def test_apply():
df = pd.DataFrame(index=pd.date_range(start='2000-01-01',
end='2000-12-31',
freq='D'),
data={
'ds0': np.repeat(0, 366),
'ds1': np.repeat(1, 366)
})
bias_matrix_old = df_metrics.pairwise_apply(df, bias)
bias_matrix_new = df_metrics.nwise_apply(df, bias, n=2, as_df=True)
assert bias_matrix_old.equals(bias_matrix_new)
# check if dict implementation and matrix implementation have same result
bias_new = df_metrics.nwise_apply(df, bias, n=2, as_df=False)
for i, v in bias_new.items():
assert bias_matrix_new.loc[i] == v
def test_apply():
df = pd.DataFrame(index=pd.date_range(start='2000-01-01',
end='2000-12-31',
freq='D'),
data={
'ds0': np.repeat(0, 366),
'ds1': np.repeat(1, 366)
})
df.loc[df.index[np.random.choice(range(366), 10)], 'ds0'] = np.nan
df.loc[df.index[np.random.choice(range(366), 10)], 'ds1'] = np.nan
with pytest.deprecated_call():
bias_matrix_old = df_metrics.pairwise_apply(df, bias)
bias_matrix_new = df_metrics.nwise_apply(df, bias, n=2, as_df=True)
r_matrix_new = df_metrics.nwise_apply(df, stats.pearsonr, n=2, as_df=True)
assert bias_matrix_old.equals(bias_matrix_new)
# check if dict implementation and matrix implementation have same result
bias_new = df_metrics.nwise_apply(df, bias, n=2, as_df=False)
for i, v in bias_new.items():
assert bias_matrix_new.loc[i] == v
def df_var_ratio(df):
return df_metrics._to_namedtuple(df_metrics.pairwise_apply(df, var_ratio),
'var_ratio')
def df_std_ratio(df):
return df_metrics._to_namedtuple(df_metrics.pairwise_apply(df, std_ratio), 'std_ratio')
def df_var_ratio(df):
return df_metrics._to_namedtuple(df_metrics.pairwise_apply(df, var_ratio), 'var_ratio')