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]]