def test_bca_errorbar_output_simple(self):
np.random.seed(1234567890)
results_default = boot.ci(self.data, np.average)
np.random.seed(1234567890)
results_errorbar = boot.ci(self.data, np.average, output='errorbar')
np.testing.assert_array_almost_equal(
results_errorbar.T,
abs(np.average(self.data) - results_default)[np.newaxis])
def test_pi_multi_2dout_multialpha(self):
np.random.seed(1234567890)
results1 = boot.ci((self.x,self.y), stats.linregress, alpha=(0.1,0.2,0.8,0.9),n_samples=2000,method='pi')
np.random.seed(1234567890)
results2 = boot.ci(np.vstack((self.x,self.y)).T, lambda a: stats.linregress(a)[0], alpha=(0.1,0.2,0.8,0.9),n_samples=2000,method='pi')
np.random.seed(1234567890)
results3 = boot.ci(np.vstack((self.x,self.y)).T, lambda a: stats.linregress(a)[1], alpha=(0.1,0.2,0.8,0.9),n_samples=2000,method='pi')
np.testing.assert_array_almost_equal(results1[:,0],results2)
np.testing.assert_array_almost_equal(results1[:,1],results3)
def test_bca_multi_multialpha(self):
np.random.seed(1234567890)
results1 = boot.ci((self.x, self.y),
lambda a, b: stats.linregress(a, b)[1],
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=1000)
np.random.seed(1234567890)
results2 = boot.ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[1],
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=1000)
np.testing.assert_array_almost_equal(results1, results2)
def main(presentation=False):
#
# Recreate STDP curve
#
theta = 51
curveparams = {"tau_pre": 0.01, "tau_post": 0.01, "pulse_rate": 5.0}
# Iterate over some pulse_delay values
pulse_delays = np.linspace(-0.1, 0.1, 100)
omegas = simulate(theta, curveparams, pulse_delays)[::-1]
omegas /= np.amax(omegas) # normalize
sim_curve_x, sim_curve_y = BCMSim.fit_stdp_curve(pulse_delays, omegas)
sim_stdp = {"x": pulse_delays, "y": omegas, "fit_x": sim_curve_x, "fit_y": sim_curve_y}
plot_stdp_curves(sim_stdp, exp_stdp, presentation)
#
# Recreate frequency effects
#
freqparams = {"tau_pre": 0.36, "tau_post": 0.0022, "pulse_delay": 0.00135, "num_pairings": 5}
theta_low = 197
theta_high = 216
pulse_rates = [1.0, 2.0, 10.0, 20.0, 100.0]
np.random.seed(6) # 1, 5
low_omegas = simulate(theta_low, freqparams, pulse_rates, trials=25, random_start=True)
low_scale = 0.2 / np.mean(low_omegas[-1])
low_omegas *= low_scale
low_conf = ci(low_omegas, axis=1)
high_omegas = simulate(theta_high, freqparams, pulse_rates, trials=25, random_start=True)
high_scale = 0.2 / np.mean(high_omegas[-1])
high_omegas *= high_scale
high_conf = ci(high_omegas, axis=1)
sim_freq_data = {
"pulse_rates": pulse_rates,
"low_l": low_conf[0],
"low_m": np.mean(low_omegas, axis=1),
"low_h": low_conf[1],
"high_l": high_conf[0],
"high_m": np.mean(high_omegas, axis=1),
"high_h": high_conf[1],
}
plot_frequencies(sim_freq_data, exp_freq_data, presentation)
def test_bca_n_samples(self):
np.random.seed(1234567890)
results = boot.ci(self.data,
np.average,
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=500)
np.testing.assert_array_almost_equal(
results, np.array([0.40027628, 0.5063184, 0.94082515, 1.05653929]))
def get_ci(self, vals):
if len(set(vals)) == 1:
return (vals[0], vals[0])
# In case bootstrap.py is missing or not working:
# loc = np.mean(vals)
# scale = np.std(vals) / np.sqrt(len(vals))
# return stats.t.interval(0.95, len(vals)-1, loc=loc, scale=scale)
return bootstrap.ci(vals, method='bca')
def get_ci(self, vals):
if len(set(vals)) == 1:
return (vals[0], vals[0])
# In case bootstrap.py is missing or not working:
# loc = np.mean(vals)
# scale = np.std(vals) / np.sqrt(len(vals))
# return stats.t.interval(0.95, len(vals)-1, loc=loc, scale=scale)
return bootstrap.ci(vals, method='bca')
def test_abc_multialpha_unified(self):
results = boot.ci(self.data,
lambda x, weights: np.average(x, weights=weights),
alpha=(0.1, 0.2, 0.8, 0.9),
method='abc')
np.testing.assert_array_almost_equal(
results, np.array([0.39472915, 0.51161304, 0.93789723,
