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main_nn_6_pre_evaluate.py
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import collections
import matplotlib.pyplot as plt
import functools
from sklearn import metrics
from sklearn.utils.multiclass import unique_labels
from keras.models import Sequential, load_model
# from keras.metrics import
from keras.layers import Dense, Conv2D, Flatten, MaxPool2D, Dropout
from sklearn.preprocessing import MinMaxScaler
# create model
import cv2
import os
import numpy as np
from os import listdir
from os.path import isfile, join
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# train_files = [f for f in listdir('data_train_preprocessed_reshape') if isfile(join('data_train', f))]
from nn_final_image.ETools import EImage
# def specificity(y_true, y_pred):
# """
# param:
# y_pred - Predicted labels
# y_true - True labels
# Returns:
# Specificity score
# """
# neg_y_true = 1 - y_true
# neg_y_pred = 1 - y_pred
# fp = K.sum(neg_y_true * y_pred)
# tn = K.sum(neg_y_true * neg_y_pred)
# specificity = tn / (tn + fp + K.epsilon())
# return specificity
def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'
# Compute confusion matrix
cm = metrics.confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
# classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
def is_eq(list1, list2):
if len(list1) != len(list2):
return False
else:
for index_row in range(len(list1)):
if list1[index_row] != list2[index_row]:
print('{} and {} are NOT equal'.format(list1[index_row], list2[index_row]))
return False
return True
# train_files = [f for f in listdir('train_2') if isfile(join('train_2', f))]
test_files = [f for f in listdir('final_data/pre_preprocessed_test') if isfile(join('final_data/pre_preprocessed_test', f))]
X_TEST_DATA = []
Y_TEST_label = []
model_id = 'model_deep_nn_6_pre.h5'
# model_id = '3'
for reshape_file in test_files:
if '.jpg' not in reshape_file:
continue
# x_image = cv2.imread('preprocessed_train/' + reshape_file)
x_image = EImage.read_image('final_data/pre_preprocessed_test/' + reshape_file, if_read_as_grayscale=True)
# print(x_image.shape)
y_label = int(reshape_file.split('_')[0])
X_TEST_DATA.append(x_image)
y_data = None
# 5, 10, 25, 50 ,100
if y_label == 0:
y_data = 0
else:
y_data = 1
# else:
# print('ERROR-Y_TRAIN_label -> unvalid classlabel ', reshape_file)
# print('Y_TRAIN_label: ', y_label)
# print('Y_TRAIN_label.type: ', type(y_label))
# break
Y_TEST_label.append(y_data)
X_TEST_DATA = np.array(X_TEST_DATA)
# Y_TEST_label = np.array(Y_TEST_label)
X_TEST_DATA_SCALED = X_TEST_DATA / 255.
# X_train, X_test, y_train, y_test = train_test_split(X_TRAIN_DATA_SCALED, Y_TEST_label, test_size=0.33)
model = load_model(model_id)
scores = model.evaluate(X_TEST_DATA_SCALED, Y_TEST_label)
# scores = model.evaluate(X_TEST_DATA_SCALED, Y_TEST_label, verbose=0)
print('score: {}'.format(scores))
Y_PREDICT = model.predict(X_TEST_DATA_SCALED)
for index_x in range(Y_PREDICT.shape[0]):
for index_y in range(Y_PREDICT.shape[1]):
Y_PREDICT[index_x,index_y] = 1 if Y_PREDICT[index_x,index_y]>=0.5 else 0
print(Y_PREDICT.shape)
print(Y_PREDICT.tolist())
print(Y_TEST_label)
# true_counts=0
# LIST_Y_PREDICT = []
# for index_row in range(len(Y_PREDICT)):
# # if collections.Counter(Y_PREDICT[index_row].tolist()) == collections.Counter([1,0,0]):
# list_index = Y_PREDICT[index_row]
# LIST_Y_PREDICT.insert(index_row, list_index.tolist().index(max(list_index.tolist())))
# # if is_eq(list_index, [1,0,0]):
# # if Y_PREDICT[index_row] == [1,0,0]:
# # LIST_Y_PREDICT.insert(index_row, 1)
# # elif is_eq(list_index, [0,1,0]):
# # LIST_Y_PREDICT.insert(index_row, 2)
# # elif is_eq(list_index, [0,0,1]):
# # LIST_Y_PREDICT.insert(index_row, 3)
# # else:
# # print('ERROR {}'.format(list_index))
#
# LIST_Y_TRUE = []
# for index_row in range(len(Y_TEST_label)):
# list_index = Y_TEST_label[index_row]
# LIST_Y_TRUE.insert(index_row, list_index.tolist().index(max(list_index.tolist())))
# # if is_eq(list_index, [1,0,0]):
# # if Y_PREDICT[index_row] == [1,0,0]:
# # LIST_Y_TRUE.insert(index_row, 1)
# # elif is_eq(list_index, [0,1,0]):
# # LIST_Y_TRUE.insert(index_row, 2)
# # elif is_eq(list_index, [0,0,1]):
# # LIST_Y_TRUE.insert(index_row, 3)
# # else:
# # print('ERROR {}'.format(list_index))
#
# print('SIZE ARE EQUAL? {}'.format(len(LIST_Y_PREDICT)==len(LIST_Y_TRUE)))
# for index_row in range(len(LIST_Y_PREDICT)):
# if LIST_Y_PREDICT[index_row]==LIST_Y_TRUE[index_row]:
# true_counts+=1
#
#
# # print(LIST_Y_TRUE)
# # print(LIST_Y_P)
# print('score: {}'.format(true_counts*100/len(Y_PREDICT)))
#
# # print("score: ", scores)
# # print("%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
#
rep_classification_report = metrics.classification_report(Y_TEST_label,
Y_PREDICT,
# target_names=[0,1,2]
)
rep_cm = metrics.confusion_matrix(Y_TEST_label,Y_PREDICT)
tn, fp, fn, tp = metrics.confusion_matrix(Y_TEST_label,Y_PREDICT).ravel()
# rep_sc = metrics.SCORERS
sensitivity = tp/(tp+fn)
specificity = tn/(tn+fp)
print(rep_classification_report)
print(rep_cm)
# print(rep_sc)
print(tn, fp, fn, tp)
print('sensitivity: {}'.format(sensitivity))
print('specificity: {}'.format(specificity))
#
# ax = plot_confusion_matrix(Y_TEST_label, Y_PREDICT,classes=[0,1], title='Confusion Matrix')
# cm = metrics.confusion_matrix(Y_TEST_label, Y_PREDICT)
# ax.imshow('cm_{}.jpg'.format(model_id))
# print(cm)
#
# model.save('model_nn3.h5')