sklearn.datasets.load_files

sklearn.datasets.load_files(container_path, description=None, categories=None, load_content=True, shuffle=True, encoding=None, decode_error='strict', random_state=0)

Load text files with categories as subfolder names.

Individual samples are assumed to be files stored a two levels folder structure such as the following:

container_folder/

category_1_folder/

file_1.txt file_2.txt ... file_42.txt

category_2_folder/

file_43.txt file_44.txt ...

The folder names are used as supervised signal label names. The individual file names are not important.

This function does not try to extract features into a numpy array or scipy sparse matrix. In addition, if load_content is false it does not try to load the files in memory.

To use text files in a scikit-learn classification or clustering algorithm, you will need to use the sklearn.feature_extraction.text module to build a feature extraction transformer that suits your problem.

If you set load_content=True, you should also specify the encoding of the text using the ‘encoding’ parameter. For many modern text files, ‘utf-8’ will be the correct encoding. If you leave encoding equal to None, then the content will be made of bytes instead of Unicode, and you will not be able to use most functions in sklearn.feature_extraction.text.

Similar feature extractors should be built for other kind of unstructured data input such as images, audio, video, ...

Parameters:

container_path : string or unicode

Path to the main folder holding one subfolder per category

description: string or unicode, optional (default=None) :

A paragraph describing the characteristic of the dataset: its source, reference, etc.

categories : A collection of strings or None, optional (default=None)

If None (default), load all the categories. If not None, list of category names to load (other categories ignored).

load_content : boolean, optional (default=True)

Whether to load or not the content of the different files. If true a ‘data’ attribute containing the text information is present in the data structure returned. If not, a filenames attribute gives the path to the files.

encoding : string or None (default is None)

If None, do not try to decode the content of the files (e.g. for images or other non-text content). If not None, encoding to use to decode text files to Unicode if load_content is True.

decode_error: {‘strict’, ‘ignore’, ‘replace’}, optional :

Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. Passed as keyword argument ‘errors’ to bytes.decode.

shuffle : bool, optional (default=True)

Whether or not to shuffle the data: might be important for models that make the assumption that the samples are independent and identically distributed (i.i.d.), such as stochastic gradient descent.

random_state : int, RandomState instance or None, optional (default=0)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Returns:

data : Bunch

Dictionary-like object, the interesting attributes are: either data, the raw text data to learn, or ‘filenames’, the files holding it, ‘target’, the classification labels (integer index), ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.