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StandardScaler()
Standardize features by removing the mean and scaling to unit variance.
The standard score of a sample x
is calculated as:z = (x - u) / s
where u
is the mean of the training samples or zero if with_mean=False
,
and s
is the standard deviation of the training samples or one if
with_std=False
.
Centering and scaling happen independently on each feature by computing
the relevant statistics on the samples in the training set. Mean and
standard deviation are then stored to be used on later data using
transform
.
Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).
Examples:
.. code-block::
from bigframes.ml.preprocessing import StandardScaler
import <xref uid="bigframes.pandas">bigframes.pandas</xref> as bpd
scaler = StandardScaler()
data = bpd.DataFrame({"a": [0, 0, 1, 1], "b":[0, 0, 1, 1]})
scaler.fit(data)
print(scaler.transform(data))
print(scaler.transform(bpd.DataFrame({"a": [2], "b":[2]})))
Methods
__repr__
__repr__()
Print the estimator's constructor with all non-default parameter values
fit
fit(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.StandardScaler
Compute the mean and std to be used for later scaling.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
The Dataframe or Series with training data. |
y |
default None
Ignored. |
Returns | |
---|---|
Type | Description |
StandardScaler | Fitted scaler. |
fit_transform
fit_transform(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
y: typing.Optional[
typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
] = None,
) -> bigframes.dataframe.DataFrame
Fit to data, then transform it.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Input samples. |
y |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). Default None. Target values (None for unsupervised transformations). |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | DataFrame of shape (n_samples, n_features_new) Transformed DataFrame. |
get_params
get_params(deep: bool = True) -> typing.Dict[str, typing.Any]
Get parameters for this estimator.
Parameter | |
---|---|
Name | Description |
deep |
bool, default True
Default |
Returns | |
---|---|
Type | Description |
Dictionary | A dictionary of parameter names mapped to their values. |
transform
transform(
X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame
Perform standardization by centering and scaling.
Parameter | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
The DataFrame or Series to be transformed. |
Returns | |
---|---|
Type | Description |
bigframes.dataframe.DataFrame | Transformed result. |