746653d9311be6c2b9841758990d3997d07dfa2a,umap/umap_.py,,nearest_neighbors,#Any#Any#Any#Any#Any#Any#Any#,191

Before Change

            // TODO: Hacked values for now
            n_trees = 5 + int(round((X.shape[0]) ** 0.5 / 20.0))
            n_iters = max(5, int(round(np.log2(X.shape[0]))))
            if verbose:
                print(ts(), "Building RP forest with",  str(n_trees), "trees")

            rp_forest = make_forest(X, n_neighbors, n_trees, rng_state, angular)

After Change

        n_trees = 5 + int(round((X.shape[0]) ** 0.5 / 20.0))
        n_iters = max(5, int(round(np.log2(X.shape[0]))))

        try:
            // Use pynndescent, if installed (python 3 only)
            from pynndescent import NNDescent
            nnd = NNDescent(
                X,
                n_neighbors=n_neighbors,
                metric=metric,
                metric_kwds=metric_kwds,
                random_state=random_state,
                n_trees=n_trees,
                n_iters=n_iters,
                max_candidates=60,
                verbose=verbose,
            )
            knn_indices, knn_dists = nnd._neighbor_graph
            rp_forest = nnd._rp_forest
        except ImportError:
            // Otherwise fall back to nn descent in umap
            if callable(metric):
                distance_func = metric
            elif metric in dist.named_distances:
                distance_func = dist.named_distances[metric]
            else:
                raise ValueError("Metric is neither callable, " + "nor a recognised string")

            if metric in (
                "cosine",
                "correlation",
                "dice",
                "jaccard",
                "ll_dirichlet",
                "hellinger",
            ):
                angular = True

            rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)

            if scipy.sparse.isspmatrix_csr(X):
                if metric in sparse.sparse_named_distances:
                    distance_func = sparse.sparse_named_distances[metric]
                    if metric in sparse.sparse_need_n_features:
                        metric_kwds["n_features"] = X.shape[1]
                elif callable(metric):
                    distance_func = metric
                else:
                    raise ValueError(
                        "Metric {} not supported for sparse " + "data".format(metric)
                    )
                // metric_nn_descent = sparse.make_sparse_nn_descent(
                //     distance_func, tuple(metric_kwds.values())
                // )

                if verbose:
                    print(ts(), "Building RP forest with",  str(n_trees), "trees")

                rp_forest = make_forest(X, n_neighbors, n_trees, rng_state, angular)
                leaf_array = rptree_leaf_array(rp_forest)

                if verbose:
                    print(ts(), "NN descent for", str(n_iters), "iterations")
                knn_indices, knn_dists = sparse_nn.sparse_nn_descent(
                    X.indices,
                    X.indptr,
                    X.data,
                    X.shape[0],
                    n_neighbors,
                    rng_state,
                    distance_func,
                    tuple(metric_kwds.values()),
                    max_candidates=60,
                    rp_tree_init=True,
                    leaf_array=leaf_array,
                    n_iters=n_iters,
                    verbose=verbose,
                )
            else:
                // metric_nn_descent = make_nn_descent(
                //     distance_func, tuple(metric_kwds.values())
                // )

                if verbose:
                    print(ts(), "Building RP forest with", str(n_trees), "trees")
                rp_forest = make_forest(X, n_neighbors, n_trees, rng_state, angular)
                leaf_array = rptree_leaf_array(rp_forest)
                if verbose:
                    print(ts(), "NN descent for", str(n_iters), "iterations")
                knn_indices, knn_dists = nn_descent(
                    X,
                    n_neighbors,
                    rng_state,
                    distance_func,
                    tuple(metric_kwds.values()),
                    max_candidates=60,
                    rp_tree_init=True,
                    leaf_array=leaf_array,
                    n_iters=n_iters,
                    verbose=verbose,
                )

            if np.any(knn_indices < 0):
                warn(
                    "Failed to correctly find n_neighbors for some samples."
                    "Results may be less than ideal. Try re-running with"
                    "different parameters."
                )
    if verbose:
        print(ts(), "Finished Nearest Neighbor Search")
    return knn_indices, knn_dists, rp_forest

Instances