text stringlengths 1 93.6k |
|---|
print("Failure: File cannot be read.")
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def test_csv_output(self):
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""" Verify that csv output file contains the expected elements"""
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try:
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mock_country = "MacedoniaMock"
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mock_data = [
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"1 : Skopje Panorama, Skopje",
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"2 : Chic & Cozy Central Loft, Skopje",
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"3 : Villa Dislievski, Ohrid",
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"4 : Velestovo House, Ohrid",
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"5 : Ibis Skopje City Center, Skopje",
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"6 : Villa Tabana, Ohrid",
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"7 : Villa Mishe, Ohrid",
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"8 : Navona 58, Skopje",
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"9 : Hotel Monako & Fish Restaurant Skopje, Skopje",
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"10 : Villa Toneli, Skopje",
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]
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b.save_data(mock_data, out_format="csv", country=mock_country)
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assert os.path.isfile("hotels-in-{}.csv".format(mock_country))
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with open("hotels-in-{}.csv".format(mock_country)) as csv_file:
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csv_reader = csv.reader(csv_file, delimiter=",")
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line_count = 0
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for row in csv_reader:
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name = mock_data[line_count].split(", ")[0].split(": ")[1]
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city = mock_data[line_count].split(", ")[1]
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# Assert that the first element in the output row is the item number
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self.assertEqual(str(line_count + 1), row[0].strip())
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# Assert that the second element in the output row is the hotel name
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self.assertEqual(name, row[1].strip())
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# Assert that the third element in the output row is the city
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self.assertEqual(city, row[2].strip())
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line_count += 1
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# Assert that the number of output items is equal to the number of input items.
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self.assertEqual(len(mock_data), line_count)
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except IOError:
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print("Failure: File cannot be read.")
|
if __name__ == "__main__":
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unittest.main()
|
# <FILESEP>
|
import argparse
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import os
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import pickle
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from multiprocessing import Process, Manager
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import numpy as np
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from torchdistill.common.file_util import get_file_path_list
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from eq.conversion import sympy2sequence, sympy2zss_module
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from eq.eval import compute_distances
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def get_argparser():
|
parser = argparse.ArgumentParser(description='Dataset comparator')
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parser.add_argument('--src_eq', required=True, help='source equation dir path')
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parser.add_argument('--src_tabular', required=True, help='source tabular data dir path')
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parser.add_argument('--src_delim', default=' ', help='delimiter in src data')
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parser.add_argument('--dst_eq', required=True, help='destination equation dir path')
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parser.add_argument('--dst_tabular', required=True, help='destination tabular data dir path')
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parser.add_argument('--dst_delim', default=' ', help='destination in src data')
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parser.add_argument('--mode', default='intersection', help='comparison mode')
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return parser
|
def load_sympy_equation(file_path):
|
with open(file_path, 'rb') as fp:
|
eq_sympy = pickle.load(fp)
|
return eq_sympy.evalf()
|
def compute_stats(eqs, message):
|
print(f'\n{message}')
|
seq_length_list = list()
|
for eq in eqs:
|
eq_tree_sequence = sympy2sequence(eq, returns_binary_tree=True)
|
seq_length_list.append(len(eq_tree_sequence))
|
seq_lengths = np.array(seq_length_list)
|
print(f'{len(eqs)} equations')
|
print('Sequence length')
|
print(f'Min: {seq_lengths.min()}')
|
print(f'Max: {seq_lengths.max()}')
|
print(f'Mean: {seq_lengths.mean()}')
|
print(f'Median: {np.median(seq_lengths)}')
|
def load_tabular_dataset(file_path, delimiter):
|
mat = np.loadtxt(file_path, delimiter=delimiter)
|
return mat
|
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