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regression.py
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regression.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""@author: David Krueger
"""
import time
t0 = time.time()
times = {}
#times['start'] = t0
import argparse
import os
import sys
import time
import numpy
np = numpy
#np.random.seed(1) # TODO
import math
import os
import random
import pandas as pd
from BHNs_MLP_Regression import MLPWeightNorm_BHN, MCdropout_MLP, Backprop_MLP
from dk_get_regression_data import get_regression_dataset
#from ops import load_mnist
from utils import log_normal, log_laplace
import lasagne
import theano
import theano.tensor as T
from lasagne.random import set_rng
from theano.tensor.shared_randomstreams import RandomStreams
#import matplotlib.pyplot as plt
import scipy
from logsumexp import logsumexp
#import shutil # for eval_only
t1 = time.time()
times['imports'] = t1 - t0
# TODO:
n_mc = 20
"""
The idea here is that we will spit out all of the jobs on all of the datasets, and aggregate performance manually.
We will use grid search for now.
"""
def save_list(path, ll):
thefile = open(path, 'w')
for item in ll:
thefile.write("%s\n" % item)
def rmse(predictions, targets):
return np.sqrt(((predictions - targets) ** 2).mean())
def get_lbda(tau, length_scale, drop_prob=None):
# this is eqn (7) from https://arxiv.org/pdf/1506.02142.pdf (Gal)
lbda = length_scale**2 / tau # we don't divide by the 2 * N(=dataset size) as in Gal, since our prior handles this scaling
if drop_prob is not None:
lbda *= (1-drop_prob)
lbda = np.cast['float32'](lbda)
return lbda
def get_LL(y_hat, y, tau):
# this is eqn (8) from https://arxiv.org/pdf/1506.02142.pdf (Gal)
n_mc = len(y_hat)
#print "get_LL... n_mc=", n_mc
return (logsumexp(-.5*tau*(y_hat-y)**2, axis=0) - np.log(n_mc) - .5*np.log(2*np.pi) - .5*np.log(tau**-1)).mean()
def train_model(model, save_,save_path,
X,Y,Xv,Yv,
Xt, Yt, # TODO: default to None
y_mean, y_std,
lr0,lrdecay,bs,epochs,anneal,
e0=0, rec=0, taus=None,
timing=True):
#save_=True):
if timing:
start_time = time.time()
eval_time = 0
print 'trainset X.shape:{}, Y.shape:{}'.format(X.shape,Y.shape)
N = X.shape[0]
tr_RMSEs = list()
va_RMSEs = list()
te_RMSEs = list()
tr_LLs = [[] for tau in taus]
va_LLs = [[] for tau in taus]
te_LLs = [[] for tau in taus]
best_va_LLs = [-np.inf,] * len(taus)
for e in range(epochs):
if e <= e0:
continue
if lrdecay:
lr = lr0 * 10**(-e/float(epochs-1))
else:
lr = lr0
if anneal:
w = min(1.0,0.001+e/(epochs/2.))
