adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix mypy error and add test for principal component analysis

Browse Source
This commit is contained in:
Robert Caulk
2022-07-25 11:46:59 +02:00
parent 4abc26b582
commit 7b105532d1
3 changed files with 33 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
freqtrade/freqai/freqai_interface.py
View File

@@ -65,7 +65,6 @@ class IFreqaiModel(ABC):
self.data_split_parameters = config.get("freqai", {}).get("data_split_parameters")
self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.model = None
self.retrain = False
self.first = True
self.set_full_path()
@@ -372,8 +371,8 @@ class IFreqaiModel(ABC):
"""
Base data cleaning method for train
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
of how outlier data points are dropped from the dataframe used for training.
based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info.get("feature_parameters", {}).get(

7
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@@ -1,6 +1,7 @@
import logging
from typing import Tuple
from typing import Any, Tuple
import numpy.typing as npt
from pandas import DataFrame
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
@@ -28,7 +29,7 @@ class BaseRegressionModel(IFreqaiModel):
def train(
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
) -> Tuple[DataFrame, DataFrame]:
) -> Any:
"""
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
for storing, saving, loading, and analyzing the data.
@@ -83,7 +84,7 @@ class BaseRegressionModel(IFreqaiModel):
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
) -> Tuple[DataFrame, DataFrame]:
) -> Tuple[DataFrame, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.