FeatureLevelDomainAdaptiveClassifier.__init__() - Code Metrics - Inspection of "Added sphinx and readthedocs." - wmkouw/libTLDA - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( f3d068...88fa67 )

by Wouter

created 2018-06-17 15:54 UTC

FeatureLevelDomainAdaptiveClassifier.init() A

↳ Parent: FeatureLevelDomainAdaptiveClassifier

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	10
CRAP Score	1

Importance

Changes	2
Bugs	0	Features	0

Metric	Value
cc	1
c	2
b	0
f	0
dl	0
loc	47
ccs	10
cts	10
cp	1
crap	1
rs	9.0303

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
import scipy.stats as st
from scipy.optimize import minimize
from scipy.sparse.linalg import eigs
from scipy.spatial.distance import cdist
import sklearn as sk
from sklearn.svm import LinearSVC
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.model_selection import cross_val_predict
from os.path import basename

from .util import is_pos_def, one_hot


class FeatureLevelDomainAdaptiveClassifier(object):
    """
    Class of feature-level domain-adaptive classifiers.

    Reference: Kouw, Krijthe, Loog & Van der Maaten (2016). Feature-level
    domain adaptation. JMLR.

    Methods contain training and prediction functions.
    """

    def __init__(self, l2=0.0, loss='logistic', transfer_model='blankout',
                 max_iter=100, tolerance=1e-5, verbose=True):
        """
        Set classifier instance parameters.

        Parameters
        ----------
        l2 : float
            l2-regularization parameter value (def:0.01)
        loss : str
            loss function for classifier, options are 'logistic' or 'quadratic'
            (def: 'logistic')
        transfer_model : str
            distribution to use for transfer model, options are 'dropout' and
            'blankout' (def: 'blankout')
        max_iter : int
            maximum number of iterations (def: 100)
        tolerance : float
            convergence criterion threshold on x (def: 1e-5)
        verbose : bool
            report training progress (def: True)

        Returns
        -------
        None

        """
        # Classifier choices
        self.l2 = l2
        self.loss = 'logistic'
        self.transfer_model = transfer_model

        # Optimization parameters
        self.max_iter = max_iter
        self.tolerance = tolerance

        # Whether model has been trained
        self.is_trained = False

        # Dimensionality of training data
        self.train_data_dim = 0

        # Classifier parameters
        self.theta = 0

        # Verbosity
        self.verbose = verbose

    def mle_transfer_dist(self, X, Z, dist='blankout'):
        """
        Maximum likelihood estimation of transfer model parameters.

        Parameters
        ----------
        X : array
            source data set (N samples by D features)
        Z : array
            target data set (M samples by D features)
        dist : str
            distribution of transfer model, options are 'blankout' or 'dropout'
            (def: 'blankout')

        Returns
        -------
        iota : array
            estimated transfer model parameters (D features by 1)

        """
        # Data shapes
        N, DX = X.shape
        M, DZ = Z.shape

        # Assert equivalent dimensionalities
        if not DX == DZ:
            raise ValueError('Dimensionalities of X and Z should be equal.')

        # Blankout and dropout have same maximum likelihood estimator
        if (dist == 'blankout') or (dist == 'dropout'):

            # Rate parameters
            eta = np.mean(X > 0, axis=0)
            zeta = np.mean(Z > 0, axis=0)

            # Ratio of rate parameters
            iota = np.clip(1 - zeta / eta, 0, None)

        else:
            raise ValueError('Distribution unknown.')

        return iota

    def moments_transfer_model(self, X, iota, dist='blankout'):
        """
        Moments of the transfer model.

        Parameters
        ----------
        X : array
            data set (N samples by D features)
        iota : array
            transfer model parameters (D samples by 1)
        dist : str
            transfer model, options are 'dropout' and 'blankout'
            (def: 'blankout')

        Returns
        -------
        E : array
            expected value of transfer model (N samples by D feautures)
        V : array
            variance of transfer model (D features by D features by N samples)

        """
        # Data shape
        N, D = X.shape

        if (dist == 'dropout'):

            # First moment of transfer distribution
            E = (1-iota) * X

            # Second moment of transfer distribution
            V = np.zeros((D, D, N))
            for i in range(N):
                V[:, :, i] = np.diag(iota * (1-iota)) * (X[i, :].T*X[i, :])

        elif (dist == 'blankout'):

            # First moment of transfer distribution
            E = X

            # Second moment of transfer distribution
            V = np.zeros((D, D, N))
            for i in range(N):
                V[:, :, i] = np.diag(iota * (1-iota)) * (X[i, :].T * X[i, :])

        else:
            raise NotImplementedError('Transfer distribution not implemented')

        return E, V

    def flda_log_loss(self, theta, X, y, E, V, l2=0.0):
        """
        Compute average loss for flda-log.

