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Alejandro Moreo Fernandez 2021-11-04 17:06:48 +01:00
parent de58849bb6 9c7c71f620
commit 4cd47cdf9f
14 changed files with 573 additions and 149 deletions
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13
LeQua2022/TODO.txt
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@ -1,8 +1,13 @@
1. los test hay que hacerlos suponiendo que las etiquetas no existen, es decir, viendo los resultados en los ficheros "prevalences" (renominar)
2. tablas?
3. fetch dataset (download, unzip, etc.)
4. model selection
5. plots
6. estoy leyendo los samples en orden, y no hace falta. Sería mejor una función genérica que lee todos los ejemplos y
que de todos modos genera un output con el mismo nombre del file
7. Make ResultSubmission class abstract, and create 4 instances thus forcing the field task_name to be set correctly
8. No me convence que la lectura de los samples (caso en que no hay ground truth) viene en orden aleatorio
9. Experimentar con vectores densos (PCA sobre tfidf por ejemplo)
10. Si cambiamos el formato de los samples (por ejemplo, en lugar de svmlight con .txt a PCA con .dat) hay que cambiar
cosas en el código. Está escrito varias veces un glob(*.txt)
11. Quitar las categorias como columnas de los ficheros de prevalences
12. sample_size cannot be set to a non-integer in GridSearchQ whith protocol="gen" (it could, but is not indicated in doc)
13. repair doc of GridSearchQ
14. reparar la calibracion en LR (lo tuve que quitar para que funcionara GridSearchQ, y lo quité en todos los ficheros)
15. podria poner que el eval_budget se usase en GridSearchQ con generator function para el progress bar de tqdm

84
LeQua2022/baselinesSVD_T1A.py Normal file
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import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants
from sklearn.decomposition import TruncatedSVD
# LeQua official baselines for task T1A (Binary/Vector)
# =====================================================
predictions_path = os.path.join('predictions', 'T1A')
os.makedirs(predictions_path, exist_ok=True)
models_path = os.path.join('models', 'T1A')
os.makedirs(models_path, exist_ok=True)
pathT1A = './data/T1A/public'
T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors')
T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv')
T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt')
train = LabelledCollection.load(T1A_trainpath, load_binary_vectors)
nF = train.instances.shape[1]
svd = TruncatedSVD(n_components=300)
train.instances = svd.fit_transform(train.instances)
qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
# classifier = CalibratedClassifierCV(LogisticRegression())
classifier = LogisticRegression()
model = quantifier(classifier).fit(train)
quantifier_name = model.__class__.__name__
predictions = ResultSubmission(categories=['negative', 'positive'])
for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF),
desc=quantifier_name, total=len(true_prevalence)):
sample = svd.transform(sample)
predictions.add(samplename, model.quantify(sample))
predictions.dump(os.path.join(predictions_path, quantifier_name + '.svd.csv'))
pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.svd.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
mae, mrae = evaluate_submission(true_prevalence, predictions)
print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}')
"""
test:
CC 0.1859 1.5406
ACC 0.0453 0.2840
PCC 0.1793 1.7187
PACC 0.0287 0.1494
EMQ 0.0225 0.1020
HDy 0.0631 0.2307
validation
CC 0.1862 1.9587
ACC 0.0394 0.2669
PCC 0.1789 2.1383
PACC 0.0354 0.1587
EMQ 0.0224 0.0960
HDy 0.0467 0.2121
"""

