Merge pull request #161 from MattScicluna/add_random_landmarking

Add random landmarking

Author: bjoaofelipe

Python/phate/phate.py

@@ -13,6 +13,7 @@
 from scipy import sparse
 import warnings
 import tasklogger
+from packaging import version
 
 import matplotlib.pyplot as plt
 
@@ -32,6 +33,15 @@
 
 _logger = tasklogger.get_tasklogger("graphtools")
 
+# Check graphtools version for random_landmarking support
+def _graphtools_supports_random_landmarking():
+    """Check if installed graphtools version supports random_landmarking parameter."""
+    try:
+        return version.parse(graphtools.__version__) >= version.parse("2.0.0")
+    except AttributeError:
+        # graphtools doesn't have __version__, assume old version
+        return False
+
 
 class PHATE(BaseEstimator):
     """PHATE operator which performs dimensionality reduction.
@@ -117,6 +127,12 @@ class PHATE(BaseEstimator):
         If an integer is given, it fixes the seed
         Defaults to the global `numpy` random number generator
 
+    random_landmarking : bool, optional, default: False   
+        Whether to use random sampling for landmarking. If True, landmarks 
+        are selected randomly. If False, landmarks are selected deterministically 
+        using spectral clustering.
+        Defaults to False.
+
     verbose : `int` or `boolean`, optional (default: 1)
         If `True` or `> 0`, print status messages
 
@@ -178,6 +194,7 @@ def __init__(
         mds="metric",
         n_jobs=1,
         random_state=None,
+        random_landmarking=False,
         verbose=1,
         **kwargs,
     ):
@@ -201,6 +218,31 @@ def __init__(
         self.mds_dist = mds_dist
         self.mds_solver = mds_solver
         self.random_state = random_state
+
+        # Validate random_landmarking parameter
+        if random_landmarking and n_landmark is None:
+            warnings.warn(
+                "random_landmarking=True has no effect when n_landmark=None. "
+                "Landmarking is disabled when n_landmark=None. "
+                "To use random landmarking, please set n_landmark to a positive integer "
+                "(e.g., n_landmark=2000).",
+                UserWarning
+            )
+            # Disable random_landmarking since it has no effect
+            random_landmarking = False
+        # Check graphtools version if random_landmarking is still requested
+        elif random_landmarking and not _graphtools_supports_random_landmarking():
+            warnings.warn(
+                "random_landmarking is not available in graphtools version < 2.0.0. "
+                "Please update graphtools to use this feature: "
+                "https://pypi.org/project/graphtools/2.0.0/. "
+                "Falling back to spectral clustering for landmark selection.",
+                UserWarning
+            )
+            # Disable random_landmarking since it's not supported
+            random_landmarking = False
+
+        self.random_landmarking = random_landmarking
         self.kwargs = kwargs
 
         self.graph = None
@@ -485,6 +527,12 @@ def set_params(self, **params):
             If an integer is given, it fixes the seed
             Defaults to the global `numpy` random number generator
 
+        random_landmarking : bool, optional, default: False   
+            Whether to use random sampling for landmarking. If True, landmarks 
+            are selected randomly. If False, landmarks are selected deterministically 
+            using spectral clustering.
+            Defaults to False.
+
         verbose : `int` or `boolean`, optional (default: 1)
             If `True` or `> 0`, print status messages
 
@@ -615,6 +663,10 @@ def set_params(self, **params):
             self.random_state = params["random_state"]
             self._set_graph_params(random_state=params["random_state"])
             del params["random_state"]
+        if "random_landmarking" in params:
+            self.random_landmarking = params["random_landmarking"]
+            self._set_graph_params(random_landmarking=params["random_landmarking"])
+            del params["random_landmarking"]
         if "verbose" in params:
             self.verbose = params["verbose"]
             _logger.set_level(self.verbose)
@@ -751,7 +803,7 @@ def _parse_input(self, X):
                 n_pca = self.n_pca
         return X, n_pca, precomputed, update_graph
 
-    def _update_graph(self, X, precomputed, n_pca, n_landmark):
+    def _update_graph(self, X, precomputed, n_pca, n_landmark, random_landmarking):
         if self.X is not None and not utils.matrix_is_equivalent(X, self.X):
             """
             If the same data is used, we can reuse existing kernel and
@@ -760,18 +812,25 @@ def _update_graph(self, X, precomputed, n_pca, n_landmark):
             self._reset_graph()
         else:
             try:
-                self.graph.set_params(
-                    decay=self.decay,
-                    knn=self.knn,
-                    knn_max=self.knn_max,
-                    distance=self.knn_dist,
-                    precomputed=precomputed,
-                    n_jobs=self.n_jobs,
-                    verbose=self.verbose,
-                    n_pca=n_pca,
-                    n_landmark=n_landmark,
-                    random_state=self.random_state,
-                )
+                # Prepare graph params
+                graph_params = {
+                    'decay': self.decay,
+                    'knn': self.knn,
+                    'knn_max': self.knn_max,
+                    'distance': self.knn_dist,
+                    'precomputed': precomputed,
+                    'n_jobs': self.n_jobs,
+                    'verbose': self.verbose,
+                    'n_pca': n_pca,
+                    'n_landmark': n_landmark,
+                    'random_state': self.random_state,
+                }
+
+                # Only add random_landmarking if graphtools supports it
+                if _graphtools_supports_random_landmarking():
+                    graph_params['random_landmarking'] = random_landmarking
+
+                self.graph.set_params(**graph_params)
                 _logger.info("Using precomputed graph and diffusion operator...")
             except ValueError as e:
                 # something changed that should have invalidated the graph
@@ -816,27 +875,35 @@ def fit(self, X):
             n_landmark = self.n_landmark
 
         if self.graph is not None and update_graph:
-            self._update_graph(X, precomputed, n_pca, n_landmark)
+            self._update_graph(X, precomputed, n_pca, n_landmark, self.random_landmarking)
 
         self.X = X
 
         if self.graph is None:
             with _logger.log_task("graph and diffusion operator"):
-                self.graph = graphtools.Graph(
-                    X,
-                    n_pca=n_pca,
-                    n_landmark=n_landmark,
-                    distance=self.knn_dist,
-                    precomputed=precomputed,
-                    knn=self.knn,
-                    knn_max=self.knn_max,
-                    decay=self.decay,
-                    thresh=1e-4,
-                    n_jobs=self.n_jobs,
-                    verbose=self.verbose,
-                    random_state=self.random_state,
-                    **(self.kwargs),
-                )
+                # Prepare graph params
+                graph_params = {
+                    'n_pca': n_pca,
+                    'n_landmark': n_landmark,
+                    'distance': self.knn_dist,
+                    'precomputed': precomputed,
+                    'knn': self.knn,
+                    'knn_max': self.knn_max,
+                    'decay': self.decay,
+                    'thresh': 1e-4,
+                    'n_jobs': self.n_jobs,
+                    'verbose': self.verbose,
+                    'random_state': self.random_state,
+                }
+
+                # Only add random_landmarking if graphtools supports it
+                if _graphtools_supports_random_landmarking():
+                    graph_params['random_landmarking'] = self.random_landmarking
+
+                # Merge with any additional kwargs
+                graph_params.update(self.kwargs)
+
+                self.graph = graphtools.Graph(X, **graph_params)
 
         # landmark op doesn't build unless forced
         self.diff_op