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refactor(Training): use date indexes instead of integers, need this to prepare for Events (#185)

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This commit is contained in:
Mark Aron Szulyovszky
2022-01-24 12:22:30 +01:00
committed by GitHub
parent e80fffdb65
commit e6e2317fe0
15 changed files with 50 additions and 52 deletions
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data_loader/load_data.py
+3 -11
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@@ -29,7 +29,6 @@ def __load_data(assets: DataCollection,
own_features: list[tuple[str, FeatureExtractor, list[int]]],
other_features: list[tuple[str, FeatureExtractor, list[int]]],
exogenous_features: list[tuple[str, FeatureExtractor, list[int]]],
index_column: Literal['date', 'int'],
no_of_classes: Literal['two', 'three-balanced', 'three-imbalanced'],
) -> tuple[pd.DataFrame, pd.Series, pd.Series]:
"""
@@ -83,15 +82,12 @@ def __load_data(assets: DataCollection,
dfs = pd.concat([df.sort_index().reindex(target_df.index) for df in dfs], axis=1).fillna(0.)
dfs.index = pd.DatetimeIndex(dfs.index)
if index_column == 'int':
dfs.reset_index(drop=True, inplace=True)
df_target_asset_only_returns.reset_index(drop=True, inplace=True)
## Create target
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)
forward_returns.index = pd.DatetimeIndex(dfs.index)
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]
@@ -216,7 +212,7 @@ def datasource_to_file(data_source: DataSource) -> str:
def load_only_returns(assets: DataCollection, index_column: Literal['date', 'int'], returns: Literal['price', 'returns']) -> pd.DataFrame:
def load_only_returns(assets: DataCollection, returns: Literal['price', 'returns']) -> pd.DataFrame:
assets_future = [__load_df.remote(
data_source=data_source,
@@ -228,10 +224,6 @@ def load_only_returns(assets: DataCollection, index_column: Literal['date', 'int
dfs = [deduplicate_indexes(df) for df in target_asset_df]
dfs = pd.concat(dfs, axis=1)
# dfs = dfs.applymap(lambda x: np.nan if x == 0 else x)
dfs.index = pd.DatetimeIndex(dfs.index)
if index_column == 'int':
dfs.reset_index(drop=True, inplace=True)
return dfs