How to use test_is_nan method in assertpy

Best Python code snippet using assertpy_python

datareader_wave.py

Source:datareader_wave.py

1import os2import numpy as np3import pandas as pd4from dataframe import DataFrame5class DataReader(object):6    def __init__(self, data_dir):7        data_cols = [8            'data',9            'is_nan',10            'page_id',11            'project',12            'access',13            'agent',14            'test_data',15            'test_is_nan'16        ]17        data = [np.load(os.path.join(data_dir, '{}.npy'.format(i))) for i in data_cols]18        self.test_df = DataFrame(columns=data_cols, data=data)19        self.train_df, self.val_df = self.test_df.train_test_split(train_size=0.95)20    21    def describe(self, logger):22        logger.info('')23        logger.info('Data dimensions:')24        logger.info('train size', len(self.train_df))25        logger.info('val size', len(self.val_df))26        logger.info('test size', len(self.test_df))27        # logger.info('    [[data]] {}'.format(self.data.shape))28        #logger.info('Split seed = {}'.format(self.seed))29        logger.info('')30    def train_batch_generator(self, batch_size):31        return self.batch_generator(32            batch_size=batch_size,33            df=self.train_df,34            shuffle=True,35            num_epochs=10000,36            is_test=False37        )38    def val_batch_generator(self, batch_size):39        return self.batch_generator(40            batch_size=batch_size,41            df=self.val_df,42            shuffle=True,43            num_epochs=10000,44            is_test=False45        )46    def test_batch_generator(self, batch_size):47        return self.batch_generator(48            batch_size=batch_size,49            df=self.test_df,50            shuffle=True,51            num_epochs=1,52            is_test=True53        )54    def batch_generator(self, batch_size, df, shuffle=True, num_epochs=10000, is_test=False):55        batch_gen = df.batch_generator(56            batch_size=batch_size,57            shuffle=shuffle,58            num_epochs=num_epochs,59            allow_smaller_final_batch=is_test60        )61        data_col = 'test_data' if is_test else 'data'62        is_nan_col = 'test_is_nan' if is_test else 'is_nan'63        for batch in batch_gen:64            num_decode_steps = 6465            full_seq_len = batch[data_col].shape[1]66            max_encode_length = full_seq_len - num_decode_steps if not is_test else full_seq_len67            x_encode = np.zeros([len(batch), max_encode_length])68            y_decode = np.zeros([len(batch), num_decode_steps])69            is_nan_encode = np.zeros([len(batch), max_encode_length])70            is_nan_decode = np.zeros([len(batch), num_decode_steps])71            encode_len = np.zeros([len(batch)])72            decode_len = np.zeros([len(batch)])73            for i, (seq, nan_seq) in enumerate(zip(batch[data_col], batch[is_nan_col])):74                rand_len = np.random.randint(max_encode_length - 365 + 1, max_encode_length + 1)75                x_encode_len = max_encode_length if is_test else rand_len76                x_encode[i, :x_encode_len] = seq[:x_encode_len]77                is_nan_encode[i, :x_encode_len] = nan_seq[:x_encode_len]78                encode_len[i] = x_encode_len79                decode_len[i] = num_decode_steps80                if not is_test:81                    y_decode[i, :] = seq[x_encode_len: x_encode_len + num_decode_steps]82                    is_nan_decode[i, :] = nan_seq[x_encode_len: x_encode_len + num_decode_steps]83            batch['x_encode'] = x_encode84            batch['encode_len'] = encode_len85            batch['y_decode'] = y_decode86            batch['decode_len'] = decode_len87            batch['is_nan_encode'] = is_nan_encode88            batch['is_nan_decode'] = is_nan_decode...

my_mod_test.py

Source:my_mod_test.py

...7class TestMyMod(unittest.TestCase):8    def test_add_col(self):9        df.add_col('Name', ['Connecticut', 'Colorado', 'California', 'Texas'])10        self.assertTrue('Name'in df.df.columns.tolist())11    def test_is_nan(self):12        self.assertTrue(isinstance(df.df.isna().sum(), pd.Series))13    def test_split_df(self):14        train, test = df.split_df()15        self.assertTrue(type(train), pd.DataFrame)16        self.assertTrue(type(test), pd.DataFrame)17if __name__ == '__main__':...

test_core.py

Source:test_core.py

1import numpy as np2import unittest3from py_ebay import is_nan4class TestCore(unittest.TestCase):5    def test_is_nan(self):6        assert(is_nan(np.NAN))7        assert(not is_nan(1))8        assert(not is_nan('1'))...

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