def copy_logistic_model(model: LogisticRegression,
max_iter=10e5,
penalty='none') -> LogisticRegression:
copied_model = LogisticRegression(max_iter=max_iter, penalty=penalty)
copied_model.coef_ = model.coef_.copy()
copied_model.classes_ = model.classes_.copy()
copied_model.intercept_ = model.intercept_
return copied_model
def deserialize_logistic_regression(model_dict):
model = LogisticRegression(model_dict['params'])
model.classes_ = np.array(model_dict['classes_'])
model.coef_ = np.array(model_dict['coef_'])
model.intercept_ = np.array(model_dict['intercept_'])
model.n_iter_ = np.array(model_dict['intercept_'])
return model
def deserialize_logistic_regression(model_dict):
model = LogisticRegression(model_dict["params"])
model.classes_ = np.array(model_dict["classes_"])
model.coef_ = np.array(model_dict["coef_"])
model.intercept_ = np.array(model_dict["intercept_"])
model.n_iter_ = np.array(model_dict["intercept_"])
return model
def choose(self, pvalues, method, outcome):
'''
Randomly choose state of node from probability distribution conditioned on *pvalues*.
This method has two parts: (1) determining the proper probability
distribution, and (2) using that probability distribution to determine
an outcome.
Arguments:
1. *pvalues* -- An array containing the assigned states of the node's parents. This must be in the same order as the parents appear in ``self.Vdataentry['parents']``.
The function creates a Gaussian distribution in the manner described in :doc:`lgbayesiannetwork`, and samples from that distribution, returning its outcome.
'''
warnings.filterwarnings("ignore", category=FutureWarning)
rand = random.random()
dispvals = []
lgpvals = []
for pval in pvalues:
if (isinstance(pval, str)):
dispvals.append(pval)
else:
lgpvals.append(pval)
# find correct Gaussian
lgdistribution = self.Vdataentry["hybcprob"][str(dispvals)]
for pvalue in lgpvals:
assert pvalue != 'default', "Graph skeleton was not topologically ordered."
model = LogisticRegression(multi_class='multinomial',
solver='newton-cg',
max_iter=100)
model.classes_ = np.array(lgdistribution["classes"], dtype=object)
if len(lgdistribution["classes"]) > 1:
model.coef_ = np.array(lgdistribution["mean_scal"],
dtype=float).reshape(-1, len(lgpvals))
model.intercept_ = np.array(lgdistribution["mean_base"],
dtype=float)
distribution = model.predict_proba(
np.array(lgpvals).reshape(1, -1))[0]
# choose
rand = random.random()
lbound = 0
ubound = 0
for interval in range(len(lgdistribution["classes"])):
ubound += distribution[interval]
if (lbound <= rand and rand < ubound):
rindex = interval
break
else:
lbound = ubound
return str(lgdistribution["classes"][rindex])
else:
return str(lgdistribution["classes"][0])
def LogR_predict():
X = json.loads(request.form['X'])
params = json.loads(request.form['params'])
reg = LogisticRegression()
reg.coef_ = np.array(params['coef'])
reg.intercept_ = np.array(params['inter'])
reg.n_iter_ = np.array(params['niter'])
reg.classes_ = np.array(params['classes'])
y = reg.predict(X)
return jsonify(pred=y.tolist())
def examineModel(X_train, y_train):
model = util.openPkl("../models/avg_logistic_model")
avg_coefficients = model['coeff_']
avg_intercepts = model['intercept_']
clf = LogisticRegression()
clf.coef_ = avg_coefficients
clf.intercept_ = avg_intercepts
clf.classes_ = util.classes
print("Averaged model train accuracy:", clf.score(X_train, y_train))
avg_preds = clf.predict(X_train)
def set_initial_params(model: LogisticRegression):
"""Sets initial parameters as zeros Required since model params are
uninitialized until model.fit is called.
But server asks for initial parameters from clients at launch. Refer
to sklearn.linear_model.LogisticRegression documentation for more
information.
"""
n_classes = 10 # MNIST has 10 classes
n_features = 784 # Number of features in dataset
model.classes_ = np.array([i for i in range(10)])
model.coef_ = np.zeros((n_classes, n_features))
if model.fit_intercept:
model.intercept_ = np.zeros((n_classes,))
def load_model_info(model_info):
"""Return a longform model from a model info JSON object.
Parameters
----------
model_info : dict
The JSON object containing the attributes of a model.
Returns
-------
longform_model : py:class:`adeft.classify.AdeftClassifier`
The classifier that was loaded from the given JSON object.
