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GridSearchQ adapted to work with generator functions and integrated for the baselines of LeQua2022; some tests with SVD

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Alejandro Moreo Fernandez 2021-10-26 18:41:10 +02:00
parent 9a08125e7e
commit a7e87e41f8
9 changed files with 315 additions and 34 deletions
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12
LeQua2022/TODO.txt
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@ -1,9 +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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@ -0,0 +1,84 @@
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
"""

16
LeQua2022/main_binary_vector.py → LeQua2022/baselines_T1A.py
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@ -13,9 +13,16 @@ from data import *
import os
import constants
predictions_path = os.path.join('predictions', 'T1A') # binary - vector
# 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')
@ -35,16 +42,19 @@ true_prevalence = ResultSubmission.load(T1A_devprevalence_path)
for quantifier in [CC, ACC, PCC, PACC, EMQ, HDy]:
classifier = CalibratedClassifierCV(LogisticRegression())
# 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_T1A(T1A_devvectors_path, nF),
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}')

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}')

3
LeQua2022/constants.py
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@ -2,5 +2,6 @@ DEV_SAMPLES = 1000
TEST_SAMPLES = 5000
T1A_SAMPLE_SIZE = 250
T1B_SAMPLE_SIZE = 1000
ERROR_TOL=1E-3
ERROR_TOL = 1E-3

39
LeQua2022/data.py
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@ -26,40 +26,45 @@ import constants
def load_category_map(path):
cat2code = {}
with open(path, 'rt') as fin:
category, code = fin.readline().split()
cat2code[category] = int(code)
return cat2code
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_with_groudtruth(path_dir:str, ground_truth_path:str, load_fn, **load_kwargs):
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)
yield filename, sample, prevalence
if return_filename:
yield filename, sample, prevalence
else:
yield sample, prevalence
def __gen_load_samples_without_groudtruth(path_dir:str, load_fn, **load_kwargs):
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)
yield os.path.basename(filepath), sample
if return_filename:
yield os.path.basename(filepath), sample
else:
yield sample
def gen_load_samples_T1A(path_dir:str, nF:int, ground_truth_path:str = None):
def gen_load_samples_T1(path_dir:str, nF:int, ground_truth_path:str = None, return_filename=True):
if ground_truth_path is None:
for filename, sample in __gen_load_samples_without_groudtruth(path_dir, load_binary_vectors, nF=nF):
yield filename, sample
# 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:
for filename, sample, prevalence in __gen_load_samples_with_groudtruth(path_dir, ground_truth_path, load_binary_vectors, nF=nF):
yield filename, sample, prevalence
def gen_load_samples_T1B(path_dir:str, ground_truth_path:str = None):
# for ... : yield
pass
# 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):

22
quapy/evaluation.py
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@ -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
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@ -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