def it_gathers_the_numeric_values_to_help(self, rows_dimension_,
_has_numeric_value_prop_):
rows_dimension_.numeric_values = (1, np.nan, 3)
_has_numeric_value_prop_.return_value = np.array([True, False, True])
scaled_counts = _ScaledCounts(rows_dimension_, None, None)
assert scaled_counts._numeric_values.tolist() == [1, 3]
def it_computes_the_total_scaled_count_to_help(self,
_weighted_counts_prop_,
_numeric_values_prop_):
_weighted_counts_prop_.return_value = np.array([10, 20, 30, 40])
_numeric_values_prop_.return_value = np.array([1, 2, 3, 4])
scaled_counts = _ScaledCounts(None, None, None)
# --- 10 + 40 + 90 + 160 = 300 ---
assert scaled_counts._total_scaled_count == 300
def it_knows_the_scale_stddev(
self,
_numeric_values_prop_,
numeric_values,
_scale_variance_prop_,
scale_variance,
expected_value,
):
_numeric_values_prop_.return_value = numeric_values
_scale_variance_prop_.return_value = scale_variance
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts.scale_stddev == expected_value
def it_retrives_the_weighted_counts_to_help(self, request,
_has_numeric_value_prop_):
weighted_cube_counts_ = instance_mock(request,
_BaseCubeCounts,
counts=np.array([1.1, 2.2, 3.3]))
property_mock(
request,
_ScaledCounts,
"_weighted_cube_counts",
return_value=weighted_cube_counts_,
)
_has_numeric_value_prop_.return_value = np.array([True, False, True])
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts._weighted_counts == pytest.approx([1.1, 3.3])
def it_knows_the_scale_median(
self,
_numeric_values_prop_,
numeric_values,
_weighted_counts_prop_,
weighted_counts,
_total_weighted_count_prop_,
expected_value,
):
_numeric_values_prop_.return_value = numeric_values
_weighted_counts_prop_.return_value = weighted_counts
_total_weighted_count_prop_.return_value = np.sum(weighted_counts)
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts.scale_median == expected_value
def it_knows_the_scale_mean(
self,
_numeric_values_prop_,
numeric_values,
_total_weighted_count_prop_,
total_weighted_count,
_total_scaled_count_prop_,
expected_value,
):
_numeric_values_prop_.return_value = numeric_values
_total_weighted_count_prop_.return_value = total_weighted_count
_total_scaled_count_prop_.return_value = 400
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts.scale_mean == expected_value
def it_knows_the_scale_stderr(
self,
_numeric_values_prop_,
numeric_values,
_scale_variance_prop_,
scale_variance,
_total_weighted_count_prop_,
total_weighted_count,
expected_value,
):
_numeric_values_prop_.return_value = numeric_values
_scale_variance_prop_.return_value = scale_variance
_total_weighted_count_prop_.return_value = total_weighted_count
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts.scale_stderr == expected_value
def it_computes_the_scale_variance_to_help(
self,
request,
_weighted_counts_prop_,
_numeric_values_prop_,
_total_weighted_count_prop_,
):
_weighted_counts_prop_.return_value = np.array([100, 200, 300])
_numeric_values_prop_.return_value = np.array([1, 2, 3])
property_mock(request,
_ScaledCounts,
"scale_mean",
return_value=2.333333)
_total_weighted_count_prop_.return_value = 600
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts._scale_variance == pytest.approx(0.5555556)
def it_computes_the_total_weighted_count_to_help(self,
_weighted_counts_prop_):
_weighted_counts_prop_.return_value = np.array([10, 20, 30, 40])
scaled_counts = _ScaledCounts(None, None, None)
assert scaled_counts._total_weighted_count == 100
def it_knows_which_elements_have_a_numeric_values_to_help(
self, rows_dimension_):
rows_dimension_.numeric_values = (1, np.nan, 3)
scaled_counts = _ScaledCounts(rows_dimension_, None, None)
assert scaled_counts._has_numeric_value.tolist() == [True, False, True]