        Parameters
        ----------
        theta : array
            classifier parameters (D features by 1)
        X : array
            source data set (N samples by D features)
        y : array
            label vector (N samples by 1)
        E : array
            expected value with respect to transfer model (N samples by
            D features)
        V : array
            variance with respect to transfer model (D features by D features
            by N samples)
        l2 : float
            regularization parameter (def: 0.0)

        Returns
        -------
        dL : array
            Value of loss function.

        """
        # Data shape
        N, D = X.shape

        # Assert y in {-1,+1}
        if not np.all(np.sort(np.unique(y)) == (-1, 1)):
            raise NotImplementedError('Labels can only be {-1, +1} for now.')

        # Precompute terms
        Xt = np.dot(X, theta)
        Et = np.dot(E, theta)
        alpha = np.exp(Xt) + np.exp(-Xt)
        beta = np.exp(Xt) - np.exp(-Xt)
        gamma = (np.exp(Xt).T * X.T).T + (np.exp(-Xt).T * X.T).T
        delta = (np.exp(Xt).T * X.T).T - (np.exp(-Xt).T * X.T).T

        # Log-partition function
        A = np.log(alpha)

        # First-order partial derivative of log-partition w.r.t. Xt
        dA = beta / alpha

        # Second-order partial derivative of log-partition w.r.t. Xt
        d2A = 1 - beta**2 / alpha**2

        # Compute pointwise loss (negative log-likelihood)
        L = np.zeros((N, 1))
        for i in range(N):
            L[i] = -y[i] * Et[i] + A[i] + dA[i] * (Et[i] - Xt[i]) + \
                   1./2*d2A[i]*np.dot(np.dot(theta.T, V[:, :, i]), theta)

        # Compute risk (average loss)
        R = np.mean(L, axis=0)

        # Add regularization
        return R + l2*np.sum(theta**2, axis=0)

    def flda_log_grad(self, theta, X, y, E, V, l2=0.0):
        """
        Compute gradient with respect to theta for flda-log.

        Parameters
        ----------
        theta : array
            classifier parameters (D features by 1)
        X : array
            source data set (N samples by D features)
        y : array
            label vector (N samples by 1)
        E : array
            expected value with respect to transfer model (N samples by
            D features)
        V : array
            variance with respect to transfer model (D features by D features
            by N samples)
        l2 : float
            regularization parameter (def: 0.0)

        Returns
        -------
        dR : array
            Value of gradient.

        """
        # Data shape
        N, D = X.shape

        # Assert y in {-1,+1}
        if not np.all(np.sort(np.unique(y)) == (-1, 1)):
            raise NotImplementedError('Labels can only be {-1, +1} for now.')

        # Precompute common terms
        Xt = np.dot(X, theta)
        Et = np.dot(E, theta)
        alpha = np.exp(Xt) + np.exp(-Xt)
        beta = np.exp(Xt) - np.exp(-Xt)
        gamma = (np.exp(Xt).T * X.T).T + (np.exp(-Xt).T * X.T).T
        delta = (np.exp(Xt).T * X.T).T - (np.exp(-Xt).T * X.T).T

        # Log-partition function
        A = np.log(alpha)

        # First-order partial derivative of log-partition w.r.t. Xt
        dA = beta / alpha

        # Second-order partial derivative of log-partition w.r.t. Xt
        d2A = 1 - beta**2 / alpha**2

        dR = 0
        for i in range(N):

