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task_similarities.py
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#!/usr/bin/env python2.5
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Written (W) 2009 Christian Widmer
# Copyright (C) 2009 Max-Planck-Society
"""
Created on 12.07.2009
@author: Christian Widmer
@summary: This class implements all ideas regarding distance measures between tasks,
including similarity matrices and taxonomies.
"""
import numpy
import yapgvb
import os
import re
from expenv import Taxonomy
def dataset_to_hierarchy(dataset_name):
"""
helper to construct taxonomy
@param dataset_name: task id
@type dataset_name: str
@param train_data: training data
@type train_data: dict<str, list<instances> >
@return: A hierarchy of tasks with data attached to the leaves
@rtype: TreeNode
"""
#factory
mymap = {"motif": create_hard_hierarchy_motif2,
"motif2": create_hard_hierarchy_motif2,
"promoter": create_hard_promoter,
"tiny promoter": create_hard_promoter,
#"medium splicing": create_hard_splicing,
"large splicing": create_hard_splicing,
#"broad_splicing": create_broad_splicing,
"complex promoter": create_hard_complex_promoter}
root = None
#TODO refactor this crappy code, really
if mymap.has_key(dataset_name):
#fetch hierarchy with appropriate hierarchy function
root = mymap[dataset_name]()
return root
#some hack to create trivial hierarchy
#task_ids = root.get_data_keys()
#root = create_simple_hierarchy(task_ids)
elif dataset_name.find("toy377") != -1:
return Taxonomy.get(18087)
elif dataset_name.find("toy") != -1:
root = create_hard_hierarchy_motif_two()
elif dataset_name.find("debug") != -1:
return Taxonomy.get(18087)
elif dataset_name.find("small splicing") != -1:
return Taxonomy.get(18092)
elif dataset_name.find("medium splicing") != -1:
return Taxonomy.get(18097)
elif dataset_name.find("broad_splicing") != -1:
return Taxonomy.get(18101)
elif dataset_name.find("deep") != -1:
# we assume that the number of tasks is encoded in dataset name
num_levels = int(numpy.log2(int(re.findall("deep(\w+)_", dataset_name)[0])))
btf = BinaryTreeFactory()
root = btf.create_binary_tree(num_levels)
elif dataset_name == "mhc":
# load pre-computed taxonomy
return Taxonomy.get(18076) #all edge_weights one
#return Taxonomy.get(18077) #all edge_weights zero
taxonomy = Taxonomy(data=root, description=dataset_name)
return taxonomy
class TreeNode(object):
"""
Simple graph implemenation for hierarchical multitask
"""
def __init__(self, name=""):
"""
define fields
@param name: node name, for leaves this defines the dataset identifier
@type name: str
"""
self.name = name
self.parent = None
self.children = []
self.predictor = None
self.edge_weight = 0
self.cost = 1.0
def add_child(self, node, weight=1.0):
"""
add node as child of current leaf
@param node: child node
@type node: TreeNode
"""
node.parent = self
node.edge_weight = weight
self.children.append(node)
def get_data_keys(self):
"""
fetch dataset names that are connected to leaves below the current node
this can be used as key to data structures
@return: list of dataset names
@rtype: list<str>
"""
return [node.name for node in self.get_leaves()]
def get_leaves(self):
"""
fetch all leaves with breadth first search from node
@return: list of leaves
@rtype: list<TreeNode>
"""
leaves = []
grey_nodes = [self]
while len(grey_nodes)>0:
node = grey_nodes.pop(0) #pop first item (go python!)
if len(node.children) == 0:
leaves.append(node)
else:
grey_nodes.extend(node.children)
return leaves
def get_nearest_neighbor(self):
"""
"""
leaves = [leaf for leaf in self.parent.get_leaves() if leaf!=self]
leftmost = leaves[0]
return leftmost
def get_all_nodes(self):
"""
fetch all nodes with breadth first search from node
@return: list of nodes
@rtype: list<TreeNode>
"""
nodes = []
grey_nodes = [self]
while len(grey_nodes)>0:
node = grey_nodes.pop(0) #pop first item (go python!)
