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add 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" | ||
} | ||
} | ||
} | ||
} |