1.04407254]))
def test_pi_multialpha(self):
np.random.seed(1234567890)
results = boot.ci(self.data,
np.average,
method='pi',
alpha=(0.1, 0.2, 0.8, 0.9))
np.testing.assert_array_almost_equal(
results, np.array([0.40351601, 0.51723236, 0.94547054,
1.05749207]))
def get_sparsity(bcm_files):
tr = []
for bcm_f in bcm_files:
t, _, transform = get_data(bcm_f)
tr.append(sparsity_v(transform))
tr = np.vstack(tr)
tr_m = np.mean(tr, axis=0)
tr_lh = ci(tr, axis=0)
return t, (tr_lh[0], tr_m, tr_lh[1])
def get_mse(control_files, other_files):
se = []
for c_f, o_f in zip(control_files, other_files):
time, control, _ = get_data(c_f)
time, other, _ = get_data(o_f)
se.append(np.sum((control - other) ** 2, axis=0))
se = np.vstack(se)
mean = np.mean(se, axis=0)
conf = ci(se, axis=0)
return time, conf[0], mean, conf[1]
def calc_bootstrap(data):
# --- >>> START stats <<< ---
# Calculate the bootstrap
CIs = bootstrap.ci(data=data, statfunction=sp.mean)
# --- >>> STOP stats <<< ---
# Print the data: the "*" turns the array CIs into a list
print(('The conficence intervals for the mean are: {0} - {1}'.format(*CIs)))
return CIs
def calc_bootstrap(data):
# --- >>> START stats <<< ---
# Calculate the bootstrap
CIs = bootstrap.ci(data=data, statfunction=sp.mean)
# --- >>> STOP stats <<< ---
# Print the data: the "*" turns the array CIs into a list
print(
('The conficence intervals for the mean are: {0} - {1}'.format(*CIs)))
return CIs
def test_pi_multi_2dout_multialpha(self):
np.random.seed(1234567890)
results1 = boot.ci((self.x, self.y),
stats.linregress,
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=2000,
method='pi')
np.random.seed(1234567890)
results2 = boot.ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[0],
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=2000,
method='pi')
np.random.seed(1234567890)
results3 = boot.ci(np.vstack((self.x, self.y)).T,
lambda a: stats.linregress(a)[1],
alpha=(0.1, 0.2, 0.8, 0.9),
n_samples=2000,
method='pi')
np.testing.assert_array_almost_equal(results1[:, 0], results2)
np.testing.assert_array_almost_equal(results1[:, 1], results3)
def test_bca_errorbar_output_simple(self):
np.random.seed(1234567890)
results_default = boot.ci(self.data,np.average)
np.random.seed(1234567890)
results_errorbar = boot.ci(self.data,np.average,output='errorbar')
np.testing.assert_array_almost_equal(results_errorbar.T,abs(np.average(self.data)-results_default)[np.newaxis])
def test_bca_simple(self):
np.random.seed(1234567890)
results = boot.ci(self.data,np.average)
np.testing.assert_array_almost_equal(results,np.array([ 0.20907826, 1.19877862]))
def test_pi_multialpha(self):
np.random.seed(1234567890)
results = boot.ci(self.data,np.average,method='pi',alpha=(0.1,0.2,0.8,0.9))
np.testing.assert_array_almost_equal(results,np.array([ 0.40351601, 0.51723236, 0.94547054, 1.05749207]))
def test_abc_multialpha_unified(self):
results = boot.ci(self.data,lambda x,weights: np.average(x,weights=weights),alpha=(0.1,0.2,0.8,0.9),method='abc')
np.testing.assert_array_almost_equal(results,np.array([ 0.39472915, 0.51161304, 0.93789723, 1.04407254]))
def test_pi_pandas_series(self):
np.random.seed(1234567890)
results = boot.ci(self.pds,np.average,method='pi')
np.testing.assert_array_almost_equal(results,np.array([ 0.2288689 , 1.21259752]))
def test_bca_pandas_series(self):
np.random.seed(1234567890)
results = boot.ci(self.pds, np.average)
np.testing.assert_array_almost_equal(
results, np.array([0.20907826, 1.19877862]))
def run(std=True, domain=''):
entities = os.listdir(properties.evaluation_dir)
_random, bayes_random, bayes_no_variation, bayes_variation, siddharthan, deemter = get_values(entities, domain)
general_random = {'accuracy':[], 'string':[], 'jaccard':[]}
general_bayes_random = {'accuracy':[], 'string':[], 'jaccard':[]}
general_bayes_no_variation = {'accuracy':[], 'string':[], 'jaccard':[]}
