fix: ensure test_size=0 is still accommodated

This commit is contained in:
robcaulk
2023-06-17 15:39:33 +02:00
parent 447feb16b4
commit b0ab400ff3
5 changed files with 34 additions and 28 deletions

View File

@@ -126,6 +126,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
dd["train_labels"],
dd["train_weights"])
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],

View File

@@ -61,6 +61,7 @@ class BaseClassifierModel(IFreqaiModel):
dd["train_labels"],
dd["train_weights"])
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],

View File

@@ -197,6 +197,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
dd["train_labels"],
dd["train_weights"])
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],

View File

@@ -96,12 +96,15 @@ class BasePyTorchRegressor(BasePyTorchModel):
dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
dd["train_labels"],
dd["train_weights"])
dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
dd["test_labels"],
dd["test_weights"])
dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
logger.info(
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"

View File

@@ -60,14 +60,14 @@ class BaseRegressionModel(IFreqaiModel):
dd["train_weights"]) = dk.feature_pipeline.fit_transform(dd["train_features"],
dd["train_labels"],
dd["train_weights"])
dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(dd["test_features"],
dd["test_labels"],
dd["test_weights"]) = dk.feature_pipeline.transform(dd["test_features"],
dd["test_labels"],
dd["test_weights"])
dd["train_labels"], _, _ = dk.label_pipeline.fit_transform(dd["train_labels"])
dd["test_labels"], _, _ = dk.label_pipeline.transform(dd["test_labels"])
logger.info(