sklearn.preprocessing¶

sklearn.preprocessing.LabelEncoder¶

sklearn.preprocessing.LabelEncoder

This class provides LabelEncoder support to encode target labels y with values between 0 and n-classes-1.

Methods¶

sklearn.preprocessing.LabelEncoder.fit¶

sklearn.preprocessing.LabelEncoder.fit(y)

Supported Arguments

- y: 1d array-like.

sklearn.preprocessing.LabelEncoder.fit_transform¶

sklearn.preprocessing.LabelEncoder.fit_transform(y)

Supported Arguments

- y: 1d array-like.

sklearn.preprocessing.LabelEncoder.transform¶

sklearn.preprocessing.LabelEncoder.transform(y)

Supported Arguments

- y: 1d array-like.

Example Usage¶

>>> import bodo
>>> import numpy as np
>>> from sklearn.preprocessing import LabelEncoder
>>> @bodo.jit
... def test_le():
...   le = LabelEncoder()
...   le.fit([1, 2, 2, 6])
...   print(le.transform([1, 1, 2, 6]))
...
>>> test_le()
[0 0 1 2]

sklearn.preprocessing.MinMaxScaler¶

sklearn.preprocessing.MinMaxScaler

This class provides MinMax Scaler support to scale your data based on the range of its features.

Methods¶

sklearn.preprocessing.MinMaxScaler.fit¶

sklearn.preprocessing.MinMaxScaler.fit(X, y=None)

Supported Arguments

- X: NumPy array or Pandas Dataframes.

sklearn.preprocessing.MinMaxScaler.inverse_transform¶

sklearn.preprocessing.MinMaxScaler.inverse_transform(X)

Supported Arguments

- X: NumPy array or Pandas Dataframes.

sklearn.preprocessing.MinMaxScaler.transform¶

sklearn.preprocessing.MinMaxScaler.transform(X)

Supported Arguments

- X: NumPy array or Pandas Dataframes.

Example Usage¶

>>> import bodo
>>> import numpy as np
>>> from sklearn.preprocessing import MinMaxScaler
>>> data = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
>>> @bodo.jit
... def test_minmax(data):
...   scaler = MinMaxScaler()
...   scaler.fit(data)
...   print(scaler.transform(data))
...
>>> test_minmax(data)
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]

sklearn.preprocessing.StandardScaler¶

sklearn.preprocessing.StandardScaler

This class provides Standard Scaler support to center your data and to scale it to achieve unit variance.

Methods¶

sklearn.preprocessing.StandardScaler.fit¶

sklearn.preprocessing.StandardScaler.fit(X, y=None, sample_weight=None)

Supported Arguments

- X: NumPy Array or Pandas Dataframes. - y: NumPy Array. - sample_weight: Numeric NumPy Array (only if data is not distributed)

sklearn.preprocessing.StandardScaler.inverse_transform¶

sklearn.preprocessing.StandardScaler.inverse_transform(X, copy=None)

Supported Arguments

- X: NumPy Array or Pandas Dataframes. - copy: bool or None.

sklearn.preprocessing.StandardScaler.transform¶

sklearn.preprocessing.StandardScaler.transform(X, copy=None)

Supported Arguments

- X: NumPy Array or Pandas Dataframes. - copy: bool or None.

Example Usage¶

>>> import bodo
>>> import numpy as np
>>> from sklearn.svm import LinearSVC
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> @bodo.jit
... def test_linearsvc(X, y):
...   scaler = StandardScaler()
...   scaler.fit(X)
...   X = scaler.transform(X)
...   clf = LinearSVC()
...   clf.fit(X, y)
...   ans = clf.predict(np.array([[0, 0, 0, 0]]))
...   print(ans)
...
>>> test_linearsvc(X, y)
[1]

sklearn.preprocessing.RobustScaler¶

sklearn.preprocessing.RobustScaler

This class provides Robust Scaler support to scale your data while being robust to outliers.

Methods¶

sklearn.preprocessing.RobustScaler.fit¶

sklearn.preprocessing.RobustScaler.fit(X, y=None)

Supported Arguments

- X: NumPy array or Pandas DataFrame. Sparse matrices are not yet supported.

sklearn.preprocessing.RobustScaler.inverse_transform¶

sklearn.preprocessing.RobustScaler.inverse_transform(X)

Supported Arguments

- X: NumPy array or Pandas DataFrame. Sparse matrices are not yet supported.

sklearn.preprocessing.RobustScaler.transform¶

sklearn.preprocessing.RobustScaler.transform(X)

Supported Arguments

- X: NumPy array or Pandas DataFrame. Sparse matrices are not yet supported.

Example Usage¶

>>> import bodo
>>> import numpy as np
>>> from sklearn.preprocessing import RobustScaler
>>> data = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18], [-100, 3], [0, 500]])
>>> @bodo.jit(distributed=["data"])
... def test_robust(data):
...   scaler = RobustScaler()
...   scaler.fit(data)
...   print(scaler.transform(data))
...
>>> test_robust(data)
[[  -0.85714286   -0.48979592]
 [  -0.28571429   -0.16326531]
 [   0.28571429    0.16326531]
 [   1.42857143    0.81632653]
 [-114.           -0.40816327]
 [   0.28571429   40.16326531]]