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How to use test_category method in tappy

Best Python code snippet using tappy_python

process_json_data_unittest.py

Source:process_json_data_unittest.py

1# Copyright (C) 2014 Google Inc. All rights reserved.2#3# Redistribution and use in source and binary forms, with or without4# modification, are permitted provided that the following conditions are5# met:6#7#     * Redistributions of source code must retain the above copyright8# notice, this list of conditions and the following disclaimer.9#     * Redistributions in binary form must reproduce the above10# copyright notice, this list of conditions and the following disclaimer11# in the documentation and/or other materials provided with the12# distribution.13#     * Neither the name of Google Inc. nor the names of its14# contributors may be used to endorse or promote products derived from15# this software without specific prior written permission.16#17# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS18# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT19# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR20# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT21# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,22# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT23# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,24# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY25# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT26# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE27# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.28import unittest29from webkitpy.layout_tests.generate_results_dashboard import ProcessJsonData30class ProcessJsonDataTester(unittest.TestCase):31    def test_check_failing_results(self):32        valid_json_data = {33            'tests': {34                'test_category': {35                    'test_sub_category': {36                        'test_name': {37                            'test.html': {'expected': 'PASS', 'actual': 'TEXT', 'is_unexpected': True}38                        }39                    }40                }41            }42        }43        valid_json_data_1 = {44            'tests': {45                'test_category': {46                    'test_sub_category': {47                        'test_name': {48                            'test.html': {'expected': 'TEXT', 'actual': 'TEXT', 'is_unexpected': True}49                        }50                    }51                }52            }53        }54        valid_json_data_2 = {55            'tests': {56                'test_category': {57                    'test_sub_category': {58                        'test_name_2': {59                            'test_2.html': {'expected': 'PASS', 'actual': 'TEXT', 'is_unexpected': True}60                        },61                        'test_name': {62                            'test.html': {'expected': 'TEXT', 'actual': 'TEXT', 'is_unexpected': True}63                        }64                    }65                }66            }67        }68        expected_result = {69            'tests': {70                'test_category': {71                    'test_sub_category': {72                        'test_name': {73                            'test.html': {'archived_results': ['TEXT', 'PASS']}74                        }75                    }76                }77            }78        }79        process_json_data = ProcessJsonData(valid_json_data, [valid_json_data_1], [valid_json_data_2])80        actual_result = process_json_data.generate_archived_result()81        self.assertEqual(expected_result, actual_result)82    def test_check_full_results(self):83        valid_json_data = {84            'tests': {85                'test_category': {86                    'test_sub_category': {87                        'test_name_2': {88                            'test_2.html': {'expected': 'PASS', 'actual': 'TEXT', 'is_unexpected': True}89                        }90                    }91                }92            }93        }94        valid_json_data_1 = {95            'tests': {96                'test_category': {97                    'test_sub_category': {98                        'test_name': {99                            'test.html': {'expected': 'TEXT', 'actual': 'TEXT', 'is_unexpected': True}100                        }101                    }102                }103            }104        }105        valid_json_data_2 = {106            'tests': {107                'test_category': {108                    'test_sub_category': {109                        'test_name_2': {110                            'test_2.html': {'expected': 'PASS', 'actual': 'TEXT', 'is_unexpected': True}111                        },112                        'test_name': {113                            'test.html': {'expected': 'TEXT', 'actual': 'TEXT', 'is_unexpected': True}114                        }115                    }116                }117            }118        }119        expected_result = {120            'tests': {121                'test_category': {122                    'test_sub_category': {123                        'test_name_2': {124                            'test_2.html': {'archived_results': ['TEXT', 'TEXT']}125                        }126                    }127                }128            }129        }130        process_json_data = ProcessJsonData(valid_json_data, [valid_json_data_1], [valid_json_data_2])131        actual_result = process_json_data.generate_archived_result()132        self.assertEqual(expected_result, actual_result)133    def test_null_check(self):134        valid_json_data = {135            'tests': {136                'test_category': {137                    'test_sub_category': {138                        'test_name': {139                            'test.html': {'expected': 'PASS', 'actual': 'TEXT', 'is_unexpected': True}140                        }141                    }142                }143            }144        }145        expected_result = {146            'tests': {147                'test_category': {148                    'test_sub_category': {149                        'test_name': {150                            'test.html': {'archived_results': ['TEXT']}}151                    }152                }153            }154        }155        process_json_data = ProcessJsonData(valid_json_data, [], [])156        actual_result = process_json_data.generate_archived_result()...

