def test__trend__computation(self):
"""
Check if trend() functions properly
"""
np.random.seed(0)
metrics, metadata = generate_random_data()
metrics['time_since_treatment'] = metrics['treatment_start_time']
exp = Experiment('B', metrics, metadata, [4, 6])
# Perform sga()
result = exp.trend()
# check uplift
df = result.statistic('trend', 'uplift', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, ('value', 'A')],
np.array([-1.009421, -0.847400, -1.119885, -1.042597, -0.868819,
-1.091165, -0.952307, -1.028234, -0.978774, -0.985696]), decimal=5)
# check pctile
df = result.statistic('trend', 'uplift_pctile', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, ('value', 'A')],
np.array([-1.137482, -0.881360, -0.970678, -0.724122, -1.245795,
-0.993975, -1.178494, -0.906699, -0.993683, -0.743954, -1.225361,
-0.956969, -1.082180, -0.822435, -1.151715, -0.904753, -1.095209,
-0.862340, -1.109407, -0.861985]), decimal=5)
# check samplesize
df = result.statistic('trend', 'sample_size', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, 'value'],
np.column_stack(([649, 595, 600, 590, 625, 602, 607, 608, 616, 616],
[405, 401, 378, 362, 377, 369, 406, 392, 414, 388])), decimal=5)
# check variant_mean
df = result.statistic('trend', 'variant_mean', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, 'value'],
np.column_stack(([0.005761, 0.057487, -0.067107, 0.001125, 0.093085,
-0.067894, -0.030500, -0.060996, 0.016257, -0.006091],
[1.015182, 0.904887, 1.052778, 1.043721, 0.961904, 1.023271,
0.921807, 0.967238, 0.995031, 0.979605])), decimal=5)
def test__trend__index_levels(self):
"""
Check if trend() returns the proper index levels
"""
np.random.seed(0)
metrics, metadata = generate_random_data()
metrics['time_since_treatment'] = metrics['treatment_start_time']
exp = Experiment('B', metrics, metadata, [4, 6])
# Perform sga()
result = exp.trend()
# Check if all index levels are present
index_levels = [
pd.Index([u'normal_same', u'normal_shifted', u'normal_shifted_by_feature', u'normal_unequal_variance'],
dtype='object', name=u'metric'),
pd.Index([u'-'], dtype='object', name=u'subgroup_metric'),
pd.Index([str(x) for x in np.arange(10.)], dtype='object', name=u'time'),
pd.Float64Index([], dtype='float64', name=u'subgroup'),
pd.Index([u'sample_size', u'uplift', u'uplift_pctile', u'variant_mean'], dtype='object', name=u'statistic'),
pd.Float64Index([2.5, 97.5], dtype='float64', name=u'pctile')
]
result_levels = list(result.df.index.levels)
# Check if all index levels match expectation TODO: Make nice
np.testing.assert_array_equal(index_levels[0], result_levels[0])
np.testing.assert_array_equal(index_levels[1], result_levels[1])
np.testing.assert_array_equal(index_levels[2], result_levels[2])
np.testing.assert_array_equal(index_levels[3], result_levels[3])
np.testing.assert_array_equal(index_levels[4], result_levels[4])
np.testing.assert_array_equal(index_levels[5], result_levels[5])
def test__trend__index_levels(self):
"""
Check if trend() returns the proper index levels
"""
np.random.seed(0)
metrics, metadata = generate_random_data()
metrics['time_since_treatment'] = metrics['treatment_start_time']
exp = Experiment('B', metrics, metadata, [4, 6])
# Perform sga()
result = exp.trend()
# Check if all index levels are present
index_levels = [
pd.Index([u'normal_same', u'normal_shifted', u'normal_shifted_by_feature', u'normal_unequal_variance'],
dtype='object', name=u'metric'),
pd.Index([u'-'], dtype='object', name=u'subgroup_metric'),
pd.Index(range(10), dtype='object', name=u'time'),
pd.Float64Index([], dtype='float64', name=u'subgroup'),
pd.Index([u'sample_size', u'uplift', u'uplift_pctile', u'variant_mean'], dtype='object', name=u'statistic'),
pd.Float64Index([2.5, 97.5], dtype='float64', name=u'pctile')
]
result_levels = list(result.df.index.levels)
# Check if all index levels match expectation TODO: Make nice
np.testing.assert_array_equal(index_levels[0], result_levels[0])
np.testing.assert_array_equal(index_levels[1], result_levels[1])
np.testing.assert_array_equal(index_levels[2], result_levels[2])
np.testing.assert_array_equal(index_levels[3], result_levels[3])
np.testing.assert_array_equal(index_levels[4], result_levels[4])
np.testing.assert_array_equal(index_levels[5], result_levels[5])
def test__trend__computation(self):
"""
Check if trend() functions properly
"""
np.random.seed(0)
metrics, metadata = generate_random_data()
metrics['time_since_treatment'] = metrics['treatment_start_time']
