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add config asserts, use .get method with default values for optional functionality, move data_cleaning_* to freqai_interface (away from user custom pred model) since it is controlled by config params.

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This commit is contained in:
robcaulk
2022-05-23 12:07:09 +02:00
parent dede128648
commit e1c068ca66
4 changed files with 162 additions and 93 deletions
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21
freqtrade/freqai/prediction_models/CatboostPredictionModel.py
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@@ -1,7 +1,6 @@
import logging
from typing import Any, Dict, Tuple
import pandas as pd
from catboost import CatBoostRegressor, Pool
from pandas import DataFrame
@@ -149,7 +148,7 @@ class CatboostPredictionModel(IFreqaiModel):
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.
"""
if self.feature_parameters["principal_component_analysis"]:
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
self.dh.principal_component_analysis()
# if self.feature_parameters["determine_statistical_distributions"]:
@@ -157,9 +156,10 @@ class CatboostPredictionModel(IFreqaiModel):
# if self.feature_parameters["remove_outliers"]:
# self.dh.remove_outliers(predict=False)
if self.feature_parameters["use_SVM_to_remove_outliers"]:
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
self.dh.use_SVM_to_remove_outliers(predict=False)
if self.feature_parameters["DI_threshold"]:
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
@@ -173,21 +173,16 @@ class CatboostPredictionModel(IFreqaiModel):
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.feature_parameters["principal_component_analysis"]:
pca_components = self.dh.pca.transform(filtered_dataframe)
self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
data=pca_components,
columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])],
index=filtered_dataframe.index,
)
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
self.dh.pca_transform()
# if self.feature_parameters["determine_statistical_distributions"]:
# self.dh.determine_statistical_distributions()
# if self.feature_parameters["remove_outliers"]:
# self.dh.remove_outliers(predict=True) # creates dropped index
if self.feature_parameters["use_SVM_to_remove_outliers"]:
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
self.dh.use_SVM_to_remove_outliers(predict=True)
if self.feature_parameters["DI_threshold"]:
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
self.dh.check_if_pred_in_training_spaces() # sets do_predict