"""
shortforms = model_info['shortforms']
pos_labels = model_info['pos_labels']
longform_model = AdeftClassifier(shortforms=shortforms,
pos_labels=pos_labels)
ngram_range = model_info['tfidf']['ngram_range']
tfidf = TfidfVectorizer(ngram_range=ngram_range, stop_words='english')
logit = LogisticRegression(multi_class='auto')
tfidf.vocabulary_ = model_info['tfidf']['vocabulary_']
tfidf.idf_ = model_info['tfidf']['idf_']
logit.classes_ = np.array(model_info['logit']['classes_'], dtype='<U64')
logit.intercept_ = np.array(model_info['logit']['intercept_'])
logit.coef_ = np.array(model_info['logit']['coef_'])
estimator = Pipeline([('tfidf', tfidf), ('logit', logit)])
longform_model.estimator = estimator
# These attributes do not exist in older adeft models.
# For backwards compatibility we check if they are present
if 'stats' in model_info:
longform_model.stats = model_info['stats']
if 'std' in model_info:
longform_model._std = np.array(model_info['std'])
if 'timestamp' in model_info:
longform_model.timestamp = model_info['timestamp']
if 'training_set_digest' in model_info:
longform_model.training_set_digest = model_info['training_set_digest']
if 'params' in model_info:
longform_model.params = model_info['params']
if 'version' in model_info:
longform_model.version == model_info['version']
if 'confusion_info' in model_info:
longform_model.confusion_info = model_info['confusion_info']
if 'other_metadata' in model_info:
longform_model.other_metadata = model_info['other_metadata']
return longform_model
def fed_integrate_model_lr(model1, model2):
coef_1 = model1.coef_
coef_2 = model2.coef_
intercept_1 = model1.intercept_
intercept_2 = model2.intercept_
classes = model1.classes_
model = LogisticRegression(solver='sag')
model.coef_ = mulkeys_add_2Darray(model1.coef_, model2.coef_)/2
model.intercept_ = mulkeys_add_array(
model1.intercept_, model1.intercept_)/2
model.classes_ = classes
return model
def convert(self, model_dict):
param_obj = model_dict["HomoLogisticRegressionParam"]
meta_obj = model_dict["HomoLogisticRegressionMeta"]
sk_lr_model = LogisticRegression(penalty=meta_obj.penalty.lower(),
tol=meta_obj.tol,
fit_intercept=meta_obj.fit_intercept,
max_iter=meta_obj.max_iter)
coefficient = np.empty((1, len(param_obj.header)))
for index in range(len(param_obj.header)):
coefficient[0][index] = param_obj.weight[param_obj.header[index]]
sk_lr_model.coef_ = coefficient
sk_lr_model.intercept_ = np.array([param_obj.intercept])
# hard-coded 0-1 classification as HomoLR only supports this for now
sk_lr_model.classes_ = np.array([0., 1.])
sk_lr_model.n_iter_ = [param_obj.iters]
return sk_lr_model
def object_hook(self, json_msg):
if '__type__' in json_msg:
if json_msg['__type__'] == 'np.ndarray':
return np.array(json_msg['data'])
elif json_msg['__type__'] == 'LogisticRegression' or json_msg[
'__type__'] == 'SGDClassifier':
if json_msg['__type__'] == 'LogisticRegression':
model = LogisticRegression()
elif json_msg['__type__'] == 'SGDClassifier':
model = SGDClassifier()
model.set_params(**json_msg['params'])
if 'intercept_' in json_msg:
model.intercept_ = json_msg['intercept_']
if 'classes_' in json_msg:
model.classes_ = json_msg['classes_']
return model
elif json_msg['__type__'] == 'pd.Series':
return pd.Series(json_msg['data'])
else:
raise TypeError()
return json_msg
def load_model(serialization_dir):
with open(os.path.join(args.model, "best_hyperparameters.json"), 'r') as f:
hyperparameters = json.load(f)
if hyperparameters.pop('stopwords') == 1:
stop_words = 'english'
else:
stop_words = None
weight = hyperparameters.pop('weight')
if weight == 'binary':
binary = True
else:
binary = False
ngram_range = hyperparameters.pop('ngram_range')
ngram_range = sorted([int(x) for x in ngram_range.split()])
if weight == 'tf-idf':
vect = TfidfVectorizer(stop_words=stop_words,
lowercase=True,
ngram_range=ngram_range)
else:
vect = CountVectorizer(binary=binary,
stop_words=stop_words,
lowercase=True,
ngram_range=ngram_range)
with open(os.path.join(args.model, "vocab.json"), 'r') as f:
vocab = json.load(f)
vect.vocabulary_ = vocab
hyperparameters['C'] = float(hyperparameters['C'])
hyperparameters['tol'] = float(hyperparameters['tol'])
classifier = LogisticRegression(**hyperparameters)
if os.path.exists(os.path.join(serialization_dir, "archive", "idf.npy")):
vect.idf_ = np.load(
os.path.join(serialization_dir, "archive", "idf.npy"))
classifier.coef_ = np.load(
os.path.join(serialization_dir, "archive", "coef.npy"))
classifier.intercept_ = np.load(
os.path.join(serialization_dir, "archive", "intercept.npy"))
classifier.classes_ = np.load(
os.path.join(serialization_dir, "archive", "classes.npy"))
return classifier, vect
def load_model_info(model_info):
"""Return a longform model from a model info JSON object.
