def traj_obs(print_num=1000):
""" Returns the trajectory observations.
If the obs have never been computed before, also stores them in a file.
Otherwise reads the cached copy from the disk.
"""
fname = "%s/traj_obs.pkl"%experiment_directory(experiment_name)
fname_test = "%s/traj_obs_test.pkl"%experiment_directory(experiment_name)
if not os.path.exists(fname):
tic("traj_obs: Saving trajectory obs in %s"%fname, experiment_name)
if num_jobs == 1:
seq = (traj_ob for date in dates
for traj_ob in getDayTrajs(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
experiment_design['trajectory_conversion'],
traj_conv(), net))
else:
from joblib import Parallel, delayed
tic("Using concurrent job code with {0} jobs".format(num_jobs),"learn_procedure")
ls = Parallel(n_jobs=num_jobs)(delayed(wrapper)(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
experiment_design['trajectory_conversion'],
traj_conv(), net) for date in dates)
seq = [traj_ob for l in ls
for traj_ob in l]
# seq = (traj_ob for tspots_seq in tspots_seqs()
# for traj_ob in traj_conv().mapTrajectory(tspots_seq))
kfold_cross_validation = data_source['kfold_cross_validation']
test_k = data_source['test_k']
assert kfold_cross_validation == 0 or test_k < kfold_cross_validation
f = open(fname, 'w')
if kfold_cross_validation > 0:
tic("traj_obs: Saving test trajectory obs in %s"%fname_test, experiment_name)
f_test = open(fname_test, 'w')
idx = 0
for traj_ob in seq:
idx += 1
if print_num > 0 and idx % print_num == 0:
tic("traj_obs: Converted so far {0} observations".format(idx), experiment_name)
if kfold_cross_validation > 0 and idx % kfold_cross_validation == test_k:
s_dump_elt(traj_ob, f_test)
else:
s_dump_elt(traj_ob, f)
yield traj_ob
else:
tic("traj_obs: opening trajectory obs in %s"%fname, experiment_name)
f = open(fname, 'r')
for traj_ob in s_load(f):
yield traj_ob
def test_traj_obs(experiment_name,print_counter=1000):
fname_test = "%s/traj_obs_test.pkl"%experiment_directory(experiment_name)
tic("test_traj_obs: opening test trajectory obs in %s"%fname_test, experiment_name)
f = open(fname_test, 'r')
c = 0
for traj_ob in s_load(f):
c += 1
if print_counter > 0 and c % print_counter == 0:
tic("test_traj_obs: Consumed so far {0} observations".format(c), experiment_name)
yield traj_ob
def gmrf():
global gmrf_
if not gmrf_:
if not os.path.exists(gmrf_fname):
tic("creating empty gmrf", experiment_name)
gmrf_ = emptyValues(tt_graph())
else:
tic("reading gmrf from %s"%gmrf_fname, experiment_name)
gmrf_ = pickle.load(open(gmrf_fname,'r'))
return gmrf_
def mode_counts():
global mode_counts_
if not mode_counts_:
tic("Loading trajectory conversion...")
fname = "%s/mode_count.pkl"%experiment_directory(experiment_name)
if not os.path.exists(fname):
pickle.dump(traj_conv().modeCounts(), open(fname,'w'))
mode_counts_ = pickle.load(open(fname,'r'))
tic("Done loading trajectory conversion and mode counts")
return mode_counts_
def tt_graph():
global tt_graph_
if not tt_graph_:
if not os.path.exists(tt_graph_fname):
tic("creating empty tt graph", experiment_name)
tt_graph_ = createTravelTimeGraph(hmm_graph(), radius=2e-4)
tt_graph_.checkInvariants()
save_ttg_structure(tt_graph_, experiment_name=experiment_name)
else:
tic("reading tt graph from %s"%tt_graph_fname, experiment_name)
tt_graph_ = pickle.load(open(tt_graph_fname,'r'))
return tt_graph_
def covsel_cvx_cholmod(
R, U, rows, cols, k, psd_tolerance=1e-6, factor=None, num_iterations=500, finish_early=True, debug=True
):
if debug:
tic("smallest ev", "covsel_cvx_cholmod")
min_ei = smallest_ev_arpack(R, U, rows, cols)
if debug:
tic("min_ei is %f" % min_ei, "covsel_cvx_cholmod")
if min_ei < 0:
R0 = R - min_ei + 1e-3
else:
R0 = R
return run_cvx_cholmod(R0, U, rows, cols, k, psd_tolerance, factor, num_iterations, finish_early)
def hmm_graph():
global hmm_graph_
if hmm_graph_ is None:
if not os.path.exists(hmm_graph_fname):
if graph_type == 'simple':
hmm_graph_ = model.createHMMGraphFromNetwork(net, mode_counts=mode_counts())
else:
# Complex model not implemented
assert False
else:
tic("Reading completed hmm graph from %s"%hmm_graph_fname)
hmm_graph_ = pickle.load(open(hmm_graph_fname,'r'))
return hmm_graph_
def validation_procedure(experiment_design,
experiment_design_indep,
experiment_design_one_mode,
experiment_design_one_mode_indep,
validate_on_network=True):
# Get the validation data
experiment_name = experiment_design['name']
# We will load the test data from this experiment
experiment_name_one_mode = experiment_design_one_mode['name']
# Get the network
basic_geometry = experiment_design['basic_geometry']
