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Merge pull request #91 from EducationalTestingService/release/0.4.0
Release/0.4.0
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‎.gitlab-ci.yml‎

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- test
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variables:
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PYVERSION: "3.8"
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PYVERSION: "3.9"
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NOSE_WITH_COV: "1"
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NOSE_COVER_PACKAGE: "factor_analyzer"
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LOGCAPTURE_LEVEL: "DEBUG"

‎README.rst‎

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Requirements
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------------
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- Python 3.4 or higher
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- Python 3.7 or higher
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- ``numpy``
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- ``pandas``
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- ``scipy``

‎conda-recipe/factor_analyzer/meta.yaml‎

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{% set name = "factor_analyzer" %}
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{% set version = "0.3.2" %}
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{% set file_ext = "tar.gz" %}
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{% set hash_type = "sha256" %}
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{% set hash_value = "94ea4c7d46e846cc7174787adce47156cf58dc257905c878edc5181b4fa300ed" %}
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{% set version = "0.4.0" %}
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package:
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name: '{{ name|lower }}'

‎factor_analyzer/__init__.py‎

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# License: GLP2
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# License: GPL2
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"""
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:author: Jeremy Biggs ([email protected])
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:organization: ETS
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:author: Nitin Madnani ([email protected])
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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from .confirmatory_factor_analyzer import ConfirmatoryFactorAnalyzer, ModelSpecification, ModelSpecificationParser
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from .factor_analyzer import FactorAnalyzer, calculate_bartlett_sphericity, calculate_kmo
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from .rotator import Rotator
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from .factor_analyzer import (FactorAnalyzer,
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calculate_bartlett_sphericity,
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calculate_kmo)
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from .confirmatory_factor_analyzer import (ConfirmatoryFactorAnalyzer,
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ModelSpecificationParser,
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ModelSpecification)
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from .utils import (cov,
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from .utils import (commutation_matrix,
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corr,
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fill_lower_diag,
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impute_values,
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smc,
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partial_correlations,
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merge_variance_covariance,
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cov,
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covariance_to_correlation,
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duplication_matrix,
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duplication_matrix_pre_post,
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covariance_to_correlation,
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commutation_matrix,
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fill_lower_diag,
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get_symmetric_lower_idxs,
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get_symmetric_upper_idxs)
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get_symmetric_upper_idxs,
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impute_values,
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merge_variance_covariance,
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partial_correlations,
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smc)

‎factor_analyzer/confirmatory_factor_analyzer.py‎

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"""
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Confirmatory factor analysis using ML.
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Confirmatory factor analysis using machine learning methods.
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:author: Jeremy Biggs ([email protected])
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:date: 2/05/2019
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:organization: ETS
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:author: Nitin Madnani ([email protected])
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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import pandas as pd
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import numpy as np
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import warnings
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from copy import deepcopy
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from scipy.optimize import minimize
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from scipy.linalg import block_diag
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import numpy as np
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import pandas as pd
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from scipy.linalg import block_diag
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from scipy.optimize import minimize
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from sklearn.base import BaseEstimator, TransformerMixin
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from sklearn.utils import check_array
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from sklearn.utils.validation import check_is_fitted
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from factor_analyzer.utils import (cov,
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covariance_to_correlation,
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commutation_matrix,
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duplication_matrix_pre_post,
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get_free_parameter_idxs,
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get_symmetric_lower_idxs,
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get_symmetric_upper_idxs,
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impute_values,
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unique_elements,
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merge_variance_covariance)
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from .utils import (commutation_matrix,
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cov,
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covariance_to_correlation,
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duplication_matrix_pre_post,
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get_free_parameter_idxs,
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get_symmetric_lower_idxs,
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get_symmetric_upper_idxs,
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impute_values,
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merge_variance_covariance,
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unique_elements)
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class ModelSpecification:

‎factor_analyzer/factor_analyzer.py‎

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"""
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Factor analysis using MINRES or ML,
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with optional rotation using Varimax or Promax.
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Factor analysis using MINRES or ML, with optional rotation using Varimax or Promax.
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:author: Jeremy Biggs ([email protected])
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:author: Nitin Madnani ([email protected])
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:date: 10/25/2017
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:organization: ETS
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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import warnings
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import numpy as np
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import scipy as sp
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import pandas as pd
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from scipy.stats import chi2, pearsonr
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import scipy as sp
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from scipy.optimize import minimize
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from scipy.stats import chi2, pearsonr
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from sklearn.base import BaseEstimator, TransformerMixin
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from factor_analyzer.utils import (corr,
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impute_values,
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partial_correlations,
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smc)
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from factor_analyzer.rotator import Rotator
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from factor_analyzer.rotator import POSSIBLE_ROTATIONS, OBLIQUE_ROTATIONS
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from sklearn.utils.extmath import randomized_svd
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from sklearn.utils import check_array
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from sklearn.utils.extmath import randomized_svd
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from sklearn.utils.validation import check_is_fitted
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from .rotator import OBLIQUE_ROTATIONS, POSSIBLE_ROTATIONS, Rotator
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from .utils import corr, impute_values, partial_correlations, smc
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POSSIBLE_SVDS = ['randomized', 'lapack']
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POSSIBLE_IMPUTATIONS = ['mean', 'median', 'drop']

‎factor_analyzer/rotator.py‎

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"""
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Rotator class to perform various
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rotations of factor loading matrices.
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Class to perform various rotations of factor loading matrices.
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:author: Jeremy Biggs ([email protected])
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:date: 05/21/2018
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:organization: ETS
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:author: Nitin Madnani ([email protected])
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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import numpy as np
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import scipy as sp
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from sklearn.base import BaseEstimator
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ORTHOGONAL_ROTATIONS = ['varimax', 'oblimax', 'quartimax', 'equamax', 'geomin_ort']

‎factor_analyzer/test_utils.py‎

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"""
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Testing utilities
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Utility functions used for testing.
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:author: Jeremy Biggs ([email protected])
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:date: 05/21/2018
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:organization: ETS
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:author: Nitin Madnani ([email protected])
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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import os
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import math
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import json
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import math
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import os
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from os.path import join
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import numpy as np
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import pandas as pd
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from os.path import join
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from factor_analyzer import ConfirmatoryFactorAnalyzer, FactorAnalyzer, ModelSpecificationParser, Rotator
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from factor_analyzer.utils import unique_elements
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from factor_analyzer import ModelSpecificationParser
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from factor_analyzer import ConfirmatoryFactorAnalyzer
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from factor_analyzer import FactorAnalyzer
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from factor_analyzer import Rotator
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DATA_DIR = os.path.join('tests', 'data')
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JSON_DIR = os.path.join('tests', 'model')

‎factor_analyzer/utils.py‎

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"""
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Utility functions, used primarily by
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the confirmatory factor analysis module.
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Utility functions, used primarily by the confirmatory factor analysis module.
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:author: Jeremy Biggs ([email protected])
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:date: 2/05/2019
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:organization: ETS
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:author: Nitin Madnani ([email protected])
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:organization: Educational Testing Service
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:date: 2021-10-18
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"""
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import numpy as np
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import warnings
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import numpy as np
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from scipy.linalg import cholesky
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‎setup.py‎

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setup(name='factor_analyzer',
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version='0.3.2',
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version='0.4.0',
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description='A Factor Analysis class',
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long_description=readme(),
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keywords='factor analysis',
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'Operating System :: Unix',
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'Operating System :: MacOS',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.4',
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'Programming Language :: Python :: 3.5',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',
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],
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zip_safe=True)

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