Update tests to use data from fixtures instead of relying on sklearn

tests/conftest.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 from tensorflow.keras import backend as K
 
@@ -11,3 +12,172 @@ def clear_session_after_test():
     yield
     if K.backend() == 'tensorflow' or K.backend() == 'cntk':
         K.clear_session()
+
+@pytest.fixture(scope="function")
+def X():
+    data = np.array(
+      [[5.1, 3.5, 1.4, 0.2],
+       [4.9, 3. , 1.4, 0.2],
+       [4.7, 3.2, 1.3, 0.2],
+       [4.6, 3.1, 1.5, 0.2],
+       [5. , 3.6, 1.4, 0.2],
+       [5.4, 3.9, 1.7, 0.4],
+       [4.6, 3.4, 1.4, 0.3],
+       [5. , 3.4, 1.5, 0.2],
+       [4.4, 2.9, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.1],
+       [5.4, 3.7, 1.5, 0.2],
+       [4.8, 3.4, 1.6, 0.2],
+       [4.8, 3. , 1.4, 0.1],
+       [4.3, 3. , 1.1, 0.1],
+       [5.8, 4. , 1.2, 0.2],
+       [5.7, 4.4, 1.5, 0.4],
+       [5.4, 3.9, 1.3, 0.4],
+       [5.1, 3.5, 1.4, 0.3],
+       [5.7, 3.8, 1.7, 0.3],
+       [5.1, 3.8, 1.5, 0.3],
+       [5.4, 3.4, 1.7, 0.2],
+       [5.1, 3.7, 1.5, 0.4],
+       [4.6, 3.6, 1. , 0.2],
+       [5.1, 3.3, 1.7, 0.5],
+       [4.8, 3.4, 1.9, 0.2],
+       [5. , 3. , 1.6, 0.2],
+       [5. , 3.4, 1.6, 0.4],
+       [5.2, 3.5, 1.5, 0.2],
+       [5.2, 3.4, 1.4, 0.2],
+       [4.7, 3.2, 1.6, 0.2],
+       [4.8, 3.1, 1.6, 0.2],
+       [5.4, 3.4, 1.5, 0.4],
+       [5.2, 4.1, 1.5, 0.1],
+       [5.5, 4.2, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.2],
+       [5. , 3.2, 1.2, 0.2],
+       [5.5, 3.5, 1.3, 0.2],
+       [4.9, 3.6, 1.4, 0.1],
+       [4.4, 3. , 1.3, 0.2],
+       [5.1, 3.4, 1.5, 0.2],
+       [5. , 3.5, 1.3, 0.3],
+       [4.5, 2.3, 1.3, 0.3],
+       [4.4, 3.2, 1.3, 0.2],
+       [5. , 3.5, 1.6, 0.6],
+       [5.1, 3.8, 1.9, 0.4],
+       [4.8, 3. , 1.4, 0.3],
+       [5.1, 3.8, 1.6, 0.2],
+       [4.6, 3.2, 1.4, 0.2],
+       [5.3, 3.7, 1.5, 0.2],
+       [5. , 3.3, 1.4, 0.2],
+       [7. , 3.2, 4.7, 1.4],
+       [6.4, 3.2, 4.5, 1.5],
+       [6.9, 3.1, 4.9, 1.5],
+       [5.5, 2.3, 4. , 1.3],
+       [6.5, 2.8, 4.6, 1.5],
+       [5.7, 2.8, 4.5, 1.3],
+       [6.3, 3.3, 4.7, 1.6],
+       [4.9, 2.4, 3.3, 1. ],
+       [6.6, 2.9, 4.6, 1.3],
+       [5.2, 2.7, 3.9, 1.4],
+       [5. , 2. , 3.5, 1. ],
+       [5.9, 3. , 4.2, 1.5],
+       [6. , 2.2, 4. , 1. ],
+       [6.1, 2.9, 4.7, 1.4],
+       [5.6, 2.9, 3.6, 1.3],
+       [6.7, 3.1, 4.4, 1.4],
+       [5.6, 3. , 4.5, 1.5],
+       [5.8, 2.7, 4.1, 1. ],
+       [6.2, 2.2, 4.5, 1.5],
+       [5.6, 2.5, 3.9, 1.1],
+       [5.9, 3.2, 4.8, 1.8],
+       [6.1, 2.8, 4. , 1.3],
+       [6.3, 2.5, 4.9, 1.5],
+       [6.1, 2.8, 4.7, 1.2],
+       [6.4, 2.9, 4.3, 1.3],
+       [6.6, 3. , 4.4, 1.4],
+       [6.8, 2.8, 4.8, 1.4],
+       [6.7, 3. , 5. , 1.7],
+       [6. , 2.9, 4.5, 1.5],
+       [5.7, 2.6, 3.5, 1. ],
+       [5.5, 2.4, 3.8, 1.1],
+       [5.5, 2.4, 3.7, 1. ],
+       [5.8, 2.7, 3.9, 1.2],
+       [6. , 2.7, 5.1, 1.6],
+       [5.4, 3. , 4.5, 1.5],
+       [6. , 3.4, 4.5, 1.6],
+       [6.7, 3.1, 4.7, 1.5],
+       [6.3, 2.3, 4.4, 1.3],
+       [5.6, 3. , 4.1, 1.3],
+       [5.5, 2.5, 4. , 1.3],
+       [5.5, 2.6, 4.4, 1.2],
+       [6.1, 3. , 4.6, 1.4],
+       [5.8, 2.6, 4. , 1.2],
+       [5. , 2.3, 3.3, 1. ],
+       [5.6, 2.7, 4.2, 1.3],
+       [5.7, 3. , 4.2, 1.2],
+       [5.7, 2.9, 4.2, 1.3],
+       [6.2, 2.9, 4.3, 1.3],
+       [5.1, 2.5, 3. , 1.1],
+       [5.7, 2.8, 4.1, 1.3],
+       [6.3, 3.3, 6. , 2.5],
+       [5.8, 2.7, 5.1, 1.9],
+       [7.1, 3. , 5.9, 2.1],
+       [6.3, 2.9, 5.6, 1.8],
+       [6.5, 3. , 5.8, 2.2],
+       [7.6, 3. , 6.6, 2.1],
+       [4.9, 2.5, 4.5, 1.7],
+       [7.3, 2.9, 6.3, 1.8],
+       [6.7, 2.5, 5.8, 1.8],
+       [7.2, 3.6, 6.1, 2.5],
+       [6.5, 3.2, 5.1, 2. ],
+       [6.4, 2.7, 5.3, 1.9],
+       [6.8, 3. , 5.5, 2.1],
+       [5.7, 2.5, 5. , 2. ],
+       [5.8, 2.8, 5.1, 2.4],
+       [6.4, 3.2, 5.3, 2.3],
+       [6.5, 3. , 5.5, 1.8],
+       [7.7, 3.8, 6.7, 2.2],
+       [7.7, 2.6, 6.9, 2.3],
+       [6. , 2.2, 5. , 1.5],
+       [6.9, 3.2, 5.7, 2.3],
+       [5.6, 2.8, 4.9, 2. ],
+       [7.7, 2.8, 6.7, 2. ],
+       [6.3, 2.7, 4.9, 1.8],
+       [6.7, 3.3, 5.7, 2.1],
+       [7.2, 3.2, 6. , 1.8],
+       [6.2, 2.8, 4.8, 1.8],
+       [6.1, 3. , 4.9, 1.8],
+       [6.4, 2.8, 5.6, 2.1],
+       [7.2, 3. , 5.8, 1.6],
+       [7.4, 2.8, 6.1, 1.9],
+       [7.9, 3.8, 6.4, 2. ],
+       [6.4, 2.8, 5.6, 2.2],
+       [6.3, 2.8, 5.1, 1.5],
+       [6.1, 2.6, 5.6, 1.4],
+       [7.7, 3. , 6.1, 2.3],
+       [6.3, 3.4, 5.6, 2.4],
+       [6.4, 3.1, 5.5, 1.8],
+       [6. , 3. , 4.8, 1.8],
+       [6.9, 3.1, 5.4, 2.1],
+       [6.7, 3.1, 5.6, 2.4],
+       [6.9, 3.1, 5.1, 2.3],
+       [5.8, 2.7, 5.1, 1.9],
+       [6.8, 3.2, 5.9, 2.3],
+       [6.7, 3.3, 5.7, 2.5],
+       [6.7, 3. , 5.2, 2.3],
+       [6.3, 2.5, 5. , 1.9],
+       [6.5, 3. , 5.2, 2. ],
+       [6.2, 3.4, 5.4, 2.3],
+       [5.9, 3. , 5.1, 1.8]]
+    )
+    return data
+
+@pytest.fixture(scope="function")
+def Y():
+    target = np.array(
+      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+    )
+    return target

