Merge branch 'devel' of github.com:HLT-ISTI/QuaPy into devel
This commit is contained in:
commit
817aab1d99
|
|
@ -69,6 +69,9 @@ instance/
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|||
# Scrapy stuff:
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.scrapy
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||||
|
||||
# vscode config:
|
||||
.vscode/
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||||
|
||||
# Sphinx documentation
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docs/_build/
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||||
|
||||
|
|
@ -85,6 +88,11 @@ ipython_config.py
|
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# pyenv
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.python-version
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|
||||
# poetry
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poetry.toml
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||||
pyproject.toml
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poetry.lock
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|
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# pipenv
|
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
|
|
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|||
|
|
@ -29,12 +29,17 @@ def newLR():
|
|||
|
||||
|
||||
def calibratedLR():
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return CalibratedClassifierCV(LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1))
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return CalibratedClassifierCV(newLR())
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|
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|
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__C_range = np.logspace(-3, 3, 7)
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lr_params = {'classifier__C': __C_range, 'classifier__class_weight': [None, 'balanced']}
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svmperf_params = {'classifier__C': __C_range}
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lr_params = {
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'classifier__C': __C_range,
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'classifier__class_weight': [None, 'balanced']
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}
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svmperf_params = {
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'classifier__C': __C_range
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}
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||||
|
||||
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def quantification_models():
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|
|
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|
|
@ -0,0 +1,124 @@
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import pickle
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import os
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||||
from time import time
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from collections import defaultdict
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|
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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|
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import quapy as qp
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from quapy.method.aggregative import PACC, EMQ, KDEyML
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import UPP
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from pathlib import Path
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||||
|
||||
|
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SEED = 1
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|
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|
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def newLR():
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return LogisticRegression(max_iter=3000)
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||||
|
||||
# typical hyperparameters explored for Logistic Regression
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||||
logreg_grid = {
|
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'C': np.logspace(-3, 3, 7),
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||||
'class_weight': ['balanced', None]
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||||
}
|
||||
|
||||
def wrap_hyper(classifier_hyper_grid:dict):
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return {'classifier__'+k:v for k, v in classifier_hyper_grid.items()}
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|
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METHODS = [
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('PACC', PACC(newLR()), wrap_hyper(logreg_grid)),
|
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('EMQ', EMQ(newLR()), wrap_hyper(logreg_grid)),
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# ('KDEy-ML', KDEyML(newLR()), {**wrap_hyper(logreg_grid), **{'bandwidth': np.linspace(0.01, 0.2, 20)}}),
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]
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|
||||
|
||||
def show_results(result_path):
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import pandas as pd
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df = pd.read_csv(result_path+'.csv', sep='\t')
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pd.set_option('display.max_columns', None)
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pd.set_option('display.max_rows', None)
|
||||
