def test_converting_to_factors():
test_data = DataFrame(
{
'colA': Series(randn(1, 5000).flatten() > 0),
'colB': Series(100 * randn(1, 5000).flatten()),
'colC': Series(100 + randn(1, 5000).flatten()),
'colD': Series(randn(1, 5000).flatten() > 0),
},
)
test_data['colA'] = test_data['colA'].map(str)
test_data['colD'] = test_data['colD'].map(str)
factor_cols = [('colA', 'True'),
('colD', 'True')]
rpy_test_df = com.convert_to_r_dataframe(test_data)
rpy_out_df = Rtools.convert_columns_to_factors(rpy_test_df, factor_cols)
test_cols = [('colA', 'factor'),
('colB', 'numeric'),
('colC', 'numeric'),
('colD', 'factor')]
for col, typ in test_cols:
if typ == 'factor':
yield eq_, rpy_out_df.rx2(col).nlevels, 2
elif typ == 'numeric':
yield ok_, (not hasattr(rpy_out_df.rx2(col), 'nlevels'))
def test_mixed_model():
test_data = DataFrame(
{
'colA': Series(randn(1, 5000).flatten() > 0),
'colB': Series(100 * randn(1, 5000).flatten()),
'colC': Series(100 + randn(1, 5000).flatten()),
'colD': Series(randn(1, 5000).flatten() > 0),
},
)
test_data['colA'] = test_data['colA'].map(str)
test_data['colD'] = test_data['colD'].map(str)
factor_cols = [('colA', 'True'),
('colD', 'True')]
rpy_test_df = com.convert_to_r_dataframe(test_data)
rpy_test_df = Rtools.convert_columns_to_factors(rpy_test_df, factor_cols)
base_formula = Formula('colC ~ as.factor(colA) + colB')
rand_formula = Formula('~1|colD')
results = Rtools.R_linear_mixed_effects_model(rpy_test_df, base_formula, rand_formula)
print results['tTable']
ok_(('tTable' in results), 'Did not have the tTable in the results')
ok_(('as.factor(colA)False' in results['tTable'].index), 'Did not have the factor in the tTable')
ok_(('colB' in results['tTable'].index), 'Did not have the variable in the tTable')
def test_quantile_normalize():
test_data = DataFrame(
{
'colA': Series(randn(1, 5000).flatten()),
'colB': Series(100 * randn(1, 5000).flatten()),
'colC': Series(100 + randn(1, 5000).flatten()),
},
)
test_data.index = Index(map(str, range(5000)))
normed_data = Rtools.quantile_norm_with_R(test_data)
yield ok_, (normed_data.index == test_data.index).all()
yield ok_, (normed_data.columns == test_data.columns).all()
means = normed_data.mean(axis=0)
for a, b in product(means.values, repeat=2):
yield ok_, abs(a - b) < 0.01
