def get(self):
if self.get_current_user():
self.redirect('/')
return
write_data(self, 'login.html', {
'login_error':None,
})
def save_predotar():
genomes = ppr.get_genomes()
data = []
vals = [
u"none",
u"mitochondrial",
u"plastid",
u"er",
u"elsewhere",
u"possibly mitochondrial",
u"possibly plastid",
u"possibly er",
u"possibly elsewhere",
]
for g in genomes:
pprs = ppr.load_records(g)
if len(pprs) < 50:
continue
row = [0] * len(vals)
total = float(len(pprs))
for p in pprs:
pred = p.annotations["predotar"]
if pred not in vals:
raise ValueError("didn't expect {}".format(pred))
else:
row[vals.index(pred)] += 1
row = [float(r) / total for r in row]
data.append([short_name(g)] + row)
data.sort(key=lambda d: d[1])
utils.write_data(["genome"] + vals, data, "output/ppr_predotar.dat")
def post(self):
if self.get_current_user():
raise tornado.web.HTTPError(403)
username = self.get_argument('form-username')
password = self.get_argument('form-password')
login_error = None
#login_error = 'err_userpass'
if login_error:
write_data(self, 'login.html', {
'login_error':login_error,
})
self.set_secure_cookie('user',username,settings.cookie_timeout)
self.redirect(self.get_argument('next','/'))
def get_localization(genome):
data = []
for g in genome:
if len(g.pprs) < 50:
continue
total = float(len(g.pprs))
loc_c = 100.0 * len([1 for p in g.pprs if p.localization() == "C"]) / total
loc_m = 100.0 * len([1 for p in g.pprs if p.localization() == "M"]) / total
loc_s = 100.0 * len([1 for p in g.pprs if p.localization() == "S"]) / total
loc_o = 100.0 * len([1 for p in g.pprs if p.localization() == "_"]) / total
loc_u = 100.0 * len([1 for p in g.pprs if p.localization() == "*"]) / total
data.append((short_name(g.name), loc_c, loc_m, loc_s, loc_o, loc_u))
data.sort(key=lambda d: d[1])
utils.write_data(("genome", "c", "m", "s", "other", "unknown"), data, "output/ppr_localization.dat")
def ppr_distance():
genomes = PPR.load_genomes()
genomes = [g for g in genomes if len(g) > 50]
data = []
for i,g in enumerate(genomes, 1):
print "Genome {}/{}: {} ({})".format(i, len(genomes),
g.name,short_name(g.name))
data.append([short_name(g.name),] + get_average(g,genomes))
utils.write_data(['',] + [reduce_name(g.name) for g in genomes],
data,
'output/Average_PPR_distance.dat')
def get_length_family(genomes):
pprs = [p for g in genomes for p in g.pprs]
type_P = [p for p in pprs if p.family() == "P"]
type_E = [p for p in pprs if p.family() == "E"]
type_Ep = [p for p in pprs if p.family() == "E+"]
type_DYW = [p for p in pprs if p.family() == "DYW"]
type_PLS = [p for p in pprs if p.family() == "PLS"]
P_hist = length_hist(type_P)
E_hist = length_hist(type_E)
Ep_hist = length_hist(type_Ep)
DYW_hist = length_hist(type_DYW)
PLS_hist = length_hist(type_PLS)
hist = fmt_hist([P_hist, E_hist, Ep_hist, DYW_hist, PLS_hist])
utils.write_data(("length", "p", "e", "ep", "dyw", "pls"), hist, "output/ppr_family_lengths.dat")
def names(request,person,do_view=False):
names = set(json.loads(open(settings.NAMES_DB,'r').read())['names'])
oked, vetoed = utils.get_data(person)
if request.method == "POST":
name = request.POST['name']
ok = 0
if 'yes' in request.POST:
oked.add(name)
ok = 1
if 'no' in request.POST:
vetoed.add(name)
if 'cancel' in request.POST:
if name in oked:
oked.remove(name)
if name in vetoed:
vetoed.remove(name)
utils.write_data(person, oked, vetoed)
response = HttpResponseRedirect('/names/%s/' % (person,))
if 'yes' in request.POST or 'no' in request.POST:
safe_name = urllib.quote(name.encode('UTF-8'))
response.set_cookie('last_decision',value='%s|%s' % (ok,safe_name),max_age=60*60)
return response
remaining = names - vetoed - oked
last_decision = request.COOKIES.get('last_decision','|').split('|')
last_decision[1] = urllib.unquote(last_decision[1])
context = {
'name': random.choice(list(remaining)) if len(remaining) else None
, 'num_remaining': len(remaining)
, 'num_ok': len(oked)
, 'num_veto': len(vetoed)
, 'last_decision': last_decision
}
if do_view:
context.update({'do_view':True, 'oked': utils.sort_nameset(oked) , 'vetoed': utils.sort_nameset(vetoed)})
template = 'names/%s.html' % ('namelist' if do_view else 'names',)
response = HttpResponse(loader.get_template(template).render(RequestContext(request,context)))
response.delete_cookie('last_decision')
return response
def write_model_parameters(hps, output_fname=None, datasets=None):
"""Save all the model parameters
Save all the parameters to hps.lfads_save_dir.
Args:
hps: The dictionary of hyperparameters.
output_fname: The prefix of the file in which to save the generated
samples.
datasets: A dictionary of data dictionaries. The dataset dict is simply a
name(string)-> data dictionary mapping (See top of lfads.py).
"""
if not output_fname:
output_fname = "model_params"
else:
output_fname = output_fname + "_model_params"
fname = os.path.join(hps.lfads_save_dir, output_fname)
print("Writing model parameters to: ", fname)
# save the optimizer params as well
model = build_model(hps, kind="write_model_params", datasets=datasets)
model_params = model.eval_model_parameters(use_nested=False,
include_strs="LFADS")
utils.write_data(fname, model_params, compression=None)
print("Done.")
def main():
genomes = ppr.get_genomes()
hist = []
numbers = []
locale = []
family = []
for g in genomes:
print g
pprs = list(ppr.load_pprs(g))
numbers.append((short_name(g),) + get_numbers(pprs))
hist.append(length_hist(pprs))
locale.append((short_name(g),) + get_localization(pprs))
family.append((short_name(g),) + get_family(pprs))
numbers.sort(key=lambda n: n[1])
family.sort(key=lambda f: sum(f[1:]))
hist = fmt_hist(hist)
utils.write_data(("genome",) + get_numbers_hdr(), numbers, "output/ppr_numbers.dat")
utils.write_data(["length"] + [short_name(g) for g in genomes], hist, "output/ppr_lengths.dat")
utils.write_data(("genome",) + get_localization_hdr(), locale, "output/ppr_localization.dat")
utils.write_data(("genome",) + get_family_hdr(), family, "output/ppr_families.dat")
def exp_gbrbm(exp_name, T=[]):
dataset = dg.one_exception_dataset(
N=6,
n=1500,
T=T,
lam=5000,
exc=1,
noise_k=1
)
utils.write_data(dataset, exp_name, "generated_data")
new_data = utils.tsne(dataset, exp_name, "generated_data", T, 2)
utils.write_data(new_data, exp_name, "generated_data_for_tsne")
_, recovery_sample, decode_res = decoder.gbrbm_decoder(
dataset,
learning_rate=0.1,
training_epochs=50,
batch_size=1001,
n_hidden=2000,
plot_every=1
)
utils.write_data(decode_res, exp_name, "decoded_data")
new_data = utils.tsne(decode_res, exp_name, "decoded_data", T, 2)
utils.write_data(new_data, exp_name, "decoded_tsne_data_for_tsne")
auc_transfer_metric, mixmode='logsigp')
alphas = np.asarray([frame.sig_alpha for frame in alpha_frames])
betas = np.asarray([frame.time for frame in alpha_frames])
gammas = np.asarray([frame.alpha for frame in alpha_frames])
alpha_list.append(alphas)
beta_list.append(betas)
gamma_list = filtered_append(gammas, gamma_list)
print(i, '/', iterations)
mean_alpha = np.mean(alpha_list, axis=0)
mean_beta = np.mean(beta_list, axis=0)
mean_gamma = np.mean(gamma_list, axis=0)
write_data("results", "baseline.txt", mean_gamma, mean_alpha, mean_beta)
for i, (a, b, c) in enumerate(zip(mean_gamma, mean_alpha, mean_beta)):
print(i + 1, a, b, c)
fig = plt.figure(figsize=(9, 5))
ax = plt.subplot(1, 1, 1)
ax.tick_params(axis='both', which='major', labelsize=13)
ax.axhline(1, c='lightgray', ls='--')
ax.axhline(0, c='lightgray', ls='--')
ax.plot(alphas, lw=2, color='k', label='N = {0}'.format(10))
ax.set_xlim([0, opt.ALPHA_NUM_ITER - 1])
ax.set_xlabel('Number of episodes', fontsize=14)
ax.set_ylabel(r'$\sigma(\gamma)$', fontsize=14)
def build_model(hps, kind="train", datasets=None):
"""Builds a model from either random initialization, or saved parameters.
