def run_single_experiment(base_dir, ntrials=10, seed=123456789):
"""
Run the actual experiment.
@param base_dir: The directory to containing the experiment to be run.
@param ntrials: The number of trials to perform with different seeds.
@param seed: The initial seed used to generate the other random seeds.
"""
# Generate the number of requested seeds
seeds = generate_seeds(ntrials, seed)
# Get the configuration
with open(os.path.join(base_dir, 'config.json'), 'rb') as f:
config = json.load(f)
# Get the data and base metric data
with open(os.path.join(base_dir, 'dataset.pkl'), 'rb') as f:
data = cPickle.load(f)
uniqueness_data, overlap_data, correlation_data = cPickle.load(f)
# Metrics
metrics = SPMetrics()
# Execute each run
for s in seeds:
# Update the seed
config['seed'] = s
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(data)
# Get the SP's output
sp_output = sp.predict(data)
# Log all of the metrics
sp._log_stats('Input Uniqueness', uniqueness_data)
sp._log_stats('Input Overlap', overlap_data)
sp._log_stats('Input Correlation', correlation_data)
sp._log_stats('SP Uniqueness', metrics.compute_uniqueness(sp_output))
sp._log_stats('SP Overlap', metrics.compute_overlap(sp_output))
sp._log_stats('SP Correlation', 1 - metrics.compute_distance(
sp_output))
def run_single_experiment(base_dir, ntrials=10, seed=123456789):
"""
Run the actual experiment.
@param base_dir: The directory to containing the experiment to be run.
@param ntrials: The number of trials to perform with different seeds.
@param seed: The initial seed used to generate the other random seeds.
"""
# Generate the number of requested seeds
seeds = generate_seeds(ntrials, seed)
# Get the configuration
with open(os.path.join(base_dir, 'config.json'), 'rb') as f:
config = json.load(f)
# Get the data and base metric data
with open(os.path.join(base_dir, 'dataset.pkl'), 'rb') as f:
data = cPickle.load(f)
uniqueness_data, overlap_data, correlation_data = cPickle.load(f)
# Metrics
metrics = SPMetrics()
# Execute each run
for s in seeds:
# Update the seed
config['seed'] = s
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(data)
# Get the SP's output
sp_output = sp.predict(data)
# Log all of the metrics
sp._log_stats('Input Uniqueness', uniqueness_data)
sp._log_stats('Input Overlap', overlap_data)
sp._log_stats('Input Correlation', correlation_data)
sp._log_stats('SP Uniqueness', metrics.compute_uniqueness(sp_output))
sp._log_stats('SP Overlap', metrics.compute_overlap(sp_output))
sp._log_stats('SP Correlation', 1 - metrics.compute_distance(
sp_output))
def base_experiment(log_dir, seed = 123456789):
"""
The base experiment.
Build an SP using SPDataset and see how it performs.
@param log_dir: The full path to the log directory.
@param seed: The random seed to use.
@return: Tuple containing: SP uniqueness, input uniqueness, SP overlap,
input overlap.
"""
# Params
nsamples, nbits, pct_active = 500, 100, 0.4
kargs = {
'ninputs': nbits,
'ncolumns': 200,
'nactive': 50,
'global_inhibition': True,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'nsynapses': 75,
'seg_th': 15,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'pwindow': 0.5,
'random_permanence': True,
'nepochs': 10,
'log_dir': log_dir
}
# Seed numpy
np.random.seed(seed)
# Build items to store results
npoints = 11
pct_noises = np.linspace(0, 1, npoints)
u_sp, u_ip = np.zeros(npoints), np.zeros(npoints)
o_sp, o_ip = np.zeros(npoints), np.zeros(npoints)
# Metrics
metrics = SPMetrics()
# Vary input noise
for i, pct_noise in enumerate(pct_noises):
# Build the dataset
ds = SPDataset(nsamples=nsamples, nbits=nbits, pct_active=pct_active,
pct_noise=pct_noise, seed=seed)
x = ds.data
# Get the dataset stats
u_ip[i] = metrics.compute_uniqueness(x) * 100
o_ip[i] = metrics.compute_overlap(x) * 100
# Build the SP
sp = SPRegion(**kargs)
# Train the region
sp.fit(x)
# Get the SP's output SDRs
sp_output = sp.predict(x)
# Get the stats
u_sp[i] = metrics.compute_uniqueness(sp_output) * 100
o_sp[i] = (metrics.compute_overlap(sp_output) +
metrics.compute_overlap(np.logical_not(sp_output))) * 50
# Log everything
sp._log_stats('% Input Uniqueness', u_ip[i])
sp._log_stats('% Input Overlap', o_ip[i])
sp._log_stats('% SP Uniqueness', u_sp[i])
sp._log_stats('% SP Overlap', o_sp[i])
return u_sp, u_ip, o_sp, o_ip
def base_experiment(log_dir, seed=123456789):
"""
The base experiment.
Build an SP using SPDataset and see how it performs.
@param log_dir: The full path to the log directory.
@param seed: The random seed to use.
@return: Tuple containing: SP uniqueness, input uniqueness, SP overlap,
input overlap.
"""
# Params
nsamples, nbits, pct_active = 500, 100, 0.4
kargs = {
'ninputs': nbits,
'ncolumns': 200,
'nactive': 50,
'global_inhibition': True,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'nsynapses': 75,
'seg_th': 15,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'pwindow': 0.5,
'random_permanence': True,
'nepochs': 10,
'log_dir': log_dir
}
# Seed numpy
np.random.seed(seed)
# Build items to store results
npoints = 11
pct_noises = np.linspace(0, 1, npoints)
u_sp, u_ip = np.zeros(npoints), np.zeros(npoints)
o_sp, o_ip = np.zeros(npoints), np.zeros(npoints)
# Metrics
metrics = SPMetrics()
# Vary input noise
for i, pct_noise in enumerate(pct_noises):
# Build the dataset
ds = SPDataset(nsamples=nsamples,
nbits=nbits,
pct_active=pct_active,
pct_noise=pct_noise,
seed=seed)
x = ds.data
# Get the dataset stats
u_ip[i] = metrics.compute_uniqueness(x) * 100
o_ip[i] = metrics.compute_overlap(x) * 100
# Build the SP
sp = SPRegion(**kargs)
# Train the region
sp.fit(x)
# Get the SP's output SDRs
sp_output = sp.predict(x)
# Get the stats
u_sp[i] = metrics.compute_uniqueness(sp_output) * 100
o_sp[i] = (metrics.compute_overlap(sp_output) +
metrics.compute_overlap(np.logical_not(sp_output))) * 50
# Log everything
sp._log_stats('% Input Uniqueness', u_ip[i])
sp._log_stats('% Input Overlap', o_ip[i])
sp._log_stats('% SP Uniqueness', u_sp[i])
sp._log_stats('% SP Overlap', o_sp[i])
return u_sp, u_ip, o_sp, o_ip
def local_experiment():
"""Run a single experiment, locally."""
seed = 123456789
config = {
'ninputs':
100,
'trim':
1e-4,
'disable_boost':
True,
'seed':
seed,
'pct_active':
None,
'random_permanence':
True,
'pwindow':
0.5,
'global_inhibition':
True,
'ncolumns':
200,
'nactive':
50,
'nsynapses':
100,
'seg_th':
5,
'syn_th':
0.5,
'pinc':
0.001,
'pdec':
0.001,
'nepochs':
10,
'log_dir':
os.path.join(os.path.expanduser('~'), 'scratch', 'param_experiments',
'1-1')
}
# Get the data
nsamples, nbits, pct_active, pct_noise = 500, 100, 0.4, 0.15
ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed)
data = ds.data
# Metrics
metrics = SPMetrics()
# Get the metrics for the dataset
uniqueness_data = metrics.compute_uniqueness(data)
overlap_data = metrics.compute_overlap(data)
correlation_data = 1 - metrics.compute_distance(data)
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(data)
# Get the SP's output
sp_output = sp.predict(data)
# Get the metrics for the SP's results
sp_uniqueness = metrics.compute_uniqueness(sp_output)
sp_overlap = metrics.compute_overlap(sp_output)
sp_correlation = 1 - metrics.compute_distance(sp_output)
# Log all of the metrics
sp._log_stats('Input Uniqueness', uniqueness_data)
sp._log_stats('Input Overlap', overlap_data)
sp._log_stats('Input Correlation', correlation_data)
sp._log_stats('SP Uniqueness', sp_uniqueness)
sp._log_stats('SP Overlap', sp_overlap)
sp._log_stats('SP Correlation', sp_correlation)
print(f'Uniqueness:\t{uniqueness_data:2.4f}\t{sp_uniqueness:2.4f}')
print(f'Overlap:\t{overlap_data:2.4f}\t{sp_overlap:2.4f}')
print(f'Correlation:\t{correlation_data:2.4f}\t{sp_correlation:2.4f}')
# Get a new random input
ds2 = SPDataset(nsamples, nbits, pct_active, pct_noise, 123)
print(f'\n% Overlapping old class to new: \
\t{(float(np.dot(ds.input, ds2.input)) / nbits) * 100:2.4f}%')
# Test the SP on the new dataset
sp_output2 = sp.predict(ds2.data)
# Get average representation of first result
original_result = np.mean(sp_output, 0)
original_result[original_result >= 0.5] = 1
original_result[original_result < 1] = 0
# Get averaged results for each metric type
sp_uniqueness2 = 0.
sp_overlap2 = 0.
sp_correlation2 = 0.
for item in sp_output2:
test = np.vstack((original_result, item))
sp_uniqueness2 = metrics.compute_uniqueness(test)
sp_overlap2 = metrics.compute_overlap(test)
sp_correlation2 = 1 - metrics.compute_distance(test)
sp_uniqueness2 /= len(sp_output2)
sp_overlap2 /= len(sp_output2)
sp_correlation2 /= len(sp_output2)
print(sp_uniqueness2, sp_overlap2, sp_correlation2)
def run_experiment(experiments,
base_dir,
nsamples=500,
nbits=100,
pct_active=0.4,
pct_noise=0.15,
seed=123456789,
ntrials=10,
partition_name='debug',
this_dir=os.getcwd()):
"""Run an experiment for the SP.
