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Fix typing issue, avoid using .get() when unnecessary, convert to fstrings

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This commit is contained in:
robcaulk
2022-07-29 08:12:50 +02:00
parent efbd83c56d
commit c84d54b35e
5 changed files with 57 additions and 57 deletions
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34
freqtrade/freqai/freqai_interface.py
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@@ -12,7 +12,7 @@ from typing import Any, Dict, Tuple
import numpy as np
import pandas as pd
from numpy.typing import ArrayLike
from numpy.typing import NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@@ -204,14 +204,9 @@ class IFreqaiModel(ABC):
dk.data_path = Path(
dk.full_path
/ str(
"sub-train"
+ "-"
+ metadata["pair"].split("/")[0]
+ "_"
+ str(int(trained_timestamp.stopts))
/
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
)
)
if not self.model_exists(
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
):
@@ -331,7 +326,8 @@ class IFreqaiModel(ABC):
return
elif self.dk.check_if_model_expired(trained_timestamp):
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
do_preds = np.ones(2, dtype=np.int_) * 2
dk.DI_values = np.zeros(2)
logger.warning(
f"Model expired for {pair}, returning null values to strategy. Strategy "
"construction should take care to consider this event with "
@@ -379,15 +375,15 @@ class IFreqaiModel(ABC):
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get(
if self.freqai_info["feature_parameters"].get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
@@ -401,15 +397,15 @@ class IFreqaiModel(ABC):
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info.get("feature_parameters", {}).get(
if self.freqai_info["feature_parameters"].get(
"principal_component_analysis", False
):
dk.pca_transform(dataframe)
if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
def model_exists(
@@ -430,9 +426,9 @@ class IFreqaiModel(ABC):
coin, _ = pair.split("/")
if not self.live:
dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
path_to_modelfile = Path(dk.data_path / str(model_filename + "_model.joblib"))
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
file_exists = path_to_modelfile.is_file()
if file_exists and not scanning:
logger.info("Found model at %s", dk.data_path / dk.model_filename)
@@ -442,7 +438,7 @@ class IFreqaiModel(ABC):
def set_full_path(self) -> None:
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_info.get("identifier"))
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
)
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
@@ -550,7 +546,7 @@ class IFreqaiModel(ABC):
@abstractmethod
def predict(
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
) -> Tuple[DataFrame, ArrayLike]:
) -> Tuple[DataFrame, NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_dataframe: Full dataframe for the current backtest period.