Args:
hps: The hyper parameters for the model.
kind: (optional) The kind of model to build. Training vs inference require
different graphs.
datasets: The datasets structure (see top of lfads.py).
Returns:
an LFADS model.
"""
build_kind = kind
if build_kind == "write_model_params":
build_kind = "train"
with tf.variable_scope("LFADS", reuse=None):
model = LFADS(hps, kind=build_kind, datasets=datasets)
if not os.path.exists(hps.lfads_save_dir):
print("Save directory %s does not exist, creating it." % hps.lfads_save_dir)
os.makedirs(hps.lfads_save_dir)
cp_pb_ln = hps.checkpoint_pb_load_name
cp_pb_ln = 'checkpoint' if cp_pb_ln == "" else cp_pb_ln
if cp_pb_ln == 'checkpoint':
print("Loading latest training checkpoint in: ", hps.lfads_save_dir)
saver = model.seso_saver
elif cp_pb_ln == 'checkpoint_lve':
print("Loading lowest validation checkpoint in: ", hps.lfads_save_dir)
saver = model.lve_saver
else:
print("Loading checkpoint: ", cp_pb_ln, ", in: ", hps.lfads_save_dir)
saver = model.seso_saver
ckpt = tf.train.get_checkpoint_state(hps.lfads_save_dir,
latest_filename=cp_pb_ln)
session = tf.get_default_session()
print("ckpt: ", ckpt)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
if kind in ["posterior_sample_and_average", "prior_sample",
"write_model_params"]:
print("Possible error!!! You are running ", kind, " on a newly \
initialized model!")
print("Are you sure you sure ", ckpt.model_checkpoint_path, " exists?")
tf.global_variables_initializer().run()
if ckpt:
train_step_str = re.search('-[0-9]+$', ckpt.model_checkpoint_path).group()
else:
train_step_str = '-0'
fname = 'hyperparameters' + train_step_str + '.txt'
hp_fname = os.path.join(hps.lfads_save_dir, fname)
hps_for_saving = jsonify_dict(hps)
utils.write_data(hp_fname, hps_for_saving, use_json=True)
return model
def build_model(hps, kind="train", datasets=None):
"""Builds a model from either random initialization, or saved parameters.
Args:
hps: The hyper parameters for the model.
kind: (optional) The kind of model to build. Training vs inference require
different graphs.
datasets: The datasets structure (see top of lfads.py).
Returns:
an LFADS model.
"""
build_kind = kind
if build_kind == "write_model_params":
build_kind = "train"
with tf.variable_scope("LFADS", reuse=None):
model = LFADS(hps, kind=build_kind, datasets=datasets)
if not os.path.exists(hps.lfads_save_dir):
print("Save directory %s does not exist, creating it." % hps.lfads_save_dir)
os.makedirs(hps.lfads_save_dir)
cp_pb_ln = hps.checkpoint_pb_load_name
cp_pb_ln = 'checkpoint' if cp_pb_ln == "" else cp_pb_ln
if cp_pb_ln == 'checkpoint':
print("Loading latest training checkpoint in: ", hps.lfads_save_dir)
saver = model.seso_saver
elif cp_pb_ln == 'checkpoint_lve':
print("Loading lowest validation checkpoint in: ", hps.lfads_save_dir)
saver = model.lve_saver
else:
print("Loading checkpoint: ", cp_pb_ln, ", in: ", hps.lfads_save_dir)
saver = model.seso_saver
ckpt = tf.train.get_checkpoint_state(hps.lfads_save_dir,
latest_filename=cp_pb_ln)
session = tf.get_default_session()
print("ckpt: ", ckpt)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
if kind in ["posterior_sample_and_average", "posterior_push_mean",
"prior_sample", "write_model_params"]:
print("Possible error!!! You are running ", kind, " on a newly \
initialized model!")
# cannot print ckpt.model_check_point path if no ckpt
print("Are you sure you sure a checkpoint in ", hps.lfads_save_dir,
" exists?")
