Evaluieren eines vorhandenen Models.

evaluate_model.py

+"""
+@author: thensel
+
+@date: 2025-05-02
+
+@description: This function contains a validation test for the model, if you dont want to create a new one.
+
+-
+"""
+
+
+# evaluate_model.py
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+import pandas as pd
+
+from bikes.model import BikeSharingModel
+from bikes.data_prep import load_dataset
+
+def main():
+    # 1. Daten laden
+    feature_names, data = load_dataset("data/hour.csv")
+    X = data[:, :-1]
+    Y = data[:, -1]
+
+    # 2. Split (gleicher Seed wie im Training)
+    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
+
+    # 3. Skalieren (Scaler muss neu fit auf Trainingsdaten)
+    scaler = StandardScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    # 4. Modell laden
+    model = BikeSharingModel(input_dim=X.shape[1])
+    model.load("models/bikesharing_model.keras")
+
+    # 5. Vorhersagen machen
+    Y_pred = model.predict(X_test_scaled)
+
+    # 6. Evaluation
+    mse = mean_squared_error(Y_test, Y_pred)
+    mae = mean_absolute_error(Y_test, Y_pred)
+
+    print(f"\nEvaluation eines gespeicherten Modells:")
+    print(f"Test-MSE:  {mse:.4f}")
+    print(f"Test-MAE:  {mae:.4f}")
+
+    # 7. Visualisierung der Vorhersagen (erste 50)
+    plt.figure(figsize=(10, 4))
+    plt.plot(Y_test[:50], label="Tatsächlich")
+    plt.plot(Y_pred[:50], label="Vorhersage", linestyle="dashed")
+    plt.legend()
+    plt.title("Modellvorhersagen vs. Tatsächliche Werte")
+    plt.xlabel("Beispielindex")
+    plt.ylabel("Anzahl Fahrten (cnt)")
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+    # 8. Als Tabelle ausgeben
+    df_results = pd.DataFrame({
+        "Actual": Y_test[:20],
+        "Predicted": Y_pred[:20]
+    })
+    print("\nBeispielergebnisse:")
+    print(df_results)
+
+if __name__ == "__main__":
+    main()