tests/data/test_knn.py

@@ -50,13 +50,10 @@ def test_dense_annoy_index(annoy_index_file):
     loaded_index.unload()
 
 
-def test_knn_retrieval():
+def test_knn_retrieval(X):
     annoy_index_filepath = 'tests/data/.test-annoy-index.index'
     expected_neighbour_list = np.load('tests/data/test_knn_k3.npy')
 
-    iris = datasets.load_iris()
-    X = iris.data
-
     k = 3
     search_k = -1
 
@@ -79,13 +76,10 @@ def test_knn_matrix_construction_params(annoy_index_file):
     for original_row, loaded_row in zip(index, loaded_index):
         assert original_row == loaded_row
 
-def test_knn_retrieval_non_verbose():
+def test_knn_retrieval_non_verbose(X):
     annoy_index_filepath = 'tests/data/.test-annoy-index.index'
     expected_neighbour_list = np.load('tests/data/test_knn_k3.npy')
 
-    iris = datasets.load_iris()
-    X = iris.data
-
     k = 3
     search_k = -1
 

tests/data/test_triplet_generator.py

@@ -7,11 +7,9 @@
 from ivis.data.generators import UnsupervisedTripletGenerator
 
 
-def test_UnsupervisedTripletGenerator():
+def test_UnsupervisedTripletGenerator(X):
     neighbour_list = np.load('tests/data/test_knn_k3.npy')
 