Parameters
----------
model_info : dict
The JSON object containing the attributes of a model.
Returns
-------
longform_model : py:class:`adeft.classify.AdeftClassifier`
The classifier that was loaded from the given JSON object.
"""
shortforms = model_info['shortforms']
pos_labels = model_info['pos_labels']
longform_model = AdeftClassifier(shortforms=shortforms,
pos_labels=pos_labels)
ngram_range = model_info['tfidf']['ngram_range']
tfidf = TfidfVectorizer(ngram_range=ngram_range, stop_words='english')
logit = LogisticRegression(multi_class='auto')
tfidf.vocabulary_ = model_info['tfidf']['vocabulary_']
tfidf.idf_ = model_info['tfidf']['idf_']
logit.classes_ = np.array(model_info['logit']['classes_'], dtype='<U64')
logit.intercept_ = np.array(model_info['logit']['intercept_'])
logit.coef_ = np.array(model_info['logit']['coef_'])
estimator = Pipeline([('tfidf', tfidf), ('logit', logit)])
longform_model.estimator = estimator
# Load model statistics if they are available
if 'stats' in model_info:
longform_model.stats = model_info['stats']
# Load standard deviations for calculating feature importances
# if they are available
if 'std' in model_info:
longform_model._std = np.array(model_info['std'])
return longform_model
classifiers = [
LogisticRegression(multi_class='multinomial',
solver='sag',
max_iter=10000,
n_jobs=-1).fit(
train_images[i * n_samples:(i + 1) * n_samples],
train_labels[i * n_samples:(i + 1) * n_samples])
for i in range(n_classifiers)
]
combined = LogisticRegression(multi_class='multinomial',
solver='sag',
max_iter=10000,
n_jobs=-1)
combined.coef_ = sum([c.coef_ for c in classifiers])
combined.intercept_ = sum([c.intercept_ for c in classifiers])
combined.classes_ = classifiers[
0].classes_ # Assumes first has. Easiest for now
end = time()
print("train completed in {} seconds".format(int(end - start)))
# Check accuracy
accuracy = combined.score(test_images, list(test_labels))
print("accuracy on test set is {} percent".format(accuracy * 100))
# save coefficients
np.savetxt(result_file_prefix + "coefficients.csv",
combined.coef_,
delimiter=",")
np.savetxt(result_file_prefix + "intercepts.csv",
combined.intercept_,
delimiter=",")
np.savetxt(result_file_prefix + "classes.csv",
from numpy import array
_clf = LogisticRegression()
_clf.coef_ = array([[
-3.09925498, -1.2233267, 0.11772731, 0.11320903, -0.61476615, 0.16965524,
0.74448574, -0.04194057, -1.35630572, -0.24676336, 0.41220109, -0.35504167,
0.92710423, -2.1255104, 0.08247018, 0.73824089, 0.31262326, 0.41799742,
-0.60178821, -0.33976708, -0.03794846, 0., -0.91812391, -0.2121092,
0.19453728, -0.60672439, -0.92668671, 0.12330509, 0.04666124, 0.,
1.68901669, -0.67069155, -0.45640558, 1.05191066, 1.03483933, 0.73240274,
0.17867271, -0.07407496, 2.06098403, 0.42573062, -0.38634422, 0.7109839,
-0.02934711, 0.05490663, 0.02008552, 0.05069223, 0., 0.2016651,
-0.28770706, -0.88722735, -0.26507582, 0.52628048, -1.28404466,
-1.96447254, 0.07607324, 0.70359565, 0.35094977, 0.01376572
]])
_clf.classes_ = array([False, True])
_clf.intercept_ = [-2.75918545]
_v = DictVectorizer()
_v.feature_names_ = [
'first_chars= ', 'first_chars="a', "first_chars=' ", "first_chars='A",
'first_chars=(0', 'first_chars=(A', 'first_chars=(a', 'first_chars=)]',
'first_chars=, ', 'first_chars=. ', 'first_chars=0', 'first_chars=0 ',
'first_chars=0,', 'first_chars=0.', 'first_chars=00', 'first_chars=0:',
'first_chars=0\\', 'first_chars=@', 'first_chars=A', 'first_chars=A ',
'first_chars=A,', 'first_chars=A-', 'first_chars=A.', 'first_chars=A0',
'first_chars=A=', 'first_chars=AA', 'first_chars=Aa', 'first_chars=[0',
'first_chars=[A', 'first_chars=[a', 'first_chars=\\A', 'first_chars=a ',
'first_chars=a(', 'first_chars=a-', 'first_chars=a.', 'first_chars=a0',
'first_chars=aA', 'first_chars=a[', 'first_chars=aa', 'isalpha', 'isdigit',
'islower', 'mean_len', 'prev_len', 'punct= ', 'punct="', 'punct=%',
n_jobs=1)
total = LogisticRegression(multi_class='multinomial',
solver='newton-cg',
