[pre-commit.ci] pre-commit autoupdate (#38)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Philipp A <[email protected]>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -13,7 +13,7 @@ repos:
       - id: no-commit-to-branch
         args: ["--branch=main"]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.1
+    rev: v0.4.2
     hooks:
       - id: ruff
         args: ["--fix"]

examples/rnn_dbscan_big.py

@@ -42,25 +42,26 @@ def run_rnn_dbscan(neighbor_transformer, n_neighbors, **kwargs):
     n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
     n_noise_ = list(labels).count(-1)
 
-    print("Estimated number of clusters: %d" % n_clusters_)
-    print("Estimated number of noise points: %d" % n_noise_)
-    print("Homogeneity: %0.3f" % metrics.homogeneity_score(y, labels))
-    print("Completeness: %0.3f" % metrics.completeness_score(y, labels))
-    print("V-measure: %0.3f" % metrics.v_measure_score(y, labels))
-    print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(y, labels))
-    print(
-        "Adjusted Mutual Information: %0.3f"
-        % metrics.adjusted_mutual_info_score(y, labels)
-    )
-    print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))
+    print(f"""\
+Estimated number of clusters: {n_clusters_}
+Estimated number of noise points: {n_noise_}
+Homogeneity: {metrics.homogeneity_score(y, labels):0.3f}
+Completeness: {metrics.completeness_score(y, labels):0.3f}
+V-measure: {metrics.v_measure_score(y, labels):0.3f}
+Adjusted Rand Index: {metrics.adjusted_rand_score(y, labels):0.3f}
+Adjusted Mutual Information: {metrics.adjusted_mutual_info_score(y, labels):0.3f}
+Silhouette Coefficient: {metrics.silhouette_score(X, labels):0.3f}\
+""")
 
 
 if __name__ == "__main__":
     import code
 
-    print("Now you can import your chosen transformer_cls and run:")
-    print("run_rnn_dbscan(transformer_cls, n_neighbors, **params)")
-    print("e.g.")
-    print("from sklearn_ann.kneighbors.pynndescent import PyNNDescentTransformer")
-    print("run_rnn_dbscan(PyNNDescentTransformer, 10)")
+    print("""\
+Now you can import your chosen transformer_cls and run:
+run_rnn_dbscan(transformer_cls, n_neighbors, **params)
+e.g.
+from sklearn_ann.kneighbors.pynndescent import PyNNDescentTransformer
+run_rnn_dbscan(PyNNDescentTransformer, 10)\
+""")
     code.interact(local=locals())

examples/rnn_dbscan_simple.py

@@ -36,17 +36,16 @@
 n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
 n_noise_ = list(labels).count(-1)
 
-print("Estimated number of clusters: %d" % n_clusters_)
-print("Estimated number of noise points: %d" % n_noise_)
-print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
-print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
-print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
-print("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(labels_true, labels))
-print(
-    "Adjusted Mutual Information: %0.3f"
-    % metrics.adjusted_mutual_info_score(labels_true, labels)
-)
-print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))
+print(f"""\
+Estimated number of clusters: {n_clusters_}
+Estimated number of noise points: {n_noise_}
+Homogeneity: {metrics.homogeneity_score(labels_true, labels):0.3f}
+Completeness: {metrics.completeness_score(labels_true, labels):0.3f}
+V-measure: {metrics.v_measure_score(labels_true, labels):0.3f}
+Adjusted Rand Index: {metrics.adjusted_rand_score(labels_true, labels):0.3f}
+Adjusted Mutual Info: {metrics.adjusted_mutual_info_score(labels_true, labels):0.3f}
+Silhouette Coefficient: {metrics.silhouette_score(X, labels):0.3f}\
+""")
 
 # #############################################################################
 # Plot result