1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
|
# -*- coding: utf-8 -*-
"""
Utility functions for GenSVM
"""
import numpy as np
from sklearn.exceptions import NotFittedError
def get_ranks(a):
"""
Rank data in an array. Low values get a small rank number. Ties are broken
by assigning the lowest value (this corresponds to ``rankdata(a,
method='min')`` in SciPy.
Examples
--------
>>> x = [7, 0.1, 0.5, 0.1, 10, 100, 200]
>>> get_ranks(x)
[4, 1, 3, 1, 5, 6, 7]
"""
orig = np.ravel(np.asarray(a))
arr = orig[~np.isnan(orig)]
sorter = np.argsort(arr, kind="quicksort")
inv = np.empty(sorter.size, dtype=np.intp)
inv[sorter] = np.arange(sorter.size, dtype=np.intp)
arr = arr[sorter]
obs = np.r_[True, arr[1:] != arr[:-1]]
dense = obs.cumsum()[inv]
count = np.r_[np.nonzero(obs)[0], len(obs)]
ranks = np.zeros_like(orig)
ranks[~np.isnan(orig)] = count[dense - 1] + 1
ranks[np.isnan(orig)] = np.max(ranks) + 1
return list(ranks)
def check_is_fitted(estimator, attribute):
msg = (
"This %(name)s instance is not fitted yet. Call 'fit' "
"with appropriate arguments before using this estimator."
)
if not hasattr(estimator, "fit"):
raise TypeError("%s is not an estimator instance" % (estimator))
if not hasattr(estimator, attribute):
raise NotFittedError(msg % {"name": type(estimator).__name__})
|
