Work in progress

RegressionModels/CaliforniaHousing/california_housing.py

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+import pandas as pd
+import torch
+import torch.nn as nn
+from sklearn.preprocessing import StandardScaler
+
+df = pd.read_csv("./RegressionModels/CaliforniaHousing/housing.csv")
+
+
+df = df.dropna(subset=df.columns[:8])
+df['ocean_proximity_encoded'] = df['ocean_proximity'].astype('category').cat.codes     #Encodes text values as numerical ones
+
+# print(df)
+# print(df.iloc[:,0:8])
+
+scaler_x = StandardScaler()
+scaled_X = scaler_x.fit_transform(df.iloc[:,0:8].join(df["ocean_proximity_encoded"]).values)
+X = torch.tensor(scaled_X, dtype=torch.float32)
+
+scaler_y = StandardScaler()
+scaled_Y = scaler_y.fit_transform(df["median_house_value"].values.reshape(-1,1))
+Y = torch.tensor(scaled_Y, dtype=torch.float32)
+
+model = torch.nn.Sequential(
+    torch.nn.Linear(9, 18),
+    torch.nn.ReLU(),
+    torch.nn.Linear(18, 1)
+)
+
+loss_fn = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+X = X.to(device)
+Y = Y.to(device)
+
+
+for epoch in range(3000):
+    pred_y = model(X)
+    loss = loss_fn(pred_y, Y)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    if epoch % 99 == 0:
+        print('Epoch: ', epoch, f"loss: {loss.item():.2f}")
+