, 2, 6, 8])],
'all_leaf_node_samples': [39, 3, 1, 1, 13],
'all_leaf_node_samples_percent': [68.421052631578945,
5.2631578947368425,
1.7543859649122806,
1.7543859649122806,
22.807017543859651],
'all_leaf_node_values': [array([[ 0, 74]]),
array([[0, 4]]),
array([[1, 0]]),
array([[0, 1]]),
array([[23, 0]])],
'all_leaf_nodes': [1, 4, 5, 7, 8],
'all_leaf_paths_features': [array([23]),
array([23, 27, 22]),
array([23, 27, 22]),
array([23, 27, 21]),
array([23, 27, 21])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.7184466]]),
array([[ 0. , 0.03883495]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.22330097, 0. ]])],
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array([22, 23, 27]),
array([22, 23, 27]),
array([21, 23, 27]),
array([21, 23, 27])],
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'tot_leaf_node_values': [74, 4, 1, 1, 23],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
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[ 1, 42]]),
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'hamming_loss': 0.10526315789473684,
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'dtree1': {'all_leaf_node_classes': [1, 0],
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'all_leaf_node_values': [array([[ 0, 78]]), array([[25, 0]])],
'all_leaf_nodes': [1, 2],
'all_leaf_paths_features': [array([22]), array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.75728155]]),
array([[ 0.24271845, 0. ]])],
'all_uniq_leaf_paths_features': [array([22]), array([22])],
'leaf_nodes_depths': [1, 1],
'max_node_depth': 1,
'n_nodes': 3,
'node_features_idx': array([22, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [78, 25],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[ 8, 6],
[ 0, 43]]),
'f1_score': 0.93478260869565222,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357448412990401,
'precision_score': 0.87755102040816324,
'recall_score': 1.0,
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6])],
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3.0769230769230771,
3.0769230769230771,
27.692307692307693],
'all_leaf_node_values': [array([[ 0, 69]]),
array([[0, 2]]),
array([[3, 0]]),
array([[29, 0]])],
'all_leaf_nodes': [1, 4, 5, 6],
'all_leaf_paths_features': [array([20]),
array([20, 22, 27]),
array([20, 22, 27]),
array([20, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.66990291]]),
array([[ 0. , 0.01941748]]),
array([[ 0.02912621, 0. ]]),
array([[ 0.2815534, 0. ]])],
'all_uniq_leaf_paths_features': [array([20]),
array([20, 22, 27]),
array([20, 22, 27]),
array([20, 22])],
'leaf_nodes_depths': [1, 3, 3, 2],
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'n_nodes': 7,
'node_features_idx': array([20, 28, 22, 27, 28, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [69, 2, 3, 29],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[11, 3],
[ 2, 41]]),
'f1_score': 0.94252873563218398,
'hamming_loss': 0.08771929824561403,
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'recall_score': 0.95348837209302328,
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'dtree3': {'all_leaf_node_classes': [1, 0],
'all_leaf_node_paths': [array([0, 1]), array([0, 2])],
'all_leaf_node_samples': [48, 17],
'all_leaf_node_samples_percent': [73.84615384615384, 26.153846153846153],
'all_leaf_node_values': [array([[ 0, 78]]), array([[25, 0]])],
'all_leaf_nodes': [1, 2],
'all_leaf_paths_features': [array([22]), array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.75728155]]),
array([[ 0.24271845, 0. ]])],
'all_uniq_leaf_paths_features': [array([22]), array([22])],
'leaf_nodes_depths': [1, 1],
'max_node_depth': 1,
'n_nodes': 3,
'node_features_idx': array([22, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [78, 25],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[ 8, 6],
[ 0, 43]]),
'f1_score': 0.93478260869565222,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357448412990401,
'precision_score': 0.87755102040816324,
'recall_score': 1.0,
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'dtree4': {'all_leaf_node_classes': [1, 1, 0],
'all_leaf_node_paths': [array([0, 1]), array([0, 2, 3]), array([0, 2, 4])],
'all_leaf_node_samples': [51, 1, 19],
'all_leaf_node_samples_percent': [71.83098591549296,
1.408450704225352,
26.760563380281692],
'all_leaf_node_values': [array([[ 0, 72]]),
array([[0, 3]]),
array([[28, 0]])],
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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'confusion_matrix': array([[ 8, 6],
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array([[40, 0]])],
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array([ 7, 21])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.38834951, 0. ]])],
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'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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min_impurity_decrease=0.0, min_impurity_split=None,
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),
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1.5625,
4.6875,
3.125,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
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'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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'confusion_matrix': array([[ 9, 5],
[ 2, 41]]),
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'hamming_loss': 0.12280701754385964,
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'zero_one_loss': 0.1228070175438597}},
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array([0, 2, 3]),
array([0, 2, 4, 5]),
array([0, 2, 4, 6, 7, 8]),
array([0, 2, 4, 6, 7, 9]),
array([ 0, 2, 4, 6, 10])],
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3.1746031746031744,
1.5873015873015872,
4.7619047619047619,
1.5873015873015872,
25.396825396825395],
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array([[0, 3]]),
array([[0, 1]]),
array([[5, 0]]),
array([[0, 1]]),
array([[28, 0]])],
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array([27, 23]),
array([27, 23, 21]),
array([27, 23, 21, 23, 22]),
array([27, 23, 21, 23, 22]),
array([27, 23, 21, 23])],
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array([[ 0. , 0.00970874]]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
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array([[4, 0]]),
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array([20, 27, 7, 7]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
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array([ 0, 10])],
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array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
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1.5384615384615385,
1.5384615384615385,
30.76923076923077],
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array([[2, 0]]),
array([[0, 1]]),
array([[30, 0]])],
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array([20, 27]),
array([20, 21]),
array([20, 21])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.67961165]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.29126214, 0. ]])],
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array([20, 27]),
array([20, 21]),
array([20, 21])],
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0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
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0.39460382, 0.03684338, 0.01440915, 0.36909691, 0. ,
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'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6, 7]),
array([0, 1, 2, 4, 6, 8]),
array([0, 1, 9]),
array([ 0, 10])],
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10.144927536231885,
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2.8985507246376812,
31.884057971014492],
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array([[1, 0]]),
array([[0, 8]]),
array([[1, 0]]),
array([[3, 0]]),
array([[37, 0]])],
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array([23, 27, 23, 20]),
array([23, 27, 23, 20, 21]),
array([23, 27, 23, 20, 21]),
array([23, 27]),
array([23])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.0776699]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.02912621, 0. ]]),
array([[ 0.3592233, 0. ]])],
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array([20, 23, 27]),
array([20, 21, 23, 27]),
array([20, 21, 23, 27]),
array([23, 27]),
array([23])],
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array([ 0, 1, 2, 8, 10]),
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array([ 7, 22, 23, 27]),
array([ 7, 23, 27])],
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0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.00962043, 0.01580266, 0.35182725, 0.32726881, 0. ,
0. , 0. , 0.11657072, 0. , 0. ]),
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14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
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0. , 0. , 0.34752536, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01212705, 0.01950059, 0.40789916, 0.36224155, 0. ,
0. , 0. , 0.08422171, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
'hamming_loss': 0.070175438596491224,
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'precision_score': 0.93333333333333335,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.070175438596491224}},
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array([0, 2, 3]),
array([0, 2, 4, 5]),
array([0, 2, 4, 6])],
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'all_leaf_node_values': [array([[ 0, 63]]),
array([[0, 2]]),
array([[0, 1]]),
array([[37, 0]])],
'all_leaf_nodes': [1, 3, 5, 6],
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array([22, 27]),
array([22, 27, 1]),
array([22, 27, 1])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.61165049]]),
array([[ 0. , 0.01941748]]),
array([[ 0. , 0.00970874]]),
array([[ 0.3592233, 0. ]])],
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array([22, 27]),
array([ 1, 22, 27]),
array([ 1, 22, 27])],
'leaf_nodes_depths': [1, 2, 3, 3],
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array([0, 1, 2, 4]),
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array([[40, 0]])],
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array([22, 7, 23]),
array([22, 7]),
array([22])],
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array([[ 0.00970874, 0. ]]),
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array([0, 1, 3]),
array([0, 4, 5, 6]),
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array([[2, 0]]),
array([[0, 1]]),
array([[1, 0]]),
array([[33, 0]])],
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array([20, 22]),
array([20, 7, 22]),
array([20, 7, 22]),
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
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array([22, 21]),
array([22, 21])],
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array([[ 0. , 0.00970874]]),
array([[ 0.38834951, 0. ]])],
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array([21, 22]),
array([21, 22])],
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[ 1, 42]]),
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'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241093,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
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'dtree4': {'all_leaf_node_classes': [1, 0, 0],
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4.615384615384615,
41.53846153846154],
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array([[3, 0]]),
array([[48, 0]])],
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array([[ 0.02912621, 0. ]]),
array([[ 0.46601942, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 22]),
array([ 7, 22]),
array([22])],
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'max_node_depth': 2,
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'tot_leaf_node_values': [52, 3, 48],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
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'hamming_loss': 0.10526315789473684,
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0. , 0. , 0.06260415, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.17512452, 0.0079062 , 0.72319709, 0.00775895, 0. ,
0. , 0. , 0.01519537, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 20, 7, 27, 1, 21, 23, 5, 9, 8, 6, 29, 4, 11, 3, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0.01642743, 0. , 0. , 0. ,
0. , 0. , 0.05466872, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.35024903, 0.0158124 , 0.31126017, 0.01551789, 0. ,