general_bayes_variation = {'accuracy':[], 'string':[], 'jaccard':[]}
general_siddharthan = {'accuracy':[], 'string':[], 'jaccard':[]}
general_deemter = {'accuracy':[], 'string':[], 'jaccard':[]}
number_samples, number_samples1 = 0, 0
for fold in bayes_random:
general_random['accuracy'].append(accuracy_score(_random[fold]['y_real'], _random[fold]['y_pred']))
general_random['string'].append(np.mean(_random[fold]['string']))
general_random['jaccard'].append(np.mean(_random[fold]['jaccard']))
general_siddharthan['accuracy'].append(accuracy_score(siddharthan[fold]['y_real'], siddharthan[fold]['y_pred']))
general_siddharthan['string'].append(np.mean(siddharthan[fold]['string']))
general_siddharthan['jaccard'].append(np.mean(siddharthan[fold]['jaccard']))
general_deemter['accuracy'].append(accuracy_score(deemter[fold]['y_real'], deemter[fold]['y_pred']))
general_deemter['string'].append(np.mean(deemter[fold]['string']))
general_deemter['jaccard'].append(np.mean(deemter[fold]['jaccard']))
general_bayes_random['accuracy'].append(accuracy_score(bayes_random[fold]['y_real'], bayes_random[fold]['y_pred']))
general_bayes_random['string'].append(np.mean(bayes_random[fold]['string']))
general_bayes_random['jaccard'].append(np.mean(bayes_random[fold]['jaccard']))
general_bayes_no_variation['accuracy'].append(accuracy_score(bayes_no_variation[fold]['y_real'], bayes_no_variation[fold]['y_pred']))
general_bayes_no_variation['string'].append(np.mean(bayes_no_variation[fold]['string']))
general_bayes_no_variation['jaccard'].append(np.mean(bayes_no_variation[fold]['jaccard']))
general_bayes_variation['accuracy'].append(accuracy_score(bayes_variation[fold]['y_real'], bayes_variation[fold]['y_pred']))
general_bayes_variation['string'].append(np.mean(bayes_variation[fold]['string']))
general_bayes_variation['jaccard'].append(np.mean(bayes_variation[fold]['jaccard']))
number_samples += len(bayes_no_variation[fold]['string'])
number_samples1 += len(siddharthan[fold]['string'])
if std and domain == '':
print 'Fold', fold
print 'Labels: '
print 'Random: ', accuracy_score(_random[fold]['y_real'], _random[fold]['y_pred'])
print 'Siddharthan: ', accuracy_score(siddharthan[fold]['y_real'], siddharthan[fold]['y_pred'])
print 'Deemter: ', accuracy_score(deemter[fold]['y_real'], deemter[fold]['y_pred'])
print 'Bayes Random: ', accuracy_score(bayes_random[fold]['y_real'], bayes_random[fold]['y_pred'])
print 'Bayes No Variation: ', accuracy_score(bayes_no_variation[fold]['y_real'], bayes_no_variation[fold]['y_pred'])
print 'Bayes Variation: ', accuracy_score(bayes_variation[fold]['y_real'], bayes_variation[fold]['y_pred'])
print 20 * '-'
print 'String Distance: '
print 'Random: ', np.mean(_random[fold]['string'])
print 'Siddharthan: ', np.mean(siddharthan[fold]['string'])
print 'Deemter: ', np.mean(deemter[fold]['string'])
print 'Bayes Random: ', np.mean(bayes_random[fold]['string'])
print 'Bayes No Variation: ', np.mean(bayes_no_variation[fold]['string'])
print 'Bayes Variation: ', np.mean(bayes_variation[fold]['string'])
print 20 * '-'
print 'Jaccard Distance: '
print 'Random: ', np.mean(_random[fold]['jaccard'])
print 'Siddharthan: ', np.mean(siddharthan[fold]['jaccard'])
print 'Deemter: ', np.mean(deemter[fold]['jaccard'])
print 'Bayes Random: ', np.mean(bayes_random[fold]['jaccard'])
print 'Bayes No Variation: ', np.mean(bayes_no_variation[fold]['jaccard'])
print 'Bayes Variation: ', np.mean(bayes_variation[fold]['jaccard'])
print 20 * '-'
print '\n'
if std:
print 'GENERAL', domain
print 'Labels: '
print 'Random: ', np.mean(general_random['accuracy'])
print 'Siddharthan: ', np.mean(general_siddharthan['accuracy'])
print 'Deemter: ', np.mean(general_deemter['accuracy'])
print 'Bayes Random: ', np.mean(general_bayes_random['accuracy'])
print 'Bayes No Variation: ', np.mean(general_bayes_no_variation['accuracy'])