tf.global_variables_initializer().run()
if ckpt:
train_step_str = re.search('-[0-9]+$', ckpt.model_checkpoint_path).group()
else:
train_step_str = '-0'
fname = 'hyperparameters' + train_step_str + '.txt'
hp_fname = os.path.join(hps.lfads_save_dir, fname)
hps_for_saving = jsonify_dict(hps)
utils.write_data(hp_fname, hps_for_saving, use_json=True)
return model
def get(self):
write_data(self, 'monitor/monitor.html',{
})
def get(self):
write_data(self, 'user/admin.html',{
})
rf = RandomForestClassifier(max_features='auto',
oob_score=False,
class_weight='balanced',
random_state=config.set_seed,
n_jobs=-1,
verbose=config.verbose)
clf = GridSearchCV(estimator=rf,
param_grid=params,
scoring='accuracy',
cv=cv,
n_jobs=-1)
clf.fit(X, y)
print('#### Best Params')
print(clf.best_params_)
print('#### Best Score')
print(clf.best_score_)
return clf.grid_scores_
if __name__ == '__main__':
# Train Data
train_x = read_data(config.d_xtrain)
train_y = read_data(config.d_ytrain)
# Test Data
test_x = read_data(config.d_xtest)
report = grid_model(param_grid, train_x, train_y, cv=5)
write_data(config.grid_report_rf, report)
def generate_lorenz_data(N_trials,
N_inits,
N_cells,
N_steps,
N_stepsinbin=1,
dt_lorenz=None,
dt_spike=None,
dt_cal=None,
base_firing_rate=5.0,
tau_c=0.4,
inc_c=1.0,
sigma=0.2,
trainp=0.8,
seed=100,
save=True,
save_dir='./'):
print('Generating Lorenz data', flush=True)
N_lorenz = 3
assert N_steps % N_stepsinbin == 0, 'Can\'t bin time steps'
N_steps_bin = int(N_steps / N_stepsinbin)
if dt_lorenz is None:
dt_lorenz = np.clip(2.0 / N_steps, 0.005, 0.02)
if dt_spike is None:
dt_spike = dt_lorenz
if dt_cal is None:
dt_cal = dt_spike * N_stepsinbin
N_train = int(N_trials * trainp)
N_steps_burn = max(N_steps, 300)
y = np.zeros((N_inits, N_steps + N_steps_burn, N_lorenz))
w_lorenz = ([10.0, 28.0, 8.0 / 3.0])
y[:, 0] = np.random.randn(N_inits, N_lorenz)
for step in range(1, N_steps + N_steps_burn):
dy = lorenz_grad(y[:, step - 1], w_lorenz)
y[:, step] = eulerStep(y[:, step - 1], dy, dt_lorenz)
print('Converting to rates and spikes', flush=True)
y = y[:, N_steps_burn:]
y = normalize(y)
W = (np.random.rand(N_lorenz, N_cells) + 1) * np.sign(
np.random.randn(N_lorenz, N_cells))
b = np.log(base_firing_rate)
rates = np.exp(y.dot(W) + b)
spikes = np.array(
[np.random.poisson(rates * dt_spike) for trial in range(N_trials)])
if N_stepsinbin > 1:
from scipy.stats import binned_statistic
binned_latent = np.zeros((N_trials, N_inits, N_steps_bin, N_lorenz))
binned_rates = np.zeros((N_trials, N_inits, N_steps_bin, N_cells))
binned_spikes = np.zeros((N_trials, N_inits, N_steps_bin, N_cells))
for ix in range(N_trials):
for jx in range(N_inits):
binned_spikes[ix, jx] = binned_statistic(x=np.arange(N_steps),
values=spikes[ix,
jx].T,
statistic='sum',
bins=N_steps_bin)[0].T
binned_rates[ix, jx] = binned_statistic(x=np.arange(N_steps),
values=rates[jx].T,
statistic='mean',
bins=N_steps_bin)[0].T
binned_latent[ix, jx] = binned_statistic(x=np.arange(N_steps),
values=y[jx].T,
statistic='mean',
bins=N_steps_bin)[0].T
rates = binned_rates
del binned_rates
spikes = binned_spikes.astype(int)
del binned_spikes
latent = binned_latent
del binned_latent
else:
latent = np.array([y for trial in range(N_trials)])
rates = np.array([rates for trial in range(N_trials)])
calcium = np.zeros_like(spikes, dtype=float)
fluor = np.zeros_like(spikes, dtype=float)
ct = spikes[:, :, 0, :] * inc_c
calcium[:, :, 0, :] = ct
fluor[:, :,
0, :] = ct + np.random.randn(N_trials, N_inits, N_cells) * sigma
print('Converting to fluorescence', flush=True)
for step in range(1, N_steps_bin):
ct = eulerStep(ct, calcium_grad(ct, tau_c), dt_spike)
ct = ct + inc_c * spikes[:, :, step, :]
calcium[:, :, step, :] = ct
fluor[:, :, step, :] = ct + np.random.randn(N_trials, N_inits,
N_cells) * sigma
print('Train and test split')
data_dict = {}
for data, name in zip([latent, rates, spikes, calcium, fluor],
['latent', 'rates', 'spikes', 'calcium', 'fluor']):
data_dict['train_%s' % name] = np.reshape(
data[:N_train], (N_train * N_inits, N_steps_bin, data.shape[-1]))
data_dict['valid_%s' % name] = np.reshape(
data[N_train:],
((N_trials - N_train) * N_inits, N_steps_bin, data.shape[-1]))
if importlib.find_loader('oasis'):
data_dict['train_oasis'] = deconvolve_calcium(data_dict['train_fluor'],
g=np.exp(-dt_cal /
tau_c))
data_dict['valid_oasis'] = deconvolve_calcium(data_dict['valid_fluor'],
g=np.exp(-dt_cal /
tau_c))
data_dict['train_data'] = data_dict['train_spikes']
data_dict['valid_data'] = data_dict['valid_spikes']
data_dict['train_truth'] = data_dict['train_rates']
data_dict['valid_truth'] = data_dict['valid_rates']
data_dict['dt'] = dt_cal
data_dict['loading_weights'] = W
data_dict['conversion_factor'] = 1. / (np.max(rates) * dt_cal)
print('Saving to %s/synth_data/lorenz_%03d' % (save_dir, seed), flush=True)
if save:
utils.write_data('%s/synth_data/lorenz_%03d' % (save_dir, seed),
data_dict)
return data_dict
def save_profile(self):
profile_name = self.profilename_edit.text()
profile_data = {
"profile_name":
profile_name,
"shipping_fname":
self.shipping_fname_edit.text(),
"shipping_lname":
self.shipping_lname_edit.text(),
"shipping_email":
self.shipping_email_edit.text(),
"shipping_phone":
self.shipping_phone_edit.text(),
"shipping_a1":
self.shipping_address1_edit.text(),
"shipping_a2":
self.shipping_address2_edit.text(),
"shipping_city":
self.shipping_city_edit.text(),
"shipping_zipcode":
self.shipping_zipcode_edit.text(),
"shipping_state":
self.shipping_state_box.currentText(),
"shipping_country":
self.shipping_country_box.currentText(),
"billing_fname":
self.billing_fname_edit.text(),
"billing_lname":
self.billing_lname_edit.text(),
"billing_email":
self.billing_email_edit.text(),
"billing_phone":
self.billing_phone_edit.text(),
"billing_a1":
self.billing_address1_edit.text(),
"billing_a2":
self.billing_address2_edit.text(),
"billing_city":
self.billing_city_edit.text(),
"billing_zipcode":
self.billing_zipcode_edit.text(),
"billing_state":
self.billing_state_box.currentText(),
"billing_country":
self.billing_country_box.currentText(),
"card_number": (Encryption().encrypt(
self.cardnumber_edit.text())).decode("utf-8"),
"card_month":
self.cardmonth_box.currentText(),
"card_year":
self.cardyear_box.currentText(),
"card_type":
self.cardtype_box.currentText(),
"card_cvv":
self.cardcvv_edit.text()
}
profiles = return_data("./data/profiles.json")
for p in profiles:
if p["profile_name"] == profile_name:
profiles.remove(p)
break
profiles.append(profile_data)
write_data("./data/profiles.json", profiles)
if self.loadprofile_box.findText(profile_name) == -1:
self.loadprofile_box.addItem(profile_name)
self.parent().parent().createdialog.profile_box.addItem(
profile_name)
QtWidgets.QMessageBox.information(self, "Bird Bot", "Saved Profile")
def main():
args = parser.parse_args()
data_name = os.path.basename(args.data_path).split('.')[0]
dir_name = os.path.dirname(args.data_path)
data_dict = read_data(args.data_path)
dt = data_dict['dt']
g = np.exp(-dt / args.tau)
train_size, steps_size, state_size = data_dict['train_fluor'].shape
valid_size, steps_size, state_size = data_dict['valid_fluor'].shape
data_size = train_size + valid_size
data = np.zeros((data_size, steps_size, state_size))
if args.undo_train_test_split:
train_idx = data_dict['train_idx']
