Source code for Orange.clustering.hierarchical

from collections import namedtuple, deque
from operator import attrgetter
from itertools import chain, count
from distutils.version import LooseVersion as _LooseVersion

import heapq
import numpy

import scipy.cluster.hierarchy
import scipy.spatial.distance

from Orange.distance import Euclidean, PearsonR

__all__ = ['HierarchicalClustering']

SINGLE = "single"
AVERAGE = "average"
COMPLETE = "complete"
WEIGHTED = "weighted"
WARD = "ward"

# Does scipy implement a O(n**2) NN chain algorithm?
_HAS_NN_CHAIN = hasattr(scipy.cluster.hierarchy, "_hierarchy") and \
                hasattr(scipy.cluster.hierarchy._hierarchy, "nn_chain")

# Prior to 0.18 scipy.cluster.hierarchical's python interface disallowed
# ward clustering from a precomputed distance matrix even though it's cython
# implementation allowed it and was documented to support it (scipy issue 5220)
    _LooseVersion(scipy.__version__) >= _LooseVersion("0.18") and \

def condensedform(X, mode="upper"):
    X = numpy.asarray(X)
    assert len(X.shape) == 2
    assert X.shape[0] == X.shape[1]

    N = X.shape[0]

    if mode == "upper":
        i, j = numpy.triu_indices(N, k=1)
    elif mode == "lower":
        i, j = numpy.tril_indices(N, k=-1)
        raise ValueError("invalid mode")
    return X[i, j]

def squareform(X, mode="upper"):
    X = numpy.asarray(X)
    k = X.shape[0]
    N = int(numpy.ceil(numpy.sqrt(k * 2)))
    assert N * (N - 1) // 2 == k
    matrix = numpy.zeros((N, N), dtype=X.dtype)
    if mode == "upper":
        i, j = numpy.triu_indices(N, k=1)
        matrix[i, j] = X
        m, n = numpy.tril_indices(N, k=-1)
        matrix[m, n] = matrix.T[m, n]
    elif mode == "lower":
        i, j = numpy.tril_indices(N, k=-1)
        matrix[i, j] = X
        m, n = numpy.triu_indices(N, k=1)
        matrix[m, n] = matrix.T[m, n]
    return matrix

def data_clustering(data, distance=Euclidean,
    Return the hierarchical clustering of the dataset's rows.

    :param data: Dataset to cluster.
    :param Orange.distance.Distance distance: A distance measure.
    :param str linkage:
    matrix = distance(data)
    return dist_matrix_clustering(matrix, linkage=linkage)

def feature_clustering(data, distance=PearsonR,
    Return the hierarchical clustering of the dataset's columns.

    :param data: Dataset to cluster.
    :param Orange.distance.Distance distance: A distance measure.
    :param str linkage:
    matrix = distance(data, axis=0)
    return dist_matrix_clustering(matrix, linkage=linkage)

def dist_matrix_linkage(matrix, linkage=AVERAGE):
    Return linkage using a precomputed distance matrix.

    :param Orange.misc.DistMatrix matrix:
    :param str linkage:
    # Extract compressed upper triangular distance matrix.
    distances = condensedform(matrix)
    if linkage == WARD and not _HAS_WARD_LINKAGE_FROM_DIST:
        # Avoid `scipy.cluster.hierarchy.linkage` and dispatch to it's
        # cython implementation directly.
        # This the core of the scipy.cluster.hierarchy.linkage in
        # scipy 0.16, 0.17. Assuming the branches are in bug fix mode
        # only so this interface will not change.
        y = numpy.asarray(distances, dtype=float)
        scipy.spatial.distance.is_valid_y(y, throw=True)
        N = scipy.spatial.distance.num_obs_y(y)
        # allocate the output linkage matrix
        Z = numpy.zeros((N - 1, 4))
        # retrieve the correct method flag
        method = scipy.cluster.hierarchy._cpy_euclid_methods["ward"]
        scipy.cluster.hierarchy._hierarchy.linkage(y, Z, int(N), int(method))
        return Z
        return scipy.cluster.hierarchy.linkage(distances, method=linkage)

def dist_matrix_clustering(matrix, linkage=AVERAGE):
    Return the hierarchical clustering using a precomputed distance matrix.