nodes.append(node)
grey_nodes.extend(node.children)
return nodes
def get_node(self, node_name):
"""
get node from subtree rooted at self by name
@param node_name: name of node to get
@type node_name: str
@return: node with name node_name
@rtype: TreeNode
"""
candidates = [node for node in self.get_all_nodes() if node.name==node_name]
assert(len(candidates)==1)
return candidates[0]
def get_path_root(self):
"""
fetch all ancesters of current node (excluding self)
until root is reached including root
@return: list of nodes on the path to root
@rtype: list<TreeNode>
"""
nodes_on_path =[]
node = self
while node != None:
nodes_on_path.append(node)
node = node.parent
return nodes_on_path
def is_root(self):
"""
returns true if self is the root node
@return: indicator if self is root
@rtype: bool
"""
if self.parent == None:
return True
else:
return False
def is_leaf(self):
"""
returns true if self is a leaf
@return: indicator if self is root
@rtype: bool
"""
if len(self.children) == 0:
return True
else:
return False
def clear_predictors(self):
"""
removes predictors from all nodes
"""
all_nodes = self.get_all_nodes()
for node in all_nodes:
node.predictor = None
def plot(self, file_name="demo", force_num=False, plot_cost=False, plot_B=False):
"""
visualizes taxonomy with help of the yetanothergraphvizbinding package
a png is created and tried to open with evince (yes, hardcoded for now)
@return: graph data structure in yapgvb format
@rtype: yapgvb.Digraph
"""
graph = yapgvb.Digraph("my_graph")
#graph.ranksep = 3
#graph.ratio = "auto"
grey_nodes = [self]
counter = 0
name = ""
if self.name=="" or force_num:
name = "root" #str(counter) + ": " + self.name
else:
name = self.name
self.node = graph.add_node(label = name)
self.node.color = "gray95"
while len(grey_nodes)>0:
node = grey_nodes.pop(0) #pop first item
print node.name
#enqueue children
if node.children != None:
grey_nodes.extend(node.children)
#add edges
for child_node in node.children:
counter += 1
child_name = ""
if child_node.name=="" or force_num:
child_name = str(counter) + ": " + child_node.name
else:
child_name = child_node.name
child_node.node = graph.add_node(label = child_name)
child_node.node.style = "filled"
if child_node.is_leaf():
child_node.node.color = "gray80"
child_node.node.shape = "doubleoctagon" #"doublecircle"
else:
child_node.node.color = "gray95"
edge = node.node >> child_node.node
tmp_label = ""
if plot_cost:
try:
tmp_label += "C=" + str(child_node.cost)
except Exception:
print "cost attribute not set"
if plot_B:
tmp_label += "B=" + str(child_node.edge_weight)
edge.label = tmp_label
print "Using dot for graph layout..."
