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plot.py
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plot.py
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import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.metrics import roc_curve, auc
from scipy import interp
from itertools import cycle
class Plot:
def __init__(self):
self.X = None
self.Y = None
# ploting the corrolation among features
def correlation(self):
corr = self.X.corr()
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(corr, cmap='coolwarm', vmin=-1, vmax=1)
fig.colorbar(cax)
ticks = np.arange(0, len(self.X.columns), 1)
ax.set_xticks(ticks)
plt.xticks(rotation=90)
ax.set_yticks(ticks)
ax.set_xticklabels(self.X.columns)
ax.set_yticklabels(self.X.columns)
plt.show()
def pairplot(self , size):
dataSize = self.X.shape[1]
keys = self.X.keys()
data = self.X
data['class'] = self.Y
i = 0
while i < dataSize:
j = 0
while j < dataSize:
sns.pairplot(self.X, x_vars=keys[i:i+size], y_vars=keys[j:j+size], hue='class')
plt.savefig('./plotResult/pairPlot' + str(i)+str(j) + '.png')
j += size
i += size
def ROCMultiClass(self , n_classes, y_test, y_score):
lw = 2
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"])
# Compute macro-average ROC curve and ROC area
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],
label='micro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["micro"]),
color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr["macro"], tpr["macro"],
label='macro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc["macro"]),
color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=lw,
label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.savefig('./plotResult/pairPlot' + 'ROCCurve' + '.png')
plt.show()