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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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""" |
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Unit tests for the Regularized Cannon model class and associated functions. |
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""" |
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import numpy as np |
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import unittest |
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from AnniesLasso import regularized, utils |
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class TestRegularizedCannonModel(unittest.TestCase): |
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View Code Duplication |
def setUp(self): |
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# Initialise some faux data and labels. |
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labels = "ABCDE" |
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N_labels = len(labels) |
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N_stars = np.random.randint(1, 500) |
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N_pixels = np.random.randint(1, 10000) |
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shape = (N_stars, N_pixels) |
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self.valid_training_labels = np.rec.array( |
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np.random.uniform(size=(N_stars, N_labels)), |
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dtype=[(label, '<f8') for label in labels]) |
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self.valid_fluxes = np.random.uniform(size=shape) |
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self.valid_flux_uncertainties = np.random.uniform(size=shape) |
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def get_model(self): |
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return regularized.RegularizedCannonModel( |
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self.valid_training_labels, self.valid_fluxes, |
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self.valid_flux_uncertainties) |
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def test_init(self): |
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self.assertIsNotNone(self.get_model()) |
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def test_remind_myself_to_write_unit_tests_for_these_functions(self): |
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m = self.get_model() |
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m.label_vector = "A + B + C" |
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self.assertIsNotNone(m.label_vector) |
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# Cannot train without regularization term. |
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with self.assertRaises(TypeError): |
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m.train() |
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# Regularization must be positive and finite. |
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for each in (-1, np.nan, +np.inf, -np.inf): |
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with self.assertRaises(ValueError): |
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m.regularization = each |
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# Regularization must be a float or match the dispersion size. |
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with self.assertRaises(ValueError): |
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m.regularization = [0., 1.] |
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m.regularization = np.zeros_like(m.dispersion) |
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m.train() |
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andycasey /
AnniesLasso
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