test_category.py

Source:test_category.py

1import unittest2from faker import Faker3from FlaskProject import create_app, db, TestCategory, FlaskProjectLogException4from FlaskProject.general import Status5from FlaskProject.controllers.test_category_controller.controller import \6    TestCategoryController7class TestTestCategoryController(unittest.TestCase):8    """9    This class is for testing TestCategoryController10    """11    def setUp(self):12        """13        Method for setUp TestCategoryController test module14        :return: OK or ERROR15        """16        self.app = create_app('development')17        self.app.testing = True18        self.client = self.app.test_client()19        self.ctx = self.app.app_context()20        self.ctx.push()21        self.faker = Faker()22        self.test_category_controller = TestCategoryController(23            test_category=TestCategory(24                name=self.faker.name()25            ))26    def test_create(self):27        """28        Method for testing creation of categories29        :return: OK or ERROR30        """31        status_insert = self.test_category_controller.create()32        self.assertEqual(33            Status.status_successfully_inserted().__dict__, status_insert)34        self.assertIsNotNone(self.test_category_controller.test_category.id)35    def test_create_already_exist(self):36        """37        Method for testing creation of categories when name is already taken38        :return: OK or ERROR39        """40        self.test_category_controller.create()41        with self.assertRaises(FlaskProjectLogException):42            self.test_category_controller.create()43    def test_alter(self):44        """45        Method for testing categories updates46        :return: OK or ERROR47        """48        self.test_category_controller.create()49        name = self.faker.name()50        self.test_category_controller.test_category.name = name51        result = self.test_category_controller.alter()52        self.assertEqual(Status.status_update_success().__dict__,53                         result)54        self.assertEqual(name, self.test_category_controller.test_category.name)55    def test_inactivate(self):56        """57        Method for testing categories inactivate58        :return: OK OR ERROR59        """60        self.test_category_controller.create()61        result = self.test_category_controller.inactivate()62        self.assertEqual(Status.status_successfully_processed().__dict__,63                         result)64        self.assertEqual(TestCategory.STATUSES['inactive'],65                         self.test_category_controller.test_category.status)66    def test_activate(self):67        """68        Method for testing categories activate69        :return: OK OR ERROR70        """71        self.test_category_controller.create()72        result = self.test_category_controller.activate()73        self.assertEqual(Status.status_successfully_processed().__dict__,74                         result)75        self.assertEqual(TestCategory.STATUSES['active'],76                         self.test_category_controller.test_category.status)77    def test_get_one(self):78        """79        Method for testing get one category by identifier80        :return: OK OR ERROR81        """82        self.test_category_controller.create()83        result = TestCategoryController.get_one(84            self.test_category_controller.test_category.id)85        self.assertEqual(str(self.test_category_controller.test_category.id),86                         str(result.test_category.id))87    def test_list_autocomplete(self):88        """89        Method for testing categories autocomplete90        :return: OK OR ERROR91        """92        self.test_category_controller.create()93        result = TestCategoryController.list_autocomplete(94            self.test_category_controller.test_category.name)95        self.assertEqual(list, type(result))96        self.assertIn(str(self.test_category_controller.test_category.id),97                      [d['id'] for d in result])98    def test_get_list_pagination(self):99        """"100        Method for testing get list of all categories with pagination101        :return: OK or Error102        """103        self.test_category_controller.create()104        result = TestCategoryController.get_list_pagination(105            start=0, limit=10,106            name=self.test_category_controller.test_category.name)107        self.assertEqual(108            Status.status_successfully_processed().__dict__, result['status'])109        self.assertEqual(110            int, type(result['total']))111        self.assertEqual(112            list, type(result['data']))113        self.assertIn(str(self.test_category_controller.test_category.id),114                      [d['id'] for d in result['data']])115    def tearDown(self):116        """117        Method for clean up data118        :return: OK or ERROR119        """120        TestCategory.query.filter(121            TestCategory.id ==122            self.test_category_controller.test_category.id).delete()123        db.session.commit()124if __name__ == '__main__':...

