def run(verbose=True, Y=1, D=2, dataset=2, in_path=None, out_path=None, steps=2):
ripl = s.make_puma_church_prime_ripl()
total_birds = 1000 if dataset == 2 else 1000000
name = "%dx%dx%d-train" % (width, height, total_birds)
params = {
"name":name,
"width":width,
"height":height,
"cells":cells,
"dataset":dataset,
"total_birds":total_birds,
"years":range(Y),
"days":[],
"hypers":hypers,
"in_path":in_path,
"out_path":out_path,
}
model = Poisson(ripl, params)
if verbose:
print "Starting run"
ripl.clear()
model.loadAssumes()
model.updateObserves(0)
logs = []
t = [time.time()] # python :(
def log():
dt = time.time() - t[0]
#logs.append((ripl.get_global_logscore(), model.computeScoreDay(model.days[-2]), dt))
logs.append((ripl.get_global_logscore(), dt))
if verbose:
print logs[-1]
t[0] += dt
for d in range(1, D):
if verbose:
print "Day %d" % d
model.updateObserves(d)
log()
for i in range(steps):
ripl.infer({"kernel":"mh", "scope":d-1, "block":"one", "transitions": Y*1000})
log()
continue
bird_locs = model.getBirdLocations(days=[d])
for y in range(Y):
path = 'bird_moves%d/%d/%02d/' % (dataset, y, d)
ensure(path)
drawBirds(bird_locs[y][d], path + '%02d.png' % i, **params)
#model.drawBirdLocations()
model.writeBirdMoves()
return logs
def run(verbose=True, Y=1, D=2, dataset=2, in_path=None, out_path=None, steps=2):
ripl = s.make_puma_church_prime_ripl()
total_birds = 1000 if dataset == 2 else 1000000
name = "%dx%dx%d-test" % (width, height, total_birds)
params = {
"name": name,
"width": width,
"height": height,
"cells": cells,
"dataset": dataset,
"total_birds": total_birds,
"years": range(Y),
"days": [],
"hypers": hypers,
"in_path": in_path,
"out_path": out_path,
}
model = Poisson(ripl, params)
if verbose:
print "Starting run"
ripl.clear()
model.loadAssumes()
predictions = {}
for d in range(D - 1):
if verbose:
print "Day %d" % d
model.updateObserves(d)
if d > 0:
for i in range(5):
ripl.infer({"kernel": "mh", "scope": d - 1, "block": "one", "transitions": Y * 1000})
last_day = d == D - 2
if last_day:
bird_moves = ripl.predict("(get_birds_moving3 %d)" % d, label="predict%d" % d)
else:
bird_moves = [ripl.predict("(get_birds_moving3 %d)" % t, label="predict%d" % t) for t in [d, d + 1]]
for y in range(Y):
for i in range(cells):
for j in range(cells):
if last_day:
predictions[(y, d - 1, i, j)] = [bird_moves[y][i][j], -1]
else:
predictions[(y, d - 1, i, j)] = [b[y][i][j] for b in bird_moves]
for t in [d] if last_day else [d, d + 1]:
ripl.forget("predict%d" % t)
# model.drawBirdLocations()
writePredictions(predictions, **params)
def run(y, days, runs, in_path=None):
ripl = s.make_puma_church_prime_ripl()
params = {
"dataset":1,
"name":name,
"cells":cells,
"years":[y],
"days":days,
"in_path":in_path
}
onebird = OneBird(ripl, params)
def sweep(r, *args):
for i in range(0):
onebird.inferMove(ripl=r)
onebird.inferHypers(ripl=r)
history, _ = onebird.runFromConditional(D, runs=runs, infer=sweep, verbose=True)
history.hypers = [avgFinalValue(history, 'hypers%d' % k) for k in range(num_features)]
history.save(directory="%s/%d" % (name, y))
def run(y):
ripl = s.make_puma_church_prime_ripl()
params = {"name": name, "cells": cells, "years": [y], "days": range(D)}
onebird = OneBird(ripl, params)
def sweep(r, *args):
for i in range(5):
onebird.inferMove(ripl=r)
#r.infer('(slice hypers one %d)' % num_features)
r.infer('(mh hypers one %d)' % 5 * (1 + num_features))