This experiment is used to vary various sets of parameters on the SP
dataset. This function uses SLURM to conduct the experiments.
@param experiments: A list containing the experiments details. Refer to one
of the examples in this module for more details.
@param base_dir: The base directory to use for logging.
@param nsamples: The number of samples to add to the dataset.
@param nbits: The number of bits each sample should have.
@param pct_active: The percentage of bits that will be active in the base
class SDR.
@param pct_noise: The percentage of noise to add to the data.
@param seed: The seed used to initialize the random number generator.
@param ntrials: The number of parameter trials to use. Each iteration will
be used to initialize the SP in a different manner.
@param partition_name: The partition name of the cluster to use.
@param this_dir: The full path to the directory where this file is located.
"""
# Create the dataset
data = SPDataset(nsamples, nbits, pct_active, pct_noise, seed).data
# Metrics
metrics = SPMetrics()
# Get the metrics for the dataset
uniqueness_data = metrics.compute_uniqueness(data)
overlap_data = metrics.compute_overlap(data)
correlation_data = 1 - metrics.compute_distance(data)
# Prep each experiment for execution
for experiment_name, time_limit, memory_limit, params in experiments:
# Iterate through each type of inhibition type
for i, global_inhibition in enumerate((True, False)):
# Get base configuration
base_config = create_base_config(base_dir, experiment_name,
global_inhibition)
# Add the parameters
for param_name, param_value in params:
base_config[param_name] = param_value
config_gen = ConfigGenerator(base_config, ntrials)
# Make the configurations
for config in config_gen.get_config():
# Make the base directory
dir = config['log_dir']
splits = os.path.basename(dir).split('-')
base_name = '-'.join(s for s in splits[:-1])
dir = os.path.join(os.path.dirname(dir), base_name)
try:
os.makedirs(dir)
except OSError:
pass
# Dump the config as JSON
s = json.dumps(config,
sort_keys=True,
indent=4,
separators=(',', ': ')).replace('},', '},\n')
with open(os.path.join(dir, 'config.json'), 'w') as f:
f.write(s)
# Dump the dataset and the metrics
with open(os.path.join(dir, 'dataset.pkl'), 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
pickle.dump(
(uniqueness_data, overlap_data, correlation_data), f,
pickle.HIGHEST_PROTOCOL)
# Create the runner
this_path = os.path.join(this_dir, 'parameter_exploration.py')
command = 'python "{0}" "{1}" {2} {3}'.format(
this_path, dir, ntrials, seed)
runner_path = os.path.join(dir, 'runner.sh')
job_name = '{0}_{1}{2}'.format(
experiment_name, 'G' if global_inhibition else 'L',
base_name)
stdio_path = os.path.join(dir, 'stdio.txt')
stderr_path = os.path.join(dir, 'stderr.txt')
create_runner(command=command,
runner_path=runner_path,
job_name=job_name,
partition_name=partition_name,
stdio_path=stdio_path,
stderr_path=stderr_path,
time_limit=time_limit[i],
memory_limit=memory_limit)
# Execute the runner
execute_runner(runner_path)
def main():
"""
Program entry.
Build an SP using SPDataset and see how it performs.
"""
# Params
nsamples, nbits, pct_active = 500, 100, 0.4
seed = 123456789
base_path = os.path.join(os.path.expanduser('~'), 'scratch', 'sp_simple')
kargs = {
'ninputs': nbits,
'ncolumns': 200,
'nactive': 50,
'global_inhibition': True,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'nsynapses': 75,
'seg_th': 15,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'pwindow': 0.5,
'random_permanence': True,
'nepochs': 10,
'log_dir': os.path.join(base_path, '1-1')
}
# Build items to store results
npoints = 25
pct_noises = np.linspace(0, 1, npoints, False)
uniqueness_sp, uniqueness_data = np.zeros(npoints), np.zeros(npoints)
similarity_sp, similarity_data = np.zeros(npoints), np.zeros(npoints)
similarity_sp1, similarity_data1 = np.zeros(npoints), np.zeros(npoints)
similarity_sp0, similarity_data0 = np.zeros(npoints), np.zeros(npoints)
dissimilarity_sp, dissimilarity_data = np.zeros(npoints), np.zeros(npoints)
overlap_sp, overlap_data = np.zeros(npoints), np.zeros(npoints)
correlation_sp, correlation_data = np.zeros(npoints), np.zeros(npoints)
# Metrics
metrics = SPMetrics()
# Vary input noise
for i, pct_noise in enumerate(pct_noises):
print 'Iteration {0} of {1}'.format(i + 1, npoints)
# Build the dataset
ds = SPDataset(nsamples=nsamples,
nbits=nbits,
pct_active=pct_active,
pct_noise=pct_noise,
seed=seed)
# Get the dataset stats
uniqueness_data[i] = metrics.compute_uniqueness(ds.data)
similarity_data[i] = metrics.compute_total_similarity(
ds.data, confidence_interval=0.9)
similarity_data1[i] = metrics.compute_one_similarity(
ds.data, confidence_interval=0.9)
similarity_data0[i] = metrics.compute_zero_similarity(
ds.data, confidence_interval=0.9)
dissimilarity_data[i] = metrics.compute_dissimilarity(
ds.data, confidence_interval=0.9)
overlap_data[i] = metrics.compute_overlap(ds.data)
correlation_data[i] = 1 - metrics.compute_distance(ds.data)
# Build the SP
sp = SPRegion(**kargs)
# Train the region
sp.fit(ds.data)
# Get the SP's output SDRs
sp_output = sp.predict(ds.data)
# Get the stats
uniqueness_sp[i] = metrics.compute_uniqueness(sp_output)
similarity_sp[i] = metrics.compute_total_similarity(
sp_output, confidence_interval=0.9)
similarity_sp1[i] = metrics.compute_one_similarity(
sp_output, confidence_interval=0.9)
similarity_sp0[i] = metrics.compute_zero_similarity(
sp_output, confidence_interval=0.9)
dissimilarity_sp[i] = metrics.compute_dissimilarity(
sp_output, confidence_interval=0.9)
overlap_sp[i] = metrics.compute_overlap(sp_output)
correlation_sp[i] = 1 - metrics.compute_distance(sp_output)
# Make some plots
print 'Showing uniqueness - 0% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[uniqueness_data * 100, uniqueness_sp * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label='Uniqueness [%]',
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'uniqueness.png'),
show=True)
print 'Showing total similarity - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[similarity_data * 100, similarity_sp * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label='Total similarity [%]',
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'similarity.png'),
show=True)
print 'Showing similarity of "1" bits - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[similarity_data1 * 100, similarity_sp1 * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label="Similarity of '1's [%]",
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'one_similarity.png'),
show=True)
print 'Showing similarity of "0" bits - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[similarity_data0 * 100, similarity_sp0 * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label="Similarity of '0's [%]",
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'zero_similarity.png'),
show=True)
print 'Showing dissimilarity - 0% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[dissimilarity_data * 100, dissimilarity_sp * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label="Dissimilarity [%]",
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'dissimilarity.png'),
show=True)
print 'Showing average normalized overlap - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[overlap_data * 100, overlap_sp * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label="% Normalized Overlap",
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'overlap.png'),
show=True)
print 'Showing % average sample correlation coefficient - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100],
[correlation_data * 100, correlation_sp * 100],
series_names=('Raw Data', 'SP Output'),
x_label='% Noise',
y_label="% Correlation",
xlim=False,
ylim=(-5, 105),
out_path=os.path.join(base_path, 'correlation.png'),
show=True)
print '*** All data saved in "{0}" ***'.format(base_path)
def base_experiment(pct_noise=0.15,
noverlap_bits=0,
exp_name='1-1',
ntrials=10,
verbose=True,
seed=123456789):
"""
Run a single experiment, locally.
@param pct_noise: The percentage of noise to add to the dataset.
@param noverlap_bits: The number of bits the base class should overlap
with the novelty class.
@param exp_name: The name of the experiment.
@param ntrials: The number of times to repeat the experiment.
@param verbose: If True print the results.
@param seed: The random seed to use.
@return: A tuple containing the percentage errors for the SP's training
and testing results and the SVM's training and testing results,
respectively.
