本文整理汇总了Python中sklearn.preprocessing.KBinsDiscretizer.fit_transform方法的典型用法代码示例。如果您正苦于以下问题：Python KBinsDiscretizer.fit_transform方法的具体用法？Python KBinsDiscretizer.fit_transform怎么用？Python KBinsDiscretizer.fit_transform使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.KBinsDiscretizer的用法示例。
在下文中一共展示了KBinsDiscretizer.fit_transform方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_inverse_transform
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
def test_inverse_transform(strategy):
X = np.random.RandomState(0).randn(100, 3)
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode='ordinal')
Xt = kbd.fit_transform(X)
assert_array_equal(Xt.max(axis=0) + 1, kbd.n_bins_)
X2 = kbd.inverse_transform(Xt)
X2t = kbd.fit_transform(X2)
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)
assert_array_equal(Xt, X2t)示例2: test_nonuniform_strategies
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
def test_nonuniform_strategies(strategy, expected_2bins, expected_3bins):
X = np.array([0, 1, 2, 3, 9, 10]).reshape(-1, 1)
# with 2 bins
est = KBinsDiscretizer(n_bins=2, strategy=strategy, encode='ordinal')
Xt = est.fit_transform(X)
assert_array_equal(expected_2bins, Xt.ravel())
# with 3 bins
est = KBinsDiscretizer(n_bins=3, strategy=strategy, encode='ordinal')
Xt = est.fit_transform(X)
assert_array_equal(expected_3bins, Xt.ravel())示例3: test_inverse_transform
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
def test_inverse_transform(strategy, encode):
X = np.random.RandomState(0).randn(100, 3)
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode=encode)
Xt = kbd.fit_transform(X)
X2 = kbd.inverse_transform(Xt)
X2t = kbd.fit_transform(X2)
if encode == 'onehot':
assert_array_equal(Xt.todense(), X2t.todense())
else:
assert_array_equal(Xt, X2t)
if 'onehot' in encode:
Xt = kbd._encoder.inverse_transform(Xt)
X2t = kbd._encoder.inverse_transform(X2t)
assert_array_equal(Xt.max(axis=0) + 1, kbd.n_bins_)
assert_array_equal(X2t.max(axis=0) + 1, kbd.n_bins_)示例4: test_overwrite
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
def test_overwrite():
X = np.array([0, 1, 2, 3])[:, None]
X_before = X.copy()
est = KBinsDiscretizer(n_bins=3, encode="ordinal")
Xt = est.fit_transform(X)
assert_array_equal(X, X_before)
Xt_before = Xt.copy()
Xinv = est.inverse_transform(Xt)
assert_array_equal(Xt, Xt_before)
assert_array_equal(Xinv, np.array([[0.5], [1.5], [2.5], [2.5]]))示例5: test_inverse_transform
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
def test_inverse_transform(strategy, encode, expected_inv):
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy, encode=encode)
Xt = kbd.fit_transform(X)
Xinv = kbd.inverse_transform(Xt)
assert_array_almost_equal(expected_inv, Xinv)示例6: print
# 需要导入模块: from sklearn.preprocessing import KBinsDiscretizer [as 别名]
# 或者: from sklearn.preprocessing.KBinsDiscretizer import fit_transform [as 别名]
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.tree import DecisionTreeRegressor
print(__doc__)
# construct the dataset
rnd = np.random.RandomState(42)
X = rnd.uniform(-3, 3, size=100)
y = np.sin(X) + rnd.normal(size=len(X)) / 3
X = X.reshape(-1, 1)
# transform the dataset with KBinsDiscretizer
enc = KBinsDiscretizer(n_bins=10, encode='onehot')
X_binned = enc.fit_transform(X)
# predict with original dataset
fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(10, 4))
line = np.linspace(-3, 3, 1000, endpoint=False).reshape(-1, 1)
reg = LinearRegression().fit(X, y)
ax1.plot(line, reg.predict(line), linewidth=2, color='green',
label="linear regression")
reg = DecisionTreeRegressor(min_samples_split=3, random_state=0).fit(X, y)
ax1.plot(line, reg.predict(line), linewidth=2, color='red',
label="decision tree")
ax1.plot(X[:, 0], y, 'o', c='k')
ax1.legend(loc="best")
ax1.set_ylabel("Regression output")
ax1.set_xlabel("Input feature")
ax1.set_title("Result before discretization")