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| from huggingface_hub import hf_hub_download |
| import joblib |
| import pandas as pd |
| import numpy as np |
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| REPO_ID = "andrewmos/lightbm-ts-forecasting-kaggle" |
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| model_path = hf_hub_download( |
| repo_id=REPO_ID, |
| filename="full_pipeline.pkl", |
| repo_type="model" |
| ) |
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| pipeline = joblib.load(model_path) |
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| print("Pipeline loaded successfully") |
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| horizon_models = pipeline["horizon_models"] |
| subcat_models = pipeline["subcat_models"] |
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| train_stats = pipeline["train_stats"] |
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| blend_scores = pipeline["blend_scores"] |
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| params = pipeline["params"] |
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| blend_power = pipeline["blend_power"] |
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| print("Number of horizon models:", len(horizon_models)) |
| print("Number of subcategory models:", len(subcat_models)) |
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| test_df = pd.read_csv("test.csv") |
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| print("Test shape:", test_df.shape) |
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| def create_features(dataframe, train_stats=None): |
| """ |
| Create engineered features for the model. |
| |
| Parameters |
| ---------- |
| dataframe : pd.DataFrame |
| Input data with raw features |
| is_train : bool |
| Whether this is training data (unused, kept for API consistency) |
| train_stats : dict or None |
| Pre-computed statistics from training data for target encoding |
| Must contain: 'sub_code_target_mean', 'global_mean' |
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| Returns |
| ------- |
| pd.DataFrame |
| DataFrame with additional engineered features |
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| Notes |
| ----- |
| - Does NOT include ts_index as a feature (causes overfitting) |
| - All features are backward-looking only (no data leakage) |
| """ |
| |
| dataframe = dataframe.copy() |
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| if 'feature_al' in dataframe.columns and 'feature_am' in dataframe.columns: |
| dataframe['feature_al_minus_feature_am'] = dataframe['feature_al'] - dataframe['feature_am'] |
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| group_cols = ['code', 'sub_code', 'sub_category', 'horizon'] |
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| if 'feature_al' in dataframe.columns: |
| dataframe['feature_al_grp_mean'] = dataframe.groupby(group_cols)['feature_al'].transform('mean') |
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| if 'feature_am' in dataframe.columns: |
| dataframe['feature_am_grp_mean'] = dataframe.groupby(group_cols)['feature_am'].transform('mean') |
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| if train_stats is not None: |
| if 'sub_code_target_mean' in train_stats: |
| dataframe['sub_code_target_mean'] = dataframe['sub_code'].map( |
| train_stats['sub_code_target_mean'] |
| ).fillna(train_stats['global_mean']) |
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| dataframe = dataframe.sort_values(['code', 'horizon', 'ts_index']) |
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| if 'feature_al' in dataframe.columns: |
| dataframe['feature_al_lag1'] = dataframe.groupby(['code', 'horizon'])['feature_al'].shift(1) |
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| if 'feature_am' in dataframe.columns: |
| dataframe['feature_am_lag1'] = dataframe.groupby(['code', 'horizon'])['feature_am'].shift(1) |
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| dataframe = dataframe.reset_index(drop=True) |
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| dataframe = dataframe.fillna(0) |
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| return dataframe |
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| test_df = create_features( |
| test_df, |
| train_stats=train_stats |
| ) |
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| print("Features created") |
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| all_predictions = {} |
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| for horizon in horizon_models.keys(): |
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| print(f"\nPredicting horizon {horizon}") |
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| model_info = horizon_models[horizon] |
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| model = model_info["model"] |
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| features = model_info["features"] |
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| X_test = test_df[features] |
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| preds = model.predict(X_test) |
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| all_predictions[horizon] = preds |
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| print(f"Done horizon {horizon}") |
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| predictions_df = pd.DataFrame(all_predictions) |
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| print("\nPredictions shape:") |
| print(predictions_df.shape) |
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| predictions_df.to_csv("predictions.csv", index=False) |
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| print("\nSaved predictions.csv") |
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| print("\nPreview:") |
| print(predictions_df.head()) |
