def test_from_time_series_transform(self, dictList_stock,
dictList_portfolio):
data = pd.DataFrame(dictList_stock)
tst = Time_Series_Transformer.from_pandas(data, 'Date', None)
stockTrans = Stock_Transformer.from_pandas(data, 'Date', None)
test = Stock_Transformer.from_time_series_transformer(tst)
assert test == stockTrans
data = pd.DataFrame(dictList_portfolio)
tst = Time_Series_Transformer.from_pandas(data, 'Date', 'symbol')
stockTrans = Stock_Transformer.from_pandas(data, 'Date', 'symbol')
test = Stock_Transformer.from_time_series_transformer(tst)
assert stockTrans == test
def transform(self, X, y=None):
"""
transform prepare data as Stock_Transformer and helper data
Parameters
----------
X : pandas DataFrame or numpy ndArray
input values
y : depreciated not used, optional
following sklearn convention (not used), by default None
Returns
-------
tst Stock_Transformer
the output Stock_Transformer
X_time list
time column list
X_header list
column name list
X_category list
category name list
"""
tst, X_time, X_header, X_category = super().transform(X, y)
tst = Stock_Transformer.from_time_series_transformer(
tst,
High=self.high,
Low=self.low,
Close=self.close,
Open=self.open,
Volume=self.volume)
return tst, X_time, X_header, X_category