feat: add SKLearnRandomForestClassifier

freqtrade/freqai/prediction_models/SKLearnRandomForestClassifier.py

@@ -0,0 +1,82 @@
+import logging
+from typing import Any, Dict, Tuple
+
+import numpy as np
+import numpy.typing as npt
+from pandas import DataFrame
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+
+from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class SKLearnRandomForestClassifier(BaseClassifierModel):
+    """
+    User created prediction model. The class inherits IFreqaiModel, which
+    means it has full access to all Frequency AI functionality. Typically,
+    users would use this to override the common `fit()`, `train()`, or
+    `predict()` methods to add their custom data handling tools or change
+    various aspects of the training that cannot be configured via the
+    top level config.json file.
+    """
+
+    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 holding all data for train, test,
+            labels, weights
+        :param dk: The datakitchen object for the current coin/model
+        """
+
+        X = data_dictionary["train_features"].to_numpy()
+        y = data_dictionary["train_labels"].to_numpy()[:, 0]
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            eval_set = None
+        else:
+            test_features = data_dictionary["test_features"].to_numpy()
+            test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
+
+            eval_set = (test_features, test_labels)
+
+        if self.freqai_info.get("continual_learning", False):
+            logger.warning("Continual learning is not supported for "
+                           "SKLearnRandomForestClassifier, ignoring.")
+
+        train_weights = data_dictionary["train_weights"]
+
+        model = RandomForestClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, sample_weight=train_weights)
+        if eval_set:
+            logger.info("Score: %s", model.score(eval_set[0], eval_set[1]))
+
+        return model
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param  unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        (pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
+
+        le = LabelEncoder()
+        label = dk.label_list[0]
+        labels_before = list(dk.data['labels_std'].keys())
+        labels_after = le.fit_transform(labels_before).tolist()
+        pred_df[label] = le.inverse_transform(pred_df[label])
+        pred_df = pred_df.rename(
+            columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
+
+        return (pred_df, dk.do_predict)

tests/freqai/test_freqai_interface.py

@@ -176,6 +176,7 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, s
     'CatboostClassifier',
     'XGBoostClassifier',
     'XGBoostRFClassifier',
+    'SKLearnRandomForestClassifier',
     'PyTorchMLPClassifier',
     ])
 def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):

tests/rpc/test_rpc_apiserver.py

@@ -1770,6 +1770,7 @@ def test_api_freqaimodels(botclient, tmp_path, mocker):
             {'name': 'LightGBMRegressorMultiTarget'},
             {'name': 'ReinforcementLearner'},
             {'name': 'ReinforcementLearner_multiproc'},
+            {'name': 'SKlearnRandomForestClassifier'},
             {'name': 'XGBoostClassifier'},
             {'name': 'XGBoostRFClassifier'},
             {'name': 'XGBoostRFRegressor'},
@@ -1788,6 +1789,7 @@ def test_api_freqaimodels(botclient, tmp_path, mocker):
         'LightGBMRegressorMultiTarget',
         'ReinforcementLearner',
         'ReinforcementLearner_multiproc',
+        'SKlearnRandomForestClassifier',
         'XGBoostClassifier',
         'XGBoostRFClassifier',
         'XGBoostRFRegressor',