print 'Bayes Variation: ', np.mean(general_bayes_variation['accuracy'])
print 20 * '-'
print 'String Distance: '
print 'Random: ', mean_confidence_interval(general_random['string']), bootstrap.ci(general_random['string'])
print 'Siddharthan: ', mean_confidence_interval(general_siddharthan['string']), bootstrap.ci(general_siddharthan['string'])
print 'Deemter: ', mean_confidence_interval(general_deemter['string']), bootstrap.ci(general_deemter['string'])
print 'Bayes Random: ', mean_confidence_interval(general_bayes_random['string']), bootstrap.ci(general_bayes_random['string'])
print 'Bayes No Variation: ', mean_confidence_interval(general_bayes_no_variation['string']), bootstrap.ci(general_bayes_no_variation['string'])
print 'Bayes Variation: ', mean_confidence_interval(general_bayes_variation['string']), bootstrap.ci(general_bayes_variation['string'])
print 20 * '-'
print 'Jaccard Distance: '
print 'Random: ', mean_confidence_interval(general_random['jaccard']), bootstrap.ci(general_random['jaccard'])
print 'Siddharthan: ', mean_confidence_interval(general_siddharthan['jaccard']), bootstrap.ci(general_siddharthan['jaccard'])
print 'Deemter: ', mean_confidence_interval(general_deemter['jaccard']), bootstrap.ci(general_deemter['jaccard'])
print 'Bayes Random: ', mean_confidence_interval(general_bayes_random['jaccard']), bootstrap.ci(general_bayes_random['jaccard'])
print 'Bayes No Variation: ', mean_confidence_interval(general_bayes_no_variation['jaccard']), bootstrap.ci(general_bayes_no_variation['jaccard'])
print 'Bayes Variation: ', mean_confidence_interval(general_bayes_variation['jaccard']), bootstrap.ci(general_bayes_variation['jaccard'])
print 20 * '-'
print '\n'
print 'String -> T-test: Random X PN-Variation'
t, p = stats.ttest_ind(general_random['string'], general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_random['string'], general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Random X PN+Variation'
t, p = stats.ttest_ind(general_random['string'], general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_random['string'], general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Siddarthan X PN-Variation'
t, p = stats.ttest_ind(general_siddharthan['string'], general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_siddharthan['string'], general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Siddarthan X PN+Variation'
t, p = stats.ttest_ind(general_siddharthan['string'], general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_siddharthan['string'], general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Deemter X PN-Variation'
t, p = stats.ttest_ind(general_deemter['string'], general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_deemter['string'], general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Deemter X PN+Variation'
t, p = stats.ttest_ind(general_deemter['string'], general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_deemter['string'], general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: PN-Variation X PN+Variation'
t, p = stats.ttest_ind(general_bayes_no_variation['string'], general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_bayes_no_variation['string'], general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> One way ANOVA'
t, p = stats.f_oneway(general_random['string'], general_siddharthan['string'], general_deemter['string'], general_bayes_no_variation['string'], general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'Number of samples: ', number_samples, number_samples1
write_dir = '/roaming/tcastrof/names/eacl/evaluation'
write_csv(general_random, general_siddharthan, general_deemter, general_bayes_no_variation, general_bayes_variation, write_dir, domain)
def calculate_similarity(self, quartile=False):
"""
Mode is either ibm or Word2Vec
"""
print "calculating similarities"
PATH = os.path.dirname(os.path.abspath(__file__))
