teradataml offers hyper-parameterization of parameters for non-model trainer functions using GridSearch algorithm. This example makes use of hyper-parameterization feature to perform Antiselect function on titanic data.
In this example, teradataml example titanic data is used to perform Antiselect function.
- Example setup.
- Load the example dataset.
>>> load_example_data("teradataml", "titanic") - Create teradataml DataFrame.
>>> titanic = DataFrame.from_table("titanic") - Slice input data for Hyper-parameterization of data.
>>> train_df1 = titanic.iloc[:200] >>> train_df2 = titanic.iloc[200:]
- Load the example dataset.
- Define hyperparameter-tuning for Antiselect (non-model trainer function).
- Define the non-model trainer function parameter space. Include input data in parameter space for non-model trainer function.
>>> params = {"data":(train_df1, train_df2), "exclude":( ['survived', 'name', 'age'], ['survived', 'age'], ["ticket", "parch", "age"], ["ticket", "parch", "sex", "age"])}Any argument in 'params' can be hyper-parameterized. - Import non-model trainer function and optimizer.
>>> from teradataml import Antiselect, GridSearch
- Initialize the GridSearch optimizer with non-model trainer function and parameter space required for non-model training.
>>> gs_obj = GridSearch(func=Antiselect, params=params)
- Define the non-model trainer function parameter space. Include input data in parameter space for non-model trainer function.
- Perform execution of Antiselect function in sequential mode, and enable progress bar by setting verbose level to “1”.
>>> gs_obj.fit(run_parallel=True, verbose=1)
completed: |████████████████████████████████████████████████████████████| 100% - 8/8
Best model selection is not supported by hyperparameter tuning for non-model trainer function. - View the non-model trainer function execution metadata. Retrieve the model metadata of "gs_obj" instance.
>>> gs_obj.models
MODEL_ID PARAMETERS STATUS 0 ANTISELECT_0 {'data': '"ALICE"."ml__select__169839646305843 PASS 1 ANTISELECT_3 {'data': '"ALICE"."ml__select__169839646305843 PASS 2 ANTISELECT_2 {'data': '"ALICE"."ml__select__169839646305843 PASS 3 ANTISELECT_1 {'data': '"ALICE"."ml__select__169839646305843 PASS 4 ANTISELECT_4 {'data': '"ALICE"."ml__select__169840161255008 PASS 5 ANTISELECT_5 {'data': '"ALICE"."ml__select__169840161255008 PASS 6 ANTISELECT_6 {'data': '"ALICE"."ml__select__169840161255008 PASS 7 ANTISELECT_7 {'data': '"ALICE"."ml__select__169840161255008 PASSAll model training has been passed. In case of failure, use get_error_log method to retrieve corresponding error logs. - Retrieve the parameter grid for non-model trainer function. Retrieve "gs_obj" object's parameter grid.
>>> pprint.pprint(gs_obj.get_parameter_grid())
[{'data': '"ALICE"."ml__select__169839646305843"', 'exclude': ['survived', 'name', 'age']}, {'data': '"ALICE"."ml__select__169839646305843"', 'exclude': ['survived', 'age']}, {'data': '"ALICE"."ml__select__169839646305843"', 'exclude': ['ticket', 'parch', 'age']}, {'data': '"ALICE"."ml__select__169839646305843"', 'exclude': ['ticket', 'parch', 'sex', 'age']}, {'data': '"ALICE"."ml__select__169840161255008"', 'exclude': ['survived', 'name', 'age']}, {'data': '"ALICE"."ml__select__169840161255008"', 'exclude': ['survived', 'age']}, {'data': '"ALICE"."ml__select__169840161255008"', 'exclude': ['ticket', 'parch', 'age']}, {'data': '"ALICE"."ml__select__169840161255008"', 'exclude': ['ticket', 'parch', 'sex', 'age']}] - Get the non-model function execution result from GridSearch instance "ANTISELECT_3".
>>> gs_obj.get_model("ANTISELECT_3")############ result Output ############ passenger survived pclass name sibsp fare cabin embarked 0 3 1 3 Heikkinen, Miss. Laina 0 7.9250 None S 1 5 0 3 Allen, Mr. William Henry 0 8.0500 None S 2 6 0 3 Moran, Mr. James 0 8.4583 None Q 3 7 0 1 McCarthy, Mr. Timothy J 0 51.8625 E46 S 4 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 0 11.1333 None S 5 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) 1 30.0708 None C 6 8 0 3 Palsson, Master. Gosta Leonard 3 21.0750 None S 7 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 53.1000 C123 S 8 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) 1 71.2833 C85 C 9 1 0 3 Braund, Mr. Owen Harris 1 7.2500 None S