numpy 2; update imports

Author: JelmerBot

multi_mst/__init__.py

@@ -0,0 +1,5 @@
+from .k_mst import kMST, KMST
+from .noisy_mst import noisyMST, NoisyMST
+from .k_mst_descent import kMSTDescent, KMSTDescent
+
+__all__ = ["kMST", "KMST", "noisyMST", "NoisyMST", "kMSTDescent", "KMSTDescent"]

multi_mst/k_mst/__init__.py

@@ -1,9 +1,3 @@
-import numpy as np
 from .api import KMST, kMST
 
-# Force JIT compilation on import
-random_state = np.random.RandomState(42)
-random_data = random_state.random(size=(50, 3))
-KMST().fit(random_data)
-
 __all__ = ["KMST", "kMST"]

multi_mst/k_mst/api.py

@@ -77,7 +77,10 @@ def kMST(data, num_neighbors=3, min_samples=1, epsilon=None, umap_kwargs=None):
     )
     with warn.catch_warnings():
         warn.filterwarnings(
-            "ignore", category=UserWarning, module="umap.umap_", lineno=2010
+            "ignore",
+            category=UserWarning,
+            module="umap.umap_",
+            message=".*is not an NNDescent object.*",
         )
         umap = UMAP(
             n_neighbors=mst_indices.shape[1],
@@ -133,7 +136,9 @@ class KMST(BaseEstimator):
         missing values. Use the graph_ and embedding_ attributes instead!
     """
 
-    def __init__(self, *, num_neighbors=3, min_samples=1, epsilon=None, umap_kwargs=None):
+    def __init__(
+        self, *, num_neighbors=3, min_samples=1, epsilon=None, umap_kwargs=None
+    ):
         self.num_neighbors = num_neighbors
         self.min_samples = min_samples
         self.epsilon = epsilon
@@ -173,9 +178,7 @@ def fit(self, X, y=None, **fit_params):
             clean_data = X
 
         kwargs = self.get_params()
-        self.mst_indices_, self.mst_distances_, self._umap = kMST(
-            clean_data, **kwargs
-        )
+        self.mst_indices_, self.mst_distances_, self._umap = kMST(clean_data, **kwargs)
         self.graph_ = self._umap.graph_.copy()
         self.embedding_ = (
             self._umap.embedding_.copy() if hasattr(self._umap, "embedding_") else None

multi_mst/k_mst_descent/__init__.py

@@ -1,9 +1,3 @@
-import numpy as np
 from .api import KMSTDescent, kMSTDescent
 
-# Force JIT compilation on import
-random_state = np.random.RandomState(42)
-random_data = random_state.random(size=(50, 3))
-KMSTDescent().fit(random_data)
-
 __all__ = ["KMSTDescent", "kMSTDescent"]

multi_mst/k_mst_descent/api.py

@@ -103,7 +103,7 @@ def kMSTDescent(
         The number of neighbors for computing the mutual reachability distance.
         Value must be lower or equal to the number of neighbors. `epsilon`
         operates on the mutual reachability distance, so always allows the
-        nearest `min_samples` points. Acts as UMAP's `local connnectivity`
+        nearest `min_samples` points. Acts as UMAP's `local connectivity`
         parameter. Default is 1.
     epsilon: float, optional
         A fraction of the initial MST edge distance to act as upper distance
@@ -114,11 +114,11 @@ def kMSTDescent(
     umap_kwargs: dict
         Additional keyword arguments passed to UMAP.
     nn_kwargs: dict
-        Additional keyword arguments passsed to NNDescent.
+        Additional keyword arguments passed to NNDescent.
     n_jobs : int, optional
         The number of threads to use for the computation. -1 means using all
         threads.
-    
+
     Returns
     -------
     mst_indices_: numpy.ndarray, shape (n_samples, num_found_neighbors)
@@ -155,7 +155,10 @@ def kMSTDescent(
         )
     with warn.catch_warnings():
         warn.filterwarnings(
-            "ignore", category=UserWarning, module="umap.umap_", lineno=2010
+            "ignore",
+            category=UserWarning,
+            module="umap.umap_",
+            message=".*is not an NNDescent object.*",
         )
         umap = UMAP(
             n_neighbors=mst_indices.shape[1],
@@ -236,7 +239,7 @@ class KMSTDescent(BaseEstimator):
         Additional keyword arguments passsed to NNDescent.
     n_jobs : int, optional
         The number of threads to use for the computation. -1 means using all
-        threads.    
+        threads.
 
     Attributes
     ----------

multi_mst/k_mst_descent_recall/__init__.py

@@ -1,6 +1,6 @@
 from .api import (
     KMSTDescentLogRecall as _KMSTDescentLogRecall,
-    kMSTDescentLogRecall as _kMSTDescentLogRecall
+    kMSTDescentLogRecall as _kMSTDescentLogRecall,
 )
 
 __all__ = ["_KMSTDescentLogRecall", "_kMSTDescentLogRecall"]

multi_mst/k_mst_descent_recall/api.py

@@ -142,7 +142,10 @@ def kMSTDescentLogRecall(
     ]
     with warn.catch_warnings():
         warn.filterwarnings(
-            "ignore", category=UserWarning, module="umap.umap_", lineno=2010
+            "ignore",
+            category=UserWarning,
+            module="umap.umap_",
+            message=".*is not an NNDescent object.*",
         )
         umap = UMAP(
             n_neighbors=mst_indices.shape[1],

multi_mst/noisy_mst/__init__.py

@@ -1,9 +1,3 @@
-import numpy as np
 from .api import NoisyMST, noisyMST
 
-# Force JIT compilation on import
-random_state = np.random.RandomState(42)
-random_data = random_state.random(size=(50, 3))
-NoisyMST().fit(random_data)
-
 __all__ = ["NoisyMST", "noisyMST"]

multi_mst/noisy_mst/api.py

@@ -76,7 +76,10 @@ def noisyMST(data, num_trees=3, noise_fraction=0.1, min_samples=1, umap_kwargs=N
     )
     with warn.catch_warnings():
         warn.filterwarnings(
-            "ignore", category=UserWarning, module="umap.umap_", lineno=2010
+            "ignore",
+            category=UserWarning,
+            module="umap.umap_",
+            message=".*is not an NNDescent object.*",
         )
         umap = UMAP(
             n_neighbors=mst_indices.shape[1],

notebooks/Benchmark MNIST.ipynb

@@ -29,8 +29,7 @@
     "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
     "\n",
     "from umap import UMAP\n",
-    "from multi_mst.k_mst import KMST\n",
-    "from multi_mst.k_mst_descent import KMSTDescent"
+    "from multi_mst import KMST, KMSTDescent"
    ]
   },
   {
@@ -393,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,