Multiple updates

- Adding different SVD methods for principal factor analysis - Fixing the way we handle non-string values for certain methods - Updating R script to generate test outputs - Adding new tests for SVD methods
EducationalTestingService · Mar 26, 2021 · 2bfd6ed · 2bfd6ed
1 parent 38827d5
commit 2bfd6ed
Show file tree

Hide file tree

Showing 11 changed files with 403 additions and 62 deletions.
diff --git a/factor_analyzer/factor_analyzer.py b/factor_analyzer/factor_analyzer.py
@@ -31,6 +31,7 @@
 from sklearn.utils import check_array
 from sklearn.utils.validation import check_is_fitted
 
+POSSIBLE_SVDS = ['randomized', 'lapack']
 
 POSSIBLE_IMPUTATIONS = ['mean', 'median', 'drop']
 
@@ -176,10 +177,19 @@ class FactorAnalyzer(BaseEstimator, TransformerMixin):
         If missing values are present in the data, either use
         list-wise deletion ('drop') or impute the column median
         ('median') or column mean ('mean').
+        Defaults to 'median'
     use_corr_matrix : bool, optional
         Set to true if the `data` is the correlation
         matrix.
         Defaults to False.
+    svd_method : {‘lapack’, ‘randomized’}
+        The SVD method to use when ``method='principal'``.
+        If 'lapack', use standard SVD from ``scipy.linalg``.
+        If 'randomized', use faster ``randomized_svd``
+        function from scikit-learn. The latter should only
+        be used if the number of columns is greater than or
+        equal to the number of rows in in the dataset. 
+        Defaults to 'randomized'
     rotation_kwargs, optional
         Additional key word arguments
         are passed to the rotation method.
@@ -249,32 +259,18 @@ def __init__(self,
                  is_corr_matrix=False,
                  bounds=(0.005, 1),
                  impute='median',
+                 svd_method='randomized',
                  rotation_kwargs=None):
 
-        rotation = rotation.lower() if isinstance(rotation, str) else rotation
-        if rotation not in POSSIBLE_ROTATIONS + [None]:
-            raise ValueError(f"The rotation must be one of the following: {POSSIBLE_ROTATIONS + [None]}")
-
-        method = method.lower()
-        if method not in POSSIBLE_METHODS:
-            raise ValueError(f"The method must be one of the following: {POSSIBLE_METHODS + [None]}")
-
-        impute = impute.lower()
-        if impute not in POSSIBLE_IMPUTATIONS:
-            raise ValueError(f"The imputation must be one of the following: {POSSIBLE_IMPUTATIONS + [None]}")
-
-        if method == 'principal' and is_corr_matrix:
-            raise ValueError('The principal method is only implemented using '
-                             'the full data set, not the correlation matrix.')
-
         self.n_factors = n_factors
         self.rotation = rotation
         self.method = method
         self.use_smc = use_smc
         self.bounds = bounds
         self.impute = impute
         self.is_corr_matrix = is_corr_matrix
-        self.rotation_kwargs = {} if rotation_kwargs is None else rotation_kwargs
+        self.svd_method = svd_method
+        self.rotation_kwargs = rotation_kwargs
 
         # default matrices to None
         self.mean_ = None
@@ -288,6 +284,34 @@ def __init__(self,
         self.rotation_matrix_ = None
         self.weights_ = None
 
+    def _arg_checker(self):
+        """
+        Check the input parameters to make sure they're properly formattted.
+        We need to do this to ensure that the FactorAnalyzer class can be properly
+        cloned when used with grid search CV, for example.
+        """
+        self.rotation = self.rotation.lower() if isinstance(self.rotation, str) else self.rotation
+        if self.rotation not in POSSIBLE_ROTATIONS + [None]:
+            raise ValueError(f"The rotation must be one of the following: {POSSIBLE_ROTATIONS + [None]}")
+
+        self.method = self.method.lower() if isinstance(self.method, str) else self.method
+        if self.method not in POSSIBLE_METHODS:
+            raise ValueError(f"The method must be one of the following: {POSSIBLE_METHODS}")
+
+        self.impute = self.impute.lower() if isinstance(self.impute, str) else self.impute
+        if self.impute not in POSSIBLE_IMPUTATIONS:
+            raise ValueError(f"The imputation must be one of the following: {POSSIBLE_IMPUTATIONS}")
+
+        self.svd_method = self.svd_method.lower() if isinstance(self.svd_method, str) else self.svd_method
+        if self.svd_method not in POSSIBLE_SVDS:
+            raise ValueError(f"The SVD method must be one of the following: {POSSIBLE_SVDS}")
+
+        if self.method == 'principal' and self.is_corr_matrix:
+            raise ValueError('The principal method is only implemented using '
+                             'the full data set, not the correlation matrix.')
+
+        self.rotation_kwargs = {} if self.rotation_kwargs is None else self.rotation_kwargs
+
     @staticmethod
     def _fit_uls_objective(psi, corr_mtx, n_factors):
         """
@@ -472,8 +496,21 @@ def _fit_principal(self, X):
         X = X.copy()
         X = (X - X.mean(0)) / X.std(0)
 