history, ripl = onebird.runFromConditional(D,
runs=runs,
infer=sweep,
verbose=True)
history.hypers = [
avgFinalValue(history, 'hypers%d' % k) for k in range(num_features)
]
#history.save(directory="%s/%d" % (name, y))
return history, ripl
def run(y):
ripl = s.make_puma_church_prime_ripl()
params = {
"name":name,
"cells":cells,
"years":[y],
"days":range(D)
}
onebird = OneBird(ripl, params)
def sweep(r, *args):
for i in range(5):
onebird.inferMove(ripl=r)
#r.infer('(slice hypers one %d)' % num_features)
r.infer('(mh hypers one %d)' % 5 * (1 + num_features))
history, ripl = onebird.runFromConditional(D, runs=runs, infer=sweep, verbose=True)
history.hypers = [avgFinalValue(history, 'hypers%d' % k) for k in range(num_features)]
#history.save(directory="%s/%d" % (name, y))
return history,ripl
import time
import venture.shortcuts as s
ripl = s.make_puma_church_prime_ripl()
from utils import *
from model_param import Poisson, num_features
def makeModel(dataset=2, D=3, Y=1, learnHypers=True, hyperPrior='(gamma 1 .1)',in_path=None, out_path=None):
width,height = 10,10
cells = width * height
total_birds = 1000 if dataset == 2 else 1000000
name = "%dx%dx%d-train" % (width, height, total_birds)
runs = 1
hypers = [5, 10, 10, 10] if not learnHypers else [hyperPrior]*4
params = {
"name":name,
"width":width,
"height":height,
"cells":cells,
"dataset":dataset,
"total_birds":total_birds,
"years":range(Y),
"days":[],
"hypers":hypers,
"maxDay":D,
"in_path":in_path,
"out_path":out_path}
model = Poisson(ripl,params)
def build(): return s.make_puma_church_prime_ripl()
self.assume("alpha_topic_word", "(gamma 1.0 1.0)")
self.assume("get_document_topic_sampler", "(mem (lambda (doc) (make_sym_dir_mult alpha_document_topic topics)))")
self.assume("get_topic_word_sampler", "(mem (lambda (topic) (make_sym_dir_mult alpha_topic_word vocab)))")
self.assume("get_word", "(mem (lambda (doc pos) ((get_topic_word_sampler ((get_document_topic_sampler doc))))))")
return
def makeObserves(self):
D = self.parameters['documents']
N = self.parameters['words_per_document']
for doc in range(D):
for pos in range(N):
self.observe("(get_word %d %d)" % (doc, pos), "atom<%d>" % 0)
return
if __name__ == '__main__':
ripl = shortcuts.make_puma_church_prime_ripl()
#parameters = {'topics' : 4, 'vocab' : 10, 'documents' : 8, 'words_per_document' : 12}
#model = LDA(ripl, parameters)
#history = model.runConditionedFromPrior(50, verbose=True)
#history = model.runFromJoint(50, verbose=True)
#history = model.sampleFromJoint(20, verbose=True)
#sample_hist, infer_hist, klHistory = model.computeJointKL(200, 50, verbose=True)
#history = model.runFromConditional(50)
#klHistory.plot(fmt='png')
parameters = {'topics' : 3, 'vocab' : 8, 'documents' : 5, 'words_per_document' : [7, 8]}
mySweeps=10
run_count = 0
def runner(params):
import venture.shortcuts as s
ripl = s.make_puma_church_prime_ripl()
from model import OneBird, num_features
from itertools import product
name = "onebird"
width = 4
height = 4
cells = width * height
Y = 1
D = 20
params = {"name": name, "cells": cells, "years": [0], "days": range(D)}
onebird = OneBird(ripl, params)
onebird.loadAssumes()
onebird.loadObserves()
def checkHypers(hypers):
print hypers
for i in range(num_features):
onebird.ripl.force('hypers%d' % i, hypers[i])
for i in range(5):
onebird.inferMove()
return onebird.ripl.get_global_logscore()
def gridHypers(grid):
