Use the generate_custom_config function to generate custom JSON file containing user customized input under current working directory which can be used for AutoML execution.
Optional Argument:
- file_name: Specifies the name of the file to be generated.
Default value is "custom".
Do not pass the file name with extension. Extension '.json' is automatically added to specified file name.
Example Setup
Before generating custom JSON file, import either AutoML, or AutoClassifier, or AutoRegressor, from teradataml based on the feature you choose.
The following example imports all three.
from teradataml import AutoML, AutoClassifier, AutoRegressor
Example 1: Generate a default file named "custom.json"
Based on the feature for your use case, generate a default file named "custom.json" file using one of the following options.
AutoML.generate_custom_config()Or,
AutoClassifier.generate_custom_config()Or,
AutoRegressor.generate_custom_config()
Example 2: Generate file with different file names using file_name argument
The following code generates JSON file with specified file name under current working directory.
AutoML.generate_custom_config("custom_titanic")Or,
AutoClassifier.generate_custom_config("custom_titanic")Or,
AutoRegressor.generate_custom_config("custom_housing")
Example system output
The generate_custom_config() method enables the generation of customized JSON by allowing you to provide appropriate responses to available options. It requires either the index value corresponding to the given option or the feature name on which customization needs to be done.
You can provide a single value or a list of values, separated by commas, based on the prompt details.
AutoRegressor.generate_custom_config("custom_housing")
Generating custom config JSON for AutoML ... Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 1 Customizing Feature Engineering Phase ... Available options for customization of feature engineering phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Missing Value Handling Index 2: Customize Bincode Encoding Index 3: Customize String Manipulation Index 4: Customize Categorical Encoding Index 5: Customize Mathematical Transformation Index 6: Customize Nonlinear Transformation Index 7: Customize Antiselect Features Index 8: Back to main menu Index 9: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in feature engineering phase: 2,4,7,8 Customizing Bincode Encoding ... Provide the following details to customize binning and coding encoding: Available binning methods with corresponding indices: Index 1: Equal-Width Index 2: Variable-Width Enter the feature or list of features for binning: bedrooms Enter the index of corresponding binning method for feature bedrooms: 2 Enter the number of bins for feature bedrooms: 2 Available value type of feature for variable binning with corresponding indices: Index 1: int Index 2: float Provide the range for bin 1 of feature bedrooms: Enter the index of corresponding value type of feature bedrooms: 1 Enter the minimum value for bin 1 of feature bedrooms: 0 Enter the maximum value for bin 1 of feature bedrooms: 2 Enter the label for bin 1 of feature bedrooms: small_house Provide the range for bin 2 of feature bedrooms: Enter the index of corresponding value type of feature bedrooms: 1 Enter the minimum value for bin 2 of feature bedrooms: 3 Enter the maximum value for bin 2 of feature bedrooms: 5 Enter the label for bin 2 of feature bedrooms: big_house Customization of bincode encoding has been completed successfully. Customizing Categorical Encoding ... Provide the following details to customize categorical encoding: Available categorical encoding methods with corresponding indices: Index 1: OneHotEncoding Index 2: OrdinalEncoding Index 3: TargetEncoding Enter the list of corresponding index categorical encoding methods you want to use: 2,3 Enter the feature or list of features for OrdinalEncoding: homestyle Enter the feature or list of features for TargetEncoding: prefarea Available target encoding methods with corresponding indices: Index 1: CBM_BETA Index 2: CBM_DIRICHLET Index 3: CBM_GAUSSIAN_INVERSE_GAMMA Enter the index of target encoding method for feature prefarea: 3 Enter the response column for target encoding method for feature prefarea: price Customization of categorical encoding has been completed successfully. Customizing Antiselect Features ... Enter the feature or list of features for antiselect: sn Customization of antiselect features has been completed successfully. Customization of feature engineering phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 2 Customizing Data Preparation Phase ... Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Train Test Split Index 2: Customize Data Imbalance Handling Index 3: Customize Outlier Handling Index 4: Customize Feature Scaling Index 5: Back to main menu Index 6: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 1,2,3,4,5 Customizing Train Test Split ... Enter the train size for train test split: 0.75 Customization of train test split has been completed successfully. Customizing Data Imbalance Handling ... Available data sampling methods with corresponding indices: Index 1: SMOTE Index 2: NearMiss Enter the corresponding index data imbalance handling method: 1 Customization of data imbalance handling has been completed successfully. Customizing Outlier Handling ... Available outlier detection methods with corresponding indices: Index 1: percentile Index 2: tukey Index 3: carling Enter the corresponding index oulier handling method: 1 Enter the lower percentile value for outlier handling: 0.1 Enter the upper percentile value for outlier handling: 0.9 Enter the feature or list of features for outlier handling: bathrms Available outlier replacement methods with corresponding indices: Index 1: delete Index 2: median Index 3: Any Numeric Value Enter the index of corresponding replacement method for feature bathrms: 1 Customization of outlier handling has been completed successfully. Available feature scaling methods with corresponding indices: Index 1: maxabs Index 2: mean Index 3: midrange Index 4: range Index 5: rescale Index 6: std Index 7: sum Index 8: ustd Enter the corresponding index feature scaling method: 6 Customization of feature scaling has been completed successfully. Customization of data preparation phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 3 Customizing Model Training Phase ... Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 1 Customizing Model Hyperparameter ... Available models for hyperparameter tuning with corresponding indices: Index 1: decision_forest Index 2: xgboost Index 3: knn Index 4: glm Index 5: svm Available hyperparamters update methods with corresponding indices: Index 1: ADD Index 2: REPLACE Enter the list of model indices for performing hyperparameter tuning: 2 Available hyperparameters for model 'xgboost' with corresponding indices: Index 1: min_impurity Index 2: max_depth Index 3: min_node_size Index 4: shrinkage_factor Index 5: iter_num Enter the list of hyperparameter indices for model 'xgboost': 3 Enter the index of corresponding update method for hyperparameters 'min_node_size' for model 'xgboost': 1 Enter the list of value for hyperparameter 'min_node_size' for model 'xgboost': 1,2 Customization of model hyperparameter has been completed successfully. Available options for customization of model training phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Model Hyperparameter Index 2: Back to main menu Index 3: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in model training phase: 2 Customization of model training phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 4 Generating custom json and exiting ... Process of generating custom config file for AutoML has been completed successfully. 'custom_housing.json' file is generated successfully under the current working directory.