Optimizers¶

class hana_automl.optimizers.base_optimizer.BaseOptimizer¶

Base optimizer class. Inherit from it to create custom optimizers.

abstract get_algorithm()¶: Return tuned AutoML algorithm

abstract get_model()¶: Return tuned HANA PAL model

abstract get_preprocessor_settings()¶: Return a PreprocessorSettings() object with preprocessor settings

abstract get_tuned_params()¶: Return hyperparameters that your optimizer has tuned

abstract objective()¶: Implement the objective function here. It must take hyperparameters to be tuned in optimizer

abstract tune()¶: Tune hyperparameters here

class hana_automl.optimizers.bayes.BayesianOptimizer(algo_list: list, data, iterations: int, time_limit: int, problem: str, categorical_features: Optional[list] = None, verbose=2, tuning_metric: Optional[str] = None)¶

Bayesian hyperparameters optimizer. (https://github.com/fmfn/BayesianOptimization)

data¶

Input data.

Type: Data

algo_list¶

List of algorithms to be tuned and compared.

Type: list

iter¶

Number of iterations.

Type: int

problem¶

Machine learning problem. Currently supported: ‘cls’, ‘reg’

Type: str

tuned_params¶

Final tuned hyperparameters of best algorithm.

Type: str

algo_index¶

Index of algorithm in algorithms list.

Type: int

time_limit¶

Time in seconds.

Type: int

categorical_features¶

List of categorical features in dataframe.

Type: list

inner_data¶

Copy of Data object to prevent from preprocessing data object multiple times while optimizing.

Type: Data

prepset¶: Imputer for preprocessing

model¶: Tuned HANA ML model in algorithm.

child_objective(**hyperparameters) → float¶

Mini objective function. It is used to tune hyperparameters of algorithm that was chosen in main objective.

Parameters: **hyperparameters – Parameters of algorithm’s model.
Returns: score – Tuning metric score of a model.
Return type: float

fit(algo, data)¶: Fits given model from data. Small method to reduce code repeating.

get_algorithm()¶: Returns tuned AutoML algorithm

get_model()¶: Returns tuned model.

get_preprocessor_settings() → hana_automl.preprocess.settings.PreprocessorSettings ¶: Returns tuned preprocessor settings.

get_tuned_params() → dict¶: Returns tuned hyperparameters.

objective(algo_index_tuned: int, num_strategy_method: int, normalizer_strategy: int, z_score_method: int, normalize_int: int, drop_outers: int)¶

Main objective function. Optimizer uses it to search for best algorithm and preprocess method.

Parameters

algo_index_tuned (int) – Index of algorithm in algo_list.
num_strategy_method (int) – Strategy to decode categorical variables.
normalizer_strategy (int) – Strategy for normalization
z_score_method (int) – A z-score (also called a standard score) gives you an idea of how far from the mean a data point is
normalize_int (int) – How to normalize integers

Returns

Optimal hyperparameters.

Return type

Best params

Note

Total number of child objective iterations is n_iter + init_points!

tune()¶: Starts hyperparameter searching.

class hana_automl.optimizers.optuna_optimizer.OptunaOptimizer(algo_list: list, data, problem: str, iterations: int, time_limit: int, algo_dict: dict, categorical_features: Optional[list] = None, droplist_columns: Optional[list] = None, verbose=2, tuning_metric: Optional[str] = None)¶

Optuna hyperparameters optimizer. (https://optuna.org/)

data¶

Input data.

Type: Data

algo_list¶

List of algorithms to be tuned and compared.

Type: list

algo_dict¶

Dictionary of algorithms to be tuned and compared.

Type: dict

iter¶

Number of iterations.

Type: int

problem¶

Machine learning problem.

Type: str

tuned_params¶

Final tuned hyperparameters of best algorithm.

Type: str

categorical_features¶

List of categorical features in dataframe.

Type: list

prepset¶: prepset for preprocessing.

model¶: Tuned HANA ML model in algorithm.

droplist_columns¶: Columns in dataframe to be dropped.

fit(algo, data)¶: Fits given model from data. Small method to reduce code repeating.

get_algorithm()¶: Returns tuned AutoML algorithm

get_model()¶: Returns tuned model.

get_preprocessor_settings() → hana_automl.preprocess.settings.PreprocessorSettings ¶: Returns tuned preprocessor settings.

get_tuned_params() → dict¶: Returns tuned hyperparameters.

objective(trial: optuna.trial._trial.Trial) → int¶

Objective function. Optimizer uses it to search for best algorithm and preprocess method.

Parameters: trial (optuna.trial.Trial) – Optuna trial. Details here: https://optuna.readthedocs.io/en/stable/reference/trial.html
Returns: acc – Model’s accuracy.
Return type: float

tune()¶: Tune hyperparameters here