mroe things

.vscode/extensions.json

@@ -1,3 +1,6 @@
 {
-  "recommendations": ["ms-python.python", "ms-toolsai.jupyter"]
+    "recommendations": [
-}
+        "ms-python.python",
+        "ms-toolsai.jupyter"
+    ]
+}

README.md

@@ -14,8 +14,10 @@ how to git? do not `push --force`. prefer pull requests. its like github
 
 we will plan to do this in multiple different ways using scikit-learn and pytorch. tensorflow is ancient.
 
-> [!IMPORTANT]
+> [!NOTE]
-> phase out tensorflow. dont use it.
+> ~phase out tensorflow. dont use it.~
+>
+> its 2 different platforms for 2 different types of people. use whatever ig
 
 maybe also fit more weather related stats? this dataset is taken from nasa so sources are there
 

data_loader.py

@@ -0,0 +1,43 @@
+import pandas as pd
+import io
+from typing import Union
+
+
+def cut_headers(stream: Union[io.StringIO, io.FileIO, io.TextIOWrapper]):
+    """Remove custom header metadata from the CSV file."""
+    headers = []
+    exist_startheader = False
+    while line := stream.readline():
+        if "-BEGIN HEADER-" in line and not exist_startheader:
+            exist_startheader = True
+            headers.append(str(line.strip()))
+        elif "-END HEADER-" in line and exist_startheader:
+            headers.append(str(line.strip()))
+            break
+        elif exist_startheader:
+            headers.append(str(line.strip()))
+        elif not exist_startheader:
+            continue
+    if not exist_startheader:
+        stream.seek(0)
+        return "", stream
+    return "\n".join(headers), stream
+
+
+def load_weather_data(file_path: str) -> pd.DataFrame:
+    """Load weather data and preprocess the datetime index."""
+    with open(file_path, "r", encoding="utf-8") as file:
+        _, csv_file = cut_headers(file)
+        data = pd.read_csv(csv_file, na_values=-999)
+
+    # Convert YEAR, MO, DY to datetime index
+    if all(col in data.columns for col in ("YEAR", "MO", "DY")):
+        data["date"] = pd.to_datetime(
+            {k: data[v] for v, k in zip(["YEAR", "MO", "DY"], ["year", "month", "day"])}
+        )
+        data.drop(columns=["YEAR", "MO", "DY"], inplace=True)
+        data.set_index("date", inplace=True)
+
+    data.dropna(inplace=True)
+    data = data.asfreq("D")  # Assumes regular daily data
+    return data

dataset_update.py

@@ -2,9 +2,11 @@ import urllib.parse
 import urllib.request
 from datetime import datetime
 
+data_file = "hanoi_weather_data.csv"
+
 # Define parameters
 latitude, longitude = 21.04815477090267, 105.80123159165096  # lmao
-start_date = "2010-01-01"
+start_date = "1985-01-01"
 end_date = datetime.now().strftime("%Y-%m-%d")  # Today's date
 
 # Define desired parameters
@@ -16,9 +18,12 @@ end_date = datetime.now().strftime("%Y-%m-%d")  # Today's date
 # WS10M           MERRA-2 Wind Speed at 10 Meters (m/s)
 # WD10M           MERRA-2 Wind Direction at 10 Meters (Degrees)
 # more at https://power.larc.nasa.gov/data-access-viewer/
+weather_params = [
+    "QV2M",
+]
 
 params = {
-    "parameters": "QV2M,T2M,PRECTOTCORR",
+    "parameters": ",".join(weather_params),
     "community": "RE",
     "longitude": longitude,
     "latitude": latitude,
@@ -36,14 +41,15 @@ base_url = "https://power.larc.nasa.gov/api/temporal/daily/point"
 url = f"{base_url}?{query_string}"
 
 # Make the API request using urllib
-try:
+if __name__ == "__main__":
-    with urllib.request.urlopen(url) as response:
+    try:
-        if response.status == 200:
+        with urllib.request.urlopen(url) as response:
-            csv_data = response.read().decode("utf-8")
+            if response.status == 200:
-            with open("hanoi_weather_data.csv", "w", encoding="utf-8") as file:
+                csv_data = response.read()
-                file.write(csv_data)
+                with open(data_file, "wb") as file:
-            print("Data successfully downloaded and saved to 'hanoi_weather_data.csv'.")
+                    file.write(csv_data)
-        else:
+                print(f"Data successfully downloaded and saved to {data_file}.")
-            print(f"Failed to retrieve data: HTTP Status Code {response.status}")
+            else:
-except urllib.error.URLError as e:
+                print(f"Failed to retrieve data: HTTP Status Code {response.status}")
-    print(f"Failed to retrieve data: {e.reason}")
+    except urllib.error.URLError as e:
+        print(f"Failed to retrieve data: {e.reason}")

dead/dead/pytorch_port.py

@@ -8,7 +8,7 @@ from sklearn.preprocessing import MinMaxScaler
 import numpy as np
 from load_data import load_this_data
 
-a = load_this_data("dataset.csv")
+a = load_this_data()
 
