def get_feature_pipelines(sort_by=FeaturePipeline.start_time,
response_type="json",
**kwargs):
assert_response_type(response_type)
q = session.query(FeaturePipeline)
q = sort_and_filter(q, sort_by=sort_by, **kwargs)
return build_response(q.all(), response_type)
def get_predictors(sort_by=Predictor.created_at,
response_type="json",
**kwargs):
assert_response_type(response_type)
q = session.query(Predictor)
q = sort_and_filter(q, sort_by=sort_by, **kwargs)
if response_type == 'object':
return q.all()
else:
return build_response(q.all(), response_type)
def get_features(self, train_config):
numeric_feature_objs = session.query(Feature).filter_by(
feature_pipeline=self,
window_start=train_config.window_start_numeric,
window_end=train_config.window_end_numeric,
feature_type=train_config.feature_type_numeric).all()
numeric_feature_dfs = [
pd.read_csv(f.path) for f in numeric_feature_objs
]
occurring_feature_objs = session.query(Feature).filter_by(
feature_pipeline=self,
window_start=train_config.window_start_occurring,
window_end=train_config.window_end_occurring,
feature_type=train_config.feature_type_occurring).all()
occurring_feature_dfs = [
pd.read_csv(f.path) for f in occurring_feature_objs
]
return numeric_feature_dfs, occurring_feature_dfs
def get_training_results(
pipeline_id=None,
columns=DEFAULT_COLUMNS,
metrics=DEFAULT_METRICS,
response_type="json",
**kwargs
):
assert_response_type(response_type)
pipeline_id = pipeline_id or request.args.get('pipeline', type=int)
assert pipeline_id is not None
q = session.query(
TrainingResult
).join(
TrainingConfiguration,
TrainingResult.training_configuration_id == TrainingConfiguration.id
).filter(
TrainingConfiguration.training_pipeline_id == pipeline_id
).with_entities(
*columns,
*metrics
)
q = sort_and_filter(q, **kwargs)
results = [row._asdict() for row in q]
return build_response(results, response_type)
def load(cls, id):
obj = session.query(cls).filter_by(id=id).first()
return obj
def load_by_config(cls, training_configuration, algorithm, sampler):
obj = session.query(cls).filter_by(
training_configuration_id=training_configuration.id,
algorithm=algorithm,
sampler=sampler).first()
return obj
def load(cls, id):
obj = session.query(cls).filter_by(
training_configuration_id=id).first()
return obj
def load_by_config(cls, training_pipeline_id, config):
obj = session.query(cls).filter_by(
training_pipeline_id=training_pipeline_id, **config).first()
return obj
def load_features_and_transform(training_configuration,
data_loader,
bin_size=30,
persist_data=True):
"""
Load features from feature pipeline.
Then apply the DataLoader for feature transformation.
"""
target = training_configuration.target
cohort = training_configuration.training_pipeline.cohort.get_fiber()
onset_df = training_configuration.training_pipeline.onset_dataframe.get_df(
target)
feature_pipeline = training_configuration.training_pipeline.feature_pipeline
numeric_feature_dfs = []
if training_configuration.feature_type_numeric == 'numeric_binned':
window = training_configuration.window_start_numeric, training_configuration.window_end_numeric
for w in range(window[0], window[1], bin_size):
numeric_feature_objs = session.query(Feature).filter_by(
feature_pipeline=feature_pipeline,
feature_type=training_configuration.feature_type_numeric,
window_start=w,
window_end=w + bin_size).all()
cur_numeric_dfs = [
pd.read_csv(f.path) for f in numeric_feature_objs
]
for df in cur_numeric_dfs:
df.set_index(OCCURRENCE_INDEX, inplace=True)
new_cols = [
get_name_for_interval(c, [w, w + bin_size])
for c in df.columns
]
df.columns = new_cols
df.reset_index(inplace=True)
numeric_feature_dfs += cur_numeric_dfs
_, occurring_feature_dfs = feature_pipeline.get_features(
training_configuration)
else:
numeric_feature_dfs, occurring_feature_dfs = feature_pipeline.get_features(
training_configuration)
cohort.occurrences.medical_record_number = cohort.occurrences.medical_record_number.astype(
int)
onset_df.medical_record_number = onset_df.medical_record_number.astype(int)
if training_configuration.feature_type_numeric == "numeric_time_series":
pivoted_dfs = []
for df in numeric_feature_dfs:
numeric_df = threshold_clip_time_series(
df=df,
cohort=cohort,
threshold=training_configuration.threshold_numeric)
pivoted_dfs.append(
pivot_time_series(
cohort=cohort,
onset_df=onset_df,
df=numeric_df,
))
numeric_df = merge_to_base(cohort.occurrences, pivoted_dfs)
else:
numeric_df = merge_to_base(cohort.occurrences, [
x.filter(regex=(data_loader.column_selector +
"|medical_record_number|age_in_days"))
for x in numeric_feature_dfs
])
numeric_df = column_threshold_clip(
df=numeric_df, threshold=training_configuration.threshold_numeric)
occurring_df = merge_to_base(cohort.occurrences, [
x.filter(regex=(data_loader.column_selector +
"|medical_record_number|age_in_days"))
for x in occurring_feature_dfs
])
occurring_df = column_threshold_clip(
df=occurring_df, threshold=training_configuration.threshold_occurring)
cohort.occurrences.medical_record_number = cohort.occurrences.medical_record_number.astype(
str)
numeric_df.medical_record_number = numeric_df.medical_record_number.astype(
str)
occurring_df.medical_record_number = occurring_df.medical_record_number.astype(
str)
onset_df.medical_record_number = onset_df.medical_record_number.astype(str)
## Merge to cohort data and use user's data loader
data = cohort.merge_patient_data(
onset_df,
numeric_df,
occurring_df,
)
## persist training data
if persist_data:
TrainingData.persist(training_configuration=training_configuration,
data=data)
X, y = data_loader.transform(X=data.drop(columns=[target]), y=data[target])
return X, y
def get_cohorts(sort_by=Cohort.created_at, response_type="json", **kwargs):
assert_response_type(response_type)
q = session.query(Cohort)
q = sort_and_filter(q, sort_by=sort_by, **kwargs)
return build_response(q.all(), response_type)
