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ravinkohliravinkohli
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Merge branch 'development' into reg_cocktails
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‎examples/40_advanced/example_custom_configuration_space.py‎

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Original file line numberDiff line numberDiff line change
@@ -5,6 +5,7 @@
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The following example shows how adjust the configuration space of
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the search. Currently, there are two changes that can be made to the space:-
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1. Adjust individual hyperparameters in the pipeline
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2. Include or exclude components:
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a) include: Dictionary containing components to include. Key is the node
@@ -66,76 +67,78 @@ def get_search_space_updates():
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return updates
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if __name__ == '__main__':
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############################################################################
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# Data Loading
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# ============
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X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
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X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
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X,
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y,
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random_state=1,
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)
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############################################################################
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# Build and fit a classifier with include components
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# ==================================================
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api = TabularClassificationTask(
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search_space_updates=get_search_space_updates(),
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include_components={'network_backbone': ['ResNetBackbone'],
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'encoder': ['OneHotEncoder']}
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)
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############################################################################
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# Search for an ensemble of machine learning algorithms
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# =====================================================
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api.search(
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X_train=X_train.copy(),
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y_train=y_train.copy(),
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X_test=X_test.copy(),
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y_test=y_test.copy(),
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optimize_metric='accuracy',
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total_walltime_limit=300,
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func_eval_time_limit_secs=50
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)
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############################################################################
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# Print the final ensemble performance
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# ====================================
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print(api.run_history, api.trajectory)
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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print(api.show_models())
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############################################################################
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# Build and fit a classifier with exclude components
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# ==================================================
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api = TabularClassificationTask(
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search_space_updates=get_search_space_updates(),
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exclude_components={'network_backbone': ['MLPBackbone'],
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'encoder': ['OneHotEncoder']}
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)
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############################################################################
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# Search for an ensemble of machine learning algorithms
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# =====================================================
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api.search(
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X_train=X_train,
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y_train=y_train,
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X_test=X_test.copy(),
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y_test=y_test.copy(),
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optimize_metric='accuracy',
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total_walltime_limit=300,
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func_eval_time_limit_secs=50
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)
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############################################################################
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# Print the final ensemble performance
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# ====================================
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print(api.run_history, api.trajectory)
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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print(api.show_models())
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############################################################################
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# Data Loading
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# ============
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X, y = sklearn.datasets.fetch_openml(data_id=40981, return_X_y=True, as_frame=True)
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X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
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X,
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y,
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random_state=1,
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)
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############################################################################
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# Build and fit a classifier with include components
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# ==================================================
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api = TabularClassificationTask(
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search_space_updates=get_search_space_updates(),
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include_components={'network_backbone': ['MLPBackbone', 'ResNetBackbone'],
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'encoder': ['OneHotEncoder']}
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)
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############################################################################
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# Search for an ensemble of machine learning algorithms
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# =====================================================
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api.search(
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X_train=X_train.copy(),
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y_train=y_train.copy(),
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X_test=X_test.copy(),
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y_test=y_test.copy(),
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optimize_metric='accuracy',
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total_walltime_limit=150,
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func_eval_time_limit_secs=30
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)
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############################################################################
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# Print the final ensemble performance
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# ====================================
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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print(api.show_models())
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# Print statistics from search
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print(api.sprint_statistics())
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############################################################################
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# Build and fit a classifier with exclude components
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# ==================================================
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api = TabularClassificationTask(
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search_space_updates=get_search_space_updates(),
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exclude_components={'network_backbone': ['MLPBackbone'],
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'encoder': ['OneHotEncoder']}
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)
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############################################################################
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# Search for an ensemble of machine learning algorithms
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# =====================================================
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api.search(
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X_train=X_train,
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y_train=y_train,
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X_test=X_test.copy(),
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y_test=y_test.copy(),
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optimize_metric='accuracy',
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total_walltime_limit=150,
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func_eval_time_limit_secs=30
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)
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############################################################################
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# Print the final ensemble performance
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# ====================================
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y_pred = api.predict(X_test)
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score = api.score(y_pred, y_test)
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print(score)
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print(api.show_models())
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# Print statistics from search
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print(api.sprint_statistics())

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