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# ===========================
# Testing (session) Fixture
# ==========================
import pytest
import numpy as np
from scipy import sparse
from sklearn.datasets import load_iris
from umap import UMAP
# Globals, used for all the tests
np.random.seed(42)
# Spatial and Binary Data
# -----------------------
@pytest.fixture(scope="session")
def spatial_data():
# - Spatial Data
spatial_data = np.random.randn(10, 20)
# Add some all zero data for corner case test
return np.vstack([spatial_data, np.zeros((2, 20))])
@pytest.fixture(scope="session")
def binary_data():
binary_data = np.random.choice(a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66])
# Add some all zero data for corner case test
binary_data = np.vstack([binary_data, np.zeros((2, 20), dtype="bool")])
return binary_data
# Sparse Spatial and Binary Data
# ------------------------------
@pytest.fixture(scope="session")
def sparse_spatial_data(spatial_data, binary_data):
return sparse.csr_matrix(spatial_data * binary_data)
@pytest.fixture(scope="session")
def sparse_binary_data(binary_data):
return sparse.csr_matrix(binary_data)
# Nearest Neighbour Data
# -----------------------
@pytest.fixture(scope="session")
def nn_data():
nn_data = np.random.uniform(0, 1, size=(1000, 5))
nn_data = np.vstack(
[nn_data, np.zeros((2, 5))]
) # Add some all zero data for corner case test
return nn_data
@pytest.fixture(scope="session")
def binary_nn_data():
binary_nn_data = np.random.choice(
a=[False, True], size=(1000, 5), p=[0.66, 1 - 0.66]
)
binary_nn_data = np.vstack(
[binary_nn_data, np.zeros((2, 5), dtype="bool")]
) # Add some all zero data for corner case test
return binary_nn_data
@pytest.fixture(scope="session")
def sparse_nn_data():
return sparse.random(1000, 50, density=0.5, format="csr")
# Data With Repetitions
# ---------------------
@pytest.fixture(scope="session")
def repetition_dense():
# Dense data for testing small n
return np.array(
[
[5, 6, 7, 8],
[5, 6, 7, 8],
[5, 6, 7, 8],
[5, 6, 7, 8],
[5, 6, 7, 8],
[5, 6, 7, 8],
[1, 1, 1, 1],
[1, 2, 3, 4],
[1, 1, 2, 1],
]
)
@pytest.fixture(scope="session")
def spatial_repeats(spatial_data):
# spatial data repeats
spatial_repeats = np.vstack(
[np.repeat(spatial_data[0:2], [2, 0], axis=0), spatial_data, np.zeros((2, 20))]
)
# Add some all zero data for corner case test. Make the first three rows identical
# binary Data Repeat
return spatial_repeats
@pytest.fixture(scope="session")
def binary_repeats(binary_data):
binary_repeats = np.vstack(
[
np.repeat(binary_data[0:2], [2, 0], axis=0),
binary_data,
np.zeros((2, 20), dtype="bool"),
]
)
# Add some all zero data for corner case test. Make the first three rows identical
return binary_repeats
@pytest.fixture(scope="session")
def sparse_spatial_data_repeats(spatial_repeats, binary_repeats):
return sparse.csr_matrix(spatial_repeats * binary_repeats)
@pytest.fixture(scope="session")
def sparse_binary_data_repeats(binary_repeats):
return sparse.csr_matrix(binary_repeats)
@pytest.fixture(scope="session")
def sparse_test_data(nn_data, binary_nn_data):
return sparse.csr_matrix(nn_data * binary_nn_data)
@pytest.fixture(scope="session")
def iris():
return load_iris()
@pytest.fixture(scope="session")
def iris_selection():
return np.random.choice([True, False], 150, replace=True, p=[0.75, 0.25])
@pytest.fixture(scope="session")
def iris_model(iris):
return UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(iris.data)
@pytest.fixture(scope="session")
def supervised_iris_model(iris):
return UMAP(n_neighbors=10, min_dist=0.01, n_epochs=200, random_state=42).fit(
iris.data, iris.target
)
# UMAP Distance Metrics
# ---------------------
@pytest.fixture(scope="session")
def spatial_distances():
return (
"euclidean",
"manhattan",
"chebyshev",
"minkowski",
"hamming",
"canberra",
"braycurtis",
"cosine",
"correlation",
)
@pytest.fixture(scope="session")
def binary_distances():
return (
"jaccard",
"matching",
"dice",
"kulsinski",
"rogerstanimoto",
"russellrao",
"sokalmichener",
"sokalsneath",
"yule",
)
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