"""
# Base parameters
ntrain, ntest = 800, 200
nsamples, nbits, pct_active = ntest + ntrain, 100, 0.4
clf_th = 0.5
log_dir = os.path.join(os.path.expanduser('~'), 'scratch',
'novelty_experiments', exp_name)
# Configure the SP
config = {
'ninputs': 100,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'pct_active': None,
'random_permanence': True,
'pwindow': 0.5,
'global_inhibition': True,
'ncolumns': 200,
'nactive': 50,
'nsynapses': 75,
'seg_th': 15,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'nepochs': 10,
'log_dir': log_dir
}
# Seed numpy
np.random.seed(seed)
# Create the base dataset
x_ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed=seed)
x_tr, x_te = x_ds.data[:ntrain], x_ds.data[ntrain:]
# Create the outlier dataset
base_indexes = set(np.where(x_ds.base_class == 1)[0])
choices = [x for x in xrange(nbits) if x not in base_indexes]
outlier_base = np.zeros(nbits, dtype='bool')
outlier_base[np.random.choice(choices, x_ds.nactive - noverlap_bits,
False)] = 1
outlier_base[np.random.permutation(list(base_indexes))[:noverlap_bits]] = 1
y_ds = SPDataset(ntest, nbits, pct_active, pct_noise, outlier_base, seed)
y_te = y_ds.data
if verbose:
print "\nBase class' test noise: {0:2.2f}".format(
1 - (np.mean(x_te, 0) * x_ds.base_class.astype('i')).sum() / 40.)
print "Outlier's class noise: {0:2.2f}".format(
1 - (np.mean(y_te, 0) * outlier_base.astype('i')).sum() / 40.)
print 'Overlap between two classes: {0}'.format(
np.dot(x_ds.base_class.astype('i'), outlier_base.astype('i')))
# Metrics
metrics = SPMetrics()
# Get the metrics for the datasets
u_x_tr = metrics.compute_uniqueness(x_tr)
o_x_tr = metrics.compute_overlap(x_tr)
c_x_tr = 1 - metrics.compute_distance(x_tr)
u_x_te = metrics.compute_uniqueness(x_te)
o_x_te = metrics.compute_overlap(x_te)
c_x_te = 1 - metrics.compute_distance(x_te)
u_y_te = metrics.compute_uniqueness(y_te)
o_y_te = metrics.compute_overlap(y_te)
c_y_te = 1 - metrics.compute_distance(y_te)
# Initialize the overall results
sp_x_results = np.zeros(ntrials)
sp_y_results = np.zeros(ntrials)
svm_x_results = np.zeros(ntrials)
svm_y_results = np.zeros(ntrials)
# Iterate across the trials:
for i in xrange(ntrials):
# Make a new seed
seed2 = np.random.randint(1000000)
config['seed'] = seed2
config['log_dir'] = '{0}-{1}'.format(log_dir, i + 1)
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(x_tr)
# Get the SP's output
sp_x_tr = sp.predict(x_tr)
sp_x_te = sp.predict(x_te)
sp_y_te = sp.predict(y_te)
# Get the metrics for the SP's results
u_sp_x_tr = metrics.compute_uniqueness(sp_x_tr)
o_sp_x_tr = metrics.compute_overlap(sp_x_tr)
c_sp_x_tr = 1 - metrics.compute_distance(sp_x_tr)
u_sp_x_te = metrics.compute_uniqueness(sp_x_te)
o_sp_x_te = metrics.compute_overlap(sp_x_te)
c_sp_x_te = 1 - metrics.compute_distance(sp_x_te)
u_sp_y_te = metrics.compute_uniqueness(sp_y_te)
o_sp_y_te = metrics.compute_overlap(sp_y_te)
c_sp_y_te = 1 - metrics.compute_distance(sp_y_te)
# Log all of the metrics
sp._log_stats('Input Base Class Train Uniqueness', u_x_tr)
sp._log_stats('Input Base Class Train Overlap', o_x_tr)
sp._log_stats('Input Base Class Train Correlation', c_x_tr)
sp._log_stats('Input Base Class Test Uniqueness', u_x_te)
sp._log_stats('Input Base Class Test Overlap', o_x_te)
sp._log_stats('Input Base Class Test Correlation', c_x_te)
sp._log_stats('Input Novelty Class Test Uniqueness', u_y_te)
sp._log_stats('Input Novelty Class Test Overlap', o_y_te)
sp._log_stats('Input Novelty Class Test Correlation', c_y_te)
sp._log_stats('SP Base Class Train Uniqueness', u_sp_x_tr)
sp._log_stats('SP Base Class Train Overlap', o_sp_x_tr)
sp._log_stats('SP Base Class Train Correlation', c_sp_x_tr)
sp._log_stats('SP Base Class Test Uniqueness', u_sp_x_te)
sp._log_stats('SP Base Class Test Overlap', o_sp_x_te)
sp._log_stats('SP Base Class Test Correlation', c_sp_x_te)
sp._log_stats('SP Novelty Class Test Uniqueness', u_sp_y_te)
sp._log_stats('SP Novelty Class Test Overlap', o_sp_y_te)
sp._log_stats('SP Novelty Class Test Correlation', c_sp_y_te)
# Print the results
fmt_s = '{0}:\t{1:2.4f}\t{2:2.4f}\t{3:2.4f}\t{4:2.4f}\t{5:2.4f}\t{5:2.4f}'
if verbose:
print '\nDescription\tx_tr\tx_te\ty_te\tsp_x_tr\tsp_x_te\tsp_y_te'
print fmt_s.format('Uniqueness', u_x_tr, u_x_te, u_y_te, u_sp_x_tr,
u_sp_x_te, u_sp_y_te)
print fmt_s.format('Overlap', o_x_tr, o_x_te, o_y_te, o_sp_x_tr,
o_sp_x_te, o_sp_y_te)
print fmt_s.format('Correlation', c_x_tr, c_x_te, c_y_te,
c_sp_x_tr, c_sp_x_te, c_sp_y_te)
# Get average representation of the base class
sp_base_result = np.mean(sp_x_tr, 0)
sp_base_result[sp_base_result >= 0.5] = 1
sp_base_result[sp_base_result < 1] = 0
# Averaged results for each metric type
u_sp_base_to_x_te = 0.
o_sp_base_to_x_te = 0.
c_sp_base_to_x_te = 0.
u_sp_base_to_y_te = 0.
o_sp_base_to_y_te = 0.
c_sp_base_to_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the sums
u_sp_base_to_x_te += metrics.compute_uniqueness(xt)
o_sp_base_to_x_te += metrics.compute_overlap(xt)
c_sp_base_to_x_te += 1 - metrics.compute_distance(xt)
u_sp_base_to_y_te += metrics.compute_uniqueness(yt)
o_sp_base_to_y_te += metrics.compute_overlap(yt)
c_sp_base_to_y_te += 1 - metrics.compute_distance(yt)
u_sp_base_to_x_te /= ntest
o_sp_base_to_x_te /= ntest
c_sp_base_to_x_te /= ntest
u_sp_base_to_y_te /= ntest
o_sp_base_to_y_te /= ntest
c_sp_base_to_y_te /= ntest
# Log the results
sp._log_stats('Base Train to Base Test Uniqueness', u_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Overlap', o_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Correlation', c_sp_base_to_x_te)
sp._log_stats('Base Train to Novelty Test Uniqueness',
u_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Overlap', o_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Correlation',
c_sp_base_to_y_te)
# Print the results
if verbose:
print '\nDescription\tx_tr->x_te\tx_tr->y_te'
print 'Uniqueness:\t{0:2.4f}\t{1:2.4f}'.format(
u_sp_base_to_x_te, u_sp_base_to_y_te)
print 'Overlap:\t{0:2.4f}\t{1:2.4f}'.format(
o_sp_base_to_x_te, o_sp_base_to_y_te)
print 'Correlation:\t{0:2.4f}\t{1:2.4f}'.format(
c_sp_base_to_x_te, c_sp_base_to_y_te)
# Create an SVM
clf = OneClassSVM(kernel='linear', nu=0.1, random_state=seed2)
# Evaluate the SVM's performance
clf.fit(x_tr)
svm_x_te = len(np.where(clf.predict(x_te) == 1)[0]) / float(ntest) * \
100
svm_y_te = len(np.where(clf.predict(y_te) == -1)[0]) / float(ntest) * \
100
# Perform classification using overlap as the feature
# -- The overlap must be above 50%
clf_x_te = 0.
clf_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the accuracy
xo = metrics.compute_overlap(xt)
yo = metrics.compute_overlap(yt)
if xo >= clf_th: clf_x_te += 1
if yo < clf_th: clf_y_te += 1
clf_x_te = (clf_x_te / ntest) * 100
clf_y_te = (clf_y_te / ntest) * 100
# Store the results as errors
sp_x_results[i] = 100 - clf_x_te
sp_y_results[i] = 100 - clf_y_te
svm_x_results[i] = 100 - svm_x_te
svm_y_results[i] = 100 - svm_y_te
# Log the results
sp._log_stats('SP % Correct Base Class', clf_x_te)
sp._log_stats('SP % Correct Novelty Class', clf_y_te)
sp._log_stats('SVM % Correct Base Class', svm_x_te)
sp._log_stats('SVM % Correct Novelty Class', svm_y_te)
# Print the results
if verbose:
print '\nSP Base Class Detection : {0:2.2f}%'.format(clf_x_te)
print 'SP Novelty Class Detection : {0:2.2f}%'.format(clf_y_te)
print 'SVM Base Class Detection : {0:2.2f}%'.format(svm_x_te)
print 'SVM Novelty Class Detection : {0:2.2f}%'.format(svm_y_te)
return sp_x_results, sp_y_results, svm_x_results, svm_y_results
def main():
"""
Program entry.
Build an SP using SPDataset and see how it performs.