pv = df.pivot_table(index='Dataset', columns="Method", values=["MAE", "MRAE", "t_train"], margins=True)
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print(pv)
|
||||
|
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def load_timings(result_path):
|
||||
import pandas as pd
|
||||
timings = defaultdict(lambda: {})
|
||||
if not Path(result_path + '.csv').exists():
|
||||
return timings
|
||||
|
||||
df = pd.read_csv(result_path+'.csv', sep='\t')
|
||||
return timings | df.pivot_table(index='Dataset', columns='Method', values='t_train').to_dict()
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||||
|
||||
if __name__ == '__main__':
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||||
|
||||
qp.environ['SAMPLE_SIZE'] = 500
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||||
qp.environ['N_JOBS'] = -1
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||||
n_bags_val = 250
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n_bags_test = 1000
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result_dir = f'results/ucimulti'
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|
||||
os.makedirs(result_dir, exist_ok=True)
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||||
|
||||
global_result_path = f'{result_dir}/allmethods'
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||||
timings = load_timings(global_result_path)
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||||
with open(global_result_path + '.csv', 'wt') as csv:
|
||||
csv.write(f'Method\tDataset\tMAE\tMRAE\tt_train\n')
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||||
|
||||
for method_name, quantifier, param_grid in METHODS:
|
||||
|
||||
print('Init method', method_name)
|
||||
|
||||
with open(global_result_path + '.csv', 'at') as csv:
|
||||
|
||||
for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
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||||
|
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print('init', dataset)
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||||
|
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local_result_path = os.path.join(Path(global_result_path).parent, method_name + '_' + dataset + '.dataframe')
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|
||||
if os.path.exists(local_result_path):
|
||||
print(f'result file {local_result_path} already exist; skipping')
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report = qp.util.load_report(local_result_path)
|
||||
|
||||
else:
|
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with qp.util.temp_seed(SEED):
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||||
|
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data = qp.datasets.fetch_UCIMulticlassDataset(dataset, verbose=True)
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||||
|
||||
# model selection
|
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train, test = data.train_test
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train, val = train.split_stratified(random_state=SEED)
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|
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protocol = UPP(val, repeats=n_bags_val)
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modsel = GridSearchQ(
|
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quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error='mae'
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||||
)
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|
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t_init = time()
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||||
try:
|
||||
modsel.fit(train)
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||||
|
||||
print(f'best params {modsel.best_params_}')
|
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print(f'best score {modsel.best_score_}')
|
||||
|
||||
quantifier = modsel.best_model()
|
||||
except:
|
||||
print('something went wrong... trying to fit the default model')
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quantifier.fit(train)
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timings[method_name][dataset] = time() - t_init
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|
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|
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protocol = UPP(test, repeats=n_bags_test)
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report = qp.evaluation.evaluation_report(
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||||
quantifier, protocol, error_metrics=['mae', 'mrae'], verbose=True
|
||||
)
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report.to_csv(local_result_path)
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|
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means = report.mean(numeric_only=True)
|
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csv.write(f'{method_name}\t{dataset}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{timings[method_name][dataset]:.3f}\n')
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csv.flush()
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|
||||
show_results(global_result_path)
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||||
|
|
@ -1,20 +1,17 @@
|
|||
import os
|
||||
import pandas as pd
|
||||
import math
|
||||
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.protocol import AbstractProtocol
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def get_sample_list(path_dir):
|
||||
"""Gets a sample list finding the csv files in a directory
|
||||
"""
|
||||
Gets a sample list finding the csv files in a directory
|
||||
|
||||
Args:
|
||||
