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adds more tests

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This commit is contained in:
Andy Eschbacher 2018-01-11 15:23:35 -05:00
parent 7eeb7faf13
commit 6fd3fbcbb7
1 changed files with 12 additions and 13 deletions
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25
src/py/crankshaft/test/test_segmentation.py
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@ -7,7 +7,8 @@ import numpy as np
from crankshaft.analysis_data_provider import AnalysisDataProvider
from crankshaft.segmentation import Segmentation
from .fixtures import fixture_file
from helper import fixture_file
from mock_plpy import MockCursor
class RawDataProvider(AnalysisDataProvider):
@ -62,23 +63,18 @@ class SegmentationTest(unittest.TestCase):
def test_replace_nan_with_mean(self):
"""test segmentation.test_replace_nan_with_mean"""
from crankshaft.segmentation import replace_nan_with_mean
# from numpy.testing import assert_array_equal
test_array = np.array([1.2, np.nan, 3.2, np.nan, np.nan])
result = replace_nan_with_mean(test_array, means=None)[0]
expectation = np.array([1.2, 2.2, 3.2, 2.2, 2.2], dtype=float)
self.assertItemsEqual(result, expectation)
# assert_array_equal(result, expectation)
def test_create_and_predict_segment(self):
"""test segmentation.test_create_and_predict"""
from crankshaft.segmentation import replace_nan_with_mean
batch_size = 1000
results = []
feature_columns = ['m1', 'm2']
target = [d['target'] for d in self.model_data]
feat = np.column_stack([np.array(self.model_data[0][col])
for col in feature_columns]).astype(float)
target_mean = replace_nan_with_mean(target[0])[1]
feature_means = replace_nan_with_mean(feat)[1]
# data_model is of the form:
@ -115,10 +111,10 @@ class SegmentationTest(unittest.TestCase):
data_predict))
result = seg.create_and_predict_segment(
'select * from segmentation_test',
'SELECT * FROM segmentation_test',
'x_value',
['m1', 'm2'],
'select * from segmentation_result',
'SELECT * FROM segmentation_result',
model_parameters,
id_col='cartodb_id')
results = [(row[1], row[2]) for row in result]
@ -127,13 +123,16 @@ class SegmentationTest(unittest.TestCase):
acc_res = [r[1] for r in self.result_seg]
# test values
for ([res_pre, res_acc], [exp_pre, exp_acc]) in zipped_values:
self.assertAlmostEqual(res_pre, exp_pre)
self.assertEqual(res_acc, exp_acc)
for (res_pre, _), (exp_pre, _) in zipped_values:
diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre])
self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff))
diff = abs(res_pre - exp_pre) / np.mean([res_pre, exp_pre])
self.assertTrue(diff <= 0.05, msg='diff: {}'.format(diff))
prediction = [r[0] for r in results]
accuracy = np.sqrt(np.mean(np.square(np.array(prediction) -
np.array(pre_res))))
accuracy = np.sqrt(np.mean(
(np.array(prediction) - np.array(pre_res))**2
))
self.assertEqual(len(results), len(self.result_seg))
self.assertTrue(accuracy < 0.3 * np.mean(pre_res))