evaluation

evaluate.py

@@ -1 +0,0 @@
-print('test')

evaluation.py

@@ -0,0 +1,38 @@
+import pandas as pd
+import numpy as np
+from tensorflow import keras
+import matplotlib.pyplot as plt
+from sklearn.metrics import mean_squared_error
+
+
+vgsales_model = 'vgsales_model.h5'
+model = keras.models.load_model(vgsales_model)
+
+vgsales_test = pd.read_csv('test.csv')
+
+vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
+
+X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
+y_test = vgsales_test[['Nintendo']]
+
+predictions = model.predict(X_test)
+
+error = mean_squared_error(y_test, predictions)
+print('Error: ', error)
+
+with open('results.txt', 'a') as f:
+    f.write(str(error) + "\n")
+
+
+with open('results.txt', 'r') as f:
+    lines = f.readlines()
+
+
+fig = plt.figure(figsize=(5,5))
+chart = fig.add_subplot()
+chart.set_ylabel("MSE")
+chart.set_xlabel("Build")
+x = np.arange(0, len(lines), 1)
+y = [float(x) for x in lines]
+plt.plot(x, y, "ro")
+plt.savefig("plot.png")