112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
|
#
|
||
|
|
# OtterTune - factor_analysis.py
|
||
|
|
#
|
||
|
|
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||
|
|
#
|
||
|
|
'''
|
||
|
|
Created on Jul 4, 2016
|
||
|
|
|
||
|
|
@author: dvanaken
|
||
|
|
'''
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from sklearn.decomposition import FactorAnalysis as SklearnFactorAnalysis
|
||
|
|
|
||
|
|
from .base import ModelBase
|
||
|
|
|
||
|
|
|
||
|
|
class FactorAnalysis(ModelBase):
|
||
|
|
"""FactorAnalysis (FA):
|
||
|
|
|
||
|
|
Fits an Sklearn FactorAnalysis model to X.
|
||
|
|
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html
|
||
|
|
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
model_ : sklearn.decomposition.FactorAnalysis
|
||
|
|
The fitted FA model
|
||
|
|
|
||
|
|
components_ : array, [n_components, n_features]
|
||
|
|
Components (i.e., factors) with maximum variance
|
||
|
|
|
||
|
|
feature_labels_ : array, [n_features]
|
||
|
|
|
||
|
|
total_variance_ : float
|
||
|
|
The total amount of variance explained by the components
|
||
|
|
|
||
|
|
pvars_ : array, [n_components]
|
||
|
|
The percentage of the variance explained by each component
|
||
|
|
|
||
|
|
pvars_noise_ : array, [n_components]
|
||
|
|
The percentage of the variance explained by each component also
|
||
|
|
accounting for noise
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
self.model_ = None
|
||
|
|
self.components_ = None
|
||
|
|
self.feature_labels_ = None
|
||
|
|
self.total_variance_ = None
|
||
|
|
self.pvars_ = None
|
||
|
|
self.pvars_noise_ = None
|
||
|
|
|
||
|
|
def _reset(self):
|
||
|
|
"""Resets all attributes (erases the model)"""
|
||
|
|
self.model_ = None
|
||
|
|
self.components_ = None
|
||
|
|
self.feature_labels_ = None
|
||
|
|
self.total_variance_ = None
|
||
|
|
self.pvars_ = None
|
||
|
|
self.pvars_noise_ = None
|
||
|
|
|
||
|
|
def fit(self, X, feature_labels=None, n_components=None, estimator_params=None):
|
||
|
|
"""Fits an Sklearn FA model to X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like, shape (n_samples, n_features)
|
||
|
|
Training data.
|
||
|
|
|
||
|
|
feature_labels : array-like, shape (n_features), optional
|
||
|
|
Labels for each of the features in X.
|
||
|
|
|
||
|
|
estimator_params : dict, optional
|
||
|
|
The parameters to pass to Sklearn's FA estimators.
|
||
|
|
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self
|
||
|
|
"""
|
||
|
|
self._reset()
|
||
|
|
if feature_labels is None:
|
||
|
|
feature_labels = ["feature_{}".format(i) for i in range(X.shape[1])]
|
||
|
|
self.feature_labels_ = feature_labels
|
||
|
|
if n_components is not None:
|
||
|
|
model = SklearnFactorAnalysis(n_components=n_components)
|
||
|
|
else:
|
||
|
|
model = SklearnFactorAnalysis()
|
||
|
|
self.model_ = model
|
||
|
|
if estimator_params is not None:
|
||
|
|
# Update Sklearn estimator params
|
||
|
|
assert isinstance(estimator_params, dict)
|
||
|
|
self.model_.set_params(**estimator_params)
|
||
|
|
self.model_.fit(X)
|
||
|
|
|
||
|
|
# Remove zero-valued components (n_components x n_features)
|
||
|
|
components_mask = np.sum(self.model_.components_ != 0.0, axis=1) > 0.0
|
||
|
|
self.components_ = self.model_.components_[components_mask]
|
||
|
|
|
||
|
|
# Compute the % variance explained (with/without noise)
|
||
|
|
c2 = np.sum(self.components_ ** 2, axis=1)
|
||
|
|
self.total_variance_ = np.sum(c2)
|
||
|
|
self.pvars_ = 100 * c2 / self.total_variance_
|
||
|
|
self.pvars_noise_ = 100 * c2 / (self.total_variance_ +
|
||
|
|
np.sum(self.model_.noise_variance_))
|
||
|
|
return self
|