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integrate inlier metric function

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This commit is contained in:
robcaulk
2022-08-18 19:15:29 +02:00
parent d3cb211283
commit b11742a4c5
2 changed files with 79 additions and 42 deletions
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36
freqtrade/freqai/freqai_interface.py
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@@ -66,7 +66,6 @@ class IFreqaiModel(ABC):
"data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.retrain = False
self.first = True
self.set_full_path()
@@ -74,11 +73,14 @@ class IFreqaiModel(ABC):
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
self.pair_it = 0
self.pair_it_train = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
@@ -403,18 +405,20 @@ class IFreqaiModel(ABC):
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
if ft_params.get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
if dk.pair in self.dd.old_DBSCAN_eps:
eps = self.dd.old_DBSCAN_eps[dk.pair]
else:
@@ -422,6 +426,11 @@ class IFreqaiModel(ABC):
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='train')
if self.freqai_info["data_split_parameters"]["test_size"] > 0:
dk.compute_inlier_metric(set_='test')
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
"""
Base data cleaning method for predict.
@@ -433,18 +442,23 @@ 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["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict')
if ft_params.get(
"principal_component_analysis", False
):
dk.pca_transform(dataframe)
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
def model_exists(