# Nearly everything will need a network.
net = get_network(**basic_geometry)
# reload the HMM and the GMRF estimator from the files
# All we need for testing is a experiment_design
test_hmm = read_hmm_pickle(experiment_name)
# Read the estimator
gmrf_estimation = experiment_design['gmrf_estimation']
test_gmrf_estimator = get_gmrf_estimator(experiment_name, gmrf_estimation['process'])
# Baseline Gaussian independent
test_hmm_one_mode = read_hmm_pickle('{0}_one_mode'.format(experiment_name))
test_gmrf_one_mode_indep = get_gmrf_estimator('{0}_one_mode_indep'.format(experiment_name), 'diagonal')
# Baseline Gaussian
test_gmrf_one_mode = get_gmrf_estimator('{0}_one_mode'.format(experiment_name), gmrf_estimation['process'])
# Baseline MultiMode Gaussian independent
test_gmrf_indep = get_gmrf_estimator('{0}_indep'.format(experiment_name), 'diagonal')
tic('Validation')
test_traj_obs_all = list(test_traj_obs(experiment_name))
test_traj_obs_one_mode_all = list(test_traj_obs(experiment_name_one_mode))
tic("Validation set: {0} trajectories".format(len(test_traj_obs_all)))
model = [(test_traj_obs_all, test_gmrf_estimator, test_hmm, 'MM-GMRF')]
baseline1 = [(test_traj_obs_one_mode_all, test_gmrf_one_mode_indep, test_hmm_one_mode, 'one mode indep')]
baseline2 = [(test_traj_obs_one_mode_all, test_gmrf_one_mode, test_hmm_one_mode, 'one mode')]
baseline3 = [(test_traj_obs_all, test_gmrf_indep, test_hmm, 'multi-modal indep')]
val_model = model + baseline1 + baseline2 + baseline3
tic('path validation')
validate.validate_on_paths(val_model, net,
estimation_sampling_process=experiment_design['estimation_sampling']['process'],
estimation_sampling_parameters=experiment_design['estimation_sampling']['parameters'], **experiment_design['evaluation'])
if validate_on_network:
tic('network validation')
validate.validate_on_network(val_model, net,
estimation_sampling_process=experiment_design['estimation_sampling']['process'],
estimation_sampling_parameters=experiment_design['estimation_sampling']['parameters'], **experiment_design['evaluation'])
tic("Evaluation finished")
def getDayTSpots(date, network):
""" Returns a list of sequences of TSpot objects for this day.
"""
all_traj_fns = list_traj_filenames(date)
tspots_groups = []
for fname in all_traj_fns:
# pylint:disable=W0142
try:
tspots = read_trajectory(fname)
except IOError:
tic("Could not read trajectory: {0}".format(fname), "getDayTSpots")
tspots = []
# tspots is a list of TSpot
# Make sure we only have data for our sub network.
for net_tspots in filterOutsideNetwork(tspots, network):
tspots_groups.append(net_tspots)
return tspots_groups
def fillTrajectoryCache(graph_type,basic_geometry,data_source,traj_conv_description,n_jobs=1):
net = get_network(**basic_geometry)
tic("Loaded network = {0} links".format(len(net)), "fillTrajectoryCache")
traj_conv = createTrajectoryConversion(graph_type=graph_type,
process=traj_conv_description['process'],
params=traj_conv_description['params'],
network=net,
max_nb_mixture=traj_conv_description['params']['max_n_modes'],
n_jobs=n_jobs)
dates = data_source['dates']
from joblib import Parallel, delayed
Parallel(n_jobs=n_jobs)(delayed(wrapper)(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
traj_conv_description,
traj_conv, net) for date in dates)
def gmrf_learn_cov_cholmod(
R,
U,
rows,
cols,
edge_count,
k,
min_variance=1e-2,
min_edge_count=10,
num_iterations=50,
psd_tolerance=1e-3,
finish_early=True,
):
n = len(R)
m = len(U)
mask = edge_count >= min_edge_count
active_m = np.sum(mask)
tic("m={0}, active m={1}".format(m, active_m), "gmrf_learn_cov_cholmod")
active_U = U[mask]
active_rows = rows[mask]
active_cols = cols[mask]
# A number of variables hare independant (due to lack of observations)
independent_mask = independent_variables(n, active_rows, active_cols)
# Put them aside and use the independent strategy to solve them.
indep_idxs = np.arange(n)[independent_mask]
R_indep = R[indep_idxs]
# Solve the regularized version for independent variables
D_indep = 1.0 / np.maximum(min_variance * np.ones_like(R_indep), R_indep)
# Putting together the dependent and independent parts
D = np.zeros_like(R)
D[independent_mask] = D_indep
P = np.zeros_like(U)
