import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
import sys
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import MultinomialNB

# Import some data to play with
df = pd.read_csv("E:\autodesk\Hourly and weather categorized2.csv")
X =df[['TTI','Max TemperatureF','Mean TemperatureF','Min TemperatureF',' Min Humidity']].values
y = df['TTI_Category'].as_matrix()
y=y.reshape(-1,1)
# Binarize the output
y = label_binarize(y, classes=['Good','Bad'])
n_classes = y.shape[1]

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
                                                    random_state=0)

# Learn to predict each class against the other
classifier = OneVsRestClassifier(KNeighborsClassifier())
y_score = classifier.fit(X_train, y_train).predict_proba(X_test)

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()

roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
plt.figure()
lw = 1
plt.plot(fpr[0], tpr[0], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[0])
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()