112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
#
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# OtterTune - factor_analysis.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 4, 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.decomposition import FactorAnalysis as SklearnFactorAnalysis
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from .base import ModelBase
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class FactorAnalysis(ModelBase):
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"""FactorAnalysis (FA):
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Fits an Sklearn FactorAnalysis model to X.
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See also
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--------
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http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html
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Attributes
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----------
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model_ : sklearn.decomposition.FactorAnalysis
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The fitted FA model
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components_ : array, [n_components, n_features]
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Components (i.e., factors) with maximum variance
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feature_labels_ : array, [n_features]
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total_variance_ : float
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The total amount of variance explained by the components
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pvars_ : array, [n_components]
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The percentage of the variance explained by each component
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pvars_noise_ : array, [n_components]
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The percentage of the variance explained by each component also
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accounting for noise
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"""
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def __init__(self):
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self.model_ = None
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self.components_ = None
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self.feature_labels_ = None
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self.total_variance_ = None
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self.pvars_ = None
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self.pvars_noise_ = None
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def _reset(self):
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"""Resets all attributes (erases the model)"""
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self.model_ = None
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self.components_ = None
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self.feature_labels_ = None
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self.total_variance_ = None
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self.pvars_ = None
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self.pvars_noise_ = None
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def fit(self, X, feature_labels=None, n_components=None, estimator_params=None):
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"""Fits an Sklearn FA model to X.
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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.
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feature_labels : array-like, shape (n_features), optional
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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 FA estimators.
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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 feature_labels is None:
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feature_labels = ["feature_{}".format(i) for i in range(X.shape[1])]
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self.feature_labels_ = feature_labels
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if n_components is not None:
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model = SklearnFactorAnalysis(n_components=n_components)
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else:
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model = SklearnFactorAnalysis()
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self.model_ = model
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if estimator_params is not None:
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# Update Sklearn estimator params
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assert isinstance(estimator_params, dict)
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self.model_.set_params(**estimator_params)
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self.model_.fit(X)
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# Remove zero-valued components (n_components x n_features)
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components_mask = np.sum(self.model_.components_ != 0.0, axis=1) > 0.0
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self.components_ = self.model_.components_[components_mask]
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# Compute the % variance explained (with/without noise)
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c2 = np.sum(self.components_ ** 2, axis=1)
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self.total_variance_ = np.sum(c2)
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self.pvars_ = 100 * c2 / self.total_variance_
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self.pvars_noise_ = 100 * c2 / (self.total_variance_ +
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np.sum(self.model_.noise_variance_))
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return self
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