# No need to solve for the outer diagonal terms, they are all zeros.
# Solve for the dependent terms
dependent_mask = ~independent_mask
n_dep = np.sum(dependent_mask)
if n_dep > 0:
idxs_dep = np.arange(n)[dependent_mask]
reverse_idxs_dep = np.zeros(n, dtype=np.int64)
reverse_idxs_dep[dependent_mask] = np.arange(n_dep)
rows_dep = reverse_idxs_dep[active_rows]
cols_dep = reverse_idxs_dep[active_cols]
R_dep = R[idxs_dep]
U_dep = active_U
(M, R_hat, U_hat) = normalized_problem(R_dep, U_dep, rows_dep, cols_dep)
tic("Computing symbolic cholesky factorization of the graph...", "gmrf_learn_cov_cholmod")
# Doing delayed import so that the rest of the code runs without sk-learn
from scikits.sparse.cholmod import analyze
Xs_dep = build_sparse(np.ones_like(R_hat), np.ones_like(U_hat), rows_dep, cols_dep)
factor = analyze(Xs_dep)
tic("Cholesky done", "gmrf_learn_cov_cholmod")
# TODO add the other parameters
(D_norm_dep, P_norm_dep) = covsel_cvx_cholmod(
R_hat, U_hat, rows_dep, cols_dep, k, psd_tolerance, factor, num_iterations, finish_early
)
D[dependent_mask] = D_norm_dep / (M ** 2)
P[mask] = P_norm_dep / (M[rows_dep] * M[cols_dep])
return (D, P)
def getDayTTOBservations(date, network, print_stats=1000):
"""
"""
all_trajs = list_traj_filenames(date)
all_groups = []
idx = 1
for traj_index in all_trajs:
idx += 1
if print_stats > 0 and idx % print_stats == 0:
tic("processed {0} observations".format(idx),"getDayTTOBservations")
# pylint:disable=W0142
try:
tspots = read_trajectory(traj_index)
except IOError:
tic("ioerror when loading a trajectory {0}".format(traj_index), "getDayTTOBservations")
tspots = []
# Make sure we only have data for our sub network.
for net_tspots in filterOutsideNetwork(tspots, network):
groups = seqGroupBy(net_tspots, keyf=lambda tsp:tsp.spot.linkId)
for g in completeGroups(groups, network):
all_groups.append(g)
return all_groups
def getMixtures(dates, network,
max_n_links=None, return_tts=False, max_nb_mixture=4,num_threads=1):
tic("Running with {0} jobs.".format(num_threads),"getMixtures")
ttob_seqs = (ttob_seq for date in dates
for ttob_seq in getDayTTOBservations(date, network))
# Get travel times for each link
all_ttobs = (ttob for ttob_seq in ttob_seqs for ttob in ttob_seq)
tic("starting groupby...","getMixtures")
all_ttobs_by_lid = sorted([(lid, list(vals)) for (lid, vals) in groupby(all_ttobs, lambda ttob:ttob.linkId)], key=lambda z:-len(z[1]))
tic("groupby done, {0} links".format(len(all_ttobs_by_lid)),"getMixtures")
if max_n_links:
all_ttobs_by_lid = all_ttobs_by_lid[:max_n_links]
tts_by_link = [(lid, np.array([tto.tt for tto in vals])) for (lid, vals) in all_ttobs_by_lid]
tic("vectorization done","getMixtures")
if num_threads != 1:
tic("Running with {0} jobs.".format(num_threads),"getMixtures")
learned_mixtures = Parallel(n_jobs=num_threads,verbose=10)(delayed(getMixtures_inner)((lid, tts, max_nb_mixture)) for (lid, tts) in tts_by_link)
else:
learned_mixtures = [(lid, learnMixtureAuto(tts, max_nb_mixture)) for (lid, tts) in tts_by_link]
if return_tts:
return (dict(learned_mixtures), dict(tts_by_link))
else:
return dict(learned_mixtures)
def gmrf_learn_cov_cvx(R, U, rows, cols, edge_count, min_variance=1e-2, min_edge_count=10, num_iterations=50):
n = len(R)
m = len(U)
mask = edge_count >= min_edge_count
active_m = np.sum(mask)
tic("m={0}, active m={1}".format(m, active_m), "gmrf_learn_cov_cvx")
active_U = U[mask]
active_rows = rows[mask]
active_cols = cols[mask]
# A number of variables hare independant (due to lack of observations)
independent_mask = independent_variables(n, active_rows, active_cols)
# Put them aside and use the independent strategy to solve them.
indep_idxs = np.arange(n)[independent_mask]
R_indep = R[indep_idxs]
# Solve the regularized version for independent variables
D_indep = 1.0 / np.maximum(min_variance * np.ones_like(R_indep), R_indep)
# Putting together the dependent and independent parts
D = np.zeros_like(R)
D[independent_mask] = D_indep
P = np.zeros_like(U)
