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feat(Transformations): replaced feature selection pre-processing step with online version (with cache) (#170)
* feat(Transformations): removed feature-selection pre-processing step completely * fix(Core): removed unnecessary `original_X` * fix(Transformations): use the X_expanding_window to transform subsequent data * fix(RFE): should check for model correctly * fix(Config): only re-train the model every 40 timestamp * fix(MetaLabeling): pass in the correct X to meta-labeling step * fix(Transformation): PCA should at least keep as many features as sliding_window_size * feat(Transformations): cache transformations across the same asset * fix(Tests): missing preloaded_transformations arg * chore(Config): got rid of unnecessary 'classification_models' and 'regression_models' dictionary keys
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@@ -47,11 +47,11 @@ parameters:
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primary_models:
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distribution: categorical
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values:
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- ["LDA", "LR_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LR_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LR_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
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- ["LDA", "LogisticRegression_two_class", "KNN", "SVC", "CART", "NB", "AB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LogisticRegression_two_class", "LDA", "NB", "RFC", "XGB_two_class", "LGBM", "StaticMom"]
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- ["LogisticRegression_two_class", "LDA", "LGBM", "RFC", "XGB_two_class"]
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meta_labeling_models:
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value: ["LGBM", "LR_two_class"]
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value: ["LGBM", "LogisticRegression_two_class"]
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own_features:
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value: ['date_days', 'level_2', 'lags_up_to_5']
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other_features:
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