-    iris = datasets.load_iris()
-    X = iris.data
     batch_size = 32
 
     data_generator = UnsupervisedTripletGenerator(X, neighbour_list,

tests/integration/test_iris.py

@@ -2,24 +2,16 @@
 from sklearn import datasets
 
 
-def test_iris_embedding():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
-
+def test_iris_embedding(X):
     ivis_iris = Ivis(epochs=5)
     ivis_iris.k = 15
     ivis_iris.batch_size = 16
 
-    y_pred_iris = ivis_iris.fit_transform(x)
-
-def test_1d_iris_embedding():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
+    y_pred_iris = ivis_iris.fit_transform(X)
 
+def test_1d_iris_embedding(X):
     ivis_iris = Ivis(epochs=5, embedding_dims=1)
     ivis_iris.k = 15
     ivis_iris.batch_size = 16
 
-    y_pred_iris = ivis_iris.fit_transform(x)
+    y_pred_iris = ivis_iris.fit_transform(X)

tests/integration/test_neighbour_matrix.py

@@ -3,16 +3,13 @@
 from ivis import Ivis
 
 
-def test_custom_ndarray_neighbour_matrix():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
+def test_custom_ndarray_neighbour_matrix(X, Y):
 
-    class_indicies = {label: np.argwhere(y == label).ravel() for label in np.unique(y)}
-    neighbour_matrix = np.array([class_indicies[label] for label in y])
+    class_indicies = {label: np.argwhere(Y == label).ravel() for label in np.unique(Y)}
+    neighbour_matrix = np.array([class_indicies[label] for label in Y])
 
     ivis_iris = Ivis(epochs=5, neighbour_matrix=neighbour_matrix)
     ivis_iris.k = 15
     ivis_iris.batch_size = 16
 
-    y_pred_iris = ivis_iris.fit_transform(x)
+    y_pred_iris = ivis_iris.fit_transform(X)

tests/integration/test_semi-supervised_iris.py

@@ -4,69 +4,55 @@
 from ivis import Ivis
 
 
-def test_iris_embedding():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
-    mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False)
-    y[mask] = -1
+def test_iris_embedding(X, Y):
+    mask = np.random.choice(range(len(Y)), size=len(Y) // 2, replace=False)
+    Y[mask] = -1
 
     ivis_iris = Ivis(epochs=5)
     ivis_iris.k = 15
     ivis_iris.batch_size = 16
 
-    y_pred_iris = ivis_iris.fit_transform(x, y)
+    y_pred_iris = ivis_iris.fit_transform(X, Y)
 
-def test_correctly_indexed_semi_supervised_classificaton_classes():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
+def test_correctly_indexed_semi_supervised_classificaton_classes(X, Y):
 
     # Mark points as unlabeled
-    mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False)
-    y[mask] = -1
+    mask = np.random.choice(range(len(Y)), size=len(Y) // 2, replace=False)
+    Y[mask] = -1
 
     supervision_metric = 'sparse_categorical_crossentropy'
     ivis_iris = Ivis(k=15, batch_size=16, epochs=5,
                      supervision_metric=supervision_metric)
 
-    embeddings = ivis_iris.fit_transform(x, y)
-
-def test_non_zero_indexed_semi_supervised_classificaton_classes():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
+    embeddings = ivis_iris.fit_transform(X, Y)
 
+def test_non_zero_indexed_semi_supervised_classificaton_classes(X, Y):
     # Make labels non-zero indexed
-    y = y + 1
+    Y = Y + 1
 
     # Mark points as unlabeled
-    mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False)
-    y[mask] = -1
+    mask = np.random.choice(range(len(Y)), size=len(Y) // 2, replace=False)
+    Y[mask] = -1
 
     supervision_metric = 'sparse_categorical_crossentropy'
     ivis_iris = Ivis(k=15, batch_size=16, epochs=5,
                      supervision_metric=supervision_metric)
 
     with pytest.raises(ValueError):
-        embeddings = ivis_iris.fit_transform(x, y)
-
+        embeddings = ivis_iris.fit_transform(X, Y)
 
-def test_non_consecutive_indexed_semi_supervised_classificaton_classes():
-    iris = datasets.load_iris()
-    x = iris.data
-    y = iris.target
 
+def test_non_consecutive_indexed_semi_supervised_classificaton_classes(X, Y):
     # Make labels non-consecutive indexed
-    y[y == max(y)] = max(y) + 1
+    Y[Y == max(Y)] = max(Y) + 1
 
     # Mark points as unlabeled
-    mask = np.random.choice(range(len(y)), size=len(y) // 2, replace=False)
-    y[mask] = -1
+    mask = np.random.choice(range(len(Y)), size=len(Y) // 2, replace=False)
+    Y[mask] = -1
 
     supervision_metric = 'sparse_categorical_crossentropy'
     ivis_iris = Ivis(k=15, batch_size=16, epochs=5,
                      supervision_metric=supervision_metric)
 
     with pytest.raises(ValueError):
-        embeddings = ivis_iris.fit_transform(x, y)
+        embeddings = ivis_iris.fit_transform(X, Y)