79
LeQua2022/baselines_T1A.py Normal file
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@ -0,0 +1,79 @@
import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants
# LeQua official baselines for task T1A (Binary/Vector)
# =====================================================
predictions_path = os.path.join('predictions', 'T1A')
os.makedirs(predictions_path, exist_ok=True)
models_path = os.path.join('models', 'T1A')
os.makedirs(models_path, exist_ok=True)
pathT1A = './data/T1A/public'
T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors')
T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv')
T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt')
train = LabelledCollection.load(T1A_trainpath, load_binary_vectors)
nF = train.instances.shape[1]
qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
# classifier = CalibratedClassifierCV(LogisticRegression(C=1))
classifier = LogisticRegression(C=1)
model = quantifier(classifier).fit(train)
quantifier_name = model.__class__.__name__
predictions = ResultSubmission(categories=['negative', 'positive'])
for samplename, sample in tqdm(gen_load_samples_T1(T1A_devvectors_path, nF),
desc=quantifier_name, total=len(true_prevalence)):
predictions.add(samplename, model.quantify(sample))
predictions.dump(os.path.join(predictions_path, quantifier_name + '.csv'))
pickle.dump(model, open(os.path.join(models_path, quantifier_name+'.pkl'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
mae, mrae = evaluate_submission(true_prevalence, predictions)
print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}')
"""
test:
CC 0.1859 1.5406
ACC 0.0453 0.2840
PCC 0.1793 1.7187
PACC 0.0287 0.1494
EMQ 0.0225 0.1020
HDy 0.0631 0.2307
validation
CC 0.1862 1.9587
ACC 0.0394 0.2669
PCC 0.1789 2.1383
PACC 0.0354 0.1587
EMQ 0.0224 0.0960
HDy 0.0467 0.2121
"""

91
LeQua2022/baselines_T1Amodsel.py Normal file
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@ -0,0 +1,91 @@
import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants
# LeQua official baselines for task T1A (Binary/Vector)
# =====================================================
predictions_path = os.path.join('predictions', 'T1A')
os.makedirs(predictions_path, exist_ok=True)
models_path = os.path.join('models', 'T1A')
os.makedirs(models_path, exist_ok=True)
pathT1A = './data/T1A/public'
T1A_devvectors_path = os.path.join(pathT1A, 'dev_vectors')
T1A_devprevalence_path = os.path.join(pathT1A, 'dev_prevalences.csv')
T1A_trainpath = os.path.join(pathT1A, 'training_vectors.txt')
train = LabelledCollection.load(T1A_trainpath, load_binary_vectors)
nF = train.instances.shape[1]
qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
param_grid = {'C': np.logspace(-3,3,7), 'class_weight': ['balanced', None]}
def gen_samples():
return gen_load_samples_T1(T1A_devvectors_path, nF, ground_truth_path=T1A_devprevalence_path, return_filename=False)
for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
#classifier = CalibratedClassifierCV(LogisticRegression(), n_jobs=-1)
classifier = LogisticRegression()
model = quantifier(classifier)
print(f'{model.__class__.__name__}: Model selection')
model = qp.model_selection.GridSearchQ(
model,
param_grid,
sample_size=None,
protocol='gen',
error=qp.error.mae,
refit=False,
verbose=True
).fit(train, gen_samples)
quantifier_name = model.best_model().__class__.__name__
print(f'{quantifier_name} mae={model.best_score_:.3f} (params: {model.best_params_})')
pickle.dump(model.best_model(),
open(os.path.join(models_path, quantifier_name+'.modsel.pkl'), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
"""
test:
CC 0.1859 1.5406
ACC 0.0453 0.2840
PCC 0.1793 1.7187
PACC 0.0287 0.1494
EMQ 0.0225 0.1020
HDy 0.0631 0.2307
validation
CC 0.1862 1.9587
ACC 0.0394 0.2669
PCC 0.1789 2.1383
PACC 0.0354 0.1587
EMQ 0.0224 0.0960
HDy 0.0467 0.2121
"""

