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* @license Apache-2.0
*
* Copyright (c) 2018 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var isPositiveInteger = require( '@stdlib/assert/is-positive-integer' ).isPrimitive;
var isVectorLike = require( '@stdlib/assert/is-vector-like' );
var isndarrayLike = require( '@stdlib/assert/is-ndarray-like' );
var setReadOnly = require( '@stdlib/utils/define-nonenumerable-read-only-property' );
var format = require( '@stdlib/string/format' );
var Model = require( './model.js' );
var LEARNING_RATE_DEFAULTS = require( './learning_rate_defaults.json' );
var validate = require( './validate.js' );
// MAIN //
/**
* Returns an accumulator function which incrementally performs binary classification using stochastic gradient descent (SGD).
*
* ## Method
*
* - The sub-gradient of the loss function is estimated for each datum and the classification model is updated incrementally, with a decreasing learning rate and regularization of model feature weights using L2 regularization.
*
* ## References
*
* - Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30. doi:10.1007/s10107-010-0420-4
*
* @param {PositiveInteger} N - number of features
* @param {Options} [options] - options object
* @param {PositiveNumber} [options.lambda=1.0e-3] - regularization parameter
* @param {ArrayLikeObject} [options.learningRate=['basic']] - learning rate function and associated parameters (one of `basic`, `constant`, or `pegasos`)
* @param {string} [options.loss='log'] - loss function (one of `hinge`, `log`, `modified-huber`, `perceptron`, or `squared-hinge`)
* @param {boolean} [options.intercept=true] - boolean indicating whether to include an intercept
* @throws {TypeError} first argument must be a positive integer
* @throws {TypeError} options argument must be an object
* @throws {TypeError} must provide valid options
* @returns {Function} accumulator
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var array = require( '@stdlib/ndarray/array' );
*
* // Create an accumulator:
* var accumulator = incrBinaryClassification( 3, {
* 'intercept': true,
* 'lambda': 1.0e-5
* });
*
* // ...
*
* // Update the model:
* var x = array( new Float64Array( [ 2.3, 1.0, 5.0 ] ) );
* var coefs = accumulator( x, 1 );
* // returns <ndarray>
*
* // ...
*
* // Create a new observation vector:
* x = array( new Float64Array( [ 2.3, 5.3, 8.6 ] ) );
*
* // Predict the response value:
* var yhat = accumulator.predict( x );
* // returns <ndarray>
*/
function incrBinaryClassification( N, options ) {
var model;
var opts;
var err;
if ( !isPositiveInteger( N ) ) {
throw new TypeError( format( 'invalid argument. First argument must be a positive integer. Value: `%s`.', N ) );
}
opts = {
'intercept': true,
'lambda': 1.0e-4,
'learningRate': LEARNING_RATE_DEFAULTS[ 'basic' ].slice(),
'loss': 'log'
};
if ( arguments.length > 1 ) {
err = validate( opts, options );
if ( err ) {
throw err;
}
}
model = new Model( N, opts );
// Attach methods to the accumulator:
setReadOnly( accumulator, 'predict', predict );
return accumulator;
/**
* If provided a feature vector and response value, the accumulator function updates a binary classification model; otherwise, the accumulator function returns the current binary classification model coefficients.
*
* @private
* @param {VectorLike} x - feature vector
* @param {integer} y - response value
* @throws {TypeError} first argument must be a one-dimensional ndarray
* @throws {TypeError} first argument must be a one-dimensional ndarray whose length matches the number of model features
* @throws {TypeError} second argument must be either `+1` or `-1`
* @returns {ndarray} one-dimensional ndarray containing model coefficients
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var array = require( '@stdlib/ndarray/array' );
*
* // Create an accumulator:
* var accumulator = incrBinaryClassification( 3 );
*
* // ...
*
* // Update the model:
* var x = array( new Float64Array( [ 2.3, 1.0, 5.0 ] ) );
* var coefs = accumulator( x, 1 );
* // returns <ndarray>
*/
function accumulator( x, y ) {
if ( arguments.length === 0 ) {
return model.coefficients;
}
if ( !isVectorLike( x ) ) {
throw new TypeError( format( 'invalid argument. First argument must be a one-dimensional ndarray. Value: `%s`.', x ) );
}
if ( y !== -1 && y !== 1 ) {
throw new TypeError( format( 'invalid argument. Second argument must be either +1 or -1. Value: `%s`.', y ) );
}
if ( x.shape[ 0 ] !== model.nfeatures ) {
throw new TypeError( format( 'invalid argument. First argument must be a one-dimensional ndarray of length %u. Actual length: `%u`.', model.nfeatures, x.shape[ 0 ] ) );
}
model.update( x, y );
return model.coefficients;
}
/**
* Predicts the response value for one or more observation vectors `X`.
*
* @private
* @param {ndarrayLike} X - ndarray (of size `(...,N)`) containing observation vectors
* @param {string} [type="label"] - prediction type (either `label`, `probability`, or `linear`)
* @throws {TypeError} first argument must be an ndarray
* @throws {TypeError} first argument must be an ndarray whose last dimension matches the number of model features
* @throws {TypeError} second argument must be a recognized/supported prediction "type"
* @throws {Error} second argument must be compatible with the model loss function
* @returns {ndarray} ndarray (of size `(...)`) containing response values
*
* @example
* var Float64Array = require( '@stdlib/array/float64' );
* var array = require( '@stdlib/ndarray/array' );
*
* // Create an accumulator:
* var accumulator = incrBinaryClassification( 3 );
*
* // ...
*
* // Create a new observation vector:
* var x = array( new Float64Array( [ 2.3, 5.3, 8.6 ] ) );
*
* // Predict the response value:
* var yhat = accumulator.predict( x );
* // returns <ndarray>
*/
function predict( X, type ) {
var sh;
var t;
if ( !isndarrayLike( X ) ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray. Value: `%s`.', X ) );
}
sh = X.shape;
if ( sh[ sh.length-1 ] !== N ) {
throw new TypeError( format( 'invalid argument. First argument must be an ndarray whose last dimension is of size %u. Actual size: `%u`.', N, sh[ sh.length-1 ] ) );
}
t = 'label';
if ( arguments.length > 1 ) {
if ( type === 'probability' ) {
if ( opts.loss !== 'log' && opts.loss !== 'modified-huber' ) {
throw new Error( format( 'invalid argument. Second argument is incompatible with model loss function. Probability predictions are only supported when the loss function is one of the following: "%s". Model loss function: `%s`.', [ 'log', 'modified-huber' ].join( '", "' ), opts.loss ) );
}
} else if ( type !== 'label' && type !== 'linear' ) {
throw new TypeError( format( 'invalid argument. Second argument must be a string value equal to either "label", "probability", or "linear". Value: `%s`.', type ) );
}
t = type;
}
return model.predict( X, t );
}
}
// EXPORTS //
module.exports = incrBinaryClassification;
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