            # Compute gradient terms
            t1 = -y[i]*E[i, :].T

            t2 = beta[i] / alpha[i] * X[i, :].T

            t3 = (gamma[i, :] / alpha[i] - beta[i]*delta[i, :] /
                  alpha[i]**2).T * (Et[i] - Xt[i])

            t4 = beta[i] / alpha[i] * (E[i, :] - X[i, :]).T

            t5 = (1 - beta[i]**2 / alpha[i]**2) * np.dot(V[:, :, i], theta)

            t6 = -(beta[i] * gamma[i, :] / alpha[i]**2 - beta[i]**2 *
                   delta[i, :] / alpha[i]**3).T * np.dot(np.dot(theta.T,
                                                         V[:, :, i]), theta)

            dR += t1 + t2 + t3 + t4 + t5 + t6

        # Add regularization
        dR += l2*2*theta

        return dR

    def fit(self, X, y, Z):
        """
        Fit/train a robust bias-aware classifier.

        Parameters
        ----------
        X : array
            source data (N samples by D features)
        y : array
            source labels (N samples by 1)
        Z : array
            target data (M samples by D features)

        Returns
        -------
        None

        """
        # Data shapes
        N, DX = X.shape
        M, DZ = Z.shape

        # Assert equivalent dimensionalities
        if not DX == DZ:
            raise ValueError('Dimensionalities of X and Z should be equal.')

        # Map to one-not-encoding
        Y, labels = one_hot(y, one_not=True)

        # Number of classes
        K = len(labels)

        # Compute transfer distribution parameters
        iota = self.mle_transfer_dist(X, Z)

        # Compute moments of transfer distribution
        E, V = self.moments_transfer_model(X, iota)

        # Select loss function
        if (self.loss == 'logistic'):

            # Preallocate parameter array
            theta = np.random.randn(DX, K)

            # Train a classifier for each class
            for k in range(K):

                # Shorthand for loss computation
                def L(theta): return self.flda_log_loss(theta, X, Y[:, k],
                                                        E, V, l2=self.l2)

                # Shorthand for gradient computation
                def J(theta): return self.flda_log_grad(theta, X, Y[:, k],
                                                        E, V, l2=self.l2)

                # Call scipy's minimizer
                results = minimize(L, theta[:, k], jac=J, method='BFGS',
                                   options={'gtol': self.tolerance,
                                            'disp': self.verbose})

                # Store resultant classifier parameters
                theta[:, k] = results.x

        elif (self.loss == 'quadratic'):

            # Compute closed-form least-squares solution
            theta = np.inv(E.T*E + np.sum(V, axis=2) + l2*np.eye(D))\
                         * (E.T * Y)

        # Store trained classifier parameters
        self.theta = theta

        # Store classes
        self.classes = labels

        # Mark classifier as trained
        self.is_trained = True

        # Store training data dimensionality
        self.train_data_dim = DX

    def predict(self, Z_):
        """
        Make predictions on new dataset.

        Parameters
        ----------
        Z : array
            new data set (M samples by D features)

        Returns
        -------
        preds : array
            label predictions (M samples by 1)

        """
        # Data shape
        M, D = Z_.shape

        # If classifier is trained, check for same dimensionality
        if self.is_trained:
            if not self.train_data_dim == D:
                raise ValueError('''Test data is of different dimensionality
                                 than training data.''')

        # Predict target labels
        preds = np.argmax(np.dot(Z_, self.theta), axis=1)

        # Map predictions back to labels
        preds = self.classes[preds]

        # Return predictions array
        return preds

    def get_params(self):
        """Get classifier parameters."""
        return self.clf.get_params()

    def is_trained(self):
        """Check whether classifier is trained."""
        return self.is_trained


