def _save(self, X, y,
save_models=True,
save_predictions=True,
save_probas=True):
# check fitted
check_has_set_attr(self, 'is_trained')
# check X, y
check_consistent_length(X, y)
# lazy don't want to handle single axis tensor
X = ensure_2d_array(X, axis=1)
y = ensure_2d_array(y, axis=1)
# check locations
if self.save_location is None:
logger.warning('Warning! Nothing gets saved. '
'Please reset save_location '
'if you want to write results to disk')
# save object
self.preds_dict = {}
self.probas_dict = {}
for i, model in self.model_dict.items():
# save model
if self.models_location and save_models:
self.save_model(model, name='model_{}'.format(i))
# predict
if hasattr(model, 'predict'):
self.preds_dict = {
**self.preds_dict,
**{i: model.predict(X[self.cv[i][1]])}
}
else:
logger.warning('Model does NOT implement predict')
# predict_proba
if hasattr(model, 'predict_proba'):
self.probas_dict = {
**self.probas_dict,
**{i: model.predict_proba(X[self.cv[i][1]])}
}
else:
logger.warning('Model does NOT implement predict_proba')
# collect data
if self.preds_dict:
preds_list = list(self.preds_dict.values())
self.pred_out_of_sample = np.vstack(preds_list)
# save pred
if self.predictions_location and save_predictions:
self.save_prediction(self.pred_out_of_sample)
if self.probas_dict:
probas_list = list(self.probas_dict.values())
self.proba_out_of_sample = np.vstack(probas_list)
# save probas
if self.probas_location and save_probas:
self.save_proba(self.proba_out_of_sample)
if self.verbose > 0:
logger.info('Saving is done')
def __init__(self,
estimator,
pct_threshold=1.0,
groups=None,
scoring=None,
cv=None,
n_jobs=1,
verbose=0,
pre_dispatch='2*n_jobs'):
check_has_set_attr(estimator, 'fit')
self.estimator = estimator
self.pct_threshold = pct_threshold
self.groups = groups
self.scoring = scoring
self.cv = cv
self.n_jobs = n_jobs
self.verbose = verbose
self.pre_dispatch = pre_dispatch
def evaluate(self, X=None, y=None,
level='date',
scoring=None,
aggregator=None,
**score_kwargs):
"""
This is a convenient method for quick evaluating out-of-sample scores
from gravity research. The score will be calculated on per level basis
NOTE it's designed specifically for gravity research. It can be further
refactored into new forms for other research
NOTE it does NOT support 'kind', because it always assume using probas
for scoring evaluations
Parameters
----------
X : X is NOT required
y : y has to be the same y passed in its train method
level : str, one of ['date', 'tradingitemid']
scoring : dictionary with {metrics name: metrics callable}
eg. {'accuracy': sklearn.metrics.accuracy_score}
Default is top_bottom_accuracy_score
aggregator: a function or a callable, to aggregate a vector
**score_kwargs : this is passed to metrics callable
Returns
-------
score_dict : a dictionary of score
eg. {
'level': ['2007-01-05', '2007-01-12', '2007-01-19'],
'accuracy': [0.84, 0.92, 0.86],
'roc_auc': [0.72, 0.77, 0.73]
}
"""
allowed_level = ['date', 'tradingitemid']
if level not in allowed_level:
raise ValueError('level must be one of {}'.format(allowed_level))
# check y
if y is None:
raise ValueError('You must pass in y')
else:
check_gravity_index(y)
# join out of sample probas with out of sample groud truth
check_has_set_attr(self, 'proba_out_of_sample')
check_gravity_index(self.proba_out_of_sample)
# check ndim of self.proba_out_of_sample
if np.ndim(self.proba_out_of_sample) == 2:
df_join = self.proba_out_of_sample.iloc[:, -1:].join(y, how='left')
else: # else if ndim is 1
df_join = self.proba_out_of_sample.join(y, how='left')
# check scoring
if scoring is None:
scoring = {'accuracy': top_bottom_accuracy_score}
# score out of sample
score_dict = \
{level: df_join.index.get_level_values(level).unique().values}
for name, score in scoring.items():
# get scores for every point on level
scores_list = df_join.groupby(level=level).apply(
lambda df: score(
df.iloc[:, 1],
df.iloc[:, 0],
**score_kwargs)
).values
# save scores with score name in score_dict
score_dict = {
**score_dict,
**{name: scores_list}
}
# aggregator
if aggregator:
score_dict = {
name: aggregator(scores)
for (name, scores) in score_dict.items() if name != level
}
return score_dict
def _save(self, X, y,
save_models=True,
save_predictions=True,
save_probas=True):
# check fitted
check_has_set_attr(self, 'is_trained')
# check X, y
check_consistent_length(X, y)
if not self.is_dataframe:
if not isinstance(X, (pd.DataFrame, pd.Series)):
X = ensure_2d_array(X, axis=1)
