

In [1]:

    
from sklearn import datasets
import pandas as pd

%matplotlib inline

Build a model



In [2]:

    
ds = datasets.load_breast_cancer();
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(min_samples_leaf=30, random_state = 1960)
NC = 12

X = ds.data[:,0:NC]
y = ds.target

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1960)

clf.fit(X_train , y_train)









    Out[2]:





DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=30, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=1960,
            splitter='best')

Decision Tree Plot



In [3]:

    
import graphviz 
from sklearn import tree

dot_data = tree.export_graphviz(clf, out_file=None, 
                         feature_names=ds.feature_names[0:NC],  
                         class_names=ds.target_names,  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = graphviz.Source(dot_data)  
graph









    Out[3]:

sklearn_explain approach



In [4]:

    
# Explain the score = ln(p(1) / (1 - p(1)))


import sklearn_explain.explainer as expl
lExplainer = expl.cModelScoreExplainer(clf)
lExplainer.mSettings.mFeatureNames = ds.feature_names[0:NC]
lExplainer.mSettings.mExplanationOrder = 1
    
lExplainer.fit(X_train)
df_rc = lExplainer.explain(X_test)

print(df_rc.columns)









    



USING_LOG_ODDS_AS_SCORE
SCORE_QUANTILES {0: -inf, 1: -11.512915464920228, 2: 1.6094379124341005, 3: 3.367295829986474, 4: 11.51291546492478}
CONST_SCORE_DETECTIOM -11.512915464920228 11.51291546492478 False
NON_USED_FEATURES ['mean radius', 'mean texture', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean symmetry', 'mean fractal dimension', 'texture error']
USED_FEATURES ['mean perimeter', 'mean area', 'mean concave points', 'radius error']
FEATURE_QUANTILES mean perimeter {0: -inf, 1: 71.884, 2: 80.624, 3: 90.566, 4: 111.44}
FEATURE_QUANTILES mean area {0: -inf, 1: 387.1, 2: 477.3, 3: 602.0, 4: 922.44}
FEATURE_QUANTILES mean concave points {0: -inf, 1: 0.017368, 2: 0.027374, 3: 0.047373999999999965, 4: 0.08419800000000001}
FEATURE_QUANTILES radius error {0: -inf, 1: 0.21352, 2: 0.26870000000000005, 3: 0.36444, 4: 0.55352}
Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',
       'mean smoothness', 'mean compactness', 'mean concavity',
       'mean concave points', 'mean symmetry', 'mean fractal dimension',
       'radius error', 'texture error', 'Score', 'BinnedScore',
       'mean perimeter_bin', 'mean area_bin', 'mean concave points_bin',
       'radius error_bin', 'mean perimeter_encoded', 'mean area_encoded',
       'mean concave points_encoded', 'radius error_encoded',
       'mean perimeter_Effect', 'mean area_Effect',
       'mean concave points_Effect', 'radius error_Effect', 'reason_1',
       'reason_2', 'reason_3', 'reason_4', 'reason_1_idx', 'detailed_reason_1',
       'reason_2_idx', 'detailed_reason_2', 'reason_3_idx',
       'detailed_reason_3', 'reason_4_idx', 'detailed_reason_4'],
      dtype='object')



In [5]:

    
df_rc_2 = lExplainer.explain(X_test[0].reshape(1, -1))



In [6]:

    
X_test[0].reshape(1, -1)









    Out[6]:





array([[1.176e+01, 2.160e+01, 7.472e+01, 4.279e+02, 8.637e-02, 4.966e-02,
        1.657e-02, 1.115e-02, 1.495e-01, 5.888e-02, 4.062e-01, 1.210e+00]])



In [7]:

    
df_rc_2









    Out[7]:







  
    
      
      mean radius
      mean texture
      mean perimeter
      mean area
      mean smoothness
      mean compactness
      mean concavity
      mean concave points
      mean symmetry
      mean fractal dimension
      ...
      reason_3
      reason_4
      reason_1_idx
      detailed_reason_1
      reason_2_idx
      detailed_reason_2
      reason_3_idx
      detailed_reason_3
      reason_4_idx
      detailed_reason_4
    
  
  
    
      0
      11.76
      21.6
      1
      1
      0.08637
      0.04966
      0.01657
      0
      0.1495
      0.05888
      ...
      mean area
      mean perimeter
      2
      [(-inf < 'mean concave points' <= 0.017368)]
      3
      [(0.36444 < 'radius error' <= 0.55352)]
      1
      [(387.1 < 'mean area' <= 477.3)]
      0
      [(71.884 < 'mean perimeter' <= 80.624)]
    
  

1 rows × 38 columns



In [8]:

    
df_rc_2[[col for col in df_rc_2.columns if col.startswith('detailed')]]









    Out[8]:







  
    
      
      detailed_reason_1
      detailed_reason_2
      detailed_reason_3
      detailed_reason_4
    
  
  
    
      0
      [(-inf < 'mean concave points' <= 0.017368)]
      [(0.36444 < 'radius error' <= 0.55352)]
      [(387.1 < 'mean area' <= 477.3)]
      [(71.884 < 'mean perimeter' <= 80.624)]



In [9]:

    
effects = [col for col in df_rc_2.columns if col.endswith('_Effect')]
transposed = df_rc_2[effects].transpose()
sorted_rc = transposed.sort_values(by=transposed.columns[0] , ascending=False)
print(sorted_rc.head())
sorted_rc.plot.barh(figsize=(12,8))









    



                                       0
mean concave points_Effect  6.380884e-04
radius error_Effect         1.576499e-05
mean perimeter_Effect      -1.110223e-15
mean area_Effect           -1.110223e-15






    Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0x7fee4694c2e8>



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