0. , 0. , 0.03039075, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[10, 4],
[ 1, 42]]),
'f1_score': 0.9438202247191011,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297733397241093,
'precision_score': 0.91304347826086951,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.087719298245614086}},
'rf_bootstrap18': {'dtree0': {'all_leaf_node_classes': [1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
'all_leaf_node_samples': [41, 1, 1, 20],
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1.5873015873015872,
1.5873015873015872,
31.746031746031747],
'all_leaf_node_values': [array([[ 0, 66]]),
array([[2, 0]]),
array([[0, 1]]),
array([[34, 0]])],
'all_leaf_nodes': [2, 3, 5, 6],
'all_leaf_paths_features': [array([ 7, 20]),
array([ 7, 20]),
array([ 7, 22]),
array([ 7, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.6407767]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.33009709, 0. ]])],
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array([0, 1, 2, 4, 6]),
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0.03315774, 0.01419524, 0.39324298, 0.05354279, 0. ,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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array([0, 1, 2, 4, 6]),
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array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
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array([ 0, 8, 12])],
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array([[0, 1]]),
array([[38, 0]])],
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array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([ 0, 1, 7, 9, 10]),
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array([ 0, 12])],
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array([22, 27, 20]),
array([22, 27, 20, 27]),
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array([ 7, 22, 1, 7]),
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array([ 7, 22, 27]),
array([ 7, 22, 7, 27]),
array([ 7, 22, 7, 27]),
array([ 7, 22, 7])],
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array([ 7, 22, 27]),
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'hamming_loss': 0.070175438596491224,
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0. , 0. , 0.16679816, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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0.02060876, 0.0076334 , 0.23069021, 0.36292832, 0. ,
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14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
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0.02936268, 0.0152668 , 0.31130642, 0.44405735, 0. ,
0. , 0. , 0.30413781, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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'confusion_matrix': array([[12, 2],
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'hamming_loss': 0.070175438596491224,
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array([22])],
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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array([0, 1, 3, 5]),
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array([[0, 1]]),
array([[36, 0]])],
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'all_leaf_paths_features': [array([22, 20, 20]),
array([22, 20, 20, 22]),
array([22, 20, 20, 22]),
array([22, 20, 22]),
array([22, 20, 22]),
array([22, 27]),
array([22, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.44660194]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.15533981]]),
array([[ 0. , 0.01941748]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.34951456, 0. ]])],
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array([20, 22]),
array([20, 22]),
array([20, 22]),
array([20, 22]),
array([22, 27]),
array([22, 27])],
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'max_node_depth': 4,
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'num_features_used': 4,
'tot_leaf_node_values': [46, 1, 16, 2, 1, 1, 36],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[11, 3],
[ 4, 39]]),
'f1_score': 0.91764705882352937,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.241646202748516,
'precision_score': 0.9285714285714286,
'recall_score': 0.90697674418604646,
'zero_one_loss': 0.1228070175438597}},
'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.01822893, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.17824135, 0.00526116, 0.73904392, 0.00577031, 0. ,
0. , 0. , 0.05345433, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 20, 27, 7, 23, 21, 5, 9, 8, 6, 29, 4, 11, 3, 2, 1, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.02360726, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.34961081, 0.01052233, 0.34034602, 0.01154062, 0. ,
0. , 0. , 0.03896873, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.85964912280701755,
'confusion_matrix': array([[ 9, 5],
[ 3, 40]]),
'f1_score': 0.90909090909090917,
'hamming_loss': 0.14035087719298245,
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'precision_score': 0.88888888888888884,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.14035087719298245}},
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'all_scaled_leaf_node_values': [array([[ 0. , 0.53398058]]),
array([[ 0.46601942, 0. ]])],
'all_uniq_leaf_paths_features': [array([7]), array([7])],
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'max_node_depth': 1,
'n_nodes': 3,
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'tot_leaf_node_values': [55, 48],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[12, 2],
[ 4, 39]]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8])],
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3.0303030303030303,
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array([[2, 0]]),
array([[50, 0]])],
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array([ 7, 22, 27, 27]),
array([ 7, 22, 27, 27]),
array([ 7, 22]),
array([7])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.02912621]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.48543689, 0. ]])],
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array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([ 7, 22]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8])],
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1.4705882352941178,
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array([[1, 0]]),
array([[47, 0]])],
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array([ 7, 22, 7, 22]),
array([ 7, 22, 7, 22]),
array([ 7, 22]),
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array([[ 0. , 0.03883495]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.45631068, 0. ]])],
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array([ 7, 22]),
array([ 7, 22]),
array([ 7, 22]),
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'max_node_depth': 1,
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'tot_leaf_node_values': [60, 43],
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'dtree4': {'all_leaf_node_classes': [1, 0, 0],
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3.278688524590164,
36.065573770491802],
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array([[4, 0]]),
array([[41, 0]])],
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array([[ 0.03883495, 0. ]]),
array([[ 0.39805825, 0. ]])],
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array([22, 27]),
array([22])],
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'max_node_depth': 2,
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'tot_leaf_node_values': [58, 4, 41],
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'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
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28, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 0]),
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0. , 0. , 0.38449652, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.32843795, 0. , 0. ,
0. , 0. , 0.05719442, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
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35.0,
1.6666666666666667],
'all_leaf_node_values': [array([[ 0, 65]]),
array([[36, 0]]),
array([[0, 2]])],
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array([[ 0.34951456, 0. ]]),
array([[ 0. , 0.01941748]])],
'all_uniq_leaf_paths_features': [array([27]),
array([26, 27]),
array([26, 27])],
'leaf_nodes_depths': [1, 2, 2],
'max_node_depth': 2,
'n_nodes': 5,
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'tot_leaf_node_values': [65, 36, 2],
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array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
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array([27, 22]),
array([27, 7]),
array([27, 7])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.29126214, 0. ]])],
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array([ 7, 27]),
array([ 7, 27])],
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array([0, 1, 2, 3, 5]),
array([0, 1, 2, 6]),
array([0, 1, 7]),
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array([[1, 0]]),
array([[ 0, 46]]),
array([[4, 0]]),
array([[32, 0]])],
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array([27, 22, 7, 27]),
array([27, 22, 7]),
array([27, 22]),
array([27])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.44660194]]),
array([[ 0.03883495, 0. ]]),
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array([ 7, 22, 27]),
array([ 7, 22, 27]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6, 7]),
array([0, 1, 2, 4, 6, 8]),
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array([ 0, 10])],
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array([[0, 1]]),
array([[3, 0]]),
array([[36, 0]])],
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array([27, 23, 1, 22, 22]),
array([27, 23, 1, 22, 22]),
array([27, 23]),
array([27])],
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array([[ 0. , 0.03883495]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
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array([ 1, 22, 23, 27]),
array([ 1, 22, 23, 27]),
array([23, 27]),
array([27])],
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'tot_leaf_node_values': [58, 4, 1, 1, 3, 36],
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'dtree4': {'all_leaf_node_classes': [1, 1, 0, 0],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
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1.5873015873015872,
23.80952380952381],
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array([[0, 1]]),
array([[1, 0]]),
array([[27, 0]])],
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array([27, 21, 23]),
array([27, 21, 23]),
array([27])],
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array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.26213592, 0. ]])],
'all_uniq_leaf_paths_features': [array([21, 27]),
array([21, 23, 27]),
array([21, 23, 27]),
array([27])],
'leaf_nodes_depths': [2, 3, 3, 1],
'max_node_depth': 3,
'n_nodes': 7,
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'num_features_used': 4,
'tot_leaf_node_values': [74, 1, 1, 27],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[10, 4],
[ 2, 41]]),
'f1_score': 0.93181818181818188,
'hamming_loss': 0.10526315789473684,
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0. , 0. , 0.03285011, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.00477569, 0.04680622, 0.02760416, 0. ,
0. , 0.01618225, 0.87054687, 0. , 0. ]),
'feature_importances_rank_idx': array([27, 22, 7, 23, 26, 21, 1, 5, 9, 8, 6, 29, 4, 3, 11, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 20, 24, 25, 0]),
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0. , 0. , 0.06501294, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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0. , 0.00955138, 0.05681919, 0.04398416, 0. ,
0. , 0.03236449, 0.05733686, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[12, 2],
[ 1, 42]]),
'f1_score': 0.96551724137931039,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178583926244296,
'precision_score': 0.95454545454545459,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.052631578947368474}},
'rf_bootstrap23': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
'all_leaf_node_samples': [36, 2, 2, 23],
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3.1746031746031744,
3.1746031746031744,
36.507936507936506],
'all_leaf_node_values': [array([[ 0, 63]]),
