Balancer

class atom.data_cleaning.Balancer(strategy="ADASYN", n_jobs=1, verbose=0, logger=None, random_state=None, **kwargs)[source]

Balance the number of samples per class in the target column.

When oversampling, the newly created samples have an increasing integer index for numerical indices, and an index of the form [estimator]_N for non-numerical indices, where N stands for the N-th sample in the data set. Use only for classification tasks.

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

Warning

The clustercentroids estimator is unavailable because of incompatibilities of the APIs.

Parameters

strategy: str or estimator, default="ADASYN"

Type of algorithm with which to balance the dataset. Choose from the name of any estimator in the imbalanced-learn package or provide a custom instance of such.

n_jobs: int, default=1

Number of cores to use for parallel processing.

If >0: Number of cores to use.
If -1: Use all available cores.
If <-1: Use number of cores - 1 - value.

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.

**kwargs

Additional keyword arguments for the strategy estimator.

Attributes

[strategy]: imblearn estimator

Object (lowercase strategy) used to balance the data, e.g. balancer.adasyn for the default strategy.

mapping: dict

Target values mapped to their respective encoded integer.

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.train)

     mean radius  mean texture  ...  worst fractal dimension  target
0         18.030         16.85  ...                  0.08225       0
1         10.950         21.35  ...                  0.09606       0
2         14.250         22.15  ...                  0.11320       0
3         17.570         15.05  ...                  0.07919       0
4         10.600         18.95  ...                  0.07587       1
..           ...           ...  ...                      ...     ...
451        8.888         14.64  ...                  0.10840       1
452       21.090         26.57  ...                  0.12840       0
453       16.160         21.54  ...                  0.07619       0
454       11.260         19.83  ...                  0.07613       1
455       12.000         15.65  ...                  0.07924       1

[456 rows x 31 columns]

>>> atom.balance(strategy="smote", verbose=2)

Oversampling with SMOTE...
    --> Adding 116 samples to class 0.

>>> # Note that the number of rows has increased
>>> print(atom.train)

     mean radius  mean texture  ...  worst fractal dimension  target
0      11.420000     20.380000  ...                 0.173000       0
1       9.876000     17.270000  ...                 0.073800       1
2      13.470000     14.060000  ...                 0.093260       1
3      16.300000     15.700000  ...                 0.072300       1
4      12.250000     17.940000  ...                 0.081320       1
..           ...           ...  ...                      ...     ...
567    12.975558     20.580996  ...                 0.118509       0
568    11.786135     17.120749  ...                 0.091266       0
569    16.194544     19.737215  ...                 0.106434       0
570    16.780524     21.261883  ...                 0.086889       0
571    20.705316     22.635645  ...                 0.085362       0

[572 rows x 31 columns]

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

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

     mean radius  mean texture  ...  worst symmetry  worst fractal dimension
0          17.99         10.38  ...          0.4601                  0.11890
1          20.57         17.77  ...          0.2750                  0.08902
2          19.69         21.25  ...          0.3613                  0.08758
3          11.42         20.38  ...          0.6638                  0.17300
4          20.29         14.34  ...          0.2364                  0.07678
..           ...           ...  ...             ...                      ...
564        21.56         22.39  ...          0.2060                  0.07115
565        20.13         28.25  ...          0.2572                  0.06637
566        16.60         28.08  ...          0.2218                  0.07820
567        20.60         29.33  ...          0.4087                  0.12400
568         7.76         24.54  ...          0.2871                  0.07039

[569 rows x 30 columns]

>>> balancer = Balancer(strategy="smote", verbose=2)
>>> X, y = balancer.transform(X, y)

Oversampling with SMOTE...
    --> Adding 145 samples to class 0.

>>> # Note that the number of rows has increased
>>> print(X)

     mean radius  mean texture  ...  worst symmetry  worst fractal dimension
0      17.990000     10.380000  ...        0.460100                 0.118900
1      20.570000     17.770000  ...        0.275000                 0.089020
2      19.690000     21.250000  ...        0.361300                 0.087580
3      11.420000     20.380000  ...        0.663800                 0.173000
4      20.290000     14.340000  ...        0.236400                 0.076780
..           ...           ...  ...             ...                      ...
709    14.824550     17.497674  ...        0.345200                 0.100678
710    20.170649     23.997572  ...        0.538881                 0.099281
711    21.006050     22.305044  ...        0.277181                 0.076740
712    20.791828     25.103989  ...        0.388202                 0.122836
713    17.081185     23.560768  ...        0.342508                 0.082558

[714 rows x 30 columns]

Methods

fit	Does nothing.
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	Balance the data.

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

Does nothing.

Implemented for continuity of the API.

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

self

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=-1)[source]

Balance the data.

Parameters	X: dataframe-like Feature set with shape=(n_samples, n_features). y: int, str or sequence, default=-1 Target column corresponding to X. 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 Balanced dataframe. pd.Series Transformed target column.