Zaktualizuj 'evaluation.py'

evaluation.py

@@ -5,13 +5,16 @@ import pandas as pd
 import numpy as np
 import sys
 import os
-from sklearn.model_selection import train_test_split
 from sklearn import preprocessing
 from sklearn.preprocessing import StandardScaler
+from datetime import datetime
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+import matplotlib.pyplot as plt
+from sklearn import svm,  datasets
+from sklearn.metrics import classification_report
 scaler = StandardScaler()
 
-EPOCHS = int(sys.argv[1])
-
 # Model
 class Model(nn.Module):
     def __init__(self):
@@ -54,13 +57,12 @@ X_testing = torch.from_numpy(X_testing.astype(np.float32))
 y_training = torch.from_numpy(y_training.astype(np.float32))
 y_testing = torch.from_numpy(y_testing.astype(np.float32))
 
-
 model = Model()
 criterion = nn.BCELoss()
 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 
 # Trening
-num_epochs = EPOCHS
+num_epochs = 1000
 for epoch in range(num_epochs):
     y_predicted = model(X_training)
     loss = criterion(y_predicted,y_training)
@@ -75,8 +77,30 @@ with torch.no_grad():
     y_predicted = model(X_testing)
     y_predicted_cls = y_predicted.round()
     acc = y_predicted_cls.eq(y_testing).sum()/float(y_testing.shape[0])
-    print(f'{acc:.4f}')
-    result = open("output",'w+')
-    result.write(f'{y_predicted}')
+    #print(f'{acc:.4f}')
 
-torch.save(model, "modelP.pkl")
+    rmse = mean_squared_error(y_testing, y_predicted)
+#print(rmse)
+
+mae = mean_absolute_error(y_testing, y_predicted)
+#print(mae)
+
+with open('metrics.txt', 'a+') as f:
+    f.write('Root mean squared error:' + str(rmse) + '\n')  
+    f.write('Mean absolute error:' + str(mae) + '\n')  
+    #count = [float(line) for line in f if line]
+    #builds = list(range(1, len(count)))
+     
+with open('metric.txt', 'a+') as f:
+    f.write(str(rmse) + '\n')
+
+with open('metric.txt') as file:
+    y_rmse = [float(line) for line in file if line]
+    x_builds = list(range(1, len(y_rmse) + 1))
+            
+plt.xlabel('Build')
+plt.ylabel('RMSE')
+plt.plot(x_builds, y_rmse, label='RMSE')
+plt.legend()
+plt.show()
+plt.savefig('metrics.png')