55
LeQua2022/baselines_T1B.py Normal file
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@ -0,0 +1,55 @@
import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import *
import os
import constants
predictions_path = os.path.join('predictions', 'T1B') # multiclass - vector
os.makedirs(predictions_path, exist_ok=True)
pathT1B = './data/T1B/public'
T1B_devvectors_path = os.path.join(pathT1B, 'dev_vectors')
T1B_devprevalence_path = os.path.join(pathT1B, 'dev_prevalences.csv')
T1B_trainpath = os.path.join(pathT1B, 'training_vectors.txt')
T1B_catmap = os.path.join(pathT1B, 'training_vectors_label_map.txt')
train = LabelledCollection.load(T1B_trainpath, load_binary_vectors)
nF = train.instances.shape[1]
qp.environ['SAMPLE_SIZE'] = constants.T1B_SAMPLE_SIZE
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
true_prevalence = ResultSubmission.load(T1B_devprevalence_path)
cat2code, categories = load_category_map(T1B_catmap)
for quantifier in [PACC]: # [CC, ACC, PCC, PACC, EMQ]:
classifier = CalibratedClassifierCV(LogisticRegression())
model = quantifier(classifier).fit(train)
quantifier_name = model.__class__.__name__
predictions = ResultSubmission(categories=categories)
for samplename, sample in tqdm(gen_load_samples_T1(T1B_devvectors_path, nF),
desc=quantifier_name, total=len(true_prevalence)):
predictions.add(samplename, model.quantify(sample))
predictions.dump(os.path.join(predictions_path, quantifier_name + '.csv'))
mae, mrae = evaluate_submission(true_prevalence, predictions)
print(f'{quantifier_name} mae={mae:.3f} mrae={mrae:.3f}')

7
LeQua2022/constants.py Normal file
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@ -0,0 +1,7 @@
DEV_SAMPLES = 1000
TEST_SAMPLES = 5000
T1A_SAMPLE_SIZE = 250
T1B_SAMPLE_SIZE = 1000
ERROR_TOL = 1E-3