"""
# Params
nsamples, nbits, pct_active = 500, 100, 0.4
ncolumns = 300
base_path = os.path.join(os.path.expanduser('~'), 'scratch', 'sp_simple')
seed = 123456789
kargs = {
'ninputs': nbits,
'ncolumns': 300,
'nactive': 0.02 * ncolumns,
'global_inhibition': True,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'nsynapses': 20,
'seg_th': 2,
'syn_th': 0.5,
'pinc': 0.01,
'pdec': 0.01,
'pwindow': 0.5,
'random_permanence': True,
'nepochs': 1,
'log_dir': os.path.join(base_path, '1-1')
}
# Build items to store results
npoints = 25
pct_noises = np.linspace(0, pct_active / 2, npoints, False)
uniqueness_sp, uniqueness_data = np.zeros(npoints), np.zeros(npoints)
similarity_sp, similarity_data = np.zeros(npoints), np.zeros(npoints)
similarity_sp1, similarity_data1 = np.zeros(npoints), np.zeros(npoints)
similarity_sp0, similarity_data0 = np.zeros(npoints), np.zeros(npoints)
dissimilarity_sp, dissimilarity_data = np.zeros(npoints), np.zeros(npoints)
overlap_sp, overlap_data = np.zeros(npoints), np.zeros(npoints)
correlation_sp, correlation_data = np.zeros(npoints), np.zeros(npoints)
# Metrics
metrics = SPMetrics()
# Vary input noise
for i, pct_noise in enumerate(pct_noises):
print 'Iteration {0} of {1}'.format(i + 1, npoints)
# Build the dataset
ds = SPDataset(nsamples=nsamples, nbits=nbits, pct_active=pct_active,
pct_noise=pct_noise, seed=seed)
# Get the dataset stats
uniqueness_data[i] = metrics.compute_uniqueness(ds.data)
similarity_data[i] = metrics.compute_total_similarity(ds.data,
confidence_interval=0.9)
similarity_data1[i] = metrics.compute_one_similarity(ds.data,
confidence_interval=0.9)
similarity_data0[i] = metrics.compute_zero_similarity(ds.data,
confidence_interval=0.9)
dissimilarity_data[i] = metrics.compute_dissimilarity(ds.data,
confidence_interval=0.9)
overlap_data[i] = metrics.compute_overlap(ds.data)
correlation_data[i] = 1 - metrics.compute_distance(ds.data)
# Build the SP
sp = SPRegion(**kargs)
# Train the region
sp.fit(ds.data)
# Get the SP's output SDRs
sp_output = sp.predict(ds.data)
# Get the stats
uniqueness_sp[i] = metrics.compute_uniqueness(sp_output)
similarity_sp[i] = metrics.compute_total_similarity(sp_output,
confidence_interval=0.9)
similarity_sp1[i] = metrics.compute_one_similarity(sp_output,
confidence_interval=0.9)
similarity_sp0[i] = metrics.compute_zero_similarity(sp_output,
confidence_interval=0.9)
dissimilarity_sp[i] = metrics.compute_dissimilarity(sp_output,
confidence_interval=0.9)
overlap_sp[i] = metrics.compute_overlap(sp_output)
correlation_sp[i] = 1 - metrics.compute_distance(sp_output)
# Make some plots
print 'Showing uniqueness - 0% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [uniqueness_data * 100,
uniqueness_sp * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label='Uniqueness [%]', xlim=False, ylim=False,
out_path=os.path.join(base_path, 'uniqueness.png'), show=True)
print 'Showing total similarity - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [similarity_data * 100,
similarity_sp * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label='Total similarity [%]', xlim=False,
ylim=False, out_path=os.path.join(base_path, 'similarity.png'),
show=True)
print 'Showing similarity of "1" bits - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [similarity_data1 * 100,
similarity_sp1 * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label="Similarity of '1's [%]", xlim=False,
ylim=False, out_path=os.path.join(base_path, 'one_similarity.png'),
show=True)
print 'Showing similarity of "0" bits - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [similarity_data0 * 100,
similarity_sp0 * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label="Similarity of '0's [%]", xlim=False,
ylim=False, out_path=os.path.join(base_path, 'zero_similarity.png'),
show=True)
print 'Showing dissimilarity - 0% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [dissimilarity_data * 100,
dissimilarity_sp * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label="Dissimilarity [%]", xlim=False,
ylim=False, out_path=os.path.join(base_path, 'dissimilarity.png'),
show=True)
print 'Showing average normalized overlap - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [overlap_data * 100,
overlap_sp * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label="% Normalized Overlap", xlim=False,
ylim=False, out_path=os.path.join(base_path, 'overlap.png'),
show=True)
print 'Showing % average sample correlation cofficient - 100% is ideal'
plot_line([pct_noises * 100, pct_noises * 100], [correlation_data * 100,
correlation_sp * 100], series_names=('Raw Data', 'SP Output'),
x_label='% Noise', y_label="% Correlation", xlim=False,
ylim=False, out_path=os.path.join(base_path, 'correlation.png'),
show=True)
print '*** All data saved in "{0}" ***'.format(base_path)
def base_experiment(pct_noise=0.15, noverlap_bits=0, exp_name='1-1',
ntrials=10, verbose=True, seed=123456789):
"""
Run a single experiment, locally.
@param pct_noise: The percentage of noise to add to the dataset.
@param noverlap_bits: The number of bits the base class should overlap
with the novelty class.
@param exp_name: The name of the experiment.
@param ntrials: The number of times to repeat the experiment.
@param verbose: If True print the results.
@param seed: The random seed to use.
@return: A tuple containing the percentage errors for the SP's training
and testing results and the SVM's training and testing results,
respectively.
"""
# Base parameters
ntrain, ntest = 800, 200
nsamples, nbits, pct_active = ntest + ntrain, 100, 0.4
clf_th = 0.5
log_dir = os.path.join(os.path.expanduser('~'), 'scratch',
'novelty_experiments', exp_name)
# Configure the SP
config = {
'ninputs': 100,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'pct_active': None,
'random_permanence': True,
'pwindow': 0.5,
'global_inhibition': True,
'ncolumns': 200,
'nactive': 50,
'nsynapses': 75,
'seg_th': 15,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'nepochs': 10,
'log_dir': log_dir
}
# Seed numpy
np.random.seed(seed)
# Create the base dataset
x_ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed=seed)
x_tr, x_te = x_ds.data[:ntrain], x_ds.data[ntrain:]
# Create the outlier dataset
base_indexes = set(np.where(x_ds.base_class == 1)[0])
choices = [x for x in xrange(nbits) if x not in base_indexes]
outlier_base = np.zeros(nbits, dtype='bool')
outlier_base[np.random.choice(choices, x_ds.nactive - noverlap_bits,
False)] = 1
outlier_base[np.random.permutation(list(base_indexes))[:noverlap_bits]] = 1
y_ds = SPDataset(ntest, nbits, pct_active, pct_noise, outlier_base, seed)
y_te = y_ds.data
if verbose:
print "\nBase class' test noise: {0:2.2f}".format(1 - (np.mean(x_te, 0)
* x_ds.base_class.astype('i')).sum() / 40.)
print "Outlier's class noise: {0:2.2f}".format(1 - (np.mean(y_te, 0) *
outlier_base.astype('i')).sum() / 40.)
print 'Overlap between two classes: {0}'.format(np.dot(
x_ds.base_class.astype('i'), outlier_base.astype('i')))
# Metrics
metrics = SPMetrics()
# Get the metrics for the datasets
u_x_tr = metrics.compute_uniqueness(x_tr)
o_x_tr = metrics.compute_overlap(x_tr)
c_x_tr = 1 - metrics.compute_distance(x_tr)
u_x_te = metrics.compute_uniqueness(x_te)
o_x_te = metrics.compute_overlap(x_te)
c_x_te = 1 - metrics.compute_distance(x_te)
u_y_te = metrics.compute_uniqueness(y_te)
o_y_te = metrics.compute_overlap(y_te)
c_y_te = 1 - metrics.compute_distance(y_te)
# Initialize the overall results
sp_x_results = np.zeros(ntrials)
sp_y_results = np.zeros(ntrials)
svm_x_results = np.zeros(ntrials)
svm_y_results = np.zeros(ntrials)
# Iterate across the trials:
for i in xrange(ntrials):
# Make a new seed
seed2 = np.random.randint(1000000)
config['seed'] = seed2
config['log_dir'] = '{0}-{1}'.format(log_dir, i + 1)
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(x_tr)
# Get the SP's output
sp_x_tr = sp.predict(x_tr)
sp_x_te = sp.predict(x_te)
sp_y_te = sp.predict(y_te)
# Get the metrics for the SP's results
u_sp_x_tr = metrics.compute_uniqueness(sp_x_tr)
o_sp_x_tr = metrics.compute_overlap(sp_x_tr)
c_sp_x_tr = 1 - metrics.compute_distance(sp_x_tr)
u_sp_x_te = metrics.compute_uniqueness(sp_x_te)
o_sp_x_te = metrics.compute_overlap(sp_x_te)
c_sp_x_te = 1 - metrics.compute_distance(sp_x_te)
u_sp_y_te = metrics.compute_uniqueness(sp_y_te)
o_sp_y_te = metrics.compute_overlap(sp_y_te)
c_sp_y_te = 1 - metrics.compute_distance(sp_y_te)
# Log all of the metrics
sp._log_stats('Input Base Class Train Uniqueness', u_x_tr)
sp._log_stats('Input Base Class Train Overlap', o_x_tr)
sp._log_stats('Input Base Class Train Correlation', c_x_tr)
sp._log_stats('Input Base Class Test Uniqueness', u_x_te)
sp._log_stats('Input Base Class Test Overlap', o_x_te)
sp._log_stats('Input Base Class Test Correlation', c_x_te)
sp._log_stats('Input Novelty Class Test Uniqueness', u_y_te)
sp._log_stats('Input Novelty Class Test Overlap', o_y_te)
sp._log_stats('Input Novelty Class Test Correlation', c_y_te)
sp._log_stats('SP Base Class Train Uniqueness', u_sp_x_tr)
sp._log_stats('SP Base Class Train Overlap', o_sp_x_tr)
sp._log_stats('SP Base Class Train Correlation', c_sp_x_tr)
sp._log_stats('SP Base Class Test Uniqueness', u_sp_x_te)
sp._log_stats('SP Base Class Test Overlap', o_sp_x_te)
sp._log_stats('SP Base Class Test Correlation', c_sp_x_te)
sp._log_stats('SP Novelty Class Test Uniqueness', u_sp_y_te)
sp._log_stats('SP Novelty Class Test Overlap', o_sp_y_te)
sp._log_stats('SP Novelty Class Test Correlation', c_sp_y_te)
# Print the results
fmt_s = '{0}:\t{1:2.4f}\t{2:2.4f}\t{3:2.4f}\t{4:2.4f}\t{5:2.4f}\t{5:2.4f}'
if verbose:
print '\nDescription\tx_tr\tx_te\ty_te\tsp_x_tr\tsp_x_te\tsp_y_te'
print fmt_s.format('Uniqueness', u_x_tr, u_x_te, u_y_te, u_sp_x_tr,
u_sp_x_te, u_sp_y_te)
print fmt_s.format('Overlap', o_x_tr, o_x_te, o_y_te, o_sp_x_tr, o_sp_x_te,
o_sp_y_te)
print fmt_s.format('Correlation', c_x_tr, c_x_te, c_y_te, c_sp_x_tr,
c_sp_x_te, c_sp_y_te)
# Get average representation of the base class
sp_base_result = np.mean(sp_x_tr, 0)
sp_base_result[sp_base_result >= 0.5] = 1
sp_base_result[sp_base_result < 1] = 0
# Averaged results for each metric type
u_sp_base_to_x_te = 0.
