add tests for dynamic models

tests/test_integration.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 import tempfile
 from pathlib import Path
 
@@ -12,9 +13,24 @@
     AutoEncodingTopicModel,
     ClusteringTopicModel,
     KeyNMF,
-    SemanticSignalSeparation,
+    SemanticSignalSeparation
 )
 
+
+def generate_dates(
+        n_dates: int,
+) -> list[datetime]:
+        """ Generate random dates to test dynamic models """
+        dates = []
+        for n in range(n_dates):
+             d = np.random.randint(low=1, high=29)
+             m = np.random.randint(low=1, high=13)
+             y = np.random.randint(low=2000, high=2020)
+             date = datetime(year=y, month=m, day=d)
+             dates.append(date)
+        return dates
+
+
 newsgroups = fetch_20newsgroups(
     subset="all",
     categories=[
@@ -25,6 +41,7 @@
 texts = newsgroups.data
 trf = SentenceTransformer("all-MiniLM-L6-v2")
 embeddings = np.asarray(trf.encode(texts))
+timestamps = generate_dates(n_dates=len(texts))
 
 models = [
     GMM(5, encoder=trf),
@@ -46,6 +63,28 @@
     AutoEncodingTopicModel(5, combined=True),
 ]
 
+dynamic_models = [
+    GMM(5, encoder=trf),
+    ClusteringTopicModel(
+        n_reduce_to=5,
+        feature_importance="centroid",
+        encoder=trf,
+        reduction_method="smallest"
+    ),
+    ClusteringTopicModel(
+        n_reduce_to=5,
+        feature_importance="soft-c-tf-idf",
+        encoder=trf,
+        reduction_method="smallest"
+    ),
+    ClusteringTopicModel(
+        n_reduce_to=5,
+        feature_importance="c-tf-idf",
+        encoder=trf,
+        reduction_method="smallest"
+    ),
+]
+
 
 @pytest.mark.parametrize("model", models)
 def test_fit_export_table(model):
@@ -56,3 +95,16 @@ def test_fit_export_table(model):
         with out_path.open("w") as out_file:
             out_file.write(table)
         df = pd.read_csv(out_path)
+
+
+@pytest.mark.parametrize("model", dynamic_models)
+def test_fit_dynamic(model):
+    doc_topic_matrix = model.fit_transform_dynamic(
+         texts, embeddings=embeddings, timestamps=timestamps,
+    )
+    table = model.export_topics(format="csv")
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        out_path = Path(tmpdirname).joinpath("topics.csv")
+        with out_path.open("w") as out_file:
+            out_file.write(table)
+        df = pd.read_csv(out_path)