TransferComponentClassifier

Complexity

Total Complexity

26

Size/Duplication

Total Lines	237
Duplicated Lines	21.52 %

Test Coverage

Coverage

70.73%

Importance

Changes

0

7 Methods

Rating	Name	Duplication	Size	Complexity
A	transfer_component_analysis()	0	55	4
B	__init__()	51	51	4
A	get_params()	0	3	1
B	fit()	0	47	6
B	kernel()	0	35	6
A	is_trained()	0	3	1
B	predict()	0	30	4

#!/usr/bin/env python

# -*- coding: utf-8 -*-


import numpy as np

import scipy.stats as st

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



class TransferComponentClassifier(object):

    """

    Class of classifiers based on Transfer Component Analysis.


    Methods contain component analysis and general utilities.

    """


    def __init__(self, loss='logistic', l2=1.0, mu=1.0, num_components=1,

This code seems to be duplicated in your project.

                 kernel_type='rbf', bandwidth=1.0, order=2.0):

        """

        Select a particular type of transfer component classifier.


        INPUT   (1) str 'loss': loss function for weighted classifier, options:

                    'logistic', 'quadratic', 'hinge' (def: 'logistic')

                (2) float 'l2': l2-regularization parameter value (def:0.01)

                (3) float 'mu': trade-off parameter (def: 1.0)

                (4) int 'num_components': number of transfer components to

                    maintain (def: 1)

                (5) str 'kernel_type': type of kernel to use, options: 'rbf'

                    (def: 'rbf')

                (6) float 'bandwidth': kernel bandwidth for transfer component

                    analysis (def: 1.0)

                (7) float 'order': order of polynomial for kernel (def: 2.0)

        """

        self.loss = loss

        self.l2 = l2

        self.mu = mu

        self.num_components = num_components


        self.kernel_type = kernel_type

        self.bandwidth = bandwidth

        self.order = order


        # Initialize untrained classifiers

        if self.loss == 'logistic':

            # Logistic regression model

            self.clf = LogisticRegression()

        elif self.loss == 'quadratic':

            # Least-squares model

            self.clf = LinearRegression()

        elif self.loss == 'hinge':

            # Linear support vector machine

            self.clf = LinearSVC()

        else:

            # Other loss functions are not implemented

            raise NotImplementedError


        # Maintain source and transfer data for computing kernels

        self.XZ = ''


        # Maintain transfer components

        self.C = ''


        # Whether model has been trained

        self.is_trained = False


        # Dimensionality of training data

        self.train_data_dim = ''


    def kernel(self, X, Z, type='rbf', order=2, bandwidth=1.0):

        """

        Compute kernel for given data set.


        INPUT   (1) array 'X': data set (N samples by D features)

                (2) array 'Z': data set (M samples by D features)

                (3) str 'type': type of kernel, options: 'linear',

                    'polynomial', 'rbf', 'sigmoid' (def: 'linear')

                (4) float 'order': order of polynomial to use for the

                    polynomial kernel (def: 2.0)

                (5) float 'bandwidth': kernel bandwidth (def: 1.0)

        OUTPUT  (1) array: kernel matrix (N+M by N+M)

        """

        # Data shapes

        N, DX = X.shape

        M, DZ = Z.shape


        # Assert equivalent dimensionalities

        assert DX == DZ


        # Select type of kernel to compute

        if type == 'linear':

            # Linear kernel is data outer product

            return np.dot(X, Z.T)

        elif type == 'polynomial':

            # Polynomial kernel is an exponentiated data outer product

            return (np.dot(X, Z.T) + 1)**p

        elif type == 'rbf':

            # Radial basis function kernel

            return np.exp(-cdist(X, Z) / (2.*bandwidth**2))

        elif type == 'sigmoid':

            # Sigmoidal kernel

            return 1./(1 + np.exp(np.dot(X, Z.T)))

        else:

            raise NotImplementedError


    def transfer_component_analysis(self, X, Z):

        """

        Transfer Component Analysis.


