/
run_quest.py
216 lines (178 loc) · 8.26 KB
/
run_quest.py
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import numpy as np
import scipy.spatial as spsp
import datetime
from wx.lib.pubsub import Publisher
import affinity
import markov
import tree_building
import bintree_construct
import dual_affinity
INIT_AFF_COS_SIM = 0
INIT_AFF_GAUSSIAN = 1
DEFAULT_INIT_AFF_THRESHOLD = 0.1
DEFAULT_INIT_AFF_EPSILON = 1.0
TREE_TYPE_BINARY = 0
TREE_TYPE_FLEXIBLE = 1
DEFAULT_TREE_BAL_CONSTANT = 1.0
DEFAULT_TREE_CONSTANT = 0.2
DUAL_EMD = 2
DUAL_GAUSSIAN = 3
DEFAULT_DUAL_EPSILON = 1.0
DEFAULT_DUAL_ALPHA = 0.5
DEFAULT_DUAL_BETA = 1.0
DEFAULT_N_ITERS = 3
DEFAULT_N_TREES = 1
class PyQuestParams(object):
def __init__(self,init_aff_type,tree_type,dual_row_type,dual_col_type,
**kwargs):
self.set_init_aff(init_aff_type,**kwargs)
self.set_tree_type(tree_type,**kwargs)
self.set_dual_aff(dual_row_type,dual_col_type,**kwargs)
self.set_iters(**kwargs)
def set_init_aff(self,affinity_type,**kwargs):
self.init_aff_type = affinity_type
if self.init_aff_type == INIT_AFF_COS_SIM:
if "threshold" in kwargs:
self.init_aff_threshold = kwargs["threshold"]
else:
self.init_aff_threshold = DEFAULT_INIT_AFF_THRESHOLD
elif self.init_aff_type == INIT_AFF_GAUSSIAN:
if "epsilon" in kwargs:
self.init_aff_epsilon = kwargs["epsilon"]
else:
self.init_aff_epsilon = DEFAULT_INIT_AFF_EPSILON
def set_tree_type(self,tree_type,**kwargs):
self.tree_type = tree_type
if self.tree_type == TREE_TYPE_BINARY:
if "bal_constant" in kwargs:
self.tree_bal_constant = kwargs["bal_constant"]
else:
self.tree_bal_constant = DEFAULT_TREE_BAL_CONSTANT
if self.tree_type == TREE_TYPE_FLEXIBLE:
if "tree_constant" in kwargs:
self.tree_constant = kwargs["tree_constant"]
else:
self.tree_constant = DEFAULT_TREE_CONSTANT
def set_dual_aff(self,row_affinity_type,col_affinity_type,**kwargs):
self.row_affinity_type = row_affinity_type
self.col_affinity_type = col_affinity_type
if self.row_affinity_type == DUAL_GAUSSIAN:
if "row_epsilon" in kwargs:
self.row_epsilon = kwargs["epsilon"]
else:
self.row_epsilon = DEFAULT_DUAL_EPSILON
if self.row_affinity_type == DUAL_EMD:
if "row_alpha" in kwargs:
self.row_alpha = kwargs["row_alpha"]
else:
self.row_alpha = DEFAULT_DUAL_ALPHA
if "row_beta" in kwargs:
self.row_beta = kwargs["row_beta"]
else:
self.row_beta = DEFAULT_DUAL_BETA
if self.col_affinity_type == DUAL_GAUSSIAN:
if "col_epsilon" in kwargs:
self.col_epsilon = kwargs["epsilon"]
else:
self.col_epsilon = DEFAULT_DUAL_EPSILON
if self.col_affinity_type == DUAL_EMD:
if "col_alpha" in kwargs:
self.col_alpha = kwargs["col_alpha"]
else:
self.col_alpha = DEFAULT_DUAL_ALPHA
if "col_beta" in kwargs:
self.col_beta = kwargs["col_beta"]
else:
self.col_beta = DEFAULT_DUAL_BETA
def set_iters(self,**kwargs):
if "n_iters" in kwargs:
self.n_iters = kwargs["n_iters"]
else:
print "default n_iters"
self.n_iters = DEFAULT_N_ITERS
if "n_trees" in kwargs:
self.n_trees = kwargs["n_trees"]
else:
self.n_trees = DEFAULT_N_TREES
class PyQuestRun(object):
def __init__(self,run_desc,row_trees,col_trees,row_tree_descs,col_tree_descs,params):
self.run_desc = run_desc
self.row_trees = row_trees
self.col_trees = col_trees
self.row_tree_descs = row_tree_descs
self.col_tree_descs = col_tree_descs
self.params = params
def pyquest(data,params):
#params should be a PyQuestParams object
Publisher.sendMessage("status.bar", "Calculating initial affinity...")
