def learning_curve_avg(classifiers, datasets, outdir, pickle_name='learning'):
with open(_get_learning_pickle_path(outdir, name=pickle_name),
'r') as results_file:
results = pickle_load(results_file)
for dataset in datasets:
print 'Dataset:', dataset
for classifier in classifiers:
print 'Classifier:', classifier
macro_avg = mean([
res_tup[0]
for res_tup in results[dataset][classifier].itervalues()
]) * 100
macro_tip = sorted((size, res_tup[0])
for size, res_tup in results[dataset]
[classifier].iteritems())[-1][1] * 100
amb_avg = mean([
res_tup[11]
for res_tup in results[dataset][classifier].itervalues()
])
amb_tip = sorted((size, res_tup[11]) for size, res_tup in
results[dataset][classifier].iteritems())[-1][1]
rec_avg = mean([
res_tup[13]
for res_tup in results[dataset][classifier].itervalues()
]) * 100
rec_tip = sorted((size, res_tup[13])
for size, res_tup in results[dataset]
[classifier].iteritems())[-1][1] * 100
print(
'{:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f} '
'MACROAVG/MACROTIP/AMBAVG/AMBTIP/RECAVG/RECTIP').format(
macro_avg, macro_tip, amb_avg, amb_tip, rec_avg, rec_tip)
def learning_curve_avg(classifiers, datasets, outdir, pickle_name='learning'):
with open(_get_learning_pickle_path(outdir, name=pickle_name), 'r') as results_file:
results = pickle_load(results_file)
for dataset in datasets:
print 'Dataset:', dataset
for classifier in classifiers:
print 'Classifier:', classifier
macro_avg = mean([res_tup[0] for res_tup
in results[dataset][classifier].itervalues()]) * 100
macro_tip = sorted((size, res_tup[0]) for size, res_tup
in results[dataset][classifier].iteritems())[-1][1] * 100
amb_avg = mean([res_tup[11] for res_tup
in results[dataset][classifier].itervalues()])
amb_tip = sorted((size, res_tup[11]) for size, res_tup
in results[dataset][classifier].iteritems())[-1][1]
rec_avg = mean([res_tup[13] for res_tup
in results[dataset][classifier].itervalues()]) * 100
rec_tip = sorted((size, res_tup[13]) for size, res_tup
in results[dataset][classifier].iteritems())[-1][1] * 100
print ('{:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f}/{:.2f} '
'MACROAVG/MACROTIP/AMBAVG/AMBTIP/RECAVG/RECTIP').format(
macro_avg, macro_tip, amb_avg, amb_tip, rec_avg, rec_tip)
def score_classifier_by_tup(classifier, test_tups):
# (TP, FP, FN) # Leaving out TN
results_by_class = {} #XXX: THIS HAS TO BE A CLASS!
for test_lbl, test_vec in izip(*test_tups):
if not isinstance(test_lbl, str):
test_lbl_type = classifier.name_by_lbl_id[test_lbl]
else:
test_lbl_type = test_lbl
# TODO: Cast annotation into span! It needs to be censored
predicted = classifier._classify(test_vec)
try:
results_by_class[test_lbl_type]
except KeyError:
results_by_class[test_lbl_type] = (0, 0, 0)
try:
results_by_class[predicted]
except KeyError:
results_by_class[predicted] = (0, 0, 0)
a_tp, a_fp, a_fn = results_by_class[test_lbl_type]
p_tp, p_fp, p_fn = results_by_class[predicted]
if predicted == test_lbl_type:
results_by_class[test_lbl_type] = (a_tp + 1, a_fp, a_fn)
if predicted != test_lbl_type:
results_by_class[test_lbl_type] = (a_tp, a_fp, a_fn + 1)
results_by_class[predicted] = (p_tp, p_fp + 1, p_fn)
# Extend the results to:
# macro, micro, {RESULTS_BY_CLASS}
tp_sum = sum([tp for tp, _, _ in results_by_class.itervalues()])
fn_sum = sum([fn for _, _, fn in results_by_class.itervalues()])
macro_score = tp_sum / float(tp_sum + fn_sum)
micro_scores = []
for res_tup in results_by_class.itervalues():
m_tp, _, m_fn = res_tup
m_tot = float(m_tp + m_fn)
if m_tot <= 0:
micro_scores.append(1.0)
else:
micro_scores.append(m_tp / float(m_tp + m_fn))
micro_score = mean(micro_scores)
return (macro_score, micro_score, tp_sum, fn_sum, results_by_class)
def score_classifier(classifier, test_set):
# (TP, FP, FN) # Leaving out TN
results_by_class = {} #XXX: THIS HAS TO BE A CLASS!