+        # if the number of rows is less than the number of columns,
+        # warn the user that the number of factors will be constrained
+        nrows, ncols = X.shape
+        if nrows < ncols and self.n_factors >= nrows:
+            warnings.warn('The number of factors will be '
+                          'constrained to min(n_samples, n_features)'
+                          '={}.'.format(min(nrows, ncols)))
+
         # perform the randomized singular value decomposition
-        U, S, V = randomized_svd(X, self.n_factors)
+        if self.svd_method == 'randomized':
+            U, S, V = randomized_svd(X, self.n_factors)
+        # otherwise, perform the full SVD
+        else:
+            U, S, V = np.linalg.svd(X, full_matrices=False)
+
         corr_mtx = np.dot(X, V.T)
         loadings = np.array([[pearsonr(x, c)[0] for c in corr_mtx.T] for x in X.T])
         return loadings
@@ -577,6 +614,9 @@ def fit(self, X, y=None):
                [ 0.81533404, -0.12494695,  0.17639683]])
         """
 
+        # check the input arguments
+        self._arg_checker()
+
         # check if the data is a data frame,
         # so we can convert it to an array
         if isinstance(X, pd.DataFrame):
@@ -650,11 +690,14 @@ def fit(self, X, y=None):
                 phi = np.dot(np.dot(np.diag(signs), phi), np.diag(signs))
                 structure = np.dot(loadings, phi) if self.rotation in OBLIQUE_ROTATIONS else None
 
-        # resort the factors according to their variance
-        variance = self._get_factor_variance(loadings)[0]
-        new_order = list(reversed(np.argsort(variance)))
-        loadings = loadings[:, new_order].copy()
+        # resort the factors according to their variance,
+        # unless the method is principal
+        if self.method != 'principal':
+            variance = self._get_factor_variance(loadings)[0]
+            new_order = list(reversed(np.argsort(variance)))
+            loadings = loadings[:, new_order].copy()
 
+        # if the structure matrix exists, reorder
         if structure is not None: 
             structure = structure[:, new_order].copy()
 

diff --git a/factor_analyzer/test_utils.py b/factor_analyzer/test_utils.py
@@ -38,6 +38,7 @@ def calculate_py_output(test_name,
                         factors,
                         method,
                         rotation,
+                        svd_method='randomized',
                         use_corr_matrix=False,
                         top_dir=None):
     """
@@ -54,6 +55,9 @@ def calculate_py_output(test_name,
         The rotation method
     rotation : str
         The type of rotation
+    svd_method : str, optional
+        The SVD method to use
+        Defaults to 'randomized'
     use_corr_matrix : bool, optional
         Whether to use the correlation matrix.
         Defaults to False.
@@ -81,7 +85,7 @@ def calculate_py_output(test_name,
     rotation = None if rotation == 'none' else rotation
     method = {'uls': 'minres'}.get(method, method)
 
-    fa = FactorAnalyzer(n_factors=factors, method=method,
+    fa = FactorAnalyzer(n_factors=factors, method=method, svd_method=svd_method,
                         rotation=rotation, is_corr_matrix=use_corr_matrix)
     fa.fit(X)
 
@@ -228,6 +232,11 @@ def check_close(data1, data2, rel_tol=0.0, abs_tol=0.1,
         data1 = normalize(data1, absolute)
         data2 = normalize(data2, absolute)
 
+    print(data1)
+    print()
+    print(data2)
+    print('------')
+
     err_msg = 'r - py: {} != {}'
     assert data1.shape == data2.shape, err_msg.format(data1.shape, data2.shape)
 
