mlxtend version: 0.23.1

CopyTransformer

CopyTransformer()

Transformer that returns a copy of the input array

For usage examples, please see
https://rasbt.github.io/mlxtend/user_guide/preprocessing/CopyTransformer/

Methods

fit(X, y=None)

Mock method. Does nothing.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

self

fit_transform(X, y=None)

Return a copy of the input array.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

  • X_copy : copy of the input X array.

get_params(deep=True)

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.

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.

Parameters

  • **params : dict

    Estimator parameters.

Returns

  • self : estimator instance

    Estimator instance.

transform(X, y=None)

Return a copy of the input array.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

  • X_copy : copy of the input X array.

DenseTransformer

DenseTransformer(return_copy=True)

Convert a sparse array into a dense array.

For usage examples, please see
https://rasbt.github.io/mlxtend/user_guide/preprocessing/DenseTransformer/

Methods

fit(X, y=None)

Mock method. Does nothing.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

self

fit_transform(X, y=None)

Return a dense version of the input array.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

  • X_dense : dense version of the input X array.

get_params(deep=True)

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.

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.

Parameters

  • **params : dict

    Estimator parameters.

Returns

  • self : estimator instance

    Estimator instance.

transform(X, y=None)

Return a dense version of the input array.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Training vectors, where n_samples is the number of samples and n_features is the number of features.

  • y : array-like, shape = [n_samples] (default: None)

Returns

  • X_dense : dense version of the input X array.

MeanCenterer

MeanCenterer()

Column centering of vectors and matrices.

Attributes

  • col_means : numpy.ndarray [n_columns]

    NumPy array storing the mean values for centering after fitting the MeanCenterer object.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/MeanCenterer/

Methods

fit(X)

Gets the column means for mean centering.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

self

fit_transform(X)

Fits and transforms an arry.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

  • X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]

    A copy of the input array with the columns centered.

transform(X)

Centers a NumPy array.

Parameters

  • X : {array-like, sparse matrix}, shape = [n_samples, n_features]

    Array of data vectors, where n_samples is the number of samples and n_features is the number of features.

Returns

  • X_tr : {array-like, sparse matrix}, shape = [n_samples, n_features]

    A copy of the input array with the columns centered.

TransactionEncoder

TransactionEncoder()

Encoder class for transaction data in Python lists

Parameters

None

Attributes

columns_: list List of unique names in the X input list of lists

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/TransactionEncoder/

Methods

fit(X)

Learn unique column names from transaction DataFrame

Parameters

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

    For example, [['Apple', 'Beer', 'Rice', 'Chicken'], ['Apple', 'Beer', 'Rice'], ['Apple', 'Beer'], ['Apple', 'Bananas'], ['Milk', 'Beer', 'Rice', 'Chicken'], ['Milk', 'Beer', 'Rice'], ['Milk', 'Beer'], ['Apple', 'Bananas']]

fit_transform(X, sparse=False)

Fit a TransactionEncoder encoder and transform a dataset.

get_params(deep=True)

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.

inverse_transform(array)

Transforms an encoded NumPy array back into transactions.

Parameters

  • array : NumPy array [n_transactions, n_unique_items]

    The NumPy one-hot encoded boolean array of the input transactions, where the columns represent the unique items found in the input array in alphabetic order

    For example, array([[True , False, True , True , False, True ], [True , False, True , False, False, True ], [True , False, True , False, False, False], [True , True , False, False, False, False], [False, False, True , True , True , True ], [False, False, True , False, True , True ], [False, False, True , False, True , False], [True , True , False, False, False, False]]) The corresponding column labels are available as self.columns_, e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']

Returns

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

    For example, [['Apple', 'Beer', 'Rice', 'Chicken'], ['Apple', 'Beer', 'Rice'], ['Apple', 'Beer'], ['Apple', 'Bananas'], ['Milk', 'Beer', 'Rice', 'Chicken'], ['Milk', 'Beer', 'Rice'], ['Milk', 'Beer'], ['Apple', 'Bananas']]

set_output(, transform=None)*

Set output container.

See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
for an example on how to use the API.

Parameters

  • transform : {"default", "pandas"}, default=None

    Configure output of transform and fit_transform.

    • "default": Default output format of a transformer
    • "pandas": DataFrame output
    • None: Transform configuration is unchanged

Returns

  • self : estimator instance

    Estimator instance.

set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.

Parameters

  • **params : dict

    Estimator parameters.

Returns

  • self : estimator instance

    Estimator instance.

transform(X, sparse=False)

Transform transactions into a one-hot encoded NumPy array.