if params.init_aff_type == INIT_AFF_COS_SIM:
init_row_aff = affinity.mutual_cosine_similarity(
data.T,False,0,threshold=params.init_aff_threshold)
elif params.init_aff_type == INIT_AFF_GAUSSIAN:
#add KNN to the page
init_row_aff = affinity.gaussian_euclidean(
data.T, 5, params.init_aff_epsilon)
#Compute diffusion embedding of initial affinities
init_row_vecs,init_row_vals = markov.markov_eigs(init_row_aff, 12)
init_row_vals[np.isnan(init_row_vals)] = 0.0
row_embedding = init_row_vecs.dot(np.diag(init_row_vals))
row_distances = spsp.distance.squareform(spsp.distance.pdist(row_embedding))
row_affinity = np.max(row_distances) - row_distances
#Generate initial tree
#print "call1 tree_constant:{}".format(tree_constant)
Publisher.sendMessage("status.bar", "Calculating initial row tree...")
if params.tree_type == TREE_TYPE_BINARY:
init_row_tree = bintree_construct.median_tree(
init_row_vecs,init_row_vals,max_levels=12)
elif params.tree_type == TREE_TYPE_FLEXIBLE:
# init_row_tree = tree_building.make_tree_embedding(
# row_affinity,params.tree_constant)
init_row_tree = tree_building.make_tree_embedding(
row_affinity,params.tree_constant)
dual_col_trees = []
dual_row_trees = [init_row_tree]
row_tree_descs = ["Initial tree"]
col_tree_descs = []
for i in xrange(params.n_iters):
message = "Iteration {}: calculating column affinity...".format(i)
Publisher.sendMessage("status.bar", message)
#print "Beginning iteration {}".format(i)
if params.col_affinity_type == DUAL_EMD:
col_emd = dual_affinity.calc_emd(data,dual_row_trees[-1],
params.col_alpha,params.col_beta)
col_aff = dual_affinity.emd_dual_aff(col_emd)
elif params.col_affinity_type == DUAL_GAUSSIAN:
print "Gaussian dual affinity not supported at the moment."
return None
message = "Iteration {}: calculating column tree...".format(i)
Publisher.sendMessage("status.bar", message)
if params.tree_type == TREE_TYPE_BINARY:
col_tree = bintree_construct.eigen_tree(data,dual_row_trees[-1],
params.col_alpha,params.col_beta,params.tree_bal_constant)
elif params.tree_type == TREE_TYPE_FLEXIBLE:
col_tree = tree_building.make_tree_embedding(col_aff,
params.tree_constant)
dual_col_trees.append(col_tree)
col_tree_descs.append("Iteration {}".format(i))
message = "Iteration {}: calculating row affinity...".format(i)
Publisher.sendMessage("status.bar", message)
if params.row_affinity_type == DUAL_EMD:
row_emd = dual_affinity.calc_emd(data.T,dual_col_trees[-1],
params.row_alpha,params.row_beta)
row_aff = dual_affinity.emd_dual_aff(row_emd)
elif params.row_affinity_type == DUAL_GAUSSIAN:
print "Gaussian dual affinity not supported at the moment."
return None
message = "Iteration {}: calculating row tree...".format(i)
Publisher.sendMessage("status.bar", message)
if params.tree_type == TREE_TYPE_BINARY:
row_tree = bintree_construct.eigen_tree(data.T,dual_col_trees[-1],
params.row_alpha,params.row_beta,params.tree_bal_constant)
elif params.tree_type == TREE_TYPE_FLEXIBLE:
row_tree = tree_building.make_tree_embedding(row_aff,
params.tree_constant)
dual_row_trees.append(row_tree)
row_tree_descs.append("Iteration {}".format(i))
quest_run_desc = "{}".format(datetime.datetime.now())
return PyQuestRun(quest_run_desc,dual_row_trees,dual_col_trees,
row_tree_descs,col_tree_descs,params)