class Experience(object):
def __init__(self, N, name=None, disp=False, noise=None):
# If no name, we create an experience
if name is None:
num = 1
fname = path.join(EXP_DIR, 'exp%i.data' % num)
while path.exists(fname):
num += 1
fname = path.join(EXP_DIR, 'exp%i.data' % num)
name = 'exp%i' % num
self.name = name
# If there exist no previous file,
# We create one from the default experience
fname = path.join(EXP_DIR, '%s.data' % name)
if not path.exists(fname):
e_name = ''.join(c for c in name if c.isalpha())
e_name = path.join(EXP_DIR, '%s.data' % e_name)
from shutil import copy
if not path.exists(e_name):
copy(path.join(EXP_DIR, 'default.data'), fname)
else:
copy(e_name, fname)
#We load the experience parameters
import json
with open(fname) as f:
params_exp = json.load(f)
self.fname = fname
self.params_exp = params_exp
# Create the instance of the experience
self.queue = Queue()
self.obs = lambda: Observer(name, self.queue, disp)
self.dbn = DBN(params_exp['lay_shape'], self.queue, noise)
#Load the model weights if it exists
self.exists = self.dbn.load(name)
#Load the dataset and split between train and test set
from DataFeeder import DataFeeder
self.data = DataFeeder(N, batch_s=1000)
def pretrain(self, epochs=30, lr=0.1):
l_r = lr
if type(l_r) == float:
l_r = lambda e: lr
self.dbn.pretrain(self.data.X_trn, epochs=epochs, lr=l_r)
for i in range(self.params_exp['N_layer']):
self.params_exp['epochs_lay'][i] += epochs
def fine_tune(self, epochs=30, lr=.1, cl=.1, lcost=False, dropout=False):
l_r = lr
c_l = cl
if type(l_r) == float:
l_r = lambda e: lr
if type(c_l) == float:
c_l = lambda e: cl
p = self.obs()
p.start()
self.dbn.fine_tune(self.data,
epochs=epochs,
lr=l_r,
cl=c_l,
lcost=lcost,
dropout=dropout)
self.params_exp['epochs_ft'] += epochs
self.queue.put(('end', ))
p.join()
def eval_perf(self):
X_tst, y_tst = self.data.get_test_set()
code = self.dbn.f_code(X_tst)
from sklearn import metrics
sil_c = metrics.silhouette_score(code, y_tst)
sil_X = metrics.silhouette_score(X_tst, y_tst)
print 'Silhouette code y', sil_c
print 'Silhouette X y', sil_X
def save(self):
'''Save the experience so we can reload it later
'''
#Save the experience parameters
import json
with open(self.fname, 'w') as f:
json.dump(self.params_exp, f)
# Save the model weights
self.dbn.save(self.name)
class Experience(object):
def __init__(self, N, name=None, disp=False, noise=None):
# If no name, we create an experience
if name is None:
num = 1
fname = path.join(EXP_DIR,'exp%i.data'%num)
while path.exists(fname):
num += 1
fname = path.join(EXP_DIR, 'exp%i.data'%num)
name = 'exp%i'%num
self.name = name
# If there exist no previous file,
# We create one from the default experience
fname = path.join(EXP_DIR, '%s.data'%name)
if not path.exists(fname):
e_name = ''.join(c for c in name if c.isalpha())
e_name = path.join(EXP_DIR, '%s.data'%e_name)
from shutil import copy
if not path.exists(e_name):
copy(path.join(EXP_DIR, 'default.data'), fname)
else:
copy(e_name, fname)
#We load the experience parameters
import json
with open(fname) as f:
params_exp = json.load(f)
self.fname = fname
self.params_exp = params_exp
# Create the instance of the experience
self.queue = Queue()
self.obs = lambda: Observer(name, self.queue, disp)
self.dbn = DBN(params_exp['lay_shape'], self.queue, noise)
#Load the model weights if it exists
self.exists = self.dbn.load(name)
#Load the dataset and split between train and test set
from DataFeeder import DataFeeder
self.data = DataFeeder(N, batch_s=1000)
def pretrain(self, epochs=30, lr=0.1):
l_r = lr
if type(l_r) == float:
l_r = lambda e: lr
self.dbn.pretrain(self.data.X_trn, epochs=epochs, lr=l_r)
for i in range(self.params_exp['N_layer']):
self.params_exp['epochs_lay'][i] += epochs
def fine_tune(self, epochs=30, lr=.1, cl=.1, lcost=False ,dropout=False):
l_r = lr
c_l = cl
if type(l_r) == float:
l_r = lambda e: lr
if type(c_l) == float:
c_l= lambda e: cl
p = self.obs()
p.start()
self.dbn.fine_tune(self.data, epochs=epochs, lr=l_r,
cl=c_l, lcost=lcost, dropout=dropout)
self.params_exp['epochs_ft'] += epochs
self.queue.put(('end',))
p.join()
def eval_perf(self):
X_tst, y_tst = self.data.get_test_set()
code = self.dbn.f_code(X_tst)
from sklearn import metrics
sil_c = metrics.silhouette_score(code, y_tst)
sil_X = metrics.silhouette_score(X_tst, y_tst)
print 'Silhouette code y', sil_c
print 'Silhouette X y', sil_X
def save(self):
'''Save the experience so we can reload it later
'''
#Save the experience parameters
import json
with open(self.fname, 'w') as f:
json.dump(self.params_exp, f)
# Save the model weights
self.dbn.save(self.name)
