add pytorch data convertor
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@ -69,12 +69,11 @@ class BasePyTorchClassifier(BasePyTorchModel):
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)
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filtered_df = dk.normalize_data_from_metadata(filtered_df)
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dk.data_dictionary["prediction_features"] = filtered_df
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self.data_cleaning_predict(dk)
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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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x = self.data_convertor.convert_x(
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dk.data_dictionary["prediction_features"],
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device=self.device
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)
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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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@ -1,4 +1,5 @@
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import logging
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from abc import ABC, abstractmethod
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from time import time
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from typing import Any
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@ -7,15 +8,17 @@ from pandas import DataFrame
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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from freqtrade.freqai.torch import PyTorchDataConvertor
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logger = logging.getLogger(__name__)
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class BasePyTorchModel(IFreqaiModel):
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class BasePyTorchModel(IFreqaiModel, ABC):
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"""
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Base class for PyTorch type models.
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User *must* inherit from this class and set fit() and predict().
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User *must* inherit from this class and set fit() and predict() and
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data_convertor property.
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"""
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def __init__(self, **kwargs):
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@ -69,3 +72,8 @@ class BasePyTorchModel(IFreqaiModel):
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f"({end_time - start_time:.2f} secs) --------------------")
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return model
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@property
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@abstractmethod
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def data_convertor(self) -> PyTorchDataConvertor:
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raise NotImplementedError("Abstract property")
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@ -3,7 +3,6 @@ from typing import Tuple
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import numpy as np
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import numpy.typing as npt
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import torch
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from pandas import DataFrame
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from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
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@ -41,9 +40,12 @@ class BasePyTorchRegressor(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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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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x = self.data_convertor.convert_x(
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dk.data_dictionary["prediction_features"],
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device=self.device
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)
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logger.info(self.model.model)
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logger.info(self.model.model)
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y = self.model.model(x)
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pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
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@ -4,6 +4,8 @@ import torch
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from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.torch import PyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchDataConvertor import DefaultPyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
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from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
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@ -38,6 +40,10 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
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}
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"""
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@property
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def data_convertor(self) -> PyTorchDataConvertor:
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return DefaultPyTorchDataConvertor(target_tensor_type=torch.long, squeeze_target_tensor=True)
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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@ -72,8 +78,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
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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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target_tensor_type=torch.long,
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squeeze_target_tensor=True,
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data_convertor=self.data_convertor,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary, self.splits)
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@ -4,6 +4,8 @@ import torch
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from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.torch import PyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchDataConvertor import DefaultPyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
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from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
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@ -39,6 +41,10 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
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}
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"""
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@property
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def data_convertor(self) -> PyTorchDataConvertor:
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return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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@ -69,7 +75,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
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criterion=criterion,
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device=self.device,
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init_model=init_model,
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target_tensor_type=torch.float,
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data_convertor=self.data_convertor,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary, self.splits)
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56
freqtrade/freqai/torch/PyTorchDataConvertor.py
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56
freqtrade/freqai/torch/PyTorchDataConvertor.py
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@ -0,0 +1,56 @@
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from abc import ABC, abstractmethod
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from typing import Optional, Tuple
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import pandas as pd
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import torch
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class PyTorchDataConvertor(ABC):
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@abstractmethod
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def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
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"""
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:param df: "*_features" dataframe.
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:param device: cpu/gpu.
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:returns: tuple of tensors.
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"""
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@abstractmethod
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def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
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"""
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:param df: "*_labels" dataframe.
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:param device: cpu/gpu.
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:returns: tuple of tensors.
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"""
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class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
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def __init__(
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self,
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target_tensor_type: Optional[torch.dtype] = None,
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squeeze_target_tensor: bool = False
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):
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self._target_tensor_type = target_tensor_type
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self._squeeze_target_tensor = squeeze_target_tensor
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def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
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x = torch.from_numpy(df.values).float()
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if device:
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x = x.to(device)
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return x,
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def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
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y = torch.from_numpy(df.values)
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if self._target_tensor_type:
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y = y.to(self._target_tensor_type)
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if self._squeeze_target_tensor:
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y = y.squeeze()
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if device:
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y = y.to(device)
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return y,
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@ -1,4 +1,5 @@
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import logging
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from typing import Tuple, List
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import torch
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import torch.nn as nn
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@ -46,7 +47,8 @@ class PyTorchMLPModel(nn.Module):
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(p=dropout_percent)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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def forward(self, x: List[torch.Tensor]) -> torch.Tensor:
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x, = x
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x = self.relu(self.input_layer(x))
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x = self.dropout(x)
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x = self.blocks(x)
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@ -9,11 +9,13 @@ import torch.nn as nn
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader, TensorDataset
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from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface
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logger = logging.getLogger(__name__)
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class PyTorchModelTrainer:
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class PyTorchModelTrainer(PyTorchTrainerInterface):
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def __init__(
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self,
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model: nn.Module,
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@ -21,8 +23,7 @@ class PyTorchModelTrainer:
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criterion: nn.Module,
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device: str,
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init_model: Dict,
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target_tensor_type: torch.dtype,
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squeeze_target_tensor: bool = False,
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data_convertor: PyTorchDataConvertor,
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model_meta_data: Dict[str, Any] = {},
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**kwargs
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):
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@ -33,11 +34,7 @@ class PyTorchModelTrainer:
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:param device: The device to use for training (e.g. 'cpu', 'cuda').