graph.layout(yapgvb.engines.dot)
#graph.layout(yapgvb.engines.neato)
#graph.layout(yapgvb.engines.twopi)
#graph.layout(yapgvb.engines.circo)
demo_formats = [
yapgvb.formats.png
#yapgvb.formats.ps
#yapgvb.formats.xdot
]
for myformat in demo_formats:
filename = file_name + ".%s" % myformat
print " Rendering .%s ..." % filename
graph.render(filename)
#os.system("evince " + filename + " &")
return graph
def create_distance_structure_from_tree(root):
"""
@param root: root of the tree
@type root: TreeNode
@return: structure containing distance between tasks
@rtype: dict<tuple(str,str), float>
"""
task_ids = [node.name for node in root.get_leaves()]
num_tasks = len(task_ids)
task_distance = {}
for i in xrange(num_tasks):
for j in xrange(i, num_tasks):
task1_id = task_ids[i]
task2_id = task_ids[j]
task_distance[(task1_id, task2_id)] = compute_hop_distance(root, task1_id, task2_id)
return task_distance
def compute_hop_distance(root, task1_id, task2_id):
"""
computes hop distance between two tasks according to a tree
@param root: root of the tree
@type root: TreeNode
@param task1_id: task name of first task
@type task1_id: str
@param task2_id:
@type task2_id: str:
@return: distance between tasks
@rtype: float
"""
if task1_id == task2_id:
return 0
leaves = root.get_leaves()
task1 = [leaf for leaf in leaves if leaf.name == task1_id][0]
task2 = [leaf for leaf in leaves if leaf.name == task2_id][0]
path1 = task1.get_path_root()
path2 = task2.get_path_root()
path_identical = True
while path_identical and len(path1) > 0 and len(path2) > 0:
tmp_node1 = path1[-1]
tmp_node2 = path2[-1]
#print "======"
#print "path1:", [node.name for node in path1]
#print "path2:", [node.name for node in path2]
if tmp_node1 != tmp_node2:
path_identical = False
else:
path1.pop()
path2.pop()
distance = float(len(path1) + len(path2) + 2)
#print "d(%s,%s)=%f" % (task1_id, task2_id, distance)
return distance
def create_simple_hierarchy(task_ids):
"""
create trivial hierarchy for debugging purposes
0
/|\
/ | \
1 2 3
@param instance_sets: task_ids
@type instance_sets: list<str>
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode()
#add all leaves directly to root node
for task_id in task_ids:
child = TreeNode(task_id)
root.add_child(child)
return root
def create_hard_hierarchy_motif_two():
"""
creates hard-coded hiearchy with two tasks
@param instance_sets: train data
@type instance_sets: dict<task_id, list<Instance> >
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode("root")
root.cost = 1.0
child1 = TreeNode("toy_0")
root.add_child(child1, weight=1.0)
child1.cost = 1.0
child2 = TreeNode("toy_1")
root.add_child(child2, weight=1.0)
child2.cost = 1.0
return root
def create_hard_hierarchy_motif2(remove_me):
"""
creates hard-coded hiearchy with four tasks
@param instance_sets: train data
@type instance_sets: dict<task_id, list<Instance> >
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode("root")
child1 = TreeNode("inner1")
root.add_child(child1, weight=0.0)
toy_0 = TreeNode("toy_0")
child1.add_child(toy_0, weight=1.0)
toy_1 = TreeNode("toy_1")
child1.add_child(toy_1, weight=1.0)
child2 = TreeNode("inner2")
root.add_child(child2, weight=0.0)
toy_2 = TreeNode("toy_2")
child2.add_child(toy_2, weight=1.0)
toy_3 = TreeNode("toy_3")
child2.add_child(toy_3, weight=remove_me)
root.plot("/tmp/mygraph")
os.system("evince /tmp/mygraph.ps &")
return root
class BinaryTreeFactory(object):
"""
Binary Tree Factory
"""
node_counter = 0
inner_node_counter = 0
leaf_counter = 0
nodes = None
leaves = None
def create_binary_tree(self, num_levels):
"""
creates binary tree with num_levels and 2^n leaves
@param num_levels: number of levels
@type num_levels: int
@return: root node with binary tree attached
@rtype: TreeNode
"""
print "creating binary tree with", pow(2, num_levels), "tasks"
# init
self.node_counter = 0
self.inner_node_counter = 0
self.leaf_counter = 0
self.nodes = []
self.leaves = []
# create root
root = TreeNode("root")
# recursive call
self.__create_subtree(root, num_levels-1)
self.__create_subtree(root, num_levels-1)
# return root node
return root
