/
pyquest_M.py
268 lines (232 loc) · 8.9 KB
/
pyquest_M.py
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from wx.lib.pubsub import Publisher
import numpy as np
import threading
import run_quest
import affinity
import dual_affinity
import markov
import barcode
import cPickle
import tree_util
class TreeData(object):
data = None
data_type = None
class PyQuestDataModel(object):
def __init__(self):
self.quest_runs = []
self.path = None
self.true_data = None
self.data = None
self.row_data = TreeData()
self.col_data = TreeData()
self.row_affinity = None
self.col_affinity = None
self.row_vecs = None
self.row_vals = None
self.col_vecs = None
self.col_vals = None
self.row_order = None
self.col_order = None
self._selected_run = None
self.selected_rowtree = None
self.selected_coltree = None
self.selected_nodes = {}
self.othertrees = {}
def save(self,filename):
fout = open(filename,"wb")
cPickle.dump(self.__dict__,fout)
fout.close()
def load(self,filename):
fin = open(filename,"rb")
self.__dict__ = cPickle.load(fin)
fin.close()
Publisher.sendMessage("data.load")
Publisher.sendMessage("data.run")
Publisher.sendMessage("rowtree.select")
Publisher.sendMessage("coltree.select")
Publisher.sendMessage("view.refresh")
def reset_calcs(self):
self.row_affinity = None
self.col_affinity = None
self.row_vecs = None
self.row_vals = None
self.col_vecs = None
self.col_vals = None
Publisher.sendMessage("embed.calc.row")
Publisher.sendMessage("embed.calc.col")
Publisher.sendMessage("affinity.calc.row")
Publisher.sendMessage("affinity.calc.col")
def load_data(self,file_path):
self.path = file_path
self.true_data = None
with np.load(file_path) as fdict:
self.data = fdict["data"]
self.row_data = TreeData()
self.col_data = TreeData()
self.row_data.data = fdict["q_descs"]
self.row_data.data_type = "descs"
self.col_data.data = (fdict["p_score_descs"],fdict["p_scores"])
self.col_data.data_type = "scores"
self.row_order = np.arange(self.data.shape[0])
self.col_order = np.arange(self.data.shape[1])
np.random.shuffle(self.row_order)
np.random.shuffle(self.col_order)
self.reset_calcs()
Publisher.sendMessage("data.load")
def select_run(self,run_selected_idx):
if run_selected_idx == self._selected_run:
return
elif run_selected_idx < len(self.quest_runs):
self._selected_run = run_selected_idx
else:
self._selected_run = None
Publisher.sendMessage("run.select")
if self._selected_run is not None:
self.select_tree("rowtree", len(self.selected_run.row_trees)-1)
self.select_tree("coltree", len(self.selected_run.col_trees)-1)
def select_tree(self,tree_id,tree_idx):
sel_run = self.quest_runs[self._selected_run]
if tree_id == "rowtree":
if tree_idx > len(sel_run.row_trees):
self.selected_rowtree = None
else:
self.selected_rowtree = tree_idx
if tree_id == "coltree":
if tree_idx > len(sel_run.col_trees):
self.selected_coltree = None
else:
self.selected_coltree = tree_idx
self.node_select(tree_id, 0)
Publisher.sendMessage(tree_id+".select")
Publisher.sendMessage(tree_id+".node.select")
def tree(self,tree_id):
if tree_id == "rowtree":
return self.row_tree
elif tree_id == "coltree":
return self.col_tree
else:
return self.othertrees[tree_id]
def tree_data(self,tree_id):
if tree_id == "rowtree":
return self.row_data
elif tree_id == "coltree":
return self.col_data
else:
return None
@property
def selected_run(self):
return self.quest_runs[self._selected_run]
@property
def row_tree(self):
if self._selected_run is None:
return None
elif self.selected_rowtree is None:
return None
else:
sel_run = self.quest_runs[self._selected_run]
return sel_run.row_trees[self.selected_rowtree]
@property
def col_tree(self):
if self._selected_run is None:
return None
elif self.selected_coltree is None:
return None
else:
sel_run = self.quest_runs[self._selected_run]
return sel_run.col_trees[self.selected_coltree]
def vecs(self,topic):
if "row" in topic:
return self.row_vecs
elif "col" in topic:
return self.col_vecs
else:
return None
def vals(self,topic):
if "row" in topic:
return self.row_vals
elif "col" in topic:
return self.col_vals
else:
return None
def tree_avgs(self,topic):
if "row" in topic:
return self.avg_tree_rows
elif "col" in topic:
return self.avg_tree_cols
else:
return None
def run_questionnaire(self,params):
t = PyQuestRunThread(params,self)
def _run_questionnaire(self,params):
Publisher.sendMessage("status.bar", "Running questionnaire...")