123
LeQua2022/data.py
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@ -7,6 +7,9 @@ import quapy as qp
import numpy as np
import sklearn
import re
from glob import glob
import constants
# def load_binary_raw_document(path):
@ -20,19 +23,48 @@ import re
# def load_multiclass_raw_document(path):
# return qp.data.from_text(path, verbose=0, class2int=False)
def load_category_map(path):
cat2code = {}
with open(path, 'rt') as fin:
for line in fin:
category, code = line.split()
cat2code[category] = int(code)
code2cat = [cat for cat, code in sorted(cat2code.items(), key=lambda x:x[1])]
return cat2code, code2cat
def load_binary_vectors(path, nF=None):
return sklearn.datasets.load_svmlight_file(path, n_features=nF)
def gen_load_samples_T1A(path_dir:str, ground_truth_path:str = None):
# for ... : yield
pass
def __gen_load_samples_with_groudtruth(path_dir:str, return_filename:bool, ground_truth_path:str, load_fn, **load_kwargs):
true_prevs = ResultSubmission.load(ground_truth_path)
for filename, prevalence in true_prevs.iterrows():
sample, _ = load_fn(os.path.join(path_dir, filename), **load_kwargs)
if return_filename:
yield filename, sample, prevalence
else:
yield sample, prevalence
def gen_load_samples_T1B(path_dir:str, ground_truth_path:str = None):
# for ... : yield
pass
def __gen_load_samples_without_groudtruth(path_dir:str, return_filename:bool, load_fn, **load_kwargs):
for filepath in glob(os.path.join(path_dir, '*_sample_*.txt')):
sample, _ = load_fn(filepath, **load_kwargs)
if return_filename:
yield os.path.basename(filepath), sample
else:
yield sample
def gen_load_samples_T1(path_dir:str, nF:int, ground_truth_path:str = None, return_filename=True):
if ground_truth_path is None:
# the generator function returns tuples (filename:str, sample:csr_matrix)
gen_fn = __gen_load_samples_without_groudtruth(path_dir, return_filename, load_binary_vectors, nF=nF)
else:
# the generator function returns tuples (filename:str, sample:csr_matrix, prevalence:ndarray)
gen_fn = __gen_load_samples_with_groudtruth(path_dir, return_filename, ground_truth_path, load_binary_vectors, nF=nF)
for r in gen_fn:
yield r
def gen_load_samples_T2A(path_dir:str, ground_truth_path:str = None):
@ -46,9 +78,6 @@ def gen_load_samples_T2B(path_dir:str, ground_truth_path:str = None):
class ResultSubmission:
DEV_LEN = 1000
TEST_LEN = 5000
ERROR_TOL = 1E-3
def __init__(self, categories: List[str]):
if not isinstance(categories, list) or len(categories) < 2:
@ -80,9 +109,9 @@ class ResultSubmission:
raise ValueError(f'error: wrong shape found for prevalence vector {prevalence_values}')
if (prevalence_values<0).any() or (prevalence_values>1).any():
raise ValueError(f'error: prevalence values out of range [0,1] for "{sample_name}"')
if np.abs(prevalence_values.sum()-1) > ResultSubmission.ERROR_TOL:
if np.abs(prevalence_values.sum()-1) > constants.ERROR_TOL:
raise ValueError(f'error: prevalence values do not sum up to one for "{sample_name}"'
f'(error tolerance {ResultSubmission.ERROR_TOL})')
f'(error tolerance {constants.ERROR_TOL})')
new_entry = dict([('filename',sample_name)]+[(col_i,prev_i) for col_i, prev_i in zip(self.categories, prevalence_values)])
self.df = self.df.append(new_entry, ignore_index=True)
@ -93,7 +122,7 @@ class ResultSubmission:
@classmethod
def load(cls, path: str) -> 'ResultSubmission':
df, inferred_type = ResultSubmission.check_file_format(path, return_inferred_type=True)
r = ResultSubmission(categories=df.columns.values.tolist())
r = ResultSubmission(categories=df.columns.values[1:].tolist())
r.inferred_type = inferred_type
r.df = df
return r
@ -102,13 +131,19 @@ class ResultSubmission:
ResultSubmission.check_dataframe_format(self.df)
self.df.to_csv(path)
def get(self, sample_name:str):
def prevalence(self, sample_name:str):
sel = self.df.loc[self.df['filename'] == sample_name]
if sel.empty:
return None
else:
return sel.loc[:,self.df.columns[1]:].values.flatten()
def iterrows(self):
for index, row in self.df.iterrows():
filename = row.filename
prevalence = row[self.df.columns[1]:].values.flatten()
yield filename, prevalence
@classmethod
def check_file_format(cls, path, return_inferred_type=False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
df = pd.read_csv(path, index_col=0)
@ -116,7 +151,7 @@ class ResultSubmission:
@classmethod
def check_dataframe_format(cls, df, path=None, return_inferred_type=False) -> Union[pd.DataFrame, Tuple[pd.DataFrame, str]]:
hint_path = '' # if given, show the data path in the error messages
hint_path = '' # if given, show the data path in the error message
if path is not None:
hint_path = f' in {path}'
@ -125,33 +160,33 @@ class ResultSubmission:
if df.empty:
raise ValueError(f'error{hint_path}: results file is empty')
elif len(df) == ResultSubmission.DEV_LEN:
elif len(df) == constants.DEV_SAMPLES:
inferred_type = 'dev'
expected_len = ResultSubmission.DEV_LEN
elif len(df) == ResultSubmission.TEST_LEN:
expected_len = constants.DEV_SAMPLES
elif len(df) == constants.TEST_SAMPLES:
inferred_type = 'test'
expected_len = ResultSubmission.TEST_LEN
expected_len = constants.TEST_SAMPLES
else:
raise ValueError(f'wrong number of prevalence values found{hint_path}; '
f'expected {ResultSubmission.DEV_LEN} for development sets and '
f'{ResultSubmission.TEST_LEN} for test sets; found {len(df)}')
f'expected {constants.DEV_SAMPLES} for development sets and '
f'{constants.TEST_SAMPLES} for test sets; found {len(df)}')
set_names = frozenset(df.filename)
for i in range(expected_len):
if f'{inferred_type}_sample_{i}.txt' not in set_names:
raise ValueError(f'{hint_path} a file with {len(df)} entries is assumed to be of type '
raise ValueError(f'error{hint_path} a file with {len(df)} entries is assumed to be of type '
f'"{inferred_type}" but entry {inferred_type}_sample_{i}.txt is missing '
f'(among perhaps many others)')
for category_name in df.columns[1:]:
if (df[category_name] < 0).any() or (df[category_name] > 1).any():
raise ValueError(f'{hint_path} column "{category_name}" contains values out of range [0,1]')
raise ValueError(f'error{hint_path} column "{category_name}" contains values out of range [0,1]')
prevs = df.loc[:, df.columns[1]:].values
round_errors = np.abs(prevs.sum(axis=-1) - 1.) > ResultSubmission.ERROR_TOL
round_errors = np.abs(prevs.sum(axis=-1) - 1.) > constants.ERROR_TOL
if round_errors.any():
raise ValueError(f'warning: prevalence values in rows with id {np.where(round_errors)[0].tolist()} '
f'do not sum up to 1 (error tolerance {ResultSubmission.ERROR_TOL}), '
f'do not sum up to 1 (error tolerance {constants.ERROR_TOL}), '
f'probably due to some rounding errors.')
if return_inferred_type:
@ -163,20 +198,31 @@ class ResultSubmission:
self.df = self.df.reindex([self.df.columns[0]] + sorted(self.df.columns[1:]), axis=1)
self.categories = sorted(self.categories)
def filenames(self):
return self.df.filename.values
def evaluate_submission(true_prevs: ResultSubmission, predicted_prevs: ResultSubmission, sample_size=1000, average=True):
def evaluate_submission(true_prevs: ResultSubmission, predicted_prevs: ResultSubmission, sample_size=None, average=True):
if sample_size is None:
if qp.environ['SAMPLE_SIZE'] is None:
raise ValueError('Relative Absolute Error cannot be computed: '
'neither sample_size nor qp.environ["SAMPLE_SIZE"] have been specified')
else:
sample_size = qp.environ['SAMPLE_SIZE']
if len(true_prevs) != len(predicted_prevs):
raise ValueError(f'size mismatch, groun truth has {len(true_prevs)} entries '
f'while predictions contain {len(predicted_prevs)} entries')
raise ValueError(f'size mismatch, ground truth file has {len(true_prevs)} entries '
f'while the file of predictions contain {len(predicted_prevs)} entries')
true_prevs.sort_categories()
predicted_prevs.sort_categories()
if true_prevs.categories != predicted_prevs.categories:
raise ValueError(f'these result files are not comparable since the categories are different')
raise ValueError(f'these result files are not comparable since the categories are different: '
f'true={true_prevs.categories} vs. predictions={predicted_prevs.categories}')
ae, rae = [], []
for sample_name in true_prevs.df.filename.values:
ae.append(qp.error.mae(true_prevs.get(sample_name), predicted_prevs.get(sample_name)))
rae.append(qp.error.mrae(true_prevs.get(sample_name), predicted_prevs.get(sample_name), eps=sample_size))
for sample_name, true_prevalence in true_prevs.iterrows():
pred_prevalence = predicted_prevs.prevalence(sample_name)
ae.append(qp.error.ae(true_prevalence, pred_prevalence))
rae.append(qp.error.rae(true_prevalence, pred_prevalence, eps=1./(2*sample_size)))
ae = np.asarray(ae)
rae = np.asarray(rae)
if average:
@ -187,21 +233,6 @@ def evaluate_submission(true_prevs: ResultSubmission, predicted_prevs: ResultSub
# r = ResultSubmission(['negative', 'positive'])
# from tqdm import tqdm
# for i in tqdm(range(1000), total=1000):
# r.add(f'dev_sample_{i}.txt', np.asarray([0.5, 0.5]))
# r.dump('./path.csv')
# r = ResultSubmission.load('./data/T1A/public/dummy_submission.csv')
# t = ResultSubmission.load('./data/T1A/public/dummy_submission (copy).csv')
# print(r.df)
# print(r.get('dev_sample_10.txt'))
# print(evaluate_submission(r, t))
# s = ResultSubmission.load('./data/T1A/public/dummy_submission.csv')
#
# print(s)