path_dir (_type_): directory to look for samples
|
||||
|
||||
Returns:
|
||||
_type_: list of samples
|
||||
:param path_dir: directory to look for samples
|
||||
:return: list of samples
|
||||
"""
|
||||
samples = []
|
||||
for filename in sorted(os.listdir(path_dir)):
|
||||
|
|
@ -23,18 +20,15 @@ def get_sample_list(path_dir):
|
|||
return samples
|
||||
|
||||
|
||||
def generate_modelselection_split(samples, split=0.3):
|
||||
"""This function generates a train/test split for model selection
|
||||
def generate_modelselection_split(samples, test_prop=0.3):
|
||||
"""This function generates a train/test partition for model selection
|
||||
without the use of random numbers so the split is always the same
|
||||
|
||||
Args:
|
||||
samples (_type_): list of samples
|
||||
split (float, optional): percentage saved for test. Defaults to 0.3.
|
||||
|
||||
Returns:
|
||||
_type_: list of samples to use as train and list of samples to use as test
|
||||
:param samples: list of samples
|
||||
:param test_prop: float, percentage saved for test. Defaults to 0.3.
|
||||
:return: list of samples to use as train and list of samples to use as test
|
||||
"""
|
||||
num_items_to_pick = math.ceil(len(samples) * split)
|
||||
num_items_to_pick = math.ceil(len(samples) * test_prop)
|
||||
step_size = math.floor(len(samples) / num_items_to_pick)
|
||||
test_indices = [i * step_size for i in range(num_items_to_pick)]
|
||||
test = [samples[i] for i in test_indices]
|
||||
|
|
|
|||
|
|
@ -14,12 +14,17 @@ from quapy.util import download_file_if_not_exists, download_file, get_quapy_hom
|
|||
|
||||
|
||||
REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
|
||||
TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders',
|
||||
|
||||
TWITTER_SENTIMENT_DATASETS_TEST = [
|
||||
'gasp', 'hcr', 'omd', 'sanders',
|
||||
'semeval13', 'semeval14', 'semeval15', 'semeval16',
|
||||
'sst', 'wa', 'wb']
|
||||
TWITTER_SENTIMENT_DATASETS_TRAIN = ['gasp', 'hcr', 'omd', 'sanders',
|
||||
'sst', 'wa', 'wb',
|
||||
]
|
||||
TWITTER_SENTIMENT_DATASETS_TRAIN = [
|
||||
'gasp', 'hcr', 'omd', 'sanders',
|
||||
'semeval', 'semeval16',
|
||||
'sst', 'wa', 'wb']
|
||||
'sst', 'wa', 'wb',
|
||||
]
|
||||
UCI_BINARY_DATASETS = [
|
||||
#'acute.a', 'acute.b',
|
||||
'balance.1',
|
||||
|
|
@ -44,14 +49,37 @@ UCI_BINARY_DATASETS = [
|
|||
'transfusion',
|
||||
'wdbc',
|
||||
'wine.1', 'wine.2', 'wine.3',
|
||||
'wine-q-red', 'wine-q-white',
|
||||
'yeast']
|
||||
'wine-q-red',
|
||||
'wine-q-white',
|
||||
'yeast',
|
||||
]
|
||||
|
||||
UCI_MULTICLASS_DATASETS = ['dry-bean',
|
||||
UCI_MULTICLASS_DATASETS = [
|
||||
'dry-bean',
|
||||
'wine-quality',
|
||||
'academic-success',
|
||||
'digits',
|
||||
'letter']
|
||||
'letter',
|
||||
'abalone',
|
||||
'obesity',
|
||||
'nursery',
|
||||
'yeast',
|
||||
'hand_digits',
|
||||
'satellite',
|
||||
'shuttle',
|
||||
'cmc',
|
||||
'isolet',
|
||||
'waveform-v1',
|
||||
'molecular',
|
||||
'poker_hand',
|
||||
'connect-4',
|
||||
'mhr',
|
||||
'chess',
|
||||
'page_block',
|
||||
'phishing',
|
||||
'image_seg',
|
||||
'hcv',
|
||||
]
|
||||
|
||||
LEQUA2022_VECTOR_TASKS = ['T1A', 'T1B']
|
||||
LEQUA2022_TEXT_TASKS = ['T2A', 'T2B']
|
||||
|
|
@ -561,7 +589,13 @@ def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, verbose=Fals
|
|||
return data
|
||||
|
||||
|
||||
def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, verbose=False) -> Dataset:
|
||||
def fetch_UCIMulticlassDataset(
|
||||
dataset_name,
|
||||
data_home=None,
|
||||
min_test_split=0.3,
|
||||
max_train_instances=25000,
|
||||
min_class_support=100,
|
||||
verbose=False) -> Dataset:
|
||||
"""
|
||||
Loads a UCI multiclass dataset as an instance of :class:`quapy.data.base.Dataset`.
|
||||
|
||||
|
|
@ -583,15 +617,28 @@ def fetch_UCIMulticlassDataset(dataset_name, data_home=None, test_split=0.3, ver
|
|||
:param dataset_name: a dataset name
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
|
||||
:param min_test_split: minimum proportion of instances to be included in the test set. This value is interpreted
|
||||
as a minimum proportion, meaning that the real proportion could be higher in case the training proportion
|
||||
(1-`min_test_split`% of the instances) surpasses `max_train_instances`. In such case, only `max_train_instances`
|
||||
are taken for training, and the rest (irrespective of `min_test_split`) is taken for test.
|
||||
:param max_train_instances: maximum number of instances to keep for training (defaults to 25000)
|
||||
:param min_class_support: minimum number of istances per class. Classes with fewer instances
|
||||
are discarded (deafult is 100)
|
||||
:param verbose: set to True (default is False) to get information (stats) about the dataset
|
||||
:return: a :class:`quapy.data.base.Dataset` instance
|
||||
"""
|
||||
data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, verbose)
|
||||
return Dataset(*data.split_stratified(1 - test_split, random_state=0), name=dataset_name)
|
||||
|
||||
data = fetch_UCIMulticlassLabelledCollection(dataset_name, data_home, min_class_support, verbose=verbose)
|
||||
n = len(data)
|
||||
train_prop = (1.-min_test_split)
|
||||
n_train = int(n*train_prop)
|
||||
if n_train > max_train_instances:
|
||||
train_prop = (max_train_instances / n)
|
||||
|
||||
return Dataset(*data.split_stratified(train_prop, random_state=0))
|
||||
|
||||
|
||||
def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection:
|
||||
def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_class_support=100, verbose=False) -> LabelledCollection:
|
||||
"""
|
||||
Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.