max_iter=1000,
n_jobs=1)
start = time()
part_A = part_A.fit(train_images[:100], train_labels[:100])
part_B = part_B.fit(train_images[100:], train_labels[100:])
total = total.fit(train_images[:], train_labels[:])
end = time()
print("train completed in {} seconds".format(int(end - start)))
# Check accuracy
accuracy_a = part_A.score(test_images, list(test_labels))
print("part A accuracy on test set is {} percent".format(accuracy_a * 100))
accuracy_b = part_B.score(test_images, list(test_labels))
print("part B accuracy on test set is {} percent".format(accuracy_b * 100))
accuracy_t = total.score(test_images, list(test_labels))
print("total accuracy on test set is {} percent".format(accuracy_t * 100))
# sum matrixes for sets
combined = LogisticRegression(multi_class='multinomial',
solver='newton-cg',
max_iter=1000,
n_jobs=1)
combined.coef_ = (part_A.coef_ + part_B.coef_) / 2
combined.intercept_ = (part_A.intercept_ + part_B.intercept_) / 2
combined.classes_ = part_A.classes_
accuracy_c = combined.score(test_images, list(test_labels))
print("summed coefficient accuracy on test set is {} percent".format(
accuracy_c * 100))
def main(seed=0,
n_train=60000,
n_test=10000,
inhib=250,
kernel_size=(16, ),
stride=(2, ),
time=100,
n_filters=25,
crop=0,
lr=1e-2,
lr_decay=0.99,
dt=1,
theta_plus=0.05,
theta_decay=1e-7,
intensity=5,
norm=0.2,
progress_interval=10,
update_interval=250,
train=True,
plot=False,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
inhib, time, dt, theta_plus, theta_decay, intensity, norm,
progress_interval, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, kernel_size, stride, n_filters, crop, lr, lr_decay, n_train,
n_test, inhib, time, dt, theta_plus, theta_decay, intensity, norm,
progress_interval, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
side_length = 28 - crop * 2
n_inpt = side_length**2
n_examples = n_train if train else n_test
n_classes = 10
# Build network.
if train:
network = LocallyConnectedNetwork(
n_inpt=n_inpt,
input_shape=[side_length, side_length],
kernel_size=kernel_size,
stride=stride,
n_filters=n_filters,
inh=inhib,
dt=dt,
nu=[0, lr],
theta_plus=theta_plus,
theta_decay=theta_decay,
wmin=0.0,
wmax=1.0,
norm=norm)
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
conv_size = network.connections['X', 'Y'].conv_size
locations = network.connections['X', 'Y'].locations
conv_prod = int(np.prod(conv_size))
n_neurons = n_filters * conv_prod
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
# Load MNIST data.
dataset = MNIST(path=data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images *= intensity
images = images[:, crop:-crop, crop:-crop]
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
full_spike_record = torch.zeros(n_examples, n_neurons)
# Neuron assignments and spike proportions.
if train:
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = np.zeros([n_classes, n_neurons])
logreg_model.intercept_ = np.zeros(n_classes)
logreg_model.classes_ = np.arange(n_classes)
else:
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
logreg_coef, logreg_intercept = torch.load(open(path, 'rb'))
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = logreg_coef
logreg_model.intercept_ = logreg_intercept
logreg_model.classes_ = np.arange(n_classes)
if train:
best_accuracy = 0
# Sequence of accuracy estimates.
curves = {'logreg': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name=f'{layer}_spikes')
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
spike_ims = None
spike_axes = None
weights_im = None
weights2_im = None
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print(f'Progress: {i} / {n_examples} ({t() - start:.4f} seconds)')
start = t()
if i % update_interval == 0 and i > 0:
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i %
len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat(
[predictions[scheme], preds[scheme]], -1)
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print(
'New best accuracy! Saving network parameters to disk.'
