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How to use test_set_target method in avocado
Best Python code snippet using avocado_python
test_optimizer.py
Source:test_optimizer.py 
1import numpy as np2import unittest3from icet.fitting import Optimizer4class TestOptimizer(unittest.TestCase):5 """Unittest class for Optimizer."""6 def __init__(self, *args, **kwargs):7 super().__init__(*args, **kwargs)8 self.n_rows = 2009 self.n_cols = 5010 self.tol = 1.0 / self.n_rows11 # set up dummy linear problem data12 self.A = np.random.normal(0, 1, (self.n_rows, self.n_cols))13 self.x = np.random.normal(0, 5, (self.n_cols, ))14 self.noise = np.random.normal(0, 0.1, (self.n_rows, ))15 self.y = np.dot(self.A, self.x) + self.noise16 def shortDescription(self):17 """Prevents unittest from printing docstring in test cases."""18 return None19 def test_get_rows_via_sizes(self):20 """Tests _get_rows_via_sizes functionality."""21 opt = Optimizer((self.A, self.y))22 # test with only train_size defined23 train_size, test_size = int(0.8 * self.n_rows), None24 train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)25 self.assertEqual(train_size, len(train_set))26 self.assertEqual(self.n_rows - train_size, len(test_set))27 # test with only test_size defined28 train_size, test_size = None, int(0.8 * self.n_rows)29 train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)30 self.assertEqual(test_size, len(test_set))31 self.assertEqual(self.n_rows - test_size, len(train_set))32 # test with both defined33 train_size, test_size = int(0.8 * self.n_rows), int(0.15 * self.n_rows)34 train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)35 self.assertEqual(train_size, len(train_set))36 self.assertEqual(test_size, len(test_set))37 # test with fractions38 train_size, test_size = 0.7, 0.239 train_set, test_set = opt._get_rows_via_sizes(train_size, test_size)40 self.assertLess(abs(train_size*self.n_rows - len(train_set)), self.tol)41 self.assertLess(abs(test_size*self.n_rows - len(test_set)), self.tol)42 # test edge case with full training set43 test_size = None44 for train_size in [1.0, self.n_rows]:45 train_set, test_set = opt._get_rows_via_sizes(46 train_size, test_size)47 self.assertEqual(len(train_set), self.n_rows)48 self.assertIsNone(test_set)49 # test invalid sizes50 with self.assertRaises(ValueError):51 train_size, test_size = None, 1.052 opt._get_rows_via_sizes(train_size, test_size)53 with self.assertRaises(ValueError):54 train_size, test_size = None, None55 opt._get_rows_via_sizes(train_size, test_size)56 def test_get_rows_from_indices(self):57 """Tests _get_rows_from_indices."""58 opt = Optimizer((self.A, self.y))59 all_rows = np.arange(self.n_rows)60 train_size = int(0.8 * self.n_rows)61 train_set_target = np.random.choice(62 all_rows, train_size, replace=False)63 test_set_target = sorted(np.setdiff1d(all_rows, train_set_target))64 # specify only train_set, test set should default to remaining rows65 train_set, test_set = opt._get_rows_from_indices(66 train_set_target, None)67 self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))68 self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))69 # specify only test_set, train set should default to remaining rows70 train_set, test_set = opt._get_rows_from_indices(None, test_set_target)71 self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))72 self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))73 # specify partial sets meaning not all rows are used74 train_set_target = np.delete(train_set_target, [0, 1, 2])75 test_set_target = np.delete(test_set_target, [0, 1, 2])76 train_set, test_set = opt._get_rows_from_indices(77 train_set_target, test_set_target)78 self.assertSequenceEqual(sorted(train_set_target), sorted(train_set))79 