exp = Experiment('B', metrics, metadata, [4, 6])
# Perform sga()
result = exp.trend()
# check uplift
df = result.statistic('trend', 'uplift', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, ('value', 'A')],
np.array([
-1.009421, -0.847400, -1.119885,
-1.042597, -0.868819, -1.091165,
-0.952307, -1.028234, -0.978774,
-0.985696
]),
decimal=5)
# check pctile
df = result.statistic('trend', 'uplift_pctile', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, ('value', 'A')],
np.array([
-1.137482, -0.881360, -0.970678, -0.724122, -1.245795,
-0.993975, -1.178494, -0.906699, -0.993683, -0.743954,
-1.225361, -0.956969, -1.082180, -0.822435, -1.151715,
-0.904753, -1.095209, -0.862340, -1.109407, -0.861985
]),
decimal=5)
# check samplesize
df = result.statistic('trend', 'sample_size', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, 'value'],
np.column_stack(
([649, 595, 600, 590, 625, 602, 607, 608, 616,
616], [405, 401, 378, 362, 377, 369, 406, 392, 414, 388])),
decimal=5)
# check variant_mean
df = result.statistic('trend', 'variant_mean', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, 'value'],
np.column_stack(([
0.005761, 0.057487, -0.067107,
0.001125, 0.093085, -0.067894,
-0.030500, -0.060996, 0.016257,
-0.006091
], [
1.015182, 0.904887, 1.052778,
1.043721, 0.961904, 1.023271,
0.921807, 0.967238, 0.995031,
0.979605
])),
decimal=5)
def test__trend__computation(self):
"""
Check if trend() functions properly
"""
np.random.seed(0)
metrics, metadata = generate_random_data()
metrics['time_since_treatment'] = metrics['treatment_start_time']
exp = Experiment('B', metrics, metadata, [4, 6])
# Perform sga() with non-cumulative results
result = exp.trend(cumulative=False)
# check uplift
df = result.statistic('trend', 'uplift', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, ('value', 'A')],
np.array([
-1.009421, -0.847400, -1.119885,
-1.042597, -0.868819, -1.091165,
-0.952307, -1.028234, -0.978774,
-0.985696
]),
decimal=5)
# check pctile
df = result.statistic('trend', 'uplift_pctile', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, ('value', 'A')],
np.array([
-1.137482, -0.881360, -0.970678, -0.724122, -1.245795,
-0.993975, -1.178494, -0.906699, -0.993683, -0.743954,
-1.225361, -0.956969, -1.082180, -0.822435, -1.151715,
-0.904753, -1.095209, -0.862340, -1.109407, -0.861985
]),
decimal=5)
# check samplesize
df = result.statistic('trend', 'sample_size', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, 'value'],
np.column_stack(
([649, 595, 600, 590, 625, 602, 607, 608, 616,
616], [405, 401, 378, 362, 377, 369, 406, 392, 414, 388])),
decimal=5)
# check variant_mean
df = result.statistic('trend', 'variant_mean', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, 'value'],
np.column_stack(([
0.005761, 0.057487, -0.067107,
0.001125, 0.093085, -0.067894,
-0.030500, -0.060996, 0.016257,
-0.006091
], [
1.015182, 0.904887, 1.052778,
1.043721, 0.961904, 1.023271,
0.921807, 0.967238, 0.995031,
0.979605
])),
decimal=5)
# Perform sga() with cumulative results
result = exp.trend()
# check uplift
df = result.statistic('trend', 'uplift', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, ('value', 'A')],
np.array([
-1.009421, -0.929807, -0.991088,
-1.003129, -0.976023, -0.994857,
-0.988167, -0.993119, -0.991571,
-0.990986
]),
decimal=5)
# check pctile
df = result.statistic('trend', 'uplift_pctile', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, ('value', 'A')],
np.array([
-1.137482, -0.881360, -1.018794, -0.840820, -1.063820,
-0.918356, -1.067283, -0.938976, -1.033110, -0.918936,
-1.047413, -0.942302, -1.036888, -0.939446, -1.038455,
-0.947784, -1.033861, -0.949280, -1.031002, -0.950970
]),
decimal=5)
# check samplesize
df = result.statistic('trend', 'sample_size', 'normal_shifted')
np.testing.assert_almost_equal(
df.loc[:, 'value'],
np.column_stack(
([649, 1244, 1844, 2434, 3059, 3661, 4268, 4876, 5492, 6108],
[405, 806, 1184, 1546, 1923, 2292, 2698, 3090, 3504, 3892])),
decimal=5)
# check variant_mean
df = result.statistic('trend', 'variant_mean', 'normal_shifted')
np.testing.assert_almost_equal(df.loc[:, 'value'],
np.column_stack(([
0.005761, 0.030501, -0.001258,
-0.000681, 0.018477, 0.004274,
-0.000671, -0.008193, -0.005451,
-0.005515
], [
1.015182, 0.960308, 0.989830,
1.002449, 0.994500, 0.999132,
0.987496, 0.984926, 0.986120,
0.985470
])),
decimal=5)
# check metadata is preserved
np.testing.assert_equal(
True,
all(item in result.metadata.items()
for item in self.testmetadata.items()))