# No need to solve for the outer diagonal terms, they are all zeros.
# Solve for the dependent terms
dependent_mask = ~independent_mask
n_dep = np.sum(dependent_mask)
if n_dep > 0:
idxs_dep = np.arange(n)[dependent_mask]
reverse_idxs_dep = np.zeros(n, dtype=np.int64)
reverse_idxs_dep[dependent_mask] = np.arange(n_dep)
rows_dep = reverse_idxs_dep[active_rows]
cols_dep = reverse_idxs_dep[active_cols]
R_dep = R[idxs_dep]
U_dep = active_U
(M, R_hat, U_hat) = normalized_problem(R_dep, U_dep, rows_dep, cols_dep)
(D_norm_dep, P_norm_dep) = covsel_cvx_dense(R_hat, U_hat, rows_dep, cols_dep, num_iterations=num_iterations)
D[dependent_mask] = D_norm_dep / (M ** 2)
P[mask] = P_norm_dep / (M[rows_dep] * M[cols_dep])
return (D, P)
def run_cvx_cholmod(
R_hat, U_hat, rows, cols, k, psd_tolerance=1e-6, factor=None, num_iterations=500, finish_early=True, debug=True
):
D = np.ones_like(R_hat)
P = np.zeros_like(U_hat)
for iters in range(num_iterations):
tic("Iter={0}".format(iters), "run_cvx_cholmod")
# Debug
# f1 = obj_dense(R_hat, U_hat, rows, cols, D, P)
# f2 = obj_cholmod(R_hat, U_hat, rows, cols, D, P, psd_tolerance, factor)
# delta = f1 - f2
# print "True objective value:",f1
# print "This objective value",f2
# print "Difference",delta
# End debug
z = iter_cholmod(R_hat, U_hat, rows, cols, D, P, k, psd_tolerance, factor)
if finish_early and z is None:
tic("done early", "run_cvx_cholmod")
return (D, P)
if z is not None:
(D2, P2, fn, lsiter) = z
D = D2
P = P2
return (D, P)
def iter_cholmod(R_hat, U_hat, rows, cols, D, P, k, psd_tolerance=1e-6, factor=None, num_lsearch_iter=10):
tic("computing gradient", "iter_cholmod")
(g_D, g_P) = grad_cholmod(R_hat, U_hat, rows, cols, D, P, k, factor)
v_D = -g_D
v_P = -g_P
# Debug
# (g_D_, g_P_) = grad_dense(R_hat, U_hat, rows, cols, D, P)
# print 'g_D diff:',la.norm(g_D-g_D_)
# print 'g_P diff:',la.norm(g_P-g_P_)
# End debug
# Stopping criterion:
sqntdecr = -v_D.dot(g_D) - v_P.dot(g_P)
tic("Newton decrement squared:%- 7.5e" % sqntdecr, "iter_cholmod")
if sqntdecr < 1e-8:
return None
# line search
dD = v_D
dP = v_P
s = 1.0
f = obj_cholmod(R_hat, U_hat, rows, cols, D, P, psd_tolerance, factor)
tic("Current objective value: {0}".format(f), "iter_cholmod")
for lsiter in range(num_lsearch_iter):
curr_D = D + s * dD
curr_P = P + s * dP
fn = obj_cholmod(R_hat, U_hat, rows, cols, curr_D, curr_P, psd_tolerance, factor)
# print 'fn ',fn
tic("lsiter={0} fn={1}".format(lsiter, fn), "iter_cholmod")
if fn == -np.Infinity:
s *= 0.5
else:
if fn < f - 0.01 * s * sqntdecr:
tic("Update lsiter={0}".format(lsiter), "iter_cholmod")
return (curr_D, curr_P, fn, lsiter)
s *= 0.5
print "Too many iterations"
return None
def wrapper(*args,**kwargs):
tic("Wrapper called")
res = getDayTrajs(*args, **kwargs)
tic("Cached {0} trajs".format(len(res)))
return res
def learning_perf(experiment_design):
""" Starting the main procedure.
This script tries to reuse as much as it can from the disk, to avoid expensive recomputations.
"""
experiment_name = experiment_design['name']
# Get the network
basic_geometry = experiment_design['basic_geometry']
# Nearly everything will need a network.
net = get_network(**basic_geometry)
tic("Loaded network = {0} links".format(len(net)), experiment_name)
graph_type = experiment_design['graph_type']
traj_conv_param = experiment_design['trajectory_conversion']['params']
# Trajectory conversion
# Needed early because it gives the number of modes.
global traj_conv_
traj_conv_ = None
def traj_conv():
global traj_conv_
if not traj_conv_:
traj_conv_ = createTrajectoryConversion(graph_type=graph_type,
process=experiment_design['trajectory_conversion']['process'],
params=traj_conv_param,
network=net,
max_nb_mixture=traj_conv_param['max_n_modes'])
return traj_conv_
# Number of modes
# Also stored on disk as pickle
global mode_counts_
mode_counts_ = None
def mode_counts():
global mode_counts_
if not mode_counts_:
tic("Loading trajectory conversion...")