else:
w = 1.0
for i in range(N/bs):
x = X[i*bs:(i+1)*bs]
y = Y[i*bs:(i+1)*bs]
loss = model.train_func(x,y,N,lr,w)
if verbose:
if timing:
eval_start = time.time()
tr_rmse, tr_LL = evaluate_model(model.predict, X, Y, n_mc=n_mc, taus=taus)
va_rmse, va_LL = evaluate_model(model.predict,Xv,Yv, n_mc=n_mc, taus=taus, y_mean=y_mean, y_std=y_std)
te_rmse, te_LL = evaluate_model(model.predict,Xt,Yt, n_mc=n_mc, taus=taus, y_mean=y_mean, y_std=y_std)
if timing:
eval_time += time.time() - eval_start
if e % 5 == 0:
if 0: #verbose
print '\n'
print 'tr LL at epochs {}: {}'.format(e,tr_LL)
print 'tr rmse at epochs {}: {}'.format(e,tr_rmse)
print 'va LL at epochs {}: {}'.format(e,va_LL)
#print 'va rmse at epochs {}: {}'.format(e,va_rmse)
tr_RMSEs.append(tr_rmse)
va_RMSEs.append(va_rmse)
te_RMSEs.append(te_rmse)
#
[tr_LLs[n].append(tr_LL[n]) for n, _ in enumerate(taus)]
[va_LLs[n].append(va_LL[n]) for n, _ in enumerate(taus)]
[te_LLs[n].append(te_LL[n]) for n, _ in enumerate(taus)]
for n, tau in enumerate(taus):
if va_LL[n] > best_va_LLs[n]:
best_va_LLs[n] = va_LL[n]
if save_:
best_params = [p.get_value() for p in model.params]#save(save_path + '_best_tau=' + str(tau))
if verbose:
np.save(save_path + '_best_tau=' + str(tau), best_params)
if timing:
train_time = time.time() - start_time - eval_time
return tr_LLs, tr_RMSEs, va_LLs, va_RMSEs, te_LLs, te_RMSEs, train_time, eval_time
else:
return tr_LLs, tr_RMSEs, va_LLs, va_RMSEs, te_LLs, te_RMSEs
def evaluate_model(predict,X,Y,
y_mean=None, y_std=None,
n_mc=100,max_n=100, taus=10.**np.arange(-3,6)):
MCt = np.zeros((n_mc,X.shape[0],1))
N = X.shape[0]
num_batches = np.ceil(N / float(max_n)).astype(int)
for i in range(n_mc):
for j in range(num_batches):
x = X[j*max_n:(j+1)*max_n]
MCt[i,j*max_n:(j+1)*max_n] = predict(x)
if y_std is not None:
MCt *= y_std
if y_mean is not None:
MCt += y_mean
LLs = [ get_LL(MCt, Y, tau) for tau in taus]
y_hat = MCt.mean(0)
RMSE = rmse(y_hat, Y)
return RMSE, LLs
if __name__ == '__main__':
taus = 10.**np.arange(-3,6)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--lrdecay',default=0.0,type=int)
parser.add_argument('--lr0',default=0.001,type=float)
parser.add_argument('--coupling',default=4,type=int)
parser.add_argument('--lbda',default=1,type=float)
parser.add_argument('--bs',default=32,type=int)
parser.add_argument('--epochs',default=1000,type=int)
parser.add_argument('--prior',default='log_normal',type=str)
parser.add_argument('--model',default='BHN',type=str, choices=['BHN', 'MCD', 'Backprop'])
parser.add_argument('--anneal',default=0,type=int)
parser.add_argument('--n_hiddens',default=1,type=int)
parser.add_argument('--n_units',default=50,type=int)
parser.add_argument('--seed',default=None,type=int)
parser.add_argument('--override',default=1,type=int)
parser.add_argument('--reinit',default=1,type=int)
#
parser.add_argument('--dataset',default='airfoil',type=str, choices=['airfoil', 'parkinsons'] + ['boston', 'concrete', 'energy', 'kin8nm', 'naval', 'power', 'protein', 'wine', 'yacht', 'year'])
parser.add_argument('--train_on_valid',default=0, type=int, help="whether to train on the validation set")
parser.add_argument('--data_path',default=None, type=str)
parser.add_argument('--flow',default='IAF',type=str, choices=['RealNVP', 'IAF'])
parser.add_argument('--save_dir',default=None, type=str)
#
parser.add_argument('--drop_prob',default=0.005, type=float) # .05, .01, .005
#
#parser.add_argument('--length_scale',default=1e-3, type=float) # 1e0,-1,-2,-4
#parser.add_argument('--tau',default=1e2, type=float) # search over
#parser.add_argument('--normalization',default='by_train_set', type=str)
parser.add_argument('--fname',default=None, type=str) # override this for launching with SLURM!!!
parser.add_argument('--split',default=0, type=str) # TODO: , help="defaults to None, in which case this script will launch a copy of itself on ALL of the available splits")
parser.add_argument('--eval_only',default=0, type=int, help="just run the final evaluation, NO training!")
parser.add_argument('--verbose',default=1, type=int)
#parser.add_argument('--analyze',default=0, type=int, help="just run the final evaluation, NO training!")