array([[0, 3]]),
array([[2, 0]]),
array([[35, 0]])],
'all_leaf_nodes': [2, 4, 5, 6],
'all_leaf_paths_features': [array([22, 7]),
array([22, 7, 21]),
array([22, 7, 21]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.61165049]]),
array([[ 0. , 0.02912621]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.33980583, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 22]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([22])],
'leaf_nodes_depths': [2, 3, 3, 1],
'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([22, 7, 28, 21, 28, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [63, 3, 2, 35],
'validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[13, 1],
[ 2, 41]]),
'f1_score': 0.96470588235294119,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178443645993385,
'precision_score': 0.97619047619047616,
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'zero_one_loss': 0.052631578947368474}},
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array([0, 1, 2, 4]),
array([0, 1, 5]),
array([0, 6, 7]),
array([0, 6, 8, 9]),
array([ 0, 6, 8, 10, 11, 12]),
array([ 0, 6, 8, 10, 11, 13]),
array([ 0, 6, 8, 10, 14])],
'all_leaf_node_samples': [3, 1, 27, 1, 1, 1, 2, 25],
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1.639344262295082,
44.26229508196721,
1.639344262295082,
1.639344262295082,
1.639344262295082,
3.278688524590164,
40.983606557377051],
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array([[1, 0]]),
array([[ 0, 46]]),
array([[0, 3]]),
array([[0, 1]]),
array([[0, 1]]),
array([[5, 0]]),
array([[37, 0]])],
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array([23, 27, 27]),
array([23, 27]),
array([23, 21]),
array([23, 21, 27]),
array([23, 21, 27, 7, 20]),
array([23, 21, 27, 7, 20]),
array([23, 21, 27, 7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.08737864]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.44660194]]),
array([[ 0. , 0.02912621]]),
array([[ 0. , 0.00970874]]),
array([[ 0. , 0.00970874]]),
array([[ 0.04854369, 0. ]]),
array([[ 0.3592233, 0. ]])],
'all_uniq_leaf_paths_features': [array([23, 27]),
array([23, 27]),
array([23, 27]),
array([21, 23]),
array([21, 23, 27]),
array([ 7, 20, 21, 23, 27]),
array([ 7, 20, 21, 23, 27]),
array([ 7, 21, 23, 27])],
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'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[10, 4],
[ 3, 40]]),
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'dtree2': {'all_leaf_node_classes': [1, 1, 1, 0, 1, 0],
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array([0, 2, 3]),
array([0, 2, 4, 5]),
array([0, 2, 4, 6, 7, 8]),
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array([ 0, 2, 4, 6, 10])],
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1.5384615384615385,
3.0769230769230771,
1.5384615384615385,
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array([[4, 0]]),
array([[0, 1]]),
array([[42, 0]])],
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array([22, 21]),
array([22, 21, 27]),
array([22, 21, 27, 22, 22]),
array([22, 21, 27, 22, 22]),
array([22, 21, 27, 22])],
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array([[ 0. , 0.01941748]]),
array([[ 0. , 0.00970874]]),
array([[ 0.03883495, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.40776699, 0. ]])],
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array([21, 22]),
array([21, 22, 27]),
array([21, 22, 27]),
array([21, 22, 27]),
array([21, 22, 27])],
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[ 1, 42]]),
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'hamming_loss': 0.070175438596491224,
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array([0, 1, 2, 3, 5]),
array([0, 1, 2, 6]),
array([0, 1, 7]),
array([0, 8])],
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1.4705882352941178,
55.882352941176471,
1.4705882352941178,
33.823529411764703],
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array([[1, 0]]),
array([[ 0, 57]]),
array([[2, 0]]),
array([[31, 0]])],
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'all_leaf_paths_features': [array([22, 27, 27, 22]),
array([22, 27, 27, 22]),
array([22, 27, 27]),
array([22, 27]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.11650485]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.55339806]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.30097087, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22])],
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'dtree4': {'all_leaf_node_classes': [1, 0, 1, 1, 0, 1, 0],
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array([0, 1, 2, 4]),
array([0, 1, 5]),
array([0, 6, 7]),
array([ 0, 6, 8, 9, 10]),
array([ 0, 6, 8, 9, 11]),
array([ 0, 6, 8, 12])],
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47.826086956521742,
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1.4492753623188406,
1.4492753623188406,
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'all_leaf_node_values': [array([[0, 5]]),
array([[1, 0]]),
array([[ 0, 52]]),
array([[0, 3]]),
array([[3, 0]]),
array([[0, 2]]),
array([[37, 0]])],
'all_leaf_nodes': [3, 4, 5, 7, 10, 11, 12],
'all_leaf_paths_features': [array([27, 27, 27]),
array([27, 27, 27]),
array([27, 27]),
array([27, 23]),
array([27, 23, 7, 27]),
array([27, 23, 7, 27]),
array([27, 23, 7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.04854369]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.50485437]]),
array([[ 0. , 0.02912621]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.3592233, 0. ]])],
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array([27]),
array([27]),
array([23, 27]),
array([ 7, 23, 27]),
array([ 7, 23, 27]),
array([ 7, 23, 27])],
'leaf_nodes_depths': [3, 3, 2, 2, 4, 4, 3],
'max_node_depth': 4,
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'tot_leaf_node_values': [5, 1, 52, 3, 3, 2, 37],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
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'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.0131087 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.00665375, 0.04440893, 0.54526231, 0.1770639 , 0. ,
0. , 0. , 0.21350242, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 27, 23, 21, 7, 20, 5, 9, 8, 6, 29, 4, 11, 3, 2, 1, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.0139022 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01330749, 0.03984014, 0.44528109, 0.30528749, 0. ,
0. , 0. , 0.32893294, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[11, 3],
[ 0, 43]]),
'f1_score': 0.96629213483146059,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178724206495209,
'precision_score': 0.93478260869565222,
'recall_score': 1.0,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8])],
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5.882352941176471,
2.9411764705882355,
1.4705882352941178,
30.882352941176471],
'all_leaf_node_values': [array([[ 0, 63]]),
array([[0, 5]]),
array([[2, 0]]),
array([[1, 0]]),
array([[32, 0]])],
'all_leaf_nodes': [3, 5, 6, 7, 8],
'all_leaf_paths_features': [array([ 7, 27, 22]),
array([ 7, 27, 22, 21]),
array([ 7, 27, 22, 21]),
array([ 7, 27]),
array([7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.61165049]]),
array([[ 0. , 0.04854369]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.31067961, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 22, 27]),
array([ 7, 21, 22, 27]),
array([ 7, 21, 22, 27]),
array([ 7, 27]),
array([7])],
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'tot_leaf_node_values': [63, 5, 2, 1, 32],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
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'hamming_loss': 0.070175438596491224,
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'dtree1': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 1, 0],
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([0, 1, 7, 9]),
array([ 0, 10, 11]),
array([ 0, 10, 12])],
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1.5625,
1.5625,
4.6875,
6.25,
1.5625,
23.4375],
'all_leaf_node_values': [array([[ 0, 62]]),
array([[1, 0]]),
array([[0, 1]]),
array([[4, 0]]),
array([[0, 6]]),
array([[0, 1]]),
array([[28, 0]])],
'all_leaf_nodes': [3, 5, 6, 8, 9, 11, 12],
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array([27, 27, 22, 22]),
array([27, 27, 22, 22]),
array([27, 27, 23]),
array([27, 27, 23]),
array([27, 21]),
array([27, 21])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.03883495, 0. ]]),
array([[ 0. , 0.05825243]]),
array([[ 0. , 0.00970874]]),
array([[ 0.27184466, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([23, 27]),
array([23, 27]),
array([21, 27]),
array([21, 27])],
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'node_features_idx': array([27, 27, 22, 28, 22, 28, 28, 23, 28, 28, 21, 28, 28]),
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'tot_leaf_node_values': [62, 1, 1, 4, 6, 1, 28],
'validation_metrics': {'accuracy_score': 0.84210526315789469,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([ 0, 8, 9, 10]),
array([ 0, 8, 9, 11]),
array([ 0, 8, 12])],
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4.2857142857142856,
1.4285714285714286,
1.4285714285714286,
1.4285714285714286,
30.0],
'all_leaf_node_values': [array([[ 0, 63]]),
array([[1, 0]]),
array([[0, 4]]),
array([[2, 0]]),
array([[0, 1]]),
array([[1, 0]]),
array([[31, 0]])],
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array([27, 22, 27, 27]),
array([27, 22, 27, 27]),
array([27, 22]),
array([27, 1, 27]),
array([27, 1, 27]),
array([27, 1])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.30097087, 0. ]])],
'all_uniq_leaf_paths_features': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([ 1, 27]),
array([ 1, 27]),
array([ 1, 27])],
'leaf_nodes_depths': [3, 4, 4, 2, 3, 3, 2],
'max_node_depth': 4,
'n_nodes': 13,
'node_features_idx': array([27, 22, 27, 28, 27, 28, 28, 28, 1, 27, 28, 28, 28]),
'num_features_used': 4,
'tot_leaf_node_values': [63, 1, 4, 2, 1, 1, 31],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[12, 2],
[ 3, 40]]),
'f1_score': 0.94117647058823517,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297452836739271,
'precision_score': 0.95238095238095233,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.087719298245614086}},
'dtree3': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([ 0, 8, 9, 10, 11]),
array([ 0, 8, 9, 10, 12]),
array([ 0, 8, 9, 13]),
array([ 0, 8, 14])],
'all_leaf_node_samples': [33, 1, 4, 1, 1, 4, 1, 15],
'all_leaf_node_samples_percent': [55.0,
1.6666666666666667,
6.666666666666667,
1.6666666666666667,
1.6666666666666667,
6.666666666666667,
1.6666666666666667,
25.0],
'all_leaf_node_values': [array([[ 0, 56]]),
array([[1, 0]]),
array([[0, 4]]),
array([[1, 0]]),
array([[0, 2]]),
array([[6, 0]]),
array([[0, 2]]),
array([[31, 0]])],
'all_leaf_nodes': [3, 5, 6, 7, 11, 12, 13, 14],
'all_leaf_paths_features': [array([27, 7, 23]),
array([27, 7, 23, 22]),
array([27, 7, 23, 22]),
array([27, 7]),
array([27, 7, 27, 21]),
array([27, 7, 27, 21]),
array([27, 7, 27]),
array([27, 7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.54368932]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.05825243, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.30097087, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 23, 27]),
array([ 7, 22, 23, 27]),
array([ 7, 22, 23, 27]),
array([ 7, 27]),
array([ 7, 21, 27]),
array([ 7, 21, 27]),
array([ 7, 27]),
array([ 7, 27])],
'leaf_nodes_depths': [3, 4, 4, 2, 4, 4, 3, 2],
'max_node_depth': 4,
'n_nodes': 15,
'node_features_idx': array([27, 7, 23, 28, 22, 28, 28, 28, 7, 27, 21, 28, 28, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [56, 1, 4, 1, 2, 6, 2, 31],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0, 1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5, 6]),
array([0, 1, 3, 5, 7]),
array([ 0, 8, 9, 10]),
array([ 0, 8, 9, 11]),
array([ 0, 8, 12])],
'all_leaf_node_samples': [35, 4, 3, 1, 1, 1, 19],
'all_leaf_node_samples_percent': [54.6875,
6.25,
4.6875,
1.5625,
1.5625,
1.5625,
29.6875],
'all_leaf_node_values': [array([[ 0, 56]]),
array([[0, 8]]),
array([[3, 0]]),
array([[0, 1]]),
array([[0, 1]]),
array([[1, 0]]),
array([[33, 0]])],
'all_leaf_nodes': [2, 4, 6, 7, 10, 11, 12],
'all_leaf_paths_features': [array([27, 21]),
array([27, 21, 23]),
array([27, 21, 23, 7]),
array([27, 21, 23, 7]),
array([27, 6, 22]),
array([27, 6, 22]),
array([27, 6])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.54368932]]),
array([[ 0. , 0.0776699]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.32038835, 0. ]])],
