create children class to PyTorchClassifier to implement the fit method where we initialize the trainer and model objects

freqtrade/freqai/base_models/PyTorchModelTrainer.py

@@ -19,35 +19,32 @@ class PyTorchModelTrainer:
             optimizer: Optimizer,
             criterion: nn.Module,
             device: str,
-            batch_size: int,
-            max_iters: int,
-            max_n_eval_batches: int,
             init_model: Dict,
             model_meta_data: Dict[str, Any] = {},
+            **kwargs
     ):
         """
         :param model: The PyTorch model to be trained.
         :param optimizer: The optimizer to use for training.
         :param criterion: The loss function to use for training.
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
-        :param batch_size: The size of the batches to use during training.
+        :param init_model: A dictionary containing the initial model/optimizer
+            state_dict and model_meta_data saved by self.save() method.
+        :param model_meta_data: Additional metadata about the model (optional).
         :param max_iters: The number of training iterations to run.
             iteration here refers to the number of times we call
             self.optimizer.step(). used to calculate n_epochs.
+        :param batch_size: The size of the batches to use during training.
         :param max_n_eval_batches: The maximum number batches to use for evaluation.
-        :param init_model: A dictionary containing the initial model/optimizer
-         state_dict and model_meta_data saved by self.save() method.
-        :param model_meta_data: Additional metadata about the model (optional).
         """
         self.model = model
         self.optimizer = optimizer
         self.criterion = criterion
         self.model_meta_data = model_meta_data
         self.device = device
-        self.max_iters = max_iters
-        self.batch_size = batch_size
-        self.max_n_eval_batches = max_n_eval_batches
-
+        self.max_iters: int = kwargs.get("max_iters", 100)
+        self.batch_size: int = kwargs.get("batch_size", 64)
+        self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
         if init_model:
             self.load_from_checkpoint(init_model)
 

freqtrade/freqai/prediction_models/MLPPyTorchClassifier.py

@@ -0,0 +1,81 @@
+from typing import Any, Dict
+
+from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.PyTorchClassifierClassifier import PyTorchClassifier
+from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
+
+import torch
+
+
+class MLPPyTorchClassifier(PyTorchClassifier):
+    """
+    This class implements the fit method of IFreqaiModel.
+    int the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchClassifier
+    predict method that expects the model to predict tensor of type long.
+    the trainer defines the training loop.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "max_iters": 5000,
+                    "batch_size": 64,
+                    "max_n_eval_batches": None,
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+
+
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        model_training_params = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
+        self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
+        self.trainer_kwargs: Dict[str, any] = model_training_params.get("trainer_kwargs", {})
+
+    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.
+        :raises ValueError: If self.class_names is not defined in the parent class.
+        """
+
+        class_names = self.get_class_names()
+        self.convert_label_column_to_int(data_dictionary, dk, class_names)
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=len(class_names),
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.CrossEntropyLoss()
+        init_model = self.get_init_model(dk.pair)
+        trainer = PyTorchModelTrainer(
+            model=model,
+            optimizer=optimizer,
+            criterion=criterion,
+            model_meta_data={"class_names": class_names},
+            device=self.device,
+            init_model=init_model,
+            **self.trainer_kwargs,
+        )
+        trainer.fit(data_dictionary)
+        return trainer

freqtrade/freqai/prediction_models/PyTorchClassifierClassifier.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Dict, List, Tuple
 
 import numpy as np
 import numpy.typing as npt
@@ -10,17 +10,16 @@ from torch.nn import functional as F
 
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
-from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
 
 
 logger = logging.getLogger(__name__)
 
 
-class PyTorchClassifierMultiTarget(BasePyTorchModel):
+class PyTorchClassifier(BasePyTorchModel):
     """
-    A PyTorch implementation of a multi-target classifier.
+    A PyTorch implementation of a classifier.
+    User must implement fit method
     """
     def __init__(self, **kwargs):
         """
@@ -34,59 +33,9 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         """
 
         super().__init__(**kwargs)
-        model_training_params = self.freqai_info.get("model_training_parameters", {})
-        self.max_iters: int = model_training_params.get("max_iters", 100)
-        self.batch_size: int = model_training_params.get("batch_size", 64)
-        self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
-        self.max_n_eval_batches: Optional[int] = model_training_params.get(
-            "max_n_eval_batches", None
-        )
-        self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
         self.class_name_to_index = None
         self.index_to_class_name = None
 
-    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.
-        :raises ValueError: If self.class_names is not defined in the parent class.
-
-        """
-
-        if not hasattr(self, "class_names"):
-            raise ValueError(
-                "Missing attribute: self.class_names "
-                "set self.freqai.class_names = [\"class a\", \"class b\", \"class c\"] "
-                "inside IStrategy.set_freqai_targets method."
-            )
-
-        self.init_class_names_to_index_mapping(self.class_names)
-        self.encode_classes_name(data_dictionary, dk)
-        n_features = data_dictionary["train_features"].shape[-1]
-        model = PyTorchMLPModel(
-            input_dim=n_features,
-            output_dim=len(self.class_names),
-            **self.model_kwargs
-        )
-        model.to(self.device)
-        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
-        criterion = torch.nn.CrossEntropyLoss()
-        init_model = self.get_init_model(dk.pair)
-        trainer = PyTorchModelTrainer(
-            model=model,
-            optimizer=optimizer,
-            criterion=criterion,
-            model_meta_data={"class_names": self.class_names},
-            device=self.device,
-            batch_size=self.batch_size,
-            max_iters=self.max_iters,
-            max_n_eval_batches=self.max_n_eval_batches,
-            init_model=init_model
-        )
-        trainer.fit(data_dictionary)
-        return trainer
-
     def predict(
         self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
@@ -97,7 +46,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         :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)
-        :raises ValueError: if 'class_name' doesn't exist in model meta_data.
+        :raises ValueError: if 'class_names' doesn't exist in model meta_data.
         """
 