|
||||
|
||||
|
|
@ -613,7 +660,9 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
|
|||
:param dataset_name: a dataset name
|
||||
:param data_home: specify the quapy home directory where the dataset will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:param test_split: proportion of documents to be included in the test set. The rest conforms the training set
|
||||
:param test_split: proportion of instances to be included in the test set. The rest conforms the training set
|
||||
:param min_class_support: minimum number of istances per class. Classes with fewer instances
|
||||
are discarded (deafult is 100)
|
||||
:param verbose: set to True (default is False) to get information (stats) about the dataset
|
||||
:return: a :class:`quapy.data.base.LabelledCollection` instance
|
||||
"""
|
||||
|
|
@ -626,19 +675,57 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
|
|||
data_home = get_quapy_home()
|
||||
|
||||
identifiers = {
|
||||
"dry-bean": 602,
|
||||
"wine-quality": 186,
|
||||
"academic-success": 697,
|
||||
"digits": 80,
|
||||
"letter": 59
|
||||
'dry-bean': 602,
|
||||
'wine-quality': 186,
|
||||
'academic-success': 697,
|
||||
'digits': 80,
|
||||
'letter': 59,
|
||||
'abalone': 1,
|
||||
'obesity': 544,
|
||||
'nursery': 76,
|
||||
'yeast': 110,
|
||||
'hand_digits': 81,
|
||||
'satellite': 146,
|
||||
'shuttle': 148,
|
||||
'cmc': 30,
|
||||
'isolet': 54,
|
||||
'waveform-v1': 107,
|
||||
'molecular': 69,
|
||||
'poker_hand': 158,
|
||||
'connect-4': 26,
|
||||
'mhr': 863,
|
||||
'chess': 23,
|
||||
'page_block': 78,
|
||||
'phishing': 379,
|
||||
'image_seg': 147,
|
||||
'hcv': 503,
|
||||
}
|
||||
|
||||
full_names = {
|
||||
"dry-bean": "Dry Bean Dataset",
|
||||
"wine-quality": "Wine Quality",
|
||||
"academic-success": "Predict students' dropout and academic success",
|
||||
"digits": "Optical Recognition of Handwritten Digits",
|
||||
"letter": "Letter Recognition"
|
||||
'dry-bean': 'Dry Bean Dataset',
|
||||
'wine-quality': 'Wine Quality',
|
||||
'academic-success': 'Predict students\' dropout and academic success',
|
||||
'digits': 'Optical Recognition of Handwritten Digits',
|
||||
'letter': 'Letter Recognition',
|
||||
'abalone': 'Abalone',
|
||||
'obesity': 'Estimation of Obesity Levels Based On Eating Habits and Physical Condition',
|
||||
'nursery': 'Nursery',
|
||||
'yeast': 'Yeast',
|
||||
'hand_digits': 'Pen-Based Recognition of Handwritten Digits',
|
||||
'satellite': 'Statlog Landsat Satellite',
|
||||
'shuttle': 'Statlog Shuttle',
|
||||
'cmc': 'Contraceptive Method Choice',
|
||||
'isolet': 'ISOLET',
|
||||
'waveform-v1': 'Waveform Database Generator (Version 1)',
|
||||
'molecular': 'Molecular Biology (Splice-junction Gene Sequences)',
|
||||
'poker_hand': 'Poker Hand',
|
||||
'connect-4': 'Connect-4',
|
||||
'mhr': 'Maternal Health Risk',
|
||||
'chess': 'Chess (King-Rook vs. King)',
|
||||
'page_block': 'Page Blocks Classification',
|
||||
'phishing': 'Website Phishing',
|
||||
'image_seg': 'Statlog (Image Segmentation)',
|
||||
'hcv': 'Hepatitis C Virus (HCV) for Egyptian patients',
|
||||
}
|
||||
|
||||
identifier = identifiers[dataset_name]
|
||||
|
|
@ -649,14 +736,36 @@ def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, verbose=
|
|||
|
||||
file = join(data_home, 'uci_multiclass', dataset_name+'.pkl')
|
||||
|
||||
def download(id):
|
||||
data = fetch_ucirepo(id=id)
|
||||
X, y = data['data']['features'].to_numpy(), data['data']['targets'].to_numpy().squeeze()
|
||||
def download(id, name):
|
||||
df = fetch_ucirepo(id=id)
|
||||
|
||||
df.data.features = pd.get_dummies(df.data.features, drop_first=True)
|
||||
X, y = df.data.features.to_numpy(), df.data.targets.to_numpy().squeeze()
|
||||
|
||||
assert y.ndim == 1, 'more than one y'
|
||||
|
||||
classes = np.sort(np.unique(y))
|
||||
y = np.searchsorted(classes, y)
|
||||
return LabelledCollection(X, y)
|
||||
|
||||
data = pickled_resource(file, download, identifier)
|
||||
def filter_classes(data: LabelledCollection, min_ipc):
|
||||
classes = data.classes_
|
||||
# restrict classes to only those with at least min_ipc instances
|
||||
classes = classes[data.counts() >= min_ipc]
|
||||
# filter X and y keeping only datapoints belonging to valid classes
|
||||
filter_idx = np.in1d(data.y, classes)
|
||||
X, y = data.X[filter_idx], data.y[filter_idx]
|
||||
# map classes to range(len(classes))
|
||||
y = np.searchsorted(classes, y)
|
||||
return LabelledCollection(X, y)
|
||||
|
||||
data = pickled_resource(file, download, identifier, dataset_name)
|
||||
data = filter_classes(data, min_class_support)
|
||||
if data.n_classes <= 2:
|
||||
raise ValueError(
|
||||
f'After filtering out classes with less than {min_class_support=} instances, the dataset {dataset_name} '
|
||||
f'is no longer multiclass. Try a reducing this value.'