for document in test_set:
for sentence in document:
for annotation in sentence:
# TODO: Cast annotation into span! It needs to be censored
predicted = classifier.classify(document, sentence, annotation)
try:
results_by_class[annotation.type]
except KeyError:
results_by_class[annotation.type] = (0, 0, 0)
try:
results_by_class[predicted]
except KeyError:
results_by_class[predicted] = (0, 0, 0)
a_tp, a_fp, a_fn = results_by_class[annotation.type]
p_tp, p_fp, p_fn = results_by_class[predicted]
if predicted == annotation.type:
results_by_class[annotation.type] = (a_tp + 1, a_fp, a_fn)
if predicted != annotation.type:
results_by_class[annotation.type] = (a_tp, a_fp, a_fn + 1)
results_by_class[predicted] = (p_tp, p_fp + 1, p_fn)
# Extend the results to:
# macro, micro, {RESULTS_BY_CLASS}
tp_sum = sum([tp for tp, _, _ in results_by_class.itervalues()])
fn_sum = sum([fn for _, _, fn in results_by_class.itervalues()])
macro_score = tp_sum / float(tp_sum + fn_sum)
micro_scores = []
for res_tup in results_by_class.itervalues():
m_tp, _, m_fn = res_tup
m_tot = float(m_tp + m_fn)
if m_tot <= 0:
micro_scores.append(1.0)
else:
micro_scores.append(m_tp / float(m_tp + m_fn))
micro_score = mean(micro_scores)
return (macro_score, micro_score, tp_sum, fn_sum, results_by_class)
def manual_describe(df: pd.DataFrame) -> pd.DataFrame:
output_df = pd.DataFrame(
columns=[columnName for (columnName, columnData) in df.iteritems()],
index=['Count', 'Mean', 'Std', 'Min', '25%', '50%', '75%', 'Max'])
for (columnName, columnData) in df.iteritems():
if columnName in output_df.columns:
my_values = [
x for x in columnData.values[~np.isnan(columnData.values)]
]
my_values.sort()
count_val = count(my_values)
mean_val = mean(my_values)
std_val = standard_deviation(my_values)
min_val = min_(my_values)
quant_25_val = quantile(my_values, 0.25)
quant_50_val = median(my_values)
quant_75_val = quantile(my_values, 0.75)
max_val = max_(my_values)
output_df[columnName] = [
count_val, mean_val, std_val, min_val, quant_25_val,
quant_50_val, quant_75_val, max_val
]
return output_df
def _knockout_pass(f_id, classifier, train_data, folds, to_censor):
macro_scores = []
for fold_num, fold in enumerate(folds, start=1):
train_set = train_data - fold
test_set = fold
assert len(train_set) + len(test_set) == len(train_data)
train_vecs = [d for d in _censor_sparse_vectors_gen(
(v for _, v in train_set), to_censor)]
train_lbls = [l for l, _ in train_set]
classifier._liblinear_train(train_lbls, train_vecs)
test_vecs = [d for d in _censor_sparse_vectors_gen(
(v for _, v in test_set), to_censor)]
test_lbls = (l for l, _ in test_set)
res_tup =score_classifier_by_tup(classifier, (test_lbls, test_vecs))
macro_scores.append(res_tup[0])
mean = mean(macro_scores)
return f_id, mean
top = input("Pick a top number? ")
guess_range = range(bottom, top+1)
ans = random.randint(bottom, top)
games = 0
average_guesses = []
again = 'y'
while again == 'y':
ans = random.randint(bottom, top)
games += 1
print "Game %d: Number picked!..." % games
guesses = 0
guess = ''
while guess != ans:
print "Guess #%d:" % (guesses+1)
guess = input("> ")
guesses += 1
if guess > ans:
print "Too high,"
elif guess < ans:
print "Too low,"
else:
pass
if guess == ans:
average_guesses.append(guesses)
again = raw_input("Yes! Play again? (y/n)")
avg_guess = maths.mean(average_guesses)
print average_guesses
print "End of game, %d games played with an average of %f guesses." % (games, avg_guess)
def _learning_curve_test_data_set(classifiers,
train,
test,
worker_pool,
verbose=False,
no_simstring_cache=False,
use_test_set=False,
folds=10,
min_perc=5,
max_perc=100,
step_perc=5,
it_factor=1):
# XXX: Not necessary any more!
if verbose:
print >> stderr, 'Calculating train set size...',
train_size = 0
for d in train:
for s in d:
for a in s:
train_size += 1
if verbose:
print >> stderr, 'Done!'
