Merge pull request #7644 from markdregan/multi-target-classifier
Support for multi target multi-class classification (FreqaiMultiOutputRegressor for Classification)
This commit is contained in:
Robert Caulk
GitHub
commit
833578716c
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@ -0,0 +1,93 @@
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import numpy as np
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from joblib import Parallel
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from sklearn.base import is_classifier
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from sklearn.multioutput import MultiOutputClassifier, _fit_estimator
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from sklearn.utils.fixes import delayed
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.utils.validation import has_fit_parameter
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from freqtrade.exceptions import OperationalException
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class FreqaiMultiOutputClassifier(MultiOutputClassifier):
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def fit(self, X, y, sample_weight=None, fit_params=None):
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"""Fit the model to data, separately for each output variable.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The input data.
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y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
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Multi-output targets. An indicator matrix turns on multilabel
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estimation.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights. If `None`, then samples are equally weighted.
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Only supported if the underlying classifier supports sample
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weights.
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fit_params : A list of dicts for the fit_params
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Parameters passed to the ``estimator.fit`` method of each step.
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Each dict may contain same or different values (e.g. different
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eval_sets or init_models)
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.. versionadded:: 0.23
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Returns
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-------
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self : object
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Returns a fitted instance.
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"""
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if not hasattr(self.estimator, "fit"):
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raise ValueError("The base estimator should implement a fit method")
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y = self._validate_data(X="no_validation", y=y, multi_output=True)
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if is_classifier(self):
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check_classification_targets(y)
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if y.ndim == 1:
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raise ValueError(
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"y must have at least two dimensions for "
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"multi-output regression but has only one."
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)
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if sample_weight is not None and not has_fit_parameter(
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self.estimator, "sample_weight"
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):
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raise ValueError("Underlying estimator does not support sample weights.")
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if not fit_params:
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fit_params = [None] * y.shape[1]
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self.estimators_ = Parallel(n_jobs=self.n_jobs)(
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delayed(_fit_estimator)(
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self.estimator, X, y[:, i], sample_weight, **fit_params[i]
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)
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for i in range(y.shape[1])
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)
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self.classes_ = []
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for estimator in self.estimators_:
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self.classes_.extend(estimator.classes_)
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if len(set(self.classes_)) != len(self.classes_):
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raise OperationalException(f"Class labels must be unique across targets: "
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f"{self.classes_}")
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if hasattr(self.estimators_[0], "n_features_in_"):
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self.n_features_in_ = self.estimators_[0].n_features_in_
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if hasattr(self.estimators_[0], "feature_names_in_"):
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self.feature_names_in_ = self.estimators_[0].feature_names_in_
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return self
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def predict_proba(self, X):
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"""
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Get predict_proba and stack arrays horizontally
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"""
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results = np.hstack(super().predict_proba(X))
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return np.squeeze(results)
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def predict(self, X):
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"""
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Get predict and squeeze into 2D array
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"""
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results = super().predict(X)
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return np.squeeze(results)
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@ -0,0 +1,74 @@
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import logging
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import sys
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from pathlib import Path
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from typing import Any, Dict
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from catboost import CatBoostClassifier, Pool
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from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
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from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class CatboostClassifierMultiTarget(BaseClassifierModel):
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"""
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User created prediction model. The class needs to override three necessary
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functions, predict(), train(), fit(). The class inherits ModelHandler which
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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cbc = CatBoostClassifier(
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allow_writing_files=True,
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loss_function='MultiClass',
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train_dir=Path(dk.data_path),
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**self.model_training_parameters,
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)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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sample_weight = data_dictionary["train_weights"]
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eval_sets = [None] * y.shape[1]
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
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eval_sets = [None] * data_dictionary['test_labels'].shape[1]
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for i in range(data_dictionary['test_labels'].shape[1]):
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eval_sets[i] = Pool(
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data=data_dictionary["test_features"],
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label=data_dictionary["test_labels"].iloc[:, i],
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weight=data_dictionary["test_weights"],
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)
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init_model = self.get_init_model(dk.pair)
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if init_model:
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init_models = init_model.estimators_
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else:
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init_models = [None] * y.shape[1]
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fit_params = []
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for i in range(len(eval_sets)):
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fit_params.append({
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'eval_set': eval_sets[i], 'init_model': init_models[i],
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'log_cout': sys.stdout, 'log_cerr': sys.stderr,
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})
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model = FreqaiMultiOutputClassifier(estimator=cbc)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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@ -0,0 +1,64 @@
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import logging
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from typing import Any, Dict
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from lightgbm import LGBMClassifier
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from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
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from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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logger = logging.getLogger(__name__)
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class LightGBMClassifierMultiTarget(BaseClassifierModel):
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"""
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User created prediction model. The class needs to override three necessary
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functions, predict(), train(), fit(). The class inherits ModelHandler which
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has its own DataHandler where data is held, saved, loaded, and managed.