Parameters

  • X : list of lists

    A python list of lists, where the outer list stores the n transactions and the inner list stores the items in each transaction.

    For example, [['Apple', 'Beer', 'Rice', 'Chicken'], ['Apple', 'Beer', 'Rice'], ['Apple', 'Beer'], ['Apple', 'Bananas'], ['Milk', 'Beer', 'Rice', 'Chicken'], ['Milk', 'Beer', 'Rice'], ['Milk', 'Beer'], ['Apple', 'Bananas']]

    sparse: bool (default=False) If True, transform will return Compressed Sparse Row matrix instead of the regular one.

Returns

  • array : NumPy array [n_transactions, n_unique_items]

    if sparse=False (default). Compressed Sparse Row matrix otherwise The one-hot encoded boolean array of the input transactions, where the columns represent the unique items found in the input array in alphabetic order. Exact representation depends on the sparse argument

    For example, array([[True , False, True , True , False, True ], [True , False, True , False, False, True ], [True , False, True , False, False, False], [True , True , False, False, False, False], [False, False, True , True , True , True ], [False, False, True , False, True , True ], [False, False, True , False, True , False], [True , True , False, False, False, False]]) The corresponding column labels are available as self.columns_, e.g., ['Apple', 'Bananas', 'Beer', 'Chicken', 'Milk', 'Rice']

minmax_scaling

minmax_scaling(array, columns, min_val=0, max_val=1)

Min max scaling of pandas' DataFrames.

Parameters

  • array : pandas DataFrame or NumPy ndarray, shape = [n_rows, n_columns].

  • columns : array-like, shape = [n_columns]

    Array-like with column names, e.g., ['col1', 'col2', ...] or column indices [0, 2, 4, ...]

  • min_val : int or float, optional (default=0)

    minimum value after rescaling.

  • max_val : int or float, optional (default=1)

    maximum value after rescaling.

Returns

  • df_new : pandas DataFrame object.

    Copy of the array or DataFrame with rescaled columns.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/minmax_scaling/

one_hot

one_hot(y, num_labels='auto', dtype='float')

One-hot encoding of class labels

Parameters

  • y : array-like, shape = [n_classlabels]

    Python list or numpy array consisting of class labels.

  • num_labels : int or 'auto'

    Number of unique labels in the class label array. Infers the number of unique labels from the input array if set to 'auto'.

  • dtype : str

    NumPy array type (float, float32, float64) of the output array.

Returns

  • ary : numpy.ndarray, shape = [n_classlabels]

    One-hot encoded array, where each sample is represented as a row vector in the returned array.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/

shuffle_arrays_unison

shuffle_arrays_unison(arrays, random_seed=None)

Shuffle NumPy arrays in unison.

Parameters

  • arrays : array-like, shape = [n_arrays]

    A list of NumPy arrays.

  • random_seed : int (default: None)

    Sets the random state.

Returns

  • shuffled_arrays : A list of NumPy arrays after shuffling.

Examples

```
>>> import numpy as np
>>> from mlxtend.preprocessing import shuffle_arrays_unison
>>> X1 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> y1 = np.array([1, 2, 3])
>>> X2, y2 = shuffle_arrays_unison(arrays=[X1, y1], random_seed=3)
>>> assert(X2.all() == np.array([[4, 5, 6], [1, 2, 3], [7, 8, 9]]).all())
>>> assert(y2.all() == np.array([2, 1, 3]).all())
>>>

For more usage examples, please see
https://rasbt.github.io/mlxtend/user_guide/preprocessing/shuffle_arrays_unison/

```

standardize

standardize(array, columns=None, ddof=0, return_params=False, params=None)

Standardize columns in pandas DataFrames.

Parameters

  • array : pandas DataFrame or NumPy ndarray, shape = [n_rows, n_columns].

  • columns : array-like, shape = [n_columns] (default: None)

    Array-like with column names, e.g., ['col1', 'col2', ...] or column indices [0, 2, 4, ...] If None, standardizes all columns.

  • ddof : int (default: 0)

    Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • return_params : dict (default: False)

    If set to True, a dictionary is returned in addition to the standardized array. The parameter dictionary contains the column means ('avgs') and standard deviations ('stds') of the individual columns.

  • params : dict (default: None)

    A dictionary with column means and standard deviations as returned by the standardize function if return_params was set to True. If a params dictionary is provided, the standardize function will use these instead of computing them from the current array.

Notes

If all values in a given column are the same, these values are all set to 0.0. The standard deviation in the parameters dictionary is consequently set to 1.0 to avoid dividing by zero.

Returns

  • df_new : pandas DataFrame object.

    Copy of the array or DataFrame with standardized columns.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/preprocessing/standardize/