1		#!/usr/bin/env python
2		# -- coding: utf-8 --
3
4	1	import numpy as np
5	1	import scipy.stats as st
6	1	from scipy.optimize import minimize
7	1	from scipy.sparse.linalg import eigs
8	1	from scipy.spatial.distance import cdist
9	1	import sklearn as sk
10	1	from sklearn.svm import LinearSVC
11	1	from sklearn.linear_model import LogisticRegression, LinearRegression
12	1	from sklearn.model_selection import cross_val_predict
13	1	from os.path import basename
14
15	1	from .util import is_pos_def, one_hot
16
17
18	1	class FeatureLevelDomainAdaptiveClassifier(object):
19		"""
20		Class of feature-level domain-adaptive classifiers.
21
22		Reference: Kouw, Krijthe, Loog & Van der Maaten (2016). Feature-level
23		domain adaptation. JMLR.
24
25		Methods contain training and prediction functions.
26		"""
27
28	1	def __init__(self, l2=0.0, loss='logistic', transfer_model='blankout',
29		max_iter=100, tolerance=1e-5, verbose=True):
30		"""
31		Set classifier instance parameters.
32
33		Parameters
34		----------
35		l2 : float
36		l2-regularization parameter value (def:0.01)
37		loss : str
38		loss function for classifier, options are 'logistic' or 'quadratic'
39		(def: 'logistic')
40		transfer_model : str
41		distribution to use for transfer model, options are 'dropout' and
42		'blankout' (def: 'blankout')
43		max_iter : int
44		maximum number of iterations (def: 100)
45		tolerance : float
46		convergence criterion threshold on x (def: 1e-5)
47		verbose : bool
48		report training progress (def: True)
49
50		Returns
51		-------
52		None
53
54		"""
55		# Classifier choices
56	1	self.l2 = l2
57	1	self.loss = 'logistic'
58	1	self.transfer_model = transfer_model
59
60		# Optimization parameters
61	1	self.max_iter = max_iter
62	1	self.tolerance = tolerance
63
64		# Whether model has been trained
65	1	self.is_trained = False
66
67		# Dimensionality of training data
68	1	self.train_data_dim = 0
69
70		# Classifier parameters
71	1	self.theta = 0
72
73		# Verbosity
74	1	self.verbose = verbose
75
76	1	def mle_transfer_dist(self, X, Z, dist='blankout'):
77		"""
78		Maximum likelihood estimation of transfer model parameters.
79
80		Parameters
81		----------
82		X : array
83		source data set (N samples by D features)
84		Z : array
85		target data set (M samples by D features)
86		dist : str
87		distribution of transfer model, options are 'blankout' or 'dropout'
88		(def: 'blankout')
89
90		Returns
91		-------
92		iota : array
93		estimated transfer model parameters (D features by 1)
94
95		"""
96		# Data shapes
97	1	N, DX = X.shape
98	1	M, DZ = Z.shape
99
100		# Assert equivalent dimensionalities
101	1	if not DX == DZ:
102		raise ValueError('Dimensionalities of X and Z should be equal.')
103
104		# Blankout and dropout have same maximum likelihood estimator
105	1	if (dist == 'blankout') or (dist == 'dropout'):
106
107		# Rate parameters
108	1	eta = np.mean(X > 0, axis=0)
109	1	zeta = np.mean(Z > 0, axis=0)
110
111		# Ratio of rate parameters
112	1	iota = np.clip(1 - zeta / eta, 0, None)
113
114		else:
115		raise ValueError('Distribution unknown.')
116
117	1	return iota
118
119	1	def moments_transfer_model(self, X, iota, dist='blankout'):
120		"""
121		Moments of the transfer model.
122
123		Parameters
124		----------
125		X : array
126		data set (N samples by D features)
127		iota : array
128		transfer model parameters (D samples by 1)
129		dist : str
130		transfer model, options are 'dropout' and 'blankout'
131		(def: 'blankout')
132
133		Returns
134		-------
135		E : array
136		expected value of transfer model (N samples by D feautures)
137		V : array
138		variance of transfer model (D features by D features by N samples)
139
140		"""
141		# Data shape
142	1	N, D = X.shape
143
144	1	if (dist == 'dropout'):
145
146		# First moment of transfer distribution
147		E = (1-iota) * X
148
