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enable continual learning and evaluation sets on multioutput models.

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This commit is contained in:
robcaulk 2022-09-10 16:54:13 +02:00
parent 170bec0438
commit 10b6aebc5f
12 changed files with 170 additions and 38 deletions
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0
freqtrade/freqai/prediction_models/BaseClassifierModel.py → freqtrade/freqai/base_models/BaseClassifierModel.py
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0
freqtrade/freqai/prediction_models/BaseRegressionModel.py → freqtrade/freqai/base_models/BaseRegressionModel.py
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0
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py → freqtrade/freqai/base_models/BaseTensorFlowModel.py
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75
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py Normal file
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@ -0,0 +1,75 @@
from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed
from sklearn.utils.validation import has_fit_parameter
class FreqaiMultiOutputRegressor(MultiOutputRegressor):
def fit(self, X, y, sample_weight=None, fit_params=None):
"""Fit the model to data, separately for each output variable.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If `None`, then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
fit_params : A list of dicts for the fit_params
Parameters passed to the ``estimator.fit`` method of each step.
Each dict may contain same or different values (e.g. different
eval_sets or init_models)
.. versionadded:: 0.23
Returns
-------
self : object
Returns a fitted instance.
"""
if not hasattr(self.estimator, "fit"):
raise ValueError("The base estimator should implement a fit method")
y = self._validate_data(X="no_validation", y=y, multi_output=True)
# if is_classifier(self):
# check_classification_targets(y)
if y.ndim == 1:
raise ValueError(
"y must have at least two dimensions for "
"multi-output regression but has only one."
)
if sample_weight is not None and not has_fit_parameter(
self.estimator, "sample_weight"
):
raise ValueError("Underlying estimator does not support sample weights.")
# fit_params_validated = _check_fit_params(X, fit_params)
if not fit_params:
fit_params = [None] * y.shape[1]
# if not init_models:
# init_models = [None] * y.shape[1]
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator, X, y[:, i], sample_weight, **fit_params[i]
# init_model=init_models[i], eval_set=eval_sets[i],
# **fit_params_validated
)
for i in range(y.shape[1])
)
if hasattr(self.estimators_[0], "n_features_in_"):
self.n_features_in_ = self.estimators_[0].n_features_in_
if hasattr(self.estimators_[0], "feature_names_in_"):
self.feature_names_in_ = self.estimators_[0].feature_names_in_
return

2
freqtrade/freqai/prediction_models/CatboostClassifier.py
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@ -3,8 +3,8 @@ from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
logger = logging.getLogger(__name__)

2
freqtrade/freqai/prediction_models/CatboostRegressor.py
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@ -3,8 +3,8 @@ from typing import Any, Dict
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)

41
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
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@ -1,11 +1,11 @@
import logging
from typing import Any, Dict
from catboost import CatBoostRegressor # , Pool
from sklearn.multioutput import MultiOutputRegressor
from catboost import CatBoostRegressor, Pool
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
@ -32,17 +32,34 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
if self.continual_learning:
logger.warning('Continual learning not supported for MultiTarget models')
model = MultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
eval_sets = [None] * data_dictionary['test_labels'].shape[1]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"].iloc[:, i],
weight=data_dictionary["test_weights"],
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=cbr)
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
return model

2
freqtrade/freqai/prediction_models/LightGBMClassifier.py
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@ -3,8 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
logger = logging.getLogger(__name__)

2
freqtrade/freqai/prediction_models/LightGBMRegressor.py
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@ -3,8 +3,8 @@ from typing import Any, Dict
from lightgbm import LGBMRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)

41
freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
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@ -2,10 +2,10 @@ import logging
from typing import Any, Dict
from lightgbm import LGBMRegressor
from sklearn.multioutput import MultiOutputRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
@ -29,15 +29,36 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
if self.continual_learning:
logger.warning('Continual learning not supported for MultiTarget models')
eval_weights = None
eval_sets = [None] * y.shape[1]
model = MultiOutputRegressor(estimator=lgb)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = ( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
'init_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=lgb)
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
# model = FreqaiMultiOutputRegressor(estimator=lgb)
# model.fit(X=X, y=y, sample_weight=sample_weight, init_models=init_models,
# eval_sets=eval_sets, eval_sample_weight=eval_weights)
return model

6
freqtrade/freqai/prediction_models/XGBoostRegressor.py
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@ -3,8 +3,8 @@ from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
@ -31,6 +31,7 @@ class XGBoostRegressor(BaseRegressionModel):
eval_set = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]
sample_weight = data_dictionary["train_weights"]
@ -38,6 +39,7 @@ class XGBoostRegressor(BaseRegressionModel):
model = XGBRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set, xgb_model=xgb_model)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
return model

37
freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py
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@ -1,11 +1,11 @@
import logging
from typing import Any, Dict
from sklearn.multioutput import MultiOutputRegressor
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
logger = logging.getLogger(__name__)
@ -29,15 +29,32 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
sample_weight = data_dictionary["train_weights"]
if self.continual_learning:
logger.warning('Continual learning not supported for MultiTarget models')
eval_weights = None
eval_sets = [None] * y.shape[1]
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
eval_weights = [data_dictionary["test_weights"]]
for i in range(data_dictionary['test_labels'].shape[1]):
eval_sets[i] = [( # type: ignore
data_dictionary["test_features"],
data_dictionary["test_labels"].iloc[:, i]
)]
init_model = self.get_init_model(dk.pair)
if init_model:
init_models = init_model.estimators_
else:
init_models = [None] * y.shape[1]
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'sample_weight_eval_set': eval_weights,
'xgb_model': init_models[i]})
model = FreqaiMultiOutputRegressor(estimator=xgb)
model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
model = MultiOutputRegressor(estimator=xgb)
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
train_score = model.score(X, y)
test_score = model.score(*eval_set)
logger.info(f"Train score {train_score}, Test score {test_score}")
return model