fname = "%s/mode_count.pkl"%experiment_directory(experiment_name)
if not os.path.exists(fname):
pickle.dump(traj_conv().modeCounts(), open(fname,'w'))
mode_counts_ = pickle.load(open(fname,'r'))
tic("Done loading trajectory conversion and mode counts")
return mode_counts_
# The HMM graph
global hmm_graph_
hmm_graph_ = None
hmm_graph_fname = "%s/hmm_graph.pkl"%experiment_directory(experiment_name)
def hmm_graph():
global hmm_graph_
if hmm_graph_ is None:
if not os.path.exists(hmm_graph_fname):
if graph_type == 'simple':
tic("creating empty hmm graph", experiment_name)
hmm_graph_ = model.createHMMGraphFromNetwork(net, mode_counts=mode_counts())
tic("done creating empty hmm graph", experiment_name)
tic("saving hmm graph pickle", experiment_name)
pickle.dump(hmm_graph(),open(hmm_graph_fname,'w'))
tic("done saving hmm graph pickle", experiment_name)
else:
# Complex model not implemented
assert False
else:
tic("Reading completed hmm graph from %s"%hmm_graph_fname)
hmm_graph_ = pickle.load(open(hmm_graph_fname,'r'))
tic("done reading completed hmm graph from %s"%hmm_graph_fname)
return hmm_graph_
# The TT gpaph
# Also stored on disk as pickle by save_ttg_values (when it is filled).
global tt_graph_
tt_graph_ = None
tt_graph_fname = "%s/tt_graph.pkl"%experiment_directory(experiment_name)
def tt_graph():
global tt_graph_
if not tt_graph_:
if not os.path.exists(tt_graph_fname):
tic("creating empty tt graph", experiment_name)
tt_graph_ = createTravelTimeGraph(hmm_graph(), radius=2e-4)
tt_graph_.checkInvariants()
save_ttg_structure(tt_graph_, experiment_name=experiment_name)
else:
tic("reading tt graph from %s"%tt_graph_fname, experiment_name)
tt_graph_ = pickle.load(open(tt_graph_fname,'r'))
return tt_graph_
# The GMFR
# Also stored on disk as pickle by save_gmrf_values (when it is filled).
global gmrf_
gmrf_ = None
gmrf_fname = "%s/gmrf.pkl"%experiment_directory(experiment_name)
def gmrf():
global gmrf_
if not gmrf_:
if not os.path.exists(gmrf_fname):
tic("creating empty gmrf", experiment_name)
gmrf_ = emptyValues(tt_graph())
tic("created empty gmrf", experiment_name)
else:
tic("reading gmrf from %s"%gmrf_fname, experiment_name)
gmrf_ = pickle.load(open(gmrf_fname,'r'))
tic("done reading gmrf from %s"%gmrf_fname, experiment_name)
return gmrf_
tic("TT graph building", experiment_name)
tic("Loaded TT graph = {0} edges, {1} variables".format(tt_graph().n,
tt_graph().m), experiment_name)
tic("simulating sstat building", experiment_name)
gmrf_learning = experiment_design['gmrf_learning']
for var in tt_graph().allVariables():
var_id = var.varID
var.parameters = GaussianParameters(0.0, 1.0)
tt_graph().variable_counts[var_id] = 1
for key in tt_graph().edges.keys():
tt_graph().edges[key] = -.1
tt_graph().edge_counts[key] = 1
tic("done simulating sstat building", experiment_name)
if not os.path.exists(tt_graph_fname):
tic("saving tt graph pickle", experiment_name)
pickle.dump(tt_graph(),open(tt_graph_fname,'w'))
tic("done saving tt graph pickle", experiment_name)
tic("GMRF learning", experiment_name)
gmrf_learning = experiment_design['gmrf_learning']
gmrf_learning_params = gmrf_learning['parameters']
gmrf_ = gmrf_learn(tt_graph(), gmrf_learning['process'],
experiment_name, gmrf_learning_params)
tic("Done GMRF learning", experiment_name)
tic("saving gmrf pickle", experiment_name)
pickle.dump(gmrf_,open(gmrf_fname,'w'))
save_gmrf_values(gmrf_, experiment_name=experiment_name)
tic("done saving gmrf pickle", experiment_name)
tic("GMRF estimation",experiment_name)
gmrf_estimation = experiment_design['gmrf_estimation']
gmrf_estimation_parameters = gmrf_estimation['parameters']
# Saves all the GMRF estimators in the different formats
# Will be reloaded when we do the estimation
gmrf_est(gmrf(), gmrf_estimation['process'], experiment_name, gmrf_estimation_parameters)
tic("End of learning", experiment_name)
def learn_procedure(experiment_design,num_jobs=1):
experiment_name = experiment_design['name']
# Get the network
basic_geometry = experiment_design['basic_geometry']
# Nearly everything will need a network.
net = get_network(**basic_geometry)
tic("Loaded network = {0} links".format(len(net)), experiment_name)
graph_type = experiment_design['graph_type']
traj_conv_param = experiment_design['trajectory_conversion']['params']
# Trajectory conversion
# Needed early because it gives the number of modes.
global traj_conv_
traj_conv_ = None
def traj_conv():
global traj_conv_
if not traj_conv_:
traj_conv_ = createTrajectoryConversion(graph_type=graph_type,
process=experiment_design['trajectory_conversion']['process'],
params=traj_conv_param,
network=net,
max_nb_mixture=traj_conv_param['max_n_modes'],
n_jobs=num_jobs)
return traj_conv_
# Number of modes
# Also stored on disk as pickle
global mode_counts_
mode_counts_ = None
def mode_counts():
global mode_counts_
if not mode_counts_:
tic("Loading trajectory conversion...")