self.quest_runs.append(run_quest.pyquest(self.data,params))
self.select_run(len(self.quest_runs)-1)
#row_tree = self.quest_runs[-1][1].row_trees[-1]
#col_tree = self.quest_runs[-1][1].col_trees[-1]
Publisher.sendMessage("data.run")
#the parameter is called row_tree either way, so the second arg is
#not an error
self.calc_row_affinity(params.row_affinity_type,
row_tree=self.tree("coltree"),
alpha=params.row_alpha,beta=params.row_beta)
self.calc_col_affinity(params.row_affinity_type,
row_tree=self.tree("rowtree"),
alpha=params.row_alpha,beta=params.row_beta)
Publisher.sendMessage("status.bar", "Ready.")
def calc_row_embedding(self):
self.row_vecs, self.row_vals = markov.markov_eigs(self.row_affinity, 8)
Publisher.sendMessage("embed.row.calc")
def calc_col_embedding(self):
self.col_vecs, self.col_vals = markov.markov_eigs(self.col_affinity, 8)
Publisher.sendMessage("embed.col.calc")
def calc_row_affinity(self,affinity_type,**kwargs):
self.row_affinity = self._calc_affinity(self.data.T,affinity_type,**kwargs)
Publisher.sendMessage("affinity.calc.row")
self.calc_row_embedding()
def calc_col_affinity(self,affinity_type,**kwargs):
self.col_affinity = self._calc_affinity(self.data,affinity_type,**kwargs)
Publisher.sendMessage("affinity.calc.col")
self.calc_col_embedding()
def _calc_affinity(self,data,affinity_type,**kwargs):
if affinity_type == run_quest.INIT_AFF_COS_SIM: #cosine similarity
affinity_matrix = affinity.mutual_cosine_similarity(data,**kwargs)
elif affinity_type == run_quest.DUAL_EMD: #EMD
emd = dual_affinity.calc_emd(data,**kwargs)
affinity_matrix = dual_affinity.emd_dual_aff(emd)
return affinity_matrix
def calc_avg_val_rows(self,row_tree,col_tree):
if col_tree is None:
print "empty column tree"
pass
else:
avg_level_rows = barcode.level_avgs(self.data.T,row_tree).T
avg_tree_rows = tree_util.tree_averages(avg_level_rows.T,col_tree).T
self.avg_tree_rows = avg_tree_rows
Publisher.sendMessage("embed.row.avg")
return avg_tree_rows
def calc_avg_val_cols(self,row_tree,col_tree):
if row_tree is None:
pass
else:
avg_level_cols = barcode.level_avgs(self.data,col_tree)
avg_tree_cols = tree_util.tree_averages(avg_level_cols,row_tree).T
self.avg_tree_cols = avg_tree_cols
Publisher.sendMessage("embed.col.avg")
return avg_tree_cols
def node_select(self,tree_id,node):
self.selected_nodes[tree_id] = node
if self.tree(tree_id) is not None:
Publisher.sendMessage(tree_id + ".node.select",node)
def tree_node(self,tree_id):
if tree_id in self.selected_nodes:
return self.selected_nodes[tree_id]
else:
return None
class PyQuestRunThread(threading.Thread):
def __init__(self,params,model):
threading.Thread.__init__(self)
self.params = params
self.model = model
self.start()
def run(self):
self.model._run_questionnaire(self.params)