allow user to multithread jobs (advanced users only)
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@@ -60,6 +60,9 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
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{'eval_set': eval_sets[i], 'init_model': init_models[i]})
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model = FreqaiMultiOutputRegressor(estimator=cbr)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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@@ -56,9 +56,9 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
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'init_model': init_models[i]})
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model = FreqaiMultiOutputRegressor(estimator=lgb)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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# model = FreqaiMultiOutputRegressor(estimator=lgb)
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# model.fit(X=X, y=y, sample_weight=sample_weight, init_models=init_models,
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# eval_sets=eval_sets, eval_sample_weight=eval_weights)
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return model
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@@ -55,6 +55,9 @@ class XGBoostRegressorMultiTarget(BaseRegressionModel):
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'xgb_model': init_models[i]})
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model = FreqaiMultiOutputRegressor(estimator=xgb)
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thread_training = self.freqai_info.get('multitarget_parallel_training', False)
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if thread_training:
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model.n_jobs = y.shape[1]
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model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
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return model
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