149		# Second moment of transfer distribution
150		V = np.zeros((D, D, N))
151		for i in range(N):
152		V[:, :, i] = np.diag(iota * (1-iota)) * (X[i, :].T*X[i, :])
153
154	1	elif (dist == 'blankout'):
155
156		# First moment of transfer distribution
157	1	E = X
158
159		# Second moment of transfer distribution
160	1	V = np.zeros((D, D, N))
161	1	for i in range(N):
162	1	V[:, :, i] = np.diag(iota * (1-iota)) * (X[i, :].T * X[i, :])
163
164		else:
165		raise NotImplementedError('Transfer distribution not implemented')
166
167	1	return E, V
168
169	1	def flda_log_loss(self, theta, X, y, E, V, l2=0.0):
170		"""
171		Compute average loss for flda-log.
172
173		Parameters
174		----------
175		theta : array
176		classifier parameters (D features by 1)
177		X : array
178		source data set (N samples by D features)
179		y : array
180		label vector (N samples by 1)
181		E : array
182		expected value with respect to transfer model (N samples by
183		D features)
184		V : array
185		variance with respect to transfer model (D features by D features
186		by N samples)
187		l2 : float
188		regularization parameter (def: 0.0)
189
190		Returns
191		-------
192		dL : array
193		Value of loss function.
194
195		"""
196		# Data shape
197	1	N, D = X.shape
198
199		# Assert y in {-1,+1}
200	1	if not np.all(np.sort(np.unique(y)) == (-1, 1)):
201		raise NotImplementedError('Labels can only be {-1, +1} for now.')
202
203		# Precompute terms
204	1	Xt = np.dot(X, theta)
205	1	Et = np.dot(E, theta)
206	1	alpha = np.exp(Xt) + np.exp(-Xt)
207	1	beta = np.exp(Xt) - np.exp(-Xt)
208	1	gamma = (np.exp(Xt).T * X.T).T + (np.exp(-Xt).T * X.T).T
209	1	delta = (np.exp(Xt).T * X.T).T - (np.exp(-Xt).T * X.T).T
210
211		# Log-partition function
212	1	A = np.log(alpha)
213
214		# First-order partial derivative of log-partition w.r.t. Xt
215	1	dA = beta / alpha
216
217		# Second-order partial derivative of log-partition w.r.t. Xt
218	1	d2A = 1 - beta2 / alpha2
219
220		# Compute pointwise loss (negative log-likelihood)
221	1	L = np.zeros((N, 1))
222	1	for i in range(N):
223	1	L[i] = -y[i] * Et[i] + A[i] + dA[i] * (Et[i] - Xt[i]) + \
224		1./2d2A[i]np.dot(np.dot(theta.T, V[:, :, i]), theta)
225
226		# Compute risk (average loss)
227	1	R = np.mean(L, axis=0)
228
229		# Add regularization
230	1	return R + l2np.sum(theta*2, axis=0)
231
232	1	def flda_log_grad(self, theta, X, y, E, V, l2=0.0):
233		"""
234		Compute gradient with respect to theta for flda-log.
235
236		Parameters
237		----------
238		theta : array
239		classifier parameters (D features by 1)
240		X : array
241		source data set (N samples by D features)
242		y : array
243		label vector (N samples by 1)
244		E : array
245		expected value with respect to transfer model (N samples by
246		D features)
247		V : array
248		variance with respect to transfer model (D features by D features
249		by N samples)
250		l2 : float
251		regularization parameter (def: 0.0)
252
253		Returns
254		-------
255		dR : array
256		Value of gradient.
257
258		"""
259		# Data shape
260	1	N, D = X.shape
261
262		# Assert y in {-1,+1}
263	1	if not np.all(np.sort(np.unique(y)) == (-1, 1)):
264		raise NotImplementedError('Labels can only be {-1, +1} for now.')
265
266		# Precompute common terms
267	1	Xt = np.dot(X, theta)
268	1	Et = np.dot(E, theta)
269	1	alpha = np.exp(Xt) + np.exp(-Xt)
270	1	beta = np.exp(Xt) - np.exp(-Xt)
271	1	gamma = (np.exp(Xt).T * X.T).T + (np.exp(-Xt).T * X.T).T
272	1	delta = (np.exp(Xt).T * X.T).T - (np.exp(-Xt).T * X.T).T
273
274		# Log-partition function
275	1	A = np.log(alpha)
276
277		# First-order partial derivative of log-partition w.r.t. Xt
278	1	dA = beta / alpha
279
280		# Second-order partial derivative of log-partition w.r.t. Xt
281	1	d2A = 1 - beta2 / alpha2
282
283	1	dR = 0
284	1	for i in range(N):
285
286		# Compute gradient terms
