Shua Dissen
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
from keras.models import model_from_json
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import numpy as np
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import csv
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import math
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model = model_from_json(open('model.json').read())
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model.load_weights('weights.h5')
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data_dir = ""
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X_test = np.load(data_dir+'VTR_test_X.npy')
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Y = np.load(data_dir+'VTR_test_Y.npy')
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names = Y[:, :1]
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Y_test = Y[:,1:]
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predictions = []
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loss1 = 0.0
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loss2 = 0.0
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loss3 = 0.0
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loss4 = 0.0
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max_1 = 0.0
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max_2 = 0.0
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max_3 = 0.0
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max_4 = 0.0
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list_1 = []
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list_2 = []
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list_3 = []
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list_4 = []
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male = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
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female = [0.0, 0.0, 0.0, 0.0, 0.0, [], [], [], []]
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karma_list = [0, 0.0, 0.0, 0.0, 0.0]
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AVG_list = [0, 0.0, 0.0, 0.0, 0.0]
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y_hat = model.predict(X_test)
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for i in range(0,len(Y_test)):
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l1 = np.abs(float(Y_test[i, 0]) - y_hat[i, 0])
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l2 = np.abs(float(Y_test[i, 1]) - y_hat[i, 1])
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l3 = np.abs(float(Y_test[i, 2]) - y_hat[i, 2])
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l4 = np.abs(float(Y_test[i, 3]) - y_hat[i, 3])
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pred = [names[i][0], float(Y_test[i, 0]), float(Y_test[i, 1]), float(Y_test[i, 2]), float(Y_test[i, 3])]
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AVG_list[0] += 1
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AVG_list[1] += float(Y_test[i, 0]) - y_hat[i, 0]
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AVG_list[2] += float(Y_test[i, 1]) - y_hat[i, 1]
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AVG_list[3] += float(Y_test[i, 2]) - y_hat[i, 2]
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AVG_list[4] += float(Y_test[i, 3]) - y_hat[i, 3]
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pred.extend([y_hat[i, 0], y_hat[i, 1], y_hat[i, 2], y_hat[i, 3]])
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if names[i][0].split('_')[3][0] == 'f':
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female[0] += 1
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female[1] += l1
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female[2] += l2
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female[3] += l3
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female[4] += l4
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female[5].append(l1)
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female[6].append(l2)
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female[7].append(l3)
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female[8].append(l4)
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elif names[i][0].split('_')[3][0] == 'm':
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male[0] += 1
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male[1] += l1
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male[2] += l2
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male[3] += l3
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male[4] += l4
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male[5].append(l1)
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male[6].append(l2)
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male[7].append(l3)
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male[8].append(l4)
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predictions.append(pred)
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list_1.append(l1)
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list_2.append(l2)
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list_3.append(l3)
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list_4.append(l4)
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max_1 = max(max_1,l1)
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max_2 = max(max_2,l2)
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max_3 = max(max_3,l3)
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max_4 = max(max_4,l4)
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loss1 += l1
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loss2 += l2
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loss3 += l3
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loss4 += l4
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karma_list[0] += 1
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karma_list[1] += l1 * l1
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karma_list[2] += l2 * l2
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karma_list[3] += l3 * l3
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karma_list[4] += l4 * l4
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loss1 /= len(Y_test)
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loss2 /= len(Y_test)
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loss3 /= len(Y_test)
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loss4 /= len(Y_test)
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total_loss = loss1+loss2+loss3+loss4
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total_loss /= 4.0
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print('standard deviation', round(np.std(list_1)*1000, 2), round(np.std(list_2)*1000, 2), round(np.std(list_3)*1000, 2), round(np.std(list_4)*1000, 2))
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print('median', round(np.median(list_1)*1000, 2), round(np.median(list_2)*1000, 2), round(np.median(list_3)*1000, 2), round(np.median(list_4)*1000, 2))
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print('max loss ', round(max_1*1000, 2), round(max_2*1000, 2), round(max_3*1000, 2), round(max_4*1000, 2))
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print('total loss ', round(total_loss*1000, 2))
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print('Real test score:', round(loss1*1000, 2), round(loss2*1000, 2), round(loss3*1000, 2), round(loss4*1000, 2))
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female[1] = round((female[1] / female[0])*1000, 2)
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female[2] = round((female[2] / female[0])*1000, 2)
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female[3] = round((female[3] / female[0])*1000, 2)
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female[4] = round((female[4] / female[0])*1000, 2)
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female[5] = round(np.std(female[5])*1000, 2)
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female[6] = round(np.std(female[6])*1000, 2)
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female[7] = round(np.std(female[7])*1000, 2)
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female[8] = round(np.std(female[8])*1000, 2)
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male[1] = round((male[1] / male[0])*1000, 2)
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male[2] = round((male[2] / male[0])*1000, 2)
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male[3] = round((male[3] / male[0])*1000, 2)
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male[4] = round((male[4] / male[0])*1000, 2)
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male[5] = round(np.std(male[5])*1000, 2)
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male[6] = round(np.std(male[6])*1000, 2)
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male[7] = round(np.std(male[7])*1000, 2)
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male[8] = round(np.std(male[8])*1000, 2)
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print("male: ", male)
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print("female: ", female)
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# karma
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karma_list[1] /= karma_list[0]
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karma_list[2] /= karma_list[0]
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karma_list[3] /= karma_list[0]
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karma_list[4] /= karma_list[0]
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print('root mean squared error ', round(math.sqrt(karma_list[1]) * 1000, 2), round(math.sqrt(karma_list[2]) * 1000, 2),
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round(math.sqrt(karma_list[3]) * 1000, 2), round(math.sqrt(karma_list[4]) * 1000, 2))
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AVG_list[1] /= AVG_list[0]
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AVG_list[2] /= AVG_list[0]
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AVG_list[3] /= AVG_list[0]
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AVG_list[4] /= AVG_list[0]
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print('AVG ', round(AVG_list[1] * 1000, 2), round(AVG_list[2] * 1000, 2), round(AVG_list[3] * 1000, 2), round(AVG_list[4] * 1000, 2))
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with open("results/VTR.csv", "wb") as f:
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writer = csv.writer(f)
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writer.writerows(predictions)
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