from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn.metrics import roc_curve, precision_recall_curve, log_loss
from sklearn.metrics import roc_auc_score as auc # noqa
import matplotlib.pyplot as plt
from ..const import EPS
def logloss(y, p):
"""Bounded log loss error.
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
bounded log loss error
"""
p[p < EPS] = EPS
p[p > 1 - EPS] = 1 - EPS
return log_loss(y, p)
def plot_roc_curve(y, p):
fpr, tpr, _ = roc_curve(y, p)
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
def plot_pr_curve(y, p):
precision, recall, _ = precision_recall_curve(y, p)
plt.step(recall, precision, color='b', alpha=0.2, where='post')
plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