fname = "%s/mode_count.pkl"%experiment_directory(experiment_name)
if not os.path.exists(fname):
pickle.dump(traj_conv().modeCounts(), open(fname,'w'))
mode_counts_ = pickle.load(open(fname,'r'))
tic("Done loading trajectory conversion and mode counts")
return mode_counts_
# The HMM graph
global hmm_graph_
hmm_graph_ = None
hmm_graph_fname = "%s/hmm_graph.pkl"%experiment_directory(experiment_name)
def hmm_graph():
global hmm_graph_
if hmm_graph_ is None:
if not os.path.exists(hmm_graph_fname):
if graph_type == 'simple':
hmm_graph_ = model.createHMMGraphFromNetwork(net, mode_counts=mode_counts())
else:
# Complex model not implemented
assert False
else:
tic("Reading completed hmm graph from %s"%hmm_graph_fname)
hmm_graph_ = pickle.load(open(hmm_graph_fname,'r'))
return hmm_graph_
# The TT gpaph
# Also stored on disk as pickle by save_ttg_values (when it is filled).
global tt_graph_
tt_graph_ = None
tt_graph_fname = "%s/tt_graph.pkl"%experiment_directory(experiment_name)
def tt_graph():
global tt_graph_
if not tt_graph_:
if not os.path.exists(tt_graph_fname):
tic("creating empty tt graph", experiment_name)
tt_graph_ = createTravelTimeGraph(hmm_graph(), radius=2e-4)
tt_graph_.checkInvariants()
save_ttg_structure(tt_graph_, experiment_name=experiment_name)
else:
tic("reading tt graph from %s"%tt_graph_fname, experiment_name)
tt_graph_ = pickle.load(open(tt_graph_fname,'r'))
return tt_graph_
# The GMFR
# Also stored on disk as pickle by save_gmrf_values (when it is filled).
global gmrf_
gmrf_ = None
gmrf_fname = "%s/gmrf.pkl"%experiment_directory(experiment_name)
def gmrf():
global gmrf_
if not gmrf_:
if not os.path.exists(gmrf_fname):
tic("creating empty gmrf", experiment_name)
gmrf_ = emptyValues(tt_graph())
else:
tic("reading gmrf from %s"%gmrf_fname, experiment_name)
gmrf_ = pickle.load(open(gmrf_fname,'r'))
return gmrf_
# The experiments data:
data_source = experiment_design['data_source']
dates = data_source['dates']
basic_geometry = experiment_design['basic_geometry']
# All this is lazy. Calling these functions does not create data.
def tspots_seqs():
return (ttob_seq for date in dates
for ttob_seq in getDayTSpots(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
net))
def traj_obs(print_num=1000):
""" Returns the trajectory observations.
If the obs have never been computed before, also stores them in a file.
Otherwise reads the cached copy from the disk.
"""
fname = "%s/traj_obs.pkl"%experiment_directory(experiment_name)
fname_test = "%s/traj_obs_test.pkl"%experiment_directory(experiment_name)
if not os.path.exists(fname):
tic("traj_obs: Saving trajectory obs in %s"%fname, experiment_name)
if num_jobs == 1:
seq = (traj_ob for date in dates
for traj_ob in getDayTrajs(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
experiment_design['trajectory_conversion'],
traj_conv(), net))
else:
from joblib import Parallel, delayed
tic("Using concurrent job code with {0} jobs".format(num_jobs),"learn_procedure")
ls = Parallel(n_jobs=num_jobs)(delayed(wrapper)(data_source['feed'],
basic_geometry['nid'],
date,
basic_geometry['net_type'],
basic_geometry['box'],
experiment_design['trajectory_conversion'],
traj_conv(), net) for date in dates)
seq = [traj_ob for l in ls
for traj_ob in l]
# seq = (traj_ob for tspots_seq in tspots_seqs()
# for traj_ob in traj_conv().mapTrajectory(tspots_seq))
kfold_cross_validation = data_source['kfold_cross_validation']
test_k = data_source['test_k']
assert kfold_cross_validation == 0 or test_k < kfold_cross_validation
f = open(fname, 'w')
if kfold_cross_validation > 0:
tic("traj_obs: Saving test trajectory obs in %s"%fname_test, experiment_name)
f_test = open(fname_test, 'w')
idx = 0
for traj_ob in seq:
idx += 1
if print_num > 0 and idx % print_num == 0:
tic("traj_obs: Converted so far {0} observations".format(idx), experiment_name)
if kfold_cross_validation > 0 and idx % kfold_cross_validation == test_k:
s_dump_elt(traj_ob, f_test)
else:
s_dump_elt(traj_ob, f)
yield traj_ob
else:
tic("traj_obs: opening trajectory obs in %s"%fname, experiment_name)
f = open(fname, 'r')
for traj_ob in s_load(f):
yield traj_ob
def var_seqs():
return ([obs.varId for obs in traj_ob.observations] for traj_ob in traj_obs())
# Starting learning here
tic("HMM learning",experiment_name)
