Commit with all files for WINS@HI - MLP network to predict PM10

projects/wins/MLP_experiments.py

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+import pickle
+from math import sqrt
+import numpy as np
+import matplotlib.pyplot as plt
+from math import sqrt
+from sklearn.neural_network import MLPClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, confusion_matrix
+import torch
+
+
+#Set mic de date:
+
+# clf = torch.load("model_small_dataset")
+#
+# with open("X_values", 'rb') as f:
+#     X = pickle.load(f)
+#
+# with open("y_values", 'rb') as f:
+#     y = pickle.load(f)
+
+
+#Set mare de date:
+
+# clf = torch.load("model_large_dataset")
+
+with open("X_values_serie_mare", 'rb') as f:
+    X = pickle.load(f)
+
+with open("y_values_serie_mare", 'rb') as f:
+    y = pickle.load(f)
+
+
+print(len(X))
+print(len(y))
+
+print()
+
+print("X: ", X)
+print("y: ", y)
+
+print()
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+# clf = MLPClassifier(solver='adam', alpha=0.00001, hidden_layer_sizes=(100,), random_state=False)
+# clf = MLPClassifier(hidden_layer_sizes=(100,), alpha=0.0001)
+clf = MLPClassifier()
+
+# clf = MLPClassifier(solver='sofmax', alpha=1000, hidden_layer_sizes=(15,), random_state=1)
+clf.fit(X_train, y_train)
+y_pred = []
+
+torch.save(clf, "model_large_dataset3")
+
+print("X_test: ", X_test)
+print("y_test: ",y_test)
+print()
+
+for i in range(len(X_test)):
+    pred = clf.predict([X_test[i]])
+    print(pred[0], y_test[i])
+    y_pred.append(pred[0])
+
+print()
+
+corr_coef = np.corrcoef(y_test, y_pred)[0,1]
+print("corelatia: ",corr_coef)
+
+mse = mean_squared_error(y_test, y_pred)
+print("Mean Squared Error:", mse)
+
+print()
+
+max_val = max(max(y_test), max(y_pred))
+
+x_graph = [x for x in range(int(round(max_val)))]
+y_graph = x_graph
+
+plt.scatter(y_pred, y_test)
+plt.plot(x_graph, y_graph, color='black')
+plt.show()
+
+print(len([x for x in y if x > 400]))
+
+lista = [x for x in y if x > 400]
+
+for x in lista:
+    print(y.index(x))
+
+plt.plot(y)
+plt.show()