Discretizer

---

class atom.data_cleaning.Discretizer(strategy="quantile", bins=5, labels=None, device="cpu", engine="sklearn", verbose=0, logger=None, random_state=None)[source]

Bin continuous data into intervals.

For each feature, the bin edges are computed during fit and, together with the number of bins, they define the intervals. Ignores categorical columns.

This class can be accessed from atom through the discretize method. Read more in the user guide.

Tip

The transformation returns categorical columns. Use the Encoder class to convert them back to numerical types.

Parameters

strategy: str, default="quantile" Strategy used to define the widths of the bins. Choose from: "uniform": All bins have identical widths. "quantile": All bins have the same number of points. "kmeans": Values in each bin have the same nearest center of a 1D k-means cluster. "custom": Use custom bin edges provided through bins. bins: int, sequence or dict, default=5 Bin number or bin edges in which to split every column. If int: Number of bins to produce for all columns. Only for strategy!="custom". If sequence: For strategy!="custom": Number of bins per column, allowing for non-uniform width. The n-th value corresponds to the n-th column that is transformed. Note that categorical columns are automatically ignored. For strategy="custom": Bin edges with length=n_bins - 1. The outermost edges are always -inf and +inf, e.g. bins [1, 2] indicate (-inf, 1], (1, 2], (2, inf]. If dict: One of the aforementioned options per column, where the key is the column's name. labels: sequence, dict or None, default=None Label names with which to replace the binned intervals. If None: Use default labels of the form (min_edge, max_edge]. If sequence: Labels to use for all columns. If dict: Labels per column, where the key is the column's name. device: str, default="cpu" Device on which to train the estimators. Use any string that follows the SYCL_DEVICE_FILTER filter selector, e.g. device="gpu" to use the GPU. Read more in the user guide. engine: str, default="sklearn" Execution engine to use for the estimators. Refer to the user guide for an explanation regarding every choice. Choose from: "sklearn" (only if device="cpu") "cuml" (only if device="gpu") verbose: int, default=0 Verbosity level of the class. Choose from: 0 to not print anything. 1 to print basic information. 2 to print detailed information. logger: str, Logger or None, 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. random_state: int or None, default=None Seed used by the random number generator. If None, the random number generator is the RandomState used by np.random. Only for strategy="quantile".

Attributes

feature_names_in_: np.array Names of features seen during fit. n_features_in_: int Number of features seen during fit.

See Also

Encoder

Perform encoding of categorical features.

Imputer

Handle missing values in the data.

Normalizer

Transform the data to follow a Normal/Gaussian distribution.

Example

atomstand-alone

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> atom = ATOMClassifier(X, y)
>>> print(atom["mean radius"])

0      17.99
1      20.57
2      19.69
3      11.42
4      20.29
       ...
564    21.56
565    20.13
566    16.60
567    20.60
568     7.76

Name: mean radius, Length: 569, dtype: float64

>>> atom.discretize(
...     strategy="custom",
...     bins=[13, 18],
...     labels=["small", "medium", "large"],
...     verbose=2,
...     columns="mean radius",
... )

Fitting Discretizer...
Binning the features...
 --> Discretizing feature mean radius in 3 bins.

>>> print(atom["mean radius"])

0       small
1      medium
2      medium
3      medium
4       small
        ...
564     large
565     small
566     large
567     small
568     small

Name: mean radius, Length: 569, dtype: category
Categories (3, object): ['small' < 'medium' < 'large']

>>> from atom.data_cleaning import Discretizer
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> print(X["mean radius"])

0      17.99
1      20.57
2      19.69
3      11.42
4      20.29
       ...
564    21.56
565    20.13
566    16.60
567    20.60
568     7.76

Name: mean radius, Length: 569, dtype: float64

>>> disc = Discretizer(
...     strategy="custom",
...     bins=[13, 18],
...     labels=["small", "medium", "large"],
...     verbose=2,
... )
>>> X["mean radius"] = disc.fit_transform(X[["mean radius"]])["mean radius"]

Fitting Discretizer...
Binning the features...
 --> Discretizing feature mean radius in 3 bins.

>>> print(X["mean radius"])

0       small
1      medium
2      medium
3      medium
4       small
        ...
564     large
565     small
566     large
567     small
568     small

Name: mean radius, Length: 569, dtype: category
Categories (3, object): ['small' < 'medium' < 'large']

Methods

fit	Fit to data.
fit_transform	Fit to data, then transform it.
get_params	Get parameters for this estimator.
inverse_transform	Does nothing.
log	Print message and save to log file.
save	Save the instance to a pickle file.
set_params	Set the parameters of this estimator.
transform	Bin the data into intervals.

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

Fit to data.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: int, str, dict, sequence or None, default=None Does nothing. Implemented for continuity of the API.
Returns	Discretizer Estimator instance.

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

Fit to data, then transform it.

Parameters

X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: int, str, dict, sequence or None, default=None Target column corresponding to X. If None: y is ignored. If int: Position of the target column in X. If str: Name of the target column in X. Else: Array with shape=(n_samples,) to use as target. **fit_params Additional keyword arguments for the fit method.

Returns

pd.DataFrame Transformed feature set. Only returned if provided. pd.Series Transformed target column. Only returned if provided.

method get_params(deep=True)[source]

Get parameters for this estimator.

Parameters	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns	params : dict Parameter names mapped to their values.

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

Does nothing.

Parameters	X: dataframe-like or None, default=None Feature set with shape=(n_samples, n_features). If None, X is ignored. y: int, str, dict, sequence or None, default=None Target column corresponding to X. If None: y is ignored. If int: Position of the target column in X. If str: Name of the target column in X. Else: Array with shape=(n_samples,) to use as target.
Returns	pd.DataFrame Transformed feature set. Only returned if provided. pd.Series Transformed target column. Only returned if provided.

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

Print message and save to log file.

Parameters

msg: int, float or str Message to save to the logger and print to stdout. level: int, default=0 Minimum verbosity level to print the message. severity: str, default="info" Severity level of the message. Choose from: debug, info, warning, error, critical.

method save(filename="auto", save_data=True)[source]

Save the instance to a pickle file.

Parameters

filename: str, default="auto" Name of the file. Use "auto" for automatic naming. save_data: bool, default=True Whether to save the dataset with the instance. This parameter is ignored if the method is not called from atom. If False, remember to add the data to ATOMLoader when loading the file.

method set_params(**params)[source]

Set the parameters of this estimator.

Parameters	**params : dict Estimator parameters.
Returns	self : estimator instance Estimator instance.

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

Bin the data into intervals.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: int, str, dict, sequence or None, default=None Does nothing. Implemented for continuity of the API.
Returns	pd.DataFrame Transformed feature set.