import pandas as pd from unittest2 import TestCase # or `from unittest import ...` if on Python 3.4+ import category_encoders.tests.helpers as th import numpy as np import warnings import category_encoders as encoders np_X = th.create_array(n_rows=100) np_X_t = th.create_array(n_rows=50, extras=True) np_y = np.random.randn(np_X.shape[0]) > 0.5 np_y_t = np.random.randn(np_X_t.shape[0]) > 0.5 X = th.create_dataset(n_rows=100) X_t = th.create_dataset(n_rows=50, extras=True) y = pd.DataFrame(np_y) y_t = pd.DataFrame(np_y_t) class TestOrdinalEncoder(TestCase): def test_ordinal(self): enc = encoders.OrdinalEncoder(verbose=1, return_df=True) enc.fit(X) out = enc.transform(X_t) self.assertEqual(len(set(out['extra'].values)), 4) self.assertIn(-1, set(out['extra'].values)) self.assertFalse(enc.mapping is None) self.assertTrue(len(enc.mapping) > 0) enc = encoders.OrdinalEncoder(verbose=1, mapping=enc.mapping, return_df=True)
# sampling rate of cpu utilization, smaller for more accurate cpu_sampling_rate = 0.2 # loop times of benchmarking in every encoding, larger for more accurate but longer benchmarking time benchmark_repeat = 3 # sample num of data data_lines = 10000 # benchmarking result format result_cols = ['encoder', 'used_processes', 'X_shape', 'min_time(s)', 'average_time(s)', 'max_cpu_utilization(%)', 'average_cpu_utilization(%)'] results = [] cpu_utilization = multiprocessing.Manager().Queue() # define data_set np_X = th.create_array(n_rows=data_lines) np_y = np.random.randn(np_X.shape[0]) > 0.5 X = th.create_dataset(n_rows=data_lines) X_t = th.create_dataset(n_rows=int(data_lines / 2), extras=True) cols = ['unique_str', 'underscore', 'extra', 'none', 'invariant', 321, 'categorical', 'na_categorical'] def get_cpu_utilization(): """ new process for recording cpu utilization record cpu utilization every [cpu_sampling_rate] second & calculate its mean value the value is the cpu utilization during every encoding """ global cpu_utilization psutil.cpu_percent(None)