 # time series forecasting
 
@@ -19,6 +19,7 @@ scaled_data = scaler.fit_transform(a)
 
 
 def create_sequences(data, seq_length):
+    "see this? return[0] is the big x and return[1] is the big y. and h(x) = y. and h(x) is the model."
     xs = []
     ys = []
     for i in range(len(data) - seq_length):

dead/global_config.py

@@ -0,0 +1 @@
+DATA_FILE_NAME = "hanoi_weather_data.csv"

dead/load_data.py

@@ -0,0 +1,28 @@
+"extracting the load data step from the notebook"
+
+import io
+
+# from itertools import islice
+from typing import Union
+
+def cut_headers(stream: Union[io.StringIO, io.FileIO, io.TextIOWrapper]):
+    headers = []
+    exist_startheader = False
+
+    while line := stream.readline():
+        if "-BEGIN HEADER-" in line and not exist_startheader:
+            exist_startheader = True
+            headers.append(str(line.strip()))
+        elif "-END HEADER-" in line and exist_startheader:
+            headers.append(str(line.strip()))
+            break
+        elif exist_startheader:
+            # print(f"{line = }")
+            headers.append(str(line.strip()))
+        elif not exist_startheader:
+            continue
+    if not exist_startheader:
+        # If no headers are found, reset the stream and return
+        stream.seek(0)
+        return "", stream
+    return "\n".join(headers), stream

dead/pytorch_port.py

@@ -0,0 +1,163 @@
+from sklearn.model_selection import train_test_split
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+from sklearn.preprocessing import MinMaxScaler
+
+import numpy as np
+from load_data import load_this_data
+
+a = load_this_data()
+
+# time series forecasting
+
+
+scaler = MinMaxScaler()
+print(a.columns)
+scaled_data = scaler.fit_transform(a)
+
+
+def create_sequences(data, seq_length):
+    "see this? return[0] is the big x and return[1] is the big y. and h(x) = y. and h(x) is the model."
+    xs = []
+    ys = []
+    for i in range(len(data) - seq_length):
+        x = data[i : (i + seq_length)]
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)
+
+
+seq_length = 20
+
+X, y = create_sequences(scaled_data, seq_length)
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+from torch.utils.data import TensorDataset, DataLoader
+
+X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
+y_train_tensor = torch.tensor(y_train, dtype=torch.float32)
+
+train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+
+
+import torch.nn as nn
+
+
+class LSTMModel(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, output_size):
+        super(LSTMModel, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
+        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
+
+        out, _ = self.lstm(x, (h0, c0))
+        out = self.fc(out[:, -1, :])  # Take the output from the last time step
+        return out
+
+
+model = LSTMModel(
+    input_size=X_train.shape[2],
+    hidden_size=64,
+    num_layers=2,
+    output_size=y_train.shape[1],
+)
+model.to(device)
+criterion = nn.MSELoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+train_losses = []
+val_losses = []
+
+
+for epoch in range(100):
+    model.train()
+    running_train_loss = 0.0
+
+    # inputs, targets
+    for batch_X, batch_y in train_loader:
+        batch_X = batch_X.to(device)
+        batch_y = batch_y.to(device)
+
+        outputs = model(batch_X)
+        loss = criterion(outputs, batch_y)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        running_train_loss += loss.item() * batch_X.size(0)
+    if (epoch + 1) % 10 == 0:
+        print(f"Epoch [{epoch+1}/100], Loss: {loss.item():.4f}")
+
+    epoch_train_loss = running_train_loss / len(train_loader.dataset)
+    train_losses.append(epoch_train_loss)
+
+model.eval()
+
+X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(device)
+y_test_tensor = torch.tensor(y_test, dtype=torch.float32).to(device)
+val_dataset = TensorDataset(X_test_tensor, y_test_tensor)
+val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
+
+# with torch.no_grad():
+#     running_val_loss = 0.0
+#     predictions = model(X_test_tensor)
+#     test_loss = criterion(predictions, y_test_tensor)
+#     print(f"Test Loss: {test_loss.item():.4f}")
+
+running_val_loss = 0.0
+predictions = []
+with torch.no_grad():
+    for inputs, targets in val_loader:
+        inputs, targets = inputs.to(device), targets.to(device)
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        running_val_loss += loss.item() * inputs.size(0)
+        predictions.append(outputs.cpu().numpy())  # Collect all batches
+
+epoch_val_loss = running_val_loss / len(val_loader.dataset)  # len(val_dataset)
+val_losses.append(epoch_val_loss)
+
+predictions = np.concatenate(predictions, axis=0)
+import matplotlib.pyplot as plt
+
+y_test_cpu = y_test_tensor.cpu().numpy()
+
+predictions_rescaled = scaler.inverse_transform(predictions)
+y_test_rescaled = scaler.inverse_transform(y_test_cpu)
+
+
+fig, (ax1, ax2) = plt.subplots(
+    1, 2, figsize=(14, 5), gridspec_kw={"width_ratios": [1, 3]}
+)
+
+# Loss curve on the left
+ax1.plot(train_losses, label="Train Loss")
+ax1.plot(val_losses, label="Val Loss")
+ax1.set_title("Loss Curve")
+ax1.set_xlabel("Epoch")
+ax1.set_ylabel("Loss")
+ax1.legend()
+ax1.grid(True)
+
+# Prediction vs Actual on the right
+ax2.plot(y_test_rescaled, label="Actual")
+ax2.plot(predictions_rescaled, label="Predicted")
+ax2.set_title("Actual vs. Predicted")
+ax2.set_xlabel("Sample")
+ax2.set_ylabel("Value")
+ax2.legend()
+
+plt.tight_layout()
+plt.show()