o_sp_base_to_x_te = 0.
c_sp_base_to_x_te = 0.
u_sp_base_to_y_te = 0.
o_sp_base_to_y_te = 0.
c_sp_base_to_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the sums
u_sp_base_to_x_te += metrics.compute_uniqueness(xt)
o_sp_base_to_x_te += metrics.compute_overlap(xt)
c_sp_base_to_x_te += 1 - metrics.compute_distance(xt)
u_sp_base_to_y_te += metrics.compute_uniqueness(yt)
o_sp_base_to_y_te += metrics.compute_overlap(yt)
c_sp_base_to_y_te += 1 - metrics.compute_distance(yt)
u_sp_base_to_x_te /= ntest
o_sp_base_to_x_te /= ntest
c_sp_base_to_x_te /= ntest
u_sp_base_to_y_te /= ntest
o_sp_base_to_y_te /= ntest
c_sp_base_to_y_te /= ntest
# Log the results
sp._log_stats('Base Train to Base Test Uniqueness',
u_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Overlap', o_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Correlation', c_sp_base_to_x_te)
sp._log_stats('Base Train to Novelty Test Uniqueness',
u_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Overlap', o_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Correlation',
c_sp_base_to_y_te)
# Print the results
if verbose:
print '\nDescription\tx_tr->x_te\tx_tr->y_te'
print 'Uniqueness:\t{0:2.4f}\t{1:2.4f}'.format(u_sp_base_to_x_te,
u_sp_base_to_y_te)
print 'Overlap:\t{0:2.4f}\t{1:2.4f}'.format(o_sp_base_to_x_te,
o_sp_base_to_y_te)
print 'Correlation:\t{0:2.4f}\t{1:2.4f}'.format(c_sp_base_to_x_te,
c_sp_base_to_y_te)
# Create an SVM
clf = OneClassSVM(kernel='linear', nu=0.1, random_state=seed2)
# Evaluate the SVM's performance
clf.fit(x_tr)
svm_x_te = len(np.where(clf.predict(x_te) == 1)[0]) / float(ntest) * \
100
svm_y_te = len(np.where(clf.predict(y_te) == -1)[0]) / float(ntest) * \
100
# Perform classification using overlap as the feature
# -- The overlap must be above 50%
clf_x_te = 0.
clf_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the accuracy
xo = metrics.compute_overlap(xt)
yo = metrics.compute_overlap(yt)
if xo >= clf_th: clf_x_te += 1
if yo < clf_th: clf_y_te += 1
clf_x_te = (clf_x_te / ntest) * 100
clf_y_te = (clf_y_te / ntest) * 100
# Store the results as errors
sp_x_results[i] = 100 - clf_x_te
sp_y_results[i] = 100 - clf_y_te
svm_x_results[i] = 100 - svm_x_te
svm_y_results[i] = 100 - svm_y_te
# Log the results
sp._log_stats('SP % Correct Base Class', clf_x_te)
sp._log_stats('SP % Correct Novelty Class', clf_y_te)
sp._log_stats('SVM % Correct Base Class', svm_x_te)
sp._log_stats('SVM % Correct Novelty Class', svm_y_te)
# Print the results
if verbose:
print '\nSP Base Class Detection : {0:2.2f}%'.format(clf_x_te)
print 'SP Novelty Class Detection : {0:2.2f}%'.format(clf_y_te)
print 'SVM Base Class Detection : {0:2.2f}%'.format(svm_x_te)
print 'SVM Novelty Class Detection : {0:2.2f}%'.format(svm_y_te)
return sp_x_results, sp_y_results, svm_x_results, svm_y_results
def base_experiment(config,
pct_noise=0.15,
noverlap_bits=0,
ntrials=10,
verbose=False,
seed=123456789):
"""
Run a single experiment, locally.
@param config: The configuration parameters.
@param pct_noise: The percentage of noise to add to the dataset.
@param noverlap_bits: The number of bits the base class should overlap
with the novelty class.
@param ntrials: The number of times to repeat the experiment.
@param verbose: If True print the results.
@param seed: The random seed to use.
"""
# Base parameters
ntrain, ntest = 800, 200
nsamples, nbits, pct_active = ntest + ntrain, 100, 0.4
clf_th = 0.5
# Build the directory, if needed
base_dir = config['log_dir']
if not os.path.exists(base_dir): os.makedirs(base_dir)
# Seed numpy
np.random.seed(seed)
# Create the base dataset
x_ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed=seed)
x_tr, x_te = x_ds.data[:ntrain], x_ds.data[ntrain:]
# Create the outlier dataset
base_indexes = set(np.where(x_ds.base_class == 1)[0])
choices = [x for x in xrange(nbits) if x not in base_indexes]
outlier_base = np.zeros(nbits, dtype='bool')
outlier_base[np.random.choice(choices, x_ds.nactive - noverlap_bits,
False)] = 1
outlier_base[np.random.permutation(list(base_indexes))[:noverlap_bits]] = 1
y_ds = SPDataset(ntest, nbits, pct_active, pct_noise, outlier_base, seed)
y_te = y_ds.data
if verbose:
print "\nBase class' test noise: {0:2.2f}".format(
1 - (np.mean(x_te, 0) * x_ds.base_class.astype('i')).sum() / 40.)
print "Outlier's class noise: {0:2.2f}".format(
1 - (np.mean(y_te, 0) * outlier_base.astype('i')).sum() / 40.)
print 'Overlap between two classes: {0}'.format(
np.dot(x_ds.base_class.astype('i'), outlier_base.astype('i')))
# Metrics
metrics = SPMetrics()
# Get the metrics for the datasets
u_x_tr = metrics.compute_uniqueness(x_tr)
o_x_tr = metrics.compute_overlap(x_tr)
u_x_te = metrics.compute_uniqueness(x_te)
o_x_te = metrics.compute_overlap(x_te)
u_y_te = metrics.compute_uniqueness(y_te)
o_y_te = metrics.compute_overlap(y_te)
# Initialize the overall results
sp_x_results = np.zeros(ntrials)
sp_y_results = np.zeros(ntrials)
svm_x_results = np.zeros(ntrials)
svm_y_results = np.zeros(ntrials)
# Iterate across the trials:
for i, seed2 in enumerate(generate_seeds(ntrials, seed)):
# Create the SP
config['seed'] = seed2
sp = SPRegion(**config)
# Fit the SP
sp.fit(x_tr)
# Get the SP's output
sp_x_tr = sp.predict(x_tr)
sp_x_te = sp.predict(x_te)
sp_y_te = sp.predict(y_te)
# Get the metrics for the SP's results
u_sp_x_tr = metrics.compute_uniqueness(sp_x_tr)
o_sp_x_tr = metrics.compute_overlap(sp_x_tr)
u_sp_x_te = metrics.compute_uniqueness(sp_x_te)
o_sp_x_te = metrics.compute_overlap(sp_x_te)
u_sp_y_te = metrics.compute_uniqueness(sp_y_te)
o_sp_y_te = metrics.compute_overlap(sp_y_te)
# Log all of the metrics
sp._log_stats('Input Base Class Train Uniqueness', u_x_tr)
sp._log_stats('Input Base Class Train Overlap', o_x_tr)
sp._log_stats('Input Base Class Test Uniqueness', u_x_te)
sp._log_stats('Input Base Class Test Overlap', o_x_te)
sp._log_stats('Input Novelty Class Test Uniqueness', u_y_te)
sp._log_stats('Input Novelty Class Test Overlap', o_y_te)
sp._log_stats('SP Base Class Train Uniqueness', u_sp_x_tr)
sp._log_stats('SP Base Class Train Overlap', o_sp_x_tr)
sp._log_stats('SP Base Class Test Uniqueness', u_sp_x_te)
sp._log_stats('SP Base Class Test Overlap', o_sp_x_te)
sp._log_stats('SP Novelty Class Test Uniqueness', u_sp_y_te)
sp._log_stats('SP Novelty Class Test Overlap', o_sp_y_te)
# Print the results
fmt_s = '{0}:\t{1:2.4f}\t{2:2.4f}\t{3:2.4f}\t{4:2.4f}\t{5:2.4f}\t{6:2.4f}'
if verbose:
print '\nDescription\tx_tr\tx_te\ty_te\tsp_x_tr\tsp_x_te\tsp_y_te'
print fmt_s.format('Uniqueness', u_x_tr, u_x_te, u_y_te, u_sp_x_tr,
u_sp_x_te, u_sp_y_te)
print fmt_s.format('Overlap', o_x_tr, o_x_te, o_y_te, o_sp_x_tr,
o_sp_x_te, o_sp_y_te)
# Get average representation of the base class
sp_base_result = np.mean(sp_x_tr, 0)
sp_base_result[sp_base_result >= 0.5] = 1
sp_base_result[sp_base_result < 1] = 0
# Averaged results for each metric type
u_sp_base_to_x_te = 0.