         class_names = self.model.model_meta_data.get("class_names", None)
@@ -106,7 +55,9 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
                 "Missing class names. "
                 "self.model.model_meta_data[\"class_names\"] is None."
             )
-        self.init_class_names_to_index_mapping(class_names)
+
+        if not self.class_name_to_index:
+            self.init_class_names_to_index_mapping(class_names)
 
         dk.find_features(unfiltered_df)
         filtered_df, _ = dk.filter_features(
@@ -116,49 +67,77 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         dk.data_dictionary["prediction_features"] = filtered_df
 
         self.data_cleaning_predict(dk)
-        dk.data_dictionary["prediction_features"] = torch.tensor(
-            dk.data_dictionary["prediction_features"].values
-        ).float().to(self.device)
+        x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
+            .float()\
+            .to(self.device)
 
-        logits = self.model.model(dk.data_dictionary["prediction_features"])
+        logits = self.model.model(x)
         probs = F.softmax(logits, dim=-1)
         predicted_classes = torch.argmax(probs, dim=-1)
-        predicted_classes_str = self.decode_classes_name(predicted_classes)
+        predicted_classes_str = self.decode_class_names(predicted_classes)
         pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
         pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
         pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
         return (pred_df, dk.do_predict)
 
-    def encode_classes_name(self, data_dictionary: Dict[str, pd.DataFrame], dk: FreqaiDataKitchen):
+    def encode_class_names(
+            self,
+            data_dictionary: Dict[str, pd.DataFrame],
+            dk: FreqaiDataKitchen,
+            class_names: List[str],
+    ):
         """
-        encode class name str -> int
-        assuming first column of *_labels data frame to contain class names
+        encode class name, str -> int
+        assuming first column of *_labels data frame to be the target column
+        containing the class names
         """
 
         target_column_name = dk.label_list[0]
         for split in ["train", "test"]:
             label_df = data_dictionary[f"{split}_labels"]
-            self.assert_valid_class_names(label_df[target_column_name])
+            self.assert_valid_class_names(label_df[target_column_name], class_names)
             label_df[target_column_name] = list(
                 map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
             )
 
-    def assert_valid_class_names(self, labels: pd.Series):
-        non_defined_labels = set(labels) - set(self.class_names)
+    @staticmethod
+    def assert_valid_class_names(
+            target_column: pd.Series,
+            class_names: List[str]
+    ):
+        non_defined_labels = set(target_column) - set(class_names)
         if len(non_defined_labels) != 0:
             raise OperationalException(
                 f"Found non defined labels: {non_defined_labels}, ",
-                f"expecting labels: {self.class_names}"
+                f"expecting labels: {class_names}"
             )
 
-    def decode_classes_name(self, classes: torch.Tensor) -> List[str]:
+    def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
         """
-        decode class name int -> str
+        decode class name, int -> str
         """
 
-        return list(map(lambda x: self.index_to_class_name[x.item()], classes))
+        return list(map(lambda x: self.index_to_class_name[x.item()], class_ints))
 
     def init_class_names_to_index_mapping(self, class_names):
         self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
         self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
-        logger.info(f"class_name_to_index: {self.class_name_to_index}")
+        logger.info(f"encoded class name to index: {self.class_name_to_index}")
+
+    def convert_label_column_to_int(
+            self,
+            data_dictionary: Dict[str, pd.DataFrame],
+            dk: FreqaiDataKitchen,
+            class_names: List[str]
+    ):
+        self.init_class_names_to_index_mapping(class_names)
+        self.encode_class_names(data_dictionary, dk, class_names)
+
+    def get_class_names(self) -> List[str]:
+        if not hasattr(self, "class_names"):
+            raise ValueError(
+                "Missing attribute: self.class_names "
+                "set self.freqai.class_names = [\"class a\", \"class b\", \"class c\"] "
+                "inside IStrategy.set_freqai_targets method."
+            )
+        return self.class_names

tests/freqai/test_freqai_interface.py

@@ -88,10 +88,12 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
     if 'PyTorchClassifierMultiTarget' in model:
         model_save_ext = 'zip'
         freqai_conf['freqai']['model_training_parameters'].update({
-            "max_iters": 1,
-            "batch_size": 64,
             "learning_rate": 3e-4,
-            "max_n_eval_batches": 1,
+            "trainer_kwargs": {
+                "max_iters": 1,
+                "batch_size": 64,
+                "max_n_eval_batches": 1,
+            },
             "model_kwargs": {
                 "hidden_dim": 32,
                 "dropout_percent": 0.2,