41
LeQua2022/evaluation.py Normal file
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@ -0,0 +1,41 @@
import argparse
import quapy as qp
from data import ResultSubmission, evaluate_submission
import constants
import os
"""
LeQua2022 Official evaluation script
"""
def main(args):
if args.task in {'T1A'}:
qp.environ['SAMPLE_SIZE'] = constants.T1A_SAMPLE_SIZE
true_prev = ResultSubmission.load(args.true_prevalences)
pred_prev = ResultSubmission.load(args.pred_prevalences)
mae, mrae = evaluate_submission(true_prev, pred_prev)
print(f'MAE: {mae:.4f}')
print(f'MRAE: {mrae:.4f}')
if args.output is not None:
outdir = os.path.dirname(args.output)
if outdir:
os.makedirs(outdir, exist_ok=True)
with open(args.output, 'wt') as foo:
foo.write(f'MAE: {mae:.4f}\n')
foo.write(f'MRAE: {mrae:.4f}\n')
if __name__=='__main__':
parser = argparse.ArgumentParser(description='LeQua2022 official evaluation script')
parser.add_argument('task', metavar='TASK', type=str, choices=['T1A', 'T1B', 'T2A', 'T2B'],
help='Task name (T1A, T1B, T2A, T2B)')
parser.add_argument('true_prevalences', metavar='TRUE-PREV-PATH', type=str,
help='Path of ground truth prevalence values file (.csv)')
parser.add_argument('pred_prevalences', metavar='PRED-PREV-PATH', type=str,
help='Path of predicted prevalence values file (.csv)')
parser.add_argument('--output', metavar='SCORES-PATH', type=str, default=None,
help='Path where to store the evaluation scores')
args = parser.parse_args()
main(args)