287	1	t1 = -y[i]*E[i, :].T
288
289	1	t2 = beta[i] / alpha[i] * X[i, :].T
290
291	1	t3 = (gamma[i, :] / alpha[i] - beta[i]*delta[i, :] /
292		alpha[i]*2).T (Et[i] - Xt[i])
293
294	1	t4 = beta[i] / alpha[i] * (E[i, :] - X[i, :]).T
295
296	1	t5 = (1 - beta[i]2 / alpha[i]2) * np.dot(V[:, :, i], theta)
297
298	1	t6 = -(beta[i] * gamma[i, :] / alpha[i]2 - beta[i]2 *
299		delta[i, :] / alpha[i]*3).T np.dot(np.dot(theta.T,
300		V[:, :, i]), theta)
301
302	1	dR += t1 + t2 + t3 + t4 + t5 + t6
303
304		# Add regularization
305	1	dR += l22theta
306
307	1	return dR
308
309	1	def fit(self, X, y, Z):
310		"""
311		Fit/train a robust bias-aware classifier.
312
313		Parameters
314		----------
315		X : array
316		source data (N samples by D features)
317		y : array
318		source labels (N samples by 1)
319		Z : array
320		target data (M samples by D features)
321
322		Returns
323		-------
324		None
325
326		"""
327		# Data shapes
328	1	N, DX = X.shape
329	1	M, DZ = Z.shape
330
331		# Assert equivalent dimensionalities
332	1	if not DX == DZ:
333		raise ValueError('Dimensionalities of X and Z should be equal.')
334
335		# Map to one-not-encoding
336	1	Y, labels = one_hot(y, one_not=True)
337
338		# Number of classes
339	1	K = len(labels)
340
341		# Compute transfer distribution parameters
342	1	iota = self.mle_transfer_dist(X, Z)
343
344		# Compute moments of transfer distribution
345	1	E, V = self.moments_transfer_model(X, iota)
346
347		# Select loss function
348	1	if (self.loss == 'logistic'):
349
350		# Preallocate parameter array
351	1	theta = np.random.randn(DX, K)
352
353		# Train a classifier for each class
354	1	for k in range(K):
355
356		# Shorthand for loss computation
357	1	def L(theta): return self.flda_log_loss(theta, X, Y[:, k],
358		E, V, l2=self.l2)
359
360		# Shorthand for gradient computation
361	1	def J(theta): return self.flda_log_grad(theta, X, Y[:, k],
362		E, V, l2=self.l2)
363
364		# Call scipy's minimizer
365	1	results = minimize(L, theta[:, k], jac=J, method='BFGS',
366		options={'gtol': self.tolerance,
367		'disp': self.verbose})
368
369		# Store resultant classifier parameters
370	1	theta[:, k] = results.x
371
372		elif (self.loss == 'quadratic'):
373
374		# Compute closed-form least-squares solution
375		theta = np.inv(E.TE + np.sum(V, axis=2) + l2np.eye(D))\
376		* (E.T * Y)
377
378		# Store trained classifier parameters
379	1	self.theta = theta
380
381		# Store classes
382	1	self.classes = labels
383
384		# Mark classifier as trained
385	1	self.is_trained = True
386
387		# Store training data dimensionality
388	1	self.train_data_dim = DX
389
390	1	def predict(self, Z_):
391		"""
392		Make predictions on new dataset.
393
394		Parameters
395		----------
396		Z : array
397		new data set (M samples by D features)
398
399		Returns
400		-------
401		preds : array
402		label predictions (M samples by 1)
403
404		"""
405		# Data shape
406	1	M, D = Z_.shape
407
408		# If classifier is trained, check for same dimensionality
409	1	if self.is_trained:
410	1	if not self.train_data_dim == D:
411		raise ValueError('''Test data is of different dimensionality
412		than training data.''')
413
414		# Predict target labels
415	1	preds = np.argmax(np.dot(Z_, self.theta), axis=1)
416
417		# Map predictions back to labels
418	1	preds = self.classes[preds]
419
420		# Return predictions array
421	1	return preds
422
423	1	def get_params(self):
424		"""Get classifier parameters."""
425		return self.clf.get_params()
426
427	1	def is_trained(self):
428		"""Check whether classifier is trained."""
429		return self.is_trained
430

wmkouw / libTLDA

Push — master ( f3d068...88fa67 )

FeatureLevelDomainAdaptiveClassifier.__init__() A

Complexity

Size

Duplication

Code Coverage

Importance

Duplication Side-by-Side

Filter issues like

FeatureLevelDomainAdaptiveClassifier.init() A