# XXX:
if not no_simstring_cache:
simstring_caching(classifiers, (train, test), verbose=verbose)
# Collect the seen type to iterate over later
seen_types = set()
results_by_classifier = {}
for classifier_id, classifier_class in classifiers.iteritems():
if verbose:
print >> stderr, 'Classifier:', classifier_id, '...',
from classifier.liblinear import hashabledict
classifier = classifier_class()
if verbose:
print >> stderr, 'featurising train:', '...',
train_lbls, train_vecs = classifier._gen_lbls_vecs(train)
train_set = [e for e in izip(train_lbls, train_vecs)]
assert len(train_lbls) == train_size, '{} != {}'.format(
len(train_lbls), train_size)
assert len(train_vecs) == train_size, '{} != {}'.format(
len(train_vecs), train_size)
assert len(train_set) == train_size, '{} != {}'.format(
len(train_set), train_size)
del train_lbls
del train_vecs
if verbose:
print >> stderr, 'Done!',
print >> stderr, 'featurising test', '...',
test_lbls, test_vecs = classifier._gen_lbls_vecs(test)
test_vecs = [hashabledict(d) for d in test_vecs]
if verbose:
print >> stderr, 'Done!',
# Fix the seed so that we get comparable folds
seed(0xd5347d33)
args = ((classifier, fold) for fold in _train_fold_gen(
train_set, min_perc, max_perc, step_perc, it_factor))
if worker_pool is None:
res_it = (_train_fold(*arg) for arg in args)
else:
res_it = worker_pool.imap(__train_fold, args)
classifier_results = defaultdict(list)
print >> stderr, 'Training and evaluating models: ...',
i = 0
for sample_size, fold_classifier in res_it:
score, new_score = _score_classifier(fold_classifier, test_lbls,
test_vecs)
classifier_results[sample_size].append((score, new_score))
i += 1
if i % 10 == 0:
print >> stderr, i, '...',
print >> stderr, 'Done!'
# Process the results
for sample_size in sorted(e for e in classifier_results):
results = classifier_results[sample_size]
scores = [score for score, _ in results]
new_scores = [new_score for _, new_score in results]
macro_scores = [ms for ms, _, _, _, _ in scores]
micro_scores = [ms for _, ms, _, _, _ in scores]
tps = [tp for _, _, tp, _, _ in scores]
fns = [fn for _, _, _, fn, _ in scores]
res_dics = [d for _, _, _, _, d in scores]
# New metrics
ranks = [mean(rs) for rs, _, _ in new_scores]
ambiguities = [mean(ambs) for _, ambs, _ in new_scores]
recalls = [r for _, _, r in new_scores]
# These are means of means
ranks_mean = mean(ranks)
ranks_stddev = stddev(ranks)
ambiguities_mean = mean(ambiguities)
ambiguities_stddev = stddev(ambiguities)
recalls_mean = mean(recalls)
recalls_stddev = stddev(recalls)
classifier_result = (
mean(macro_scores),
stddev(macro_scores),
mean(micro_scores),
stddev(micro_scores),
mean(tps),
stddev(tps),
mean(fns),
stddev(fns),
res_dics,
# New metrics
ranks_mean,
ranks_stddev,
ambiguities_mean,
ambiguities_stddev,
recalls_mean,
recalls_stddev)
classifier_results[sample_size] = classifier_result
if verbose:
res_str = (
'Results {size}: '
'MACRO: {0:.3f} MACRO_STDDEV: {1:.3f} '
'MICRO: {2:.3f} MICRO_STDDEV: {3:.3f} '
'TP: {4:.3f} FP: {5:.3f} '
'MEAN_RANK: {mean_rank:.3f} MEAN_RANK_STDDEV: {mean_rank_stddev:.3f} '
'AVG_AMB: {avg_amb:.3f} AVG_AMB_STDDEV: {avg_amb_stddev:.3f} '
'RECALL: {recall:.3f} RECALL_STDDEV: {recall_stddev:.3f}'
).format(*classifier_result,
size=sample_size,
mean_rank=ranks_mean,
mean_rank_stddev=ranks_stddev,
avg_amb=ambiguities_mean,
avg_amb_stddev=ambiguities_stddev,
recall=recalls_mean,
recall_stddev=recalls_stddev)
print res_str
results_by_classifier[classifier_id] = classifier_results
return results_by_classifier
def _learning_curve_test_data_set(classifiers, train, test,
worker_pool, verbose=False, no_simstring_cache=False,
use_test_set=False, folds=10, min_perc=5, max_perc=100, step_perc=5,
it_factor=1):
# XXX: Not necessary any more!
if verbose:
print >> stderr, 'Calculating train set size...',
train_size = 0
for d in train:
for s in d:
for a in s:
train_size += 1
if verbose:
print >> stderr, 'Done!'