27
LeQua2022/format_checker.py Normal file
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@ -0,0 +1,27 @@
import argparse
import quapy as qp
from data import ResultSubmission, evaluate_submission
import constants
import os
"""
LeQua2022 Official format-checker script
"""
def main(args):
try:
ResultSubmission.check_file_format(args.prevalence_file)
except Exception as e:
print(e)
print('Format check: not passed')
else:
print('Format check: passed')
if __name__=='__main__':
parser = argparse.ArgumentParser(description='LeQua2022 official format-checker script')
parser.add_argument('prevalence_file', metavar='PREV-PATH', type=str,
help='Path of the file containing prevalence values to check')
args = parser.parse_args()
main(args)

89
LeQua2022/main_binary_vector.py
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@ -1,89 +0,0 @@
import pickle
import numpy as np
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import pandas as pd
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from data import load_binary_vectors
import os
path_binary_vector = './data/T1A'
result_path = os.path.join('results', 'T1A') # binary - vector
os.makedirs(result_path, exist_ok=True)
train_file = os.path.join(path_binary_vector, 'public', 'training_vectors.txt')
train = LabelledCollection.load(train_file, load_binary_vectors)
nF = train.instances.shape[1]
print(f'number of classes: {len(train.classes_)}')
print(f'number of training documents: {len(train)}')
print(f'training prevalence: {F.strprev(train.prevalence())}')
print(f'training matrix shape: {train.instances.shape}')
dev_prev = pd.read_csv(os.path.join(path_binary_vector, 'public', 'dev_prevalences.csv'), index_col=0)
print(dev_prev)
scores = {}
for quantifier in [CC]: #, ACC, PCC, PACC, EMQ, HDy]:
classifier = CalibratedClassifierCV(LogisticRegression())
model = quantifier(classifier).fit(train)
quantifier_name = model.__class__.__name__
scores[quantifier_name]={}
for sample_set, sample_size in [('dev', 1000)]:
ae_errors, rae_errors = [], []
for i, row in tqdm(dev_prev.iterrows(), total=len(dev_prev), desc=f'testing {quantifier_name} in {sample_set}'):
filename = row['filename']
prev_true = row[1:].values
sample_path = os.path.join(path_binary_vector, 'public', f'{sample_set}_vectors', filename)
sample, _ = load_binary_vectors(sample_path, nF)
qp.environ['SAMPLE_SIZE'] = sample.shape[0]
prev_estim = model.quantify(sample)
# prev_true = sample.prevalence()
ae_errors.append(qp.error.mae(prev_true, prev_estim))
rae_errors.append(qp.error.mrae(prev_true, prev_estim))
ae_errors = np.asarray(ae_errors)
rae_errors = np.asarray(rae_errors)
mae = ae_errors.mean()
mrae = rae_errors.mean()
scores[quantifier_name][sample_set] = {'mae': mae, 'mrae': mrae}
pickle.dump(ae_errors, open(os.path.join(result_path, f'{quantifier_name}.{sample_set}.ae.pickle'), 'wb'), pickle.HIGHEST_PROTOCOL)
pickle.dump(rae_errors, open(os.path.join(result_path, f'{quantifier_name}.{sample_set}.rae.pickle'), 'wb'), pickle.HIGHEST_PROTOCOL)
print(f'{quantifier_name} {sample_set} MAE={mae:.4f}')
print(f'{quantifier_name} {sample_set} MRAE={mrae:.4f}')
for model in scores:
for sample_set in ['validation']:#, 'test']:
print(f'{model}\t{scores[model][sample_set]["mae"]:.4f}\t{scores[model][sample_set]["mrae"]:.4f}')
"""
test:
CC 0.1859 1.5406
ACC 0.0453 0.2840
PCC 0.1793 1.7187
PACC 0.0287 0.1494
EMQ 0.0225 0.1020
HDy 0.0631 0.2307
validation
CC 0.1862 1.9587
ACC 0.0394 0.2669
PCC 0.1789 2.1383
PACC 0.0354 0.1587
EMQ 0.0224 0.0960
HDy 0.0467 0.2121
"""

