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feat(Evaluation): created a unified evaluation framework for both regression / classification

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Mark Aron Szulyovszky
2021-12-14 21:25:43 +01:00
parent 1ef314c034
commit beb281fc3a
4 changed files with 46 additions and 50 deletions
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load_data.py
+4 -4
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@@ -56,9 +56,9 @@ def load_data(path: str,
target_col = 'target'
returns_col = target_asset + '_returns'
if method == 'regression':
dfs = create_target_cum_forward_returns(dfs, returns_col, 1)
dfs = __create_target_cum_forward_returns(dfs, returns_col, 1)
elif method == 'classification':
dfs = create_target_classes(dfs, returns_col, 1, 'two')
dfs = __create_target_classes(dfs, returns_col, 1, 'two')
X = dfs.drop(columns=[target_col])
y = dfs[target_col]
@@ -130,13 +130,13 @@ def __augment_derived_features(df: pd.DataFrame, log_returns: bool, technical_fe
# %%
def create_target_cum_forward_returns(df: pd.DataFrame, source_column: str, period: int) -> pd.DataFrame:
def __create_target_cum_forward_returns(df: pd.DataFrame, source_column: str, period: int) -> pd.DataFrame:
df['target'] = df[source_column].diff(period).shift(-period)
df = df.iloc[:-period]
return df
def create_target_classes(df: pd.DataFrame, source_column: str, period: int, no_of_classes: Literal["two", "three"]) -> pd.DataFrame:
def __create_target_classes(df: pd.DataFrame, source_column: str, period: int, no_of_classes: Literal["two", "three"]) -> pd.DataFrame:
def get_class_binary(x):
return 0 if x <= 0.0 else 1