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:param init_model: A dictionary containing the initial model/optimizer
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state_dict and model_meta_data saved by self.save() method.
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:param target_tensor_type: type of target tensor, for classification usually
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torch.long, for regressor usually torch.float.
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:param model_meta_data: Additional metadata about the model (optional).
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:param squeeze_target_tensor: controls the target shape, used for loss functions
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that requires 0D or 1D.
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:param max_iters: The number of training iterations to run.
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iteration here refers to the number of times we call
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self.optimizer.step(). used to calculate n_epochs.
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@ -49,11 +46,10 @@ class PyTorchModelTrainer:
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self.criterion = criterion
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self.model_meta_data = model_meta_data
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self.device = device
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self.target_tensor_type = target_tensor_type
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self.max_iters: int = kwargs.get("max_iters", 100)
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self.batch_size: int = kwargs.get("batch_size", 64)
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self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
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self.squeeze_target_tensor = squeeze_target_tensor
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self.data_convertor = data_convertor
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if init_model:
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self.load_from_checkpoint(init_model)
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@ -81,9 +77,12 @@ class PyTorchModelTrainer:
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# training
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losses = []
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for i, batch_data in enumerate(data_loaders_dictionary["train"]):
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xb, yb = batch_data
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xb = xb.to(self.device)
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yb = yb.to(self.device)
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for tensor in batch_data:
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tensor.to(self.device)
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xb = batch_data[:-1]
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yb = batch_data[-1]
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yb_pred = self.model(xb)
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loss = self.criterion(yb_pred, yb)
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@ -115,14 +114,16 @@ class PyTorchModelTrainer:
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self.model.eval()
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n_batches = 0
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losses = []
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for i, batch in enumerate(data_loader_dictionary[split]):
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for i, batch_data in enumerate(data_loader_dictionary[split]):
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if max_n_eval_batches and i > max_n_eval_batches:
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n_batches += 1
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break
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xb, yb = batch
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xb = xb.to(self.device)
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yb = yb.to(self.device)
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for tensor in batch_data:
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tensor.to(self.device)
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xb = batch_data[:-1]
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yb = batch_data[-1]
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yb_pred = self.model(xb)
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loss = self.criterion(yb_pred, yb)
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losses.append(loss.item())
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@ -140,14 +141,9 @@ class PyTorchModelTrainer:
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"""
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data_loader_dictionary = {}
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for split in splits:
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x = torch.from_numpy(data_dictionary[f"{split}_features"].values).float()
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y = torch.from_numpy(data_dictionary[f"{split}_labels"].values)\
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.to(self.target_tensor_type)
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if self.squeeze_target_tensor:
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y = y.squeeze()
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dataset = TensorDataset(x, y)
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x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"])
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y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"])
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dataset = TensorDataset(*x, *y)
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data_loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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@ -186,7 +182,7 @@ class PyTorchModelTrainer:
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"model_meta_data": self.model_meta_data,
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}, path)
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def load_from_file(self, path: Path):
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def load(self, path: Path):
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checkpoint = torch.load(path)
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return self.load_from_checkpoint(checkpoint)
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54
freqtrade/freqai/torch/PyTorchTrainerInterface.py
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54
freqtrade/freqai/torch/PyTorchTrainerInterface.py
Normal file
@ -0,0 +1,54 @@
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Tuple
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import pandas as pd
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import torch
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import torch.nn as nn
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from pathlib import Path
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class PyTorchTrainerInterface(ABC):
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@abstractmethod
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def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:
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"""
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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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:param splits: splits to use in training, splits must contain "train",
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optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
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in the config file.
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- Calculates the predicted output for the batch using the PyTorch model.
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- Calculates the loss between the predicted and actual output using a loss function.
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- Computes the gradients of the loss with respect to the model's parameters using
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backpropagation.
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- Updates the model's parameters using an optimizer.
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"""
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@abstractmethod
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def save(self, path: Path) -> None:
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"""
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- Saving any nn.Module state_dict
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- Saving model_meta_data, this dict should contain any additional data that the
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user needs to store. e.g class_names for classification models.
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"""
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def load(self, path: Path) -> nn.Module:
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"""
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:param path: path to zip file.
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:returns: pytorch model.
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"""
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checkpoint = torch.load(path)
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return self.load_from_checkpoint(checkpoint)
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@abstractmethod
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def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module:
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"""
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when using continual_learning, DataDrawer will load the dictionary
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(containing state dicts and model_meta_data) by calling torch.load(path).
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you can access this dict from any class that inherits IFreqaiModel by calling
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get_init_model method.
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:checkpoint checkpoint: dict containing the model & optimizer state dicts,
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model_meta_data, etc..
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"""
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