make BaseClassifierModel. Add predict_proba to lightgbm

freqtrade/freqai/data_drawer.py

@@ -12,7 +12,7 @@ import pandas as pd
 import rapidjson
 from joblib import dump, load
 from joblib.externals import cloudpickle
-from numpy.typing import ArrayLike, NDArray
+from numpy.typing import NDArray
 from pandas import DataFrame
 
 from freqtrade.configuration import TimeRange

freqtrade/freqai/prediction_models/BaseClassifierModel.py

@@ -0,0 +1,99 @@
+import logging
+from typing import Any, Tuple
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.freqai_interface import IFreqaiModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseClassifierModel(IFreqaiModel):
+    """
+    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
+    User *must* inherit from this class and set fit() and predict(). See example scripts
+    such as prediction_models/CatboostPredictionModel.py for guidance.
+    """
+
+    def train(
+        self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
+    ) -> Any:
+        """
+        Filter the training data and train a model to it. Train makes heavy use of the datakitchen
+        for storing, saving, loading, and analyzing the data.
+        :param unfiltered_dataframe: Full dataframe for the current training period
+        :param metadata: pair metadata from strategy.
+        :return:
+        :model: Trained model which can be used to inference (self.predict)
+        """
+
+        logger.info("-------------------- Starting training " f"{pair} --------------------")
+
+        # filter the features requested by user in the configuration file and elegantly handle NaNs
+        features_filtered, labels_filtered = dk.filter_features(
+            unfiltered_dataframe,
+            dk.training_features_list,
+            dk.label_list,
+            training_filter=True,
+        )
+
+        start_date = unfiltered_dataframe["date"].iloc[0].strftime("%Y-%m-%d")
+        end_date = unfiltered_dataframe["date"].iloc[-1].strftime("%Y-%m-%d")
+        logger.info(f"-------------------- Training on data from {start_date} to "
+                    f"{end_date}--------------------")
+        # split data into train/test data.
+        data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
+        if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
+            dk.fit_labels()
+        # normalize all data based on train_dataset only
+        data_dictionary = dk.normalize_data(data_dictionary)
+
+        # optional additional data cleaning/analysis
+        self.data_cleaning_train(dk)
+
+        logger.info(
+            f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
+        )
+        logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
+
+        model = self.fit(data_dictionary)
+
+        logger.info(f"--------------------done training {pair}--------------------")
+
+        return model
+
+    def predict(
+        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param: unfiltered_dataframe: 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)
+        """
+
+        dk.find_features(unfiltered_dataframe)
+        filtered_dataframe, _ = dk.filter_features(
+            unfiltered_dataframe, dk.training_features_list, training_filter=False
+        )
+        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
+        dk.data_dictionary["prediction_features"] = filtered_dataframe
+
+        self.data_cleaning_predict(dk, filtered_dataframe)
+
+        predictions = self.model.predict(dk.data_dictionary["prediction_features"])
+        pred_df = DataFrame(predictions, columns=dk.label_list)
+
+        predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
+        pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
+
+        pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
+
+        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -1,17 +1,15 @@
 import logging
-from typing import Any, Dict, Tuple
-import pandas as pd
-from pandas import DataFrame
+from typing import Any, Dict
+
 from catboost import CatBoostClassifier, Pool
-import numpy.typing as npt
-import numpy as np
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
+
 
 logger = logging.getLogger(__name__)
 
 
-class CatboostClassifier(BaseRegressionModel):
+class CatboostClassifier(BaseClassifierModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which
@@ -41,34 +39,3 @@ class CatboostClassifier(BaseRegressionModel):
         cbr.fit(train_data)
 
         return cbr
-
-    def predict(
-        self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
-    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
-        """
-        Filter the prediction features data and predict with it.
-        :param: unfiltered_dataframe: 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)
-        """
-
-        dk.find_features(unfiltered_dataframe)
-        filtered_dataframe, _ = dk.filter_features(
-            unfiltered_dataframe, dk.training_features_list, training_filter=False
-        )
-        filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
-        dk.data_dictionary["prediction_features"] = filtered_dataframe
-
-        self.data_cleaning_predict(dk, filtered_dataframe)
-
-        predictions = self.model.predict(dk.data_dictionary["prediction_features"])
-        pred_df = DataFrame(predictions, columns=dk.label_list)
-
-        predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
-        pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
-
-        pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
-
-        return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -3,13 +3,13 @@ from typing import Any, Dict
 
 from lightgbm import LGBMClassifier
 
-from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
+from freqtrade.freqai.prediction_models.BaseClassifierModel import BaseClassifierModel
 
 
 logger = logging.getLogger(__name__)
 
 
-class LightGBMClassifier(BaseRegressionModel):
+class LightGBMClassifier(BaseClassifierModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which