/
evaluation.py
138 lines (121 loc) · 4.74 KB
/
evaluation.py
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"""Evaluation functions for LatticeRNN."""
import os
import sys
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, precision_recall_curve, auc
def log(array):
"""Clip the elements of arrays before taking log."""
return np.log(np.clip(array, a_min=1e-8, a_max=1.0))
def nce(labels, predictions):
"""Calculate normalised cross entropy.
Arguments:
labels {array} -- numpy array of labels {0, 1}
predictions {array} -- numpy array of predictions, [0, 1]
Returns:
float -- NCE score
"""
assert len(labels) == len(predictions), \
"dimension of labels is not the same as predictions"
percentage_correct = np.sum(labels) / len(labels)
label_entropy = - percentage_correct * log(percentage_correct) \
- (1 - percentage_correct) * log(1 - percentage_correct)
conditional_entropy = (np.dot(labels, log(predictions)) \
+ np.dot((1 - labels), log(1 - predictions))) \
/ (-len(labels))
score = (label_entropy - conditional_entropy) / label_entropy
return score
def roc(labels, predictions):
"""Compute ROC curve and its AUC.
Arguments:
labels {array} -- numpy array of labels {0, 1}
predictions {array} -- numpy array of predictions, [0, 1]
Returns:
tuple -- fpr array, tpr array, area float
"""
fpr, tpr, _ = roc_curve(labels, predictions)
area = auc(fpr, tpr)
return fpr, tpr, area
def pr(labels, predictions):
"""Compute precision-recall curve and its AUC.
Arguments:
labels {array} -- numpy array of labels {0, 1}
predictions {array} -- numpy array of predictions, [0, 1]
Returns:
tuple -- precision array, recall array, area float
"""
precision, recall, _ = precision_recall_curve(labels, predictions)
area = auc(recall, precision)
return precision, recall, area
def plot_roc(fpr, tpr, area, name, dst_dir):
"""Plotting ROC curve.
Arguments:
tpr {list} -- a list of numpy 1D array for true positive rate
fpr {list} -- a list of numpy 1D array for false positive rate
area {list} -- a list of floats for area under curve
name {str} -- text for the legend
dst_dir {str} -- output figure directory, file name `roc.pdf`
"""
plt.clf()
plt.figure(figsize=(3, 3))
plt.rcParams["font.family"] = "Times New Roman"
for (x_val, y_val, a_val, string) in zip(fpr, tpr, area, name):
label = string + ' (AUC = %0.4f)' %a_val
plt.plot(x_val, y_val, label=label)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig(os.path.join(dst_dir, 'roc.pdf'), bbox_inches='tight')
plt.close()
def plot_pr(precision, recall, area, name, dst_dir):
"""Plotting ROC curve.
Arguments:
tpr {list} -- a list of numpy 1D array for true positive rate
fpr {list} -- a list of numpy 1D array for false positive rate
area {list} -- a list of floats for area under curve
name {str} -- text for the legend
dst_dir {str} -- output figure directory, file name `roc.pdf`
"""
plt.clf()
plt.figure(figsize=(3, 3))
plt.rcParams["font.family"] = "Times New Roman"
for (x_val, y_val, a_val, string) in zip(recall, precision, area, name):
label = string + ' (AUC = %0.4f)' %a_val
plt.plot(x_val, y_val, label=label)
plt.legend(loc='lower right')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('Precision')
plt.xlabel('Recall')
plt.savefig(os.path.join(dst_dir, 'pr.pdf'), bbox_inches='tight')
plt.close()
def plot_det(fnr, fpr, name, dst_dir):
"""Plotting DET curve.
Arguments:
fnr {list} -- a list of numpy 1D array for true positive rate
fpr {list} -- a list of numpy 1D array for false positive rate
name {str} -- text for the legend
dst_dir {str} -- output figure directory, file name `det.pdf`
"""
plt.clf()
plt.figure(figsize=(3, 3))
plt.rcParams["font.family"] = "Times New Roman"
for (x_val, y_val, string) in zip(fpr, fnr, name):
plt.plot(x_val, y_val, label=string)
plt.legend(loc='upper right')
plt.xlim([0.01, 1])
plt.ylim([0.01, 1])
plt.xscale('log')
plt.yscale('log')
plt.ylabel('False Negative Rate')
plt.xlabel('False Positive Rate')
axes = plt.gca()
axes.xaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.2f'))
axes.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.2f'))
plt.savefig(os.path.join(dst_dir, 'det.pdf'), bbox_inches='tight')