#parser.add_argument('--save_results',default='./results/',type=str)
args = parser.parse_args()
print args
args_dict = args.__dict__
# moved from below!
locals().update(args_dict)
eval_only = int(args_dict.pop('eval_only'))
verbose = int(args_dict.pop('verbose'))
flags = [flag.lstrip('--') for flag in sys.argv[1:] if (not flag.startswith('--save_dir') and
not flag.startswith('--eval_only') and
not flag.startswith('--verbose'))]
exp_description = '_'.join(flags)
fname = args_dict.pop('fname')
if fname is None:
fname = os.path.basename(__file__)
# prepare to save model
if args_dict['save_dir'] is None:
save_ = False
save_path = None
print "\n\n\n\t\t\t\t WARNING: save_dir is None! Results will not be saved! \n\n\n"
else:
save_ = True
# save_dir = filename + PROVIDED parser arguments
save_dir = os.path.join(args_dict.pop('save_dir'), fname)
save_path = save_dir + '___' + '_'.join(flags)
print"\t\t save_dir=", save_dir
# make directory for results
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# save ALL parser arguments
with open (save_path + '__exp_settings.txt', 'w') as f:
for key in sorted(args_dict):
f.write(key+'\t'+str(args_dict[key])+'\n')
#assert lbda is None # we'
if seed is None:
seed = np.random.randint(2**32 - 1)
set_rng(np.random.RandomState(seed))
np.random.seed(seed+1000)
if data_path is None:
data_path = os.path.join(os.environ['HOME'], 'BayesianHypernetCW/')
t2 = time.time()
times['setup'] = t2 - t1
#
if 1:
input_dim, tr_x, tr_y, va_x, va_y, te_x, te_y, y_mean, y_std = get_regression_dataset(dataset, split, data_path=data_path)
if train_on_valid:
print tr_x.shape
tr_x = np.concatenate((tr_x, va_x), axis=0)
print tr_x.shape
tr_y = np.concatenate((tr_y, va_y), axis=0)
if model == 'MCD':
#lbda = get_lbda(tau, length_scale, drop_prob)
#print drop_prob, lbda
network = MCdropout_MLP(n_hiddens=n_hiddens,
n_units=n_units,
lbda=lbda,
input_dim=input_dim)
elif model == 'BHN':
#lbda = get_lbda(tau, length_scale)
prior = log_normal
#print lbda
if reinit:
init_batch_size = 64
init_batch = tr_x[-init_batch_size:]
else:
init_batch = None
network = MLPWeightNorm_BHN(lbda=lbda,
srng = RandomStreams(seed=seed+2000),
prior=prior,
coupling=coupling,
n_hiddens=n_hiddens,
n_units=n_units,
input_dim=input_dim,
flow=flow,
init_batch=init_batch)
t3 = time.time()
times['compile'] = t3 - t2
if eval_only:
network.load(save_path + '_final.npy')
print "skipping training (eval_only=1)"
t4 = time.time()
else:
print "begin training"
result = train_model(network, save_, save_path,
tr_x,tr_y,
va_x,va_y,
te_x, te_y,
y_mean, y_std,
lr0,lrdecay,bs,epochs,anneal,
taus=taus)
tr_LLs, tr_RMSEs, va_LLs, va_RMSEs, te_LLs, te_RMSEs, train_time, eval_time = result
t4 = time.time()
times['train'] = train_time
times['eval'] = eval_time
print "done training, begin final evaluation"
#tr_RMSE, tr_LL = evaluate_model(network.predict, tr_x, tr_y, n_mc=10000, taus=taus)
va_RMSE, va_LL = evaluate_model(network.predict, va_x, va_y, n_mc=1000, taus=taus, y_mean=y_mean, y_std=y_std)
te_RMSE, te_LL = evaluate_model(network.predict, te_x, te_y, n_mc=1000, taus=taus, y_mean=y_mean, y_std=y_std)
