def make_fixture(binary=False, balanced=False, split=False):
"""
Make a dataset for testing ClassBalance based on the specified params.
"""
kwargs = {
"n_samples": 100,
"n_features": 20,
"n_informative": 8,
"n_redundant": 2,
"n_clusters_per_class": 1,
"random_state": 89092,
}
if binary:
kwargs["n_classes"] = 2
kwargs["weights"] = None if balanced else [0.3, 0.7]
else:
kwargs["n_classes"] = 5
kwargs["weights"] = None if balanced else [0.1, 0.2, 0.4, 0.2, 0.01]
X, y = make_classification(**kwargs)
if split:
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=101)
return Dataset(Split(X_train, X_test), Split(y_train, y_test))
return Dataset(X, y)
def s_curves(request):
"""
Creates a random regressor fixture.
"""
X, y = make_s_curve(1000, random_state=888)
# Set a class attribute for continuous data
request.cls.s_curves = Dataset(X, y)
def clusters(request):
# TODO: replace with make_blobs
X = np.array([
[-0.40020753, -4.67055317, -0.27191127, -1.49156318],
[0.37143349, -4.89391622, -1.23893945, 0.48318165],
[8.625142, -1.2372284, 1.39301471, 4.3394457],
[7.65803596, -2.21017215, 1.99175714, 3.71004654],
[0.89319875, -5.37152317, 1.50313598, 1.95284886],
[2.68362166, -5.78810913, -0.41233406, 1.94638989],
[7.63541182, -1.99606076, 0.9241231, 4.53478238],
[9.04699415, -0.74540679, 0.98042851, 5.99569071],
[1.02552122, -5.73874278, -1.74804915, -0.07831216],
[7.18135665, -3.49473178, 1.14300963, 4.46065816],
[0.58812902, -4.66559815, -0.72831685, 1.40171779],
[1.48620862, -5.9963108, 0.19145963, -1.11369256],
[7.6625556, -1.21328083, 2.06361094, 6.2643551],
[9.45050727, -1.36536078, 1.31154384, 3.89103468],
[6.88203724, -1.62040255, 3.89961049, 2.12865388],
[5.60842705, -2.10693356, 1.93328514, 3.90825432],
[2.35150936, -6.62836131, -1.84278374, 0.51540886],
[1.17446451, -5.62506058, -2.18420699, 1.21385128],
])
y = np.array([0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0])
request.cls.clusters = Dataset(X, y)
def blobs4(request):
"""
Creates a fixture of 400 instances in 4 clusters with 16 features.
"""
X, y = make_blobs(
centers=4, n_samples=400, n_features=16, shuffle=True, random_state=1212
)
request.cls.blobs4 = Dataset(X, y)
def blobs12(request):
"""
Creates a fixture of 1000 instances in 12 clusters with 16 features.
"""
X, y = make_blobs(
centers=12, n_samples=1000, n_features=16, shuffle=True, random_state=2121
)
request.cls.blobs12 = Dataset(X, y)
def clusters(request):
"""
Creates a random regression dataset fixture
"""
X, y = make_blobs(n_samples=500, n_features=20, centers=3, random_state=743)
dataset = Dataset(X, y)
request.cls.clusters = dataset
def continuous(request):
"""
Creates a random regressor fixture.
"""
X, y = make_regression(
n_samples=500, n_features=22, n_informative=8, random_state=2019
)
# Set a class attribute for continuous data
request.cls.continuous = Dataset(X, y)
def blobs(request):
"""
Create a random blobs clustering dataset fixture
"""
X, y = make_blobs(
n_samples=1000, n_features=12, centers=6, shuffle=True, random_state=42
)
# Set a class attribute for blobs
request.cls.blobs = Dataset(X, y)
def continuous(request):
"""
Creates a random regression dataset fixture.
"""
X, y = make_regression(n_samples=500,
n_features=22,
n_informative=8,
random_state=2019)
# Dataset is accessible on the class so it is only generated once
request.cls.continuous = Dataset(X, y)
def digits(request):
"""
Creates a fixture of train and test splits for the sklearn digits dataset
For ease of use returns a Dataset named tuple composed of two Split tuples.
