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%matplotlib inline


    

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%load ../ud120-projects/evaluation/evaluate_poi_identifier.py


    

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#%%writefile ../ud120-projects/evaluation/evaluate_poi_identifier.py
#!/usr/bin/python


"""
    starter code for the evaluation mini-project
    start by copying your trained/tested POI identifier from
    that you built in the validation mini-project

    the second step toward building your POI identifier!

    start by loading/formatting the data

"""

import pickle
import sys
sys.path.append("../ud120-projects/tools/")
from feature_format import featureFormat, targetFeatureSplit

data_dict = pickle.load(open("../ud120-projects/final_project/final_project_dataset.pkl", "r") )

### add more features to features_list!
features_list = ["poi", "salary"]

data = featureFormat(data_dict, features_list)
labels, features = targetFeatureSplit(data)

### Splitting train/test

from sklearn.cross_validation import train_test_split

train_features, test_features, train_labels, test_labels = train_test_split(features, labels,
                                                                            test_size=0.3,
                                                                            random_state=42)

### Train decision tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

clf = DecisionTreeClassifier()
clf.fit(train_features, train_labels)

pred = clf.predict(test_features)

print "Confusion Matrix:\n", confusion_matrix(test_labels, pred), "\n"
print "Classification Report:\n", classification_report(test_labels, pred)
print "Accuracy:", accuracy_score(test_labels, pred)


    

    




Overwriting ../ud120-projects/evaluation/evaluate_poi_identifier.py

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sum(test_labels)


    

    
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5.0

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len(test_labels)


    

    
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29

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24./29


    

    
Out[17]:





0.8275862068965517

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from sklearn.metrics import precision_score
from sklearn.metrics import recall_score


    

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precision_score(test_labels, pred)


    

    
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0.0

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recall_score(test_labels, pred)


    

    
Out[21]:





0.0

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predictions = [0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1] 
true_labels = [0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0]


    

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(6)/9.


    

    
Out[27]:





0.6666666666666666

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6/8.


    

    
Out[28]:





0.75

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