o_sp_base_to_x_te = 0.
u_sp_base_to_y_te = 0.
o_sp_base_to_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the sums
u_sp_base_to_x_te += metrics.compute_uniqueness(xt)
o_sp_base_to_x_te += metrics.compute_overlap(xt)
u_sp_base_to_y_te += metrics.compute_uniqueness(yt)
o_sp_base_to_y_te += metrics.compute_overlap(yt)
u_sp_base_to_x_te /= ntest
o_sp_base_to_x_te /= ntest
u_sp_base_to_y_te /= ntest
o_sp_base_to_y_te /= ntest
# Log the results
sp._log_stats('Base Train to Base Test Uniqueness', u_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Overlap', o_sp_base_to_x_te)
sp._log_stats('Base Train to Novelty Test Uniqueness',
u_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Overlap', o_sp_base_to_y_te)
# Print the results
if verbose:
print '\nDescription\tx_tr->x_te\tx_tr->y_te'
print 'Uniqueness:\t{0:2.4f}\t{1:2.4f}'.format(
u_sp_base_to_x_te, u_sp_base_to_y_te)
print 'Overlap:\t{0:2.4f}\t{1:2.4f}'.format(
o_sp_base_to_x_te, o_sp_base_to_y_te)
# Create an SVM
clf = OneClassSVM(kernel='linear', nu=0.1, random_state=seed2)
# Evaluate the SVM's performance
clf.fit(x_tr)
svm_x_te = len(np.where(clf.predict(x_te) == 1)[0]) / float(ntest) * \
100
svm_y_te = len(np.where(clf.predict(y_te) == -1)[0]) / float(ntest) * \
100
# Perform classification using overlap as the feature
# -- The overlap must be above 50%
clf_x_te = 0.
clf_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the accuracy
xo = metrics.compute_overlap(xt)
yo = metrics.compute_overlap(yt)
if xo >= clf_th: clf_x_te += 1
if yo < clf_th: clf_y_te += 1
clf_x_te = (clf_x_te / ntest) * 100
clf_y_te = (clf_y_te / ntest) * 100
# Store the results as errors
sp_x_results[i] = 100 - clf_x_te
sp_y_results[i] = 100 - clf_y_te
svm_x_results[i] = 100 - svm_x_te
svm_y_results[i] = 100 - svm_y_te
# Log the results
sp._log_stats('SP % Correct Base Class', clf_x_te)
sp._log_stats('SP % Correct Novelty Class', clf_y_te)
sp._log_stats('SVM % Correct Base Class', svm_x_te)
sp._log_stats('SVM % Correct Novelty Class', svm_y_te)
# Print the results
if verbose:
print '\nSP Base Class Detection : {0:2.2f}%'.format(clf_x_te)
print 'SP Novelty Class Detection : {0:2.2f}%'.format(clf_y_te)
print 'SVM Base Class Detection : {0:2.2f}%'.format(svm_x_te)
print 'SVM Novelty Class Detection : {0:2.2f}%'.format(svm_y_te)
# Save the results
with open(os.path.join(base_dir, 'results.pkl'), 'wb') as f:
cPickle.dump(
(sp_x_results, sp_y_results, svm_x_results, svm_y_results), f,
cPickle.HIGHEST_PROTOCOL)
def base_experiment(config, ntrials=1, seed=123456789):
"""
Run a single experiment, locally.
@param config: The configuration parameters to use for the SP.
@param ntrials: The number of times to repeat the experiment.
@param seed: The random seed to use.
@return: A tuple containing the percentage errors for the SP's training
and testing results and the SVM's training and testing results,
respectively.
"""
# Base parameters
ntrain, ntest = 800, 200
clf_th = 0.5
# Seed numpy
np.random.seed(seed)
# Get the data
(tr_x, tr_y), (te_x, te_y) = load_mnist()
tr_x_0 = np.random.permutation(tr_x[tr_y == 0])
x_tr = tr_x_0[:ntrain]
x_te = tr_x_0[ntrain:ntrain + ntest]
outliers = [np.random.permutation(tr_x[tr_y == i])[:ntest] for i in
xrange(1, 10)]
# Metrics
metrics = SPMetrics()
# Get the metrics for the datasets
u_x_tr = metrics.compute_uniqueness(x_tr)
o_x_tr = metrics.compute_overlap(x_tr)
c_x_tr = 1 - metrics.compute_distance(x_tr)
u_x_te = metrics.compute_uniqueness(x_te)
o_x_te = metrics.compute_overlap(x_te)
c_x_te = 1 - metrics.compute_distance(x_te)
u_y_te, o_y_te, c_y_te = [], [], []
for outlier in outliers:
u_y_te.append(metrics.compute_uniqueness(outlier))
o_y_te.append(metrics.compute_overlap(outlier))
c_y_te.append(1 - metrics.compute_distance(outlier))
# Initialize the overall results
sp_x_results = np.zeros(ntrials)
sp_y_results = [np.zeros(ntrials) for _ in xrange(9)]
svm_x_results = np.zeros(ntrials)
svm_y_results = [np.zeros(ntrials) for _ in xrange(9)]
# Iterate across the trials:
for nt in xrange(ntrials):
# Make a new seeod
seed2 = np.random.randint(1000000)
config['seed'] = seed2
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(x_tr)
# Get the SP's output
sp_x_tr = sp.predict(x_tr)
sp_x_te = sp.predict(x_te)
sp_y_te = [sp.predict(outlier) for outlier in outliers]
# Get the metrics for the SP's results
u_sp_x_tr = metrics.compute_uniqueness(sp_x_tr)
o_sp_x_tr = metrics.compute_overlap(sp_x_tr)
c_sp_x_tr = 1 - metrics.compute_distance(sp_x_tr)
u_sp_x_te = metrics.compute_uniqueness(sp_x_te)
o_sp_x_te = metrics.compute_overlap(sp_x_te)
c_sp_x_te = 1 - metrics.compute_distance(sp_x_te)
u_sp_y_te, o_sp_y_te, c_sp_y_te = [], [], []
for y in sp_y_te:
u_sp_y_te.append(metrics.compute_uniqueness(y))
o_sp_y_te.append(metrics.compute_overlap(y))
c_sp_y_te.append(1 - metrics.compute_distance(y))
# Log all of the metrics
sp._log_stats('Input Base Class Train Uniqueness', u_x_tr)
sp._log_stats('Input Base Class Train Overlap', o_x_tr)
sp._log_stats('Input Base Class Train Correlation', c_x_tr)
sp._log_stats('Input Base Class Test Uniqueness', u_x_te)
sp._log_stats('Input Base Class Test Overlap', o_x_te)
sp._log_stats('Input Base Class Test Correlation', c_x_te)
sp._log_stats('SP Base Class Train Uniqueness', u_sp_x_tr)
sp._log_stats('SP Base Class Train Overlap', o_sp_x_tr)
sp._log_stats('SP Base Class Train Correlation', c_sp_x_tr)
sp._log_stats('SP Base Class Test Uniqueness', u_sp_x_te)
sp._log_stats('SP Base Class Test Overlap', o_sp_x_te)
sp._log_stats('SP Base Class Test Correlation', c_sp_x_te)
for i, (a, b, c, d, e, f) in enumerate(zip(u_y_te, o_y_te, c_y_te,
u_sp_y_te, o_sp_y_te, c_sp_y_te), 1):
sp._log_stats('Input Novelty Class {0} Uniqueness'.format(i), a)
sp._log_stats('Input Novelty Class {0} Overlap'.format(i), b)
sp._log_stats('Input Novelty Class {0} Correlation'.format(i), c)
sp._log_stats('SP Novelty Class {0} Uniqueness'.format(i), d)
sp._log_stats('SP Novelty Class {0} Overlap'.format(i), e)
sp._log_stats('SP Novelty Class {0} Correlation'.format(i), f)
# Get average representation of the base class
sp_base_result = np.mean(sp_x_tr, 0)
sp_base_result[sp_base_result >= 0.5] = 1
sp_base_result[sp_base_result < 1] = 0
# Averaged results for each metric type
u_sp_base_to_x_te = 0.
o_sp_base_to_x_te = 0.
c_sp_base_to_x_te = 0.
