Discretizers

AutoCarver implements Discretizers. It provides the following Data Preparation tools:

Discretizer / Data Type

Data Preparation

Continuous Discretizer:

Continuous Data

Discrete Data

Over-represented values are set as there own modality

Automatic quantile bucketization of under-represented values

Modalities are ordered by default real number ordering

Ordinal Discretizer:

Ordinal Data

Under-represented modalities are grouped with the closest modality

Modalities are ordered according to provided modality ranking

Categorical Discretizer:

Categorical Data

Under-represented modalities are grouped into a default value

Modalities are ordered by target rate

Note

Representativity threshold of modalities is user selected (min_freq attribute).
At this step, if any, numpy.nan are set as there own modality (no given order).
Helps improve modality relevancy and reduces the set of possible combinations to test from.
Included in all carving pipelines: BinaryCarver, MulticlassCarver, ContinuousCarver.

Discretizer, a complete discretization pipeline

class AutoCarver.discretizers.Discretizer(quantitative_features: list[str], qualitative_features: list[str], min_freq: float, *, ordinal_features: list[str] | None = None, values_orders: dict[str, GroupedList] | None = None, copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__', str_default: str = '__OTHER__')

Automatic discretization pipeline of continuous, discrete, categorical and ordinal features.

Pipeline steps: Complete pipeline for continuous and discrete features, Complete pipeline for categorical and ordinal features.

Modalities/values of features are grouped according to there respective orders:

[Categorical features] order based on modality target rate.
[Ordinal features] user-specified order.
[Continuous/Discrete features] real order of the values.

Parameters:

quantitative_features (list[str]) – List of column names of quantitative features (continuous and discrete) to be dicretized
qualitative_features (list[str]) – List of column names of qualitative features (non-ordinal) to be discretized
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
ordinal_features (list[str], optional) – List of column names of ordinal features to be discretized. For those features a list of values has to be provided in the values_orders dict, by default None
str_default (str, optional) – String representation for default qualitative values, i.e. values less frequent than min_freq, by default "__OTHER__"
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Quantitative Data

Complete pipeline for continuous and discrete features

class AutoCarver.discretizers.QuantitativeDiscretizer(quantitative_features: list[str], min_freq: float, *, values_orders: dict[str, GroupedList] | None = None, input_dtypes: str | dict[str, str] = 'float', verbose: bool = False, copy: bool = False, str_nan: str = '__NAN__')

Automatic discretization pipeline of continuous and discrete features.

Pipeline steps: Continuous Discretizer, Ordinal Discretizer

Modalities/values of features are grouped according to there respective orders:

[Continuous/Discrete features] real order of the values.

Parameters:

quantitative_features (list[str]) – List of column names of quantitative features (continuous and discrete) to be dicretized
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
input_dtypes (Union[str, dict[str, str]], optional) –
Input data type, converted to a dict of the provided type for each feature, by default "str"
- If "str", features are considered as qualitative.
- If "float", features are considered as quantitative.
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Continuous Discretizer

class AutoCarver.discretizers.ContinuousDiscretizer(quantitative_features: list[str], min_freq: float, *, values_orders: dict[str, Any] | None = None, copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__')

Automatic discretizing of continuous and discrete features, building simple groups of quantiles of values.

Quantile discretization creates a lot of modalities (for example: 100 modalities for min_freq=0.01). Set min_freq with caution.

The number of quantiles depends on overrepresented modalities and nans:

Values more frequent than min_freq are set as there own modalities.
Other values are cut in quantiles using numpy.quantile.
The number of quantiles is set as (1-freq_of_frequent_modalities)/(min_freq).
Nans are considered as a modality (and are taken into account in freq_of_frequent_modalities).

Parameters:

quantitative_features (list[str]) – List of column names of quantitative features (continuous and discrete) to be dicretized
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series | None = None) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Qualitative Data

Complete pipeline for categorical and ordinal features

class AutoCarver.discretizers.QualitativeDiscretizer(qualitative_features: list[str], min_freq: float, *, ordinal_features: list[str] | None = None, values_orders: dict[str, GroupedList] | None = None, input_dtypes: str | dict[str, str] = 'str', copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__', str_default: str = '__OTHER__')

Automatic discretiziation pipeline of categorical and ordinal features.

Pipeline steps: Categorical Discretizer, String Discretizer, Ordinal Discretizer.