'all_uniq_leaf_paths_features': [array([21, 27]),
array([21, 23, 27]),
array([ 7, 21, 23, 27]),
array([ 7, 21, 23, 27]),
array([ 6, 22, 27]),
array([ 6, 22, 27]),
array([ 6, 27])],
'leaf_nodes_depths': [2, 3, 4, 4, 3, 3, 2],
'max_node_depth': 4,
'n_nodes': 13,
'node_features_idx': array([27, 21, 28, 23, 28, 7, 28, 28, 6, 22, 28, 28, 28]),
'num_features_used': 7,
'tot_leaf_node_values': [56, 8, 3, 1, 1, 1, 33],
'validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[12, 2],
[ 1, 42]]),
'f1_score': 0.96551724137931039,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178583926244296,
'precision_score': 0.95454545454545459,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.052631578947368474}},
'feature_importances': array([ 0. , 0.00406544, 0. , 0. , 0. ,
0. , 0.00397683, 0.19929797, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.03856528, 0.04037557, 0.0355715 , 0. ,
0. , 0. , 0.67814739, 0. , 0. ]),
'feature_importances_rank_idx': array([27, 7, 22, 21, 23, 1, 6, 10, 9, 8, 29, 5, 4, 12, 3, 2, 11,
14, 13, 28, 15, 16, 17, 18, 19, 20, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0.00813089, 0. , 0. , 0. ,
0. , 0.00795367, 0.33974375, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.0234498 , 0.02221931, 0.04286104, 0. ,
0. , 0. , 0.32066368, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[13, 1],
[ 2, 41]]),
'f1_score': 0.96470588235294119,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178443645993385,
'precision_score': 0.97619047619047616,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.052631578947368474}},
'rf_bootstrap25': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([ 0, 6, 8, 9, 10]),
array([ 0, 6, 8, 9, 11]),
array([ 0, 6, 8, 12])],
'all_leaf_node_samples': [39, 2, 1, 2, 1, 1, 21],
'all_leaf_node_samples_percent': [58.208955223880594,
2.9850746268656718,
1.4925373134328359,
2.9850746268656718,
1.4925373134328359,
1.4925373134328359,
31.343283582089551],
'all_leaf_node_values': [array([[ 0, 61]]),
array([[0, 4]]),
array([[1, 0]]),
array([[0, 3]]),
array([[0, 1]]),
array([[2, 0]]),
array([[31, 0]])],
'all_leaf_nodes': [2, 4, 5, 7, 10, 11, 12],
'all_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([ 7, 23]),
array([ 7, 23, 21, 22]),
array([ 7, 23, 21, 22]),
array([ 7, 23, 21])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.59223301]]),
array([[ 0. , 0.03883495]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.02912621]]),
array([[ 0. , 0.00970874]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.30097087, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([ 7, 23]),
array([ 7, 21, 22, 23]),
array([ 7, 21, 22, 23]),
array([ 7, 21, 23])],
'leaf_nodes_depths': [2, 3, 3, 2, 4, 4, 3],
'max_node_depth': 4,
'n_nodes': 13,
'node_features_idx': array([ 7, 21, 28, 22, 28, 28, 23, 28, 21, 22, 28, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [61, 4, 1, 3, 1, 2, 31],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[10, 4],
[ 2, 41]]),
'f1_score': 0.93181818181818188,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357167852488583,
'precision_score': 0.91111111111111109,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.10526315789473684}},
'dtree1': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([ 0, 8, 9, 10, 11]),
array([ 0, 8, 9, 10, 12]),
array([ 0, 8, 9, 13]),
array([ 0, 8, 14])],
'all_leaf_node_samples': [33, 1, 9, 2, 1, 4, 2, 18],
'all_leaf_node_samples_percent': [47.142857142857146,
1.4285714285714286,
12.857142857142858,
2.8571428571428572,
1.4285714285714286,
5.7142857142857144,
2.8571428571428572,
25.714285714285715],
'all_leaf_node_values': [array([[ 0, 49]]),
array([[1, 0]]),
array([[ 0, 12]]),
array([[2, 0]]),
array([[0, 1]]),
array([[7, 0]]),
array([[0, 2]]),
array([[29, 0]])],
'all_leaf_nodes': [3, 5, 6, 7, 11, 12, 13, 14],
'all_leaf_paths_features': [array([ 7, 22, 27]),
array([ 7, 22, 27, 27]),
array([ 7, 22, 27, 27]),
array([ 7, 22]),
array([ 7, 23, 20, 27]),
array([ 7, 23, 20, 27]),
array([ 7, 23, 20]),
array([ 7, 23])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.47572816]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.11650485]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.06796117, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.2815534, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([ 7, 22]),
array([ 7, 20, 23, 27]),
array([ 7, 20, 23, 27]),
array([ 7, 20, 23]),
array([ 7, 23])],
'leaf_nodes_depths': [3, 4, 4, 2, 4, 4, 3, 2],
'max_node_depth': 4,
'n_nodes': 15,
'node_features_idx': array([ 7, 22, 27, 28, 27, 28, 28, 28, 23, 20, 27, 28, 28, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [49, 1, 12, 2, 1, 7, 2, 29],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[13, 1],
[ 5, 38]]),
'f1_score': 0.92682926829268308,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6356747011735844,
'precision_score': 0.97435897435897434,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.10526315789473684}},
'dtree2': {'all_leaf_node_classes': [1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1]),
array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6])],
'all_leaf_node_samples': [33, 3, 2, 17],
'all_leaf_node_samples_percent': [60.0,
5.4545454545454541,
3.6363636363636362,
30.90909090909091],
'all_leaf_node_values': [array([[ 0, 66]]),
array([[6, 0]]),
array([[0, 4]]),
array([[27, 0]])],
'all_leaf_nodes': [1, 4, 5, 6],
'all_leaf_paths_features': [array([22]),
array([22, 22, 22]),
array([22, 22, 22]),
array([22, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.6407767]]),
array([[ 0.05825243, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.26213592, 0. ]])],
'all_uniq_leaf_paths_features': [array([22]),
array([22]),
array([22]),
array([22])],
'leaf_nodes_depths': [1, 3, 3, 2],
'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([22, 28, 22, 22, 28, 28, 28]),
'num_features_used': 2,
'tot_leaf_node_values': [66, 6, 4, 27],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[11, 3],
[ 4, 39]]),
'f1_score': 0.91764705882352937,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.241646202748516,
'precision_score': 0.9285714285714286,
'recall_score': 0.90697674418604646,
'zero_one_loss': 0.1228070175438597}},
'dtree3': {'all_leaf_node_classes': [1, 0, 1, 1, 0, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2, 3]),
array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([ 0, 1, 7, 9, 10]),
array([ 0, 1, 7, 9, 11, 12]),
array([ 0, 1, 7, 9, 11, 13]),
array([ 0, 14])],
'all_leaf_node_samples': [27, 1, 10, 2, 4, 1, 1, 22],
'all_leaf_node_samples_percent': [39.705882352941174,
1.4705882352941178,
14.705882352941176,
2.9411764705882355,
5.882352941176471,
1.4705882352941178,
1.4705882352941178,
32.352941176470587],
'all_leaf_node_values': [array([[ 0, 40]]),
array([[1, 0]]),
array([[ 0, 16]]),
array([[0, 2]]),
array([[5, 0]]),
array([[1, 0]]),
array([[0, 1]]),
array([[37, 0]])],
'all_leaf_nodes': [3, 5, 6, 8, 10, 12, 13, 14],
'all_leaf_paths_features': [array([22, 27, 20]),
array([22, 27, 20, 22]),
array([22, 27, 20, 22]),
array([22, 27, 22]),
array([22, 27, 22, 7]),
array([22, 27, 22, 7, 23]),
array([22, 27, 22, 7, 23]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.38834951]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.15533981]]),
array([[ 0. , 0.01941748]]),
array([[ 0.04854369, 0. ]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.3592233, 0. ]])],
'all_uniq_leaf_paths_features': [array([20, 22, 27]),
array([20, 22, 27]),
array([20, 22, 27]),
array([22, 27]),
array([ 7, 22, 27]),
array([ 7, 22, 23, 27]),
array([ 7, 22, 23, 27]),
array([22])],
'leaf_nodes_depths': [3, 4, 4, 3, 4, 5, 5, 1],
'max_node_depth': 5,
'n_nodes': 15,
'node_features_idx': array([22, 27, 20, 28, 22, 28, 28, 22, 28, 7, 28, 23, 28, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [40, 1, 16, 2, 5, 1, 1, 37],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[11, 3],
[ 3, 40]]),
'f1_score': 0.93023255813953487,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357027572237675,
'precision_score': 0.93023255813953487,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.10526315789473684}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0, 1, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([0, 6, 8, 9]),
array([ 0, 6, 8, 10])],
'all_leaf_node_samples': [32, 8, 1, 2, 1, 19],
'all_leaf_node_samples_percent': [50.793650793650791,
12.698412698412698,
1.5873015873015872,
3.1746031746031744,
1.5873015873015872,
30.158730158730158],
'all_leaf_node_values': [array([[ 0, 58]]),
array([[ 0, 12]]),
array([[1, 0]]),
array([[0, 2]]),
array([[0, 1]]),
array([[29, 0]])],
'all_leaf_nodes': [2, 4, 5, 7, 9, 10],
'all_leaf_paths_features': [array([ 7, 23]),
array([ 7, 23, 21]),
array([ 7, 23, 21]),
array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.5631068]]),
array([[ 0. , 0.11650485]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0. , 0.00970874]]),
array([[ 0.2815534, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 7, 23]),
array([ 7, 21, 23]),
array([ 7, 21, 23]),
array([ 7, 21]),
array([ 7, 21, 22]),
array([ 7, 21, 22])],
'leaf_nodes_depths': [2, 3, 3, 2, 3, 3],
'max_node_depth': 3,
'n_nodes': 11,
'node_features_idx': array([ 7, 23, 28, 21, 28, 28, 21, 28, 22, 28, 28]),
'num_features_used': 5,
'tot_leaf_node_values': [58, 12, 1, 2, 1, 29],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[11, 3],
[ 1, 42]]),
'f1_score': 0.95454545454545447,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238158661742695,
'precision_score': 0.93333333333333335,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.070175438596491224}},
'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.48155813, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01043774, 0.02945545, 0.40433218, 0.03288585, 0. ,
0. , 0. , 0.04133065, 0. , 0. ]),
'feature_importances_rank_idx': array([ 7, 22, 27, 23, 21, 20, 5, 9, 8, 6, 29, 4, 11, 3, 2, 1, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.38786789, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.0200666 , 0.04889286, 0.42214189, 0.04185194, 0. ,
0. , 0. , 0.05334557, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[11, 3],
[ 4, 39]]),
'f1_score': 0.91764705882352937,
'hamming_loss': 0.12280701754385964,
'log_loss': 4.241646202748516,
'precision_score': 0.9285714285714286,
'recall_score': 0.90697674418604646,
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array([0, 1, 3]),
array([0, 4, 5, 6]),
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array([[3, 0]]),
array([[8, 0]]),
array([[0, 2]]),
array([[42, 0]])],
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array([23, 7, 7]),
array([23, 7])],
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array([[ 0.02912621, 0. ]]),
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array([[ 0. , 0.01941748]]),
array([[ 0.40776699, 0. ]])],
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array([0, 4, 5, 6]),
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array([23, 21, 20]),
array([23, 21, 20]),
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
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array([ 0, 10])],
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array([22, 27, 22]),
array([22])],
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array([[3, 0]]),
array([[35, 0]])],
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array([20, 21])],
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array([[45, 0]])],
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array([23, 27])],
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array([[ 0.03883495, 0. ]]),
array([[ 0.37864078, 0. ]])],
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array([[0, 1]]),
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0. , 0. , 0. , 0. , 0. ,
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14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
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0.36192052, 0.06169666, 0.44349483, 0.31504343, 0. ,
0. , 0. , 0.05012238, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[12, 2],
[ 3, 40]]),
'f1_score': 0.94117647058823517,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297452836739267,
'precision_score': 0.95238095238095233,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.087719298245614086}},
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([0, 8, 9]),
array([ 0, 8, 10])],
'all_leaf_node_samples': [30, 1, 5, 1, 1, 28],
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1.5151515151515151,