)
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(
params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
best_accuracy = max([x[-1] for x in curves.values()])
# Refit logistic regression model.
logreg_model = logreg_fit(full_spike_record[:i], labels[:i],
logreg_model)
print()
# Get next input sample.
image = images[i % len(images)].contiguous().view(-1)
sample = bernoulli(datum=image, time=time, dt=dt)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
retries = 0
while spikes['Y'].get('s').sum() < 5 and retries < 3:
retries += 1
image *= 2
sample = bernoulli(datum=image, time=time, dt=dt)
inpts = {'X': sample}
network.run(inpts=inpts, time=time)
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y'].get('s').view(time, -1)
full_spike_record[i] = spikes['Y'].get('s').view(time, -1).sum(0)
if plot:
# Optionally plot various simulation information.
_spikes = {
'X': spikes['X'].get('s').view(side_length**2, time),
'Y': spikes['Y'].get('s').view(n_filters * conv_prod, time)
}
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_locally_connected_weights(
network.connections[('X', 'Y')].w,
n_filters,
kernel_size,
conv_size,
locations,
side_length,
im=weights_im)
weights2_im = plot_weights(logreg_model.coef_, im=weights2_im)
plt.pause(1e-8)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i % len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]],
-1)
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
print('Average accuracies:\n')
for scheme in curves.keys():
print('\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
if train:
to_write = ['train'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
# Save results to disk.
results = [np.mean(curves['logreg']), np.std(curves['logreg'])]
to_write = params + results if train else test_params + results
to_write = [str(x) for x in to_write]
if train:
name = 'train.csv'
else:
name = 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(results_path, name), 'w') as f:
if train:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,inhib,time,timestep,theta_plus,'
'theta_decay,intensity,norm,progress_interval,update_interval,mean_logreg,std_logreg\n'
)
else:
f.write(
'random_seed,kernel_size,stride,n_filters,crop,lr,lr_decay,n_train,n_test,inhib,time,timestep,'
'theta_plus,theta_decay,intensity,norm,progress_interval,update_interval,mean_logreg,std_logreg\n'
)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat(
[labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(labels, predictions[scheme])
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join(confusion_path, f))
n_neurons = int(np.prod(network.layers['Y'].shape))
update_interval = 100
progress_interval = 10
path = os.path.join(ROOT_DIR, 'data', 'MNIST')
dataset = MNIST(path=path, download=True, shuffle=True)
images, labels = dataset.get_train()
images = images[:, 4:-4, 4:-4].contiguous()
images = images.view(-1, n_input) * 0.5
labels = labels.long()
model = LogisticRegression(solver='lbfgs', multi_class='multinomial')
model.coef_ = np.zeros([n_classes, n_neurons])
model.intercept_ = np.zeros(n_classes)
model.classes_ = np.arange(n_classes)
full_spike_record = torch.zeros(len(images), n_neurons)
locations = network.connections['X', 'Y'].locations
spike_ims = None
spike_axes = None
weights_im = None
weights2_im = None
weights3_im = None
start = t()
for i in range(len(images)):
if i % progress_interval == 0:
print(f'Progress: {i} / {len(images)} ({t() - start:.4f} seconds)')
start = t()
def _fake_lr(self):
lr = LogisticRegression()
lr.coef_ = self.coef_
lr.intercept_ = self.intercept_
lr.classes_ = self.classes_
return lr
'fit_intercept': True,
'intercept_scaling': 1,
'max_iter': 100,
'multi_class': 'warn',
'n_jobs': None,
'penalty': 'l2',
'random_state': 0,
'solver': 'warn',
'tol': 0.0001,
'verbose': 0,
'warm_start': False
})
clf.coef_ = np.fromfile('coef.npy').reshape(-1, n_features)
clf.intercept_ = np.fromfile('intercept.npy')
clf.classes_ = np.loadtxt('classes.txt', delimiter=',', dtype=str)
estimator = make_pipeline(lemmatizer, vectorizer, clf)
def predict(query):
pred = estimator.predict_proba([[query]])
pred = {j: i for i, j in zip(pred[0], clf.classes_)}
return pred
@app.route('/get')
def get():
query = request.args.get('query', '')
pred = predict(query)
monitor_directory = os.environ['MONITOR_DIRECTORY'] if os.environ.has_key('MONITOR_DIRECTORY') else '/var/lib/motion'
label_directory = os.environ['LABEL_DIRECTORY'] if os.environ.has_key('LABEL_DIRECTORY') else monitor_directory+'/label'
poll = os.environ['POLL'] == 'true' if os.environ.has_key('POLL') else False
print 'Monitor directory:', monitor_directory
print 'Poll:', poll
with open('classifier.json') as data_file:
clf_dict = json.load(data_file)
clf = LogisticRegression()
clf.set_params(**clf_dict['params'])
clf.coef_ = np.asarray(clf_dict['coef_'])
clf.intercept_ = np.asarray(clf_dict['intercept_'])
clf.n_iter_ = np.asarray(clf_dict['n_iter_'])
clf.classes_ = np.asarray(clf_dict['classes_'])
labelled = set()
while True:
candidates = set(glob(monitor_directory+'/*.jpg'))
for candidate in candidates:
name = candidate.split('/')[-1]
print name
if name in labelled:
continue
if os.path.isfile(label_directory+'/error/'+name) or os.path.isfile(label_directory+'/guess/open/'+name) or os.path.isfile(label_directory+'/guess/closed/'+name) or os.path.isfile(label_directory+'/known/open/'+name) or os.path.isfile(label_directory+'/known/closed/'+name):
labelled.add(name)
continue
def train_confidence_model(argv=None):
import pickle
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.preprocessing import MaxAbsScaler
from ..utils import get_logger
parser = argparse.ArgumentParser()
parser.add_argument("output", type=str, help="the path to save the model to")
parser.add_argument("-s", "--summary", type=str, help='the summarized sequence NN outputs')
parser.add_argument('-O', '--onnx', metavar='PATH', nargs='?', const=True, default=False, type=str,
help='Save data in ONNX format. If an argument is passed, it is assumed to be the path to a pickled model to convert to ONNX format')
parser.add_argument("-k", "--topk", metavar="TOPK", type=int, help="use the TOPK probabilities for building confidence model", default=None)
parser.add_argument('-j', '--n_jobs', metavar='NJOBS', nargs='?', const=True, default=None, type=int,
help='the number of jobs to use for cross-validation. if NJOBS is not specified, use the number of folds i.e. 5')
parser.add_argument('-c', '--cvs', metavar='NFOLDS', default=5, type=int,
help='the number of cross-validation folds to use. default is 5')
args = parser.parse_args(argv)
if args.n_jobs and isinstance(args.n_jobs, bool):
args.n_jobs = args.cvs
logger = get_logger()
if isinstance(args.onnx, str):
# just convert the given model to ONNX
with open(args.output, 'rb') as f:
lr = pickle.load(f)
args.output = args.onnx
else:
# train a new model
logger.info(f"reading outputs summary data from {args.summary}")
f = h5py.File(args.summary, 'r')
true = f['labels'][:]
pred = f['preds'][:]
# the top-k probabilities, in descending order
maxprobs = f['maxprob'][:, :args.topk]
lengths = f['lengths'][:]
f.close()
X = np.concatenate([lengths[:, np.newaxis], maxprobs], axis=1)
y = (true == pred).astype(int)
scaler = MaxAbsScaler()
scaler.fit(X)
lrcv = LogisticRegressionCV(penalty='elasticnet', solver='saga', l1_ratios=[.1, .5, .7, .9, .95, .99, 1], n_jobs=args.n_jobs, cv=args.cvs)
logger.info(f"building confidence model with \n{lrcv}")
lrcv.fit(scaler.transform(X), y)
lr = LogisticRegression()
lr.coef_ = scaler.transform(lrcv.coef_)
lr.intercept_ = lrcv.intercept_
lr.classes_ = lrcv.classes_
if args.onnx:
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
logger.info(f"saving {lr} to ONNX file {args.output}")
initial_type = [('float_input', FloatTensorType([None, lr.coef_.shape[1]]))]
onx = convert_sklearn(lr, target_opset=12, initial_types=initial_type, options={type(lr): {'zipmap': False}})
with open(args.output, "wb") as f:
f.write(onx.SerializeToString())
else:
logger.info(f"pickling {lr} to {args.output}")
with open(args.output, 'wb') as f:
pickle.dump(lr, f)
def main(seed=0,
n_train=60000,
n_test=10000,
kernel_size=(16, ),
stride=(4, ),
n_filters=25,
padding=0,
inhib=100,
time=25,
lr=1e-3,
lr_decay=0.99,
dt=1,
intensity=1,
progress_interval=10,
update_interval=250,
plot=False,
train=True,
gpu=False):
assert n_train % update_interval == 0 and n_test % update_interval == 0, \
'No. examples must be divisible by update_interval'
params = [
seed, n_train, kernel_size, stride, n_filters, padding, inhib, time,
lr, lr_decay, dt, intensity, update_interval
]
model_name = '_'.join([str(x) for x in params])
if not train:
test_params = [
seed, n_train, n_test, kernel_size, stride, n_filters, padding,
inhib, time, lr, lr_decay, dt, intensity, update_interval
]
np.random.seed(seed)
if gpu:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
torch.cuda.manual_seed_all(seed)
else:
torch.manual_seed(seed)
n_examples = n_train if train else n_test
input_shape = [20, 20]
if kernel_size == input_shape:
conv_size = [1, 1]
else:
conv_size = (int((input_shape[0] - kernel_size[0]) / stride[0]) + 1,
int((input_shape[1] - kernel_size[1]) / stride[1]) + 1)
n_classes = 10
n_neurons = n_filters * np.prod(conv_size)
total_kernel_size = int(np.prod(kernel_size))
total_conv_size = int(np.prod(conv_size))
