Merge pull request #7644 from markdregan/multi-target-classifier

Support for multi target multi-class classification (FreqaiMultiOutputRegressor for Classification)

freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py

@@ -0,0 +1,74 @@
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Dict
+
+from catboost import CatBoostClassifier, Pool
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostClassifierMultiTarget(BaseClassifierModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        cbc = CatBoostClassifier(
+            allow_writing_files=True,
+            loss_function='MultiClass',
+            train_dir=Path(dk.data_path),
+            **self.model_training_parameters,
+        )
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        sample_weight = data_dictionary["train_weights"]
+
+        eval_sets = [None] * y.shape[1]
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
+            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],
+                'log_cout': sys.stdout, 'log_cerr': sys.stderr,
+            })
+
+        model = FreqaiMultiOutputClassifier(estimator=cbc)
+        thread_training = self.freqai_info.get('multitarget_parallel_training', False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model

freqtrade/freqai/prediction_models/LightGBMClassifierMultiTarget.py

@@ -0,0 +1,64 @@
+import logging
+from typing import Any, Dict
+
+from lightgbm import LGBMClassifier
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMClassifierMultiTarget(BaseClassifierModel):
+    """
+    User created prediction model. The class needs to override three necessary
+    functions, predict(), train(), fit(). The class inherits ModelHandler which
+    has its own DataHandler where data is held, saved, loaded, and managed.
+    """
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        lgb = LGBMClassifier(**self.model_training_parameters)
+
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+        sample_weight = data_dictionary["train_weights"]
+
+        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"]]
+            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 = FreqaiMultiOutputClassifier(estimator=lgb)
+        thread_training = self.freqai_info.get('multitarget_parallel_training', False)
+        if thread_training:
+            model.n_jobs = y.shape[1]
+        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
+
+        return model