def __create_subtree(self, parent_node, level):
"""
recursive call to generate binary tree
@param parent_node: parent node
@type parent_node: TreeNode
@param level: current level
@type level: int
"""
current_node = TreeNode("no name")
parent_node.add_child(current_node)
if level == 0:
current_node.name = "toy_" + str(self.leaf_counter)
self.leaf_counter += 1
else:
current_node.name = "inner_" + str(self.inner_node_counter)
self.inner_node_counter += 1
self.__create_subtree(current_node, level-1)
self.__create_subtree(current_node, level-1)
self.nodes.append(current_node)
self.node_counter += 1
def create_hard_hierarchy_deep():
"""
creates hard-coded hierarchy with eight tasks
@param instance_sets: train data
@type instance_sets: dict<task_id, list<Instance> >
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode("root")
inner1 = TreeNode("inner1")
root.add_child(inner1, 1.0)
inner1_a = TreeNode("inner1_a")
inner1.add_child(inner1_a, 1.0)
toy_0 = TreeNode("toy_0")
inner1_a.add_child(toy_0, 1.0)
toy_1 = TreeNode("toy_1")
inner1_a.add_child(toy_1, 1.0)
inner1_b = TreeNode("inner1_b")
inner1.add_child(inner1_b, 1.0)
toy_2 = TreeNode("toy_2")
inner1_b.add_child(toy_2, 1.0)
toy_3 = TreeNode("toy_3")
inner1_b.add_child(toy_3, 1.0)
inner2 = TreeNode("inner2")
root.add_child(inner2, 1.0)
inner2_a = TreeNode("inner2_a")
inner2.add_child(inner2_a, 1.0)
toy_4 = TreeNode("toy_4")
inner2_a.add_child(toy_4, 1.0)
toy_5 = TreeNode("toy_5")
inner2_a.add_child(toy_5, 1.0)
inner2_b = TreeNode("inner2_b")
inner2.add_child(inner2_b, 1.0)
toy_6 = TreeNode("toy_6")
inner2_b.add_child(toy_6, 1.0)
toy_7 = TreeNode("toy_7")
inner2_b.add_child(toy_7, 1.0)
return root
def create_broad_splicing():
"""
@return: root node of deep splicing dataset
@rtype: TreeNode
"""
root = TreeNode()
nidulans = TreeNode("A.nidulans")
root.add_child(nidulans)
plantae = TreeNode("plantae")
root.add_child(plantae)
animalia = TreeNode("animalia")
root.add_child(animalia)
trichocarpa = TreeNode("P.trichocarpa")
plantae.add_child(trichocarpa)
angiosperms = TreeNode("angiosperms")
plantae.add_child(angiosperms)
thaliana = TreeNode("A.thaliana")
angiosperms.add_child(thaliana)
sativa = TreeNode("O.sativa")
angiosperms.add_child(sativa)
chordata = TreeNode("chordata")
animalia.add_child(chordata)
savignyi = TreeNode("C.savignyi")
chordata.add_child(savignyi)
vertebrata = TreeNode("vertebrata")
chordata.add_child(vertebrata)
actinopterygii = TreeNode("actinopterygii")
vertebrata.add_child(actinopterygii)
rerio = TreeNode("D.rerio")
actinopterygii.add_child(rerio)
latipes = TreeNode("O.latipes")
actinopterygii.add_child(latipes)
aculeatus = TreeNode("G.aculeatus")
actinopterygii.add_child(aculeatus)
nigroviridis = TreeNode("T.nigroviridis")
actinopterygii.add_child(nigroviridis)
mammals = TreeNode("mammals")
vertebrata.add_child(mammals)
taurus = TreeNode("B.taurus")
mammals.add_child(taurus)
sapiens = TreeNode("H.sapiens")
mammals.add_child(sapiens)
musculus = TreeNode("M.musculus")
mammals.add_child(musculus)
protostomia = TreeNode("protostomia")
animalia.add_child(protostomia)
#nematoda = TreeNode("nematoda")
elegans = TreeNode("C.elegans")
protostomia.add_child(elegans)
diptera = TreeNode("diptera")
protostomia.add_child(diptera)
gambiae = TreeNode("A.gambiae")
diptera.add_child(gambiae)
melanogaster = TreeNode("D.melanogaster")
diptera.add_child(melanogaster)
return root
def create_hard_splicing():
"""
@param instance_sets: train data
@type instance_sets: dict<task_id, list<Instance> >
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode()
thaliana = TreeNode("thaliana")
root.add_child(thaliana)
child1 = TreeNode()
root.add_child(child1)
drosophila = TreeNode("drosophila")
child1.add_child(drosophila)
child2 = TreeNode()
child1.add_child(child2)
pacificus = TreeNode("pacificus")
child2.add_child(pacificus)
remanei = TreeNode("remanei")
child2.add_child(remanei)
return root
def create_hard_complex_promoter_simplified(train_data):
"""
create hierarchy
Gg Gallus gallus (chicken)
HSV-1 Human herpes simplex virus type 1
Xl Xenopus laevis (African clawed frog)
Ps Pisum sativum (pea).