dead/sk2_port.py

@@ -0,0 +1,185 @@
+import sys
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import io
+from typing import Union
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from statsmodels.tsa.ar_model import AutoReg
+
+# ==== LOAD & CLEAN DATA ====
+DATA_FILE_NAME = "hanoi_weather_data.csv"
+
+def cut_headers(stream: Union[io.StringIO, io.FileIO, io.TextIOWrapper]):
+    headers = []
+    exist_startheader = False
+    while line := stream.readline():
+        if "-BEGIN HEADER-" in line and not exist_startheader:
+            exist_startheader = True
+            headers.append(str(line.strip()))
+        elif "-END HEADER-" in line and exist_startheader:
+            headers.append(str(line.strip()))
+            break
+        elif exist_startheader:
+            headers.append(str(line.strip()))
+        elif not exist_startheader:
+            continue
+    if not exist_startheader:
+        stream.seek(0)
+        return "", stream
+    return "\n".join(headers), stream
+
+with open(DATA_FILE_NAME, "r", encoding="utf-8") as file:
+    data_header, csv_file = cut_headers(file)
+    data = pd.read_csv(csv_file, na_values=-999)
+
+if all(z in data.columns for z in ("YEAR", "MO", "DY")):
+    data["date"] = pd.to_datetime(
+        {k: data[v] for v, k in zip(["YEAR", "MO", "DY"], ["year", "month", "day"])}
+    )
+    data.drop(columns=["YEAR", "MO", "DY"], inplace=True)
+    data.set_index("date", inplace=True)
+
+data.dropna(inplace=True)
+
+# ==== VISUALIZE CORRELATION ====
+plt.figure(figsize=(12, 8))
+sns.heatmap(data.corr(), annot=True, cmap="coolwarm")
+plt.title("Feature Correlation Heatmap")
+plt.show()
+
+# ==== SCALE DATA ====
+scaler = MinMaxScaler()
+scaled_data = scaler.fit_transform(data)
+
+# ==== CREATE SEQUENCES ====
+def create_sequences(data, seq_length):
+    xs, ys = [], []
+    for i in range(len(data) - seq_length):
+        x = data[i:i + seq_length].flatten()
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)
+
+seq_length = 50
+X, y = create_sequences(scaled_data, seq_length)
+
+# ==== SPLIT ====
+date_index = data.index[seq_length:]
+X_train, X_test, y_train, y_test, date_train, date_test = train_test_split(
+    X, y, date_index, test_size=0.02, shuffle=False
+)
+
+# ==== TRAIN LINEAR REGRESSION ====
+model = LinearRegression()
+model.fit(X_train, y_train)
+predictions = model.predict(X_test)
+
+# ==== EVALUATION ====
+mse = mean_squared_error(y_test, predictions)
+r2 = r2_score(y_test, predictions)
+
+print(f"Test MSE: {mse:.4f}")
+print(f"Test R2: {r2:.4f}")
+print(f"Model Score: {model.score(X_test, y_test):.4f}")
+
+# ==== INVERSE TRANSFORM ====
+predictions_rescaled = scaler.inverse_transform(
+    predictions.reshape(-1, data.shape[1])
+)
+y_test_rescaled = scaler.inverse_transform(
+    y_test.reshape(-1, data.shape[1])
+)
+
+# ==== PLOTS ====
+
+# Actual vs Predicted with Real Dates
+plt.figure(figsize=(12, 6))
+plt.plot(date_test, y_test_rescaled[:, 0], label="Actual")
+plt.plot(date_test, predictions_rescaled[:, 0], label="Predicted")
+plt.title("Actual vs Predicted (First Feature)")
+plt.xlabel("Date")
+plt.ylabel("Original Scale")
+plt.xticks(rotation=45)
+plt.legend()
+plt.grid(True)
+plt.tight_layout()
+plt.show()
+
+# Residual Plot
+residuals = y_test[:, 0] - predictions[:, 0]
+plt.figure(figsize=(10, 4))
+plt.plot(date_test, residuals, label="Residuals")
+plt.hlines(0, 0, len(residuals), colors="red")
+plt.title("Residuals Over Time (First Feature)")
+plt.xlabel("Date")
+plt.ylabel("Error")
+plt.xticks(rotation=45)
+plt.legend()
+plt.tight_layout()
+plt.grid(True)
+plt.show()
+
+# Error Distribution
+errors = np.abs(y_test[:, 0] - predictions[:, 0])
+plt.hist(errors, bins=50)
+plt.title("Error Distribution (First Feature)")
+plt.xlabel("Absolute Error")
+plt.ylabel("Frequency")
+plt.grid(True)
+plt.show()
+
+# Scatter Plot
+plt.scatter(y_test[:, 0], predictions[:, 0], alpha=0.5)
+plt.plot([0, 1], [0, 1], "r--")
+plt.title("Predicted vs Actual (Normalized)")
+plt.xlabel("Actual")
+plt.ylabel("Predicted")
+plt.grid(True)
+plt.show()
+
+# ==== AUTOREGRESSION FORECAST ====
+target_column = data.columns[0]
+series = data[target_column]
+
+train_size = int(len(series) * 0.98)
+train_series = series[:train_size]
+test_series = series[train_size:]
+
+ar_model = AutoReg(train_series, lags=30).fit()
+ar_preds = ar_model.predict(start=len(train_series), end=len(series)-1)
+
+# Plot AR Forecast
+plt.figure(figsize=(12, 5))
+plt.plot(test_series.index, test_series, label="Actual")
+plt.plot(test_series.index, ar_preds, label="AR Forecast", linestyle="--")
+plt.title(f"AutoRegression Forecast - {target_column}")
+plt.xlabel("Date")
+plt.ylabel(target_column)
+plt.xticks(rotation=45)
+plt.legend()
+plt.grid(True)
+plt.tight_layout()
+plt.show()
+
+# ==== FUTURE FORECAST ====
+future_steps = 30
+future_index = pd.date_range(start=series.index[-1], periods=future_steps+1, freq="D")[1:]
+future_forecast = ar_model.predict(start=len(series), end=len(series) + future_steps - 1)
+
+plt.figure(figsize=(12, 5))
+plt.plot(series[-100:], label="Recent Data")
+plt.plot(future_index, future_forecast, label="Future Forecast", color="red")
+plt.title(f"{target_column} - 30 Day Forecast (AutoRegression)")
+plt.xlabel("Date")
+plt.ylabel(target_column)
+plt.xticks(rotation=45)
+plt.legend()
+plt.tight_layout()
+plt.grid(True)
+plt.show()