tic("Loaded HMM = {0} nodes, {1} transitions".format(len(hmm_graph().allNodes()),
len(hmm_graph().allTransitions())), experiment_name)
fillProbabilitiesObservations(hmm_graph(), var_seqs(), **experiment_design['hmm_learning']['parameters'])
# Save to disk as well
pickle.dump(hmm_graph(),open(hmm_graph_fname,'w'))
save_hmm(hmm_graph(),experiment_name)
tic("TT graph building", experiment_name)
tic("Loaded TT graph = {0} edges, {1} variables".format(tt_graph().n,
tt_graph().m), experiment_name)
gmrf_learning = experiment_design['gmrf_learning']
fillTTGraph(tt_graph(), traj_obs(),traj_obs_copy=traj_obs(),**gmrf_learning['tt_graph_parameters'])
pickle.dump(tt_graph(),open(tt_graph_fname,'w'))
tic("GMRF learning", experiment_name)
gmrf_learning = experiment_design['gmrf_learning']
gmrf_learning_params = gmrf_learning['parameters']
gmrf_ = gmrf_learn(tt_graph(), gmrf_learning['process'],
experiment_name, gmrf_learning_params)
pickle.dump(gmrf_,open(gmrf_fname,'w'))
save_gmrf_values(gmrf(), experiment_name=experiment_name)
tic("GMRF estimation",experiment_name)
gmrf_estimation = experiment_design['gmrf_estimation']
gmrf_estimation_parameters = gmrf_estimation['parameters']
# Saves all the GMRF estimators in the different formats
# Will be reloaded when we do the estimation
gmrf_est(gmrf(), gmrf_estimation['process'], experiment_name, gmrf_estimation_parameters)
tic("End of learning", experiment_name)
def hmm_graph():
global hmm_graph_
if hmm_graph_ is None:
if not os.path.exists(hmm_graph_fname):
if graph_type == 'simple':
tic("creating empty hmm graph", experiment_name)
hmm_graph_ = model.createHMMGraphFromNetwork(net, mode_counts=mode_counts())
tic("done creating empty hmm graph", experiment_name)
tic("saving hmm graph pickle", experiment_name)
pickle.dump(hmm_graph(),open(hmm_graph_fname,'w'))
tic("done saving hmm graph pickle", experiment_name)
else:
# Complex model not implemented
assert False
else:
tic("Reading completed hmm graph from %s"%hmm_graph_fname)
hmm_graph_ = pickle.load(open(hmm_graph_fname,'r'))
tic("done reading completed hmm graph from %s"%hmm_graph_fname)
return hmm_graph_
def read_hmm_pickle(experiment_name):
hmm_graph_fname = "%s/hmm_graph.pkl"%experiment_directory(experiment_name)
tic("Reading hmm from %s"%hmm_graph_fname, experiment_name)
return pickle.load(open(hmm_graph_fname,'r'))
def fillTTGraph(tt_graph, traj_obs, min_variance=1e-2,
variance_prior=0.0, variance_prior_count=0.0, traj_obs_copy=None):
""" Computes the sufficient statistics of the travel time graph, and fills the
corresponding TT graph.
Arguments:
tt_graph -- a travel time graph
traj_obs -- an iterable of trajectory observations
TODO: returns some stats about the number of elements seen
"""
# This function is one of the most complicated
# Compute the sufficient stats
# First moments for means
# The prior is on white noise
sstats0 = dict([(var_id, 0.0) for var_id in tt_graph.variable_keys])
sstats0_counts = dict([(var_id, variance_prior_count) for var_id in tt_graph.variable_keys])
sstats0_true_counts = dict([(var_id, 0) for var_id in tt_graph.variable_keys])
sstats1_var = dict([(var_id, variance_prior_count*variance_prior) for var_id in tt_graph.variable_keys])
# Compute the sufficient statistics first for central elements
tic_n_obs = 1000
count_traj_obs = 0
for traj_ob in traj_obs:
count_traj_obs += 1
if count_traj_obs % tic_n_obs == 0:
tic("fillTTGraph: processed %d observations in pass #1"%count_traj_obs)
obs = traj_ob.observations
for ob in obs:
sstats0[ob.varId] += ob.value
sstats0_counts[ob.varId] += 1.0
sstats0_true_counts[ob.varId] += 1
sstats1_var[ob.varId] += ob.value * ob.value
del obs,ob
tic("fillTTGraph: processed %d observations in pass #1"%count_traj_obs)
# Compute the means
means = {}
for var in tt_graph.allVariables():
var_id = var.varID
count0 = sstats0_counts[var_id]
mean = sstats0[var_id] / float(count0) if count0 > 0 else 0.0
means[var_id] = mean
assert mean >= -EPSI # Specific to traffic problem
mean = max(mean,0)
del mean, count0,var,var_id
# Compute the variances
variances = {}
for var in tt_graph.allVariables():
var_id = var.varID
count0 = sstats0_counts[var_id]
sstat1 = sstats1_var[var_id] / float(count0) if count0 > 0 else 0.0
mean = means[var_id]
