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)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.