dead/sklearn_port.py

@@ -0,0 +1,110 @@
+import sys
+import numpy as np
+import pandas as pd
+from sklearn.metrics import r2_score
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import matplotlib.pyplot as plt
+# from global_config import DATA_FILE_NAME
+DATA_FILE_NAME = "hanoi_weather_data.csv"
+
+# from load_data import cut_headers
+
+import io
+
+# from itertools import islice
+from typing import Union
+
+def cut_headers(stream: Union[io.StringIO, io.FileIO, io.TextIOWrapper]):
+    headers = []
+    exist_startheader = False
+
+    while line := stream.readline():
+        if "-BEGIN HEADER-" in line and not exist_startheader:
+            exist_startheader = True
+            headers.append(str(line.strip()))
+        elif "-END HEADER-" in line and exist_startheader:
+            headers.append(str(line.strip()))
+            break
+        elif exist_startheader:
+            # print(f"{line = }")
+            headers.append(str(line.strip()))
+        elif not exist_startheader:
+            continue
+    if not exist_startheader:
+        # If no headers are found, reset the stream and return
+        stream.seek(0)
+        return "", stream
+    return "\n".join(headers), stream
+
+# 1. Load data
+
+with open(DATA_FILE_NAME, "r", encoding="utf-8") as file:
+    data_header, csv_file = cut_headers(file)
+    data = pd.read_csv(csv_file, na_values=-999)
+
+if all(z in data.columns for z in ("YEAR", "MO", "DY")):
+    data["date"] = pd.to_datetime(
+        {k: data[v] for v, k in (zip(["YEAR", "MO", "DY"], ["year", "month", "day"]))}
+    )
+    data.drop(inplace=True, columns=["YEAR", "MO", "DY"])
+    data.set_index(["date"], inplace=True)
+data.dropna(inplace=True)
+
+# 2. Normalize data
+scaler = MinMaxScaler()
+print(data.columns)
+scaled_data = scaler.fit_transform(data)
+
+
+# 3. Create sequences for supervised learning
+def create_sequences(data, seq_length):
+    xs, ys = [], []
+    for i in range(len(data) - seq_length):
+        x = data[i : i + seq_length].flatten()
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)
+
+
+seq_length = 50
+X, y = create_sequences(scaled_data, seq_length)
+
+# 4. Split into training and testing
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.02)
+
+# 5. Train linear regression model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# 6. Make predictions
+predictions = model.predict(X_test)
+
+# 7. Evaluate performance
+mse = mean_squared_error(y_test, predictions)
+r2 = r2_score(y_test, predictions)
+
+print(f"Test MSE: {mse:.4f}")
+print(f"Test r2: {r2:.4f}")
+
+print(f"Score: {model.score(X_test, y_test)}")
+# 8. Inverse transform predictions
+predictions_rescaled = scaler.inverse_transform(predictions.reshape(-1, data.shape[1]))
+y_test_rescaled = scaler.inverse_transform(y_test.reshape(-1, data.shape[1]))
+
+# 9. Plot results
+
+# Loss bar
+
+
+# Prediction vs Actual
+plt.plot(y_test_rescaled, label="Actual")
+plt.plot(predictions_rescaled, label="Predicted")
+plt.title("Actual vs. Predicted")
+plt.xlabel("Sample")
+plt.ylabel("Value")
+plt.legend()
+plt.show()

load_data.py

@@ -1,87 +0,0 @@
-"extracting the load data step from the notebook"
-
-import io
-
-# from itertools import islice
-from typing import Union
-
-import pandas as pd
-
-
-def cut_headers(stream: Union[io.StringIO, io.FileIO, io.TextIOWrapper]):
-    headers = []
-    exist_startheader = False
-
-    while line := stream.readline():
-        if "-BEGIN HEADER-" in line and not exist_startheader:
-            exist_startheader = True
-            headers.append(str(line.strip()))
-        elif "-END HEADER-" in line and exist_startheader:
-            headers.append(str(line.strip()))
-            break
-        elif exist_startheader:
-            # print(f"{line = }")
-            headers.append(str(line.strip()))
-        elif not exist_startheader:
-            continue
-    if not exist_startheader:
-        # If no headers are found, reset the stream and return
-        stream.seek(0)
-        return "", stream
-    return "\n".join(headers), stream
-
-
-# if __name__ == "__main__":
-#     with open("hanoi_qv2m_latest.csv", "r", encoding="utf-8") as file:
-#         header, file = cut_headers(file)
-#         print("Extracted Header:")
-#         print(header)
-#         data = pd.read_csv(file)
-#         print("\nFirst 5 Lines of Data:")
-#         print(data.head())
-#         print(data.isnull().sum())
-
-
-def load_data(
-    weather_file_name, columns=["Year", "Month", "Day", "Humidity"].copy(), na=-999
-):
-    with open(weather_file_name, "r", encoding="utf-8") as file:
-        _header, file = cut_headers(file)
-        data = pd.read_csv(file, na_values=na)
-    # print(header)
-    data.columns = columns
-    return data
-
-
-def date_combine(data: pd.DataFrame):
-    data["Date"] = pd.to_datetime(data[["Year", "Month", "Day"]])
-    data = data.drop(columns=["Year", "Month", "Day"])
-    data = data.set_index("Date")
-    return data
-
-
-def validate_view(data: pd.DataFrame, verbose=False):
-    if verbose:
-        print(f"Shape with N/A values: {data.shape}")
-        isn = data.isnull().sum().iloc[0]
-        print(f"Count of N/A values: {isn}")
-    data = data.dropna()  # Drop the rows with missing values
-    if verbose:
-        isn = data.isnull().sum().iloc[0]
-        print(f"Count after drop: {isn}")
-        print(data.describe())
-    return data
-
-
-# if __name__ == "__main__":
-def load_this_data(filename, verbose=False):
-    d = load_data(filename)
-    # if verbose:
-    #     print(d.columns)
-    d = date_combine(d)
-    # if verbose:
-    #     print(d.columns)
-    d = validate_view(d, verbose)
-    # if verbose:
-    #     print(d.columns)
-    return d