7.5757575757575761,
1.5151515151515151,
1.5151515151515151,
42.424242424242422],
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array([[2, 0]]),
array([[0, 8]]),
array([[2, 0]]),
array([[0, 1]]),
array([[48, 0]])],
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array([27, 22, 7, 27]),
array([27, 22, 7, 27]),
array([27, 22]),
array([27, 22]),
array([27, 22])],
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.0776699]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.46601942, 0. ]])],
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array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([22, 27]),
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
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array([[54, 0]])],
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array([27, 7, 27]),
array([27, 7, 27]),
array([27])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.08737864]]),
array([[ 0.52427184, 0. ]])],
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array([ 7, 27]),
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array([0, 1, 7]),
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array([0, 1, 3, 5]),
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array([[ 0.00970874, 0. ]]),
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array([0, 1, 2, 4, 6]),
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25.0,
1.5625,
1.5625,
35.9375],
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array([23, 27])],
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'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
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max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
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array([0, 1, 7, 8]),
array([ 0, 1, 7, 9, 10]),
array([ 0, 1, 7, 9, 11]),
array([ 0, 12])],
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array([[4, 0]]),
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array([[41, 0]])],
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array([22, 7, 27, 22]),
array([22, 7, 27, 22]),
array([22, 7, 27]),
array([22, 7, 27, 27]),
array([22, 7, 27, 27]),
array([22])],
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array([[ 0.01941748, 0. ]]),
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array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
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array([[1, 0]]),
array([[38, 0]])],
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array([ 7, 20, 7, 27]),
array([ 7, 20]),
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array([[ 0.01941748, 0. ]]),
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array([ 0, 1, 11]),
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
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'all_leaf_node_values': [array([[ 0, 63]]),
array([[0, 5]]),
array([[2, 0]]),
array([[2, 0]]),
array([[31, 0]])],
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array([23, 27, 1, 27]),
array([23, 27, 1, 27]),
array([23, 27]),
array([23])],
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array([[ 0. , 0.04854369]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.01941748, 0. ]]),
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array([ 1, 23, 27]),
array([ 1, 23, 27]),
array([23, 27]),
array([23])],
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0. , 0. , 0.2137563 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.32672565, 0.00380043, 0.18653726, 0.16730159, 0. ,
0. , 0. , 0.09742739, 0. , 0. ]),
'feature_importances_rank_idx': array([20, 7, 22, 23, 27, 1, 21, 5, 9, 8, 6, 29, 4, 11, 3, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
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0. , 0. , 0.34061111, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.3851487 , 0.00760087, 0.3070858 , 0.33460317, 0. ,
0. , 0. , 0.05718793, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.85964912280701755,
'confusion_matrix': array([[ 9, 5],
[ 3, 40]]),
'f1_score': 0.90909090909090917,
'hamming_loss': 0.14035087719298245,
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'precision_score': 0.88888888888888884,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.14035087719298245}},
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6])],
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1.5384615384615385,
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array([[2, 0]]),
array([[0, 1]]),
array([[53, 0]])],
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array([27, 7, 7]),
array([27, 7, 7]),
array([27, 7])],
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.51456311, 0. ]])],
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array([ 7, 27]),
array([ 7, 27]),
array([ 7, 27])],
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'max_node_depth': 3,
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array([0, 2, 3, 4]),
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array([0, 2, 6])],
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array([[0, 1]]),
array([[49, 0]])],
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array([27, 27, 22]),
array([27, 27, 22]),
array([27, 27])],
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array([[ 0. , 0.00970874]]),
array([[ 0.47572816, 0. ]])],
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array([0, 4, 6])],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
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array([ 0, 6, 8, 12])],
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1.6666666666666667,
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array([[0, 3]]),
array([[1, 0]]),
array([[0, 1]]),
array([[0, 1]]),
array([[3, 0]]),
array([[51, 0]])],
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array([27, 22, 27]),
array([27, 22, 27]),
array([27, 22]),
array([27, 22, 21, 27]),
array([27, 22, 21, 27]),
array([27, 22, 21])],
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array([[ 0. , 0.02912621]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.00970874]]),
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array([22, 27]),
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0. , 0.00180888, 0.0287093 , 0. , 0. ,
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13, 28, 15, 16, 17, 18, 19, 20, 23, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.01532738, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.00361777, 0.02994351, 0. , 0. ,
0. , 0. , 0.02718307, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[12, 2],
[ 3, 40]]),
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'hamming_loss': 0.08771929824561403,
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array([0, 2, 3]),
array([0, 2, 4, 5, 6]),
array([0, 2, 4, 5, 7]),
array([0, 2, 4, 8])],
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array([[0, 1]]),
array([[2, 0]]),
array([[0, 1]]),
array([[40, 0]])],
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array([27, 22]),
array([27, 22, 20, 27]),
array([27, 22, 20, 27]),
array([27, 22, 20])],
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array([[ 0. , 0.00970874]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.38834951, 0. ]])],
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array([22, 27]),
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35.384615384615387],
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array([[1, 0]]),
array([[32, 0]])],
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array([[ 0.00970874, 0. ]]),
array([[ 0.31067961, 0. ]])],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
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array([[3, 0]]),
array([[0, 1]]),
array([[37, 0]])],
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array([ 7, 20, 23]),
array([7])],
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array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.00970874]]),
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array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6, 7, 8]),
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array([22, 23, 27]),
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'recall_score': 0.95348837209302328,
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'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
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1.4705882352941178,
2.9411764705882355,
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array([[0, 2]]),
array([[40, 0]])],
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array([22, 27]),
array([22, 27]),
array([22, 27])],
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array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.38834951, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([22, 27])],
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0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01977135, 0. , 0.36322794, 0.01044793, 0. ,
0. , 0. , 0.42996188, 0. , 0. ]),
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14, 13, 28, 15, 16, 17, 18, 19, 21, 24, 25, 26, 0]),
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0.03365776, 0. , 0.41200425, 0.01311354, 0. ,
0. , 0. , 0.42942234, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[12, 2],
[ 3, 40]]),
'f1_score': 0.94117647058823517,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297452836739276,
'precision_score': 0.95238095238095233,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.087719298245614086}},
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
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1.5151515151515151,
3.0303030303030303,
30.303030303030305],
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array([[0, 2]]),
array([[3, 0]]),
array([[27, 0]])],
'all_leaf_nodes': [2, 4, 5, 6],
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array([23, 27, 22]),
array([23, 27, 22]),
array([23])],
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array([[ 0. , 0.01941748]]),
array([[ 0.02912621, 0. ]]),
array([[ 0.26213592, 0. ]])],
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array([22, 23, 27]),
array([22, 23, 27]),
array([23])],
'leaf_nodes_depths': [2, 3, 3, 1],
'max_node_depth': 3,
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'node_features_idx': array([23, 27, 28, 22, 28, 28, 28]),
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[ 1, 42]]),
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array([0, 1, 3, 4]),
array([0, 1, 3, 5, 6]),
array([0, 1, 3, 5, 7]),
array([0, 8])],
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3.1746031746031744,
1.5873015873015872,
4.7619047619047619,
38.095238095238095],
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array([[0, 3]]),
array([[0, 1]]),
array([[4, 0]]),
array([[43, 0]])],
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'all_leaf_paths_features': [array([23, 6]),
array([23, 6, 23]),
array([23, 6, 23, 27]),
array([23, 6, 23, 27]),
array([23])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.50485437]]),
array([[ 0. , 0.02912621]]),
array([[ 0. , 0.00970874]]),
array([[ 0.03883495, 0. ]]),
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array([ 6, 23]),
array([ 6, 23, 27]),
array([ 6, 23, 27]),
array([23])],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
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array([0, 6, 8])],
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array([[2, 0]]),
array([[0, 3]]),
array([[35, 0]])],
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array([27, 23, 27]),
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
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array([27, 26, 22]),
array([27, 26, 22]),
array([27, 22]),
array([27, 22])],
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array([[ 0. , 0.06796117]]),
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array([22, 27]),
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array([0, 1, 2, 4, 5, 7]),
array([0, 1, 2, 4, 8]),
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array([ 0, 10, 12])],
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array([[0, 1]]),
array([[2, 0]]),
array([[0, 3]]),
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array([[0, 1]]),
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array([22, 23, 27, 22, 22]),
array([22, 23, 27, 22, 22]),
array([22, 23, 27, 22]),
array([22, 23]),
array([22, 27]),
array([22, 27])],
