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pipelines.py
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pipelines.py
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from functools import partial
from sklearn.metrics import roc_auc_score
import feature_extraction as fe
from hyperparameter_tuning import RandomSearchOptimizer, NeptuneMonitor, SaveResults
from steps.adapters import to_numpy_label_inputs, identity_inputs
from steps.base import Step, Dummy
from steps.misc import LightGBM
def baseline(config, train_mode):
if train_mode:
features, features_valid = feature_extraction_v0(config, train_mode)
light_gbm = classifier_lgbm((features, features_valid), config, train_mode)
else:
features = feature_extraction_v0(config, train_mode)
light_gbm = classifier_lgbm(features, config, train_mode)
output = Step(name='output',
transformer=Dummy(),
input_steps=[light_gbm],
adapter={'y_pred': ([(light_gbm.name, 'prediction')]),
},
cache_dirpath=config.env.cache_dirpath)
return output
def solution_1(config, train_mode):
if train_mode:
features, features_valid = feature_extraction_v1(config, train_mode,
save_output=True, cache_output=True, load_saved_output=False)
light_gbm = classifier_lgbm((features, features_valid), config, train_mode)
else:
features = feature_extraction_v1(config, train_mode, cache_output=True)
light_gbm = classifier_lgbm(features, config, train_mode)
output = Step(name='output',
transformer=Dummy(),
input_steps=[light_gbm],
adapter={'y_pred': ([(light_gbm.name, 'prediction')]),
},
cache_dirpath=config.env.cache_dirpath)
return output
def feature_extraction_v0(config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = _feature_by_type_splits(config, train_mode)
categorical_features = Step(name='categorical_features',
transformer=Dummy(),
input_steps=[feature_by_type_split],
adapter={'categorical_features': (
[(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
categorical_features_valid = Step(name='categorical_features_valid',
transformer=Dummy(),
input_steps=[feature_by_type_split_valid],
adapter={'categorical_features': (
[(feature_by_type_split_valid.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
feature_combiner = _join_features(numerical_features=[],
numerical_features_valid=[],
categorical_features=[categorical_features],
categorical_features_valid=[categorical_features_valid],
config=config, train_mode=train_mode)
return feature_combiner
else:
feature_by_type_split = _feature_by_type_splits(config, train_mode)
categorical_features = Step(name='categorical_features',
transformer=Dummy(),
input_steps=[feature_by_type_split],
adapter={'categorical_features': (
[(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
feature_combiner = _join_features(numerical_features=[],
numerical_features_valid=[],
categorical_features=[categorical_features],
categorical_features_valid=[],
config=config, train_mode=train_mode)
return feature_combiner
def feature_extraction_v1(config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = _feature_by_type_splits(config, train_mode)
time_delta, time_delta_valid = _time_deltas((feature_by_type_split, feature_by_type_split_valid),
config, train_mode, **kwargs)
confidence_rate, confidence_rate_valid = _confidence_rates((feature_by_type_split, feature_by_type_split_valid),
config, train_mode, **kwargs)
feature_combiner, feature_combiner_valid = _join_features(numerical_features=[time_delta, confidence_rate],
numerical_features_valid=[time_delta_valid,
confidence_rate_valid],
categorical_features=[time_delta, confidence_rate],
categorical_features_valid=[time_delta_valid,
confidence_rate_valid],
config=config, train_mode=train_mode,
**kwargs)
return feature_combiner, feature_combiner_valid
else:
feature_by_type_split = _feature_by_type_splits(config, train_mode)
time_delta = _time_deltas(feature_by_type_split, config, train_mode, **kwargs)
confidence_rate = _confidence_rates(feature_by_type_split, config, train_mode, **kwargs)
feature_combiner = _join_features(numerical_features=[time_delta, confidence_rate],
numerical_features_valid=[],
categorical_features=[time_delta, confidence_rate],
categorical_features_valid=[],
config=config, train_mode=train_mode,
**kwargs)
return feature_combiner
def classifier_lgbm(features, config, train_mode, **kwargs):
if train_mode:
features_train, features_valid = features
if config.random_search.light_gbm.n_runs:
transformer = RandomSearchOptimizer(LightGBM, config.light_gbm,
train_input_keys=[],