variance_ = sstat1 - mean * mean
assert variance_ >= -EPSI, (sstat1 - mean * mean, sstat1, mean)
variance = max(variance_, min_variance)
variances[var_id] = variance
del var,count0,sstat1,variance_,variance,var_id
# Update the gaussian parameters
for var in tt_graph.allVariables():
var_id = var.varID
mean = means[var_id]
variance = variances[var_id]
var.parameters = GaussianParameters(mean, variance)
tt_graph.variable_counts[var_id] = sstats0_true_counts[var_id]
# Second moments for outer terms
all_edge_keys = tt_graph.edges.keys()
# Covariance term
# pvid = pair of var_ids
sstats1 = dict([(pvid, 0.0) for pvid in all_edge_keys])
# First order stat for the start
sstats1_from = dict([(pvid, variance_prior * variance_prior_count)
for pvid in all_edge_keys])
sstats1_to = dict([(pvid, variance_prior * variance_prior_count)
for pvid in all_edge_keys])
sstats0_from = dict([(pvid, 0.0) for pvid in all_edge_keys])
sstats0_to = dict([(pvid, 0.0) for pvid in all_edge_keys])
sstats1_counts = dict([(pvid, variance_prior_count) for pvid in all_edge_keys])
sstats1_true_counts = dict([(pvid, 0) for pvid in all_edge_keys])
# Fill the sufficient stats for the variance and the mean:
# Updates the sufficient stats
# hack to make sure we can run twice over the data
if traj_obs_copy is None:
traj_obs_copy=traj_obs
count_traj_obs = 0
for traj_ob in traj_obs_copy:
count_traj_obs += 1
if count_traj_obs % tic_n_obs == 0:
tic("fillTTGraph: processed %d observations in pass #2"%count_traj_obs)
obs = traj_ob.observations
l = len(obs)
for i in range(l):
for j in range(i + 1, l):
from_ob = obs[i]
to_ob = obs[j]
from_vid = from_ob.varId
to_vid = to_ob.varId
assert not ((from_vid, to_vid) in sstats1 and (to_vid, from_vid) in sstats1)
if (from_vid, to_vid) not in sstats1 and (to_vid, from_vid) not in sstats1:
continue
# We may need to flip around the vids and values because of the ordering in
# the variables
if (from_vid, to_vid) in sstats1:
from_vid_c = from_vid
to_vid_c = to_vid
from_val_c = from_ob.value
to_val_c = to_ob.value
else:
assert (to_vid, from_vid) in sstats1
from_vid_c = to_vid
to_vid_c = from_vid
from_val_c = to_ob.value
to_val_c = from_ob.value
# Update the stats
key = (from_vid_c, to_vid_c)
sstats1[key] += from_val_c * to_val_c
sstats1_from[key] += from_val_c * from_val_c
sstats1_to[key] += to_val_c * to_val_c
sstats0_from[key] += from_val_c
sstats0_to[key] += to_val_c
sstats1_counts[key] += 1.0
sstats1_true_counts[key] += 1
tic("fillTTGraph: processed %d observations in pass #2"%count_traj_obs)
# Compute the new covariance terms
for key in all_edge_keys:
count = sstats1_counts[key]
# The local means
local_mean_from = sstats0_from[key] / float(count) if count > 0 else 0.0
local_mean_to = sstats0_to[key] / float(count) if count > 0 else 0.0
# The local variances
sstat1_from = sstats1_from[key] / float(count) if count > 0 else 0.0
sstat1_to = sstats1_to[key] / float(count) if count > 0 else 0.0
local_var_from_ = sstat1_from - (local_mean_from ** 2)
local_var_to_ = sstat1_to - (local_mean_to ** 2)
# This epsilon should prevent the assertion from failing due to rounding
# errors and a low number of samples.
local_var_from = max(local_var_from_, min_variance)+1e-7
local_var_to = max(local_var_to_, min_variance)+1e-7
# The local covariance term
sstat1 = sstats1[key] / float(count) if count > 0 else 0.0
local_cov = sstat1 - local_mean_from * local_mean_to
assert abs(local_cov) <= np.sqrt(local_var_from * local_var_to),()
# The global variance terms
(from_vid,to_vid) = key
variance_from = variances[from_vid]
variance_to = variances[to_vid]
scale = np.sqrt((variance_from * variance_to) / (local_var_from * local_var_to))
cov = scale * local_cov
assert np.abs(cov) <= np.sqrt(variance_from * variance_to)+EPSI, \
(np.abs(cov), np.sqrt(variance_from * variance_to))
tt_graph.edges[key] = cov
tt_graph.edge_counts[key] = sstats1_true_counts[key]
del from_vid,to_vid,count,sstat1,local_mean_from,local_mean_to,sstat1_from,sstat1_to
def learnMixtureAuto(tts, max_nb_mixtures):
mixs = [learnMixture(tts, n) for n in range(1, min(len(tts) + 1, max_nb_mixtures + 1))]
bics = [mix.bic(tts) for mix in mixs]
best_idx = np.argmin(bics)
tic("{0}:{1}".format(len(tts),best_idx+1),"learnMixtureAuto")
return mixs[best_idx]
