def get_feature_frames_or_fetch(self, df, symbol, features_and_labels):
fkey = f'{symbol}__features'
# note! features can be a MultiFrameDecorator !!
if isinstance(features_and_labels.features, tuple):
fkeys = [
f'{symbol}__features__{i}'
for i in range(len(features_and_labels.features))
]
features = [
self.file_cache[fkey] for fk in self.file_cache if fk in fkeys
]
if len(features) == len(fkeys):
features = MultiFrameDecorator(features)
else:
features, _, _ = extract_features(df, features_and_labels)
for fk, f in zip(fkeys, features.frames()):
self.file_cache[fk] = f
else:
if fkey in self.file_cache:
features = self.file_cache[fkey]
else:
print(f"fetch data for: {symbol}")
features, _, _ = extract_features(df, features_and_labels)
self.file_cache[fkey] = features
return features._.values, features.index
def get_feature_frames_or_fetch(
self, df, symbol,
features_and_labels) -> Tuple[np.ndarray, pd.Index]:
if symbol not in self.features:
features, _, _ = extract_features(df, features_and_labels)
self.features[symbol] = (features._.values, features.index)
return self.features[symbol]
def get_feature_frames_or_fetch(
self, df, symbol,
features_and_labels) -> Tuple[np.ndarray, pd.Index]:
_, features, self._targets = extract_features(df, features_and_labels)
features_index = features.index
features = features._.values
return features, features_index