valid_idx = data_dict['valid_idx']
data[train_idx] = data_dict['train_fluor']
data[valid_idx] = data_dict['valid_fluor']
else:
data[:train_size] = data_dict['train_fluor']
data[train_size:] = data_dict['valid_fluor']
if args.flatten:
data = data.reshape(data_size * steps_size, state_size).transpose()
else:
data = data.transpose(0, 2, 1)
data = data.reshape(data_size * state_size, steps_size)
data = np.hstack((np.zeros((data_size * state_size, 1)), data))
if args.known:
S, C = deconvolve_calcium_known(data, g=g, s_min=args.scale / 2)
else:
if args.normalize:
data = max_normalize(data.T, axis=0).T
S, C, bias, G, gain, rval = deconvolve_calcium_unknown(
data, g=g, snr_thresh=args.scale)
tau = -dt / (np.log(G))
if args.flatten:
data = data.reshape(data_size, steps_size, state_size)
S = S.reshape(data_size, steps_size, state_size)
C = C.reshape(data_size, steps_size, state_size)
else:
data = data.reshape(data_size, state_size,
steps_size + 1).transpose(0, 2, 1)[:, 1:]
S = S.reshape(data_size, state_size,
steps_size + 1).transpose(0, 2, 1)[:, 1:]
C = C.reshape(data_size, state_size,
steps_size + 1).transpose(0, 2, 1)[:, 1:]
if not args.known:
bias = bias.reshape(data_size, state_size).mean(axis=0)
tau = tau.reshape(data_size, state_size).mean(axis=0)
gain = gain.reshape(data_size, state_size).mean(axis=0)
if args.undo_train_test_split:
train_fluor = data[train_idx]
valid_fluor = data[valid_idx]
train_ospikes = S[train_idx]
valid_ospikes = S[valid_idx]
train_ocalcium = C[train_idx]
valid_ocalcium = C[valid_idx]
else:
train_fluor = data[:train_size]
valid_fluor = data[train_size:]
train_ospikes = S[:train_size]
valid_ospikes = S[train_size:]
train_ocalcium = C[:train_size]
valid_ocalcium = C[train_size:]
data_dict['train_fluor'] = train_fluor
data_dict['valid_fluor'] = valid_fluor
data_dict['train_ospikes'] = train_ospikes
data_dict['valid_ospikes'] = valid_ospikes
data_dict['train_ocalcium'] = train_ocalcium
data_dict['valid_ocalcium'] = valid_ocalcium
if not args.known:
data_dict['obs_gain_init'] = gain
data_dict['obs_bias_init'] = bias
data_dict['obs_tau_init'] = tau
data_dict['obs_var_init'] = (gain / args.scale)**2
arg_string = '_o%s' % ('k' if args.known else 'u')
arg_string += '_t%s' % (str(args.tau))
arg_string += '_s%s' % (str(args.scale))
arg_string += '_f' if args.flatten else ''
arg_string += '_z' if args.undo_train_test_split else ''
arg_string += '_n' if args.normalize else ''
write_data(os.path.join(dir_name, data_name) + arg_string, data_dict)
def run_filter():
data = utils.read_json(Const.origin_train_filename)
saved_data = utils.filter_out(data)
f = open(Const.origin_train_filtered_filename, 'w')
utils.write_data(f, saved_data)
print 'filter finish, saved in %s' % Const.origin_train_filtered_filename
sys.path.append("../")
from param_config import config
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import ADASYN, SMOTE, RandomOverSampler
from utils import read_data
from utils import write_data
import numpy as np
np.random.seed(config.set_seed)
def join_split(X, y):
x_train, x_val, y_train, y_val = train_test_split(
X, y, stratify=y, test_size=config.train_val_split_pct)
return x_train, y_train, x_val, y_val
# Try imbalance learn
if __name__ == '__main__':
train_x = read_data(config.e_xtrain)
train_y = read_data(config.e_ytrain)
# split data into train and validation
# 20 percent stratified
x_train, y_train, x_val, y_val = join_split(train_x, train_y)
print('#### Writing Pickle 09: Split ####')
write_data(config.f_xtrain, x_train)
write_data(config.f_ytrain, y_train)
write_data(config.f_xval, x_val)
write_data(config.f_yval, y_val)
c = Counter(lab60)
# Return most common label. Make sure that ratio between second most common
# and most common is < 0.8
counts = c.most_common(2)
if counts[1][1] / float(counts[0][1]) > 0.8:
print 'Ambiguous label for sampleid = %d : counts : %s' % (sampleid,
counts)
#print counts
return counts[0][0]
##
newlabels = np.array([knn_DM(DM, sampleid) for sampleid in xrange(len(labels))])
modified = np.flatnonzero(newlabels - labels)
## Write a new file
with open('out.txt', 'w') as f:
utils.write_data(f, accel, gyro, labels)
##
sample1 = 18
sample2 = 151
dist, cost, path = mlpy.dtw_std(accel[sample1,0,:], accel[sample2,0,:],
dist_only=False)
pl.figure()
pl.suptitle('dist = %f' % dist)
pl.subplot(211); pl.title('%d'%sample1); pl.plot(accel[sample1,0,:]); pl.ylim(0, 5000); pl.subplot(212); pl.title('%d'%sample2); pl.plot(accel[sample2,0,:]); pl.ylim(0,5000);
pl.figure()
pl.title('%d - %d' % (sample1, sample2))
pl.imshow(cost.T, origin='lower', cmap=cm.gray, interpolation='nearest')
pl.plot(path[0], path[1], 'w')
from utils import read_data
from utils import write_data
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
np.random.seed(config.set_seed)
num_cols = ['amount_tsh', 'gps_height', 'population', 'total_missing']
# Scale and Fit
# standard mean and unit variance scale
def pca(x_train, x_test):
std_clf = make_pipeline(StandardScaler(),
PCA(n_components=config.pca_components))
std_clf.fit(x_train)
train_pca = std_clf.transform(x_train)
test_pca = std_clf.transform(x_test)
return train_pca, test_pca
if __name__ == '__main__':
train_x = read_data(config.d_xtrain)
test_x = read_data(config.d_xtest)
train_pca, test_pca = pca(train_x[num_cols], test_x[num_cols])
print('#### Writing Pickle 1: PCA ####')
write_data(config.pca_xtrain, train_pca)
write_data(config.pca_xtest, test_pca)
def deproject(self, config_path='params.cfg'):
'''
Deproject a rotation measure map by dividing it by a smooth model
of the cluster with the parameters taken from the configuration file.
Args:
config_path (str): path to the configuration file
'''
# Observed cluster parameters parameters (use only when need to deproject 3D PDS)
config = configparser.ConfigParser()
try:
config.read(config_path)
except IOError:
print('Error: No config file found at ' + config_path)
return
# beta model parameters
self.cluster_params['ne0'] = config.getfloat("cluster_params", "ne0")
self.cluster_params['rc'] = config.getfloat("cluster_params", "rc")
self.cluster_params['beta'] = config.getfloat("cluster_params", "beta")
# size of pixel in kiloparsecs
self.cluster_params['kpc_px'] = config.getfloat(
"cluster_params", "kpc_px")
# Galactic background to subtract from the observed RM map
self.cluster_params['gal_bg'] = config.getfloat(
"cluster_params", "gal_bg")
# location of the cluster center on the sky in pixels
self.cluster_params['center'][0] += config.getint(
"cluster_params", "ix0")
self.cluster_params['center'][1] += config.getint(
"cluster_params", "iy0")
#recovery
self.recovery_params = {}
self.recovery_params['alpha'] = config.getfloat(
"recovery_params", "alpha")
self.recovery_params['inclin'] = config.getfloat(
"recovery_params", "inclin")
# depth of the 3D box used to generate the smooth model
self.recovery_params['lz'] = config.getint("recovery_params", "Lz")
# subtract the background
self.data -= self.cluster_params['gal_bg']
kpc_px = self.cluster_params['kpc_px']
lx, ly = self.data.shape
print('generate a 3D smooth model of the cluster...')
smod = gen3d_smooth_model(shape=(lx, ly),
cluster_params=self.cluster_params,
recovery_params=self.recovery_params)
write_data(self.output_paths['smooth3d'],
smod[:, ly // 2, :],
ftype='npy')
print('deproject the image...')
