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chore: update freqai to modern typing syntax

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This commit is contained in:
Matthias
2024-10-04 06:50:31 +02:00
parent f369151e8e
commit 96f737f13e
32 changed files with 148 additions and 149 deletions
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4
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@@ -1,6 +1,6 @@
import logging
from time import time
from typing import Any, Tuple
from typing import Any
import numpy as np
import numpy.typing as npt
@@ -86,7 +86,7 @@ class BaseClassifierModel(IFreqaiModel):
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
) -> 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.

18
freqtrade/freqai/base_models/BasePyTorchClassifier.py
View File

@@ -1,6 +1,6 @@
import logging
from time import time
from typing import Any, Dict, List, Tuple
from typing import Any
import numpy as np
import numpy.typing as npt
@@ -44,7 +44,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
) -> tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param dk: dk: The datakitchen object
@@ -100,9 +100,9 @@ class BasePyTorchClassifier(BasePyTorchModel):
def encode_class_names(
self,
data_dictionary: Dict[str, pd.DataFrame],
data_dictionary: dict[str, pd.DataFrame],
dk: FreqaiDataKitchen,
class_names: List[str],
class_names: list[str],
):
"""
encode class name, str -> int
@@ -119,7 +119,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
)
@staticmethod
def assert_valid_class_names(target_column: pd.Series, class_names: List[str]):
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(
@@ -127,7 +127,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
f"expecting labels: {class_names}",
)
def decode_class_names(self, class_ints: torch.Tensor) -> List[str]:
def decode_class_names(self, class_ints: torch.Tensor) -> list[str]:
"""
decode class name, int -> str
"""
@@ -141,14 +141,14 @@ class BasePyTorchClassifier(BasePyTorchModel):
def convert_label_column_to_int(
self,
data_dictionary: Dict[str, pd.DataFrame],
data_dictionary: dict[str, pd.DataFrame],
dk: FreqaiDataKitchen,
class_names: List[str],
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]:
def get_class_names(self) -> list[str]:
if not self.class_names:
raise ValueError(
"self.class_names is empty, "

4
freqtrade/freqai/base_models/BasePyTorchRegressor.py
View File

@@ -1,6 +1,6 @@
import logging
from time import time
from typing import Any, Tuple
from typing import Any
import numpy as np
import numpy.typing as npt
@@ -24,7 +24,7 @@ class BasePyTorchRegressor(BasePyTorchModel):
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
) -> 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.

4
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@@ -1,6 +1,6 @@
import logging
from time import time
from typing import Any, Tuple
from typing import Any
import numpy as np
import numpy.typing as npt
@@ -88,7 +88,7 @@ class BaseRegressionModel(IFreqaiModel):
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
) -> 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.