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sgd_regression.py
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sgd_regression.py
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#!/usr/bin/env python3
# Copyright (C) 2017 LREN CHUV for Human Brain Project
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from mip_helper import io_helper, shapes, errors, utils, parameters
from sklearn_to_pfa.sklearn_to_pfa import sklearn_to_pfa
from sklearn_to_pfa.featurizer import Featurizer, Standardize, OneHotEncoding
from sklearn_to_pfa.mixed_nb import MixedNB
import logging
from pandas.io import json
import argparse
from sklearn.linear_model import SGDRegressor, SGDClassifier
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
import jsonpickle
import jsonpickle.ext.numpy as jsonpickle_numpy
jsonpickle_numpy.register_handlers()
# Configure logging
logging.basicConfig(level=logging.INFO)
DEFAULT_DOCKER_IMAGE = "python-sgd-regression"
@utils.catch_user_error
def main(job_id, generate_pfa):
inputs = io_helper.fetch_data()
dep_var = inputs["data"]["dependent"][0]
indep_vars = inputs["data"]["independent"]
if dep_var['type']['name'] in ('polynominal', 'binominal'):
job_type = 'classification'
else:
job_type = 'regression'
# Get existing results with partial model if they exist
if job_id:
job_result = io_helper.get_results(job_id=str(job_id))
logging.info('Loading existing estimator')
estimator = deserialize_sklearn_estimator(job_result.data['estimator'])
else:
logging.info('Creating new estimator')
estimator = _create_estimator(job_type)
# featurization
featurizer = _create_featurizer(indep_vars, estimator)
# convert variables into dataframe
X = io_helper.fetch_dataframe(variables=[dep_var] + indep_vars)
X = utils.remove_nulls(X, errors='ignore')
y = X.pop(dep_var['name'])
X = featurizer.transform(X)
if len(X) == 0:
# log error, but still save the estimator
logging.warning("All data are NULL, cannot fit model")
else:
# Train single step
if hasattr(estimator, 'partial_fit'):
if job_type == 'classification':
estimator.partial_fit(X, y, classes=dep_var['type']['enumeration'])
else:
estimator.partial_fit(X, y)
else:
if not generate_pfa:
logging.warning('{} does not support partial fit.'.format(estimator))
estimator.fit(X, y)
if generate_pfa:
# Create PFA from the estimator
types = [(var['name'], var['type']['name']) for var in indep_vars]
# Estimator was not trained on any data
if not _is_fitted(estimator):
raise errors.UserError('Model was not fitted on any data, cannot generate PFA.')
pfa = sklearn_to_pfa(estimator, types, featurizer.generate_pretty_pfa())
# Add serialized model as metadata
pfa['metadata'] = _estimator_metadata(estimator, X, y, featurizer)
model_type = parameters.get_parameter('type', str, 'linear_model')
pfa['name'] = model_type
# Save or update job_result
logging.info('Saving PFA to job_results table')
pfa = json.dumps(pfa)
io_helper.save_results(pfa, shapes.Shapes.PFA)
else:
# Save or update job_result
logging.info('Saving serialized estimator into job_results table')
io_helper.save_results(_estimator_metadata(estimator, X, y, featurizer), shapes.Shapes.JSON)
def _estimator_metadata(estimator, X, y, featurizer):
"""Serialize estimator and add score and other metadata."""
meta = {
'estimator': serialize_sklearn_estimator(estimator),
}
if len(X) and hasattr(estimator, 'score'):
meta['score'] = str(estimator.score(X, y))
if hasattr(estimator, 'coef_'):
meta['coef_'] = str(estimator.coef_)
if hasattr(estimator, 'intercept_'):
meta['intercept_'] = str(estimator.intercept_)
if hasattr(estimator, 'feature_importances_') and hasattr(featurizer, 'columns'):
meta['feature_importances_'] = str(dict(zip(featurizer.columns, estimator.feature_importances_)))
return meta
def serialize_sklearn_estimator(estimator):
"""Serialize model to JSON, see https://cmry.github.io/notes/serialize for inspiration."""
return jsonpickle.encode(estimator)
def deserialize_sklearn_estimator(js):
"""Deserialize model from JSON."""
return jsonpickle.decode(js)
def _create_estimator(job_type):
model_parameters = parameters.fetch_parameters()
model_type = model_parameters.pop('type', 'linear_model')
if job_type == 'regression':
if model_type == 'linear_model':
estimator = SGDRegressor(**model_parameters)
elif model_type == 'neural_network':
estimator = MLPRegressor(**model_parameters)
elif model_type == 'gradient_boosting':
estimator = GradientBoostingRegressor(**model_parameters)
else:
raise errors.UserError('Unknown model type {} for regression'.format(model_type))
elif job_type == 'classification':
if model_type == 'linear_model':
estimator = SGDClassifier(**model_parameters)
elif model_type == 'neural_network':
estimator = MLPClassifier(**model_parameters)
elif model_type == 'naive_bayes':
estimator = MixedNB(**model_parameters)
elif model_type == 'gradient_boosting':
estimator = GradientBoostingClassifier(**model_parameters)
else:
raise errors.UserError('Unknown model type {} for classification'.format(model_type))
return estimator
def _is_fitted(estimator):
"""Return whether estimator is fitted or not."""
# TODO: put to utils
if isinstance(estimator, MixedNB):
return hasattr(estimator.multi_nb, 'classes_') or hasattr(estimator.gauss_nb, 'classes_')
elif isinstance(estimator, (SGDRegressor, SGDClassifier)):
return hasattr(estimator, 'coef_')
elif isinstance(estimator, (MLPRegressor, MLPClassifier)):
return hasattr(estimator, 'coefs_')
elif isinstance(estimator, (GradientBoostingRegressor, GradientBoostingClassifier)):
return estimator._is_initialized()
else:
raise NotImplementedError('_is_fitted method is not implemented for {}'.format(estimator))
def _create_featurizer(indep_vars, estimator):
transforms = []
for var in indep_vars:
if var['type']['name'] in ('integer', 'real'):
if 'mean' not in var:
logging.warning('Mean not available for variable {}, using default value 0.'.format(var['name']))
if 'std' not in var:
logging.warning('Standard deviation not available for variable {}, using default value 1.'.format(var['name']))
transforms.append(Standardize(var['name'], var.get('mean', 0), var.get('std', 1)))
elif var["type"]["name"] in ['polynominal', 'binominal']:
transforms.append(OneHotEncoding(var['name'], var['type']['enumeration']))
# for NaiveBayes, continuous variables must go before nominal ones
if isinstance(estimator, MixedNB):
transforms = sorted(transforms, key=lambda x: not isinstance(x, Standardize))
is_nominal = []
for tf in transforms:
if isinstance(tf, Standardize):
is_nominal.append(False)
elif isinstance(tf, OneHotEncoding):
is_nominal += [True] * len(tf.enumerations)
estimator.is_nominal = is_nominal
return Featurizer(transforms)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('compute', choices=['compute'])
parser.add_argument('--mode', choices=['partial', 'final'], default='final')
parser.add_argument('--job-id', type=str)
args = parser.parse_args()
# > compute partial --job-id 12
if args.mode == 'partial':
main(args.job_id, generate_pfa=False)
# > compute final --job-id 13
elif args.mode == 'final':
main(args.job_id, generate_pfa=True)