@@ -253,6 +262,7 @@ def check_scenario(test_name,
                    check_scores=False,
                    check_structure=False,
                    use_corr_matrix=False,
+                   svd_method='randomized',
                    data_dir=None,
                    expected_dir=None,
                    rel_tol=0,
@@ -321,8 +331,10 @@ def check_scenario(test_name,
     if check_structure:
         output_types.append('structure')
 
-    r_output = collect_r_output(test_name, factors, method, rotation, output_types, expected_dir)
-    py_output = calculate_py_output(test_name, factors, method, rotation, use_corr_matrix, data_dir)
+    r_output = collect_r_output(test_name, factors, method, rotation,
+                                output_types, expected_dir)
+    py_output = calculate_py_output(test_name, factors, method, rotation, svd_method,
+                                    use_corr_matrix, data_dir)
 
     for output_type in output_types:
 

diff --git a/tests/data/test15.csv b/tests/data/test15.csv
@@ -0,0 +1,10 @@
+pers01,pers02,pers03,pers04,pers05,pers06,pers07,pers08,pers09,pers10,pers11,pers12,pers13,pers14,pers15,pers16,pers17,pers18,pers19,pers20,pers21,pers22,pers23,pers24,pers25,pers26,pers27,pers28,pers29,pers30,pers31,pers32,pers33,pers34,pers35,pers36,pers37,pers38,pers39,pers40,pers41,pers42,pers43,pers44
+5,4,5,1,4,3,3,1,2,3,2,4,5,4,4,3,5,1,3,4,2,4,2,3,2,5,4,5,4,2,5,4,5,3,1,3,4,4,4,3,3,3,5,4
+1,1,5,2,1,2,5,1,5,1,5,3,5,4,1,2,1,3,5,1,5,5,1,3,1,2,3,5,3,3,5,5,5,5,5,3,1,1,2,3,3,5,2,1
+4,1,5,3,3,4,5,3,1,4,2,1,5,4,3,2,5,1,5,4,4,4,3,2,4,1,3,4,5,4,3,3,5,5,4,2,3,1,5,4,2,3,5,3
+4,2,5,1,4,3,4,4,4,5,4,1,4,5,3,3,4,2,5,4,4,4,3,2,4,2,1,5,3,4,4,4,4,4,3,3,3,4,5,5,3,5,2,4
+2,3,5,1,2,4,5,2,3,3,4,2,5,3,3,3,5,1,4,2,5,5,1,3,2,2,4,4,4,3,4,4,5,4,4,2,5,5,4,3,2,4,3,2
+1,1,5,4,3,4,4,2,1,4,3,3,5,5,3,2,4,1,5,3,5,4,1,3,3,1,2,5,5,4,5,3,4,2,3,1,1,3,5,4,2,4,3,5
+3,2,5,1,2,1,1,2,5,4,4,1,5,1,1,4,5,5,1,3,2,5,1,4,2,1,1,5,1,2,1,5,4,5,5,5,2,5,1,1,4,5,1,1
+5,2,4,2,4,1,4,3,3,5,4,1,4,3,2,4,4,4,2,5,2,4,3,3,1,1,3,4,3,3,2,5,4,4,2,4,2,4,2,3,2,5,4,4
+5,1,4,3,2,1,4,4,2,3,4,1,4,4,2,5,5,5,4,4,2,5,2,4,2,4,4,4,3,5,2,5,4,2,4,5,1,2,4,3,2,5,4,2
diff --git a/tests/expected/test15/communalities_principal_none_20_test15.csv b/tests/expected/test15/communalities_principal_none_20_test15.csv
@@ -0,0 +1,45 @@
+,x
+pers01,2.147636456
+pers02,3.05860696
+pers03,2.634980867
+pers04,4.167044602
+pers05,3.239628161
+pers06,2.855673949
+pers07,2.846150806
+pers08,2.342990801
+pers09,3.030327258
+pers10,2.975790993
+pers11,2.395566544
+pers12,2.220826511
+pers13,2.038321064
+pers14,3.265232908
+pers15,2.425621063
+pers16,3.061295019
+pers17,2.017025954
+pers18,3.049338556
+pers19,3.122061642
+pers20,2.728356141
+pers21,2.594510111
+pers22,3.782658794
+pers23,3.070056277
+pers24,4.111231609
+pers25,3.064079366
+pers26,1.832456264
+pers27,2.162362221
+pers28,1.758259243
+pers29,3.225984183
+pers30,3.166512309
+pers31,2.790080337
+pers32,3.175142177
+pers33,2.391267257
+pers34,2.403458399
+pers35,2.514121013
+pers36,2.983086234
+pers37,2.746601113
+pers38,3.495223869
+pers39,2.864928829
+pers40,3.571532646
