refactor(Naming): use new convention, added Ensemble model parameter back, support multiple Meta-Labeling models (#132)

* refactor(Naming): use `primary_models` & `meta_labeling_models` * refactor(Naming): using primary * meta_labeling across config and in pipeline * feat(Pipeline): added back Ensemble models * fix(Pipeline): compiler error * fix(Config): typo * chore(Pipeline): removed unused averaging step * revert the changes in discretizing * chore(Pipeline): remove sharpe improvement logging * fix(Pipeline): ensemble predictions should be a pd.Series instead of a DataFrame * fix(Pipeline): discard unnecessary ensemble_probabilities * fix(Pipeline): fixes regarding various meta-labeling ensemble bugs * fix(Reporting): use the new naming convention * fix(Reporting): use the right variable * feat(Sweep): new sweep for ensemble models * fix(Sweep): config reference * fix(Config): simplified dev config * fix(Models): use the faster LR model * fix(Models): use LGBM in the meta-labeling model for speed * fix(Selection): always use the first model for feature selection, commented out caching from select_features() as it's close to redundant in terms of speed
2026-08-13 02:48:07 +00:00 · 2022-01-09 17:21:06 +01:00
parent 22b3167cb9
commit b1c04afb13
20 changed files with 202 additions and 239 deletions
@@ -0,0 +1,67 @@
+import pandas as pd
+from typing import Literal
+from training.walk_forward import walk_forward_train_test
+from utils.evaluate import evaluate_predictions
+from models.base import Model
+from utils.scaler import get_scaler
+from utils.types import ScalerTypes
+
+def train_primary_model(
+                    ticker_to_predict: str,
+                    original_X: pd.DataFrame,
+                    X: pd.DataFrame,
+                    y: pd.Series,
+                    target_returns: pd.Series,
+                    models: list[tuple[str, Model]],
+                    method: Literal['regression', 'classification'],
+                    expanding_window: bool,
+                    sliding_window_size: int,
+                    retrain_every: int,
+                    scaler: ScalerTypes,
+                    no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
+                    level: str,
+                    print_results: bool
+    ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, dict]:
+
+    scaler = get_scaler(scaler)
+
+    results = pd.DataFrame()
+    all_models_single_asset = dict()
+    predictions = pd.DataFrame(index=y.index)
+    probabilities = pd.DataFrame(index=y.index)
+    
+    for model_name, model in models:
+        model_over_time, preds, probs = walk_forward_train_test(
+            model_name=model_name,
+            model = model,
+            X = X if model.feature_selection == 'on' else original_X,
+            y = y,
+            target_returns = target_returns,
+            expanding_window = expanding_window,
+            window_size = sliding_window_size,
+            retrain_every = retrain_every,
+            scaler = scaler
+        )
+        
+        assert len(preds) == len(y)
+        result = evaluate_predictions(
+            model_name = model_name,
+            target_returns = target_returns,
+            y_pred = preds,
+            y_true = y,
+            method = method,
+            no_of_classes=no_of_classes,
+            print_results = print_results,
+            discretize=True
+        )
+        column_name = "model_" + ticker_to_predict + "_" + model_name + "_" + level
+        results[column_name] = result
+        all_models_single_asset[model_name] = model_over_time
+        # column names for model outputs should be different, so we can differentiate between original data and model predictions later, where necessary
+        predictions[column_name] = preds
+        probs_column_name = "probs_" + ticker_to_predict + "_" + model_name + "_" + level
+        probs.columns = [probs_column_name + "_" + c for c in probs.columns]
+        probabilities = pd.concat([probabilities, probs], axis=1)
+        
+
+    return results, predictions, probabilities, all_models_single_asset