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array([[ 0. , 0.00970874]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.02912621]]),
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array([[ 0. , 0.00970874]]),
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array([22, 23, 27]),
array([22, 23, 27]),
array([22, 23, 27]),
array([22, 23]),
array([22, 27]),
array([22, 27])],
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'tot_leaf_node_values': [66, 1, 2, 3, 1, 1, 29],
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'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0.01356636, 0.02330919, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.21915595, 0.36943634, 0. ,
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14, 13, 28, 15, 16, 17, 18, 19, 20, 21, 24, 25, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0.02713273, 0.04661839, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
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0. , 0. , 0.33818739, 0.41947649, 0. ,
0. , 0.00690772, 0.3805802 , 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[12, 2],
[ 1, 42]]),
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array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
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3.225806451612903,
24.193548387096776],
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array([[1, 0]]),
array([[0, 2]]),
array([[31, 0]])],
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array([23, 22]),
array([23, 27]),
array([23, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.66990291]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.30097087, 0. ]])],
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array([22, 23]),
array([23, 27]),
array([23, 27])],
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array([0, 2, 3, 5]),
array([0, 2, 6])],
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3.0303030303030303,
30.303030303030305],
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array([[0, 2]]),
array([[2, 0]]),
array([[33, 0]])],
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array([27, 27, 22]),
array([27, 27, 22]),
array([27, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.6407767]]),
array([[ 0. , 0.01941748]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.32038835, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([27])],
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'node_features_idx': array([27, 28, 27, 22, 28, 28, 28]),
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'tot_leaf_node_values': [66, 2, 2, 33],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([0, 6, 8])],
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9.67741935483871,
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array([[5, 0]]),
array([[0, 8]]),
array([[0, 1]]),
array([[32, 0]])],
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array([22, 20, 22]),
array([22, 20, 22]),
array([22, 27]),
array([22, 27])],
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array([[ 0.04854369, 0. ]]),
array([[ 0. , 0.0776699]]),
array([[ 0. , 0.00970874]]),
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
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array([[0, 6]]),
array([[5, 0]]),
array([[19, 0]])],
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array([ 7, 21, 7]),
array([ 7, 21, 7]),
array([7])],
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array([[ 0. , 0.05825243]]),
array([[ 0.04854369, 0. ]]),
array([[ 0.18446602, 0. ]])],
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array([ 7, 21]),
array([ 7, 21]),
array([7])],
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'hamming_loss': 0.10526315789473684,
'log_loss': 3.6356747011735844,
'precision_score': 0.97435897435897434,
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'zero_one_loss': 0.10526315789473684}},
'dtree4': {'all_leaf_node_classes': [1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
'all_leaf_node_samples': [38, 2, 1, 16],
'all_leaf_node_samples_percent': [66.666666666666671,
3.5087719298245612,
1.7543859649122806,
28.07017543859649],
'all_leaf_node_values': [array([[ 0, 68]]),
array([[0, 2]]),
array([[2, 0]]),
array([[31, 0]])],
'all_leaf_nodes': [2, 4, 5, 6],
'all_leaf_paths_features': [array([22, 21]),
array([22, 21, 22]),
array([22, 21, 22]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.66019417]]),
array([[ 0. , 0.01941748]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.30097087, 0. ]])],
'all_uniq_leaf_paths_features': [array([21, 22]),
array([21, 22]),
array([21, 22]),
array([22])],
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'max_node_depth': 3,
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'node_features_idx': array([22, 21, 28, 22, 28, 28, 28]),
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'tot_leaf_node_values': [68, 2, 2, 31],
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'hamming_loss': 0.08771929824561403,
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'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.17854049, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.01320976, 0.02988183, 0.38778067, 0.17402784, 0. ,
0. , 0. , 0.2165594 , 0. , 0. ]),
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14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.35708098, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.02641953, 0.04200365, 0.43979324, 0.34805568, 0. ,
0. , 0. , 0.37141779, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[ 9, 5],
[ 1, 42]]),
'f1_score': 0.93333333333333324,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357308132739496,
'precision_score': 0.8936170212765957,
'recall_score': 0.97674418604651159,
'zero_one_loss': 0.10526315789473684}},
'rf_bootstrap7': {'dtree0': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6, 7, 8]),
array([0, 4, 6, 7, 9]),
array([ 0, 4, 6, 10])],
'all_leaf_node_samples': [34, 1, 2, 1, 1, 25],
'all_leaf_node_samples_percent': [53.125,
1.5625,
3.125,
1.5625,
1.5625,
39.0625],
'all_leaf_node_values': [array([[ 0, 48]]),
array([[1, 0]]),
array([[0, 4]]),
array([[3, 0]]),
array([[0, 1]]),
array([[46, 0]])],
'all_leaf_nodes': [2, 3, 5, 8, 9, 10],
'all_leaf_paths_features': [array([22, 27]),
array([22, 27]),
array([22, 21]),
array([22, 21, 7, 21]),
array([22, 21, 7, 21]),
array([22, 21, 7])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.46601942]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.44660194, 0. ]])],
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array([22, 27]),
array([21, 22]),
array([ 7, 21, 22]),
array([ 7, 21, 22]),
array([ 7, 21, 22])],
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'max_node_depth': 4,
'n_nodes': 11,
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'tot_leaf_node_values': [48, 1, 4, 3, 1, 46],
'validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[11, 3],
[ 2, 41]]),
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'hamming_loss': 0.08771929824561403,
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'recall_score': 0.95348837209302328,
'zero_one_loss': 0.087719298245614086}},
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6])],
'all_leaf_node_samples': [37, 3, 1, 30],
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4.225352112676056,
1.408450704225352,
42.25352112676056],
'all_leaf_node_values': [array([[ 0, 55]]),
array([[0, 4]]),
array([[2, 0]]),
array([[42, 0]])],
'all_leaf_nodes': [1, 4, 5, 6],
'all_leaf_paths_features': [array([23]),
array([23, 27, 21]),
array([23, 27, 21]),
array([23, 27])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.53398058]]),
array([[ 0. , 0.03883495]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.40776699, 0. ]])],
'all_uniq_leaf_paths_features': [array([23]),
array([21, 23, 27]),
array([21, 23, 27]),
array([23, 27])],
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'node_features_idx': array([23, 28, 27, 21, 28, 28, 28]),
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'tot_leaf_node_values': [55, 4, 2, 42],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[12, 2],
[ 5, 38]]),
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'hamming_loss': 0.12280701754385964,
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'recall_score': 0.88372093023255816,
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'dtree2': {'all_leaf_node_classes': [1, 0, 1, 1, 0],
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array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6, 7]),
array([0, 6, 8])],
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3.278688524590164,
1.639344262295082,
4.918032786885246,
37.704918032786885],
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array([[2, 0]]),
array([[0, 4]]),
array([[0, 6]]),
array([[37, 0]])],
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array([20, 23, 22]),
array([20, 23, 22]),
array([20, 7]),
array([20, 7])],
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0. , 0.05825243]]),
array([[ 0.3592233, 0. ]])],
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array([20, 22, 23]),
array([20, 22, 23]),
array([ 7, 20]),
array([ 7, 20])],
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'tot_leaf_node_values': [54, 2, 4, 6, 37],
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'confusion_matrix': array([[11, 3],
[ 3, 40]]),
'f1_score': 0.93023255813953487,
'hamming_loss': 0.10526315789473684,
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'precision_score': 0.93023255813953487,
'recall_score': 0.93023255813953487,
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'dtree3': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 0],
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6, 7, 8]),
array([0, 2, 6, 7, 9]),
array([ 0, 2, 6, 10])],
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1.5151515151515151,
3.0303030303030303,
4.5454545454545459,
1.5151515151515151,
45.454545454545453],
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array([[2, 0]]),
array([[0, 4]]),
array([[5, 0]]),
array([[0, 1]]),
array([[47, 0]])],
'all_leaf_nodes': [1, 4, 5, 8, 9, 10],
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array([23, 27, 27]),
array([23, 27, 27]),
array([23, 27, 7, 7]),
array([23, 27, 7, 7]),
array([23, 27, 7])],
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array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0.04854369, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.45631068, 0. ]])],
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array([23, 27]),
array([23, 27]),
array([ 7, 23, 27]),
array([ 7, 23, 27]),
array([ 7, 23, 27])],
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'tot_leaf_node_values': [44, 2, 4, 5, 1, 47],
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[ 5, 38]]),
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'dtree4': {'all_leaf_node_classes': [1, 0, 1, 0, 1, 0, 0],
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array([0, 1, 2, 3, 5, 6]),
array([0, 1, 2, 3, 5, 7]),
array([0, 1, 2, 8]),
array([ 0, 1, 9, 10]),
array([ 0, 1, 9, 11]),
array([ 0, 12])],
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1.5873015873015872,
3.1746031746031744,
1.5873015873015872,
1.5873015873015872,
6.3492063492063489,
33.333333333333336],
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array([[1, 0]]),
array([[0, 3]]),
array([[2, 0]]),
array([[0, 1]]),
array([[4, 0]]),
array([[36, 0]])],
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'all_leaf_paths_features': [array([ 6, 23, 27, 21]),
array([ 6, 23, 27, 21, 1]),
array([ 6, 23, 27, 21, 1]),
array([ 6, 23, 27]),
array([ 6, 23, 27]),
array([ 6, 23, 27]),
array([6])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.54368932]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.02912621]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.03883495, 0. ]]),
array([[ 0.34951456, 0. ]])],
'all_uniq_leaf_paths_features': [array([ 6, 21, 23, 27]),
array([ 1, 6, 21, 23, 27]),
array([ 1, 6, 21, 23, 27]),
array([ 6, 23, 27]),
array([ 6, 23, 27]),
array([ 6, 23, 27]),
array([6])],
'leaf_nodes_depths': [4, 5, 5, 3, 3, 3, 1],
'max_node_depth': 5,
'n_nodes': 13,
'node_features_idx': array([ 6, 23, 27, 21, 28, 1, 28, 28, 28, 27, 28, 28, 28]),
'num_features_used': 6,
'tot_leaf_node_values': [56, 1, 3, 2, 1, 4, 36],
'validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[13, 1],
[ 5, 38]]),
'f1_score': 0.92682926829268308,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6356747011735844,
'precision_score': 0.97435897435897434,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.10526315789473684}},
