create children class to PyTorchClassifier to implement the fit method where we initialize the trainer and model objects
This commit is contained in:
Yinon Polak
81
freqtrade/freqai/prediction_models/MLPPyTorchClassifier.py
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81
freqtrade/freqai/prediction_models/MLPPyTorchClassifier.py
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from typing import Any, Dict
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from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.PyTorchClassifierClassifier import PyTorchClassifier
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from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
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import torch
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class MLPPyTorchClassifier(PyTorchClassifier):
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"""
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This class implements the fit method of IFreqaiModel.
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int the fit method we initialize the model and trainer objects.
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the only requirement from the model is to be aligned to PyTorchClassifier
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predict method that expects the model to predict tensor of type long.
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the trainer defines the training loop.
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parameters are passed via `model_training_parameters` under the freqai
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section in the config file. e.g:
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{
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...
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"freqai": {
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...
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"model_training_parameters" : {
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"learning_rate": 3e-4,
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"trainer_kwargs": {
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"max_iters": 5000,
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"batch_size": 64,
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"max_n_eval_batches": None,
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},
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"model_kwargs": {
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"hidden_dim": 512,
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"dropout_percent": 0.2,
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"n_layer": 1,
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},
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}
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}
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}
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"""
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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model_training_params = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
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self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
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self.trainer_kwargs: Dict[str, any] = model_training_params.get("trainer_kwargs", {})
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:raises ValueError: If self.class_names is not defined in the parent class.
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"""
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class_names = self.get_class_names()
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self.convert_label_column_to_int(data_dictionary, dk, class_names)
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n_features = data_dictionary["train_features"].shape[-1]
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model = PyTorchMLPModel(
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input_dim=n_features,
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output_dim=len(class_names),
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**self.model_kwargs
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.CrossEntropyLoss()
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init_model = self.get_init_model(dk.pair)
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trainer = PyTorchModelTrainer(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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model_meta_data={"class_names": class_names},
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device=self.device,
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init_model=init_model,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary)
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return trainer
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@@ -1,5 +1,5 @@
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import logging
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from typing import Any, Dict, List, Optional, Tuple
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from typing import Dict, List, Tuple
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import numpy as np
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import numpy.typing as npt
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@@ -10,17 +10,16 @@ from torch.nn import functional as F
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
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from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
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logger = logging.getLogger(__name__)
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class PyTorchClassifierMultiTarget(BasePyTorchModel):
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class PyTorchClassifier(BasePyTorchModel):
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"""
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A PyTorch implementation of a multi-target classifier.
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A PyTorch implementation of a classifier.
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User must implement fit method
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"""
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def __init__(self, **kwargs):
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"""
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@@ -34,59 +33,9 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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"""
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super().__init__(**kwargs)
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model_training_params = self.freqai_info.get("model_training_parameters", {})
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self.max_iters: int = model_training_params.get("max_iters", 100)
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self.batch_size: int = model_training_params.get("batch_size", 64)
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self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
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self.max_n_eval_batches: Optional[int] = model_training_params.get(
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"max_n_eval_batches", None
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)
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self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
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self.class_name_to_index = None
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self.index_to_class_name = None
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:raises ValueError: If self.class_names is not defined in the parent class.
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"""
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if not hasattr(self, "class_names"):
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raise ValueError(
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"Missing attribute: self.class_names "
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"set self.freqai.class_names = [\"class a\", \"class b\", \"class c\"] "
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"inside IStrategy.set_freqai_targets method."
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)
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self.init_class_names_to_index_mapping(self.class_names)
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self.encode_classes_name(data_dictionary, dk)
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n_features = data_dictionary["train_features"].shape[-1]
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model = PyTorchMLPModel(
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input_dim=n_features,
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output_dim=len(self.class_names),
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**self.model_kwargs
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.CrossEntropyLoss()
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init_model = self.get_init_model(dk.pair)
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trainer = PyTorchModelTrainer(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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model_meta_data={"class_names": self.class_names},
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device=self.device,
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batch_size=self.batch_size,
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max_iters=self.max_iters,
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max_n_eval_batches=self.max_n_eval_batches,
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init_model=init_model
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)
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trainer.fit(data_dictionary)
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return trainer
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def predict(
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self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
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) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
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@@ -97,7 +46,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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:pred_df: dataframe containing the predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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:raises ValueError: if 'class_name' doesn't exist in model meta_data.
