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predicting.py
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predicting.py
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from data import get_data_set
from cnn import Convolutional_Neural_Network
from viterbi import run_viterbi
import numpy as np
import TRAINING_VARIABLES
import pandas as pd
import matplotlib.pyplot as plt
import json
V = TRAINING_VARIABLES.VARS()
def main():
''' Load test data '''
# Input: Testing, generate new windows, oversampling, viterbi training
DATA_TYPE = "predicting"
GENERATE_NEW_WINDOWS = True
OVERSAMPLING = False
VITERBI = False
data_set = get_data_set(DATA_TYPE, GENERATE_NEW_WINDOWS, OVERSAMPLING, VITERBI)
''' Create network '''
cnn = Convolutional_Neural_Network()
cnn.set_data_set(data_set)
cnn.load_model()
''''''
cnn_result = cnn.get_predictions()
viterbi_result = run_viterbi()
print 'Prediction saved at path', V.VITERBI_RESULT_PREDICTING
def produce_statistics_json(result):
score = get_score(result)
specificity = {}
precision = {}
recall = {}
for i in range(0, len(score[1])):
specificity[V.ACTIVITY_NAMES_CONVERTION[i+1]] = score[1][i]
precision[V.ACTIVITY_NAMES_CONVERTION[i+1]] = score[2][i]
recall[V.ACTIVITY_NAMES_CONVERTION[i+1]] = score[3][i]
statistics = {
'ACCURACY' : score[0],
'SPECIFICITY': specificity,
'PRECISION': precision,
'RECALL': recall
}
path = 'RESULTS/TEST_STATISTICS.json'
with open(path, "w") as outfile:
json.dump(statistics, outfile)
return statistics
def get_score(result_matrix):
activities = V.ACTIVITIES
'''TP / (FP - TP)
Correctly classified walking / Classified as walking
'''
TP = np.zeros(len(activities))
TN = np.zeros(len(activities))
FP_TP = np.zeros(len(activities))
TP_FN = np.zeros(len(activities))
FP_TN = np.zeros(len(activities))
actual = result_matrix[:,0]
predicted = result_matrix[:,2]
for activity in activities:
''' FP - TP'''
FP_TP[activity-1] = np.sum(predicted == activity) #len(df[df[0]==activity])
''' TP - FN '''
TP_FN[activity-1] = np.sum(actual == activity) #len(df_actual[df_actual[0]==activity])
''' FP - TN '''
FP_TN[activity-1] = np.sum(actual != activity)#len(df_actual[df_actual[0] != activity])
for i in range(0, len(predicted)):
if predicted[i] == actual[i]:
TP[actual[i]-1] += 1.0
for activity in activities:
if actual[i] != activity and predicted[i] != activity:
TN[activity-1] += 1.0
accuracy = sum(TP) / sum(TP_FN)
specificity = TN / FP_TN
precision = TP / FP_TP
recall = TP / TP_FN
return [accuracy, specificity, precision, recall]
def visualize(result_matrix):
for i in range(0,len(result_matrix)):
result_matrix[i][0] = V.VISUALIZATION_CONVERTION[result_matrix[i][0]+1]
result_matrix[i][1] = V.VISUALIZATION_CONVERTION[result_matrix[i][1]+1]
result_matrix[i][2] = V.VISUALIZATION_CONVERTION[result_matrix[i][2]+1]
start = 0
stop = 1000
actual = result_matrix[:,0][start:stop]
cnn = result_matrix[:,1][start:stop]
viterbi = result_matrix[:,2][start:stop]
t = cnn != viterbi
#actual = actual[t]
#cnn = cnn[t]
#viterbi = viterbi[t]
y_values = ["Lying", "Sit", "Stand", "Walk", "Walk(up)", "Walk(down)", "Cycle (sit)", "Cycle(Stand)", "Bending", "Running"]
y_axis = np.arange(1,11,1)
plt.figure(1)
plt.subplot(311)
axes = plt.gca()
axes.set_ylim([0.9,10.4])
plt.yticks(y_axis, y_values)
plt.plot(actual)
plt.subplot(312)
axes = plt.gca()
axes.set_ylim([0.9,10.4])
plt.yticks(y_axis, y_values)
plt.plot(cnn)
plt.subplot(313)
axes = plt.gca()
axes.set_ylim([0.9,10.4])
plt.yticks(y_axis, y_values)
plt.plot(viterbi)
plt.show()
def confusion_matrix(result_matrix, index):
for i in range(0,len(result_matrix)):
result_matrix[i][0] = V.VISUALIZATION_CONVERTION[result_matrix[i][0]+1]
result_matrix[i][1] = V.VISUALIZATION_CONVERTION[result_matrix[i][1]+1]
result_matrix[i][2] = V.VISUALIZATION_CONVERTION[result_matrix[i][2]+1]
confusion_matrix = np.zeros((len(V.ACTIVITIES), len(V.ACTIVITIES)))
for i in range(0, len(result_matrix)):
actual = result_matrix[i][0]
predicted = result_matrix[i][index]
confusion_matrix[actual-1][predicted-1] += 1.0
row_sums = confusion_matrix.sum(axis=1)
norm_conf = confusion_matrix / row_sums[:, np.newaxis]
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.set_aspect(1)
res = ax.imshow(np.array(norm_conf), cmap=plt.cm.summer,
interpolation='nearest')
width = len(confusion_matrix)
height = len(confusion_matrix[0])
for x in xrange(width):
for y in xrange(height):
ax.annotate(str(confusion_matrix[x][y]), xy=(y, x),
horizontalalignment='center',
verticalalignment='center')
cb = fig.colorbar(res)
plt.title('Confusion Matrix')
labels = ['Lying', 'Sitting','Standing','Walking','Stairs (up)','Stairs (down)', 'Cycling (sit)','Cycling (stand)', 'Bending', 'Running']
plt.xticks(range(width), labels,rotation='vertical')
plt.yticks(range(height), labels)
plt.show()
if __name__ == "__main__":
main()