# integrate the smooth model along the line of sight
I0 = np.sqrt((smod**2).sum(axis=2)) * kpc_px * 812.
write_data(self.output_paths['smooth'], I0, ftype='npy')
# divide the image by the smooth model
ind = (I0 != 0.) * np.invert(np.isnan(self.data))
self.data[ind] /= I0[ind]
write_data(self.output_paths['deproj'], self.data, ftype='npy')
print('deprojection done\n')
def generate_chaotic_rnn_data(Ninits=400,
Ntrial=10,
Ncells=50,
Nsteps=200,
trainp=0.8,
dt_rnn=0.1,
dt_spike=0.1,
tau=0.25,
gamma=1.5,
maxRate=5,
B=20,
tau_c=0.4,
inc_c=1.0,
sigma=0.2,
seed=5,
save=False,
save_dir='./'):
'''
Generate synthetic calcium fluorescence data from chaotic recurrent neural network system
Arguments:
- T (int or float): total time in seconds to run
- dt_rnn (float): time step of chaotic RNN
- dt_spike (float): time step of calcium trace
- Ninits (int): Number of network initialisations
- Ntrial (int): Number of instances with same network initialisations
- Ncells (int): Number of cells in network
- trainp (float): proportion of dataset to partition into training set
- tau (float): time constant of chaotic RNN
- gamma (float):
- maxRate (float): maximum firing rate of chaotic RNN
- B (int, or float): amplitude of perturbation to network
- tau_c (float): time constant of calcium decay
- inc_c (float): increment size of calcium influx
- sigma (float): standard deviation of fluorescence noise
- save (bool): save output
'''
np.random.seed(seed)
Nsteps = int(T / dt_rnn)
Ntrial_train = int(trainp * Ntrial)
# Chaotic RNN weight matrix
W = gamma * np.random.randn(Ncells, Ncells) / np.sqrt(Ncells)
rates, spikes, calcium, fluor = np.zeros(
(4, Ninits, Ntrial, Nsteps, Ncells))
perturb_steps = []
for init in range(Ninits):
y0 = np.random.randn(Ncells)
for trial in range(Ntrial):
perturb_step = np.random.randint(0.25 * Nsteps, 0.75 * Nsteps)
perturb_steps.append(perturb_step)
perturb_amp = np.random.randn(Ncells) * B
b = 0
yt = y0
rt = rateScale(np.tanh(yt), maxRate=maxRate)
st = spikify_rates(rt, dt=dt_spike)
ct = inc_c * st
rates[init, trial, 0, :] = rt
spikes[init, trial, 0, :] = st
calcium[init, trial, 0, :] = ct
fluor[init, trial, 0, :] = ct + np.random.randn(Ncells) * sigma
for step in range(1, Nsteps):
yt = eulerStep(yt, RNNgrad(yt + b, W, tau), dt_rnn)
ct = eulerStep(ct, calciumgrad(ct, tau_c), dt_spike)
if step == perturb_step:
b = perturb_amp * dt_rnn / tau
else:
b = 0
rt = rateScale(np.tanh(yt), maxRate=maxRate)
st = spikify_rates(rt, dt=dt_spike)
ct = ct + inc_c * st
rates[init, trial, step, :] = rt
spikes[init, trial, step, :] = st
calcium[init, trial, step, :] = ct
fluor[init, trial,
step, :] = ct + np.random.randn(Ncells) * sigma
# Construct data dictionary
for data, name in zip([rates, spikes, calcium, fluor],
['rates', 'spikes', 'calcium', 'fluor']):
data_dict['train_%s' % name] = np.reshape(
data[:N_train], (N_train * N_inits, N_steps_bin, data.shape[-1]))
data_dict['valid_%s' % name] = np.reshape(
data[N_train:],
((N_trials - N_train) * N_inits, N_steps_bin, data.shape[-1]))
if importlib.find_loader('oasis'):
data_dict['train_oasis'] = deconvolve_calcium(data_dict['train_fluor'],
g=np.exp(-dt_spike /
tau_c))
data_dict['valid_oasis'] = deconvolve_calcium(data_dict['valid_fluor'],
g=np.exp(-dt_spike /
tau_c))
data_dict['train_data'] = data_dict['train_spikes']
data_dict['valid_data'] = data_dict['valid_spikes']
data_dict['train_truth'] = data_dict['train_rates']
data_dict['valid_truth'] = data_dict['valid_rates']
data_dict['dt'] = dt_spike
data_dict['perturb_times'] = np.array(perturb_steps) * dt_spike
if save:
utils.write_data('%s/synth_data/chaotic_rnn_%03d' % (save_dir, seed),
data_dict)
return data_dict
def get(self):
write_data(self, 'left.html', {})
def get(self):
write_data(self, 'manage/manage.html',{
})
def most_frequent(x_train):
na_cols = {}
for c in x_train.columns:
if x_train[c].isnull().sum() > 0:
na_cols[c] = test_x[c].value_counts().index[0]
return na_cols
def target_encoding(y_train):
y_train = [target_mapping[i] for i in y_train]
return y_train
if __name__ == '__main__':
train_x = read_data(config.c_xtrain)
test_x = read_data(config.c_xtest)
train_y = read_data(config.b_ytrain)
train_y = target_encoding(train_y)
impute_data = most_frequent(train_x)
train_x, test_x = label_encoding(train_x, test_x, impute_data)
assert len(train_x.columns) == len(test_x.columns)
print('#### Writing Pickle 04: Imputation ####')
write_data(config.d_xtrain, train_x)
write_data(config.d_xtest, test_x)
write_data(config.d_ytrain, train_y)
def get(self):
write_data(self, 'index.html', {
})
def get_bs2_combined_features(df_policy, df_claim):
'''
In:
DataFrame(df_policy),
DataFrame(df_claim),
Out:
DataFrame(X_fs),
DataFrame(y_fs),
Description:
create train dataset with additional columns
'''
print('Getting labels')
y_train_all = read_data('training-set.csv', path='raw')
y_test = read_data('testing-set.csv', path='raw')
print('Getting neural network processed premiums')
X_fs = read_data('premium_60_1.csv')
# insured
print('Getting column cat_ins')
X_fs = X_fs.assign(
cat_ins=get_bs2_cat(df_policy, X_fs.index, "Insured's_ID"))
print('Getting column cat_assured')
X_fs = X_fs.assign(
cat_assured=get_bs2_cat(df_policy, X_fs.index, 'fassured'))
print('Getting column real_age')
X_fs = X_fs.assign(real_age=get_bs2_real_age(df_policy, X_fs.index))
print('Getting column cat_sex')
X_fs = X_fs.assign(cat_sex=get_bs2_cat(df_policy, X_fs.index, 'fsex'))
print('Getting column cat_marriage')
X_fs = X_fs.assign(
cat_marriage=get_bs2_cat(df_policy, X_fs.index, 'fmarriage'))
# policy
print('Getting column real_cancel')
X_fs = X_fs.assign(real_cancel=get_bs2_real_cancel(df_policy, X_fs.index))
print('Getting column cat_area')
X_fs = X_fs.assign(
cat_area=get_bs2_cat(df_policy, X_fs.index, 'iply_area'))
print('Getting column cat_ic_combo')
X_fs = X_fs.assign(
cat_ic_combo=get_bs2_cat_ic_combo(df_policy, X_fs.index))
print('Getting column cat_ic_grp_combo')
X_fs = X_fs.assign(
cat_ic_grp_combo=get_bs2_cat_ic_grp_combo(df_policy, X_fs.index))
print('Getting column cat_distr')
X_fs = X_fs.assign(
cat_distr=get_bs2_cat(df_policy, X_fs.index, 'Distribution_Channel'))
print('Getting column real_acc_dmg')
X_fs = X_fs.assign(
real_acc_dmg=get_bs2_cat(df_policy, X_fs.index, 'pdmg_acc'))
print('Getting column real_acc_lia')
X_fs = X_fs.assign(
real_acc_lia=get_bs2_cat(df_policy, X_fs.index, 'plia_acc'))
print('Getting column real_dage')
X_fs = X_fs.assign(real_dage=get_bs2_real_dage(df_policy, X_fs.index))
print('Getting column real_prem_terminate')
X_fs = X_fs.assign(
real_prem_terminate=get_bs2_real_prem_terminate(df_policy, X_fs.index))
# vehicle
print('Getting column cat_vmm1')
X_fs = X_fs.assign(
cat_vmm1=get_bs2_cat(df_policy, X_fs.index, 'Vehicle_Make_and_Model1'))
print('Getting column cat_vmm2')
X_fs = X_fs.assign(