62
LeQua2022/predict.py Normal file
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@ -0,0 +1,62 @@
import argparse
import quapy as qp
from data import ResultSubmission, evaluate_submission
import constants
import os
import pickle
from tqdm import tqdm
from data import gen_load_samples_T1, load_category_map
from glob import glob
import constants
"""
LeQua2022 prediction script
"""
def main(args):
# check the number of samples
nsamples = len(glob(os.path.join(args.samples, '*.txt')))
if nsamples not in {constants.DEV_SAMPLES, constants.TEST_SAMPLES}:
print(f'Warning: The number of samples does neither coincide with the expected number of '
f'dev samples ({constants.DEV_SAMPLES}) nor with the expected number of '
f'test samples ({constants.TEST_SAMPLES}).')
_, categories = load_category_map(args.catmap)
# load pickled model
model = pickle.load(open(args.model, 'rb'))
# predictions
predictions = ResultSubmission(categories=categories)
for samplename, sample in tqdm(gen_load_samples_T1(args.samples, args.nf),
desc='predicting', total=nsamples):
predictions.add(samplename, model.quantify(sample))
# saving
basedir = os.path.basename(args.output)
if basedir:
os.makedirs(basedir, exist_ok=True)
predictions.dump(args.output)
if __name__=='__main__':
parser = argparse.ArgumentParser(description='LeQua2022 prediction script')
parser.add_argument('model', metavar='MODEL-PATH', type=str,
help='Path of saved model')
parser.add_argument('samples', metavar='SAMPLES-PATH', type=str,
help='Path to the directory containing the samples')
parser.add_argument('output', metavar='PREDICTIONS-PATH', type=str,
help='Path where to store the predictions file')
parser.add_argument('catmap', metavar='CATEGORY-MAP-PATH', type=str,
help='Path to the category map file')
parser.add_argument('nf', metavar='NUM-FEATURES', type=int,
help='Number of features seen during training')
args = parser.parse_args()
if not os.path.exists(args.samples):
raise FileNotFoundError(f'path {args.samples} does not exist')
if not os.path.isdir(args.samples):
raise ValueError(f'path {args.samples} is not a valid directory')
main(args)

2
quapy/data/preprocessing.py
View File

@ -149,7 +149,7 @@ class IndexTransformer:
def index(self, documents):
vocab = self.vocabulary_.copy()
return [[vocab.get(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
return [[vocab.prevalence(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
def fit_transform(self, X, n_jobs=-1):
return self.fit(X).transform(X, n_jobs=n_jobs)

22
quapy/evaluation.py
View File

@ -9,6 +9,7 @@ from quapy.method.base import BaseQuantifier
from quapy.util import temp_seed
import quapy.functional as F
import pandas as pd
import inspect
def artificial_prevalence_prediction(
@ -78,6 +79,27 @@ def natural_prevalence_prediction(
return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
def gen_prevalence_prediction(model: BaseQuantifier, gen_fn: Callable, eval_budget=None):
if not inspect.isgenerator(gen_fn()):
raise ValueError('param "gen_fun" is not a generator')
if not isinstance(eval_budget, int):
eval_budget = -1
true_prevalences, estim_prevalences = [], []
for sample_instances, true_prev in gen_fn():
true_prevalences.append(true_prev)
estim_prevalences.append(model.quantify(sample_instances))
eval_budget -= 1
if eval_budget == 0:
break
true_prevalences = np.asarray(true_prevalences)
estim_prevalences = np.asarray(estim_prevalences)
return true_prevalences, estim_prevalences
def _predict_from_indexes(
indexes,
model: BaseQuantifier,

