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How to use test_input_values method in lettuce_webdriver

Best Python code snippet using lettuce_webdriver_python

test_model.py

Source:test_model.py

1import pandas as pd2import pytest3from src.recsys.model import mean_center, normalize, softmax4def test_mean_center():5    test_input_values = [6        0.0,7        0.0,8        0.0,9        247.0,10        4.0,11        0.0,12        0.0,13        0.0,14        0.0,15        0.0,16        0.0,17        5.0,18        0.0,19        0.0,20        0.0,21        0.0,22        0.0,23        0.0,24        2.0,25        6.0,26        264.0,27    ]28    test_input_index = pd.Index(29        [30            "brazilian",31            "british",32            "cajun_creole",33            "chinese",34            "filipino",35            "french",36            "greek",37            "indian",38            "irish",39            "italian",40            "jamaican",41            "japanese",42            "korean",43            "mexican",44            "moroccan",45            "russian",46            "southern_us",47            "spanish",48            "thai",49            "vietnamese",50            "ingr_sum",51        ],52        name="cuisine",53    )54    test_input = pd.Series(test_input_values, index=test_input_index)55    test = mean_center(test_input)56    true_values = [57        -13.2,58        -13.2,59        -13.2,60        233.8,61        -9.2,62        -13.2,63        -13.2,64        -13.2,65        -13.2,66        -13.2,67        -13.2,68        -8.2,69        -13.2,70        -13.2,71        -13.2,72        -13.2,73        -13.2,74        -13.2,75        -11.2,76        -7.2,77        264.0,78    ]79    true_index = pd.Index(80        data=[81            "brazilian",82            "british",83            "cajun_creole",84            "chinese",85            "filipino",86            "french",87            "greek",88            "indian",89            "irish",90            "italian",91            "jamaican",92            "japanese",93            "korean",94            "mexican",95            "moroccan",96            "russian",97            "southern_us",98            "spanish",99            "thai",100            "vietnamese",101            "ingr_sum",102        ],103        name="cuisine",104    )105    true = pd.Series(true_values, index=true_index)106    pd.testing.assert_series_equal(test, true)107def test_mean_center_empty():108    test_input = pd.Series([], dtype="object")109    with pytest.raises(ValueError):110        mean_center(test_input)111def test_normalize():112    test_input_values = [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0]113    test_input_index = pd.Index(114        [115            "Gochujang base",116            "Italian bread",117            "Italian parsley leaves",118            "Mexican cheese blend",119            "Mexican oregano",120            "Shaoxing wine",121            "Sriracha",122            "Tabasco Pepper Sauce",123            "Thai fish sauce",124            "Thai red curry paste",125        ],126        name="ingredient",127    )128    test_input = pd.Series(test_input_values, index=test_input_index)129    test = normalize(test_input)130    true_values = [-0.1, -0.1, 0.15, -0.1, -0.1, -0.1, -0.1, 0.65, -0.1, -0.1]131    true_index = pd.Index(132        [133            "Gochujang base",134            "Italian bread",135            "Italian parsley leaves",136            "Mexican cheese blend",137            "Mexican oregano",138            "Shaoxing wine",139            "Sriracha",140            "Tabasco Pepper Sauce",141            "Thai fish sauce",142            "Thai red curry paste",143        ],144        name="ingredient",145    )146    true = pd.Series(true_values, index=true_index)147    pd.testing.assert_series_equal(test, true)148def test_normalize_empty():149    test = normalize(pd.Series([], dtype="object"))150    true = pd.Series([], dtype="object")151    pd.testing.assert_series_equal(test, true)152def test_softmax():153    test_input_values = [154        0.0,155        0.0,156        0.0,157        247.0,158        4.0,159        0.0,160        0.0,161        0.0,162        0.0,163        0.0,164        0.0,165        5.0,166        0.0,167        0.0,168        0.0,169        0.0,170        0.0,171        0.0,172        2.0,173        6.0,174        264.0,175    ]176    test_input_index = pd.Index(177        [178            "brazilian",179            "british",180            "cajun_creole",181            "chinese",182            "filipino",183            "french",184            "greek",185            "indian",186            "irish",187            "italian",188            "jamaican",189            "japanese",190            "korean",191            "mexican",192            "moroccan",193            "russian",194            "southern_us",195            "spanish",196            "thai",197            "vietnamese",198            "ingr_sum",199        ],200        name="cuisine",201    )202    test_input = pd.Series(test_input_values, index=test_input_index)203    test = softmax(test_input).astype("str")204    true_values = [205        "2.2195082290865866e-115",206        "2.2195082290865866e-115",207        "2.2195082290865866e-115",208        "4.1399375473943345e-08",209        "1.2118104329146771e-113",210        "2.2195082290865866e-115",211        "2.2195082290865866e-115",212        "2.2195082290865866e-115",213        "2.2195082290865866e-115",214        "2.2195082290865866e-115",215        "2.2195082290865866e-115",216        "3.294042279329056e-113",217        "2.2195082290865866e-115",218        "2.2195082290865866e-115",219        "2.2195082290865866e-115",220        "2.2195082290865866e-115",221        "2.2195082290865866e-115",222        "2.2195082290865866e-115",223        "1.6400070816759008e-114",224        "8.954135270075986e-113",225        "0.9999999586006246",226    ]227    true_index = pd.Index(228        [229            "brazilian",230            "british",231            "cajun_creole",232            "chinese",233            "filipino",234            "french",235            "greek",236            "indian",237            "irish",238            "italian",239            "jamaican",240            "japanese",241            "korean",242            "mexican",243            "moroccan",244            "russian",245            "southern_us",246            "spanish",247            "thai",248            "vietnamese",249            "ingr_sum",250        ],251        name="cuisine",252    )253    true = pd.Series(true_values, true_index)254    pd.testing.assert_series_equal(test, true)255def test_softmax_empty():256    test = softmax(pd.Series([], dtype="object"))257    true = pd.Series([], dtype="object")...

