from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from sklearn.utils import extmath
from sklearn.svm import SVC
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from matplotlib import pyplot
from itertools import cycle
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_curve, auc
iris = load_iris()
# Load iris into a dataframe and set the field names
df = pd.DataFrame(iris['data'], columns=iris['feature_names'])
df.head()
y = iris.target
X = df.iloc[:, 0:4]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)
clf = SVC(kernel='linear')
# fitting x samples and y classes
clf.fit(X_train, y_train)
ypred = clf.predict(X_test)
y_test = pd.DataFrame(y_test)
ypred=pd.DataFrame(ypred)
y_test=y_test.values.reshape(-1,1)
ypred=ypred.values.reshape(-1,1)
onehotencoder = OneHotEncoder()
y_test= onehotencoder.fit_transform(y_test).toarray()
ypred = onehotencoder.fit_transform(ypred).toarray()
n_classes = ypred.shape[1]
# Plotting and estimation of FPR, TPR
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_test[:, i], ypred[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
colors = cycle(['blue', 'green', 'red','darkorange','olive','purple','navy'])
for i, color in zip(range(n_classes), colors):
pyplot.plot(fpr[i], tpr[i], color=color, lw=1.5, label='ROC curve of class {0}(area = {1:0.2f})' ''.format(i+1, roc_auc[i]))
pyplot.plot([0, 1], [0, 1], 'k--', lw=1.5)
pyplot.xlim([-0.05, 1.0])
pyplot.ylim([0.0, 1.05])
pyplot.xlabel('False Positive Rate',fontsize=12, fontweight='bold')
pyplot.ylabel('True Positive Rate',fontsize=12, fontweight='bold')
pyplot.tick_params(labelsize=12)
pyplot.legend(loc="lower right")
ax = pyplot.axes()
pyplot.show()