stable/freqtrade/freqai/prediction_models/PyTorchClassifier.py

146 lines
5.3 KiB
Python
Raw Normal View History

2023-03-05 14:59:24 +00:00
import logging
from typing import Dict, List, Tuple
2023-03-05 14:59:24 +00:00
import numpy as np
2023-03-08 14:03:36 +00:00
import numpy.typing as npt
2023-03-05 14:59:24 +00:00
import pandas as pd
import torch
from pandas import DataFrame
from torch.nn import functional as F
2023-03-08 14:11:51 +00:00
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
2023-03-08 14:03:36 +00:00
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
2023-03-05 14:59:24 +00:00
logger = logging.getLogger(__name__)
class PyTorchClassifier(BasePyTorchModel):
2023-03-09 09:14:54 +00:00
"""
A PyTorch implementation of a classifier.
User must implement fit method
Important!
User must declare the target class names in the strategy, under
IStrategy.set_freqai_targets method.
```
def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
self.freqai.class_names = ["down", "up"]
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-100) >
dataframe["close"], 'up', 'down')
return dataframe
```
2023-03-09 09:14:54 +00:00
"""
2023-03-05 14:59:24 +00:00
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.class_name_to_index = None
self.index_to_class_name = None
2023-03-05 14:59:24 +00:00
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
: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_names' doesn't exist in model meta_data.
2023-03-05 14:59:24 +00:00
"""
2023-03-09 12:55:52 +00:00
class_names = self.model.model_meta_data.get("class_names", None)
if not class_names:
raise ValueError(
"Missing class names. "
"self.model.model_meta_data[\"class_names\"] is None."
)
if not self.class_name_to_index:
self.init_class_names_to_index_mapping(class_names)
2023-03-05 14:59:24 +00:00
dk.find_features(unfiltered_df)
filtered_df, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk)
x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
.float()\
.to(self.device)
2023-03-05 14:59:24 +00:00
logits = self.model.model(x)
2023-03-05 14:59:24 +00:00
probs = F.softmax(logits, dim=-1)
predicted_classes = torch.argmax(probs, dim=-1)
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]])
2023-03-05 14:59:24 +00:00
pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
return (pred_df, dk.do_predict)
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 be the target column
containing the class names
"""
2023-03-09 12:55:52 +00:00
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], class_names)
label_df[target_column_name] = list(
map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
)
@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(
2023-03-08 13:38:22 +00:00
f"Found non defined labels: {non_defined_labels}, ",
f"expecting labels: {class_names}"
)
def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
2023-03-08 13:38:22 +00:00
"""
decode class name, int -> str
2023-03-08 13:38:22 +00:00
"""
2023-03-09 12:55:52 +00:00
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"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