# XXX:
if not no_simstring_cache:
simstring_caching(classifiers, (train, test), verbose=verbose)
# Collect the seen type to iterate over later
seen_types = set()
results_by_classifier = {}
for classifier_id, classifier_class in classifiers.iteritems():
if verbose:
print >> stderr, 'Classifier:', classifier_id, '...',
from classifier.liblinear import hashabledict
classifier = classifier_class()
if verbose:
print >> stderr, 'featurising train:', '...',
train_lbls, train_vecs = classifier._gen_lbls_vecs(train)
train_set = [e for e in izip(train_lbls, train_vecs)]
assert len(train_lbls) == train_size, '{} != {}'.format(
len(train_lbls), train_size)
assert len(train_vecs) == train_size, '{} != {}'.format(
len(train_vecs), train_size)
assert len(train_set) == train_size, '{} != {}'.format(
len(train_set), train_size)
del train_lbls
del train_vecs
if verbose:
print >> stderr, 'Done!',
print >> stderr, 'featurising test', '...',
test_lbls, test_vecs = classifier._gen_lbls_vecs(test)
test_vecs = [hashabledict(d) for d in test_vecs]
if verbose:
print >> stderr, 'Done!',
# Fix the seed so that we get comparable folds
seed(0xd5347d33)
args = ((classifier, fold) for fold in _train_fold_gen(train_set,
min_perc, max_perc, step_perc, it_factor))
if worker_pool is None:
res_it = (_train_fold(*arg) for arg in args)
else:
res_it = worker_pool.imap(__train_fold, args)
classifier_results = defaultdict(list)
print >> stderr, 'Training and evaluating models: ...',
i = 0
for sample_size, fold_classifier in res_it:
score, new_score = _score_classifier(fold_classifier, test_lbls,
test_vecs)
classifier_results[sample_size].append((score, new_score))
i += 1
if i % 10 == 0:
print >> stderr, i, '...',
print >> stderr, 'Done!'
# Process the results
for sample_size in sorted(e for e in classifier_results):
results = classifier_results[sample_size]
scores = [score for score, _ in results]
new_scores = [new_score for _, new_score in results]
macro_scores = [ms for ms, _, _, _, _ in scores]
micro_scores = [ms for _, ms, _, _, _ in scores]
tps = [tp for _, _, tp, _, _ in scores]
fns = [fn for _, _, _, fn, _ in scores]
res_dics = [d for _, _, _, _, d in scores]
# New metrics
ranks = [mean(rs) for rs, _, _ in new_scores]
ambiguities = [mean(ambs) for _, ambs, _ in new_scores]
recalls = [r for _, _, r in new_scores]
# These are means of means
ranks_mean = mean(ranks)
ranks_stddev = stddev(ranks)
ambiguities_mean = mean(ambiguities)
ambiguities_stddev = stddev(ambiguities)
recalls_mean = mean(recalls)
recalls_stddev = stddev(recalls)
classifier_result = (
mean(macro_scores), stddev(macro_scores),
mean(micro_scores), stddev(micro_scores),
mean(tps), stddev(tps),
mean(fns), stddev(fns),
res_dics,
# New metrics
ranks_mean, ranks_stddev,
ambiguities_mean, ambiguities_stddev,
recalls_mean, recalls_stddev
)
classifier_results[sample_size] = classifier_result
if verbose:
res_str = ('Results {size}: '
'MACRO: {0:.3f} MACRO_STDDEV: {1:.3f} '
'MICRO: {2:.3f} MICRO_STDDEV: {3:.3f} '
'TP: {4:.3f} FP: {5:.3f} '
'MEAN_RANK: {mean_rank:.3f} MEAN_RANK_STDDEV: {mean_rank_stddev:.3f} '
'AVG_AMB: {avg_amb:.3f} AVG_AMB_STDDEV: {avg_amb_stddev:.3f} '
'RECALL: {recall:.3f} RECALL_STDDEV: {recall_stddev:.3f}'
).format(*classifier_result,
size=sample_size,
mean_rank=ranks_mean,
mean_rank_stddev=ranks_stddev,
avg_amb=ambiguities_mean,
avg_amb_stddev=ambiguities_stddev,
recall=recalls_mean,
recall_stddev=recalls_stddev
)
print res_str
results_by_classifier[classifier_id] = classifier_results
return results_by_classifier
guess_range = range(bottom, top + 1)
ans = random.randint(bottom, top)
games = 0
average_guesses = []
again = 'y'
while again == 'y':
ans = random.randint(bottom, top)
games += 1
print "Game %d: Number picked!..." % games
guesses = 0
guess = ''
while guess != ans:
print "Guess #%d:" % (guesses + 1)
guess = input("> ")
guesses += 1
if guess > ans:
print "Too high,"
elif guess < ans:
print "Too low,"
else:
pass
if guess == ans:
average_guesses.append(guesses)
again = raw_input("Yes! Play again? (y/n)")
avg_guess = maths.mean(average_guesses)
print average_guesses
print "End of game, %d games played with an average of %f guesses." % (
games, avg_guess)