main.py

@@ -0,0 +1,71 @@
+# import pandas as pd
+from data_loader import load_weather_data
+from preprocessing import scale_data, create_sequences, inverse_scale
+from model_train import (
+    train_linear_regression_gd,
+    predict_linear_regression,
+    evaluate_model,
+)
+from visualize import (
+    plot_original_vs_scaled,
+    plot_predictions,
+    plot_residuals,
+    plot_error_hist,
+    plot_scatter,
+    plot_loss_curve,
+    plot_value_distribution,
+    visualize_sequence_window,
+)
+from sklearn.model_selection import train_test_split
+
+# Step 1: Load data
+data = load_weather_data("hanoi_weather_data.csv")
+
+plot_value_distribution(data)
+
+# Step 2: Preprocess (scale and sequence)
+scaled_data, scaler = scale_data(data)
+plot_original_vs_scaled(data, scaled_data)
+
+seq_length = 50
+X, y = create_sequences(scaled_data, seq_length)
+
+visualize_sequence_window(data["QV2M"], seq_length=10, start_idx=1000)
+
+# Step 3: Train/test split
+date_index = data.index[seq_length:]
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y.reshape(-1), test_size=0.02, shuffle=False
+)
+date_train, date_test = train_test_split(date_index, test_size=0.02, shuffle=False)
+
+# Step 4: Train model with gradient descent
+# import math
+# lr_schedule = lambda epoch: max(0.005, 0.01 * (1 + math.cos(epoch / 1000 * math.pi)) / 2)
+
+theta, losses = train_linear_regression_gd(
+    X_train,
+    y_train,
+    # lr_schedule=lr_schedule,
+    n_epochs=10000,
+)
+
+predictions = predict_linear_regression(X_test, theta)
+
+# Step 5: Evaluate
+mse, r2 = evaluate_model(y_test, predictions)
+print(f"MSE: {mse:.4f}, R2: {r2:.4f}")
+
+# Step 6: Rescale predictions
+y_test_rescaled = inverse_scale(y_test, scaler, data.shape[1])
+predictions_rescaled = inverse_scale(predictions, scaler, data.shape[1])
+
+# Step 7: Visualize
+residuals = y_test - predictions
+errors = abs(residuals)
+
+plot_predictions(date_test, y_test_rescaled, predictions_rescaled)
+plot_residuals(date_test.to_numpy(), residuals)
+plot_error_hist(errors)
+plot_scatter(y_test.reshape(-1, 1), predictions.reshape(-1, 1))
+plot_loss_curve(losses)

model_train.py

@@ -0,0 +1,45 @@
+import numpy as np
+from sklearn.metrics import mean_squared_error, r2_score
+
+
+def train_linear_regression_gd(X_train, y_train, lr_schedule= lambda _e: 0.01, n_epochs=1000):
+    """
+    Train linear regression model using batch gradient descent.
+
+    Returns:
+        theta: learned weights
+        losses: list of MSE loss per epoch
+    """
+    m, n = X_train.shape
+    X_b = np.hstack([np.ones((m, 1)), X_train])
+    theta = np.zeros(n + 1)
+    losses = []
+
+    for epoch in range(n_epochs):
+        lr = lr_schedule(epoch)
+        predictions = X_b @ theta
+        errors = predictions - y_train
+        gradients = (2 / m) * X_b.T @ errors
+        gradients = np.clip(gradients, -1, 1)  # gradient clipping
+        theta -= lr * gradients
+        loss = np.mean(errors ** 2)
+        losses.append(loss)
+
+        if epoch % 1000 == 0:
+            print(f"Epoch {epoch}: Loss = {loss:.6f}, LR: {lr:.6f}")
+
+    print(f"Final Loss: {losses[-1]:.6f}")
+    return theta, losses
+
+
+def predict_linear_regression(X, theta):
+    """Predict using learned linear model."""
+    X_b = np.hstack([np.ones((X.shape[0], 1)), X])
+    return X_b @ theta
+
+
+def evaluate_model(y_true, y_pred):
+    """Evaluate using sklearn's built-in MSE and R² functions."""
+    mse = mean_squared_error(y_true, y_pred)
+    r2 = r2_score(y_true, y_pred)
+    return mse, r2

preprocessing.py

@@ -0,0 +1,26 @@
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+
+
+def scale_data(data):
+    """Normalize the data using MinMaxScaler."""
+    scaler = MinMaxScaler()
+    scaled_data = scaler.fit_transform(data)
+    return scaled_data, scaler
+
+
+def inverse_scale(scaled, scaler, n_features):
+    """Inverse transform using stored scaler."""
+    return scaler.inverse_transform(scaled.reshape(-1, n_features))
+
+
+def create_sequences(data, seq_length):
+    """Transform the time series into overlapping sequences."""
+    xs, ys = [], []
+    for i in range(len(data) - seq_length):
+        x = data[i : i + seq_length].flatten()
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)