wide_deep_test.py

Source:wide_deep_test.py

1from __future__ import absolute_import2from __future__ import division3from __future__ import print_function4import os5import tensorflow as tf6import sys7PACKAGE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))8sys.path.insert(0, PACKAGE_DIR)9from lib.read_conf import Config10from lib.dataset import input_fn11from lib.build_estimator import build_estimator12TEST_CSV = os.path.join(os.path.dirname(PACKAGE_DIR), 'data/test/test2')13USED_FEATURE_KEY = Config().get_feature_name('used')14def _read_test_input():15    for line in open(TEST_CSV):16        return line17TEST_INPUT_VALUES = _read_test_input()18TEST_INPUT_KEYS = Config().get_feature_name()19TEST_INPUT = zip(TEST_INPUT_KEYS, TEST_INPUT_VALUES)20class BaseTest(tf.test.TestCase):21    def setUp(self):22        # Create temporary CSV file23        self.temp_dir = self.get_temp_dir()24        self.input_csv = os.path.join(self.temp_dir, 'test.csv')25        with tf.gfile.Open(self.input_csv, 'w') as temp_csv:26            temp_csv.write(TEST_INPUT_VALUES)27    def test_input_fn(self):28        features, labels = input_fn(self.input_csv, 'eval', batch_size=1)29        with tf.Session() as sess:30            features, labels = sess.run((features, labels))31        # Compare the two features dictionaries.32        for key in USED_FEATURE_KEY:33            self.assertTrue(key in features)34            self.assertEqual(len(features[key]), 1)35            feature_value = features[key][0]36            # Convert from bytes to string for Python 3.37            if isinstance(feature_value, bytes):38                feature_value = feature_value.decode()39            self.assertEqual(TEST_INPUT[key], feature_value)40        self.assertFalse(labels)41    def build_and_test_estimator(self, model_type):42        """Ensure that model trains and minimizes loss."""43        model = build_estimator(self.temp_dir, model_type)44        # Train for 1 step to initialize model and evaluate initial loss45        model.train(46            input_fn=lambda: input_fn(47                TEST_CSV, None, 'eval', batch_size=1),48            steps=1)49        initial_results = model.evaluate(50            input_fn=lambda: input_fn(51                TEST_CSV, None, 'eval', batch_size=1))52        # Train for 100 epochs at batch size 3 and evaluate final loss53        model.train(54            input_fn=lambda: input_fn(55                TEST_CSV, None, 'eval', batch_size=8))56        final_results = model.evaluate(57            input_fn=lambda: input_fn(58                TEST_CSV, None, 'eval', batch_size=1))59        print('%s initial results:' % model_type, initial_results)60        print('%s final results:' % model_type, final_results)61        # Ensure loss has decreased, while accuracy and both AUCs have increased.62        self.assertLess(final_results['loss'], initial_results['loss'])63        self.assertGreater(final_results['auc'], initial_results['auc'])64        self.assertGreater(final_results['auc_precision_recall'],65                           initial_results['auc_precision_recall'])66        self.assertGreater(final_results['accuracy'], initial_results['accuracy'])67    def test_wide_deep_estimator_training(self):68        self.build_and_test_estimator('wide_deep')69if __name__ == '__main__':70    tf.logging.set_verbosity(tf.logging.ERROR)...

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