add classifier, improve model naming scheme

freqtrade/freqai/data_drawer.py

@@ -226,6 +226,7 @@ class FreqaiDataDrawer:
         historical candles, and also stores historical predictions despite retrainings (so stored
         predictions are true predictions, not just inferencing on trained data)
         """
+
         # dynamic df returned to strategy and plotted in frequi
         mrv_df = self.model_return_values[pair] = pd.DataFrame()
 
@@ -246,6 +247,8 @@ class FreqaiDataDrawer:
         else:
             for label in dk.label_list:
                 mrv_df[label] = pred_df[label]
+                if mrv_df[label].dtype == object:
+                    continue
                 mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
                 mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
 
@@ -295,6 +298,8 @@ class FreqaiDataDrawer:
 
         for label in dk.label_list:
             df[label].iloc[-1] = predictions[label].iloc[-1]
+            if df[label].dtype == object:
+                continue
             df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
             df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
 

freqtrade/freqai/data_kitchen.py

@@ -294,7 +294,7 @@ class FreqaiDataKitchen:
             self.data[item + "_min"] = train_min[item]
 
         for item in data_dictionary["train_labels"].keys():
-            if data_dictionary["train_labels"][item].dtype == str:
+            if data_dictionary["train_labels"][item].dtype == object:
                 continue
             train_labels_max = data_dictionary["train_labels"][item].max()
             train_labels_min = data_dictionary["train_labels"][item].min()
@@ -1010,6 +1010,8 @@ class FreqaiDataKitchen:
 
         self.data["labels_mean"], self.data["labels_std"] = {}, {}
         for label in self.label_list:
+            if self.data_dictionary["train_labels"][label].dtype == object:
+                continue
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
 

freqtrade/freqai/freqai_interface.py

@@ -123,7 +123,7 @@ class IFreqaiModel(ABC):
 
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
         del dk
-        return self.return_values(dataframe)
+        return dataframe
 
     @threaded
     def start_scanning(self, strategy: IStrategy) -> None:
@@ -609,17 +609,6 @@ class IFreqaiModel(ABC):
         data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
         """
 
-    @abstractmethod
-    def return_values(self, dataframe: DataFrame) -> DataFrame:
-        """
-        User defines the dataframe to be returned to strategy here.
-        :param dataframe: DataFrame = the full dataframe for the current prediction (live)
-                                      or --timerange (backtesting)
-        :return: dataframe: DataFrame = dataframe filled with user defined data
-        """
-
-        return
-
     def analyze_trade_database(self, dk: FreqaiDataKitchen, pair: str) -> None:
         """
         User analyzes the trade database here and returns summary stats which will be passed back

freqtrade/freqai/prediction_models/BaseRegressionModel.py

@@ -19,15 +19,6 @@ class BaseRegressionModel(IFreqaiModel):
     such as prediction_models/CatboostPredictionModel.py for guidance.
     """
 
-    def return_values(self, dataframe: DataFrame) -> DataFrame:
-        """
-        User uses this function to add any additional return values to the dataframe.
-        e.g.
-        dataframe['volatility'] = dk.volatility_values
-        """
-
-        return dataframe
-
     def train(
         self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
     ) -> Any:

freqtrade/freqai/prediction_models/BaseTensorFlowModel.py

@@ -16,15 +16,6 @@ class BaseTensorFlowModel(IFreqaiModel):
     User *must* inherit from this class and set fit() and predict().
     """
 
-    def return_values(self, dataframe: DataFrame) -> DataFrame:
-        """
-        User uses this function to add any additional return values to the dataframe.
-        e.g.
-        dataframe['volatility'] = dk.volatility_values
-        """
-
-        return dataframe
-
     def train(
         self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
     ) -> Any:

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -0,0 +1,44 @@
+import logging
+from typing import Any, Dict
+
+from catboost import CatBoostClassifier, Pool
+
+from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class CatboostClassifier(BaseRegressionModel):
+    """
+    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) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :params:
+        :data_dictionary: the dictionary constructed by DataHandler to hold
+        all the training and test data/labels.
+        """
+
+        train_data = Pool(
+            data=data_dictionary["train_features"],
+            label=data_dictionary["train_labels"],
+            weight=data_dictionary["train_weights"],
+        )
+
+        cbr = CatBoostClassifier(
+            allow_writing_files=False,
+            gpu_ram_part=0.5,
+            verbose=100,
+            early_stopping_rounds=400,
+            loss_function='MultiClass',
+            **self.model_training_parameters,
+        )
+
+        cbr.fit(train_data)
+
+        return cbr

freqtrade/freqai/prediction_models/CatboostRegressor.py

@@ -10,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
 logger = logging.getLogger(__name__)
 
 
-class CatboostPredictionModel(BaseRegressionModel):
+class CatboostRegressor(BaseRegressionModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which

freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py

@@ -10,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
 logger = logging.getLogger(__name__)
 
 
-class CatboostPredictionMultiModel(BaseRegressionModel):
+class CatboostRegressorMultiTarget(BaseRegressionModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which

freqtrade/freqai/prediction_models/LightGBMRegressor.py

@@ -9,7 +9,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
 logger = logging.getLogger(__name__)
 
 
-class LightGBMPredictionModel(BaseRegressionModel):
+class LightGBMRegressor(BaseRegressionModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which

freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py

@@ -10,7 +10,7 @@ from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressio
 logger = logging.getLogger(__name__)
 
 
-class LightGBMPredictionMultiModel(BaseRegressionModel):
+class LightGBMRegressorMultiTarget(BaseRegressionModel):
     """
     User created prediction model. The class needs to override three necessary
     functions, predict(), train(), fit(). The class inherits ModelHandler which

tests/freqai/conftest.py

@@ -21,7 +21,7 @@ def freqai_conf(default_conf, tmpdir):
             "strategy": "freqai_test_strat",
             "user_data_dir": Path(tmpdir),
             "strategy-path": "freqtrade/tests/strategy/strats",
-            "freqaimodel": "LightGBMPredictionModel",
+            "freqaimodel": "LightGBMRegressor",
             "freqaimodel_path": "freqai/prediction_models",
             "timerange": "20180110-20180115",
             "freqai": {

tests/freqai/test_freqai_interface.py

@@ -43,7 +43,7 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
 def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
     freqai_conf.update({"timerange": "20180110-20180130"})
     freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
-    freqai_conf.update({"freqaimodel": "LightGBMPredictionMultiModel"})
+    freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -73,8 +73,9 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
 @pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...")
 def test_train_model_in_series_Catboost(mocker, freqai_conf):
     freqai_conf.update({"timerange": "20180110-20180130"})
-    freqai_conf.update({"freqaimodel": "CatboostPredictionModel"})
-    del freqai_conf['freqai']['model_training_parameters']['verbosity']
+    freqai_conf.update({"freqaimodel": "CatboostRegressor"})
+    freqai_conf.get('freqai', {}).update(
+        {'model_training_parameters': {"n_estimators": 100, "verbose": 0}})
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)