Modalities/values of features are grouped according to there respective orders:

[Categorical features] order based on modality target rate.
[Ordinal features] user-specified order.

Parameters:

qualitative_features (list[str]) – List of column names of qualitative features (non-ordinal) to be discretized
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
ordinal_features (list[str], optional) – List of column names of ordinal features to be discretized. For those features a list of values has to be provided in the values_orders dict, by default None
input_dtypes (Union[str, dict[str, str]], optional) –
Input data type, converted to a dict of the provided type for each feature, by default "str"
- If "str", features are considered as qualitative.
- If "float", features are considered as quantitative.
str_default (str, optional) – String representation for default qualitative values, i.e. values less frequent than min_freq, by default "__OTHER__"

values_ordersdict[str, GroupedList], optional: Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copybool, optional: If True, applies transform to a copy of the provided DataFrame, by default False
verbosebool, optional: If True, prints raw Discretizers Fit and Transform steps, by default False
str_nanstr, optional: String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Categorical Discretizer

class AutoCarver.discretizers.CategoricalDiscretizer(qualitative_features: list[str], min_freq: float, *, values_orders: dict[str, GroupedList] | None = None, copy: bool = False, verbose: bool = False, str_default: str = '__OTHER__', str_nan: str = '__NAN__')

Automatic discretization of categorical features, building simple groups frequent enough.

Groups a qualitative features’ values less frequent than min_freq into a str_default string.

NaNs are left untouched.

Only use for qualitative non-ordinal features.

Parameters:

qualitative_features (list[str]) – List of column names of qualitative features (non-ordinal) to be discretized
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
str_default (str, optional) – String representation for default qualitative values, i.e. values less frequent than min_freq, by default "__OTHER__"
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Ordinal Discretizer

class AutoCarver.discretizers.OrdinalDiscretizer(ordinal_features: list[str], min_freq: float, values_orders: dict[str, GroupedList], *, input_dtypes: str | dict[str, str] = 'str', copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__')

Automatic discretization of ordinal features, grouping less frequent modalities with the closest modlity in target rate or by frequency.

NaNs are left untouched.

Only use for qualitative ordinal features.

Fisrt fits String Discretizer if neccesary.

Parameters:

ordinal_features (list[str]) – List of column names of ordinal features to be discretized. For those features a list of values has to be provided in the values_orders dict.
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
input_dtypes (Union[str, dict[str, str]], optional) –
Input data type, converted to a dict of the provided type for each feature, by default "str"
- "str", features are considered as qualitative.
- 'float", features are considered as quantitative.
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

Chained Discretizer

ChainedDiscretizer can be used prior to using any carving pipeline or any other discretizer to group categorical modalities more intelligently. By providing a set of modality groups, the user can introduce use case specific knowledge into the discretization process. The fitted ordering can then be passed as values_orders parameter for further discretization.

class AutoCarver.discretizers.ChainedDiscretizer(qualitative_features: list[str], min_freq: float, chained_orders: list[GroupedList], *, values_orders: dict[str, GroupedList] | None = None, unknown_handling: str = 'raise', copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__')

Automatic discretization of categorical features, joining rare modalities into higher level groups.

For each provided GroupedList from chained_orders attribute, values less frequent than min_freq are grouped in there respective group, as defined by GroupedList.

Parameters:

qualitative_features (list[str]) – List of column names of qualitative features (non-ordinal) to be discretized
chained_orders (list[GroupedList]) – A list of interlocked higher level groups for each modalities of each ordianl feature. Values of chained_orders[0] have to be grouped in chained_order[1] etc.
min_freq (float) –
Minimum frequency per grouped modalities.
- Features whose most frequent modality is less frequent than min_freq will not be discretized.
- Sets the number of quantiles in which to discretize the continuous features.
- Sets the minimum frequency of a quantitative feature’s modality.
Tip: should be set between 0.02 (slower, preciser, less robust) and 0.05 (faster, more robust)
unknown_handling (str, optional) –
Whether or not to remove unknown values, by default 'raise'.
- 'raise', unknown values raise an AssertionError.
- 'drop', unknown values are grouped with str_nan.
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series | None = None) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

String Discretizer

StringDiscretizer is used as a data preparation tool to convert qualitative data to str type.

class AutoCarver.discretizers.StringDiscretizer(qualitative_features: list[str], *, values_orders: dict[str, GroupedList] | None = None, copy: bool = False, verbose: bool = False, str_nan: str = '__NAN__')

Converts specified columns of a DataFrame into strings. First step of a Qualitative discretization pipeline.