#total_runtime = time.time() - t0
#print "total_runtime=", total_runtime
t5 = time.time()
times['final_eval'] = t5 - t4
if save_:
if eval_only:
# we only overwrite the _FINAL_ results
np.savetxt(save_path + "__eval_only__FINAL_va_RMSE=" + str(np.round(va_RMSE, 3)), [va_RMSE])
np.savetxt(save_path + "__eval_only__FINAL_te_RMSE=" + str(np.round(te_RMSE, 3)), [te_RMSE])
for n, tau in enumerate(taus):
np.savetxt(save_path + '_tau=' + str(tau) + "__eval_only__FINAL_va_LL=" + str(np.round(va_LL[n], 3)), [va_LL[n]])
np.savetxt(save_path + '_tau=' + str(tau) + "__eval_only__FINAL_te_LL=" + str(np.round(te_LL[n], 3)), [te_LL[n]])
elif verbose:
# final params
network.save(save_path + '_final')
# learning curves
save_list(save_path + "_tr_RMSEs", tr_RMSEs)
save_list(save_path + "_va_RMSEs", va_RMSEs)
save_list(save_path + "_te_RMSEs", te_RMSEs)
for n, tau in enumerate(taus):
save_list(save_path + '_tau=' + str(tau) + "_tr_LLs", tr_LLs[n])
save_list(save_path + '_tau=' + str(tau) + "_va_LLs", va_LLs[n])
save_list(save_path + '_tau=' + str(tau) + "_te_LLs", te_LLs[n])
# final results (with full evaluation) TODO: EARLY STOPPING!!!
#np.savetxt(save_path + "_FINAL_tr_RMSE=" + str(np.round(tr_RMSE, 3)), [tr_RMSE])
np.savetxt(save_path + "_FINAL_va_RMSE=" + str(np.round(va_RMSE, 3)), [va_RMSE])
np.savetxt(save_path + "_FINAL_te_RMSE=" + str(np.round(te_RMSE, 3)), [te_RMSE])
for n, tau in enumerate(taus):
#np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_tr_LL=" + str(np.round(tr_LL[n], 3)), [tr_LL[n]])
np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_va_LL=" + str(np.round(va_LL[n], 3)), [va_LL[n]])
np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_te_LL=" + str(np.round(te_LL[n], 3)), [te_LL[n]])
t6 = time.time()
times['saving'] = t6 - t5
total_runtime = t6 - t0
print "total_runtime=", total_runtime
print "sum(times.values())", sum(times.values())
for k,v in times.items():
print k, np.round(v, 4)
# TODO: save times
np.savetxt(save_path + "________________total_runtime=" + str(total_runtime), [total_runtime])
def to_str(times_dict):
rval = '____timing____'
for k, v in times_dict.items():
rval += k + '_' + str(np.round(v, 3)) + '__'
return rval
np.save(save_path + to_str(times), times)
else:
np.savetxt(save_path + "_FINAL_va_RMSE=" + str(np.round(va_RMSE, 3)), [va_RMSE])
np.savetxt(save_path + "_FINAL_te_RMSE=" + str(np.round(te_RMSE, 3)), [te_RMSE])
for n, tau in enumerate(taus):
#np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_tr_LL=" + str(np.round(tr_LL[n], 3)), [tr_LL[n]])
np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_va_LL=" + str(np.round(va_LL[n], 3)), [va_LL[n]])
np.savetxt(save_path + '_tau=' + str(tau) + "_FINAL_te_LL=" + str(np.round(te_LL[n], 3)), [te_LL[n]])
t6 = time.time()
times['saving'] = t6 - t5
total_runtime = t6 - t0
print "total_runtime=", total_runtime
print "sum(times.values())", sum(times.values())
for k,v in times.items():
print k, np.round(v, 4)
def to_str(times_dict):
rval = '____timing____'
for k, v in times_dict.items():
rval += k + '_' + str(np.round(v, 3)) + '__'
return rval
np.save(save_path + to_str(times), times)