    :param Orange.misc.DistMatrix matrix:
    :param str linkage:
    Z = dist_matrix_linkage(matrix, linkage=linkage)
    return tree_from_linkage(Z)

def sample_clustering(X, linkage=AVERAGE, metric="euclidean"):
    assert len(X.shape) == 2
    Z = scipy.cluster.hierarchy.linkage(X, method=linkage, metric=metric)
    return tree_from_linkage(Z)

class Tree(object):
    __slots__ = ("__value", "__branches", "__hash")

    def __init__(self, value, branches=()):
        if not isinstance(branches, tuple):
            raise TypeError()
        self.__value = value
        self.__branches = branches
        # preemptively cache the hash value
        self.__hash = hash((value, branches))

    def __hash__(self):
        return self.__hash

    def __eq__(self, other):
        return isinstance(other, Tree) and tuple(self) == tuple(other)

    def __lt__(self, other):
        if not isinstance(other, Tree):
            return NotImplemented
        return tuple(self) < tuple(other)

    def __le__(self, other):
        if not isinstance(other, Tree):
            return NotImplemented
        return tuple(self) <= tuple(other)

    def __getnewargs__(self):
        return tuple(self)

    def __iter__(self):
        return iter((self.__value, self.__branches))

    def __repr__(self):
        return ("{0.__name__}(value={1!r}, branches={2!r})"
                .format(type(self), self.value, self.branches))

    def is_leaf(self):
        return not bool(self.branches)

    def left(self):
        return self.branches[0]

    def right(self):
        return self.branches[-1]

    value = property(attrgetter("_Tree__value"))
    branches = property(attrgetter("_Tree__branches"))

ClusterData = namedtuple("Cluster", ["range", "height"])
SingletonData = namedtuple("Singleton", ["range", "height", "index"])

class _Ranged:

    def first(self):
        return self.range[0]

    def last(self):
        return self.range[-1]

class ClusterData(ClusterData, _Ranged):
    __slots__ = ()

class SingletonData(SingletonData, _Ranged):
    __slots__ = ()

def tree_from_linkage(linkage):
    Return a Tree representation of a clustering encoded in a linkage matrix.

    .. seealso:: scipy.cluster.hierarchy.linkage

        linkage, throw=True, name="linkage")
    T = {}
    N, _ = linkage.shape
    N = N + 1
    order = []
    for i, (c1, c2, d, _) in enumerate(linkage):
        if c1 < N:
            left = Tree(SingletonData(range=(len(order), len(order) + 1),
                                      height=0.0, index=int(c1)),
            left = T[c1]

        if c2 < N:
            right = Tree(SingletonData(range=(len(order), len(order) + 1),
                                       height=0.0, index=int(c2)),
            right = T[c2]

        t = Tree(ClusterData(range=(left.value.first, right.value.last),
                 (left, right))
        T[N + i] = t

    root = T[N + N - 2]
    T = {}

    leaf_idx = 0
    for node in postorder(root):
        if node.is_leaf:
            T[node] = Tree(
                node.value._replace(range=(leaf_idx, leaf_idx + 1)), ())
            leaf_idx += 1
            left, right = T[node.left].value, T[node.right].value
            assert left.first < right.first

            t = Tree(
                node.value._replace(range=(left.range[0], right.range[1])),
                tuple(T[ch] for ch in node.branches)
            assert t.value.range[0] <= t.value.range[-1]
            assert left.first == t.value.first and right.last == t.value.last
            assert t.value.first < right.first
            assert t.value.last > left.last
            T[node] = t

    return T[root]

def postorder(tree, branches=attrgetter("branches")):
    stack = deque([tree])
    visited = set()

    while stack:
        current = stack.popleft()
        children = branches(current)
        if children:
            # yield the item on the way up
            if current in visited:
                yield current
                # stack = children + [current] + stack

            yield current

def preorder(tree, branches=attrgetter("branches")):
    stack = deque([tree])
    while stack:
        current = stack.popleft()
        yield current
        children = branches(current)
        if children:

def leaves(tree, branches=attrgetter("branches")):
    Return an iterator over the leaf nodes in a tree structure.
    return (node for node in postorder(tree, branches)
            if node.is_leaf)

def prune(cluster, level=None, height=None, condition=None):
    Prune the clustering instance ``cluster``.

    :param Tree cluster: Cluster root node to prune.
    :param int level: If not `None` prune all clusters deeper then `level`.
    :param float height:
        If not `None` prune all clusters with height lower then `height`.
    :param function condition:
        If not `None condition must be a `Tree -> bool` function
        evaluating to `True` if the cluster should be pruned.

