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Merge pull request #116 from Hector-hedb12/issue_104_ElasticNet
Issue 104: primitive for `sklearn.linear_model.ElasticNet`
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mlprimitives/jsons/sklearn.linear_model.ElasticNet.json
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| { | ||
| "name": "sklearn.linear_model.ElasticNet", | ||
| "contributors": [ | ||
| "Hector Dominguez <hedb_12@hotmail.com>" | ||
| ], | ||
| "documentation": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html", | ||
| "description": "Linear regression with combined L1 and L2 priors as regularizer", | ||
| "classifiers": { | ||
| "type": "estimator", | ||
| "subtype": "regressor" | ||
| }, | ||
| "modalities": [], | ||
| "primitive": "sklearn.linear_model.ElasticNet", | ||
| "fit": { | ||
| "method": "fit", | ||
| "args": [ | ||
| { | ||
| "name": "X", | ||
| "type": "ndarray" | ||
| }, | ||
| { | ||
| "name": "y", | ||
| "type": "array" | ||
| } | ||
| ] | ||
| }, | ||
| "produce": { | ||
| "method": "predict", | ||
| "args": [ | ||
| { | ||
| "name": "X", | ||
| "type": "ndarray" | ||
| } | ||
| ], | ||
| "output": [ | ||
| { | ||
| "name": "y", | ||
| "type": "array" | ||
| } | ||
| ] | ||
| }, | ||
| "hyperparameters": { | ||
| "fixed": { | ||
| "precompute": { | ||
| "type": "bool or list", | ||
| "default": false, | ||
| "description": "Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always True to preserve sparsity" | ||
| }, | ||
| "copy_X": { | ||
| "type": "bool", | ||
| "description": "If True, X will be copied; else, it may be overwritten", | ||
| "default": true | ||
| }, | ||
| "warm_start": { | ||
| "type": "bool", | ||
| "description": "When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution", | ||
| "default": false | ||
| }, | ||
| "random_state": { | ||
| "type": "multitype", | ||
| "description": "The seed of the pseudo random number generator that selects a random feature to update", | ||
| "types": { | ||
| "int": { | ||
| "description": "random_state is the seed used by the random number generator" | ||
| }, | ||
| "RandomState": { | ||
| "description": "random_state is the random number generator" | ||
| }, | ||
| "None": { | ||
| "description": "the random number generator is the RandomState instance used by np.random" | ||
| } | ||
| }, | ||
| "default": null | ||
| } | ||
| }, | ||
| "tunable": { | ||
| "alpha": { | ||
| "type": "float", | ||
| "description": "Constant that multiplies the penalty terms", | ||
| "default": 1.0, | ||
| "range": [ | ||
| 0.01, | ||
| 10.0 | ||
| ] | ||
| }, | ||
| "l1_ratio": { | ||
| "type": "float", | ||
| "description": "The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2", | ||
| "default": 0.5, | ||
| "range": [ | ||
| 0.0, | ||
| 1.0 | ||
| ] | ||
| }, | ||
| "fit_intercept": { | ||
| "type": "bool", | ||
| "default": true, | ||
| "description": "Whether the intercept should be estimated or not. If False, the data is assumed to be already centered" | ||
| }, | ||
| "normalize": { | ||
| "type": "bool", | ||
| "default": false, | ||
| "description": "This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm" | ||
| }, | ||
| "max_iter": { | ||
| "type": "int", | ||
| "description": "The maximum number of iterations", | ||
| "default": 1000, | ||
| "range": [ | ||
| 1, | ||
| 10000 | ||
| ] | ||
| }, | ||
| "tol": { | ||
| "type": "float", | ||
| "description": "The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol", | ||
| "default": 0.0001, | ||
| "range": [ | ||
| 1e-06, | ||
| 0.01 | ||
| ] | ||
| }, | ||
| "positive": { | ||
| "type": "bool", | ||
| "description": "When set to True, forces the coefficients to be positive", | ||
| "default": false | ||
| }, | ||
| "selection": { | ||
| "type": "str", | ||
| "description": "If set to random, a random coefficient is updated every iteration rather than looping over features sequentially by default", | ||
| "values": [ | ||
| "cyclic", | ||
| "random" | ||
| ], | ||
| "default": "cyclic" | ||
| } | ||
| } | ||
| } | ||
| } |
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| { | ||
| "metadata": { | ||
| "name": "sklearn.linear_model.ElasticNet", | ||
| "data_type": "single_table", | ||
| "task_type": "regression" | ||
| }, | ||
| "validation": { | ||
| "dataset": "boston" | ||
| }, | ||
| "primitives": [ | ||
| "sklearn.linear_model.ElasticNet" | ||
| ] | ||
| } |