9394559170

Committed 26 Apr 2024 06:36AM UTC coverage: 94.656% (-0.02%) from 94.674%

Build # 9394559170

Build Type

push

github

Committed by

xmatthias

Commit Message

Loader should be passed as kwarg for clarity

Run Details

20280 of 21425 relevant lines covered (94.66%)

0.95 hits per line

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87.5

/freqtrade/freqai/torch/PyTorchTrainerInterface.py

from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List

import pandas as pd
import torch
from torch import nn


class PyTorchTrainerInterface(ABC):

    @abstractmethod
    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:
        """
        :param data_dictionary: the dictionary constructed by DataHandler to hold
        all the training and test data/labels.
        :param splits: splits to use in training, splits must contain "train",
        optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
        in the config file.

         - Calculates the predicted output for the batch using the PyTorch model.
         - Calculates the loss between the predicted and actual output using a loss function.
         - Computes the gradients of the loss with respect to the model's parameters using
           backpropagation.
         - Updates the model's parameters using an optimizer.
        """

    @abstractmethod
    def save(self, path: Path) -> None:
        """
        - Saving any nn.Module state_dict
        - Saving model_meta_data, this dict should contain any additional data that the
          user needs to store. e.g class_names for classification models.
        """

    def load(self, path: Path) -> nn.Module:
        """
        :param path: path to zip file.
        :returns: pytorch model.
        """
        checkpoint = torch.load(path)
        return self.load_from_checkpoint(checkpoint)

    @abstractmethod
    def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module:
        """
        when using continual_learning, DataDrawer will load the dictionary
        (containing state dicts and model_meta_data) by calling torch.load(path).
        you can access this dict from any class that inherits IFreqaiModel by calling
        get_init_model method.
        :checkpoint checkpoint: dict containing the model & optimizer state dicts,
        model_meta_data, etc..
        """

1	from abc import ABC, abstractmethod	1✔
2	from pathlib import Path	1✔
3	from typing import Dict, List	1✔
4
5	import pandas as pd	1✔
6	import torch	1✔
7	from torch import nn	1✔
8
9
10	class PyTorchTrainerInterface(ABC):	1✔
11
12	@abstractmethod	1✔
13	def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:	1✔
14	"""
15	:param data_dictionary: the dictionary constructed by DataHandler to hold
16	all the training and test data/labels.
17	:param splits: splits to use in training, splits must contain "train",
18	optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
19	in the config file.
20
21	- Calculates the predicted output for the batch using the PyTorch model.
22	- Calculates the loss between the predicted and actual output using a loss function.
23	- Computes the gradients of the loss with respect to the model's parameters using
24	backpropagation.
25	- Updates the model's parameters using an optimizer.
26	"""
27
28	@abstractmethod	1✔
29	def save(self, path: Path) -> None:	1✔
30	"""
31	- Saving any nn.Module state_dict
32	- Saving model_meta_data, this dict should contain any additional data that the
33	user needs to store. e.g class_names for classification models.
34	"""
35
36	def load(self, path: Path) -> nn.Module:	1✔
37	"""
38	:param path: path to zip file.
39	:returns: pytorch model.
40	"""
41	checkpoint = torch.load(path)	×
42	return self.load_from_checkpoint(checkpoint)	×
43
44	@abstractmethod	1✔
45	def load_from_checkpoint(self, checkpoint: Dict) -> nn.Module:	1✔
46	"""
47	when using continual_learning, DataDrawer will load the dictionary
48	(containing state dicts and model_meta_data) by calling torch.load(path).
49	you can access this dict from any class that inherits IFreqaiModel by calling
50	get_init_model method.
51	:checkpoint checkpoint: dict containing the model & optimizer state dicts,
52	model_meta_data, etc..
53	"""

freqtrade / freqtrade / 9394559170

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