cat_vmm2=get_bs2_cat(df_policy, X_fs.index, 'Vehicle_Make_and_Model2'))
print('Getting column real_vmy')
X_fs = X_fs.assign(real_vmy=get_bs2_real_vmy(df_policy, X_fs.index))
print('Getting column real_vengine')
X_fs = X_fs.assign(real_vengine=get_bs2_cat(
df_policy, X_fs.index, 'Engine_Displacement_(Cubic_Centimeter)'))
print('Getting column cat_vregion')
X_fs = X_fs.assign(cat_vregion=get_bs2_cat(df_policy, X_fs.index,
'Imported_or_Domestic_Car'))
print('Getting column cat_vc')
X_fs = X_fs.assign(cat_vc=get_bs2_cat(df_policy, X_fs.index,
'Coding_of_Vehicle_Branding_&_Type'))
print('Getting column real_vqpt')
X_fs = X_fs.assign(real_vqpt=get_bs2_cat(df_policy, X_fs.index, 'qpt'))
print('Getting column real_vcost')
X_fs = X_fs.assign(real_vcost=get_bs2_cat(
df_policy, X_fs.index, 'Replacement_cost_of_insured_vehicle'))
# claim
print('Getting column real_num_claim')
X_fs = X_fs.assign(
real_num_claim=get_bs2_real_num_claim(df_claim, X_fs.index))
print('Getting column real_nearest_claim')
X_fs = X_fs.assign(
real_nearest_claim=get_bs2_real_nearest_claim(df_claim, X_fs.index))
print('Getting column cat_claim_cause')
X_fs = X_fs.assign(
cat_claim_cause=get_bs2_cat_claim_cause(df_claim, X_fs.index))
print('Getting column real_loss')
X_fs = X_fs.assign(
real_loss=get_bs2_real_claim(df_claim, X_fs.index, 'Paid_Loss_Amount'))
print('Getting column real_loss_ins')
X_fs = X_fs.assign(
real_loss_ins=get_bs2_real_loss_ins(df_policy, df_claim, X_fs.index))
print('Getting column real_salvage')
X_fs = X_fs.assign(real_salvage=get_bs2_real_claim(
df_claim, X_fs.index, 'Salvage_or_Subrogation?'))
print('Getting column real_claim_fault')
X_fs = X_fs.assign(
real_claim_fault=get_bs2_real_claim_fault(df_claim, X_fs.index))
print('Getting column cat_claim_area')
X_fs = X_fs.assign(
cat_claim_area=get_bs2_cat_claim_area(df_claim, X_fs.index))
print('Getting column real_claimants')
X_fs = X_fs.assign(
real_claimants=get_bs2_real_claimants(df_claim, X_fs.index))
# helper columns
print('Getting column real_prem_plc, for mean encoding use')
X_fs = X_fs.assign(
real_prem_plc=get_bs2_real_prem_plc(df_policy, X_fs.index))
print('\nSplitting train valid test features\n')
X_train_all = X_fs.loc[y_train_all.index]
X_test = X_fs.loc[y_test.index]
# add mean encoding on mean of diff btw next_premium and premium
cols_cat = ['cat_vmm1', 'cat_vmm2', 'cat_vc']
for col_cat in cols_cat:
col_mean = col_cat.replace('cat_', 'real_mc_mean_diff_')
print('Getting column ' + col_mean)
X_test[col_mean] = get_bs2_real_mc_mean_diff(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_mean] = get_bs2_real_mc_mean_diff(
col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
# add mean encoding on mean of dividend btw next_premium and premium
cols_cat = ['cat_claim_cause']
for col_cat in cols_cat:
col_mean = col_cat.replace('cat_', 'real_mc_mean_div_')
print('Getting column ' + col_mean)
X_test[col_mean] = get_bs2_real_mc_mean_div(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_mean] = get_bs2_real_mc_mean_div(
col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
# add median encoding on median of next_premium
cols_cat = [
'cat_ins', 'cat_assured', 'cat_sex', 'cat_distr', 'cat_ic_combo'
]
for col_cat in cols_cat:
col_median = col_cat.replace('cat_', 'real_mc_median_')
print('Getting column ' + col_median)
X_test[col_median] = get_bs2_real_mc_median(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_median] = get_bs2_real_mc_median(
col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
# add median encoding on median of diff btw next_premium and premium
cols_cat = ['cat_assured', 'cat_sex']
for col_cat in cols_cat:
col_median = col_cat.replace('cat_', 'real_mc_median_diff_')
print('Getting column ' + col_median)
X_test[col_median] = get_bs2_real_mc_median_diff(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_median] = get_bs2_real_mc_median_diff(
col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
# add median encoding on median of div btw next_premium and premium
cols_cat = [
'cat_ins', 'cat_assured', 'cat_sex', 'cat_distr', 'cat_ic_combo',
'cat_ic_grp_combo', 'cat_area', 'cat_vregion'
]
for col_cat in cols_cat:
col_median = col_cat.replace('cat_', 'real_mc_median_div_')
print('Getting column ' + col_median)
X_test[col_median] = get_bs2_real_mc_median_div(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_median] = get_bs2_real_mc_median_div(
col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
# add mean encoding on probability of next_premium being 0
cols_cat = ['cat_ins', 'cat_marriage', 'cat_claim_cause', 'cat_claim_area']
for col_cat in cols_cat:
col_prob = col_cat.replace('cat_', 'real_mc_prob_')
print('Getting column ' + col_prob)
X_test[col_prob] = get_bs2_real_mc_prob(col_cat,
X_train_all,
y_train_all,
X_valid=X_test,
train_only=False,
fold=5,
prior=1000)
X_train_all[col_prob] = get_bs2_real_mc_prob(col_cat,
X_train_all,
y_train_all,
X_valid=pd.DataFrame(),
train_only=True,
fold=5,
prior=1000)
print('Writing results to file')
write_data(X_train_all, "X_train_all_bs2.csv")
write_data(y_train_all, "y_train_all_bs2.csv")
write_data(X_test, "X_test_bs2.csv")
write_data(y_test, "y_test_bs2.csv")
return (None)
def get(self):
write_data(self, 'main.html', {})
def main(args):
# Parameters
initial_seed = 123456 # Used to generate the set of seeds for repetitions
n_repetitions = 30
n_iterations = args.iterations
initial_pheromone = 0.5
t_min = 0.001 # Min pheromone level
t_max = 0.999 # Max pheromone level
rho = args.rho # Pheromone decay rate
alpha = args.alpha
beta = args.beta
# Initializations
random_seeds = utils.generate_seeds(initial_seed, n_repetitions)
n, p, nodes = utils.read_data(args.dataset)
world = World(n, p, nodes)
n_ants = (n - p) if args.ants is None else args.ants
colony = Colony(n_ants)
ni = aco.information_heuristic(world) # Information Heuristic
dataset_name = args.dataset.split('/')[-1].split('.')[0]
output = np.zeros((n_repetitions, n_iterations, 3))
output_dir = "../results/{}it{}rho{}alpha{}beta{}ants{}/".format(
dataset_name, n_iterations, rho, alpha, beta, n_ants)
# Main loop
for repetition in range(n_repetitions):
np.random.seed(random_seeds[repetition])
# Reset things for new repetition
g_best = Solution(distance=np.inf)
world.reset_pheromones(initial_pheromone)
print("Repetition {}\n".format(repetition))
for iteration in tqdm(range(n_iterations)):
for ant in colony.ants:
ant.build_solution(world, ni, alpha, beta)
l_best, l_worst = aco.evaluate_solutions(world, colony)
world.update_pheromones(rho, g_best, l_best, l_worst)
# Check algorithm stagnation
if aco.is_stagnated(world, t_min, t_max):
world.reset_pheromones(initial_pheromone)
# Update global solution
if l_best.distance < g_best.distance:
g_best = l_best
# Reset for next iteration
colony.reset_solutions()
# Store output data
output[repetition][iteration][0] = g_best.distance