Mm Mus musculus (mouse)
Zm Zea mays (maize)
Hv Hordeum vulgare (barley). (Gerste)
Ce Caenorhabditis elegans.
Bt Bos taurus (cattle)
Ss Sus scrofa (pig).
Sp Strongylocentrotus purpuratus. (Art Seestern)
EBV Human Epstein-Barr virus
Ath A.thaliana
HCMV Human Cytomegalovirus (HCMV)
Rn Rattus norvegicus (rat)
Nt Nicotiana tabacum (common tobacco).
Gm Glycine max (soybean).
Ta Triticum aestivum (wheat).
@param instance_sets: list of datasets
@type instance_sets: list<list<Instance>>
@return: root node of simple tree
@rtype: TreeNode
"""
root = TreeNode()
# plants
plants = TreeNode()
root.add_child(plants)
tmp = TreeNode()
plants.add_child(tmp)
tabacco = TreeNode()
tabacco.data = train_data["Nt"]
tmp.add_child(tabacco)
thaliana = TreeNode()
thaliana.data = train_data["Ath"]
plants.add_child(thaliana)
barley = TreeNode()
barley.data = train_data["Hv"]
plants.add_child(barley)
wheat = TreeNode()
wheat.data = train_data["Ta"]
plants.add_child(wheat)
maize = TreeNode()
maize.data = train_data["Zm"]
plants.add_child(maize)
pea = TreeNode()
pea.data = train_data["Ps"]
plants.add_child(pea)
soy_bean = TreeNode()
soy_bean.data = train_data["Gm"]
plants.add_child(soy_bean)
# animals
animals = TreeNode()
root.add_child(animals)
elegans = TreeNode()
elegans.data = train_data["Ce"]
animals.add_child(elegans)
purpuratus = TreeNode()
purpuratus.data = train_data["Sp"]
animals.add_child(purpuratus)
frog = TreeNode()
frog.data = train_data["Xl"]
#TODO experimental
root.add_child(frog)
chicken = TreeNode()
chicken.data = train_data["Gg"]
animals.add_child(chicken)
cow = TreeNode()
cow.data = train_data["Bt"]
animals.add_child(cow)
pig = TreeNode()
pig.data = train_data["Ss"]
animals.add_child(pig)
mouse = TreeNode()
mouse.data = train_data["Mm"]
animals.add_child(mouse)
rat = TreeNode()
rat.data = train_data["Rn"]
animals.add_child(rat)
# viruses
human_viruses = TreeNode()
root.add_child(human_viruses)
herpes = TreeNode()
herpes.data = train_data["HSV-1"]
human_viruses.add_child(herpes)
hcmv = TreeNode()
hcmv.data = train_data["HCMV"]
human_viruses.add_child(hcmv)
viruses = TreeNode()
root.add_child(viruses)
bah = TreeNode()
viruses.add_child(bah)
epv = TreeNode()
epv.data = train_data["EBV"]
bah.add_child(epv)
return root
def create_hard_complex_promoter(train_data):
"""
create hierarchy
Gg Gallus gallus (chicken)
HSV-1 Human herpes simplex virus type 1
Xl Xenopus laevis (African clawed frog)
Ps Pisum sativum (pea).
Mm Mus musculus (mouse)
Zm Zea mays (maize)
Hv Hordeum vulgare (barley). (Gerste)
Ce Caenorhabditis elegans.
Bt Bos taurus (cattle)
Ss Sus scrofa (pig).
Sp Strongylocentrotus purpuratus. (Art Seestern)