arjoly

# Author: Arnaud Joly
# Licence: BSD3

import numpy as np

from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.base import RegressorMixin
from sklearn.base import clone
from sklearn.externals.joblib import Memory

arjoly

import numpy as np
from time import time
from sklearn.datasets import make_multilabel_classification

X, y = make_multilabel_classification(n_samples=10000, sparse=True,
                                      random_state=0, return_indicator=True)


from sklearn.ensemble import RandomForestClassifier

arjoly

import gc
import argparse
import sys
import time
import warnings

from sklearn.neural_network import ELMClassifier
from sklearn.datasets import make_classification

ELMClassifier._fit = profile(ELMClassifier._fit)

arjoly

import gc
import argparse
import sys
import numpy as np
import joblib
import time
import scipy.sparse as sp
import warnings
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multiclass import fit_ovr

arjoly

from __future__ import division
from __future__ import print_function

from timeit import timeit
from functools import partial

# from sklearn.metrics import roc_auc_score
# from sklearn.metrics import average_precision_score
from sklearn.metrics import label_ranking_average_precision_score

arjoly

""" Some results on the 20 news dataset

Classifier   train-time test-time error-rate
--------------------------------------------
5-nn            0.0047s    13.6651s    0.5916
random forest 263.3146s   3.9985s     0.2459
sgd             0.2265s    0.0657s     0.2604

"""

arjoly

from __future__ import print_function
from __future__ import division

from collections import defaultdict
from functools import partial
from pprint import pprint
import numpy as np

from sklearn.datasets import fetch_mldata

arjoly

def _check_clf_targets(y_true, y_pred):
    """Check that y_true and y_pred belong to the same classification task

    This converts multiclass or binary types to a common shape, and raises a
    ValueError for a mix of multilabel and multiclass targets, a mix of
    multilabel formats, for the presence of continuous-valued or multioutput
    targets, or for targets of different lengths.

    Column vectors are squeezed to 1d.

arjoly

def test_unique_labels():
    # Empty iterable
    assert_raises(ValueError, unique_labels)

    # Multiclass problem
    assert_array_equal(unique_labels(xrange(10)), np.arange(10))
    assert_array_equal(unique_labels(np.arange(10)), np.arange(10))
    assert_array_equal(unique_labels([4, 0, 2]), np.array([0, 2, 4]))

    # Multilabels

arjoly

def test_unique_labels():
    # Empty iterable
    assert_raises(ValueError, unique_labels)

    # Multiclass problem
    assert_array_equal(unique_labels(xrange(10)), np.arange(10))
    assert_array_equal(unique_labels(np.arange(10)), np.arange(10))
    assert_array_equal(unique_labels([4, 0, 2]), np.array([0, 2, 4]))

    # Multilabels