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					@ -742,15 +742,21 @@ repr(optuna_study.best_params)
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					# %%
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					regressor_best_params = dict(optuna_study.best_params.items())
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					regressor_best_params = dict(optuna_study.best_params.items())
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					# %% [markdown]
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					# %% [markdown]
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					# Составной пайплайн:
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					# Составной пайплайн:
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					# %%
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					pipeline = build_pipeline(
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					def build_pipeline_optimized_best():
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					    regressor_best_params['n_estimators'],
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					    return build_pipeline(
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					    regressor_max_depth=regressor_best_params['max_depth'],
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					        regressor_best_params['n_estimators'],
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					    regressor_max_features=regressor_best_params['max_features'],
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					        regressor_max_depth=regressor_best_params['max_depth'],
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					)
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					        regressor_max_features=regressor_best_params['max_features'],
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					    )
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					pipeline = build_pipeline_optimized_best()
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					pipeline
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					pipeline
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					| 
						
							
								
							
						
						
							
								
							
						
						
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					@ -804,4 +810,49 @@ mlflow_log_model(
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					    ),
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					    ),
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					)
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					)
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					# %% [markdown]
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					# ### И в продакшн
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					# %% [markdown]
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					# Лучшая выбранная модель — с автоматически подобранными гиперпараметрами.
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					pipeline = build_pipeline_optimized_best()
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					pipeline
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					# %%
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					model_params = filter_params(
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					    pipeline.get_params(),
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					    include={
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					        'preprocess': (False, PREPROCESS_AUGMENTING_TRANSFORMER_PARAMS_COMMON_INCLUDE.copy()),
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					        'select_features': (False, FEATURE_SELECTOR_PARAMS_COMMON_INCLUDE.copy()),
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					        'regress': (False, True),
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					    },
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					    exclude={
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					        'preprocess': PREPROCESS_AUGMENTING_TRANSFORMER_PARAMS_COMMON_EXCLUDE.copy(),
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					        'select_features': FEATURE_SELECTOR_PARAMS_COMMON_EXCLUDE,
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					        'regress': RANDOM_FOREST_REGRESSOR_PARAMS_COMMON_EXCLUDE,
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					    },
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					)
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					model_params
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					# %%
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					_ = pipeline.fit(df_orig_features, df_target.iloc[:, 0])
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					# %%
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					mlflow_log_model(
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					    pipeline,
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					    model_params=model_params,
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					    metrics=None,
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					    nested_run_name='Final model',
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					    model_signature=mlflow_model_signature,
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					    input_example=df_orig_features.head(MODEL_INOUT_EXAMPLE_SIZE),
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					    #pip_requirements=str(MODEL_PIP_REQUIREMENTS_PATH),
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					    comment_file_path=(
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					        model_comment_path
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					        if model_comment_path is not None
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					        else (BASE_PATH / 'research' / model_comment_relpath)
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					    ),
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					)
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					# %%
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					# %%
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