85 lines
2.4 KiB
Python
85 lines
2.4 KiB
Python
|
# Author: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
|
||
|
|
#
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import assert_array_equal
|
||
|
|
import scipy.sparse as sp
|
||
|
|
|
||
|
|
from sklearn.utils.seq_dataset import ArrayDataset, CSRDataset
|
||
|
|
from sklearn.datasets import load_iris
|
||
|
|
|
||
|
|
from sklearn.utils.testing import assert_equal
|
||
|
|
|
||
|
|
iris = load_iris()
|
||
|
|
X = iris.data.astype(np.float64)
|
||
|
|
y = iris.target.astype(np.float64)
|
||
|
|
X_csr = sp.csr_matrix(X)
|
||
|
|
sample_weight = np.arange(y.size, dtype=np.float64)
|
||
|
|
|
||
|
|
|
||
|
|
def assert_csr_equal(X, Y):
|
||
|
|
X.eliminate_zeros()
|
||
|
|
Y.eliminate_zeros()
|
||
|
|
assert_equal(X.shape[0], Y.shape[0])
|
||
|
|
assert_equal(X.shape[1], Y.shape[1])
|
||
|
|
assert_array_equal(X.data, Y.data)
|
||
|
|
assert_array_equal(X.indices, Y.indices)
|
||
|
|
assert_array_equal(X.indptr, Y.indptr)
|
||
|
|
|
||
|
|
|
||
|
|
def test_seq_dataset():
|
||
|
|
dataset1 = ArrayDataset(X, y, sample_weight, seed=42)
|
||
|
|
dataset2 = CSRDataset(X_csr.data, X_csr.indptr, X_csr.indices,
|
||
|
|
y, sample_weight, seed=42)
|
||
|
|
|
||
|
|
for dataset in (dataset1, dataset2):
|
||
|
|
for i in range(5):
|
||
|
|
# next sample
|
||
|
|
xi_, yi, swi, idx = dataset._next_py()
|
||
|
|
xi = sp.csr_matrix((xi_), shape=(1, X.shape[1]))
|
||
|
|
|
||
|
|
assert_csr_equal(xi, X_csr[idx])
|
||
|
|
assert_equal(yi, y[idx])
|
||
|
|
assert_equal(swi, sample_weight[idx])
|
||
|
|
|
||
|
|
# random sample
|
||
|
|
xi_, yi, swi, idx = dataset._random_py()
|
||
|
|
xi = sp.csr_matrix((xi_), shape=(1, X.shape[1]))
|
||
|
|
|
||
|
|
assert_csr_equal(xi, X_csr[idx])
|
||
|
|
assert_equal(yi, y[idx])
|
||
|
|
assert_equal(swi, sample_weight[idx])
|
||
|
|
|
||
|
|
|
||
|
|
def test_seq_dataset_shuffle():
|
||
|
|
dataset1 = ArrayDataset(X, y, sample_weight, seed=42)
|
||
|
|
dataset2 = CSRDataset(X_csr.data, X_csr.indptr, X_csr.indices,
|
||
|
|
y, sample_weight, seed=42)
|
||
|
|
|
||
|
|
# not shuffled
|
||
|
|
for i in range(5):
|
||
|
|
_, _, _, idx1 = dataset1._next_py()
|
||
|
|
_, _, _, idx2 = dataset2._next_py()
|
||
|
|
assert_equal(idx1, i)
|
||
|
|
assert_equal(idx2, i)
|
||
|
|
|
||
|
|
for i in range(5):
|
||
|
|
_, _, _, idx1 = dataset1._random_py()
|
||
|
|
_, _, _, idx2 = dataset2._random_py()
|
||
|
|
assert_equal(idx1, idx2)
|
||
|
|
|
||
|
|
seed = 77
|
||
|
|
dataset1._shuffle_py(seed)
|
||
|
|
dataset2._shuffle_py(seed)
|
||
|
|
|
||
|
|
for i in range(5):
|
||
|
|
_, _, _, idx1 = dataset1._next_py()
|
||
|
|
_, _, _, idx2 = dataset2._next_py()
|
||
|
|
assert_equal(idx1, idx2)
|
||
|
|
|
||
|
|
_, _, _, idx1 = dataset1._random_py()
|
||
|
|
_, _, _, idx2 = dataset2._random_py()
|
||
|
|
assert_equal(idx1, idx2)
|
||
|
|
|