benchmarks = os.listdir(PATH+"/word-sim-data/")
mode = self.mode
vocab = self.vocab
word_vectors = self.word_vectors
rhos = []
p_values = []
num_pairs = []
reports = ''
cis = []
if quartile != False:
conc = self.conc
counter = 0
print "Using benchmarks:", benchmarks
print "Number of benchmarks:", len(benchmarks)
for name in benchmarks:
counter += 1
print "At benchmark:", name
print "Remaining:", len(benchmarks)-counter
benchmark = pd.read_csv(PATH+"/word-sim-data/"+name, sep='\t', header=None)
a = zip(benchmark[benchmark.columns[0]], benchmark[benchmark.columns[1]])
benchmark = dict(zip(a, benchmark[benchmark.columns[2]]))
wordpairs = [x for x in benchmark.keys() if x[0] in vocab and x[1] in vocab]
if quartile == 'lower':
sorted_wordpairs = sorted([(x,y,conc[x]*conc[y]) for x, y in wordpairs if x in conc
and y in conc and x != y], key=lambda x: x[2])
wordpairs = [(x, y) for x,y,z in sorted_wordpairs[:int(len(sorted_wordpairs)*0.5)]]
elif quartile == 'upper':
sorted_wordpairs= sorted([(x,y,conc[x]*conc[y]) for x, y in wordpairs if x in conc
and y in conc and x != y], key=lambda x: x[2])
wordpairs = [(x, y) for x,y,z in sorted_wordpairs[int(len(sorted_wordpairs)*0.5):]]
overlap = 0
orig_sim = []
predicted_sim = []
count = 0
for i in wordpairs:
count+=1
word1 = i[0]
word2 = i[1]
if word1 in vocab and word2 in vocab:
orig_sim.append(benchmark[i])
sim = self.similarity(word_vectors[word1], word_vectors[word2])
predicted_sim.append(sim)
if quartile != False:
reports += ' '.join([word1, word2, str(conc[word1]), str(conc[word2]),
str(benchmark[i]), str(sim), name])+'\n'
num_pairs.append(len(wordpairs))
corr = spearmanr(orig_sim, predicted_sim)
CIs = bootstrap.ci(data=(orig_sim, predicted_sim), statfunction=spearmanr, method='pi')
performance_record = dict(zip(wordpairs, zip(orig_sim, predicted_sim)))
print "Bootstrapped 95% confidence intervals\n, ", CIs[:, 0]
try:
rhos.append(round(corr[0], 3))
p_values.append(round(corr[1], 3))
cis.append(CIs[:, 0])
except:
rhos.append('-')
p_values.append('-')
cis.append('-')
benchmarks = map(lambda x: x.replace('.txt', '').replace('EN-', ''), benchmarks)
return benchmarks, p_values, rhos, num_pairs, cis
def get_ci(vals):
"""Bootstrapped 95% confidence intervals."""
return bootstrap.ci(vals, method='bca')
def run(std=True, domain=''):
entities = os.listdir(properties.evaluation_dir)
_random, bayes_random, bayes_no_variation, bayes_variation, siddharthan, deemter = get_values(
entities, domain)
general_random = {'accuracy': [], 'string': [], 'jaccard': []}
general_bayes_random = {'accuracy': [], 'string': [], 'jaccard': []}
general_bayes_no_variation = {'accuracy': [], 'string': [], 'jaccard': []}
general_bayes_variation = {'accuracy': [], 'string': [], 'jaccard': []}
general_siddharthan = {'accuracy': [], 'string': [], 'jaccard': []}
general_deemter = {'accuracy': [], 'string': [], 'jaccard': []}
number_samples, number_samples1 = 0, 0
for fold in bayes_random:
general_random['accuracy'].append(
accuracy_score(_random[fold]['y_real'], _random[fold]['y_pred']))
general_random['string'].append(np.mean(_random[fold]['string']))
general_random['jaccard'].append(np.mean(_random[fold]['jaccard']))
general_siddharthan['accuracy'].append(
accuracy_score(siddharthan[fold]['y_real'],
siddharthan[fold]['y_pred']))
general_siddharthan['string'].append(
np.mean(siddharthan[fold]['string']))
general_siddharthan['jaccard'].append(
np.mean(siddharthan[fold]['jaccard']))
general_deemter['accuracy'].append(
accuracy_score(deemter[fold]['y_real'], deemter[fold]['y_pred']))
general_deemter['string'].append(np.mean(deemter[fold]['string']))
general_deemter['jaccard'].append(np.mean(deemter[fold]['jaccard']))
general_bayes_random['accuracy'].append(
accuracy_score(bayes_random[fold]['y_real'],
bayes_random[fold]['y_pred']))
general_bayes_random['string'].append(
np.mean(bayes_random[fold]['string']))
