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https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
c611481eb6
* refactor(WalkForward): separate train / test functions (draft) to potentially help with inference later * fix(Training): use the new separate train / test functions * feat(Training): return and pass in scalers that are necessary for inference * fix(Project): runtime errors * fix(WalkForward): use the correct `train_from` value * fix(Tests): for new walk_forward functions() * refactor(WalkForward): rename `walk_forward_test()` to `walk_forward_inference()`
41 lines
1.8 KiB
Python
41 lines
1.8 KiB
Python
from sklearn.feature_selection import RFE
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from sklearn.model_selection import TimeSeriesSplit
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import pandas as pd
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from models.base import Model
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from models.sklearn import SKLearnModel
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from utils.scaler import get_scaler
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from utils.types import ScalerTypes
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# from utils.hashing import hash_df, hash_series
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# from diskcache import Cache
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# cache = Cache(".cachedir/feature_selection")
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# def select_features(**kwargs) -> pd.DataFrame:
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# hashed = kwargs['data_config_hash'] + kwargs['model'].get_name() + str(kwargs['n_features_to_select']) + kwargs['backup_model'].get_name() + kwargs['scaling']
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# if hashed in cache:
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# return cache.get(hashed)
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# else:
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# return_value = __select_features(**kwargs)
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# cache[hashed] = return_value
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# return return_value
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def select_features(X: pd.DataFrame, y: pd.Series, model: Model, n_features_to_select: int, backup_model: SKLearnModel, scaling: ScalerTypes) -> pd.DataFrame:
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''' Select features using RFECV, returns a pd.DataFrame (X) with only the selected features.'''
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if model.model_type != 'ml': return X
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# 2. Recursive feature selection
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cv = TimeSeriesSplit(n_splits=5)
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scaler = get_scaler(scaling)
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X_scaled = scaler.fit_transform(X)
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feat_selector_model = model.model
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if hasattr(feat_selector_model, 'feature_importances_') == False and hasattr(feat_selector_model, 'coef_') == False:
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feat_selector_model = backup_model.model
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# selector = RFECV(feat_selector_model, cv = cv, step=5, min_features_to_select=min_features_to_select)
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step = 0.05
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selector = RFE(feat_selector_model, n_features_to_select= n_features_to_select, step=step)
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selector = selector.fit(X_scaled, y)
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print("Kept %d features out of %d" % (selector.n_features_, X_scaled.shape[1]))
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return pd.DataFrame(X[X.columns[selector.support_]], index= X.index)
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