In [1]:

    

import pandas as pd
%matplotlib inline


    

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from sklearn import tree


    

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from sklearn import datasets


    

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from pandas.tools.plotting import scatter_matrix


    

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import matplotlib.pyplot as plt


    

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iris = datasets.load_iris() # load iris data set


    

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x = iris.data[:,2:] # the attributes
y = iris.target # the target variable


    

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dt = tree.DecisionTreeClassifier()


    

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dt = dt.fit(x,y)


    

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from sklearn.externals.six import StringIO
import pydotplus #pip install pydotplus


    

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with open("iris.dot", 'w') as f:
    f = tree.export_graphviz(dt, out_file=f)


    

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import os
os.unlink('iris.dot')


    

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dot_data = StringIO() 
tree.export_graphviz(dt, out_file=dot_data) #brew install graphviz
graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 
graph.write_pdf("iris.pdf")


    

    
Out[61]:





True

In [62]:

    

# from http://stackoverflow.com/questions/19470099/view-pdf-image-in-an-ipython-notebook
from wand.image import Image as WImage #pip install wand; brew install freetype imagemagick
img = WImage(filename='iris.pdf')
img


    

    
Out[62]:

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from sklearn.cross_validation import train_test_split


    

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x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.33,train_size=0.66)


    

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dt = dt.fit(x_train,y_train)


    

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from sklearn import metrics


    

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def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confussion_matrix=True):
    y_pred=clf.predict(X)
    if show_accuracy:
        print "Accuracy:{0:.3f}".format(metrics.accuracy_score(y, y_pred)),"\n"
    if show_classification_report:
        print "Classification report"
        print metrics.classification_report(y,y_pred),"\n"
    if show_confussion_matrix:
        print "Confusion matrix"
        print metrics.confusion_matrix(y,y_pred),"\n"


    

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measure_performance(x_train,y_train,dt)


    

    




Accuracy:0.990 

Classification report
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        35
          1       0.97      1.00      0.98        32
          2       1.00      0.97      0.98        32

avg / total       0.99      0.99      0.99        99


Confusion matrix
[[35  0  0]
 [ 0 32  0]
 [ 0  1 31]] 

In [52]:

    

measure_performance(x_test,y_test,dt)


    

    




Accuracy:0.980 

Classification report
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        15
          1       0.94      1.00      0.97        17
          2       1.00      0.94      0.97        18

avg / total       0.98      0.98      0.98        50


Confusion matrix
[[15  0  0]
 [ 0 17  0]
 [ 0  1 17]] 

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from sklearn.cross_validation import cross_val_score


    

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# http://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics
scores = cross_val_score(dt,x_test,y_test,cv=10)


    

In [60]:

    

scores


    

    
Out[60]:





array([ 1. ,  1. ,  0.8,  0.8,  1. ,  1. ,  1. ,  0.8,  1. ,  1. ])

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