general_bayes_random['jaccard'].append(
np.mean(bayes_random[fold]['jaccard']))
general_bayes_no_variation['accuracy'].append(
accuracy_score(bayes_no_variation[fold]['y_real'],
bayes_no_variation[fold]['y_pred']))
general_bayes_no_variation['string'].append(
np.mean(bayes_no_variation[fold]['string']))
general_bayes_no_variation['jaccard'].append(
np.mean(bayes_no_variation[fold]['jaccard']))
general_bayes_variation['accuracy'].append(
accuracy_score(bayes_variation[fold]['y_real'],
bayes_variation[fold]['y_pred']))
general_bayes_variation['string'].append(
np.mean(bayes_variation[fold]['string']))
general_bayes_variation['jaccard'].append(
np.mean(bayes_variation[fold]['jaccard']))
number_samples += len(bayes_no_variation[fold]['string'])
number_samples1 += len(siddharthan[fold]['string'])
if std and domain == '':
print 'Fold', fold
print 'Labels: '
print 'Random: ', accuracy_score(_random[fold]['y_real'],
_random[fold]['y_pred'])
print 'Siddharthan: ', accuracy_score(siddharthan[fold]['y_real'],
siddharthan[fold]['y_pred'])
print 'Deemter: ', accuracy_score(deemter[fold]['y_real'],
deemter[fold]['y_pred'])
print 'Bayes Random: ', accuracy_score(
bayes_random[fold]['y_real'], bayes_random[fold]['y_pred'])
print 'Bayes No Variation: ', accuracy_score(
bayes_no_variation[fold]['y_real'],
bayes_no_variation[fold]['y_pred'])
print 'Bayes Variation: ', accuracy_score(
bayes_variation[fold]['y_real'],
bayes_variation[fold]['y_pred'])
print 20 * '-'
print 'String Distance: '
print 'Random: ', np.mean(_random[fold]['string'])
print 'Siddharthan: ', np.mean(siddharthan[fold]['string'])
print 'Deemter: ', np.mean(deemter[fold]['string'])
print 'Bayes Random: ', np.mean(bayes_random[fold]['string'])
print 'Bayes No Variation: ', np.mean(
bayes_no_variation[fold]['string'])
print 'Bayes Variation: ', np.mean(bayes_variation[fold]['string'])
print 20 * '-'
print 'Jaccard Distance: '
print 'Random: ', np.mean(_random[fold]['jaccard'])
print 'Siddharthan: ', np.mean(siddharthan[fold]['jaccard'])
print 'Deemter: ', np.mean(deemter[fold]['jaccard'])
print 'Bayes Random: ', np.mean(bayes_random[fold]['jaccard'])
print 'Bayes No Variation: ', np.mean(
bayes_no_variation[fold]['jaccard'])
print 'Bayes Variation: ', np.mean(
bayes_variation[fold]['jaccard'])
print 20 * '-'
print '\n'
if std:
print 'GENERAL', domain
print 'Labels: '
print 'Random: ', np.mean(general_random['accuracy'])
print 'Siddharthan: ', np.mean(general_siddharthan['accuracy'])
print 'Deemter: ', np.mean(general_deemter['accuracy'])
print 'Bayes Random: ', np.mean(general_bayes_random['accuracy'])
print 'Bayes No Variation: ', np.mean(
general_bayes_no_variation['accuracy'])
print 'Bayes Variation: ', np.mean(general_bayes_variation['accuracy'])
print 20 * '-'
print 'String Distance: '
print 'Random: ', mean_confidence_interval(
general_random['string']), bootstrap.ci(general_random['string'])
print 'Siddharthan: ', mean_confidence_interval(
general_siddharthan['string']), bootstrap.ci(
general_siddharthan['string'])
print 'Deemter: ', mean_confidence_interval(
general_deemter['string']), bootstrap.ci(general_deemter['string'])
print 'Bayes Random: ', mean_confidence_interval(
general_bayes_random['string']), bootstrap.ci(
general_bayes_random['string'])
print 'Bayes No Variation: ', mean_confidence_interval(
general_bayes_no_variation['string']), bootstrap.ci(
general_bayes_no_variation['string'])
print 'Bayes Variation: ', mean_confidence_interval(
general_bayes_variation['string']), bootstrap.ci(
general_bayes_variation['string'])
print 20 * '-'
print 'Jaccard Distance: '
print 'Random: ', mean_confidence_interval(
general_random['jaccard']), bootstrap.ci(general_random['jaccard'])
print 'Siddharthan: ', mean_confidence_interval(
general_siddharthan['jaccard']), bootstrap.ci(
general_siddharthan['jaccard'])
print 'Deemter: ', mean_confidence_interval(
