def test_na_vals_reg_plot_raise_warning():
X, y = load_diabetes(return_X_y=True)
X = pd.DataFrame(X)
y[::50] = np.NaN
X['target_col'] = y
scatter_alpha = _get_scatter_alpha('auto', X['target_col'])
scatter_size = _get_scatter_size('auto', X['target_col'])
with pytest.warns(UserWarning,
match="Missing values in target_col have "
"been removed for regression"):
plot(X, 'target_col')
with pytest.warns(UserWarning,
match="Missing values in target_col have "
"been removed for regression"):
plot_regression_continuous(X,
'target_col',
scatter_alpha=scatter_alpha,
scatter_size=scatter_size)
with pytest.warns(UserWarning,
match="Missing values in target_col have "
"been removed for regression"):
plot_regression_categorical(X,
'target_col',
scatter_alpha=scatter_alpha,
scatter_size=scatter_size)
def test_plot_string_target():
X, y = make_blobs(n_samples=30)
data = pd.DataFrame(X)
y = pd.Series(y)
y[y == 0] = 'a'
y[y == 1] = 'b'
y[y == 2] = 'c'
data['target'] = y
plot(data, target_col='target')
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(data, target_col='target')
# same with "actual float" - we need to specify classification for that :-/
data['target'] = y.astype(np.float) + .2
plot(data, target_col='target', type_hints={'target': 'categorical'})
plt.close("all")
def test_negative_ordinal():
# check that a low card int with negative values is plotted correctly
data = pd.DataFrame([
np.random.randint(0, 10, size=1000) - 5,
np.random.randint(0, 2, size=1000)
]).T
# ensure first column is low_card_int
assert (detect_types(data).T.idxmax() == ['low_card_int',
'categorical']).all()
assert guess_ordinal(data[0])
# smoke test
plot(data, target_col=1)
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(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_plot_regression_missing_categories():
df = pd.DataFrame(
{'cat_col': np.random.choice(['a', 'b', 'c', 'd'], size=100)})
df['target'] = np.NaN
counts = df.cat_col.value_counts()
df.loc[df.cat_col == "a", 'target'] = np.random.normal(size=counts['a'])
df.loc[df.cat_col == "b", 'target'] = np.random.normal(1, size=counts['b'])
axes = plot(df, target_col="target")
ticklabels = axes[-1][0, 0].get_yticklabels()
assert [label.get_text() for label in ticklabels] == ['a', 'b']
def test_plot_regression_categorical_missing_value():
df = pd.DataFrame({'y': np.random.normal(size=300)})
df.loc[100:200, 'y'] += 1
df.loc[200:300, 'y'] += 2
df['x'] = 'a'
df.loc[100:200, 'x'] = 'b'
df.loc[200:300, 'x'] = np.NaN
res = plot(df, target_col='y')
assert len(res[2][0, 0].get_yticklabels()) == 3
assert res[2][0, 0].get_yticklabels()[2].get_text() == 'dabl_mi...'
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(X_clean, target_col='target')
plt.close("all")
def test_plot_regression_with_target_outliers():
df = pd.DataFrame(
data={
"feature": np.random.randint(low=1, high=100, size=200),
# target values are bound between 50 and 100
"target": np.random.randint(low=50, high=100, size=200)
})
# append single outlier record with target value 0
df = df.append({"feature": 50, "target": 0}, ignore_index=True)
with pytest.warns(UserWarning,
match="Dropped 1 outliers in column target."):
plot_regression_continuous(df, target_col='target')
with pytest.warns(UserWarning,
match="Dropped 1 outliers in column target."):
plot_regression_categorical(df, target_col='target')
res = plot(df, target_col='target')
assert len(res) == 3
ax = res[0]
# ensure outlier at 0 was removed
assert ax.get_xticks()[0] == 40
def test_plot_int_column_name():
X, y = make_blobs()
X = pd.DataFrame(X)
X[3] = y
plot(X, target_col=3)
def test_plot_lda_binary():
X, y = make_blobs(centers=2)
X = pd.DataFrame(X)
plot(X, y, univariate_plot='kde')
def test_plot_regression_numpy():
X, y = make_regression()
plot(X, y)
def test_plot_X_y():
X, y = make_blobs()
X = pd.DataFrame(X)
plot(X, y)
def test_plot_target_low_card_int():
data = load_digits()
df = data_df_from_bunch(data)
plot(df[::10], target_col='target')