    .. note::
        At least one `level`, `height` or `condition` argument needs to
        be supplied.

    if not any(arg is not None for arg in [level, height, condition]):
        raise ValueError("At least one pruning argument must be supplied")

    level_check = height_check = condition_check = lambda cl: False

    if level is not None:
        cluster_depth = cluster_depths(cluster)
        level_check = lambda cl: cluster_depth[cl] >= level

    if height is not None:
        height_check = lambda cl: cl.value.height <= height

    if condition is not None:
        condition_check = condition

    def check_all(cl):
        return level_check(cl) or height_check(cl) or condition_check(cl)

    T = {}

    for node in postorder(cluster):
        if check_all(node):
            if node.is_leaf:
                T[node] = node
                T[node] = Tree(node.value, ())
            T[node] = Tree(node.value,
                           tuple(T[ch] for ch in node.branches))
    return T[cluster]

def cluster_depths(cluster):
    Return a dictionary mapping :class:`Tree` instances to their depth.

    :param Tree cluster: Root cluster
    :rtype: class:`dict`

    depths = {}
    depths[cluster] = 0
    for cluster in preorder(cluster):
        cl_depth = depths[cluster]
        depths.update(dict.fromkeys(cluster.branches, cl_depth + 1))
    return depths

def top_clusters(tree, k):
    Return `k` topmost clusters from hierarchical clustering.

    :param Tree root: Root cluster.
    :param int k: Number of top clusters.

    :rtype: list of :class:`Tree` instances
    def item(node):
        return ((node.is_leaf, -node.value.height), node)

    heap = [item(tree)]

    while len(heap) < k:
        _, cl = heap[0]  # peek
        if cl.is_leaf:
            assert all(n.is_leaf for _, n in heap)
        key, cl = heapq.heappop(heap)
        left, right = cl.left, cl.right
        heapq.heappush(heap, item(left))
        heapq.heappush(heap, item(right))

    return [n for _, n in heap]

def optimal_leaf_ordering(tree, distances, progress_callback=None):
    Order the leaves in the clustering tree.

    (based on Ziv Bar-Joseph et al. Fast optimal leaf ordering for
    hierarchical clustering)

    :param Tree tree:
        Binary hierarchical clustering tree.
    :param numpy.ndarray distances:
        A (N, N) numpy.ndarray of distances that were used to compute
        the clustering.
    :param function progress_callback:
        Function used to report on progress.

    distances = numpy.asarray(distances)
    M = numpy.zeros_like(distances)

    # rearrange distances by order defined by tree's leaves
    indices = numpy.array([leaf.value.index for leaf in leaves(tree)])
    distances = distances[indices[numpy.newaxis, :],
                          indices[:, numpy.newaxis]]
    distances = numpy.ascontiguousarray(distances)

    # This is the 'fast' early termination search described in the paper
    # (it is slower in the pure python implementation)
    def argmin_ordered_xpypZ(x, y, Z, sorter_x=None, sorter_y=None):
        C = numpy.min(Z)
        if sorter_x is None:
            sorter_x = range(len(x))
            ordered_x = x
            ordered_x = x[sorter_x]
        if sorter_y is None:
            sorter_y = range(len(y))
            ordered_y = y
            ordered_y = y[sorter_y]

        y0 = ordered_y[0]

        best_val = numpy.inf
        best_i, best_j = 0, 0

        y0pC = y0 + C
        for i, x in zip(sorter_x, ordered_x):
            if x + y0pC >= best_val:
            xpC = x + C
            for j, y in zip(sorter_y, ordered_y):
                if xpC + y >= best_val:
                val = x + y + Z[i, j]
                if val < best_val:
                    best_val, best_i, best_j = val, i, j
        return best_i, best_j

    def argmin_xpypZ(x, y, Z):
        _, h = Z.shape
        A = Z + numpy.reshape(x, (-1, 1))
        A += numpy.reshape(y, (1, -1))
        i = numpy.argmin(A)
        return i // h, i % h

    def optimal_ordering(tree, M, ordering):
        if tree.is_leaf:
            M[tree.value.first, tree.value.first] = 0.0
            left, right = tree.left, tree.right
            if not left.is_leaf:
                V_ll = range(*left.left.value.range)
                V_lr = range(*left.right.value.range)
                u_iter = chain(((u, V_lr) for u in V_ll),
                               ((u, V_ll) for u in V_lr))
                V_lr = range(*left.value.range)
                u_iter = ((u, V_lr) for u in V_lr)

            u_iter = list(u_iter)
            assert [u for u, _ in u_iter] == list(range(*left.value.range))

