Utilities for Developers

Scikit-learn contains a number of utilities to help with development. These are located in sklearn.utils, and include tools in a number of categories. All the following functions and classes are in the module sklearn.utils.

Warning

These utilities are meant to be used internally within the scikit-learn package. They are not guaranteed to be stable between versions of scikit-learn. Backports, in particular, will be removed as the scikit-learn dependencies evolve.

Validation Tools

These are tools used to check and validate input. When you write a function which accepts arrays, matrices, or sparse matrices as arguments, the following should be used when applicable.

  • assert_all_finite: Throw an error if array contains NaNs or Infs.
  • as_float_array: convert input to an array of floats. If a sparse matrix is passed, a sparse matrix will be returned.
  • check_array: check that input is a 2D array, raise error on sparse matrices. Allowed sparse matrix formats can be given optionally, as well as allowing 1D or N-dimensional arrays. Calls assert_all_finite by default.
  • check_X_y: check that X and y have consistent length, calls check_array on X, and column_or_1d on y. For multilabel classification or multitarget regression, specify multi_output=True, in which case check_array will be called on y.
  • indexable: check that all input arrays have consistent length and can be sliced or indexed using safe_index. This is used to validate input for cross-validation.
  • validation.check_memory checks that input is joblib.Memory-like, which means that it can be converted into a sklearn.utils.Memory instance (typically a str denoting the cachedir) or has the same interface.

If your code relies on a random number generator, it should never use functions like numpy.random.random or numpy.random.normal. This approach can lead to repeatability issues in unit tests. Instead, a numpy.random.RandomState object should be used, which is built from a random_state argument passed to the class or function. The function check_random_state, below, can then be used to create a random number generator object.

  • check_random_state: create a np.random.RandomState object from a parameter random_state.
    • If random_state is None or np.random, then a randomly-initialized RandomState object is returned.
    • If random_state is an integer, then it is used to seed a new RandomState object.
    • If random_state is a RandomState object, then it is passed through.

For example:

>>> from sklearn.utils import check_random_state
>>> random_state = 0
>>> random_state = check_random_state(random_state)
>>> random_state.rand(4)
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318])

When developing your own scikit-learn compatible estimator, the following helpers are available.

  • validation.check_is_fitted: check that the estimator has been fitted before calling transform, predict, or similar methods. This helper allows to raise a standardized error message across estimator.
  • validation.has_fit_parameter: check that a given parameter is supported in the fit method of a given estimator.

Efficient Linear Algebra & Array Operations

Efficient Random Sampling

Efficient Routines for Sparse Matrices

The sklearn.utils.sparsefuncs cython module hosts compiled extensions to efficiently process scipy.sparse data.

Graph Routines

  • graph.single_source_shortest_path_length: (not currently used in scikit-learn) Return the shortest path from a single source to all connected nodes on a graph. Code is adapted from networkx. If this is ever needed again, it would be far faster to use a single iteration of Dijkstra’s algorithm from graph_shortest_path.
  • graph_shortest_path.graph_shortest_path: (used in sklearn.manifold.Isomap) Return the shortest path between all pairs of connected points on a directed or undirected graph. Both the Floyd-Warshall algorithm and Dijkstra’s algorithm are available. The algorithm is most efficient when the connectivity matrix is a scipy.sparse.csr_matrix.

Benchmarking

  • bench.total_seconds (back-ported from timedelta.total_seconds in Python 2.7). Used in benchmarks/bench_glm.py.

Testing Functions

Multiclass and multilabel utility function

Helper Functions

  • gen_even_slices: generator to create n-packs of slices going up to n. Used in sklearn.decomposition.dict_learning and sklearn.cluster.k_means.
  • safe_mask: Helper function to convert a mask to the format expected by the numpy array or scipy sparse matrix on which to use it (sparse matrices support integer indices only while numpy arrays support both boolean masks and integer indices).
  • safe_sqr: Helper function for unified squaring (**2) of array-likes, matrices and sparse matrices.

Hash Functions

  • murmurhash3_32 provides a python wrapper for the MurmurHash3_x86_32 C++ non cryptographic hash function. This hash function is suitable for implementing lookup tables, Bloom filters, Count Min Sketch, feature hashing and implicitly defined sparse random projections:

    >>> from sklearn.utils import murmurhash3_32
>>> murmurhash3_32("some feature", seed=0) == -384616559
True

>>> murmurhash3_32("some feature", seed=0, positive=True) == 3910350737
True

    The sklearn.utils.murmurhash module can also be “cimported” from other cython modules so as to benefit from the high performance of MurmurHash while skipping the overhead of the Python interpreter.

Warnings and Exceptions