Create dbscan_batch.py

exp/exp3/dcnn/native/dbscan_batch.py

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+from sklearn.neighbors import NearestNeighbors
+from .native_cc import dfs1_cc, dfs2_cc, dfs3_cc
+from ..sample import *
+import numpy as np
+
+# for exp
+
+class dbscan:
+
+    def __init__(self, algorithm='dbscan', eps=0.1, minPts=5,
+                    metric="euclidean", query="kd_tree", sample_size=1000,
+                 leaf_size=30, n_jobs=1, seed=42):
+
+        self.algorithm = algorithm
+        self.eps = eps
+        self.minPts = minPts
+        self.metric = metric
+        self.query = query
+        self.leaf_size = leaf_size
+        self.n_jobs = n_jobs
+        self.sample_size = sample_size
+        np.random.seed(seed)
+
+    def fit_transform(self, X):
+        self.fit(X)
+        return self.labels
+
+
+    def fit(self, X):
+        size = X.shape[0]
+        neighborsModel = NearestNeighbors(
+            radius=self.eps,
+            algorithm=self.query,
+            leaf_size=self.leaf_size,
+            metric=self.metric,
+            n_jobs=self.n_jobs
+        )
+
+        neighborsModel.fit(X)
+
+        if self.algorithm == 'dbscan':
+
+            # if self.query == 'kd_tree':
+                # neighborsModel = KDTree(X, leaf_size=self.leaf_size)
+            #     neighborhoods=[neighborsModel.radius_neighbors(X[i:i+1], self.eps, 
+            #                                 return_distance=False)[0] for i in range(size)]
+            # elif self.query == 'ball_tree':
+            #     # neighborsModel = BallTree(X, leaf_size=self.leaf_size)
+            #     neighborhoods=[neighborsModel.radius_neighbors(X[i:i+1], self.eps, 
+            #                                 return_distance=False)[0] for i in range(size)]
+            # else:
+
+            self.neighborhoods = neighborsModel.radius_neighbors(X, radius=self.eps, 
+                          return_distance=False).tolist()
+
+            n_neighbors = np.array([len(neighbors) for neighbors in self.neighborhoods])
+            corePoints = np.asarray(n_neighbors >= self.minPts, dtype=np.uint8)
+            self.labels = np.full(size, -1, dtype=np.intp)
+            dfs1_cc(corePoints, self.neighborhoods, self.labels)
+
+        elif self.algorithm == 'dbscan*':
+
+            self.neighborhoods=  neighborsModel.radius_neighbors(X, self.eps, 
+                                            return_distance=False).tolist()
+
+            n_neighbors = np.array([len(neighbors) for neighbors in self.neighborhoods])
+            corePoints = np.asarray(n_neighbors >= self.minPts, dtype=bool)
+            self.labels = np.full(size, -1, dtype=np.intp)
+            dfs2_cc(corePoints, self.neighborhoods, self.labels)
+
+        elif self.algorithm == 'dbscan++':
+            if self.sample_size > size:
+                self.sample_size = int(round(0.5*size))
+
+            subsampleID = uniform_sample(X, size=self.sample_size)
+            self.neighborhoods = neighborsModel.radius_neighbors(X[subsampleID], self.eps,
+                                                            return_distance=False).tolist()
+            n_neighbors = np.array([len(neighbors) for neighbors in self.neighborhoods])
+            corePoints = np.zeros(size, dtype=np.uint8)
+            corePoints[subsampleID[n_neighbors >= self.minPts]]=1
+            self.labels = np.full(size, -1, dtype=np.intp)
+            dfs3_cc(corePoints, self.neighborhoods, subsampleID, self.labels)
+
+
+
+
+
+
+
+