def test_ttest_one_target(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target = columns[0])
assert(isinstance(result, pandas.core.series.Series))
assert(len(result) == (len(columns)-1))
def test_ttest_one_target(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target=columns[0])
assert (isinstance(result, pandas.core.series.Series))
assert (len(result) == (len(columns) - 1))
def test_ttest_multiple_target(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target = [columns[0],columns[1]])
assert(isinstance(result, pandas.core.frame.DataFrame))
assert(len(result.columns) == 2)
assert(len(result.index) == len(columns))
def test_ttest_multiple_target(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target=[columns[0], columns[1]])
assert (isinstance(result, pandas.core.frame.DataFrame))
assert (len(result.columns) == 2)
assert (len(result.index) == len(columns))
def test_ttest_default(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, features = columns)
assert(isinstance(result, pandas.core.frame.DataFrame))
assert(len(result.columns) == len(columns))
assert(len(result.index) == len(columns))
result = result.fillna(0) # np.nan values are not equal when compared
result = result[result.index]
assert(all(result != result.T)) # asymmetry
def test_ttest_default(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, features=columns)
assert (isinstance(result, pandas.core.frame.DataFrame))
assert (len(result.columns) == len(columns))
assert (len(result.index) == len(columns))
result = result.fillna(
0) # np.nan values are not equal when compared
result = result[result.index]
assert (all(result != result.T)) # asymmetry
def test_ttest_valueError(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] != "NUMERIC"].index)
if len(columns) > 0: # Raise no numerical features
with pytest.raises(ValueError):
ttest(idadf, features = columns)
if len(idadf.columns) > 0:
with pytest.raises(ValueError): # Cannot compute correlation coefficients of only one column (...), need at least 2
ttest(idadf, features = idadf.columns[0])
with pytest.raises(ValueError): # The correlation value of two same columns is always maximal
ttest(idadf, target= idadf.columns[0], features = idadf.columns[0])
def test_ttest_valueError(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] != "NUMERIC"].index)
if len(columns) > 0: # Raise no numerical features
with pytest.raises(ValueError):
ttest(idadf, features=columns)
if len(idadf.columns) > 0:
with pytest.raises(
ValueError
): # Cannot compute correlation coefficients of only one column (...), need at least 2
ttest(idadf, features=idadf.columns[0])
with pytest.raises(
ValueError
): # The correlation value of two same columns is always maximal
ttest(idadf,
target=idadf.columns[0],
features=idadf.columns[0])
def test_ttest_one_target_one_feature(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target = columns[0], features=[columns[1]])
assert(isinstance(result, float))
def test_ttest_TypeError(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] != "NUMERIC"].index)
if len(columns) > 1:
with pytest.raises(TypeError):
ttest(idadf, features = columns)
def test_ttest_one_target_one_feature(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] == "NUMERIC"].index)
if len(columns) > 1:
result = ttest(idadf, target=columns[0], features=[columns[1]])
assert (isinstance(result, float))
def test_ttest_TypeError(self, idadf):
data = idadf._table_def()
columns = list(data.loc[data['VALTYPE'] != "NUMERIC"].index)
if len(columns) > 1:
with pytest.raises(TypeError):
ttest(idadf, features=columns)