"""
data = load_digits()
X_train, X_test, y_train, y_test = tts(
data.data, data.target, test_size=0.2, random_state=11
)
# Set a class attribute for digits
request.cls.digits = Dataset(Split(X_train, X_test), Split(y_train, y_test))
def discrete(request):
"""
Create a random classification dataset fixture.
"""
X, y = make_classification(
n_classes=5,
n_samples=400,
n_features=12,
n_informative=10,
n_redundant=0,
random_state=2019,
)
# Dataset is accessible on the class so it is only generated once
request.cls.discrete = Dataset(X, y)
def regression(request):
"""
Creates a random regression dataset fixture
"""
X, y = make_regression(
n_samples=500,
n_features=20,
n_informative=8,
noise=0.01,
bias=1.4,
random_state=953,
)
dataset = Dataset(X, y)
request.cls.regression = dataset
def data(request):
"""
Creates a random regression fixture that has a R2 score below 0.85 and several
outliers that best demonstrate the effectiveness of influence visualizers.
"""
X, y = make_regression(
n_samples=100,
n_features=14,
n_informative=6,
bias=1.2,
noise=49.8,
tail_strength=0.6,
random_state=637,
)
request.cls.data = Dataset(X, y)
def classification(request):
"""
Creates a random multiclass classification dataset fixture
"""
X, y = make_classification(
n_samples=500,
n_features=20,
n_informative=8,
n_redundant=2,
n_classes=3,
n_clusters_per_class=3,
random_state=3902,
)
dataset = Dataset(X, y)
request.cls.classification = dataset
def dataset(request):
"""
Creates a multiclass classification dataset fixture for RFECV
"""
X, y = make_classification(
n_samples=300,
n_features=5,
n_informative=3,
n_repeated=0,
n_classes=4,
n_clusters_per_class=1,
random_state=0,
)
dataset = Dataset(X, y)
request.cls.dataset = dataset
def continuous(request):
"""
Creates a random continuous regression dataset fixture
"""
X, y = make_regression(
n_samples=500,
n_features=22,
n_informative=8,
random_state=42,
noise=0.2,
bias=0.2,
)
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=11)
# Set a class attribute for regression
request.cls.continuous = Dataset(Split(X_train, X_test), Split(y_train, y_test))
def multiclass(request):
"""
Creates a random multiclass classification dataset fixture
"""
X, y = make_classification(
n_samples=500,
n_features=20,
n_informative=8,
n_redundant=2,
n_classes=6,
n_clusters_per_class=3,
random_state=87,
)
X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=93)
dataset = Dataset(Split(X_train, X_test), Split(y_train, y_test))
request.cls.multiclass = dataset
def discrete(request):
"""
Creates a fixture of train and test splits for the sklearn digits dataset
For ease of use returns a Dataset named tuple composed of two Split tuples.
"""
X, y = make_classification(
n_samples=400,
n_features=12,
n_informative=8,
n_redundant=0,
n_classes=5,
n_clusters_per_class=1,
class_sep=1.8,
random_state=854,
scale=[14.2, 2.1, 0.32, 0.001, 32.3, 44.1, 102.3, 2.3, 2.4, 38.2, 0.05, 1.0],
)
# Set a class attribute for discrete data.
request.cls.discrete = Dataset(X, y)
def data(request):
"""
Creates a fixture of train and test splits for the sklearn digits dataset
For ease of use returns a Dataset named tuple composed of two Split tuples.
"""
X, y = make_regression(
n_samples=500,
n_features=22,
n_informative=8,
random_state=42,
noise=0.2,
bias=0.2,
)
X_train, X_test, y_train, y_test = tts(X,
y,
test_size=0.2,
random_state=11)
# Set a class attribute for digits
request.cls.data = Dataset(Split(X_train, X_test), Split(y_train, y_test))