Add lightgbm classifier, add classifier check test, fix classifier bug.

freqtrade/freqai/freqai_interface.py

@@ -566,6 +566,8 @@ class IFreqaiModel(ABC):
         num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
         for label in dk.label_list:
+            if self.dd.historic_predictions[dk.pair][label].dtype == object:
+                continue
             f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
             dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
 

freqtrade/freqai/prediction_models/CatboostClassifier.py

@@ -32,9 +32,6 @@ class CatboostClassifier(BaseRegressionModel):
 
         cbr = CatBoostClassifier(
             allow_writing_files=False,
-            gpu_ram_part=0.5,
-            verbose=100,
-            early_stopping_rounds=400,
             loss_function='MultiClass',
             **self.model_training_parameters,
         )

freqtrade/freqai/prediction_models/LightGBMClassifier.py

@@ -0,0 +1,38 @@
+import logging
+from typing import Any, Dict
+
+from lightgbm import LGBMClassifier
+
+from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
+
+
+logger = logging.getLogger(__name__)
+
+
+class LightGBMClassifier(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.
+        """
+
+        if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
+            eval_set = None
+        else:
+            eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
+        X = data_dictionary["train_features"]
+        y = data_dictionary["train_labels"]
+
+        model = LGBMClassifier(**self.model_training_parameters)
+
+        model.fit(X=X, y=y, eval_set=eval_set)
+
+        return model

freqtrade/templates/FreqaiExampleStrategy.py

@@ -155,6 +155,10 @@ class FreqaiExampleStrategy(IStrategy):
                     - 1
                 )
 
+                # Classifiers are typically set up with strings as targets:
+                # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
+                #                                 df["close"], 'up', 'down')
+
                 # If user wishes to use multiple targets, they can add more by
                 # appending more columns with '&'. User should keep in mind that multi targets
                 # requires a multioutput prediction model such as