def backup_results(results_path, results, file_name):
"""
Backs up the test results to a PKL file
Parameters
----------
results_path - path to backup file
results - the results object
file_name - name of back up file.
-------
"""
io.save_results_to_pkl(results_path, results, file_name)
if __name__ == '__main__':
benchmark_timestamp = df.get_date()
results_path = RESULTS_DIRECTORY + BENCHMARK_DIRECTORY + benchmark_timestamp + "/"
functions_under_test = get_functions_under_test()
items_pro_dimension = [1000, 2000, 3000, 4000, 5000]
number_of_timings_pro_function_and_matrix_dimension = 5
results = run_performance_test(items_pro_dimension, number_of_timings_pro_function_and_matrix_dimension,
functions_under_test)
backup_results(results_path, results, 'dense_dot_sparse')
timings = get_timings_from_results(results)
functions_ranked_by_time = rank_functions_by_performance(timings)
functions_labels = create_functions_aliases()
table_data = tf.TableData(functions_labels, items_pro_dimension, functions_ranked_by_time, results, timings)
ranked_times = [ranked_label for time, ranked_label in functions_ranked_by_time]
def test_get_date(self):
date = datetime.datetime.now().strftime("%y-%m-%d-%H-%M")
self.assertEquals(date[:-2], df.get_date()[:-2])
def create_functions_aliases():
"""
Creates aliases to the function names in order to display them in the plots.
Returns a dictionary with the function names as keys and aliases as values.
-------
"""
return {'dot_numpy':'Numpy x Numpy (Referenz)', 'scipy_csc_dot_numpy_with_swap':'Compressed Sparse Column x Numpy',
'scipy_csr_dot_numpy_with_swap':'Compressed Sparse Row x Numpy',
'scipy_bsr_dot_numpy_with_swap':'Block Sparse Row x Numpy'}
if __name__ == '__main__':
benchmark_timestamp = df.get_date()
results_path = RESULTS_DIRECTORY + BENCHMARK_DIRECTORY + benchmark_timestamp + "/"
functions_under_test = get_functions_under_test()
items_pro_dimension = [500, 1000, 2000, 3000, 4000, 5000]
number_of_timings_pro_function_and_matrix_dimension = 5
results = dds.run_performance_test(items_pro_dimension, number_of_timings_pro_function_and_matrix_dimension,
functions_under_test)
dds.backup_results(results_path, results, FILENAME)
timings = dds.get_timings_from_results(results)
functions_ranked_by_time = dds.rank_functions_by_performance(timings)
functions_labels = create_functions_aliases()
table_data = tf.TableData(functions_labels, items_pro_dimension, functions_ranked_by_time, results, timings)
ranked_times = [ranked_label for time, ranked_label in functions_ranked_by_time]
def test_get_date(self):
date = datetime.datetime.now().strftime("%y-%m-%d-%H-%M")
self.assertEquals(date[:-2], df.get_date()[:-2])