self.assertSequenceEqual(sorted(test_set_target), sorted(test_set))80 # test invalid input81 with self.assertRaises(ValueError):82 opt._get_rows_from_indices(None, None)83 def test_setup_rows(self):84 """85 Tests _setup_rows.86 Simply test that function raise when no training data available87 """88 opt = Optimizer((self.A, self.y))89 # no training data from train_size90 with self.assertRaises(ValueError):91 train_size, test_size = 0, 0.592 opt._setup_rows(train_size, test_size, None, None)93 # no training data from train_set94 with self.assertRaises(ValueError):95 train_set, test_set = [], np.arange(0, int(0.5*self.n_rows))96 opt._setup_rows(None, None, train_set, test_set)97 # overlapping indices in train_set and test_set98 with self.assertRaises(ValueError):99 train_set, test_set = [1, 2, 3, 4, 5], [5, 6, 7, 8, 9, 10]100 opt._setup_rows(None, None, train_set, test_set)101 def test_train(self):102 """Tests train."""103 # with test set104 train_size = 0.75105 opt = Optimizer((self.A, self.y), train_size=train_size)106 self.assertIsNone(opt._rmse_train)107 self.assertIsNone(opt._rmse_test)108 self.assertIsNone(opt.train_scatter_data)109 self.assertIsNone(opt.test_scatter_data)110 opt.train()111 self.assertIsNotNone(opt._rmse_train)112 self.assertIsNotNone(opt._rmse_test)113 self.assertIsNotNone(opt.train_scatter_data)114 self.assertIsNotNone(opt.test_scatter_data)115 # without testing116 train_size = 1.0117 opt = Optimizer((self.A, self.y), train_size=train_size)118 self.assertIsNone(opt._rmse_train)119 self.assertIsNone(opt._rmse_test)120 self.assertIsNone(opt.train_scatter_data)121 self.assertIsNone(opt.test_scatter_data)122 opt.train()123 self.assertIsNotNone(opt._rmse_train)124 self.assertIsNone(opt._rmse_test)125 self.assertIsNotNone(opt.train_scatter_data)126 self.assertIsNone(opt.test_scatter_data)127 def test_summary_property(self):128 """Tests summary property."""129 # without having trained130 opt = Optimizer((self.A, self.y))131 self.assertIsInstance(opt.summary, dict)132 # with having trained133 opt.train()134 self.assertIsInstance(opt.summary, dict)135 self.assertIn('rmse_train', opt.summary.keys())136 self.assertIn('rmse_test', opt.summary.keys())137 def test_repr(self):138 """Tests repr dunder."""139 opt = Optimizer((self.A, self.y))140 self.assertIsInstance(repr(opt), str)141 def test_size_properties(self):142 """Tests the properties in regards to training/test sets and sizes."""143 # test without test_set144 train_set = np.arange(0, self.n_rows)145 opt = Optimizer((self.A, self.y), train_set=train_set)146 self.assertSequenceEqual(opt.train_set.tolist(), train_set.tolist())147 self.assertEqual(len(train_set), opt.train_size)148 self.assertEqual(1.0, opt.train_fraction)149 self.assertIsNone(opt.test_set)150 self.assertEqual(opt.test_size, 0)151 self.assertEqual(opt.test_fraction, 0)152 # test with test set153 test_set = np.arange(int(0.7 * self.n_rows), int(0.8 * self.n_rows))154 opt = Optimizer((self.A, self.y), test_set=test_set)155 self.assertSequenceEqual(test_set.tolist(), opt.test_set.tolist())156 self.assertEqual(opt.test_size, len(test_set))157 self.assertAlmostEqual(opt.test_fraction, len(test_set) / self.n_rows)158 def test_zero_error_with_least_square_fit(self):159 """ Test that the error is zero if training without noise and with160 least-squares. """161 # set up dummy linear problem data162 for standardize in [True, False]:163 y = np.dot(self.A, self.x)164 opt = Optimizer((self.A, y), fit_method='least-squares', standardize=standardize)165 opt.train()166 self.assertAlmostEqual(opt.rmse_train, 0.0)167 self.assertAlmostEqual(opt.rmse_test, 0.0)168 self.assertAlmostEqual(np.abs(self.x - opt.parameters).max(), 0)169if __name__ == '__main__':...