Q10SVNNB.py

Source:Q10SVNNB.py

1# -*- coding: utf-8 -*-2"""3Created on Sun Mar 11 18:30:09 20184@author: chaoran5"""6from sklearn.datasets import fetch_20newsgroups7from sklearn.feature_extraction.text import CountVectorizer8from sklearn.feature_extraction.text import TfidfTransformer9from sklearn.model_selection import train_test_split10from sklearn.naive_bayes import MultinomialNB11from sklearn.svm import LinearSVC12from sklearn.svm import SVC13from sklearn.linear_model import SGDClassifier14from sklearn.pipeline import Pipeline15from sklearn import metrics16import numpy as np17# NaiveBayes algorithm18def NB(categories):19    #get the data from 4 folder20    all_four = fetch_20newsgroups(subset='all',categories=categories, shuffle=True, random_state=42)21    #split the data to training data and test data    22    train_data, test_data, train_category, test_category = train_test_split(all_four.data, all_four.target, test_size = 0.3)   23    #unigram24    #pipeline for text feature extraction and evaluation    25    #tokenizer => transformer => MultinomialNB classifier26    text_clf = Pipeline([('vect', CountVectorizer( analyzer = 'word',ngram_range = (1,1), stop_words='english')),('tfidf', TfidfTransformer()),('clf', MultinomialNB(alpha=.01))])27    text_clf = text_clf.fit(train_data, train_category)28    #evaluate on test set29    predicted = text_clf.predict(test_data)30    print("************************* Naive Bayes Model *************************")31    print("Newsgroup Categories : ", categories )32    print("Unigram Accuracy : {}% \n".format(np.mean(predicted == test_category)*100))33    print(metrics.classification_report(test_category, predicted, target_names=categories))34    print("Unigram Confusion Matrix : \n", metrics.confusion_matrix(test_category, predicted))35    print("\n")36    #bigram37    #pipeline for text feature extraction and evaluation    38    #tokenizer => transformer => MultinomialNB classifier    39    bigram_clf = Pipeline([('vect', CountVectorizer( analyzer = 'word',ngram_range = (2,2), stop_words='english')),('tfidf', TfidfTransformer()),('clf', MultinomialNB(alpha=.01))])40    bigram_clf = bigram_clf.fit(train_data, train_category) 41    #evaluate on test set    42    predicted = bigram_clf.predict(test_data)43    print("Bigram Accuracy : {}% \n".format(np.mean(predicted == test_category)*100))44    print(metrics.classification_report(test_category, predicted, target_names=categories))45    print("Bigram Confusion Matrix : \n", metrics.confusion_matrix(test_category, predicted)) 46    47    48# SVM algorithm49def SVM(categories):50    #get the data from 4 folder51    all_four = fetch_20newsgroups(subset='all',categories=categories, shuffle=True, random_state=42)52    #split the data to training data and test data     53    train_data, test_data, train_category, test_category = train_test_split(all_four.data, all_four.target, test_size = 0.3)54    #unigram55    #pipeline for text feature extraction and evaluation    56    #tokenizer => transformer => MultinomialNB classifier57    #LinearSVC()58    unigram_clf = Pipeline([('vect', CountVectorizer( analyzer = 'word',ngram_range = (1,1), stop_words='english')),('tfidf', TfidfTransformer()),('clf', LinearSVC())])59    unigram_clf = unigram_clf.fit(train_data, train_category) 60    #evaluate on test set    61    predicted = unigram_clf.predict(test_data)62    print("************************* SVM Model *************************")63    print("Newsgroup Categories : ", categories )64    print("Unigram Accuracy : {}% \n".format(np.mean(predicted == test_category)*100))65    print(metrics.classification_report(test_category, predicted, target_names=categories))66    print("Unigram Confusion Matrix : \n", metrics.confusion_matrix(test_category, predicted))67    print("\n")68    #bigram   69    #pipeline for text feature extraction and evaluation    70    #tokenizer => transformer => MultinomialNB classifier71    #SGDClassifier(loss='hinge')72    bigram_clf = Pipeline([('vect', CountVectorizer( analyzer = 'word',ngram_range = (2,2), stop_words='english')),('tfidf', TfidfTransformer()),('clf', LinearSVC())])73    bigram_clf = bigram_clf.fit(train_data, train_category) 74    #evaluate on test set    75    predicted = bigram_clf.predict(test_data)76    print("Bigram Accuracy : {}% \n".format(np.mean(predicted == test_category)*100))77    print(metrics.classification_report(test_category, predicted, target_names=categories))78    print("Bigram Confusion Matrix : \n", metrics.confusion_matrix(test_category, predicted))    79    80# selected categories81categories = ['alt.atheism','talk.religion.misc','comp.graphics','sci.space']82NB(categories)...

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