general_deemter['jaccard']), bootstrap.ci(
general_deemter['jaccard'])
print 'Bayes Random: ', mean_confidence_interval(
general_bayes_random['jaccard']), bootstrap.ci(
general_bayes_random['jaccard'])
print 'Bayes No Variation: ', mean_confidence_interval(
general_bayes_no_variation['jaccard']), bootstrap.ci(
general_bayes_no_variation['jaccard'])
print 'Bayes Variation: ', mean_confidence_interval(
general_bayes_variation['jaccard']), bootstrap.ci(
general_bayes_variation['jaccard'])
print 20 * '-'
print '\n'
print 'String -> T-test: Random X PN-Variation'
t, p = stats.ttest_ind(general_random['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_random['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Random X PN+Variation'
t, p = stats.ttest_ind(general_random['string'],
general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_random['string'],
general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Siddarthan X PN-Variation'
t, p = stats.ttest_ind(general_siddharthan['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_siddharthan['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Siddarthan X PN+Variation'
t, p = stats.ttest_ind(general_siddharthan['string'],
general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_siddharthan['string'],
general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Deemter X PN-Variation'
t, p = stats.ttest_ind(general_deemter['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_deemter['string'],
general_bayes_no_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: Deemter X PN+Variation'
t, p = stats.ttest_ind(general_deemter['string'],
general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_deemter['string'],
general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> T-test: PN-Variation X PN+Variation'
t, p = stats.ttest_ind(general_bayes_no_variation['string'],
general_bayes_variation['string'])
print round(t, 6), p
t, p = stats.ttest_rel(general_bayes_no_variation['string'],
general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'String -> One way ANOVA'
t, p = stats.f_oneway(general_random['string'],
general_siddharthan['string'],
general_deemter['string'],
general_bayes_no_variation['string'],
general_bayes_variation['string'])
print round(t, 6), p
print 10 * '-'
print 'Number of samples: ', number_samples, number_samples1
write_dir = '/roaming/tcastrof/names/eacl/evaluation'
write_csv(general_random, general_siddharthan, general_deemter,
general_bayes_no_variation, general_bayes_variation, write_dir,
domain)
def test_bca_multialpha(self):
np.random.seed(1234567890)
results = boot.ci(self.data, np.average, alpha=(0.1, 0.2, 0.8, 0.9))
np.testing.assert_array_almost_equal(
results, np.array([0.39210727, 0.50775386, 0.93673299, 1.0476729]))
def test_bca_multialpha(self):
np.random.seed(1234567890)
results = boot.ci(self.data,np.average,alpha=(0.1,0.2,0.8,0.9))
np.testing.assert_array_almost_equal(results,np.array([ 0.39210727, 0.50775386, 0.93673299, 1.0476729 ]))
def plot_learn_curves(channel_zips, conv_zips, rules=("PES", "hPES"), presentation=False):
group_by = "learn_type"
figsize = (8, 6) if presentation else (5, 4)
if presentation:
matplotlib.rc("font", size=18)
ext = "svg" if presentation else "pdf"
else:
plt.figure(figsize=figsize)
for func, zips in zip(("channel", "conv"), (channel_zips, conv_zips)):
if presentation:
plt.figure(figsize=figsize)
filenames = {"PES": [], "hPES": [], "control": []}
# Group files by group_by
for zfn in zips:
zfn_b = os.path.basename("%s" % zfn)
with zipfile.ZipFile("%s.zip" % zfn) as zf:
for l in zf.open("%s.txt" % zfn_b):
if group_by in l:
group = l.split("=")[1].strip()
filenames[group].append(zfn)
# Get the control results
control = []
for zfn in filenames["control"]:
with zipfile.ZipFile("%s.zip" % zfn) as zf:
zfn_b = os.path.basename("%s" % zfn)
with zf.open("%s.csv" % zfn_b) as fp:
