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"""
explore the confusion matrix and ROC curve using scikit-learn
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
"""

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

import perceptron2

###############################################################################
# first let's read in the heart dataset

data=np.genfromtxt("../data/heart_scale.data", delimiter=",")
X=data[:,1:]
y=data[:,0]
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=3)

#############################################
# confusion matrix

classifier = perceptron2.Perceptron()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)

# By definition a confusion matrix C is such that C_{i, j} is equal to the
# number of observations known to be in group i but predicted to be in group j.

print(confusion_matrix(y_test, y_pred))

#############################################
#  ROC analysis

# compute ROC curve for the perceptron
classifier = perceptron2.Perceptron()
classifier.fit(X_train, y_train)
scores = classifier.decision_function(X_test)

fpr_per, tpr_per, _ = roc_curve(y_test, scores)

# compute ROC curve for logistic regression
# we'll compare two variants with varying levels
# of regularization; note that the parameter C of
# the scikit-learn LogisticRegression class is inversely
# proportional to the strength of regularization
classifier = LogisticRegression(C=0.1)
classifier.fit(X_train, y_train)
scores = classifier.decision_function(X_test)

fpr_lr, tpr_lr, _ = roc_curve(y_test, scores)

classifier = LogisticRegression(C=1000)
classifier.fit(X_train, y_train)
scores = classifier.decision_function(X_test)

fpr_lr2, tpr_lr2, _ = roc_curve(y_test, scores)

plt.figure(1)
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr_per, tpr_per, label='perceptron')
plt.plot(fpr_lr, tpr_lr, label='logistic regression')
plt.plot(fpr_lr2, tpr_lr2, label='logistic regression')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.show()