tutorial/pt_tuto.py

@@ -1,304 +0,0 @@
-"""
-This script demonstrates how to port a TensorFlow LSTM model to PyTorch.
-It includes detailed explanations for every step.
-"""
-
-# Step 1: Import necessary libraries
-# ----------------------------------
-# PyTorch is the core library for building and training neural networks.
-# Torch.nn contains modules for defining layers and loss functions.
-# Torch.optim provides optimizers like Adam for training.
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import MinMaxScaler
-from torch import nn, optim
-from torch.utils.data import DataLoader, TensorDataset
-
-import sys
-
-sys.path.append(r"c:\Users\Admin\Documents\lecture ass shit\ml1\codemidterm\ml_midterm")
-
-from load_data import load_this_data
-
-# Step 2: Data Preparation
-# ------------------------
-# In PyTorch, data must be converted into tensors. We'll simulate the data
-# preprocessing steps from your TensorFlow code.
-
-
-# Simulate some data (replace this with your actual data loading logic)
-np.random.seed(42)  # For reproducibility
-# data = np.sin(np.linspace(0, 100, 1000))  # Example: Sine wave data
-# data = data.reshape(-1, 1)  # Reshape to (samples, features)
-
-
-# Load the dataset (replace "dataset.csv" with your actual file path)
-data = load_this_data("dataset.csv")
-
-
-# Extract the "Humidity" column as the target variable
-data = data["Humidity"].values.reshape(-1, 1)
-
-# Normalize the data using MinMaxScaler (same as in TensorFlow)
-scaler = MinMaxScaler()
-data_scaled = scaler.fit_transform(data)
-
-# Define lookback window (sequence length for LSTM)
-n_lookback = 100  # Number of past timesteps to consider
-n_features = 1  # Number of features (e.g., 1 for univariate time series)
-
-
-# Helper function to create sequences
-def create_sequences(data, n_lookback):
-    """
-    Create sequences of length n_lookback for LSTM input.
-
-    Args:
-        data: Scaled data as a NumPy array or PyTorch tensor.
-        n_lookback: Number of past timesteps to consider.
-
-    Returns:
-        x: Input sequences of shape (num_samples, n_lookback, n_features).
-        y: Target values of shape (num_samples, 1).
-    """
-    x, y = [], []
-    for i in range(len(data) - n_lookback):
-        x.append(data[i : i + n_lookback])
-        y.append(data[i + n_lookback])
-    return np.array(x), np.array(y)
-
-
-# Split into training and testing sets
-split_index = int(0.8 * len(data_scaled))
-train_data = data_scaled[:split_index]
-test_data = data_scaled[split_index:]
-
-# Create sequences for training and testing
-train_x, train_y = create_sequences(train_data, n_lookback)
-test_x, test_y = create_sequences(test_data, n_lookback)
-
-# train_y = scaler.transform(train_y)
-# test_y = scaler.transform(test_y)
-# Convert sequences to PyTorch tensors
-train_x = torch.tensor(train_x, dtype=torch.float32)
-train_y = torch.tensor(train_y, dtype=torch.float32)
-test_x = torch.tensor(test_x, dtype=torch.float32)
-test_y = torch.tensor(test_y, dtype=torch.float32)
-
-
-# Create datasets and dataloaders
-train_dataset = TensorDataset(train_x, train_y)
-test_dataset = TensorDataset(test_x, test_y)
-
-train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
-
-# Step 3: Define the LSTM Model
-# -----------------------------
-# PyTorch models are defined by subclassing `nn.Module`.
-# The forward method specifies how data flows through the model.
-
-
-# class LSTMModel(nn.Module):
-#     def __init__(self, n_features, n_lookback, n_neurons):
-#         """
-#         Initialize the LSTM model.
-
-#         Args:
-#             n_features: Number of input features (e.g., 1 for univariate time series).
-#             n_lookback: Number of past timesteps to consider.
-#             n_neurons: Number of neurons in each LSTM layer.
-#         """
-#         super(LSTMModel, self).__init__()
-#         self.lstm1 = nn.LSTM(
-#             n_features,
-#             n_neurons,
-#             batch_first=True,
-#             dropout=0.2,
-#         )
-#         self.lstm2 = nn.LSTM(
-#             n_neurons,
-#             n_neurons,
-#             batch_first=True,
-#         )
-#         self.lstm3 = nn.LSTM(n_neurons, n_neurons, batch_first=True)
-#         self.lstm4 = nn.LSTM(n_neurons, n_neurons, batch_first=True)
-#         self.fc = nn.Linear(n_neurons, 1)
-#         self.dropout = nn.Dropout(0.2)
-
-#     def forward(self, x):
-#         """
-#         Forward pass through the model.
-
-#         Args:
-#             x: Input tensor of shape (batch, seq_len, n_features).
-
-#         Returns:
-#             Output tensor of shape (batch, 1).
-#         """
-#         x, _ = self.lstm1(x)
-#         x, _ = self.lstm2(x)
-#         x, _ = self.lstm3(x)
-#         x, _ = self.lstm4(x)
-#         x = self.dropout(x)
-#         x = self.fc(x[:, -1, :])  # Use the last timestep's output
-#         return x
-
-print("Input sequences (x):", train_x.shape)
-print("Target values (y):", train_y.shape)
-
-print("Sample input:", train_x[0])
-print("Corresponding target:", train_y[0])
-
-class LSTMModel(nn.Module):
-    def __init__(self, n_features, n_lookback, n_neurons):
-        super(LSTMModel, self).__init__()
-        self.lstm = nn.LSTM(n_features, n_neurons, batch_first=True, dropout=0.2)
-        self.lstm2 = nn.LSTM(n_neurons, n_neurons, batch_first=True, dropout=0.2)
-
-        self.fc = nn.Linear(n_neurons, 1)
-
-    def forward(self, x):
-        x, _ = self.lstm(x)
-        x, _ = self.lstm2(x)
-        x = self.fc(x[:, -1, :])  # Use the last timestep's output
-        return x
-
-
-# Instantiate the model
-n_neurons = 128  # Number of neurons in the LSTM
-model = LSTMModel(n_features, n_lookback, n_neurons)
-
-# Step 4: Define Loss Function and Optimizer
-# ------------------------------------------
-# Loss function: Mean Squared Error (MSE) for regression tasks.
-# Optimizer: Adam optimizer for efficient training.
-criterion = nn.MSELoss()
-optimizer = optim.Adam(model.parameters(), lr=0.001)
-
-# Step 5: Train the Model
-# -----------------------
-# PyTorch requires an explicit training loop. This gives you full control
-# over the training process.
-
-epochs = 50  # Number of training epochs
-
-# Check for CUDA and set the device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Using device: {device}")
-
-# Move the model to the device
-model = LSTMModel(n_features, n_lookback, n_neurons).to(device)
-
-# scheduler = optim.lr_scheduler.ReduceLROnPlateau(
-    # optimizer, mode="min", patience=5, factor=0.5, min_lr=1e-6
-# )
-# Move data to the device during training and evaluation
-for epoch in range(epochs):
-    model.train()  # Set the model to training mode
-    train_loss = 0.0
-
-    for x_batch, y_batch in train_loader:
-        # Move data to the device
-        x_batch = x_batch.to(device)
-        y_batch = y_batch.to(device)
-        # Reshape input to (batch, seq_len, n_features)
-        x_batch = x_batch.view(-1, n_lookback, n_features)
-        # y_batch = y_batch.view(-1, 1)
-        # print("x_batch shape:", x_batch.shape)
-        # print("y_batch shape:", y_batch.shape)
-        # print("x_batch dtype:", x_batch.dtype)
-        # print("y_batch dtype:", y_batch.dtype)  
-        # break
-        # Zero the gradients from the previous step
-        optimizer.zero_grad()
-
-        # Forward pass: Compute predictions
-        predictions = model(x_batch)
-        # if epoch == 32:
-            # for name, param in model.named_parameters():
-            #     if param.grad is not None:
-            #         print(f"{name} gradient mean: {param.grad.mean().item()}")
-            #     else:
-            #         print(f"{name} has no gradient")
-            # print("Predictions device:", predictions.device)
-            # print("y_batch device:", y_batch.device)
-            # print("Predictions shape:", predictions.shape)
-            # print("y_batch shape:", y_batch.shape)
-        # Compute the loss
-        loss = criterion(predictions, y_batch)
-
-        # Backward pass: Compute gradients
-        loss.backward()
-
-        # Clip gradients to prevent exploding gradients
-        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
-
-        # Update model parameters
-        optimizer.step()
-        # scheduler.step(train_loss / len(train_loader))
-        # Accumulate the loss for reporting
-        train_loss += loss.item()
-
-    # Print epoch loss
-    print(f"Epoch {epoch+1}/{epochs}, Loss: {train_loss/len(train_loader):.4f}")
-
-    if epoch % 10 == 0:  # Print every 10 epochs
-        print(
-            f"Sample prediction: {predictions[0].item()}, Actual: {y_batch[0].item()}"
-        )
-# Step 6: Evaluate the Model
-# --------------------------
-# Switch to evaluation mode and disable gradient computation.
-model.eval()
-with torch.no_grad():
-    predictions = []
-    actuals = []
-
-    for x_batch, y_batch in test_loader:
-        # Move data to the device
-        x_batch = x_batch.to(device)
-        y_batch = y_batch.to(device)
-
-        # Reshape input to (batch, seq_len, n_features)
-        # x_batch = x_batch.view(-1, n_lookback, n_features)
-
-        # Make predictions
-        preds = model(x_batch)
-
-        # Move predictions and actuals back to CPU for further processing
-        predictions.append(preds.cpu().numpy())
-        actuals.append(y_batch.cpu().numpy())
-
-# Convert predictions and actuals to NumPy arrays for comparison
-predictions = np.concatenate(predictions)
-actuals = np.concatenate(actuals)
-
-# Step 7: Post-Processing and Visualization
-# -----------------------------------------
-# Inverse transform the predictions and actuals to get original scale
-predictions = scaler.inverse_transform(predictions)
-actuals = scaler.inverse_transform(actuals)
-
-# Plot the results
-print("Sample scaled train_y:", train_y[:5])
-print("Sample scaled test_y:", test_y[:5])
-
-print("Predictions shape:", predictions.shape)
-print("Actuals shape:", actuals.shape)
-
-print("Sample inverse-transformed predictions:", predictions[:5])
-print("Sample inverse-transformed actuals:", actuals[:5])
-
-plt.figure(figsize=(20, 5))
-plt.plot(actuals, label="Actual")
-plt.plot(predictions, label="Predicted")
-plt.legend()
-plt.show()
-
-from sklearn.metrics import r2_score, mean_squared_error
-
-print("R² Score:", r2_score(actuals, predictions))
-print("MSE:", mean_squared_error(actuals, predictions))

