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