valid_input_keys=['X_valid', 'y_valid'],
score_func=roc_auc_score,
maximize=True,
n_runs=config.random_search.light_gbm.n_runs,
callbacks=[NeptuneMonitor(
**config.random_search.light_gbm.callbacks.neptune_monitor),
SaveResults(
**config.random_search.light_gbm.callbacks.save_results),
]
)
else:
transformer = LightGBM(**config.light_gbm)
light_gbm = Step(name='light_gbm',
transformer=transformer,
input_data=['input'],
input_steps=[features_train, features_valid],
adapter={'X': ([(features_train.name, 'features')]),
'y': ([('input', 'y')], to_numpy_label_inputs),
'feature_names': ([(features_train.name, 'feature_names')]),
'categorical_features': ([(features_train.name, 'categorical_features')]),
'X_valid': ([(features_valid.name, 'features')]),
'y_valid': ([('input', 'y_valid')], to_numpy_label_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
else:
light_gbm = Step(name='light_gbm',
transformer=LightGBM(**config.light_gbm),
input_steps=[features],
adapter={'X': ([(features.name, 'features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return light_gbm
def _feature_by_type_splits(config, train_mode):
if train_mode:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(**config.dataframe_by_type_splitter),
input_data=['input'],
adapter={'X': ([('input', 'X')]),
},
cache_dirpath=config.env.cache_dirpath)
feature_by_type_split_valid = Step(name='feature_by_type_split_valid',
transformer=feature_by_type_split,
input_data=['input'],
adapter={'X': ([('input', 'X_valid')]),
},
cache_dirpath=config.env.cache_dirpath)
return feature_by_type_split, feature_by_type_split_valid
else:
feature_by_type_split = Step(name='feature_by_type_split',
transformer=fe.DataFrameByTypeSplitter(**config.dataframe_by_type_splitter),
input_data=['input'],
adapter={'X': ([('input', 'X')]),
},
cache_dirpath=config.env.cache_dirpath)
return feature_by_type_split
def _categorical_frequency_filters(dispatchers, config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = dispatchers
categorical_filter = Step(name='categorical_filter',
transformer=fe.CategoricalFilter(**config.categorical_filter),
input_steps=[feature_by_type_split],
adapter={
'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
categorical_filter_valid = Step(name='categorical_filter_valid',
transformer=categorical_filter,
input_steps=[feature_by_type_split_valid],
adapter={'categorical_features': (
[(feature_by_type_split_valid.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return categorical_filter, categorical_filter_valid
else:
feature_by_type_split = dispatchers
categorical_filter = Step(name='categorical_filter',
transformer=fe.CategoricalFilter(**config.categorical_filter),
input_data=['input'],
input_steps=[feature_by_type_split],
adapter={
'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return categorical_filter
def _target_encoders(dispatchers, config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = dispatchers
target_encoder = Step(name='target_encoder',
transformer=fe.TargetEncoder(**config.target_encoder),
input_data=['input'],
input_steps=[feature_by_type_split],
adapter={'X': ([(feature_by_type_split.name, 'categorical_features')]),
'y': ([('input', 'y')], to_numpy_label_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
target_encoder_valid = Step(name='target_encoder_valid',
transformer=target_encoder,
input_data=['input'],
input_steps=[feature_by_type_split_valid],
adapter={'X': ([(feature_by_type_split_valid.name, 'categorical_features')]),
'y': ([('input', 'y_valid')], to_numpy_label_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return target_encoder, target_encoder_valid
else:
feature_by_type_split = dispatchers
target_encoder = Step(name='target_encoder',
transformer=fe.TargetEncoder(**config.target_encoder),
input_data=['input'],
input_steps=[feature_by_type_split],
adapter={'X': ([(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return target_encoder
def _binary_encoders(dispatchers, config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = dispatchers
binary_encoder = Step(name='binary_encoder',
transformer=fe.BinaryEncoder(),
input_steps=[feature_by_type_split],
adapter={'X': ([(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
binary_encoder_valid = Step(name='binary_encoder_valid',
transformer=binary_encoder,
input_steps=[feature_by_type_split_valid],
adapter={'X': ([(feature_by_type_split_valid.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return binary_encoder, binary_encoder_valid
else:
feature_by_type_split = dispatchers
binary_encoder = Step(name='binary_encoder',
transformer=fe.BinaryEncoder(),
input_steps=[feature_by_type_split],
adapter={'X': ([(feature_by_type_split.name, 'categorical_features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return binary_encoder
def _time_deltas(dispatchers, config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = dispatchers
time_delta = Step(name='time_delta',
transformer=fe.TimeDelta(**config.time_delta),
input_steps=[feature_by_type_split],
adapter={'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
'timestamp_features': ([(feature_by_type_split.name, 'timestamp_features')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
time_delta_valid = Step(name='time_delta_valid',
transformer=time_delta,
input_steps=[feature_by_type_split_valid],
adapter={'categorical_features': (
[(feature_by_type_split_valid.name, 'categorical_features')]),
'timestamp_features': (
[(feature_by_type_split_valid.name, 'timestamp_features')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return time_delta, time_delta_valid
else:
feature_by_type_split = dispatchers
time_delta = Step(name='time_delta',
transformer=fe.TimeDelta(**config.time_delta),
input_steps=[feature_by_type_split],
adapter={'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
'timestamp_features': ([(feature_by_type_split.name, 'timestamp_features')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return time_delta
def _confidence_rates(dispatchers, config, train_mode, **kwargs):
if train_mode:
feature_by_type_split, feature_by_type_split_valid = dispatchers
confidence_rates = Step(name='confidence_rates',
transformer=fe.ConfidenceRate(**config.confidence_rate),
input_data=['input'],
input_steps=[feature_by_type_split],
adapter={
'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
'target': ([('input', 'y')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
confidence_rates_valid = Step(name='confidence_rates_valid',
transformer=confidence_rates,
input_data=['input'],
input_steps=[feature_by_type_split_valid],
adapter={'categorical_features': (
[(feature_by_type_split_valid.name, 'categorical_features')]),
'target': ([('input', 'y_valid')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return confidence_rates, confidence_rates_valid
else:
feature_by_type_split = dispatchers
confidence_rates = Step(name='confidence_rates',
transformer=fe.ConfidenceRate(**config.confidence_rate),
input_data=['input'],
input_steps=[feature_by_type_split],
adapter={
'categorical_features': ([(feature_by_type_split.name, 'categorical_features')]),
'target': ([('input', 'y')])
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return confidence_rates
def _join_features(numerical_features, numerical_features_valid,
categorical_features, categorical_features_valid,
config, train_mode,
**kwargs):
if train_mode:
feature_joiner = Step(name='feature_joiner',
transformer=fe.FeatureJoiner(),
input_steps=numerical_features + categorical_features,
adapter={
'numerical_feature_list': (
[(feature.name, 'numerical_features') for feature in numerical_features],
identity_inputs),
'categorical_feature_list': (
[(feature.name, 'categorical_features') for feature in categorical_features],
identity_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
feature_joiner_valid = Step(name='feature_joiner_valid',
transformer=feature_joiner,
input_steps=numerical_features_valid + categorical_features_valid,
adapter={'numerical_feature_list': (
[(feature.name, 'numerical_features') for feature in numerical_features_valid],
identity_inputs),
'categorical_feature_list': (
[(feature.name, 'categorical_features') for feature in
categorical_features_valid],
identity_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return feature_joiner, feature_joiner_valid
else:
feature_joiner = Step(name='feature_joiner',
transformer=fe.FeatureJoiner(),
input_steps=numerical_features + categorical_features,
adapter={
'numerical_feature_list': (
[(feature.name, 'numerical_features') for feature in numerical_features],
identity_inputs),
'categorical_feature_list': (
[(feature.name, 'categorical_features') for feature in categorical_features],
identity_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return feature_joiner
PIPELINES = {'baseline': {'train': partial(baseline, train_mode=True),
'inference': partial(baseline, train_mode=False)},
'solution_1': {'train': partial(solution_1, train_mode=True),
'inference': partial(solution_1, train_mode=False)},
}