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#!/usr/bin/python
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import scikitplot as skplt
from sklearn.preprocessing import label_binarize
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import auc
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
flags = tf.app.flags
flags.DEFINE_string('softmax', None, 'The softmax.npz file contained labels and probas')
flags.DEFINE_string('dinfo', None, 'The dinfo.npz file')
flags.DEFINE_integer('chunks', 4, 'The number of plots to produce')
FLAGS = flags.FLAGS
softmax = np.load(FLAGS.softmax)
dinfo = np.load(FLAGS.dinfo)
class_names=dinfo['classes']
y_true = softmax['labels']
y_proba = softmax['predictions']
def plot_precision_recall(y_true, y_probas,
plot_micro=True,
classes_to_plot=None, ax=None,
figsize=None, cmap='nipy_spectral',
text_fontsize="medium"):
y_true = np.array(y_true)
y_probas = np.array(y_probas)
classes = np.unique(y_true)
probas = y_probas
if classes_to_plot is None:
classes_to_plot = classes
binarized_y_true = label_binarize(y_true, classes=classes)
if len(classes) == 2:
binarized_y_true = np.hstack(
(1 - binarized_y_true, binarized_y_true))
fig, ax = plt.subplots(int(FLAGS.chunks/2), 2, figsize=figsize)
chunk_size = int(len(classes)/FLAGS.chunks) + int(len(classes) % FLAGS.chunks > 0)
print('Chunk size', chunk_size)
indices_to_plot = np.in1d(classes, classes_to_plot)
for i, img_class in enumerate(classes):
average_precision = average_precision_score(
binarized_y_true[:, i],
probas[:, i])
precision, recall, _ = precision_recall_curve(
y_true, probas[:, i], pos_label=img_class)
color = plt.cm.get_cmap(cmap)(float(i%chunk_size) / chunk_size)
ax[int(i/(chunk_size*2)), int(i%(chunk_size*2) > chunk_size)].plot(recall, precision, lw=2,
label='{0} '
'(area = {1:0.3f})'.format(class_names[int(img_class)],
average_precision),
color=color)
if plot_micro:
precision, recall, _ = precision_recall_curve(
binarized_y_true.ravel(), probas.ravel())
average_precision = average_precision_score(binarized_y_true,
probas,
average='micro')
ax[int(FLAGS.chunks/2)-1,1].plot(recall, precision,
label='micro-average PR '
'(area = {0:0.3f})'.format(average_precision),
color='navy', linestyle=':', linewidth=4)
for x in range(int(FLAGS.chunks/2)):
for y in range(2):
ax[x,y].set_xlim([0.0, 1.0])
ax[x,y].set_ylim([0.0, 1.05])
ax[x,y].set_xlabel('Recall')
ax[x,y].set_ylabel('Precision')
ax[x,y].tick_params(labelsize=text_fontsize)
ax[x,y].legend(loc='lower left', fontsize=text_fontsize)
return ax
plot_precision_recall(y_true, y_proba, text_fontsize="xx-small", classes_to_plot=[3,16,41,70,77,82])
plt.show()
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