|
||||
)
|
||||
|
||||
if verbose:
|
||||
data.stats()
|
||||
|
|
@ -746,8 +855,8 @@ def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=No
|
|||
information on this dataset, please follow the zenodo link).
|
||||
This dataset is based on the data available publicly at
|
||||
`WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>`_.
|
||||
The scripts for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.
|
||||
Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.
|
||||
The dataset already comes with processed features.
|
||||
The scripts used for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.
|
||||
|
||||
The datasets are downloaded only once, and stored for fast reuse.
|
||||
|
||||
|
|
@ -803,7 +912,7 @@ def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=No
|
|||
if for_model_selection:
|
||||
# In this case, return 70% of training data as the training set and 30% as the test set
|
||||
samples = get_sample_list(train_samples_path)
|
||||
train, test = generate_modelselection_split(samples, split=0.3)
|
||||
train, test = generate_modelselection_split(samples, test_prop=0.3)
|
||||
train_gen = IFCBTrainSamplesFromDir(path_dir=train_samples_path, classes=classes, samples=train)
|
||||
|
||||
# Test prevalence is computed from class labels
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ class QuaNetTrainer(BaseQuantifier):
|
|||
Example:
|
||||
|
||||
>>> import quapy as qp
|
||||
>>> from quapy.method.meta import QuaNet
|
||||
>>> from quapy.method_name.meta import QuaNet
|
||||
>>> from quapy.classification.neural import NeuralClassifierTrainer, CNNnet
|
||||
>>>
|
||||
>>> # use samples of 100 elements
|
||||
|
|
|
|||
|
|
@ -593,6 +593,7 @@ class PACC(AggregativeSoftQuantifier):
|
|||
if self.norm not in ACC.NORMALIZATIONS:
|
||||
raise ValueError(f"unknown normalization; valid ones are {ACC.NORMALIZATIONS}")
|
||||
|
||||
|
||||
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
|
||||
"""
|
||||
Estimates the misclassification rates
|
||||
|
|
|
|||
|
|
@ -6,6 +6,9 @@ import pickle
|
|||
import urllib
|
||||
from pathlib import Path
|
||||
from contextlib import ExitStack
|
||||
|
||||
import pandas as pd
|
||||
|
||||
import quapy as qp
|
||||
|
||||
import numpy as np
|
||||
|
|
@ -248,6 +251,28 @@ def _check_sample_size(sample_size):
|
|||
return sample_size
|
||||
|
||||
|
||||
def load_report(path, as_dict=False):
|
||||
def str2prev_arr(strprev):
|
||||
within = strprev.strip('[]').split()
|
||||
float_list = [float(p) for p in within]
|
||||
float_list[-1] = 1. - sum(float_list[:-1])
|
||||
return np.asarray(float_list)
|
||||
|
||||
df = pd.read_csv(path, index_col=0)
|
||||
df['true-prev'] = df['true-prev'].apply(str2prev_arr)
|
||||
df['estim-prev'] = df['estim-prev'].apply(str2prev_arr)
|
||||
if as_dict:
|
||||
d = {}
|
||||
for col in df.columns.values:
|
||||
vals = df[col].values
|
||||
if col in ['true-prev', 'estim-prev']:
|
||||
vals = np.vstack(vals)
|
||||
d[col] = vals
|
||||
return d
|
||||
else:
|
||||
return df
|
||||
|
||||
|
||||
class EarlyStop:
|
||||
"""
|
||||
A class implementing the early-stopping condition typically used for training neural networks.
|
||||
|
|
|
|||
Loading…
Reference in New Issue