tutorial/pytorch_test.py

@@ -1,124 +0,0 @@
-"""this uses new tech
-make by conda new -p path python=3.12; conda activate -p path; conda install all those shi
-
-conda install pytorch torchvision torchaudio pytorch-cuda -c pytorch -c nvidia
-"""
-
-import sys
-
-sys.path.append(".")
-from load_data import load_this_data
-import torch
-
-# a = load_this_data("dataset.csv")
-
-x = torch.tensor([[1, 2], [3, 4]])
-print(x)
-
-
-print(x.shape)       # Output: torch.Size([2, 2])
-print(x.dtype)       # Output: torch.int64
-print(x.device)      # Output: cpu
-
-y = torch.ones_like(x)
-print(x + y)
-print(x * y)
-
-
-x = torch.randn(3, requires_grad=True)
-y = x * 2
-z = y.mean()
-z.backward()
-print(x.grad)
-
-import torch.nn as nn
-
-class SimpleNet(nn.Module):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.fc = nn.Linear(2, 1)
-
-    def forward(self, x):
-        return self.fc(x)
-
-
-model = SimpleNet()
-sample_input = torch.tensor([[1.0, 2.0]])
-output = model(sample_input)
-print(output)
-
-import torch.optim as optim
-
-criterion = nn.MSELoss()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-
-for epoch in range(100):
-    optimizer.zero_grad()
-    outputs = model(sample_input)
-    loss = criterion(outputs, torch.tensor([[1.0]]))
-    loss.backward()
-    optimizer.step()
-    if (epoch+1) % 10 == 0:
-        print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}')
-
-model.eval()
-
-sample_input_2 = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
-output_2 = model(sample_input_2)
-print(output_2)
-
-
-from torchvision import datasets, transforms
-from torch.utils.data import DataLoader
-
-# Define a transform to normalize the data
-transform = transforms.Compose([transforms.ToTensor()])
-
-# Download and load the training data
-train_dataset = datasets.MNIST(root='data', train=True, download=True, transform=transform)
-train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
-
-
-# import torch.nn as nn
-import torch.nn.functional as F
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(28 * 28, 128)
-        self.fc2 = nn.Linear(128, 10)
-
-    def forward(self, x):
-        x = x.view(-1, 28 * 28)  # Flatten the input
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-model_2 = Net()
-criterion_2 = nn.CrossEntropyLoss()
-optimizer_2 = torch.optim.SGD(model.parameters(), lr=0.01)
-
-
-for epoch in range(35):  # loop over the dataset multiple times
-    for inputs, labels in train_loader:
-        optimizer_2.zero_grad()           # zero the parameter gradients
-        outputs = model_2(inputs)         # forward
-        loss = criterion_2(outputs, labels)  # compute loss
-        loss.backward()                 # backward
-        optimizer_2.step()                # optimize
-    print(f'Epoch {epoch+1}, Loss: {loss.item()}')
-
-
-test_dataset = datasets.MNIST(root='data', train=False, download=True, transform=transform)
-test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
-
-
-correct = 0
-total = 0
-with torch.no_grad():
-    for inputs, labels in test_loader:
-        outputs = model_2(inputs)
-        _, predicted = torch.max(outputs.data, 1)
-        total += labels.size(0)
-        correct += (predicted == labels).sum().item()
-print(f'Accuracy: {100 * correct / total}%')