        INPUT   (1) array 'X': source data set (N samples by D features)

                (2) array 'Z': target data set (M samples by D features)

        OUTPUT  (1) array 'C': transfer components (D features

                    by num_components)

                (2) array 'K': source and target data kernel distances

        """

        # Data shapes

        N, DX = X.shape

        M, DZ = Z.shape


        # Assert equivalent dimensionalities

        assert DX == DZ


        # Compute kernel matrix

        XZ = np.concatenate((X, Z), axis=0)

        K = self.kernel(XZ, XZ, type=self.kernel_type,

                        bandwidth=self.bandwidth)


        # Ensure positive-definiteness

        if not is_pos_def(K):

            print('Warning: covariate matrices not PSD.')


            regct = -6

            while not is_pos_def(K):

                print('Adding regularization: ' + str(10**regct))


                # Add regularization

                K += np.eye(N + M)*10.**regct


                # Increment regularization counter

                regct += 1


        # Normalization matrix

        L = np.vstack((np.hstack((np.ones((N, N))/N**2,

                                  -1*np.ones((N, M))/(N*M))),

                       np.hstack((-1*np.ones((M, N))/(N*M),

                                  np.ones((M, M))/M**2))))


        # Centering matrix

        H = np.eye(N + M) - np.ones((N + M, N + M)) / float(N + M)


        # Matrix Lagrangian objective function: (I + mu*K*L*K)^{-1}*K*H*K

        J = np.dot(np.linalg.inv(np.eye(N + M) +

                   self.mu*np.dot(np.dot(K, L), K)),

                   np.dot(np.dot(K, H), K))


        # Eigenvector decomposition as solution to trace minimization

        _, C = eigs(J, k=self.num_components)


        # Discard imaginary numbers (possible computation issue)

        return np.real(C), K


    def fit(self, X, y, Z):

        """

        Fit/train a classifier on data mapped onto transfer components.


        INPUT   (1) array 'X': source data (N samples by D features)

                (2) array 'y': source labels (N samples by 1)

                (3) array 'Z': target data (M samples by D features)

        OUTPUT

        """

        # Data shapes

        N, DX = X.shape

        M, DZ = Z.shape


        # Assert equivalent dimensionalities

        assert DX == DZ


        # Assert correct number of components for given dataset

        assert self.num_components <= N + M - 1


        # Maintain source and target data for later kernel computations

        self.XZ = np.concatenate((X, Z), axis=0)


        # Transfer component analysis

        self.C, K = self.transfer_component_analysis(X, Z)


        # Map source data onto transfer components

        X = np.dot(K[:N, :], self.C)


        # Train a weighted classifier

        if self.loss == 'logistic':

            # Logistic regression model with sample weights

            self.clf.fit(X, y)

        elif self.loss == 'quadratic':

            # Least-squares model with sample weights

            self.clf.fit(X, y)

        elif self.loss == 'hinge':

            # Linear support vector machine with sample weights

            self.clf.fit(X, y)

        else:

            # Other loss functions are not implemented

            raise NotImplementedError


        # 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.


        INPUT   (1) array 'Z_': new data set (M samples by D features)

        OUTPUT  (2) array 'preds': label predictions (M samples by 1)

        """

        # Data shape

        M, D = Z_.shape


        # If classifier is trained, check for same dimensionality

        if self.is_trained:

            assert self.train_data_dim == D


        # Compute kernel for new data

        K = self.kernel(Z_, self.XZ, type=self.kernel_type,

                        bandwidth=self.bandwidth, order=self.order)


        # Map new data onto transfer components

        Z_ = np.dot(K, self.C)


        # Call scikit's predict function

        preds = self.clf.predict(Z_)


        # For quadratic loss function, correct predictions

        if self.loss == 'quadratic':

            preds = (np.sign(preds)+1)/2.


        # 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