output[repetition][iteration][1] = l_best.distance
output[repetition][iteration][2] = l_worst.distance
print("\nBest solution\n"
"-------------\n"
"Distance: {}\n"
"Medians: {}\n".format(g_best.distance, g_best.medians))
utils.write_data(output_dir, output)
#!/usr/bin/env python3
import sys
sys.path.append("../")
from param_config import config
from imblearn.over_sampling import RandomOverSampler, SMOTE
from utils import read_data, write_data
import numpy as np
from scipy import stats
np.random.seed(config.set_seed)
def imbalance_split(X, y):
ros = SMOTE(random_state=config.set_seed)
X_res, y_res = ros.fit_sample(X, y)
return X_res, y_res
# Try imbalance learn
if __name__ == '__main__':
train_x = read_data(config.d_xtrain)
train_y = read_data(config.d_ytrain)
X, y = imbalance_split(train_x, train_y)
write_data(config.e_xtrain, X)
write_data(config.e_ytrain, y)
def total_missing(train, test):
header_str = 'total_missing'
train[header_str] = train.isnull().sum(axis=1)
test[header_str] = test.isnull().sum(axis=1)
return train, test
def missing(x_train, x_test):
train_missing = get_missing(x_train)
test_missing = get_missing(x_test)
assert len(train_missing) == len(test_missing)
train = create_missing(train_missing.values, x_train)
test = create_missing(train_missing.values, x_test)
train, test = total_missing(train, test)
return train, test
if __name__ == '__main__':
train_x = pd.read_csv(config.train_file)
test_x = pd.read_csv(config.test_file)
# Shuffle training data
train_x = shuffle(train_x, random_state=config.set_seed)
train_x, test_x = missing(train_x, test_x)
print('#### Writing Pickle 01: Missing ####')
write_data(config.a_xtrain, train_x)
write_data(config.a_xtest, test_x)
def get(self):
write_data(self, 'server/server.html',{
})
def main(corpus_dir, labels_dir, output_dir, sample_rate=16000, use_reference=False):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
datadir = os.path.join(corpus_dir, 'core')
wav_base = 'FILEID sox WAVPATH -r {0} -t .wav - |'.format(sample_rate)
if use_reference:
ref_dir = os.path.join(labels_dir, 'reference_labels', 'speaker_labels', 'lab')
reference_list = [f.replace('.lab', '') for f in os.listdir(ref_dir)]
# utterances with issues, ignore these
reject_list = ['02F-Therapy_07-004A', '20M-BL2-009A']
speaker_utts = {}
text, wav = [], []
utt2spk, spk2utt = [], []
utt2dur = []
speakers = os.listdir(datadir)
for speaker in speakers:
sessions = os.listdir(os.path.join(datadir, speaker))
for session in sessions:
session_dir = os.path.join(datadir, speaker, session)
flist = [f for f in os.listdir(session_dir) if f.endswith('.wav')]
for f in flist:
f = f.replace('.wav', '')
fileid = '-'.join([speaker, session, f])
if fileid in reject_list:
continue
if use_reference:
if fileid not in reference_list:
continue
# use prompt for text, although it will be ignored for decoding
txt_f = os.path.join(session_dir, f+'.txt')
with open(txt_f, 'r') as fid:
txt = fid.readline().rstrip()
words = []
for w in txt.split():
w = w.upper()
words.append(w)
words = ' '.join([fileid] + words)
text.append(words)
# prepare wav.scp
wavpath = os.path.join(session_dir, f+'.wav')
file_wav = wav_base.replace('FILEID', fileid)
file_wav = file_wav.replace('WAVPATH', wavpath)
wav.append(file_wav)
# prepare utt2dur
dur = get_duration(wavpath)
utt2dur.append('{0} {1}'.format(fileid, dur))
# prepare utt2spk
utt2spk.append('{0} {1}'.format(fileid, speaker))
if speaker in speaker_utts:
speaker_utts[speaker].append(fileid)
else:
speaker_utts[speaker] = [fileid]
# prepare spk2utt
for speaker in speaker_utts:
spk_utts = '{0} {1}'.format(speaker, ' '.join(sorted(speaker_utts[speaker])))
spk2utt.append(spk_utts)
text_f = os.path.join(output_dir, 'text')
wav_f = os.path.join(output_dir, 'wav.scp')
utt2spk_f = os.path.join(output_dir, 'utt2spk')
spk2utt_f = os.path.join(output_dir, 'spk2utt')
utt2dur_f = os.path.join(output_dir, 'utt2dur')
write_data(text, text_f)
write_data(wav, wav_f)
write_data(utt2spk, utt2spk_f)
write_data(spk2utt, spk2utt_f)
write_data(utt2dur, utt2dur_f)
# validate data directory
validate_cmd = './utils/validate_data_dir.sh --no-feats {0}'.format(output_dir)
os.system(validate_cmd)
MOVEMENT_REGEXP = r'(\w+)_movement_\d+_\d+.txt'
mvtfiles = os.listdir(dirname)
mvtfiles = filter(lambda f: re.match(MOVEMENT_REGEXP, f), mvtfiles)
mvtfiles = sorted(mvtfiles)
all_accel = []
all_gyro = []
all_labels = []
for fname in mvtfiles:
with open(os.path.join(dirname, fname)) as f:
accel, gyro, labels = utils.load_data(f)
shimmerid = re.match(MOVEMENT_REGEXP, fname).group(1)
print shimmerid
acalib = utils.load_calibration_from_properties(os.path.join(calib_dirname,
'1_5_%s.accel.properties' % shimmerid))
gcalib = utils.load_calibration_from_properties(os.path.join(calib_dirname,
'1_5_%s.gyro.properties' % shimmerid))
accel = 9.81 * (accel - acalib['offset'][:,None]) / acalib['gain'][:,None]
gyro = (gyro - gcalib['offset'][:,None]) / gcalib['gain'][:,None]
all_accel.append(accel)
all_gyro.append(gyro)
all_labels.append(labels)
##
all_accel = np.squeeze(np.array(all_accel))
all_gyro = np.squeeze(np.array(all_gyro))
all_labels = np.squeeze(np.array(all_labels))
##
outfname = os.path.join(dirname, 'out_calib.txt')
with open(outfname, 'w') as f:
utils.write_data(f, all_accel, all_gyro, all_labels)
##
os.system('touch %s' % data_file)
it = 0
## Coarse calibration
original_use_auto_judge = use_auto_judge
use_auto_judge = True
if not use_mp:
for i in range(len(path)):
target = path[i]
im_name, index, p_robot = move_shoot(target, mode)
p_camera = circle_fitting(im_name,
("flag" + str(i) + "_" + index),
need_judge=False)
write_data(data_file, [p_robot, p_camera],
index,
type="point_pair")
if use_mp:
for i in range(len(path)):
print("Working Process")
print(working_worker_pipe)
target = path[i]
im_name, index, p_robot = move_shoot(target, mode)
worker[i % worker_num].inQ.put(
[im_name, ("flag" + str(i) + "_" + index), p_robot])
working_worker_pipe.append(i % worker_num)
if len(working_worker_pipe) == worker_num:
last_idx = working_worker_pipe[0]
result = worker[last_idx].outQ.get()
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1)
yp = model.predict(X_dev)
yp = np.argmax(yp, axis=1)
y_dev = np.argmax(y_dev, axis=1)
#performance on dev set
print_classfication_report('Keras', y_dev, yp, stem='keras_dev')
pr_curve(y_dev, yp, num_classes, 'keras_dev')
# prediction is done for every sample and
# prediction for whole block is done through consensus voting
# blocks_consensus tell me about block prediction distrubution
# making sure there is no bimodal distribution
blocks_pred, blocks_consensus = score_model(model, X_test)
#writing csv file
write_data(blocks_pred, 'khan_speaker_labels_MLP.csv')
def __init__(self, input_path, output_dir='out'):
'''
Loads and preprocesses the data, generates the mask.
