Bunch object not callable - scikit-learn rcv1 dataset - scikit-learn

Question

I want to split the train and test set for RCV1 inbuilt dataset and apply k-means algorithm, however while trying to split the data, an error is shown saying bunch object not callable
from sklearn.datasets import fetch_rcv1
rcv1 = fetch_rcv1()
x_train = rcv1(subset='train')

Answer 1

Indeed it is not; neither it is a dataframe - see the docs. Some extra info is included in the DESCR attribute:
from sklearn.datasets import fetch_rcv1
rcv1 = fetch_rcv1()
print(rcv1.DESCR)
Result:
.. _rcv1_dataset:
RCV1 dataset
------------
Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually
categorized newswire stories made available by Reuters, Ltd. for research
purposes. The dataset is extensively described in [1]_.
**Data Set Characteristics:**
============== =====================
Classes 103
Samples total 804414
Dimensionality 47236
Features real, between 0 and 1
============== =====================
:func:`sklearn.datasets.fetch_rcv1` will load the following
version: RCV1-v2, vectors, full sets, topics multilabels::
>>> from sklearn.datasets import fetch_rcv1
>>> rcv1 = fetch_rcv1()
It returns a dictionary-like object, with the following attributes:
``data``:
The feature matrix is a scipy CSR sparse matrix, with 804414 samples and
47236 features. Non-zero values contains cosine-normalized, log TF-IDF vectors.
A nearly chronological split is proposed in [1]_: The first 23149 samples are
the training set. The last 781265 samples are the testing set. This follows
the official LYRL2004 chronological split. The array has 0.16% of non zero
values::
>>> rcv1.data.shape
(804414, 47236)
``target``:
The target values are stored in a scipy CSR sparse matrix, with 804414 samples
and 103 categories. Each sample has a value of 1 in its categories, and 0 in
others. The array has 3.15% of non zero values::
>>> rcv1.target.shape
(804414, 103)
``sample_id``:
Each sample can be identified by its ID, ranging (with gaps) from 2286
to 810596::
>>> rcv1.sample_id[:3]
array([2286, 2287, 2288], dtype=uint32)
``target_names``:
The target values are the topics of each sample. Each sample belongs to at
least one topic, and to up to 17 topics. There are 103 topics, each
represented by a string. Their corpus frequencies span five orders of
magnitude, from 5 occurrences for 'GMIL', to 381327 for 'CCAT'::
>>> rcv1.target_names[:3].tolist() # doctest: +SKIP
['E11', 'ECAT', 'M11']
The dataset will be downloaded from the `rcv1 homepage`_ if necessary.
The compressed size is about 656 MB.
.. _rcv1 homepage: http://jmlr.csail.mit.edu/papers/volume5/lewis04a/
.. topic:: References
.. [1] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004).
RCV1: A new benchmark collection for text categorization research.
The Journal of Machine Learning Research, 5, 361-397.
So, if you want to stick to the original training & test subsets, as described above, you should simply do:
X_train = rcv1.data[0:23149,]
X.train.shape
# (23149, 47236)
X_test = rcv1.data[23149:,]
X_test.shape
# (781265, 47236)
and similarly for your y_train and y_test, using rcv1.target.
If you want to use a different training & test partition, use:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
rcv1.data, rcv1.target, test_size=0.33, random_state=42)
adjusting your test_size accordingly.