u_sp, o_sp, c_sp = np.zeros(9), np.zeros(9), np.zeros(9)
for i, x in enumerate(sp_x_te):
xt = np.vstack((sp_base_result, x))
u_sp_base_to_x_te += metrics.compute_uniqueness(xt)
o_sp_base_to_x_te += metrics.compute_overlap(xt)
c_sp_base_to_x_te += 1 - metrics.compute_distance(xt)
for j, yi in enumerate(sp_y_te):
yt = np.vstack((sp_base_result, yi[i]))
u_sp[j] += metrics.compute_uniqueness(yt)
o_sp[j] += metrics.compute_overlap(yt)
c_sp[j] += 1 - metrics.compute_distance(yt)
u_sp_base_to_x_te /= ntest
o_sp_base_to_x_te /= ntest
c_sp_base_to_x_te /= ntest
for i in xrange(9):
u_sp[i] /= ntest
o_sp[i] /= ntest
c_sp[i] /= ntest
# Log the results
sp._log_stats('Base Train to Base Test Uniqueness',
u_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Overlap', o_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Correlation', c_sp_base_to_x_te)
for i, j in enumerate(xrange(1, 10)):
sp._log_stats('Base Train to Novelty {0} Uniqueness'.format(j),
u_sp[i])
sp._log_stats('Base Train to Novelty {0} Overlap'.format(j),
o_sp[i])
sp._log_stats('Base Train to Novelty {0} Correlation'.format(j),
c_sp[i])
# Create an SVM
clf = OneClassSVM(kernel='linear', nu=0.1, random_state=seed2)
# Evaluate the SVM's performance
clf.fit(x_tr)
svm_x_te = len(np.where(clf.predict(x_te) == 1)[0]) / float(ntest) * \
100
svm_y_te = np.array([len(np.where(clf.predict(outlier) == -1)[0]) /
float(ntest) * 100 for outlier in outliers])
# Perform classification using overlap as the feature
# -- The overlap must be above 50%
clf_x_te = 0.
clf_y_te = np.zeros(9)
for i, x in enumerate(sp_x_te):
xt = np.vstack((sp_base_result, x))
xo = metrics.compute_overlap(xt)
if xo >= clf_th: clf_x_te += 1
for j, yi in enumerate(sp_y_te):
yt = np.vstack((sp_base_result, yi[i]))
yo = metrics.compute_overlap(yt)
if yo < clf_th: clf_y_te[j] += 1
clf_x_te = (clf_x_te / ntest) * 100
clf_y_te = (clf_y_te / ntest) * 100
# Store the results as errors
sp_x_results[nt] = 100 - clf_x_te
sp_y_results[nt] = 100 - clf_y_te
svm_x_results[nt] = 100 - svm_x_te
svm_y_results[nt] = 100 - svm_y_te
# Log the results
sp._log_stats('SP % Correct Base Class', clf_x_te)
sp._log_stats('SVM % Correct Base Class', svm_x_te)
for i, j in enumerate(xrange(1, 10)):
sp._log_stats('SP % Correct Novelty Class {0}'.format(j),
clf_y_te[i])
sp._log_stats('SVM % Correct Novelty Class {0}'.format(j),
svm_y_te[i])
sp._log_stats('SP % Mean Correct Novelty Class', np.mean(clf_y_te))
sp._log_stats('SVM % Mean Correct Novelty Class', np.mean(svm_y_te))
sp._log_stats('SP % Adjusted Score', (np.mean(clf_y_te) * clf_x_te) /
100)
sp._log_stats('SVM % Adjusted Score', (np.mean(svm_y_te) * svm_x_te) /
100)
return sp_x_results, sp_y_results, svm_x_results, svm_y_results
def run_experiment(experiments, base_dir, nsamples=500, nbits=100,
pct_active=0.4, pct_noise=0.15, seed=123456789, ntrials=10,
partition_name='debug', this_dir=os.getcwd()):
"""
Run an experiment for the SP. This experiment is used to vary various sets
of parameters on the SP dataset. This function uses SLURM to conduct the
experiments.
@param experiments: A list containing the experiments details. Refer to one
of the examples in this module for more details.
@param base_dir: The base directory to use for logging.
@param nsamples: The number of samples to add to the dataset.
@param nbits: The number of bits each sample should have.
@param pct_active: The percentage of bits that will be active in the base
class SDR.
@param pct_noise: The percentage of noise to add to the data.
@param seed: The seed used to initialize the random number generator.
@param ntrials: The number of parameter trials to use. Each iteration will
be used to initialize the SP in a different manner.
@param partition_name: The partition name of the cluster to use.
@param this_dir: The full path to the directory where this file is located.
"""
# Create the dataset
data = SPDataset(nsamples, nbits, pct_active, pct_noise, seed).data
# Metrics
metrics = SPMetrics()
# Get the metrics for the dataset
uniqueness_data = metrics.compute_uniqueness(data)
overlap_data = metrics.compute_overlap(data)
correlation_data = 1 - metrics.compute_distance(data)
# Prep each experiment for execution
for experiment_name, time_limit, memory_limit, params in experiments:
# Iterate through each type of inhibition type
for i, global_inhibition in enumerate((True, False)):
# Get base configuration
base_config = create_base_config(base_dir, experiment_name,
global_inhibition)
# Add the parameters
for param_name, param_value in params:
base_config[param_name] = param_value
config_gen = ConfigGenerator(base_config, ntrials)
# Make the configurations
for config in config_gen.get_config():
# Make the base directory
dir = config['log_dir']
splits = os.path.basename(dir).split('-')
base_name = '-'.join(s for s in splits[:-1])
dir = os.path.join(os.path.dirname(dir), base_name)
try:
os.makedirs(dir)
except OSError:
pass
# Dump the config as JSON
s = json.dumps(config, sort_keys=True, indent=4,
separators=(',', ': ')).replace('},', '},\n')
with open(os.path.join(dir, 'config.json'), 'wb') as f:
f.write(s)
# Dump the dataset and the metrics
with open(os.path.join(dir, 'dataset.pkl'), 'wb') as f:
cPickle.dump(data, f, cPickle.HIGHEST_PROTOCOL)
cPickle.dump((uniqueness_data, overlap_data,
correlation_data), f, cPickle.HIGHEST_PROTOCOL)
# Create the runner
this_path = os.path.join(this_dir, 'parameter_exploration.py')
command = 'python "{0}" "{1}" {2} {3}'.format(this_path, dir,
ntrials, seed)
runner_path = os.path.join(dir, 'runner.sh')
job_name = '{0}_{1}{2}'.format(experiment_name, 'G' if
global_inhibition else 'L', base_name)
stdio_path = os.path.join(dir, 'stdio.txt')
stderr_path = os.path.join(dir, 'stderr.txt')
create_runner(command=command, runner_path=runner_path,
job_name=job_name, partition_name=partition_name,
stdio_path=stdio_path, stderr_path=stderr_path,
time_limit=time_limit[i], memory_limit=memory_limit)
# Execute the runner
execute_runner(runner_path)
def local_experiment():
"""
Run a single experiment, locally.
"""
seed = 123456789
config = {
'ninputs': 100,
'trim': 1e-4,
'disable_boost': True,
'seed': seed,
'pct_active': None,
'random_permanence': True,
'pwindow': 0.5,
'global_inhibition': True,
'ncolumns': 200,
'nactive': 50,
'nsynapses': 100,
'seg_th': 5,
'syn_th': 0.5,
'pinc': 0.001,
'pdec': 0.001,
'nepochs': 10,
'log_dir': os.path.join(os.path.expanduser('~'), 'scratch',
'param_experiments', '1-1')
}
# Get the data
nsamples, nbits, pct_active, pct_noise = 500, 100, 0.4, 0.15
ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed)
data = ds.data
# Metrics
metrics = SPMetrics()
# Get the metrics for the dataset
uniqueness_data = metrics.compute_uniqueness(data)
overlap_data = metrics.compute_overlap(data)
correlation_data = 1 - metrics.compute_distance(data)
# Create the SP
sp = SPRegion(**config)
# Fit the SP
sp.fit(data)
# Get the SP's output
sp_output = sp.predict(data)
# Get the metrics for the SP's results
sp_uniqueness = metrics.compute_uniqueness(sp_output)
sp_overlap = metrics.compute_overlap(sp_output)
sp_correlation = 1 - metrics.compute_distance(sp_output)
# Log all of the metrics
sp._log_stats('Input Uniqueness', uniqueness_data)
sp._log_stats('Input Overlap', overlap_data)
sp._log_stats('Input Correlation', correlation_data)
sp._log_stats('SP Uniqueness', sp_uniqueness)
sp._log_stats('SP Overlap', sp_overlap)
sp._log_stats('SP Correlation', sp_correlation)
print 'Uniqueness:\t{0:2.4f}\t{1:2.4f}'.format(uniqueness_data,
sp_uniqueness)
print 'Overlap:\t{0:2.4f}\t{1:2.4f}'.format(overlap_data, sp_overlap)
print 'Correlation:\t{0:2.4f}\t{1:2.4f}'.format(correlation_data,
sp_correlation)
# Get a new random input
ds2 = SPDataset(nsamples, nbits, pct_active, pct_noise, 123)
print '\n% Overlapping old class to new: \t{0:2.4f}%'.format(
(float(np.dot(ds.input, ds2.input)) / nbits) * 100)
# Test the SP on the new dataset
sp_output2 = sp.predict(ds2.data)
# Get average representation of first result
original_result = np.mean(sp_output, 0)
original_result[original_result >= 0.5] = 1
original_result[original_result < 1] = 0
# Get averaged results for each metric type
sp_uniqueness2 = 0.
sp_overlap2 = 0.
sp_correlation2 = 0.
for item in sp_output2:
test = np.vstack((original_result, item))
sp_uniqueness2 = metrics.compute_uniqueness(test)
sp_overlap2 = metrics.compute_overlap(test)
sp_correlation2 = 1 - metrics.compute_distance(test)
sp_uniqueness2 /= len(sp_output2)
sp_overlap2 /= len(sp_output2)
sp_correlation2 /= len(sp_output2)
print sp_uniqueness2, sp_overlap2, sp_correlation2
def base_experiment(config, pct_noise=0.15, noverlap_bits=0, ntrials=10,
verbose=False, seed=123456789):
"""
Run a single experiment, locally.
