add tests

src/flowsom/models/batch/_som.py

@@ -1,4 +1,4 @@
-from __future__ import annotations
+"""Code adapted from student assignment Computational Biology 2024, Ghent University."""
 
 from typing import Callable
 

src/flowsom/models/batch/som_estimator.py

@@ -8,6 +8,7 @@
 from . import SOM_Batch, map_data_to_codes
 
 
+# TODO: try to use the same code for both SOMEstimator and BatchSOMEstimator
 class BatchSOMEstimator(BaseClusterEstimator):
     """Estimate a Self-Organizing Map (SOM) clustering model."""
 

tests/models/test_BatchFlowSOM.py

@@ -0,0 +1,35 @@
+from sklearn.metrics import v_measure_score
+
+from flowsom.models import BatchFlowSOMEstimator
+
+
+def test_clustering(X):
+    fsom = BatchFlowSOMEstimator(n_clusters=10)
+    y_pred = fsom.fit_predict(X)
+    assert y_pred.shape == (100,)
+
+
+def test_clustering_v_measure(X_and_y):
+    som = BatchFlowSOMEstimator(n_clusters=10)
+    X, y_true = X_and_y
+    y_pred = som.fit_predict(X)
+    score = v_measure_score(y_true, y_pred)
+    assert score > 0.7
+
+
+def test_reproducibility_no_seed(X):
+    fsom_1 = BatchFlowSOMEstimator(n_clusters=10)
+    fsom_2 = BatchFlowSOMEstimator(n_clusters=10)
+    y_pred_1 = fsom_1.fit_predict(X)
+    y_pred_2 = fsom_2.fit_predict(X)
+
+    assert not all(y_pred_1 == y_pred_2)
+
+
+def test_reproducibility_seed(X):
+    fsom_1 = BatchFlowSOMEstimator(n_clusters=10, seed=0)
+    fsom_2 = BatchFlowSOMEstimator(n_clusters=10, seed=0)
+    y_pred_1 = fsom_1.fit_predict(X)
+    y_pred_2 = fsom_2.fit_predict(X)
+
+    assert all(y_pred_1 == y_pred_2)