text stringlengths 0 93.6k |
|---|
"Overfitting Score": overfitting_score, |
} |
) |
pbar.update(1) |
# Create a DataFrame with model statistics |
stats_df = pd.DataFrame(model_stats) |
stats_df = stats_df.sort_values("OOF R² Score", ascending=False).reset_index( |
drop=True |
) |
# Add overfitting indicator |
stats_df["Overfit"] = stats_df["Overfitting Score"].apply( |
lambda x: "Yes" if x > 0.05 else "No" |
) |
# Print the table |
print("\nModel Performance Summary:") |
print(tabulate(stats_df, headers="keys", tablefmt="pretty", floatfmt=".4f")) |
print("\nCalculating ensemble weights...") |
ensemble_weights = calculate_ensemble_weights(models, X_test, y_test) |
print(f"Ensemble weights: {ensemble_weights}") |
print("Making ensemble predictions...") |
ensemble_predictions = weighted_ensemble_predict( |
[model for _, model in models], X, ensemble_weights |
) |
print(f"Predicting future data for the next {future_days} days...") |
future_predictions = [] |
for name, model in models: |
print(f" Making future predictions with {name} model...") |
future_pred = predict_future(model, X[-1], scaler, future_days) |
future_predictions.append(future_pred) |
future_predictions = np.mean(future_predictions, axis=0) |
print("Inverse transforming predictions...") |
close_price_scaler = MinMaxScaler(feature_range=(0, 1)) |
close_price_scaler.fit(data["Close"].values.reshape(-1, 1)) |
ensemble_predictions = close_price_scaler.inverse_transform( |
ensemble_predictions.reshape(-1, 1) |
) |
future_predictions = close_price_scaler.inverse_transform( |
future_predictions.reshape(-1, 1) |
) |
# Ensure ensemble_predictions matches the length of the actual data |
ensemble_predictions = ensemble_predictions[-len(data) :] |
print("Plotting results...") |
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(20, 24)) |
# Price prediction plot |
plot_data = data.iloc[-len(ensemble_predictions) :] |
future_dates = pd.date_range( |
start=plot_data.index[-1] + pd.Timedelta(days=1), periods=future_days |
) |
ax1.plot(plot_data.index, plot_data["Close"], label="Actual Price", color="blue") |
ax1.plot( |
plot_data.index, |
ensemble_predictions, |
label="Predicted Price", |
color="red", |
linestyle="--", |
) |
ax1.plot( |
future_dates, |
future_predictions, |
label="Future Predictions", |
color="green", |
linestyle="--", |
) |
# Add price indications for every day (initially invisible) |
annotations = [] |
for i, (date, price) in enumerate(zip(plot_data.index, ensemble_predictions)): |
ann = ax1.annotate( |
f"${price[0]:.2f}", |
(date, price[0]), |
xytext=(0, 10), |
textcoords="offset points", |
ha="center", |
va="bottom", |
fontsize=8, |
alpha=0.7, |
visible=False, |
) |
annotations.append(ann) |
for i, (date, price) in enumerate(zip(future_dates, future_predictions)): |
ann = ax1.annotate( |
f"${price[0]:.2f}", |
(date, price[0]), |
xytext=(0, -10), |
textcoords="offset points", |
ha="center", |
va="top", |
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