# Build network.
if train:
network = Network()
input_layer = Input(n=400, shape=(1, 1, 20, 20), traces=True)
conv_layer = DiehlAndCookNodes(n=n_filters * total_conv_size,
shape=(1, n_filters, *conv_size),
thresh=-64.0,
traces=True,
theta_plus=0.05 * (kernel_size[0] / 20),
refrac=0)
conv_layer2 = LIFNodes(n=n_filters * total_conv_size,
shape=(1, n_filters, *conv_size),
refrac=0)
conv_conn = Conv2dConnection(input_layer,
conv_layer,
kernel_size=kernel_size,
stride=stride,
update_rule=WeightDependentPostPre,
norm=0.05 * total_kernel_size,
nu=[0, lr],
wmin=0,
wmax=0.25)
conv_conn2 = Conv2dConnection(input_layer,
conv_layer2,
w=conv_conn.w,
kernel_size=kernel_size,
stride=stride,
update_rule=None,
wmax=0.25)
w = -inhib * torch.ones(n_filters, conv_size[0], conv_size[1],
n_filters, conv_size[0], conv_size[1])
for f in range(n_filters):
for f2 in range(n_filters):
if f != f2:
w[f, :, :f2, :, :] = 0
w = w.view(n_filters * conv_size[0] * conv_size[1],
n_filters * conv_size[0] * conv_size[1])
recurrent_conn = Connection(conv_layer, conv_layer, w=w)
network.add_layer(input_layer, name='X')
network.add_layer(conv_layer, name='Y')
network.add_layer(conv_layer2, name='Y_')
network.add_connection(conv_conn, source='X', target='Y')
network.add_connection(conv_conn2, source='X', target='Y_')
network.add_connection(recurrent_conn, source='Y', target='Y')
# Voltage recording for excitatory and inhibitory layers.
voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time)
network.add_monitor(voltage_monitor, name='output_voltage')
else:
network = load_network(os.path.join(params_path, model_name + '.pt'))
network.connections['X', 'Y'].update_rule = NoOp(
connection=network.connections['X', 'Y'],
nu=network.connections['X', 'Y'].nu)
network.layers['Y'].theta_decay = 0
network.layers['Y'].theta_plus = 0
# Load MNIST data.
dataset = MNIST(data_path, download=True)
if train:
images, labels = dataset.get_train()
else:
images, labels = dataset.get_test()
images *= intensity
images = images[:, 4:-4, 4:-4].contiguous()
# Record spikes during the simulation.
spike_record = torch.zeros(update_interval, time, n_neurons)
full_spike_record = torch.zeros(n_examples, n_neurons)
# Neuron assignments and spike proportions.
if train:
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = np.zeros([n_classes, n_neurons])
logreg_model.intercept_ = np.zeros(n_classes)
logreg_model.classes_ = np.arange(n_classes)
else:
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
logreg_coef, logreg_intercept = torch.load(open(path, 'rb'))
logreg_model = LogisticRegression(warm_start=True,
n_jobs=-1,
solver='lbfgs',
max_iter=1000,
multi_class='multinomial')
logreg_model.coef_ = logreg_coef
logreg_model.intercept_ = logreg_intercept
logreg_model.classes_ = np.arange(n_classes)
# Sequence of accuracy estimates.
curves = {'logreg': []}
predictions = {scheme: torch.Tensor().long() for scheme in curves.keys()}
if train:
best_accuracy = 0
spikes = {}
for layer in set(network.layers):
spikes[layer] = Monitor(network.layers[layer],
state_vars=['s'],
time=time)
network.add_monitor(spikes[layer], name='%s_spikes' % layer)
# Train the network.
if train:
print('\nBegin training.\n')
else:
print('\nBegin test.\n')
inpt_ims = None
inpt_axes = None
spike_ims = None
spike_axes = None
weights_im = None
plot_update_interval = 100
start = t()
for i in range(n_examples):
if i % progress_interval == 0:
print('Progress: %d / %d (%.4f seconds)' %
(i, n_examples, t() - start))
start = t()
if i % update_interval == 0 and i > 0:
if train:
network.connections['X', 'Y'].update_rule.nu[1] *= lr_decay
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i %
len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat(
[predictions[scheme], preds[scheme]], -1)
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print(
'New best accuracy! Saving network parameters to disk.'
)
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(
params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
best_accuracy = max([x[-1] for x in curves.values()])
# Refit logistic regression model.
logreg_model = logreg_fit(full_spike_record[:i], labels[:i],
logreg_model)
print()
# Get next input sample.
image = images[i % len(images)]
sample = bernoulli(datum=image, time=time, dt=dt,
max_prob=1).unsqueeze(1).unsqueeze(1)
inpts = {'X': sample}
# Run the network on the input.
network.run(inpts=inpts, time=time)
network.connections['X', 'Y_'].w = network.connections['X', 'Y'].w
# Add to spikes recording.
spike_record[i % update_interval] = spikes['Y_'].get('s').view(
time, -1)
full_spike_record[i] = spikes['Y_'].get('s').view(time, -1).sum(0)
