test

lkmeans/clustering.py

@@ -1,11 +1,17 @@
 from copy import deepcopy
+from typing import Any
 
 import numpy as np
 from numpy.typing import NDArray
 
 from lkmeans.distance import pairwise_minkowski_distance
-from lkmeans.optimizers import (bound_optimizer, mean_optimizer,
-                                median_optimizer, slsqp_optimizer)
+from lkmeans.optimizers import bound_optimizer, mean_optimizer, median_optimizer, slsqp_optimizer
+
+
+def set_type(data: Any) -> NDArray:
+    if not isinstance(data, np.ndarray):
+        data = np.array(data)
+    return data
 
 
 def assign_to_cluster(
@@ -79,6 +85,7 @@ def _inertia(X: NDArray, centroids: NDArray) -> float:
         return np.sum(np.min(distances, axis=1))
 
     def fit(self, X: NDArray) -> None:
+        X = set_type(X)
         centroids = self._init_centroids(X, self.n_clusters)
 
         iter_with_no_progress = 0
@@ -106,11 +113,13 @@ def fit(self, X: NDArray) -> None:
         self.cluster_centers_ = deepcopy(centroids)
 
     def predict(self, X: NDArray) -> list[int]:
+        X = set_type(X)
         _, labels = assign_to_cluster(
             X, self.cluster_centers_, self.n_clusters, self.p)
         return labels
 
     def fit_predict(self, X: NDArray) -> list[int]:
+        X = set_type(X)
         self.fit(X)
         labels = self.predict(X)
         return labels

lkmeans/tests/test_api.py

@@ -1,16 +1,44 @@
 import numpy as np
+import pandas as pd
 import pytest
+from numpy.typing import NDArray
 
 from lkmeans import LKMeans
 
 p_values = [0.5, 1, 2, 5]
 
+
+def get_data() -> NDArray:
+    return np.random.uniform(-10,10, size=(100, 50))
+
+
 @pytest.mark.api
-@pytest.mark.parametrize("p", p_values)
-def test_segment_slsqp_calculation(p) -> None:
-    data = np.random.uniform(-10,10, size=(100, 50))
+@pytest.mark.parametrize('p', p_values)
+def test_general_processing(p: float | int) -> None:
+    data = get_data()
 
     lkmeans = LKMeans(n_clusters=2, p=p)
     lkmeans.fit_predict(data)
     print('Inertia', lkmeans.inertia_)
     print('Centers', lkmeans.cluster_centers_)
+
+
+def convert_from_ndarray(data: NDArray, type: str) -> list | pd.DataFrame | pd.Series:
+    if type == 'list':
+        return data.tolist()
+    if type == 'frame':
+        return pd.DataFrame(data.tolist())
+    if type == 'series':
+        return pd.Series(data.tolist())
+
+
+@pytest.mark.api
+@pytest.mark.parametrize('type', ['list', 'frame', 'series'])
+def test_input_data_conversion(type: str) -> None:
+    data = get_data()
+    data = convert_from_ndarray(data, type)
+
+    lkmeans = LKMeans(n_clusters=2, p=2)
+    lkmeans.fit_predict(data)
+    print('Inertia', lkmeans.inertia_)
+    print('Centers', lkmeans.cluster_centers_)