def pyquest_newtree(data,tree_constant=0.25,row_alpha=0.5,col_alpha=0.5,beta=1.0,n_iters=3):
init_row_aff = affinity.mutual_cosine_similarity(data.T,False,0,threshold=0.1)
#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)
init_row_tree = tree_building.make_tree_embedding(row_affinity,tree_constant)
dual_col_trees = []
dual_row_trees = [init_row_tree]
for _ in xrange(n_iters):
#print "Beginning iteration {}".format(i)
col_emd = dual_affinity.calc_emd(data,dual_row_trees[-1],alpha=col_alpha,beta=beta)
col_aff = dual_affinity.emd_dual_aff(col_emd)
#print "call2 tree_constant:{}".format(tree_constant)
dual_col_trees.append(tree_building.make_tree_embedding(col_aff,tree_constant))
row_emd = dual_affinity.calc_emd(data.T,dual_col_trees[-1],alpha=row_alpha,beta=beta)
row_aff = dual_affinity.emd_dual_aff(row_emd)
#print "call3 tree_constant:{}".format(tree_constant)
dual_row_trees.append(tree_building.make_tree_embedding(row_aff,tree_constant))
col_tree = dual_col_trees[-1]
row_tree = dual_row_trees[-1]
col_emd = dual_affinity.calc_emd(data,row_tree,alpha=col_alpha,beta=beta)
row_emd = dual_affinity.calc_emd(data.T,col_tree,alpha=row_alpha,beta=beta)
row_aff = dual_affinity.emd_dual_aff(row_emd)
col_aff = dual_affinity.emd_dual_aff(col_emd)
row_vecs,row_vals = markov.markov_eigs(row_aff, 12)
col_vecs,col_vals = markov.markov_eigs(col_aff, 12)
return row_tree,col_tree,row_vecs,col_vecs,row_vals,col_vals
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)
