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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]:













Tree


0

mean concave points ≤ 0.051
gini = 0.462
samples = 284
value = [103, 181]
class = benign


1

mean area ≤ 571.85
gini = 0.076
samples = 177
value = [7, 170]
class = benign


0->1


True


6

mean perimeter ≤ 98.32
gini = 0.184
samples = 107
value = [96, 11]
class = malignant


0->6


False


2

radius error ≤ 0.354
gini = 0.014
samples = 141
value = [1, 140]
class = benign


1->2




5

gini = 0.278
samples = 36
value = [6, 30]
class = benign


1->5




3

gini = 0.0
samples = 111
value = [0, 111]
class = benign


2->3




4

gini = 0.064
samples = 30
value = [1, 29]
class = benign


2->4




7

gini = 0.464
samples = 30
value = [19, 11]
class = malignant


6->7




8

gini = 0.0
samples = 77
value = [77, 0]
class = malignant


6->8

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: 3.3672958299864741, 4: 11.512915464924779}
CONST_SCORE_DETECTIOM -11.5129154649 11.5129154649 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: 80.623999999999995, 3: 90.566000000000003, 4: 111.44}
FEATURE_QUANTILES mean area {0: -inf, 1: 387.10000000000002, 2: 477.30000000000001, 3: 602.0, 4: 922.44000000000005}
FEATURE_QUANTILES mean concave points {0: -inf, 1: 0.017368000000000001, 2: 0.027373999999999999, 3: 0.047373999999999965, 4: 0.084198000000000009}
FEATURE_QUANTILES radius error {0: -inf, 1: 0.21351999999999999, 2: 0.26870000000000005, 3: 0.36443999999999999, 4: 0.55352000000000001}
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([[  1.17600000e+01,   2.16000000e+01,   7.47200000e+01,
          4.27900000e+02,   8.63700000e-02,   4.96600000e-02,
          1.65700000e-02,   1.11500000e-02,   1.49500000e-01,
          5.88800000e-02,   4.06200000e-01,   1.21000000e+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)]

Scorecard





In [11]:



    


scorecard_df = lExplainer.get_local_score_card(X_test[0].reshape(1, -1))



    


















    






GET_LOCAL_SCORE_CARD [[  1.17600000e+01   2.16000000e+01   7.47200000e+01   4.27900000e+02
    8.63700000e-02   4.96600000e-02   1.65700000e-02   1.11500000e-02
    1.49500000e-01   5.88800000e-02   4.06200000e-01   1.21000000e+00]] [ 3.36729583]

















In [12]:



    


scorecard_df.head(scorecard_df.shape[0])



    


















    
Out[12]:











  
    

      
      feature_1
      feature_min_1
      feature_max_1
      points
    

  
  
    

      0
      mean perimeter
      -inf
      71.884000
      1.682561
    

    

      1
      mean perimeter
      71.884000
      80.624000
      1.682561
    

    

      2
      mean perimeter
      80.624000
      90.566000
      1.682561
    

    

      3
      mean perimeter
      90.566000
      111.440000
      -0.079875
    

    

      4
      mean perimeter
      111.440000
      inf
      -1.682561
    

    

      5
      mean area
      -inf
      387.100000
      1.682561
    

    

      6
      mean area
      387.100000
      477.300000
      1.682561
    

    

      7
      mean area
      477.300000
      602.000000
      1.682561
    

    

      8
      mean area
      602.000000
      922.440000
      -0.079875
    

    

      9
      mean area
      922.440000
      inf
      -1.682561
    

    

      10
      mean concave points
      -inf
      0.017368
      0.002357
    

    

      11
      mean concave points
      0.017368
      0.027374
      0.002357
    

    

      12
      mean concave points
      0.027374
      0.047374
      0.000689
    

    

      13
      mean concave points
      0.047374
      0.084198
      -0.000112
    

    

      14
      mean concave points
      0.084198
      inf
      -0.002357
    

    

      15
      radius error
      -inf
      0.213520
      0.000347
    

    

      16
      radius error
      0.213520
      0.268700
      0.000347
    

    

      17
      radius error
      0.268700
      0.364440
      0.000101
    

    

      18
      radius error
      0.364440
      0.553520
      0.000048
    

    

      19
      radius error
      0.553520
      inf
      -0.000347
    

  




















In [13]:



    


scorecard_df.columns



    


















    
Out[13]:








Index(['feature_1', 'feature_min_1', 'feature_max_1', 'points'], dtype='object')

Content source: antoinecarme/sklearn_explain

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