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"""
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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lgb = LGBMClassifier(**self.model_training_parameters)
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X = data_dictionary["train_features"]
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y = data_dictionary["train_labels"]
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sample_weight = data_dictionary["train_weights"]
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eval_weights = None
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eval_sets = [None] * y.shape[1]
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if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
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eval_weights = [data_dictionary["test_weights"]]
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eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1] # type: ignore
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for i in range(data_dictionary['test_labels'].shape[1]):
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eval_sets[i] = ( # type: ignore
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data_dictionary["test_features"],
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data_dictionary["test_labels"].iloc[:, i]
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)
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init_model = self.get_init_model(dk.pair)
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if init_model:
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init_models = init_model.estimators_
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else:
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init_models = [None] * y.shape[1]
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fit_params = []
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for i in range(len(eval_sets)):
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fit_params.append(
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{'eval_set': eval_sets[i], 'eval_sample_weight': eval_weights,
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'init_model': init_models[i]})
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model = FreqaiMultiOutputClassifier(estimator=lgb)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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@ -77,17 +77,19 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMRegressorMultiTarget',
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'XGBoostRegressorMultiTarget',
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'CatboostRegressorMultiTarget',
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@pytest.mark.parametrize('model, strat', [
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('LightGBMRegressorMultiTarget', "freqai_test_multimodel_strat"),
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('XGBoostRegressorMultiTarget', "freqai_test_multimodel_strat"),
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('CatboostRegressorMultiTarget', "freqai_test_multimodel_strat"),
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('LightGBMClassifierMultiTarget', "freqai_test_multimodel_classifier_strat"),
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('CatboostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat")
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])
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def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
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if is_arm() and model == 'CatboostRegressorMultiTarget':
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def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat):
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if is_arm() and 'Catboost' in model:
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pytest.skip("CatBoost is not supported on ARM")
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
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freqai_conf.update({"strategy": strat})
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freqai_conf.update({"freqaimodel": model})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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@ -1460,6 +1460,7 @@ def test_api_strategies(botclient, tmpdir):
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'StrategyTestV3CustomEntryPrice',
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'StrategyTestV3Futures',
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'freqai_test_classifier',
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'freqai_test_multimodel_classifier_strat',
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'freqai_test_multimodel_strat',
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'freqai_test_strat'
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]}
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@ -0,0 +1,138 @@
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import logging
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from functools import reduce
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import numpy as np
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import pandas as pd
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
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logger = logging.getLogger(__name__)
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class freqai_test_multimodel_classifier_strat(IStrategy):
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"""
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Test strategy - used for testing freqAI multimodel functionalities.
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DO not use in production.
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"""
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minimal_roi = {"0": 0.1, "240": -1}
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plot_config = {
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"main_plot": {},
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"subplots": {
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"prediction": {"prediction": {"color": "blue"}},
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"target_roi": {
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"target_roi": {"color": "brown"},
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},
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"do_predict": {
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"do_predict": {"color": "brown"},
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},
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},
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 300
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can_short = False
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linear_roi_offset = DecimalParameter(
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0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
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)
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max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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coin = pair.split('/')[0]
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
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informative[f"%-{coin}raw_volume"] = informative["volume"]
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informative[f"%-{coin}raw_price"] = informative["close"]
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# Add generalized indicators here (because in live, it will call this
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# function to populate indicators during training). Notice how we ensure not to
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# add them multiple times
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if set_generalized_indicators:
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df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
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df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
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# user adds targets here by prepending them with &- (see convention below)
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# If user wishes to use multiple targets, a multioutput prediction model
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# needs to be used such as templates/CatboostPredictionMultiModel.py
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df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
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df["close"], 'up', 'down')
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df['&s-up_or_down2'] = np.where(df["close"].shift(-50) >
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df["close"], 'up2', 'down2')
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return df
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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self.freqai_info = self.config["freqai"]
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dataframe = self.freqai.start(dataframe, metadata, self)
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dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
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dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
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] = (1, "long")
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enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
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if enter_short_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
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] = (1, "short")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
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if exit_long_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
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exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
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if exit_short_conditions:
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df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
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return df
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@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
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directory = Path(__file__).parent / "strats"
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strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
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assert isinstance(strategies, list)
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assert len(strategies) == 10
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assert len(strategies) == 11
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assert isinstance(strategies[0], dict)
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@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
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directory = Path(__file__).parent / "strats"
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strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
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assert isinstance(strategies, list)
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assert len(strategies) == 11
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assert len(strategies) == 12
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# with enum_failed=True search_all_objects() shall find 2 good strategies
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# and 1 which fails to load
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assert len([x for x in strategies if x['class'] is not None]) == 10
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assert len([x for x in strategies if x['class'] is not None]) == 11
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assert len([x for x in strategies if x['class'] is None]) == 1
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