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:raises ValueError: if 'class_names' doesn't exist in model meta_data.
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"""
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class_names = self.model.model_meta_data.get("class_names", None)
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@@ -106,7 +55,9 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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"Missing class names. "
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"self.model.model_meta_data[\"class_names\"] is None."
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)
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self.init_class_names_to_index_mapping(class_names)
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if not self.class_name_to_index:
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self.init_class_names_to_index_mapping(class_names)
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dk.find_features(unfiltered_df)
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filtered_df, _ = dk.filter_features(
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@@ -116,49 +67,77 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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dk.data_dictionary["prediction_features"] = filtered_df
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self.data_cleaning_predict(dk)
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dk.data_dictionary["prediction_features"] = torch.tensor(
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dk.data_dictionary["prediction_features"].values
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).float().to(self.device)
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x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
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.float()\
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.to(self.device)
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logits = self.model.model(dk.data_dictionary["prediction_features"])
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logits = self.model.model(x)
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probs = F.softmax(logits, dim=-1)
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predicted_classes = torch.argmax(probs, dim=-1)
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predicted_classes_str = self.decode_classes_name(predicted_classes)
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predicted_classes_str = self.decode_class_names(predicted_classes)
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pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
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pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
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pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
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return (pred_df, dk.do_predict)
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def encode_classes_name(self, data_dictionary: Dict[str, pd.DataFrame], dk: FreqaiDataKitchen):
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def encode_class_names(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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dk: FreqaiDataKitchen,
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class_names: List[str],
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):
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"""
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encode class name str -> int
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assuming first column of *_labels data frame to contain class names
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encode class name, str -> int
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assuming first column of *_labels data frame to be the target column
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containing the class names
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"""
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target_column_name = dk.label_list[0]
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for split in ["train", "test"]:
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label_df = data_dictionary[f"{split}_labels"]
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self.assert_valid_class_names(label_df[target_column_name])
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self.assert_valid_class_names(label_df[target_column_name], class_names)
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label_df[target_column_name] = list(
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map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
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)
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def assert_valid_class_names(self, labels: pd.Series):
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non_defined_labels = set(labels) - set(self.class_names)
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@staticmethod
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def assert_valid_class_names(
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target_column: pd.Series,
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class_names: List[str]
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):
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non_defined_labels = set(target_column) - set(class_names)
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if len(non_defined_labels) != 0:
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raise OperationalException(
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f"Found non defined labels: {non_defined_labels}, ",
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f"expecting labels: {self.class_names}"
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f"expecting labels: {class_names}"
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)
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def decode_classes_name(self, classes: torch.Tensor) -> List[str]:
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def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
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"""
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decode class name int -> str
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decode class name, int -> str
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"""
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return list(map(lambda x: self.index_to_class_name[x.item()], classes))
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return list(map(lambda x: self.index_to_class_name[x.item()], class_ints))
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def init_class_names_to_index_mapping(self, class_names):
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self.class_name_to_index = {s: i for i, s in enumerate(class_names)}
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self.index_to_class_name = {i: s for i, s in enumerate(class_names)}
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logger.info(f"class_name_to_index: {self.class_name_to_index}")
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logger.info(f"encoded class name to index: {self.class_name_to_index}")
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def convert_label_column_to_int(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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dk: FreqaiDataKitchen,
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class_names: List[str]
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):
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self.init_class_names_to_index_mapping(class_names)
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self.encode_class_names(data_dictionary, dk, class_names)
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def get_class_names(self) -> List[str]:
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if not hasattr(self, "class_names"):
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raise ValueError(
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"Missing attribute: self.class_names "
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"set self.freqai.class_names = [\"class a\", \"class b\", \"class c\"] "
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"inside IStrategy.set_freqai_targets method."
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)
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return self.class_names
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