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refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classification later) (#182)

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* refactor(Project): removed regression method (we can still use regression models, but we'll need map them to classes later)

* fix(Training): removed mistakenly left in `method` parameter
This commit is contained in:
Mark Aron Szulyovszky
2022-01-23 17:15:08 +01:00
committed by GitHub
parent 516c8bcc87
commit 5c4a5b0cf1
12 changed files with 11 additions and 44 deletions
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data_loader/load_data.py
+1 -5
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@@ -30,7 +30,6 @@ def __load_data(assets: DataCollection,
other_features: list[tuple[str, FeatureExtractor, list[int]]],
exogenous_features: list[tuple[str, FeatureExtractor, list[int]]],
index_column: Literal['date', 'int'],
method: Literal['regression', 'classification'],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
narrow_format: bool = False
) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
@@ -105,10 +104,7 @@ def __load_data(assets: DataCollection,
target_col = 'target'
returns_col = target_asset[1] + '_returns'
forward_returns = __create_target_cum_forward_returns(df_target_asset_only_returns, returns_col, forecasting_horizon)
if method == 'regression':
dfs[target_col] = forward_returns
elif method == 'classification':
dfs[target_col] = __create_target_classes(dfs, returns_col, forecasting_horizon, no_of_classes)
dfs[target_col] = __create_target_classes(dfs, returns_col, forecasting_horizon, no_of_classes)
# we need to drop the last row, because we forward-shift the target (see what happens if you call .shift[-1] on a pd.Series)
dfs = dfs.iloc[:-forecasting_horizon]
forward_returns = forward_returns.iloc[:-forecasting_horizon]