+pers41,2.654887486
+pers42,2.126933791
+pers43,2.482551325
+pers44,3.105868335
diff --git a/tests/expected/test15/evalues_principal_none_20_test15.csv b/tests/expected/test15/evalues_principal_none_20_test15.csv
@@ -0,0 +1,45 @@
+,x
+1,14.25162169
+2,9.294533097
+3,6.498852485
+4,4.620861291
+5,3.059319317
+6,2.625005382
+7,1.991825606
+8,1.657981133
+9,2.69E-15
+10,1.67E-15
+11,1.22E-15
+12,9.84E-16
+13,6.64E-16
+14,5.94E-16
+15,5.04E-16
+16,4.43E-16
+17,4.28E-16
+18,3.86E-16
+19,3.62E-16
+20,2.83E-16
+21,1.92E-16
+22,1.41E-16
+23,1.16E-16
+24,7.91E-17
+25,2.42E-17
+26,1.45E-17
+27,-1.52E-17
+28,-3.58E-17
+29,-9.31E-17
+30,-2.07E-16
+31,-2.23E-16
+32,-2.53E-16
+33,-3.39E-16
+34,-4.00E-16
+35,-4.38E-16
+36,-4.54E-16
+37,-5.18E-16
+38,-5.69E-16
+39,-7.36E-16
+40,-8.59E-16
+41,-9.67E-16
+42,-1.20E-15
+43,-1.50E-15
+44,-3.05E-15
diff --git a/tests/expected/test15/loading_principal_none_20_test15.csv b/tests/expected/test15/loading_principal_none_20_test15.csv
@@ -0,0 +1,45 @@
+,MR1,MR2,MR3,MR4,MR5,MR6,MR7,MR8,MR9
+pers01,-0.04828462,0.872685807,0.239695145,0.1783874,-0.070266058,0.233832136,-0.127745213,0.266230396,0.109949125
+pers02,0.065050861,0.087111805,0.917108625,0.09829389,0.045889312,0.259212544,0.240458465,-0.101551737,-0.438491274
+pers03,0.335344379,-0.692295104,0.352908838,-0.448246064,-0.105717303,-0.075752288,0.072948636,0.246102405,-0.026867838
+pers04,0.369038127,0.065051128,-0.659481751,0.216562195,0.122770915,-0.550263386,-0.238255766,-0.055995953,0.650878605
+pers05,0.460360115,0.673786618,0.357376302,-0.321981352,0.22082246,0.141451804,-0.140351837,-0.119249891,0.088835419
+pers06,0.87171974,-0.336822521,0.112582984,-0.145152604,-0.211905593,-0.080225718,0.198171197,-0.047949025,0.530776467
+pers07,0.576151802,-0.206133915,-0.510224335,0.311050617,-0.18298515,0.43465408,0.004627584,-0.214586587,0.581738459
+pers08,0.013465102,0.72669014,-0.534236413,-0.20871943,-0.19468342,0.119898964,0.269297945,0.134048523,0.395215471
+pers09,-0.748916302,-0.361166248,0.065624042,-0.340057899,0.067146842,0.42644553,0.043870457,0.021173528,-0.771983064
+pers10,0.137766271,0.729061174,0.055714597,-0.601408949,-0.108200226,-0.133706349,0.019605779,-0.233932375,0.119752545
+pers11,-0.675924401,-0.273483264,-0.433155063,-0.064071358,0.143265802,0.359476466,0.248424031,-0.255228802,-0.544707831
+pers12,0.355725628,-0.531383218,0.427897805,0.322997529,0.543285483,-0.056315934,-0.072021539,-0.012227634,-0.211014824
+pers13,0.191984172,-0.752696697,0.397452888,0.0537552,-0.217179515,-0.397121986,-0.166976097,0.05456656,0.03461962
+pers14,0.761628997,0.018888667,-0.389796375,0.02405038,0.439146501,0.204641161,0.08654672,0.15756543,0.476955155
+pers15,0.841854905,0.251761235,0.412010313,0.053732916,0.042505833,0.029407325,0.226082677,0.038366163,0.402422072
+pers16,-0.64430474,0.655136206,0.025197088,0.259591771,0.01078642,-0.024935079,0.293030345,0.032253453,-0.309892468
+pers17,0.133885448,0.599835612,0.405812074,0.039838088,-0.411211008,-0.402493253,0.338007923,0.103231512,0.273106526
+pers18,-0.899018777,0.306544161,-0.263593624,0.086024622,0.073959402,-0.061269493,-0.070891304,0.081636764,-0.419225537