X = pd.DataFrame(X)
if not isinstance(y, (pd.DataFrame, pd.Series)):
y = ensure_2d_array(y, axis=1)
y = pd.DataFrame(y)
# check locations
if self.save_location is None:
logger.warning('Warning! Nothing gets saved. '
'Please reset save_location '
'if you want to write results to disk')
# save object
self.preds_dict = {}
self.probas_dict = {}
for i, model in self.model_dict.items():
# save model
if save_models:
self.save_model(model, name='model_{}'.format(i))
# pred
if hasattr(model, 'predict'):
self.preds_dict = {
**self.preds_dict,
**{
i: pd.DataFrame(
model.predict(X.iloc[self.cv[i][1]]),
index=X.iloc[self.cv[i][1]].index
)
}
}
else:
logger.warning('Model does NOT implement predict')
# probas
if hasattr(model, 'predict_proba'):
self.probas_dict = {
**self.probas_dict,
**{
i: pd.DataFrame(
model.predict_proba(X.iloc[self.cv[i][1]]),
index=X.iloc[self.cv[i][1]].index
)
}
}
else:
logger.warning('Model does NOT implement predict_proba')
if self.preds_dict:
preds_list = list(self.preds_dict.values())
self.pred_out_of_sample = \
pd.concat(preds_list, verify_integrity=True).sort_index()
# save pred
if self.predictions_location and save_predictions:
self.save_prediction(self.pred_out_of_sample)
if self.probas_dict:
probas_list = list(self.probas_dict.values())
self.proba_out_of_sample = \
pd.concat(probas_list, verify_integrity=True).sort_index()
# save probas
if self.probas_location and save_probas:
self.save_proba(self.proba_out_of_sample)
if self.verbose > 0:
logger.info('Saving is done')
def evaluate(self, X=None, y=None,
kind='prediction',
scoring=None,
aggregator=None,
**score_kwargs):
"""
This is a convenient method for quick evaluating out-of-sample scores
Parameters
----------
X : X is NOT required
y : y has to be the same y passed in its train method
kind : str, one of ['prediction', 'proba']. If 'prediction' is chosen,
then it will score prediction against out of sample targets
If 'proba' is chosen, then it will score proba against
out of sample targets
scoring : dictionary with {metrics name: metrics callable}
eg. {'accuracy': sklearn.metrics.accuracy_score}
Default is accuracy
aggregator: a function or a callable, to aggregate a vector
**score_kwargs : this is passed to metrics callable
Returns
-------
score_dict : a dictionary of score
eg. {
'accuracy': [0.84, 0.92, 0.86, 0.78],
'roc_auc': [0.72, 0.77, 0.73, 0.69]
}
"""
allowed_kind = ['prediction', 'proba']
if kind not in allowed_kind:
raise ValueError('kind must be one of {}'.format(allowed_kind))
if kind == 'prediction':
check_has_set_attr(self, 'preds_dict')
y_hat_dict = self.preds_dict
else: # kind == 'proba'
check_has_set_attr(self, 'probas_dict')
y_hat_dict = self.probas_dict
for i, y_probas in y_hat_dict.items():
if np.dim(y_probas) == 2:
y_hat_dict[i] = y_probas[:, -1]
# check y
if y is None:
raise ValueError('You must pass in y')
else:
y = force_array(y)
# check scoring
if scoring is None:
scoring = {'accuracy': accuracy_score}
# score out of sample
score_dict = {}
for name, score in scoring.items():
# get scores for every folds
scores_list = [
score(y[self.cv[i][1]], y_hat_dict[i], **score_kwargs)
for i in range(len(self.cv))
]
# save scores with score name in score_dict
score_dict = {
**score_dict,
**{name: scores_list}
}
# aggregator
if aggregator:
score_dict = {
name: aggregator(scores)
for (name, scores) in score_dict.items()
}
return score_dict
def get_probas_dict(self):
# check fitted
check_has_set_attr(self, 'probas_dict')
return self.probas_dict
def get_out_of_sample_probas(self):
# check fitted
check_has_set_attr(self, 'proba_out_of_sample')
return self.proba_out_of_sample
def get_out_of_sample_predictions(self):
# check fitted
check_has_set_attr(self, 'pred_out_of_sample')
return self.pred_out_of_sample
def get_trained_model_dict(self):
# check fitted
check_has_set_attr(self, 'is_trained')
return self.model_dict
def get_model_dict(self):
# check fitted
check_has_set_attr(self, 'model_dict')
return self.model_dict
def save_proba(self, proba, name='proba'):
check_has_set_attr(self, 'probas_location')
pathlib.Path(self.probas_location).mkdir(parents=True, exist_ok=True)
filepath = os.path.join(self.probas_location, '{}.pkl'.format(name)) # noqa
save_object(proba, filepath)
def save_model(self, model, name='model'):
check_has_set_attr(self, 'models_location')
pathlib.Path(self.models_location).mkdir(parents=True, exist_ok=True)
filepath = os.path.join(self.models_location, '{}.pkl'.format(name))
save_object(model, filepath)
