def test_summary_w_report_output(self, monkeypatch, inference_input,
summary_report_filename):
inferences_df = pd.DataFrame(inference_input)
causal = CausalImpact()
params = {'alpha': 0.05, 'post_period': [2, 4]}
causal.params = params
causal.inferences = inferences_df
dedent_mock = mock.Mock()
expected = open(summary_report_filename).read()
expected = re.sub(r'\s+', ' ', expected)
expected = expected.strip()
tmpdir = mkdtemp()
tmp_file = os.path.join(tmpdir, 'summary_test')
def dedent_side_effect(msg):
with open(tmp_file, 'a') as file_obj:
msg = re.sub(r'\s+', ' ', msg)
msg = msg.strip()
file_obj.write(msg)
return msg
dedent_mock.side_effect = dedent_side_effect
monkeypatch.setattr('textwrap.dedent', dedent_mock)
causal.summary(output='report')
result_str = open(tmp_file, 'r').read()
assert result_str == expected
def test_summary_wrong_argument_raises(self, inference_input):
inferences_df = pd.DataFrame(inference_input)
causal = CausalImpact()
params = {'alpha': 0.05, 'post_period': [2, 4]}
causal.params = params
causal.inferences = inferences_df
with pytest.raises(ValueError):
causal.summary(output='wrong_argument')
def test_summary(self, inference_input):
inferences_df = pd.DataFrame(inference_input)
causal = CausalImpact()
params = {'alpha': 0.05, 'post_period': [2, 4]}
causal.params = params
causal.inferences = inferences_df
expected = [
[3, 7],
[3, 7],
[[3, 3], [7, 7]],
[' ', ' '],
[0, 0],
[[0, 0], [0, 0]],
[' ', ' '],
['-2.8%', '-2.8%'],
[['0.0%', '-11.1%'], ['0.0%', '-11.1%']],
[' ', ' '],
['0.0%', ' '],
['100.0%', ' '],
]
expected = pd.DataFrame(expected,
columns=['Average', 'Cumulative'],
index=[
'Actual', 'Predicted', '95% CI', ' ',
'Absolute Effect', '95% CI', ' ',
'Relative Effect', '95% CI', " ",
"P-value", "Prob. of Causal Effect"
])
tmpdir = mkdtemp()
tmp_expected = 'tmp_expected'
tmp_result = 'tmp_test_summary'
result_file = os.path.join(tmpdir, tmp_result)
expected_file = os.path.join(tmpdir, tmp_expected)
expected.to_csv(expected_file)
expected_str = open(expected_file).read()
causal.summary(path=result_file)
result = open(result_file).read()
assert result == expected_str
def test_plot(self, monkeypatch):
causal = CausalImpact()
params = {'alpha': 0.05, 'post_period': [2, 4], 'pre_period': [0, 1]}
inferences_mock = {
'point_pred': 'points predicted',
'response': 'y obs',
'point_pred_lower': 'lower predictions',
'point_pred_upper': 'upper predictions'
}
class Inferences(object):
@property
def iloc(self):
class Iloc(object):
def __getitem__(*args, **kwargs):
class EnhancedDict(dict):
@property
def index(self):
return [0, 1]
@property
def point_effect(self):
return 'lift'
@property
def point_effect_lower(self):
return 'point effect lower'
@property
def point_effect_upper(self):
return 'point effect upper'
@property
def cum_effect(self):
return 'cum effect'
@property
def cum_effect_upper(self):
return 'cum effect upper'
@property
def cum_effect_lower(self):
return 'cum effect lower'
return EnhancedDict(inferences_mock)
return Iloc()
class Data(object):
@property
def index(self):
return 'index'
@property
def shape(self):
return [(1, 2)]
plot_mock = mock.Mock()
fill_mock = mock.Mock()
show_mock = mock.Mock()
np_zeros_mock = mock.Mock()
np_zeros_mock.side_effect = lambda x: [0, 0]
get_lib_mock = mock.Mock(return_value=plot_mock)
monkeypatch.setattr('causalimpact.analysis.get_matplotlib',
get_lib_mock)
monkeypatch.setattr('numpy.zeros', np_zeros_mock)
causal.params = params
causal.inferences = Inferences()
causal.data = Data()
causal.plot(panels=['original', 'pointwise', 'cumulative'])
causal.plot(panels=['pointwise', 'cumulative'])
causal.plot(panels=['original'])
plot_mock.plot.assert_any_call('y obs',
'k',
label='endog',
linewidth=2)
plot_mock.plot.assert_any_call('points predicted',
'r--',
label='model',
linewidth=2)
plot_mock.fill_between.assert_any_call([0, 1],
'lower predictions',
'upper predictions',
facecolor='gray',
interpolate=True,
alpha=0.25)
causal.plot(panels=['pointwise'])
plot_mock.plot.assert_any_call('lift', 'r--', linewidth=2)
plot_mock.plot.assert_any_call('index', [0, 0], 'g-', linewidth=2)
causal.plot(panels=['cumulative'])
plot_mock.plot.assert_any_call([0, 1],
'cum effect',
'r--',
linewidth=2)
plot_mock.plot.assert_any_call('index', [0, 0], 'g-', linewidth=2)
