Package utils

Return the CPU time or real time since the start of the process or since the first call to clock(). This has as much precision as the system records.

Returns: floating point number

Return the current time in seconds since the Epoch. Fractions of a second may be present if the system clock provides them.

Returns: floating point number

Return the CPU time or real time since the start of the process or since the first call to clock(). This has as much precision as the system records.

Returns: floating point number

This decorator can be used to mark functions as deprecated.

Useful when phasing out old API functions.

Parameters
----------
replaced_by : String. The name of the function that should be used instead.

Checks that:
    - Lists are converted to numpy arrays.
    - All arrays are cast to float.
    - All arrays have consistent first dimensions.
    - Arrays are at least 2D arrays, if not they are reshaped.

Parameters
----------
*arrays: Sequence of arrays or scipy.sparse matrices with same shape[0]
        Python lists or tuples occurring in arrays are converted to 2D
        numpy arrays.

Examples
--------
>>> import numpy as np
>>> check_arrays([1, 2], np.array([3, 4]), np.array([[1., 2.], [3., 4.]]))
[array([[ 1.],
       [ 2.]]), array([[ 3.],
       [ 4.]]), array([[ 1.,  2.],
       [ 3.,  4.]])]

Estimate class weights for unbalanced datasets.

Parameters
----------
class_weight : dict, 'auto' or None
    If 'auto', class weights will be given inverse proportional
    to the frequency of the class in the data. sample_weight will sum
    to n_sample.
    If a dictionary is given, keys are classes and values
    are corresponding class weights. With two classes in {1, 0},
    class_weight = {0:0.5, 1:0.5} is equivalent to class_weight == "auto"
    If None is given, the class weights will be uniform sample_weight==1.

y : array-like, shape (n_samples,)
    Array of original class labels per sample;

Returns
-------
weight_vect : ndarray, shape (n_samples,)
    Array with weight_vect[i] the weight for i-th sample

Example
-------
>>> y = [1, 1, 1, 0, 0, 2]
>>> w = class_weight_to_sample_weight("auto", y)
>>> print w.sum() == len(y)
True
>>> print ["%i:%.2f" % (l, np.sum(w[y==l])) for l in np.unique(y)]
['0:2.00', '1:2.00', '2:2.00']
>>> y = [1, 1, 1, 0, 0, 2]
>>> w2 = class_weight_to_sample_weight({0:1./3, 1:1./3, 2:1./3}, y)
>>> np.all(w2 == w)
True