#!/usr/bin/env python
# Copyright 2016-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# 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.
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_is_fitted
from sklearn.utils.multiclass import unique_labels
from WORC.classification.RankedSVM import RankSVM_train, RankSVM_test
[docs]class RankedSVM(BaseEstimator, ClassifierMixin):
""" An example classifier which implements a 1-NN algorithm.
Parameters
----------
demo_param : str, optional
A parameter used for demonstation of how to pass and store paramters.
Attributes
----------
X_ : array, shape = [n_samples, n_features]
The input passed during :meth:`fit`
y_ : array, shape = [n_samples]
The labels passed during :meth:`fit`
"""
[docs] def __init__(self, cost=1, lambda_tol=1e-6,
norm_tol=1e-4, max_iter=500, svm='Poly', gamma=0.05,
coefficient=0.05, degree=3):
self.cost = cost
self.lambda_tol = lambda_tol
self.norm_tol = norm_tol
self.max_iter = max_iter
self.svm = svm
self.gamma = gamma
self.coefficient = coefficient
self.degree = 3
[docs] def fit(self, X, y):
"""A reference implementation of a fitting function for a classifier.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values. An array of int.
Returns
-------
self : object
Returns self.
"""
# Store the classes seen during fit
self.classes_ = unique_labels(y)
# RankedSVM requires a very specific format of y
# Each row should represent a label, consisiting of ones and minus ones
y = np.transpose(y).astype(np.int16)
y[y == 0] = -1
self.X_ = X
self.y_ = y
self.num_class = y.shape[0]
Weights, Bias, SVs =\
RankSVM_train(train_data=X,
train_target=y,
cost=self.cost,
lambda_tol=self.lambda_tol,
norm_tol=self.norm_tol,
max_iter=self.max_iter,
svm=self.svm, gamma=self.gamma,
coefficient=self.coefficient,
degree=self.degree)
self.Weights = Weights
self.Bias = Bias
self.SVs = SVs
return self
[docs] def predict(self, X, y=None):
""" A reference implementation of a prediction for a classifier.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : array of int of shape = [n_samples]
The label for each sample is the label of the closest sample
seen udring fit.
"""
# Check is fit had been called
check_is_fitted(self, ['X_', 'y_'])
_, Predicted_Labels =\
RankSVM_test(test_data=X,
num_class=self.num_class,
Weights=self.Weights,
Bias=self.Bias,
SVs=self.SVs,
svm=self.svm, gamma=self.gamma,
coefficient=self.coefficient,
degree=self.degree)
return Predicted_Labels
[docs] def predict_proba(self, X, y):
""" A reference implementation of a prediction for a classifier.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : array of int of shape = [n_samples]
The label for each sample is the label of the closest sample
seen udring fit.
"""
# Check is fit had been called
check_is_fitted(self, ['X_', 'y_'])
Probs, _ =\
RankSVM_test(test_data=X,
num_class=self.num_class,
Weights=self.Weights,
Bias=self.Bias,
svm=self.svm, gamma=self.gamma,
coefficient=self.coefficient,
degree=self.degree)
return Probs