'feature_importances': array([ 0. , 0.00598837, 0. , 0. , 0. ,
0. , 0.14994793, 0.05203594, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.14143423, 0.04592455, 0.16812029, 0.36109809, 0. ,
0. , 0. , 0.0754506 , 0. , 0. ]),
'feature_importances_rank_idx': array([23, 22, 6, 20, 27, 7, 21, 1, 5, 9, 8, 29, 4, 11, 3, 2, 10,
14, 12, 13, 28, 15, 16, 17, 18, 19, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0.01197674, 0. , 0. , 0. ,
0. , 0.29989587, 0.08171812, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.28286847, 0.06381958, 0.30946446, 0.39120386, 0. ,
0. , 0. , 0.05003293, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.89473684210526316,
'confusion_matrix': array([[11, 3],
[ 3, 40]]),
'f1_score': 0.93023255813953487,
'hamming_loss': 0.10526315789473684,
'log_loss': 3.6357027572237675,
'precision_score': 0.93023255813953487,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.10526315789473684}},
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([0, 1, 7, 9]),
array([ 0, 10])],
'all_leaf_node_samples': [35, 1, 3, 2, 3, 18],
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1.6129032258064515,
4.838709677419355,
3.225806451612903,
4.838709677419355,
29.032258064516128],
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array([[1, 0]]),
array([[0, 6]]),
array([[0, 2]]),
array([[6, 0]]),
array([[32, 0]])],
'all_leaf_nodes': [3, 5, 6, 8, 9, 10],
'all_leaf_paths_features': [array([22, 27, 27]),
array([22, 27, 27, 27]),
array([22, 27, 27, 27]),
array([22, 27, 22]),
array([22, 27, 22]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.54368932]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.05825243]]),
array([[ 0. , 0.01941748]]),
array([[ 0.05825243, 0. ]]),
array([[ 0.31067961, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22])],
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'n_nodes': 11,
'node_features_idx': array([22, 27, 27, 28, 27, 28, 28, 22, 28, 28, 28]),
'num_features_used': 3,
'tot_leaf_node_values': [56, 1, 6, 2, 6, 32],
'validation_metrics': {'accuracy_score': 0.8771929824561403,
'confusion_matrix': array([[12, 2],
[ 5, 38]]),
'f1_score': 0.91566265060240959,
'hamming_loss': 0.12280701754385964,
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'precision_score': 0.94999999999999996,
'recall_score': 0.88372093023255816,
'zero_one_loss': 0.1228070175438597}},
'dtree1': {'all_leaf_node_classes': [1, 0, 1, 0, 0, 0, 0],
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array([0, 1, 2, 3, 5, 6, 7]),
array([0, 1, 2, 3, 5, 6, 8]),
array([0, 1, 2, 3, 5, 9]),
array([ 0, 1, 2, 10]),
array([ 0, 1, 11]),
array([ 0, 12])],
'all_leaf_node_samples': [34, 1, 6, 1, 1, 3, 23],
'all_leaf_node_samples_percent': [49.275362318840578,
1.4492753623188406,
8.695652173913043,
1.4492753623188406,
1.4492753623188406,
4.3478260869565215,
33.333333333333336],
'all_leaf_node_values': [array([[ 0, 49]]),
array([[1, 0]]),
array([[ 0, 11]]),
array([[2, 0]]),
array([[1, 0]]),
array([[4, 0]]),
array([[35, 0]])],
'all_leaf_nodes': [4, 7, 8, 9, 10, 11, 12],
'all_leaf_paths_features': [array([22, 27, 23, 23]),
array([22, 27, 23, 23, 27, 22]),
array([22, 27, 23, 23, 27, 22]),
array([22, 27, 23, 23, 27]),
array([22, 27, 23]),
array([22, 27]),
array([22])],
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array([[ 0. , 0.10679612]]),
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array([22, 23, 27]),
array([22, 23, 27]),
array([22, 23, 27]),
array([22, 27]),
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array([0, 1, 3, 4, 5]),
array([0, 1, 3, 4, 6]),
array([0, 1, 3, 7]),
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1.5873015873015872,
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array([[40, 0]])],
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array([ 7, 22]),
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array([ 7, 27])],
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'tot_leaf_node_values': [52, 1, 3, 4, 3, 40],
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array([0, 1, 3]),
array([0, 4, 5]),
array([0, 4, 6])],
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3.1746031746031744,
1.5873015873015872,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7]),
array([ 0, 8, 9, 10]),
array([ 0, 8, 9, 11]),
array([ 0, 8, 12])],
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4.2857142857142856,
1.4285714285714286,
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1.4285714285714286,
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array([[1, 0]]),
array([[3, 0]]),
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array([22, 27, 23, 27]),
array([22, 27, 23, 27]),
array([22, 27]),
array([22, 27, 22]),
array([22, 27, 22]),
array([22, 27])],
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array([[ 0. , 0.02912621]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.00970874, 0. ]]),
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array([22, 23, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])],
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'validation_metrics': {'accuracy_score': 0.92982456140350878,
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'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
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0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.50831641, 0.02082389, 0. ,
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13, 28, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 0]),
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0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.353917 , 0.02257051, 0. ,
0. , 0. , 0.27029396, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.91228070175438591,
'confusion_matrix': array([[12, 2],
[ 3, 40]]),
'f1_score': 0.94117647058823517,
'hamming_loss': 0.08771929824561403,
'log_loss': 3.0297452836739276,
'precision_score': 0.95238095238095233,
'recall_score': 0.93023255813953487,
'zero_one_loss': 0.087719298245614086}},
'rf_bootstrap9': {'dtree0': {'all_leaf_node_classes': [1, 1, 0, 0],
'all_leaf_node_paths': [array([0, 1, 2]),
array([0, 1, 3, 4]),
array([0, 1, 3, 5]),
array([0, 6])],
'all_leaf_node_samples': [37, 1, 1, 26],
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1.5384615384615385,
1.5384615384615385,
40.0],
'all_leaf_node_values': [array([[ 0, 57]]),
array([[0, 2]]),
array([[1, 0]]),
array([[43, 0]])],
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'all_leaf_paths_features': [array([22, 27]),
array([22, 27, 22]),
array([22, 27, 22]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.55339806]]),
array([[ 0. , 0.01941748]]),
array([[ 0.00970874, 0. ]]),
array([[ 0.41747573, 0. ]])],
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array([22, 27]),
array([22, 27]),
array([22])],
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'max_node_depth': 3,
'n_nodes': 7,
'node_features_idx': array([22, 27, 28, 22, 28, 28, 28]),
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'confusion_matrix': array([[10, 4],
[ 0, 43]]),
'f1_score': 0.9555555555555556,
'hamming_loss': 0.070175438596491224,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6, 7]),
array([0, 1, 2, 4, 6, 8]),
array([0, 1, 9]),
array([ 0, 10, 11]),
array([ 0, 10, 12])],
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3.125,
1.5625,
3.125,
1.5625,
1.5625,
32.8125],
'all_leaf_node_values': [array([[ 0, 59]]),
array([[0, 5]]),
array([[3, 0]]),
array([[0, 2]]),
array([[2, 0]]),
array([[0, 1]]),
array([[31, 0]])],
'all_leaf_nodes': [3, 5, 7, 8, 9, 11, 12],
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array([27, 22, 27, 6]),
array([27, 22, 27, 6, 7]),
array([27, 22, 27, 6, 7]),
array([27, 22]),
array([27, 23]),
array([27, 23])],
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array([[ 0. , 0.04854369]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.01941748]]),
array([[ 0.01941748, 0. ]]),
array([[ 0. , 0.00970874]]),
array([[ 0.30097087, 0. ]])],
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array([ 6, 22, 27]),
array([ 6, 7, 22, 27]),
array([ 6, 7, 22, 27]),
array([22, 27]),
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array([23, 27])],
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'node_features_idx': array([27, 22, 27, 28, 6, 28, 7, 28, 28, 28, 23, 28, 28]),
'num_features_used': 6,
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array([0, 2, 3, 4]),
array([0, 2, 3, 5]),
array([0, 2, 6])],
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2.8985507246376812,
2.8985507246376812,
36.231884057971016],
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array([[0, 3]]),
array([[40, 0]])],
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array([ 7, 22, 22]),
array([ 7, 22, 22]),
array([ 7, 22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.55339806]]),
array([[ 0.02912621, 0. ]]),
array([[ 0. , 0.02912621]]),
array([[ 0.38834951, 0. ]])],
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array([ 7, 22]),
array([ 7, 22]),
array([ 7, 22])],
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[ 2, 41]]),
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'hamming_loss': 0.12280701754385964,
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'recall_score': 0.95348837209302328,
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([0, 1, 7, 9]),
array([ 0, 10])],
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2.816901408450704,
1.408450704225352,
1.408450704225352,
4.225352112676056,
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array([[1, 0]]),
array([[0, 3]]),
array([[5, 0]]),
array([[34, 0]])],
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array([22, 27, 21, 7]),
array([22, 27, 21, 7]),
array([22, 27, 21]),
array([22, 27, 21]),
array([22])],
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array([[ 0. , 0.01941748]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.02912621]]),
array([[ 0.04854369, 0. ]]),
array([[ 0.33009709, 0. ]])],
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array([ 7, 21, 22, 27]),
array([ 7, 21, 22, 27]),
array([21, 22, 27]),
array([21, 22, 27]),
array([22])],
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'tot_leaf_node_values': [58, 2, 1, 3, 5, 34],
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'dtree4': {'all_leaf_node_classes': [1, 0, 1, 1, 0, 0],
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array([0, 1, 2, 4, 5]),
array([0, 1, 2, 4, 6]),
array([0, 1, 7, 8]),
array([0, 1, 7, 9]),
array([ 0, 10])],
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1.5873015873015872,
4.7619047619047619,
1.5873015873015872,
3.1746031746031744,
30.158730158730158],
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array([[1, 0]]),
array([[0, 4]]),
array([[0, 1]]),
array([[2, 0]]),
array([[40, 0]])],
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array([22, 27, 7, 7]),
array([22, 27, 7, 7]),
array([22, 27, 22]),
array([22, 27, 22]),
array([22])],
'all_scaled_leaf_node_values': [array([[ 0. , 0.53398058]]),
array([[ 0.00970874, 0. ]]),
array([[ 0. , 0.03883495]]),
array([[ 0. , 0.00970874]]),
array([[ 0.01941748, 0. ]]),
array([[ 0.38834951, 0. ]])],
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array([ 7, 22, 27]),
array([ 7, 22, 27]),
array([22, 27]),
array([22, 27]),
array([22])],
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'max_node_depth': 4,
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'tot_leaf_node_values': [55, 1, 4, 1, 2, 40],
'validation_metrics': {'accuracy_score': 0.92982456140350878,
'confusion_matrix': array([[12, 2],
[ 2, 41]]),
'f1_score': 0.95348837209302328,
'hamming_loss': 0.070175438596491224,
'log_loss': 2.4238018381491786,
'precision_score': 0.95348837209302328,
'recall_score': 0.95348837209302328,
'zero_one_loss': 0.070175438596491224}},
'feature_importances': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0.00768657, 0.20115861, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.01791824, 0.572794 , 0.00827374, 0. ,
0. , 0. , 0.19216885, 0. , 0. ]),
'feature_importances_rank_idx': array([22, 7, 27, 21, 23, 6, 5, 10, 9, 8, 29, 4, 12, 3, 2, 1, 11,
14, 13, 28, 15, 16, 17, 18, 19, 20, 24, 25, 26, 0]),
'feature_importances_std': array([ 0. , 0. , 0. , 0. , 0. ,
0. , 0.01537313, 0.34386242, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.03583648, 0.39696993, 0.01654747, 0. ,
0. , 0. , 0.30271209, 0. , 0. ]),
'get_params': <bound method BaseEstimator.get_params of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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)>,
