deephyper.sklearn.regressor.run_autosklearn1#

deephyper.sklearn.regressor.run_autosklearn1(config: dict, load_data: callable) → float[source]#

Run function which can be used for AutoML regression.

It has to be used with the deephyper.sklearn.regressor.problem_autosklearn1 problem definition which corresponds to:

Configuration space object:
    Hyperparameters:
        C, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
        alpha, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
        gamma, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
        kernel, Type: Categorical, Choices: {linear, poly, rbf, sigmoid}, Default: linear
        max_depth, Type: UniformInteger, Range: [2, 100], Default: 14, on log-scale
        n_estimators, Type: UniformInteger, Range: [1, 2000], Default: 45, on log-scale
        n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 50
        regressor, Type: Categorical, Choices: {RandomForest, Linear, AdaBoost, KNeighbors, MLP, SVR, XGBoost}, Default: RandomForest
    Conditions:
        (gamma | kernel == 'rbf' || gamma | kernel == 'poly' || gamma | kernel == 'sigmoid')
        (n_estimators | regressor == 'RandomForest' || n_estimators | regressor == 'AdaBoost')
        C | regressor == 'SVR'
        alpha | regressor == 'MLP'
        kernel | regressor == 'SVR'
        max_depth | regressor == 'RandomForest'
        n_neighbors | regressor == 'KNeighbors'
Parameters:

  • config (dict) – an hyperparameter configuration dict corresponding to the deephyper.sklearn.regressor.problem_autosklearn1.

  • load_data (callable) – a function returning data as Numpy arrays (X, y).

Returns:

returns the \(R^2\) on the validation set.

Return type:

float