            if not right.is_leaf:
                V_rl = range(*right.left.value.range)
                V_rr = range(*right.right.value.range)
                w_iter = chain(((w, V_rr) for w in V_rl),
                               ((w, V_rl) for w in V_rr))
                V_rr = range(*right.value.range)
                w_iter = ((w, V_rr) for w in V_rr)

            w_iter = list(w_iter)
            assert [w for w, _ in w_iter] == list(range(*right.value.range))

            for u, left_inner in u_iter:
                left_inner_slice = slice(left_inner.start, left_inner.stop)
                M_left_inner = M[u, left_inner_slice]
#                 left_inner_sort = numpy.argsort(M_left_inner)
                for w, right_inner in w_iter:
                    right_inner_slice = slice(right_inner.start,
                    M_right_inner = M[w, right_inner_slice]
#                     right_inner_sort = numpy.argsort(M_right_inner)
#                     i, j = argmin_ordered_xpypZ(
#                         M_left_inner, M_right_inner,
#                         distances[left_inner_slice, right_inner_slice],
#                         sorter_x=left_inner_sort, sorter_y=right_inner_sort,
#                     )
                    i, j = argmin_xpypZ(
                        M_left_inner, M_right_inner,
                        distances[left_inner_slice, right_inner_slice]
                    m, k = left_inner.start + i, right_inner.start + j
                    score = M[u, m] + M[k, w] + distances[m, k]
                    M[u, w] = M[w, u] = score
                    ordering[u, w] = (m, k)

        return M, ordering

    subtrees = list(postorder(tree))
    ordering_dtype = numpy.dtype(
        [("m", numpy.uint32),
         ("k", numpy.uint32)])

    ordering = numpy.empty(distances.shape, dtype=ordering_dtype)

    for i, subtree in enumerate(subtrees):
        M, ordering = optimal_ordering(subtree, M, ordering)

        if progress_callback:
            progress_callback(100.0 * i / len(subtrees))

    def min_uw(tree, u=None, w=None):
        if tree.is_leaf:
            return 0.0
            if u is None:
                U = slice(*tree.left.value.range)
                U = slice(u, u + 1)
            if w is None:
                W = slice(*tree.right.value.range)
                W = slice(w, w + 1)

            M_ = M[U, W]
            _, w = M_.shape
            i = numpy.argmin(M_.ravel())
            i, j = i // w, i % w
            return U.start + i, W.start + j

    def optimal_swap(root, M):
        opt_uw = {root: min_uw(root)}
        # run down the tree applying u, w constraints from parents.
        for tree in preorder(root):
            if tree.is_leaf:
                u, w = opt_uw[tree]
                assert u in range(*tree.value.range)
                assert w in range(*tree.value.range)
                if u < w:
                    m, k = ordering[u, w]

                    opt_uw[tree.left] = (u, m)
                    opt_uw[tree.right] = (k, w)
                    k, m = ordering[w, u]
                    opt_uw[tree.right] = (u, m)

                    opt_uw[tree.left] = (k, w)

        def is_swapped(tree):
            "Is `tree` swapped based on computed optimal ordering"
            if tree.is_leaf:
                return False
                u, w = opt_uw[tree]
                return u > w

        def swaped_branches(tree):
            "Return branches from `tree` in optimal order"
            if tree.is_leaf:
                return ()
            elif is_swapped(tree):
                return tree.branches
                return tuple(reversed(tree.branches))

        # Create a new tree structure with optimally swapped branches.
        T = {}
        counter = count(0)
        for tree in postorder(root, branches=swaped_branches):
            if tree.is_leaf:
                # we need to 're-enumerate' the leaves
                i = next(counter)
                T[tree] = Tree(tree.value._replace(range=(i, i + 1)), ())
                left, right = T[tree.left], T[tree.right]

                if left.value.first > right.value.first:
                    right, left = left, right

                assert left.value.first < right.value.last
                assert left.value.last == right.value.first

                T[tree] = Tree(tree.value._replace(range=(left.value.first,
                               (left, right))
        return T[root]

    return optimal_swap(tree, M)

[docs]class HierarchicalClustering: def __init__(self, n_clusters=2, linkage=AVERAGE): self.n_clusters = n_clusters self.linkage = linkage def fit(self, X): self.tree = dist_matrix_clustering(X, linkage=self.linkage) cut = top_clusters(self.tree, self.n_clusters) labels = numpy.zeros(self.tree.value.last) for i, cl in enumerate(cut): indices = [leaf.value.index for leaf in leaves(cl)] labels[indices] = i self.labels = labels def fit_predict(self, X, y=None): return self.labels