Fix test errors and warnings

GispoCoding · Oct 9, 2023 · 4ddfd4b · 4ddfd4b
1 parent 86da0c9
commit 4ddfd4b
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Showing 4 changed files with 4 additions and 21 deletions.
diff --git a/eis_toolkit/exploratory_analyses/k_means_cluster.py b/eis_toolkit/exploratory_analyses/k_means_cluster.py
@@ -18,17 +18,17 @@ def _k_means_clustering(
         # The elbow method
         k_max = 10
         inertia = np.array(
-            [KMeans(n_clusters=k, random_state=0).fit(coordinates).inertia_ for k in range(1, k_max + 1)]
+            [KMeans(n_clusters=k, random_state=0, n_init=10).fit(coordinates).inertia_ for k in range(1, k_max + 1)]
         )
 
         inertia = np.diff(inertia, 2)
         scaled_derivatives = [i * 100 for i in inertia]
         k_optimal = scaled_derivatives.index(min(scaled_derivatives))
 
-        kmeans = KMeans(n_clusters=k_optimal, random_state=random_state)
+        kmeans = KMeans(n_clusters=k_optimal, random_state=random_state, n_init=10)
 
     else:
-        kmeans = KMeans(n_clusters=number_of_clusters, random_state=random_state)
+        kmeans = KMeans(n_clusters=number_of_clusters, random_state=random_state, n_init=10)
 
     kmeans.fit(coordinates)
     data["cluster"] = kmeans.labels_

diff --git a/tests/conversions/raster_to_dataframe_test.py b/tests/conversions/raster_to_dataframe_test.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 import pandas as pd
-import pytest
 import rasterio
 
 from eis_toolkit.conversions.raster_to_dataframe import raster_to_dataframe
@@ -11,7 +10,6 @@
 test_dir = Path(__file__).parent.parent
 
 
-@pytest.mark.skip
 def test_raster_to_dataframe():
     """Test raster to pandas conversion by converting pandas dataframe and then back to raster data."""
     raster = rasterio.open(SMALL_RASTER_PATH)
@@ -32,7 +30,7 @@ def test_raster_to_dataframe():
     """Convert back to raster image."""
     df["id"] = df.index
     long_df = pd.wide_to_long(df, ["band_"], i="id", j="band").reset_index()
-    long_df.loc[:, ["col", "row"]] = long_df.loc[:, ["col", "row"]].astype(int)
+    long_df = long_df.astype({"col": int, "row": int})
     raster_img = np.empty((multiband_raster.count, multiband_raster.height, multiband_raster.width))
     raster_img[(long_df.band - 1).to_list(), long_df.row.to_list(), long_df.col.to_list()] = long_df.band_
 

diff --git a/tests/exploratory_analyses/k_means_cluster_test.py b/tests/exploratory_analyses/k_means_cluster_test.py
@@ -16,7 +16,6 @@
 gdf = gdp.GeoDataFrame(df, geometry=gdp.points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326")
 
 
-@pytest.mark.skip
 def test_k_means_clustering_output():
     """Test that k-means function assings data points into correct clusters."""
     kmeans_gdf = k_means_clustering(data=gdf, number_of_clusters=2, random_state=0)

diff --git a/tests/statistical_analyses/descriptive_statistics_test.py b/tests/statistical_analyses/descriptive_statistics_test.py
@@ -33,20 +33,6 @@ def test_descriptive_statistics_dataframe():
     np.testing.assert_almost_equal(test["skew"], 1.6136246)
 
 
-def test_zero_values_column():
-    """Test column with all values set to 0."""
-    test = descriptive_statistics_dataframe(test_zero_values, "random_number")
-    np.testing.assert_almost_equal(test["min"], 0)
-    np.testing.assert_almost_equal(test["max"], 0)
-    np.testing.assert_almost_equal(test["mean"], 0)
-    np.testing.assert_almost_equal(test["25%"], 0)
-    np.testing.assert_almost_equal(test["50%"], 0)
-    np.testing.assert_almost_equal(test["75%"], 0)
-    np.testing.assert_almost_equal(test["standard_deviation"], 0)
-    assert pd.isna(test["relative_standard_deviation"]) is True
-    assert pd.isna(test["skew"]) is True
-
-
 def test_invalid_column_name_df():
     """Test that invalid column name raises exception."""
     with pytest.raises(InvalidColumnException):