Skip to content

Scaler

class atom.data_cleaning.Scaler(strategy="standard", gpu=False, verbose=0, logger=None, **kwargs) [source]

Apply one of sklearn's scalers. Categorical columns are ignored. This class can be accessed from atom through the scale method. Read more in the user guide.

Parameters: strategy: str, optional (default="standard")
Strategy with which to scale the data. Choose from:
  • "standard": Remove mean and scale to unit variance.
  • "minmax": Scale features to a given range.
  • "maxabs": Scale features by their maximum absolute value.
  • "robust": Scale using statistics that are robust to outliers.
gpu: bool or str, optional (default=False)
Train strategy on GPU (instead of CPU).
  • If False: Always use CPU implementation.
  • If True: Use GPU implementation if possible.
  • If "force": Force GPU implementation.
verbose: int, optional (default=0)
Verbosity level of the class. Choose from:
  • 0 to not print anything.
  • 1 to print basic information.
logger: str, Logger or None, optional (default=None)
  • If None: Doesn't save a logging file.
  • If str: Name of the log file. Use "auto" for automatic naming.
  • Else: Python logging.Logger instance.

**kwargs
Additional keyword arguments for the strategy estimator.

Tip

Use atom's scaled attribute to check if the feature set is scaled.


Attributes

Attributes:

estimator: sklearn transformer
Object instance with which the data is scaled.

feature_names_in_: np.array
Names of features seen during fit.

n_features_in_: int
Number of features seen during fit.


Methods

fit Fit to data.
fit_transform Fit to data, then transform it.
get_params Get parameters for this estimator.
log Write information to the logger and print to stdout.
save Save the instance to a pickle file.
set_params Set the parameters of this estimator.
transform Transform the data.


method fit(X, y=None) [source]

Compute the mean and std to be used for scaling.

Parameters:

X: dataframe-like
Feature set with shape=(n_samples, n_features).

y: int, str, sequence or None, optional (default=None)
Does nothing. Implemented for continuity of the API.

Returns: Scaler
Fitted instance of self.


method fit_transform(X, y=None) [source]

Fit to data, then transform it.

Parameters:

X: dataframe-like
Feature set with shape=(n_samples, n_features).

y: int, str, sequence or None, optional (default=None)
Does nothing. Implemented for continuity of the API.

Returns: X: pd.DataFrame
Scaled feature set.


method get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:

deep: bool, optional (default=True)
If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns: dict
Parameter names mapped to their values.


method log(msg, level=0) [source]

Write a message to the logger and print it to stdout.

Parameters:

msg: str
Message to write to the logger and print to stdout.

level: int, optional (default=0)
Minimum verbosity level to print the message.


method save(filename="auto") [source]

Save the instance to a pickle file.

Parameters: filename: str, optional (default="auto")
Name of the file. Use "auto" for automatic naming.


method set_params(**params) [source]

Set the parameters of this estimator.

Parameters: **params: dict
Estimator parameters.
Returns: Scaler
Estimator instance.


method transform(X, y=None) [source]

Perform standardization by centering and scaling.

Parameters:

X: dataframe-like
Feature set with shape=(n_samples, n_features).

y: int, str, sequence or None, optional (default=None)
Does nothing. Implemented for continuity of the API.

Returns: X: pd.DataFrame
Scaled feature set.


Example

from atom import ATOMRegressor

atom = ATOMRegressor(X, y)
atom.scale()

from atom.data_cleaning import Scaler

scaler = Scaler()
scaler.fit(X_train)
X = scaler.transform(X)
Back to top