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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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