visualize.py

@@ -0,0 +1,137 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_predictions(date_test, y_test_rescaled, predictions_rescaled):
+    plt.figure(figsize=(12, 6))
+    plt.plot(date_test, y_test_rescaled[:, 0], label="Actual")
+    plt.plot(date_test, predictions_rescaled[:, 0], label="Predicted")
+    plt.title("Actual vs Predicted (First Feature)")
+    plt.xlabel("Date")
+    plt.ylabel("Value")
+    plt.xticks(rotation=45)
+    plt.legend()
+    plt.tight_layout()
+    plt.grid(True)
+    plt.show()
+
+
+def plot_residuals(date_test, residuals):
+    plt.figure(figsize=(10, 4))
+    plt.plot(date_test, residuals, label="Residuals")
+    plt.hlines(0, 0, len(residuals), colors="red")
+    plt.xlim(date_test.min(), date_test.max())
+    plt.title("Residuals Over Time")
+    plt.xlabel("Date")
+    plt.ylabel("Error")
+    plt.xticks(rotation=45)
+    plt.grid(True)
+    plt.tight_layout()
+    plt.legend()
+    plt.show()
+
+
+def plot_error_hist(errors):
+    plt.hist(errors, bins=50)
+    plt.title("Error Distribution")
+    plt.xlabel("Absolute Error")
+    plt.ylabel("Frequency")
+    plt.grid(True)
+    plt.show()
+
+
+def plot_scatter(y_test, predictions):
+    plt.scatter(y_test[:, 0], predictions[:, 0], alpha=0.5)
+    plt.plot([0, 1], [0, 1], "r--")
+    plt.title("Predicted vs Actual (Normalized)")
+    plt.xlabel("Actual")
+    plt.ylabel("Predicted")
+    plt.grid(True)
+    plt.show()
+
+
+def plot_loss_curve(losses):
+    plt.plot(losses)
+    plt.title("Training Loss Curve")
+    plt.xlabel("Epoch")
+    plt.ylabel("MSE Loss")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_original_vs_scaled(original_data, scaled_data, feature_names=None):
+    """
+    Plot original and scaled data, aligned by date.
+    """
+    if feature_names is None:
+        feature_names = original_data.columns[:2]
+
+    dates = original_data.index  # Grab the datetime index
+
+    fig, axs = plt.subplots(2, 1, figsize=(12, 6), sharex=True)
+
+    for col in feature_names:
+        axs[0].plot(dates, original_data[col], label=f"Original {col}")
+    axs[0].set_title("Original Data")
+    axs[0].legend()
+    axs[0].grid(True)
+
+    for i, col in enumerate(feature_names):
+        col_index = original_data.columns.get_loc(col)
+        axs[1].plot(dates, scaled_data[:, col_index], label=f"Scaled {col}")
+    axs[1].set_title("Scaled Data (MinMaxScaler)")
+    axs[1].legend()
+    axs[1].grid(True)
+
+    plt.xlabel("Date")
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_value_distribution(data):
+    # import matplotlib.pyplot as plt
+
+    plt.figure(figsize=(8, 4))
+    plt.hist(data.iloc[:, 0], bins=40, color="skyblue", edgecolor="black")
+    plt.title("Distribution of Values")
+    plt.xlabel(data.columns[0])
+    plt.ylabel("Frequency")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+
+def visualize_sequence_window(series, seq_length=5, start_idx=0):
+    """
+    Visualize how a sequence window is used to create X (input) and y (target).
+
+    Parameters:
+        series (pd.Series or np.ndarray): 1D time series data
+        seq_length (int): Length of input window
+        start_idx (int): Where to start slicing from for demo
+    """
+    if isinstance(series, np.ndarray):
+        data = series
+    else:
+        data = series.to_numpy()
+
+    x_seq = data[start_idx:start_idx + seq_length]
+    y_val = data[start_idx + seq_length]
+
+    full_seq = np.concatenate([x_seq, [y_val]])
+
+    plt.figure(figsize=(8, 3))
+    plt.plot(range(start_idx, start_idx + seq_length + 1), full_seq, marker='o')
+    plt.axvline(x=start_idx + seq_length - 0.5, linestyle="--", color="gray")
+
+    for i in range(seq_length):
+        plt.text(start_idx + i, x_seq[i], f"x[{i}]", ha="center", va="bottom", fontsize=9)
+    plt.text(start_idx + seq_length, y_val, "y", ha="center", va="bottom", color="red", fontsize=9)
+
+    plt.title(f"Sequence Window (seq_length={seq_length}) → Predict Next")
+    plt.xlabel("Time Step")
+    plt.ylabel("Value")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()