Keeps NaN inplace
Converts floats of int to int

Parameters:

qualitative_features (list[str]) – List of column names of qualitative features (non-ordinal) to be discretized
values_orders (dict[str, GroupedList], optional) – Dict of column names and there associated ordering. If lists are passed, a GroupedList will automatically be initiated, by default None
copy (bool, optional) – If True, applies transform to a copy of the provided DataFrame, by default False
verbose (bool, optional) – If True, prints raw Discretizers Fit and Transform steps, by default False
str_nan (str, optional) – String representation to input numpy.nan. If dropna=False, numpy.nan will be left unchanged, by default "__NAN__"

Examples

See Discretizers examples

fit(X: DataFrame, y: Series | None = None) → None

Learns simple discretization of values of X according to values of y.

Parameters:

X (DataFrame) – Training dataset, to determine features’ optimal carving Needs to have columns has specified in features attribute.
y (Series) – Target with wich the association is maximized.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

summary() → DataFrame

Summarizes the data discretization process.

Returns:: A summary of features’ values per modalities.
Return type:: DataFrame

to_json() → str

Converts to .json format.

To be used with json.dump.

Returns:: JSON serialized object
Return type:: str

transform(X: DataFrame, y: Series = None) → DataFrame

Applies discretization to a DataFrame’s columns.

Parameters:

X (DataFrame) – Dataset to be carved. Needs to have columns has specified in features attribute.
y (Series, optional) – Target, by default None

Returns:

Discretized X.

Return type:

DataFrame

GroupedList

Note

AutoCarver would not exist without GroupedList. It allows for a complete historization of the data processing steps, thanks to its content dictionnary attribute. All modalities are stored inside the GroupedList and can safely be linked to there respective group label.

class AutoCarver.discretizers.GroupedList(iterable: ndarray | dict | list | tuple = ())

An ordered list that’s extended with a per-value content dict.

Parameters:: iterable (Union[ndarray, dict, list, tuple], optional) – List-like or GroupedList, by default ()

append(new_value: Any) → None

Appends a new_value to the GroupedList

Parameters:: new_value (Any) – New key to be added.

contains(value: Any) → bool

Checks if a value is content in any group, also matches NaNs.

Parameters:: value (Any) – Value to search for
Returns:: Whether the value is in the GroupedList
Return type:: bool

get(key: Any, default: Any | None = None) → list[Any]

List of values content in key

Parameters:

key (Any) – Group.
default (Any, optional) – Value to return if key was not found, by default None

Returns:

Values content in key

Return type:

list[Any]

get_group(value: Any) → Any

Returns the key (group) containing the specified value

Parameters:: value (Any) – Value for which to find the group.
Returns:: Corresponding key (group)
Return type:: Any

get_repr(char_limit: int = 6) → list[str]

Returns a representative list of strings of values of groups.

Parameters:: char_limit (int, optional) – Maximum number of character per string, by default 6
Returns:: List of short str representation of the keys’ values
Return type:: list[str]

group(discarded: Any, kept: Any) → None

Groups the discarded value with the kept value

Parameters:

discarded (Any) – Value to be grouped into the key to_keep.
kept (Any) – Key value in which to group discarded.

group_list(to_discard: list[Any], to_keep: Any) → None

Groups elements to_discard into values to_keep

Parameters:

to_discard (list[Any]) – Values to be grouped into the key to_keep.
to_keep (Any) – Key value in which to group to_discard values.

pop(idx: int) → None

Pop a value from the GroupedList by index

Parameters:: idx (int) – Index of the value to be popped out

remove(value: Any) → None

Removes a value from the GroupedList

Parameters:: value (Any) – value to be removed

sort()

Sorts the values of the list and dict (if any, NaNs are last).

Returns:: Sorted GroupedList
Return type:: GroupedList

sort_by(ordering: list[Any]) → None

Sorts the values of the list and dict according to ordering, if any, NaNs are the last.

Parameters:: ordering (list[Any]) – Order used for ordering of the list of keys.
Returns:: Sorted GroupedList
Return type:: GroupedList

update(new_value: dict[Any, list[Any]]) → None

Updates the GroupedList via a dict

Parameters:: new_value (dict[Any, list[Any]]) – Dict of key, values to updated content dict

values() → list[Any]

All values content in all groups

Returns:: List of all values in the GroupedList
Return type:: list[Any]