time, error = get_data(fp, func, "full")
control.append(error)
control = np.vstack(control)
# Just take the mean for the control
control = np.mean(control, axis=0)
# Get the non-control results
colors = ("b", "g")
for rule, color in zip(rules, colors):
err = []
for zfn in filenames[rule]:
zfn_b = os.path.basename("%s" % zfn)
with zipfile.ZipFile("%s.zip" % zfn) as zf:
with zf.open("%s.csv" % zfn_b) as fp:
time, error = get_data(fp, func, "full")
err.append(error)
err = np.vstack(err) / control
mean = np.mean(err, axis=0)
conf = ci(err, axis=0)
if func == "channel":
if not presentation:
plt.subplot(122)
plt.title("Learning transmission")
plt.ylim(-0.4, 9)
plt.gca().yaxis.tick_right()
elif func == "conv":
if not presentation:
plt.subplot(121)
plt.title("Learning binding")
plt.ylabel("Error relative to control mean")
if not presentation:
plt.xticks(np.arange(0, 80, 20))
plt.xlim((0, 80))
plt.ylim(0.9, 1.6)
plt.gca().yaxis.set_ticks_position("left")
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["top"].set_visible(False)
plt.gca().xaxis.set_ticks_position("bottom")
plt.xlabel("Learning time (seconds)")
plt.axhline(1.0, lw=1, color="0.3")
plt.fill_between(time, y1=conf[1], y2=conf[0], color=color, alpha=0.3)
if presentation:
rule = "Combined, $S$=0.73" if rule == "hPES" else "Supervised, $S$=1"
plt.plot(time, mean, color=color, lw=1, label=rule)
if len(rules) > 1:
if presentation:
plt.legend(prop={"size": 16})
else:
plt.legend(prop={"size": 12})
if presentation:
plt.tight_layout()
name = func + "-learncurve" if len(rules) == 2 else func + "-learncurve-pes"
plt.savefig("%s/%s.%s" % (figuredir, name, ext), transparent=True)
print "Saved %s.%s" % (name, ext)
if not presentation:
plt.tight_layout()
plt.subplots_adjust(wspace=0)
name = "fig4-learn-curves" if len(rules) == 2 else "learncurve-pes"
plt.savefig("%s/%s.%s" % (figuredir, name, ext), transparent=True)
print "Saved fig4-learn-curves.%s" % ext
plt.close()
def test_bca_multi_multialpha(self):
np.random.seed(1234567890)
results1 = boot.ci((self.x,self.y), lambda a,b: stats.linregress(a,b)[1], alpha=(0.1,0.2,0.8,0.9),n_samples=1000)
np.random.seed(1234567890)
results2 = boot.ci(np.vstack((self.x,self.y)).T, lambda a: stats.linregress(a)[1], alpha=(0.1,0.2,0.8,0.9),n_samples=1000)
np.testing.assert_array_almost_equal(results1,results2)
def test_bca_n_samples(self):
np.random.seed(1234567890)
results = boot.ci(self.data,np.average,alpha=(0.1,0.2,0.8,0.9),n_samples=500)
np.testing.assert_array_almost_equal(results,np.array([ 0.40027628, 0.5063184 , 0.94082515, 1.05653929]))
''''''''' tr_COV_C = norm(C_mat_hat,'fro')**2/n tr_COV_C_neg = norm(C_mat_neg_hat,'fro')**2/n tr_COV_C # |C|_F^2/n for (X_1, X_2) tr_COV_C_neg # |C|_F^2/n for (X_1, -X_2) #Note: specify the global variables r_1, r_2, r_12 and P_mat #for the function compute_tr_COV_C(Y_1_t, Y_2_t) stated at the lines 15-21 r_1 = r_1_hat r_2 = r_2_hat r_12 = r_12_hat P_mat = P_mat_hat np.random.seed(0) # seed for the random number geneator of bootstrap tr_COV_C_interval = boot.ci((Y_1.T, Y_2.T), statfunction=compute_tr_COV_C, alpha=0.05, n_samples=5000, method='bca') tr_COV_C_interval[0,0],tr_COV_C_interval[1,0] # the 95% bootstrap CI for tr_COV_C tr_COV_C_interval[0,1],tr_COV_C_interval[1,1] # the 95% bootstrap CI for tr_COV_C_neg ''''''''' Choose the C of (X_1, X_2) or that of (X_1, -X_2) by the larger one of tr_COV_C and tr_COV_C_neg. See Remark 2 in the paper for details ''''''''' #The common-pattern matrix C rescaled with the magnitude of X_k C_scaled_1_hat = C_mat_hat * norm(X_1_hat,'fro')/np.sqrt(n) C_scaled_2_hat = C_mat_hat * norm(X_2_hat,'fro')/np.sqrt(n) #The distinctive-pattern matrix Delta_k
def test_pi_pandas_series(self):
np.random.seed(1234567890)
results = boot.ci(self.pds, np.average, method='pi')
np.testing.assert_array_almost_equal(results,
np.array([0.2288689, 1.21259752]))
