def test_float_classification_target():
# check we can plot even if we do classification with a float target
X, y = make_blobs()
data = pd.DataFrame(X)
data['target'] = y.astype(np.float)
types = detect_types(data)
assert types.categorical['target']
plot_supervised(data, 'target')
# same with "actual float" - we need to specify classification for that :-/
data['target'] = y.astype(np.float) + .2
plot_supervised(data, 'target', type_hints={'target': 'categorical'})
plt.close("all")
def test_type_hints(add, feature_type, target_type):
X = pd.DataFrame(np.random.randint(4, size=100)) + add
X['target'] = np.random.uniform(size=100)
plot_supervised(X,
type_hints={
0: feature_type,
'target': target_type
},
target_col='target')
# get title of figure
text = plt.gcf()._suptitle.get_text()
assert feature_type.capitalize() in text
ax = plt.gca()
# one of the labels is 'target' iif regression
labels = ax.get_ylabel() + ax.get_xlabel()
assert ('target' in labels) == (target_type == 'continuous')
plt.close("all")
def test_plots_smoke(continuous_features, categorical_features, task):
# simple smoke test
# should be parametrized
n_samples = 100
X_cont, y_cont = make_regression(n_samples=n_samples,
n_features=continuous_features,
n_informative=min(continuous_features, 2))
X_cat, y_cat = make_regression(n_samples=n_samples,
n_features=categorical_features,
n_informative=min(categorical_features, 2))
if X_cat.shape[1] > 0:
X_cat = KBinsDiscretizer(encode='ordinal').fit_transform(X_cat)
cont_columns = ["asdf_%d_cont" % i for i in range(continuous_features)]
df_cont = pd.DataFrame(X_cont, columns=cont_columns)
if categorical_features > 0:
cat_columns = ["asdf_%d_cat" % i for i in range(categorical_features)]
df_cat = pd.DataFrame(X_cat, columns=cat_columns).astype('int')
df_cat = df_cat.astype("category")
X_df = pd.concat([df_cont, df_cat], axis=1)
else:
X_df = df_cont
assert (X_df.shape[1] == continuous_features + categorical_features)
X_clean = clean(X_df.copy())
y = y_cont + y_cat
if X_df.shape[1] == 0:
y = np.random.uniform(size=n_samples)
if task == "classification":
y = np.digitize(y, np.percentile(y, [5, 10, 60, 85]))
X_clean['target'] = y
if task == "classification":
X_clean['target'] = X_clean['target'].astype('category')
types = detect_types(X_clean)
column_types = types.T.idxmax()
assert np.all(column_types[:continuous_features] == 'continuous')
assert np.all(column_types[continuous_features:-1] == 'categorical')
if task == "classification":
assert column_types[-1] == 'categorical'
else:
assert column_types[-1] == 'continuous'
plot_supervised(X_clean, 'target')
plt.close("all")
def test_plot_target_low_card_int():
data = load_digits()
df = data_df_from_bunch(data)
plot_supervised(df[::10], target_col='target')