Args:
input_path (str): path to the input file, overrides the config file
output_dir (str): path to the output directory
'''
# Input (path to the input image)
self.input_path = input_path
input_fname_parts = os.path.split(self.input_path)[-1].split('.')
if len(input_fname_parts) == 1:
print(
'Error: the input file has no extension, please use .npy or .fits'
)
sys.exit()
input_fname = ('.').join(
input_fname_parts[:-1]) # file name w/o extension
# output folder
if not os.path.exists(output_dir): os.mkdir(output_dir)
# output file names
self.output_paths = {}
out_files = ['deproj', 'smooth', 'mask', 'smooth3d']
for f in out_files:
self.output_paths[f] = os.path.join(output_dir,
f + '_' + input_fname + '.npy')
self.output_paths['pds'] = os.path.join(output_dir,
'pds_' + input_fname + '.txt')
#input
# mask_path = config.get("input", "mask_path")
#
# #output
# rm_out_path = config.get("output", "rm_out_path")
#
# if regime=="2d":
# #gen2d_params
# lx = config.getint("gen2d_params", "Lx")
# ly = config.getint("gen2d_params", "Ly")
# p1 = config.getfloat("gen2d_params", "p1")
# p2 = config.getfloat("gen2d_params", "p2")
# kb = config.getfloat("gen2d_params", "kb")
# C = config.getfloat("gen2d_params","C")
# apply_mask = config.getboolean("gen2d_params", "apply_mask")
# bw = config.getfloat("gen2d_params", "beam_width")
#
# elif regime=="3d":
# #gen3d_params
# lx = config.getint("gen3d_params", "Lx")
# ly = config.getint("gen3d_params", "Ly")
# lz = config.getint("gen3d_params", "Lz")
# p1 = config.getfloat("gen3d_params", "p1")
# p2 = config.getfloat("gen3d_params", "p2")
# kb = config.getfloat("gen3d_params", "kb")
# C = config.getfloat("gen3d_params", "C")
# apply_mask = config.getboolean("gen3d_params", "apply_mask")
# inclin = config.getfloat("gen3d_params", "inclin")
# alpha = config.getfloat("gen3d_params", "alpha")
ftype = self.input_path.split('.')[-1].lower()
# load data
d = load_data(self.input_path, ftype=ftype, print_info=True)
# crop it
ir, jr = np.nonzero(np.invert(np.isnan(d)))
imin, jmin, imax, jmax = ir.min(), jr.min(), ir.max(), jr.max()
self.data = d[imin:imax + 1, jmin:jmax + 1]
self.data_dim = self.data.shape
# adjust the cluster center location after the cropping
self.cluster_params = {}
self.cluster_params['center'] = [-imin, -jmin]
# make the mask given the cropped input data
self.mask = np.array(np.invert(np.isnan(self.data)), dtype=float)
# save the mask
write_data(self.output_paths['mask'], self.mask, ftype='npy')
# This code generates fake weather data with Location, Latitude, Longitude, Local Time,
# Weather condition, Temperature, Pressure and Humidity
##########################
import utils
# Returns the full weather data for all cities
def generate_weather_data(cities):
full_weather_data = []
for city in cities:
latitude, longitude = utils.get_lat_and_lon()
altitude = utils.get_altitude()
local_time = utils.get_local_time()
temperature = utils.get_temperature()
pressure = utils.get_pressure()
weather_condition = utils.get_condition(temperature)
humidity = utils.get_humidity()
entry = utils.create_weather_entry(city, latitude, longitude, altitude,
local_time, weather_condition,
temperature, pressure, humidity)
full_weather_data.append(entry)
return full_weather_data
if __name__ == '__main__':
city_names = utils.read_cities()
final_weather_data = generate_weather_data(city_names)
utils.write_data(final_weather_data)
def set_data(self):
settings_default = return_data("./data/settings_default.json")
if data_exists("./data/settings.json"):
settings = return_data("./data/settings.json")
else:
logger.alt(
"Set-Settings-Data",
"No existing settings found to be parsed, creating new from default."
)
write_data("./data/settings.json", settings_default)
settings = return_data("./data/settings.json")
if not validate_data(settings, settings_default):
logger.error(
"Set-Settings-Data", "Parsed settings data is malformed! "
"This will most likely cause a fatal exception. "
"Try removing existing settings.json")
self.webhook_edit.setText(settings["webhook"])
if settings["webhookonbrowser"]:
self.browser_checkbox.setChecked(True)
if settings["webhookonorder"]:
self.order_checkbox.setChecked(True)
if settings["webhookonfailed"]:
self.paymentfailed_checkbox.setChecked(True)
if settings["browseronfailed"]:
self.onfailed_checkbox.setChecked(True)
if settings["runheadless"]:
self.headless_checkbox.setChecked(True)
if settings["bb_ac_beta"]:
self.bb_ac_beta_checkbox.setChecked(True)
if settings['onlybuyone']:
self.buy_one_checkbox.setChecked(True)
if settings['dont_buy']:
self.dont_buy_checkbox.setChecked(True)
if settings['random_delay_start']:
self.random_delay_start.setText(settings["random_delay_start"])
if settings['random_delay_stop']:
self.random_delay_stop.setText(settings["random_delay_stop"])
self.geckodriver_path = settings["geckodriver"]
# try:
# self.geckodriver.setText(settings["geckodriver"])
# except:
# self.geckodriver.setText("")
try:
self.bestbuy_user_edit.setText(settings["bestbuy_user"])
except:
self.bestbuy_user_edit.setText("")
try:
self.bestbuy_pass_edit.setText((Encryption().decrypt(
settings["bestbuy_pass"].encode("utf-8"))).decode("utf-8"))
except:
self.bestbuy_pass_edit.setText("")
try:
self.target_user_edit.setText(settings["target_user"])
except:
self.target_user_edit.setText("")
try:
self.target_pass_edit.setText((Encryption().decrypt(
settings["target_pass"].encode("utf-8"))).decode("utf-8"))
except:
self.target_pass_edit.setText("")
try:
self.gamestop_user_edit.setText(settings["gamestop_user"])
except:
self.gamestop_user_edit.setText("")
try:
self.gamestop_pass_edit.setText((Encryption().decrypt(
settings["gamestop_pass"].encode("utf-8"))).decode("utf-8"))
except:
self.gamestop_pass_edit.setText("")
self.update_settings(settings)