27
quapy/model_selection.py
View File

@ -5,8 +5,9 @@ from typing import Union, Callable
import quapy as qp
from quapy.data.base import LabelledCollection
from quapy.evaluation import artificial_prevalence_prediction, natural_prevalence_prediction
from quapy.evaluation import artificial_prevalence_prediction, natural_prevalence_prediction, gen_prevalence_prediction
from quapy.method.aggregative import BaseQuantifier
import inspect
class GridSearchQ(BaseQuantifier):
@ -74,8 +75,10 @@ class GridSearchQ(BaseQuantifier):
self.timeout = timeout
self.verbose = verbose
self.__check_error(error)
assert self.protocol in {'app', 'npp'}, \
'unknown protocol; valid ones are "app" or "npp" for the "artificial" or the "natural" prevalence protocols'
assert self.protocol in {'app', 'npp', 'gen'}, \
'unknown protocol: valid ones are "app" or "npp" for the "artificial" or the "natural" prevalence ' \
'protocols. Use protocol="gen" when passing a generator function thorough val_split that yields a ' \
'sample (instances) and their prevalence (ndarray) at each iteration.'
if self.protocol == 'npp':
if self.n_repetitions is None or self.n_repetitions == 1:
if self.eval_budget is not None:
@ -99,9 +102,14 @@ class GridSearchQ(BaseQuantifier):
assert 0. < validation < 1., 'validation proportion should be in (0,1)'
training, validation = training.split_stratified(train_prop=1 - validation)
return training, validation
elif self.protocol=='gen' and inspect.isgenerator(validation()):
return training, validation
else:
raise ValueError(f'"validation" must either be a LabelledCollection or a float in (0,1) indicating the'
f'proportion of training documents to extract (type found: {type(validation)})')
f'proportion of training documents to extract (type found: {type(validation)}). '
f'Optionally, "validation" can be a callable function returning a generator that yields '
f'the sample instances along with their true prevalence at each iteration by '
f'setting protocol="gen".')
def __check_error(self, error):
if error in qp.error.QUANTIFICATION_ERROR:
@ -132,6 +140,8 @@ class GridSearchQ(BaseQuantifier):
return natural_prevalence_prediction(
model, val_split, self.sample_size,
**commons)
elif self.protocol == 'gen':
return gen_prevalence_prediction(model, gen_fn=val_split, eval_budget=self.eval_budget)
else:
raise ValueError('unknown protocol')
@ -144,7 +154,8 @@ class GridSearchQ(BaseQuantifier):
if val_split is None:
val_split = self.val_split
training, val_split = self.__check_training_validation(training, val_split)
assert isinstance(self.sample_size, int) and self.sample_size > 0, 'sample_size must be a positive integer'
if self.protocol != 'gen':
assert isinstance(self.sample_size, int) and self.sample_size > 0, 'sample_size must be a positive integer'
params_keys = list(self.param_grid.keys())
params_values = list(self.param_grid.values())
@ -192,8 +203,6 @@ class GridSearchQ(BaseQuantifier):
raise TimeoutError('all jobs took more than the timeout time to end')
self.sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f})')
# model.set_params(**self.best_params_)
# self.best_model_ = deepcopy(model)
if self.refit:
self.sout(f'refitting on the whole development set')
@ -203,11 +212,11 @@ class GridSearchQ(BaseQuantifier):
def quantify(self, instances):
assert hasattr(self, 'best_model_'), 'quantify called before fit'
return self.best_model_.quantify(instances)
return self.best_model().quantify(instances)
@property
def classes_(self):
return self.best_model_.classes_
return self.best_model().classes_
def set_params(self, **parameters):
self.param_grid = parameters