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How to use _construct_test method in Testify

Best Python code snippet using Testify_python

test_env_context.py

Source:test_env_context.py

...136                    # __enter__() test137                    if (_test_enter):138                        self.conduct_tests(139                            func=func,140                            tests=_construct_test(_test_enter.get("answer", None)),141                        )142                # __exit__() test143                if (_test_exit):144                    self.conduct_tests(145                        func=func,146                        tests=_construct_test(_test_exit.get("answer", None)),147                    )148    def test_EnvironmentContext(self):149        _old_environment = dict(os.environ)150        _test_envs = {151            "SAMPLE_ENVVAR1":"Testing Temporary System Variable 1",152            "SAMPLE_ENVVAR2":"Testing Temporary System Variable 2",153            "SAMPLE_ENVVAR3":"Testing Temporary System Variable 3",154        }155        _tests_enter = [156            {157                # These are args for the context manager, not the tests158                "args":{159                    # Just update the variables160                    "update":_test_envs,...

dataset.py

Source:dataset.py

...32        df, self.num_users, self.num_items = self._load_data(file_path)33        self.pos_dict = self._construct_pos_dict(df)34        self.train_df, self.test_df = self._split_train_test(df)35        self.train_dict = self._construct_train(self.train_df)36        self.test_dict = self._construct_test(self.test_df)373839    def _load_data(self, file_path):40        df = pd.read_csv(file_path, sep=',', usecols=[0, 1])4142        # constructing index43        uiterator = count(0)44        udict = defaultdict(lambda: next(uiterator))45        [udict[user] for user in sorted(df['reviewerID'].tolist())]46        iiterator = count(0)47        idict = defaultdict(lambda: next(iiterator))48        [idict[item] for item in sorted(df['asin'].tolist())]4950        self.udict = udict51        self.idict = idict5253        df['uidx'] = df['reviewerID'].map(lambda x: udict[x])54        df['iidx'] = df['asin'].map(lambda x: idict[x])55        del df['reviewerID'], df['asin']56        print('Load %s data successfully with %d users, %d products and %d interactions.'57              %(self.name, len(udict), len(idict), df.shape[0]))5859        return df, len(udict), len(idict)606162    def _construct_pos_dict(self, df):63        # we can't build a negative dictionary cause it'll cost huge memory64        pos_dict = defaultdict(set)65        for user, item in zip(df['uidx'], df['iidx']):66            pos_dict[user].add(item)6768        return pos_dict697071    def _split_train_test(self, df):72        test_list = []73        print('Spliting data of train and test...')74        with Pool(self.args.processor_num) as pool:75            nargs = [(user, df, self.args.test_size) for user in range(self.num_users)]76            test_list = pool.map(self._split, nargs)7778        test_df = pd.concat(test_list)79        train_df = df.drop(test_df.index)8081        train_df = train_df.reset_index(drop=True)82        test_df = test_df.reset_index(drop=True)8384        train_df.to_csv(self.path+'/%s_train_df.csv'% self.name, index=False)85        test_df.to_csv(self.path+'/%s_test_df.csv'% self.name, index=False)8687        return train_df, test_df888990    def _construct_train(self, df):91        # It's desperate to use df to calculate... so slow!!!92        print('Adding negative data to train_df...')93        users = []94        items = []95        labels = []96        with Pool(self.args.processor_num) as pool:97            nargs = [(user, item, self.num_items, self.pos_dict, self.args.train_neg_num, True)98                     for user, item in zip(df['uidx'], df['iidx'])]99            res_list = pool.map(self._add_negtive, nargs)100101        for (batch_users, batch_items, batch_labels) in res_list:102            users += batch_users103            items += batch_items104            labels += batch_labels105106        data_dict = {'user': users, 'item': items, 'label': labels}107        np.save(self.train_npy_path, data_dict)108109        return data_dict110111112    def _construct_test(self, df):113        print('Adding negative data to test_df...')114        users = []115        items = []116        labels = []117118        with Pool(self.args.processor_num) as pool:119            nargs = [(user, item, self.num_items, self.pos_dict, self.args.test_neg_num, False)120                     for user, item in zip(df['uidx'], df['iidx'])]121            res_list = pool.map(self._add_negtive, nargs)122123        for batch_users, batch_items, batch_labels in res_list:124            users += batch_users125            items += batch_items126            labels += batch_labels
...

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