# Optionally plot various simulation information.
if plot and i % plot_update_interval == 0:
_input = inpts['X'].view(time, 400).sum(0).view(20, 20)
w = network.connections['X', 'Y'].w
_spikes = {
'X': spikes['X'].get('s').view(400, time),
'Y': spikes['Y'].get('s').view(n_filters * total_conv_size,
time),
'Y_': spikes['Y_'].get('s').view(n_filters * total_conv_size,
time)
}
inpt_axes, inpt_ims = plot_input(image.view(20, 20),
_input,
label=labels[i % len(labels)],
ims=inpt_ims,
axes=inpt_axes)
spike_ims, spike_axes = plot_spikes(spikes=_spikes,
ims=spike_ims,
axes=spike_axes)
weights_im = plot_conv2d_weights(
w, im=weights_im, wmax=network.connections['X', 'Y'].wmax)
plt.pause(1e-2)
network.reset_() # Reset state variables.
print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)')
i += 1
if i % len(labels) == 0:
current_labels = labels[-update_interval:]
current_record = full_spike_record[-update_interval:]
else:
current_labels = labels[i % len(labels) - update_interval:i %
len(labels)]
current_record = full_spike_record[i % len(labels) -
update_interval:i % len(labels)]
# Update and print accuracy evaluations.
curves, preds = update_curves(curves,
current_labels,
n_classes,
full_spike_record=current_record,
logreg=logreg_model)
print_results(curves)
for scheme in preds:
predictions[scheme] = torch.cat([predictions[scheme], preds[scheme]],
-1)
if train:
if any([x[-1] > best_accuracy for x in curves.values()]):
print('New best accuracy! Saving network parameters to disk.')
# Save network to disk.
network.save(os.path.join(params_path, model_name + '.pt'))
path = os.path.join(params_path,
'_'.join(['auxiliary', model_name]) + '.pt')
torch.save((logreg_model.coef_, logreg_model.intercept_),
open(path, 'wb'))
if train:
print('\nTraining complete.\n')
else:
print('\nTest complete.\n')
print('Average accuracies:\n')
for scheme in curves.keys():
print('\t%s: %.2f' % (scheme, float(np.mean(curves[scheme]))))
# Save accuracy curves to disk.
to_write = ['train'] + params if train else ['test'] + params
to_write = [str(x) for x in to_write]
f = '_'.join(to_write) + '.pt'
torch.save((curves, update_interval, n_examples),
open(os.path.join(curves_path, f), 'wb'))
# Save results to disk.
results = [np.mean(curves['logreg']), np.std(curves['logreg'])]
to_write = params + results if train else test_params + results
to_write = [str(x) for x in to_write]
name = 'train.csv' if train else 'test.csv'
if not os.path.isfile(os.path.join(results_path, name)):
with open(os.path.join(results_path, name), 'w') as f:
if train:
columns = [
'seed', 'n_train', 'kernel_size', 'stride', 'n_filters',
'padding', 'inhib', 'time', 'lr', 'lr_decay', 'dt',
'intensity', 'update_interval', 'mean_logreg', 'std_logreg'
]
header = ','.join(columns) + '\n'
f.write(header)
else:
columns = [
'seed', 'n_train', 'n_test', 'kernel_size', 'stride',
'n_filters', 'padding', 'inhib', 'time', 'lr', 'lr_decay',
'dt', 'intensity', 'update_interval', 'mean_logreg',
'std_logreg'
]
header = ','.join(columns) + '\n'
f.write(header)
with open(os.path.join(results_path, name), 'a') as f:
f.write(','.join(to_write) + '\n')
if labels.numel() > n_examples:
labels = labels[:n_examples]
else:
while labels.numel() < n_examples:
if 2 * labels.numel() > n_examples:
labels = torch.cat(
[labels, labels[:n_examples - labels.numel()]])
else:
labels = torch.cat([labels, labels])
# Compute confusion matrices and save them to disk.
confusions = {}
for scheme in predictions:
confusions[scheme] = confusion_matrix(labels, predictions[scheme])
to_write = ['train'] + params if train else ['test'] + test_params
f = '_'.join([str(x) for x in to_write]) + '.pt'
torch.save(confusions, os.path.join(confusion_path, f))
labels = np.load("labels.npy")
print("labels")
print(labels.shape)
labels = np.sort(labels, axis=0)
X_train, X_test, y_train, y_test = train_test_split(X_in,
y_in,
test_size=0.2,
random_state=42)
print("done splitting")
LR = LogisticRegression(random_state=0,
verbose=2,
n_jobs=4,
multi_class='multinomial',
max_iter=2000)
LR.classes_ = labels
LR.fit(X_train, y_train.flatten())
print("done fitting")
print(LR.predict(X_test))
temp = LR.predict_proba(X_test)
print(temp)
print(list(temp[0]))
print(y_test)
score = LR.score(X_test, y_test)
print(score)
# yhat = LR.predict_proba(X_test)
# print("predicted")
# print(yhat)
# print("actual")
# print(y_test)
# print("done predicting")