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from __future__ import division
import pytest
from random import Random
from dlib import (vectors, vector, sparse_vectors, sparse_vector, pair, array,
cross_validate_trainer,
svm_c_trainer_radial_basis,
svm_c_trainer_sparse_radial_basis,
svm_c_trainer_histogram_intersection,
svm_c_trainer_sparse_histogram_intersection,
svm_c_trainer_linear,
svm_c_trainer_sparse_linear,
rvm_trainer_radial_basis,
rvm_trainer_sparse_radial_basis,
rvm_trainer_histogram_intersection,
rvm_trainer_sparse_histogram_intersection,
rvm_trainer_linear,
rvm_trainer_sparse_linear)
@pytest.fixture
def training_data():
r = Random(0)
predictors = vectors()
sparse_predictors = sparse_vectors()
response = array()
for i in range(30):
for c in [-1, 1]:
response.append(c)
values = [r.random() + c * 0.5 for _ in range(3)]
predictors.append(vector(values))
sp = sparse_vector()
for i, v in enumerate(values):
sp.append(pair(i, v))
sparse_predictors.append(sp)
return predictors, sparse_predictors, response
@pytest.mark.parametrize('trainer, class1_accuracy, class2_accuracy', [
(svm_c_trainer_radial_basis, 1.0, 1.0),
(svm_c_trainer_sparse_radial_basis, 1.0, 1.0),
(svm_c_trainer_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_sparse_histogram_intersection, 1.0, 1.0),
(svm_c_trainer_linear, 1.0, 23 / 30),
(svm_c_trainer_sparse_linear, 1.0, 23 / 30),
(rvm_trainer_radial_basis, 1.0, 1.0),
(rvm_trainer_sparse_radial_basis, 1.0, 1.0),
(rvm_trainer_histogram_intersection, 1.0, 1.0),
(rvm_trainer_sparse_histogram_intersection, 1.0, 1.0),
(rvm_trainer_linear, 1.0, 0.6),
(rvm_trainer_sparse_linear, 1.0, 0.6)
])
def test_trainers(training_data, trainer, class1_accuracy, class2_accuracy):
predictors, sparse_predictors, response = training_data
if 'sparse' in trainer.__name__:
predictors = sparse_predictors
cv = cross_validate_trainer(trainer(), predictors, response, folds=10)
assert cv.class1_accuracy == pytest.approx(class1_accuracy)
assert cv.class2_accuracy == pytest.approx(class2_accuracy)
decision_function = trainer().train(predictors, response)
assert decision_function(predictors[2]) < 0
assert decision_function(predictors[3]) > 0
if 'linear' in trainer.__name__:
assert len(decision_function.weights) == 3
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