+pers19,0.690934528,-0.329647629,-0.551624951,0.0182762,0.064019853,0.196192485,0.15420495,0.207240475,0.606635059
+pers20,0.155163821,0.957389706,0.108910582,-0.089609788,0.022891194,-0.02826736,-0.193441382,0.026364585,0.239325582
+pers21,0.485799705,-0.70406665,-0.364281338,-0.193071399,-0.097510953,0.090140204,0.1946385,-0.206866024,0.285201141
+pers22,-0.681026118,-0.43653348,-0.152606385,0.349435475,-0.227476649,-0.024178065,0.372173269,0.096979075,-0.32989024
+pers23,0.298251743,0.738460588,-0.104605406,-0.18269442,-0.149645341,0.429224993,-0.312246211,0.131441227,0.397777692
+pers24,-0.735050328,0.022838942,0.070148594,0.42301257,0.17584153,-0.452732468,0.197949991,-0.015741936,-0.467726762
+pers25,0.612945178,0.179319088,-0.143944907,-0.536182675,-0.246095779,-0.144123534,0.178983737,0.412990281,0.615785245
+pers26,0.075493078,0.164186546,0.361855321,0.60623896,0.363141969,0.212132397,0.266854716,0.469888221,-0.107909185
+pers27,0.222367812,0.025808466,0.118818476,0.937412093,-0.130937606,0.1757883,0.031728968,-0.089298973,0.271281581
+pers28,-0.066730222,-0.434588822,0.269651574,-0.478297293,0.639249191,-0.095051394,-0.032966158,0.294003909,-0.528846167
+pers29,0.943968963,-0.072840218,-0.105035164,0.233586597,-0.059723312,-0.116892943,-0.06291015,-0.129743188,0.741046671
+pers30,0.329973251,0.309685066,-0.828180961,0.108154302,-0.016580396,-0.057603016,0.255376313,0.169769485,0.679006138
+pers31,0.663368245,-0.546831933,0.099996746,0.073101125,0.436956147,0.219166869,0.07969368,-0.016075172,0.068306007
+pers32,-0.896103875,0.087076721,-0.032225109,0.233865369,0.167003644,0.322598699,-0.024188096,-0.033746387,-0.67512874
+pers33,0.368927175,-0.55505924,0.330315318,0.404087257,-0.358903058,0.316887491,-0.186347221,0.139473596,0.227565878
+pers34,-0.297896347,-0.356547533,0.108006974,-0.380935237,-0.55636427,0.376008538,-0.417555873,0.045584353,-0.143322194
+pers35,-0.466026995,-0.521274333,-0.486631795,-0.090454241,-0.437389454,-0.144597074,0.10906771,0.204904425,0.03733149
+pers36,-0.849143547,0.423510789,0.024744724,0.136594471,0.030216843,0.09148576,-0.026121108,0.265251737,-0.457345574
+pers37,0.341668592,0.007337606,0.66756131,-0.008476775,-0.46117769,0.331304008,0.328795419,-0.083337506,0.029768105
+pers38,-0.263669214,0.20346738,0.640196465,-0.321078152,0.009810988,-0.038680089,0.470802886,-0.391010377,-0.606810668
+pers39,0.882113868,0.17203835,-0.16887364,-0.023210193,-0.013321485,-0.040546264,0.34238953,0.210163886,0.712370485
+pers40,0.791007623,0.146938,-0.379995394,-0.239363727,0.157728703,0.334133052,0.109024279,0.051138425,0.546598278
+pers41,-0.56246979,-0.273713014,0.436724979,-0.447447065,0.177598914,0.035751419,-0.068604277,0.424553004,-0.688649095
+pers42,-0.742317116,0.071452222,-0.469083975,-0.218919485,0.263651376,0.217525709,0.182249823,-0.160778989,-0.53833395
+pers43,0.587977857,0.446104906,0.144926452,0.575703527,-0.127295674,0.010140071,-0.290820054,0.044169119,0.615268623
+pers44,0.697943291,0.446797496,0.096467909,-0.230890491,0.408446905,-0.09793622,-0.066987721,-0.264051038,0.260242219