@param config: The configuration parameters.
@param pct_noise: The percentage of noise to add to the dataset.
@param noverlap_bits: The number of bits the base class should overlap
with the novelty class.
@param ntrials: The number of times to repeat the experiment.
@param verbose: If True print the results.
@param seed: The random seed to use.
"""
# Base parameters
ntrain, ntest = 800, 200
nsamples, nbits, pct_active = ntest + ntrain, 100, 0.4
clf_th = 0.5
# Build the directory, if needed
base_dir = config['log_dir']
if not os.path.exists(base_dir): os.makedirs(base_dir)
# Seed numpy
np.random.seed(seed)
# Create the base dataset
x_ds = SPDataset(nsamples, nbits, pct_active, pct_noise, seed=seed)
x_tr, x_te = x_ds.data[:ntrain], x_ds.data[ntrain:]
# Create the outlier dataset
base_indexes = set(np.where(x_ds.base_class == 1)[0])
choices = [x for x in xrange(nbits) if x not in base_indexes]
outlier_base = np.zeros(nbits, dtype='bool')
outlier_base[np.random.choice(choices, x_ds.nactive - noverlap_bits,
False)] = 1
outlier_base[np.random.permutation(list(base_indexes))[:noverlap_bits]] = 1
y_ds = SPDataset(ntest, nbits, pct_active, pct_noise, outlier_base, seed)
y_te = y_ds.data
if verbose:
print "\nBase class' test noise: {0:2.2f}".format(1 - (np.mean(x_te, 0)
* x_ds.base_class.astype('i')).sum() / 40.)
print "Outlier's class noise: {0:2.2f}".format(1 - (np.mean(y_te, 0) *
outlier_base.astype('i')).sum() / 40.)
print 'Overlap between two classes: {0}'.format(np.dot(
x_ds.base_class.astype('i'), outlier_base.astype('i')))
# Metrics
metrics = SPMetrics()
# Get the metrics for the datasets
u_x_tr = metrics.compute_uniqueness(x_tr)
o_x_tr = metrics.compute_overlap(x_tr)
u_x_te = metrics.compute_uniqueness(x_te)
o_x_te = metrics.compute_overlap(x_te)
u_y_te = metrics.compute_uniqueness(y_te)
o_y_te = metrics.compute_overlap(y_te)
# Initialize the overall results
sp_x_results = np.zeros(ntrials)
sp_y_results = np.zeros(ntrials)
svm_x_results = np.zeros(ntrials)
svm_y_results = np.zeros(ntrials)
# Iterate across the trials:
for i, seed2 in enumerate(generate_seeds(ntrials, seed)):
# Create the SP
config['seed'] = seed2
sp = SPRegion(**config)
# Fit the SP
sp.fit(x_tr)
# Get the SP's output
sp_x_tr = sp.predict(x_tr)
sp_x_te = sp.predict(x_te)
sp_y_te = sp.predict(y_te)
# Get the metrics for the SP's results
u_sp_x_tr = metrics.compute_uniqueness(sp_x_tr)
o_sp_x_tr = metrics.compute_overlap(sp_x_tr)
u_sp_x_te = metrics.compute_uniqueness(sp_x_te)
o_sp_x_te = metrics.compute_overlap(sp_x_te)
u_sp_y_te = metrics.compute_uniqueness(sp_y_te)
o_sp_y_te = metrics.compute_overlap(sp_y_te)
# Log all of the metrics
sp._log_stats('Input Base Class Train Uniqueness', u_x_tr)
sp._log_stats('Input Base Class Train Overlap', o_x_tr)
sp._log_stats('Input Base Class Test Uniqueness', u_x_te)
sp._log_stats('Input Base Class Test Overlap', o_x_te)
sp._log_stats('Input Novelty Class Test Uniqueness', u_y_te)
sp._log_stats('Input Novelty Class Test Overlap', o_y_te)
sp._log_stats('SP Base Class Train Uniqueness', u_sp_x_tr)
sp._log_stats('SP Base Class Train Overlap', o_sp_x_tr)
sp._log_stats('SP Base Class Test Uniqueness', u_sp_x_te)
sp._log_stats('SP Base Class Test Overlap', o_sp_x_te)
sp._log_stats('SP Novelty Class Test Uniqueness', u_sp_y_te)
sp._log_stats('SP Novelty Class Test Overlap', o_sp_y_te)
# Print the results
fmt_s = '{0}:\t{1:2.4f}\t{2:2.4f}\t{3:2.4f}\t{4:2.4f}\t{5:2.4f}\t{6:2.4f}'
if verbose:
print '\nDescription\tx_tr\tx_te\ty_te\tsp_x_tr\tsp_x_te\tsp_y_te'
print fmt_s.format('Uniqueness', u_x_tr, u_x_te, u_y_te, u_sp_x_tr,
u_sp_x_te, u_sp_y_te)
print fmt_s.format('Overlap', o_x_tr, o_x_te, o_y_te, o_sp_x_tr,
o_sp_x_te, o_sp_y_te)
# Get average representation of the base class
sp_base_result = np.mean(sp_x_tr, 0)
sp_base_result[sp_base_result >= 0.5] = 1
sp_base_result[sp_base_result < 1] = 0
# Averaged results for each metric type
u_sp_base_to_x_te = 0.
o_sp_base_to_x_te = 0.
u_sp_base_to_y_te = 0.
o_sp_base_to_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the sums
u_sp_base_to_x_te += metrics.compute_uniqueness(xt)
o_sp_base_to_x_te += metrics.compute_overlap(xt)
u_sp_base_to_y_te += metrics.compute_uniqueness(yt)
o_sp_base_to_y_te += metrics.compute_overlap(yt)
u_sp_base_to_x_te /= ntest
o_sp_base_to_x_te /= ntest
u_sp_base_to_y_te /= ntest
o_sp_base_to_y_te /= ntest
# Log the results
sp._log_stats('Base Train to Base Test Uniqueness',
u_sp_base_to_x_te)
sp._log_stats('Base Train to Base Test Overlap', o_sp_base_to_x_te)
sp._log_stats('Base Train to Novelty Test Uniqueness',
u_sp_base_to_y_te)
sp._log_stats('Base Train to Novelty Test Overlap', o_sp_base_to_y_te)
# Print the results
if verbose:
print '\nDescription\tx_tr->x_te\tx_tr->y_te'
print 'Uniqueness:\t{0:2.4f}\t{1:2.4f}'.format(u_sp_base_to_x_te,
u_sp_base_to_y_te)
print 'Overlap:\t{0:2.4f}\t{1:2.4f}'.format(o_sp_base_to_x_te,
o_sp_base_to_y_te)
# Create an SVM
clf = OneClassSVM(kernel='linear', nu=0.1, random_state=seed2)
# Evaluate the SVM's performance
clf.fit(x_tr)
svm_x_te = len(np.where(clf.predict(x_te) == 1)[0]) / float(ntest) * \
100
svm_y_te = len(np.where(clf.predict(y_te) == -1)[0]) / float(ntest) * \
100
# Perform classification using overlap as the feature
# -- The overlap must be above 50%
clf_x_te = 0.
clf_y_te = 0.
for x, y in zip(sp_x_te, sp_y_te):
# Refactor
xt = np.vstack((sp_base_result, x))
yt = np.vstack((sp_base_result, y))
# Compute the accuracy
xo = metrics.compute_overlap(xt)
yo = metrics.compute_overlap(yt)
if xo >= clf_th: clf_x_te += 1
if yo < clf_th: clf_y_te += 1
clf_x_te = (clf_x_te / ntest) * 100
clf_y_te = (clf_y_te / ntest) * 100
# Store the results as errors
sp_x_results[i] = 100 - clf_x_te
sp_y_results[i] = 100 - clf_y_te
svm_x_results[i] = 100 - svm_x_te
svm_y_results[i] = 100 - svm_y_te
# Log the results
sp._log_stats('SP % Correct Base Class', clf_x_te)
sp._log_stats('SP % Correct Novelty Class', clf_y_te)
sp._log_stats('SVM % Correct Base Class', svm_x_te)
sp._log_stats('SVM % Correct Novelty Class', svm_y_te)
# Print the results
if verbose:
print '\nSP Base Class Detection : {0:2.2f}%'.format(clf_x_te)
print 'SP Novelty Class Detection : {0:2.2f}%'.format(clf_y_te)
print 'SVM Base Class Detection : {0:2.2f}%'.format(svm_x_te)
print 'SVM Novelty Class Detection : {0:2.2f}%'.format(svm_y_te)
# Save the results
with open(os.path.join(base_dir, 'results.pkl'), 'wb') as f:
cPickle.dump((sp_x_results, sp_y_results, svm_x_results,
svm_y_results), f, cPickle.HIGHEST_PROTOCOL)