'rf_obj': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
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),
'rf_validation_metrics': {'accuracy_score': 0.94736842105263153,
'confusion_matrix': array([[11, 3],
[ 0, 43]]),
'f1_score': 0.96629213483146059,
'hamming_loss': 0.052631578947368418,
'log_loss': 1.8178724206495205,
'precision_score': 0.93478260869565222,
'recall_score': 1.0,
'zero_one_loss': 0.052631578947368474}}}
In [27]:
all_rit_bootstrap_output
Out[27]:
{'rf_bootstrap0': {'rit0': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f412e8>,
'rit_intersected_values': [array([22, 27]),
array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([], dtype=int64),
array([22, 27]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit1': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f419e8>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit10': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f41d68>,
'rit_intersected_values': [array([22, 27]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([22])],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22])]},
'rit11': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5b3c8>,
'rit_intersected_values': [array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([20]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64)]},
'rit12': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5b6d8>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit13': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5b828>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([22])],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22])]},
'rit14': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5bb38>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit15': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5beb8>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit16': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5bf60>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit17': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f60208>,
'rit_intersected_values': [array([20, 27]),
array([27]),
array([], dtype=int64),
array([27]),
array([27]),
array([], dtype=int64),
array([27]),
array([], dtype=int64),
array([27]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([27]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit18': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f60518>,
'rit_intersected_values': [array([22, 27]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64)]},
'rit19': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f60898>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([20]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit2': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f41dd8>,
'rit_intersected_values': [array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit3': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f59128>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([20]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64)]},
'rit4': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f593c8>,
'rit_intersected_values': [array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit5': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f597b8>,
'rit_intersected_values': [array([22]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit6': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f59908>,
'rit_intersected_values': [array([22, 27]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([22, 27])],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27])]},
'rit7': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f59d68>,
'rit_intersected_values': [array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64)]},
'rit8': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5b128>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit9': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f59748>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]}},
'rf_bootstrap1': {'rit0': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f60c88>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([23]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit1': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f69128>,
'rit_intersected_values': [array([ 7, 21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([7]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit10': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f69dd8>,
'rit_intersected_values': [array([20, 21, 27]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([20, 21, 27]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([21]),
array([21]),
array([21]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([], dtype=int64)]},
'rit11': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6a208>,
'rit_intersected_values': [array([ 7, 21]),
array([7]),
array([7]),
array([], dtype=int64),
array([7]),
array([], dtype=int64),
array([], dtype=int64),
array([7]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64)]},
'rit12': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6a668>,
'rit_intersected_values': [array([20]),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([20]),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([20]),
'rit_leaf_node_values': [array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit13': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6aba8>,
'rit_intersected_values': [array([20, 21, 22, 27]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21])],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([21])]},
'rit14': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6af28>,
'rit_intersected_values': [array([20, 21, 27]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit15': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6e358>,
'rit_intersected_values': [array([ 7, 21]),
array([ 7, 21]),
array([ 7, 21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit16': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6e6d8>,
'rit_intersected_values': [array([ 7, 21]),
array([ 7, 21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([ 7, 21]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit17': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6ea58>,
'rit_intersected_values': [array([ 7, 21]),
array([ 7, 21]),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([], dtype=int64),
array([ 7, 21]),
array([21]),
array([], dtype=int64),
array([21]),
array([ 7, 21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit18': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f6eeb8>,
'rit_intersected_values': [array([ 7, 21]),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit19': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f70208>,
'rit_intersected_values': [array([20]),
array([], dtype=int64),
array([20]),
array([20]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit2': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f694a8>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit3': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f695f8>,
'rit_intersected_values': [array([23]),
array([23]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64)]},
'rit4': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f697b8>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit5': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f69908>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit6': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f69a58>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([], dtype=int64),
'rit_leaf_node_values': [array([], dtype=int64), array([], dtype=int64)]},
'rit7': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f60eb8>,
'rit_intersected_values': [array([20, 21, 27]),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21]),
array([21]),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([21])],
'rit_leaf_node_union_value': array([21]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([21]),
array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([21])]},
'rit8': {'rit': <sklearn.tree.irf_utils.RITTree at 0x114f5b668>,
'rit_intersected_values': [array([23]),
array([], dtype=int64),
array([23]),
array([], dtype=int64),
array([23]),
array([], dtype=int64),
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array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64)]},
'rit3': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150b2908>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit4': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150b2f28>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])]},
'rit5': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150ba2e8>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
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array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([27]),
array([27]),
array([27]),
array([27]),
array([], dtype=int64),
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array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
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array([22, 27]),
array([22, 27]),
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array([27]),
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array([], dtype=int64),
array([22, 27]),
array([], dtype=int64)]},
'rit6': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150ba908>,
'rit_intersected_values': [array([22, 27]),
array([22]),
array([22]),
array([22]),
array([22]),
array([], dtype=int64),
array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([22]),
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array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([22]),
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array([], dtype=int64),
array([22]),
array([22]),
array([22]),
array([22]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit7': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150baf28>,
'rit_intersected_values': [array([21, 22, 27]),
array([21, 22, 27]),
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array([22, 27]),
array([22, 27]),
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array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64)]},
'rit8': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150bf518>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])]},
'rit9': {'rit': <sklearn.tree.irf_utils.RITTree at 0x1150bfb38>,
'rit_intersected_values': [array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])],
'rit_leaf_node_union_value': array([22, 27]),
'rit_leaf_node_values': [array([], dtype=int64),
array([], dtype=int64),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([], dtype=int64),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27]),
array([22, 27])]}}}
In [28]:
stability_score
Out[28]:
{'1_21_22_27': 0.03333333333333333,
'1_22': 0.03333333333333333,
'1_22_27': 0.03333333333333333,
'1_23_27': 0.1,
'1_6_21_23_27': 0.03333333333333333,
'1_7_22': 0.06666666666666667,
'1_7_22_27': 0.03333333333333333,
'20': 0.3,
'20_21': 0.03333333333333333,
'20_21_22': 0.03333333333333333,
'20_21_22_27': 0.06666666666666667,
'20_21_27': 0.03333333333333333,
'20_22': 0.13333333333333333,
'20_22_23': 0.03333333333333333,
'20_22_27': 0.06666666666666667,
'20_23': 0.03333333333333333,
'20_23_27': 0.03333333333333333,
'20_27': 0.1,
'21': 0.16666666666666666,
'21_22': 0.13333333333333333,
'21_22_27': 0.06666666666666667,
'21_23': 0.06666666666666667,
'21_23_27': 0.03333333333333333,
'21_27': 0.13333333333333333,
'22': 0.6333333333333333,
'22_23': 0.03333333333333333,
'22_23_27': 0.23333333333333334,
'22_26_27': 0.03333333333333333,
'22_27': 0.7,
'23': 0.26666666666666666,
'23_27': 0.23333333333333334,
'26_27': 0.03333333333333333,
'27': 0.6,
'6_21_23_27': 0.03333333333333333,
'6_23': 0.03333333333333333,
'7': 0.36666666666666664,
'7_20': 0.1,
'7_20_21_23_27': 0.03333333333333333,
'7_20_22': 0.06666666666666667,
'7_20_23': 0.03333333333333333,
'7_20_27': 0.06666666666666667,
'7_21': 0.2,
'7_21_22': 0.03333333333333333,
'7_21_22_23': 0.03333333333333333,
'7_21_23': 0.06666666666666667,
'7_21_23_27': 0.03333333333333333,
'7_21_27': 0.06666666666666667,
'7_22': 0.4,
'7_22_23': 0.06666666666666667,
'7_22_23_27': 0.03333333333333333,
'7_22_27': 0.3333333333333333,
'7_23': 0.1,
'7_23_27': 0.03333333333333333,
'7_27': 0.23333333333333334}
Content source: Yu-Group/scikit-learn-sandbox
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