110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
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# OtterTune - lasso.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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'''
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Created on Jul 8, 2016
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@author: dvanaken
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'''
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import numpy as np
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from sklearn.linear_model import lasso_path
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from .base import ModelBase
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class LassoPath(ModelBase):
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"""Lasso:
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Computes the Lasso path using Sklearn's lasso_path method.
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See also
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--------
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http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html
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Attributes
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----------
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feature_labels_ : array, [n_features]
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Labels for each of the features in X.
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alphas_ : array, [n_alphas]
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The alphas along the path where models are computed. (These are
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the decreasing values of the penalty along the path).
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coefs_ : array, [n_outputs, n_features, n_alphas]
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Coefficients along the path.
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rankings_ : array, [n_features]
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The average ranking of each feature across all target values.
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"""
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def __init__(self):
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self.feature_labels_ = None
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self.alphas_ = None
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self.coefs_ = None
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self.rankings_ = None
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def _reset(self):
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"""Resets all attributes (erases the model)"""
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self.feature_labels_ = None
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self.alphas_ = None
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self.coefs_ = None
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self.rankings_ = None
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def fit(self, X, y, feature_labels, estimator_params=None):
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"""Computes the Lasso path using Sklearn's lasso_path method.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Training data (the independent variables).
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y : array-like, shape (n_samples, n_outputs)
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Training data (the output/target values).
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feature_labels : array-like, shape (n_features)
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Labels for each of the features in X.
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estimator_params : dict, optional
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The parameters to pass to Sklearn's Lasso estimator.
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Returns
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-------
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self
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"""
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self._reset()
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if estimator_params is None:
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estimator_params = {}
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self.feature_labels_ = feature_labels
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alphas, coefs, _ = lasso_path(X, y, **estimator_params)
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self.alphas_ = alphas.copy()
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self.coefs_ = coefs.copy()
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# Rank the features in X by order of importance. This ranking is based
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# on how early a given features enter the regression (the earlier a
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# feature enters the regression, the MORE important it is).
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feature_rankings = [[] for _ in range(X.shape[1])]
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for target_coef_paths in self.coefs_:
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for i, feature_path in enumerate(target_coef_paths):
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entrance_step = 1
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for val_at_step in feature_path:
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if val_at_step == 0:
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entrance_step += 1
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else:
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break
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feature_rankings[i].append(entrance_step)
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self.rankings_ = np.array([np.mean(ranks) for ranks in feature_rankings])
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return self
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def get_ranked_features(self):
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if self.rankings_ is None:
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raise Exception("No lasso path has been fit yet!")
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rank_idxs = np.argsort(self.rankings_)
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return [self.feature_labels_[i] for i in rank_idxs]
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