week11.py
Source:week11.py
1from __future__ import division2import hashlib3import json4from sklearn.ensemble import RandomForestClassifier as rfc5directory = "data/"6def bag_of_words(lines):7 uniq = set()8 for line in lines:9 # Iterate over all words10 for word in line[0].split():11 # Put words not already found into dictionary12 uniq.add(word)13 uniq_dict = {}14 for j, word in enumerate(uniq):15 uniq_dict[word] = j16 uniq_len = len(uniq)17 # Use word dictionary to create bag-of-words18 bag = []19 for line in lines:20 vec = [0] * uniq_len21 for word in line[0].split():22 vec[uniq_dict[word]] += 123 bag.append((vec, (1 if 'earn' in line[1] else 0)))24 return bag25def bag_of_words_feature_hashed(lines, N):26 uniq = set()27 for line in lines:28 # Iterate over all words29 for word in line[0].split():30 # Put words not already found into dictionary31 uniq.add(word)32 uniq_dict = {}33 for j, word in enumerate(uniq):34 uniq_dict[word] = j35 # Use word dictionary to create bag-of-words36 bag = []37 for line in lines:38 vec = [0] * N39 for word in line[0].split():40 h = int(hashlib.md5(word).hexdigest(), 16) % N41 vec[h] += 142 bag.append((vec, (1 if 'earn' in line[1] else 0)))43 return bag44if __name__ == "__main__":45 articles = []46 for i in range(21):47 f = open(directory + "reuters-0" + (str(i) if i >= 10 else "0" + str(i)) + ".json", 'r')48 articles += json.loads(f.read())49 f.close()50 articles = reduce(lambda x, y: x + ([y] if ('topics' in y.keys())51 and ('body' in y.keys())52 and (len(y['topics']) > 0)53 and (len(y['body']) > 0)54 else []), articles, [])55 lines = map(lambda x: (x['body'].lower().encode('ascii', errors='ignore'), x['topics']), articles)56 print "Calcualting bag of words."57 bow = bag_of_words(lines)58 print "Amount of lines: " + str(len(bow))59 print "Amount of words: " + str(len(bow[0][0]))60 training_set = bow[:int(round(len(bow)*0.8))]61 training_set_data = [row[0] for row in training_set]62 training_set_target = [row[1] for row in training_set]63 test_set = bow[-int(round(len(bow)*0.2)):]64 test_set_data = [row[0] for row in test_set]65 test_set_target = [row[1] for row in test_set]66 classifier = rfc(n_estimators=50)67 classifier.fit(training_set_data, training_set_target)68 predictions = classifier.predict(test_set_data)69 accuracy = []70 for i, prediction in enumerate(predictions):71 accuracy += [test_set_target[i] == prediction]72 accuracy = (accuracy.count(True) / len(test_set_target)) * 10073 print "Accuracy of classifier: " + str(accuracy) + "%"74 print "\nCalculating bag of words using feature hashing."75 bow = bag_of_words_feature_hashed(lines, 1000)76 print "Amount of lines: " + str(len(bow))77 print "Amount of words: " + str(len(bow[0][0]))78 training_set = bow[:int(round(len(bow)*0.8))]79 training_set_data = [row[0] for row in training_set]80 training_set_target = [row[1] for row in training_set]81 test_set = bow[-int(round(len(bow)*0.2)):]82 test_set_data = [row[0] for row in test_set]83 test_set_target = [row[1] for row in test_set]84 classifier = rfc(n_estimators=50)85 classifier.fit(training_set_data, training_set_target)86 predictions = classifier.predict(test_set_data)87 accuracy = []88 for i, prediction in enumerate(predictions):89 accuracy += [test_set_target[i] == prediction]90 accuracy = (accuracy.count(True) / len(test_set_target)) * 100...
evaluate_digit_classifier.py
Source:evaluate_digit_classifier.py
1import numpy as np2from sklearn import datasets3# initialize test set4def load_digits_and_test_set(test_start=0, test_count=1000):5 global digits6 global t_start, t_count, t_end7 global test_set_images, test_set_target8 digits = datasets.load_digits()9 t_images = len(digits.images)10 t_start = test_start11 t_count = test_count12 t_end = t_start + t_count13 if (t_images < t_end):14 t_end = t_images15 test_set_images = digits.images[t_start:t_end]16 test_set_target = digits.target[t_start:t_end]17 return len(test_set_images)18# test classifier over test set19def test_digit_classifier(classifier):20 digits = datasets.load_digits()21 correct = 022 print(f'test classifier for {t_count} images: {t_start} to {t_end-1}.')23 for img, target in zip(test_set_images, test_set_target):24 v = np.matrix.flatten(img) / 15.25 output = classifier(v)26 answer = list(output).index(max(output))27 if answer == target:28 correct += 129 return (correct/t_count)30# calculate total cost for classifier over test set31def y_vec(digit):32 return np.array([1 if i == digit else 0 for i in range(0,10)])33def cost_one(classifier,x,i):34 return sum([(classifier(x)[j] - y_vec(i)[j])**2 for j in range(10)])35def calculate_total_cost(classifier):36 digits = datasets.load_digits()37 print(f'calculate total cost for {t_count} images: {t_start} to {t_end-1}.')38 x = np.array([np.matrix.flatten(img) for img in test_set_images]) / 15.039 y = test_set_target...
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