def test_subsample_classification_unique_labels_stay_in_training_set(task, y):
n_samples = 10000
X = np.random.random(size=(n_samples, 3))
memory_limit = 1 # Force subsampling
mock = unittest.mock.Mock()
# Make sure our test assumptions are correct
assert len(y) == n_samples, "Ensure tests are correctly setup"
values, counts = np.unique(y, axis=0, return_counts=True)
unique_labels = [
value for value, count in zip(values, counts) if count == 1
]
assert len(unique_labels), "Ensure we have unique labels in the test"
_, y_sampled = AutoML.subsample_if_too_large(X,
y,
logger=mock,
seed=1,
memory_limit=memory_limit,
task=task)
assert len(y_sampled) <= len(y), \
"Ensure sampling took place"
assert all(label in y_sampled for label in unique_labels), \
"All unique labels present in the return sampled set"
def test_subsample_if_too_large(memory_limit, task):
fixture = {
BINARY_CLASSIFICATION: {
1: 436,
10: 569,
None: 569
},
MULTICLASS_CLASSIFICATION: {
1: 204,
10: 1797,
None: 1797
},
MULTILABEL_CLASSIFICATION: {
1: 204,
10: 1797,
None: 1797
},
REGRESSION: {
1: 1310,
10: 1326,
None: 1326
},
MULTIOUTPUT_REGRESSION: {
1: 1310,
10: 1326,
None: 1326
}
}
mock = unittest.mock.Mock()
if task == BINARY_CLASSIFICATION:
X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
elif task == MULTICLASS_CLASSIFICATION:
X, y = sklearn.datasets.load_digits(return_X_y=True)
elif task == MULTILABEL_CLASSIFICATION:
X, y_ = sklearn.datasets.load_digits(return_X_y=True)
y = np.zeros((X.shape[0], 10))
for i, j in enumerate(y_):
y[i, j] = 1
elif task == REGRESSION:
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
X = np.vstack((X, X, X))
y = np.vstack((y.reshape((-1, 1)), y.reshape(
(-1, 1)), y.reshape((-1, 1))))
elif task == MULTIOUTPUT_REGRESSION:
X, y = sklearn.datasets.load_diabetes(return_X_y=True)
y = np.vstack((y, y)).transpose()
X = np.vstack((X, X, X))
y = np.vstack((y, y, y))
else:
raise ValueError(task)
assert X.shape[0] == y.shape[0]
X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit,
task)
assert X_new.shape[0] == fixture[task][memory_limit]
if memory_limit == 1:
assert mock.warning.call_count == 1
else:
assert mock.warning.call_count == 0
def test_subsample_if_too_large(memory_limit, precision, task):
fixture = {
BINARY_CLASSIFICATION: {
1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655},
100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000},
None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000},
},
MULTICLASS_CLASSIFICATION: {
1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102},
100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
},
MULTILABEL_CLASSIFICATION: {
1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102},
100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797},
},
REGRESSION: {
1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327},
100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
},
MULTIOUTPUT_REGRESSION: {
1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327},
100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000},
}
}
mock = unittest.mock.Mock()
if task == BINARY_CLASSIFICATION:
X, y = sklearn.datasets.make_hastie_10_2()
elif task == MULTICLASS_CLASSIFICATION:
X, y = sklearn.datasets.load_digits(return_X_y=True)
elif task == MULTILABEL_CLASSIFICATION:
X, y_ = sklearn.datasets.load_digits(return_X_y=True)
y = np.zeros((X.shape[0], 10))
for i, j in enumerate(y_):
y[i, j] = 1
elif task == REGRESSION:
X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20)
elif task == MULTIOUTPUT_REGRESSION:
X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20)
y = np.vstack((y, y)).transpose()
else:
raise ValueError(task)
X = X.astype(precision)
assert X.shape[0] == y.shape[0]
X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task)
assert X_new.shape[0] == fixture[task][memory_limit][precision]
if memory_limit == 1:
if precision in (np.float128, np.float64, float):
assert mock.warning.call_count == 2
else:
assert mock.warning.call_count == 1
else:
assert mock.warning.call_count == 0
