1.

#!/usr/bin/python3
import numpy as np
from sklearn import tree
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split
data = np.array([[1.5, 50, 'thin'], [1.5, 60, 'fat'], [1.6, 40, 'thin'], [1.7, 60, 'thin'], [1.7, 80, 'fat'],
               [1.8, 60, 'thin'], [1.8, 90, 'fat'], [1.9, 70, 'thin'], [1.9, 80, 'thin']])
labels = data[:, -1]
y =np.zeros(labels.shape)
y[labels == 'fat'] = 1
y[labels == 'thin'] = 0
x = data[:, :2]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf.fit(x_train, y_train)
with open("tree.dot", 'w') as f:
    f = tree.export_graphviz(clf, out_file=f)
print(clf.feature_importances_)
precision, recall, thresholds = precision_recall_curve(y_train, clf.predict(x_train))
answer = clf.predict_proba(x)[:, 1]
print(classification_report(y, answer, target_names=['thin', 'fat']))

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