125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
import itertools
|
|
import os
|
|
|
|
import quapy as qp
|
|
from ClassifierAccuracy.util.plotting import plot_diagonal
|
|
from quapy.protocol import UPP
|
|
|
|
from quacc.dataset import save_dataset_stats
|
|
from quacc.experiments.generators import (
|
|
any_missing,
|
|
gen_acc_measure,
|
|
gen_bin_datasets,
|
|
gen_CAP,
|
|
gen_CAP_cont_table,
|
|
gen_classifiers,
|
|
gen_multi_datasets,
|
|
gen_tweet_datasets,
|
|
)
|
|
from quacc.experiments.report import TestReport
|
|
from quacc.experiments.util import (
|
|
fit_method,
|
|
predictionsCAP,
|
|
predictionsCAPcont_table,
|
|
prevs_from_prot,
|
|
true_acc,
|
|
)
|
|
|
|
PROBLEM = "binary"
|
|
ORACLE = False
|
|
basedir = PROBLEM + ("-oracle" if ORACLE else "")
|
|
|
|
|
|
if PROBLEM == "binary":
|
|
qp.environ["SAMPLE_SIZE"] = 1000
|
|
NUM_TEST = 1000
|
|
gen_datasets = gen_bin_datasets
|
|
elif PROBLEM == "multiclass":
|
|
qp.environ["SAMPLE_SIZE"] = 250
|
|
NUM_TEST = 1000
|
|
gen_datasets = gen_multi_datasets
|
|
elif PROBLEM == "tweet":
|
|
qp.environ["SAMPLE_SIZE"] = 100
|
|
NUM_TEST = 1000
|
|
gen_datasets = gen_tweet_datasets
|
|
|
|
|
|
for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(
|
|
gen_classifiers(), gen_datasets()
|
|
):
|
|
print(f"training {cls_name} in {dataset_name}")
|
|
h.fit(*L.Xy)
|
|
|
|
# test generation protocol
|
|
test_prot = UPP(
|
|
U, repeats=NUM_TEST, return_type="labelled_collection", random_state=0
|
|
)
|
|
|
|
# compute some stats of the dataset
|
|
save_dataset_stats(f"dataset_stats/{dataset_name}.json", test_prot, L, V)
|
|
|
|
# precompute the actual accuracy values
|
|
true_accs = {}
|
|
for acc_name, acc_fn in gen_acc_measure():
|
|
true_accs[acc_name] = [true_acc(h, acc_fn, Ui) for Ui in test_prot()]
|
|
|
|
# instances of ClassifierAccuracyPrediction are bound to the evaluation measure, so they
|
|
# must be nested in the acc-for
|
|
for acc_name, acc_fn in gen_acc_measure():
|
|
print(f"\tfor measure {acc_name}")
|
|
for method_name, method in gen_CAP(h, acc_fn, with_oracle=ORACLE):
|
|
report = TestReport(cls_name, acc_name, dataset_name, method_name)
|
|
if os.path.exists(report.path(basedir)):
|
|
print(f"\t\t{method_name}-{acc_name} exists, skipping")
|
|
continue
|
|
|
|
print(f"\t\t{method_name} computing...")
|
|
method, t_train = fit_method(method, V)
|
|
estim_accs, t_test_ave = predictionsCAP(method, test_prot, ORACLE)
|
|
test_prevs = prevs_from_prot(test_prot)
|
|
report.add_result(
|
|
test_prevs=test_prevs,
|
|
true_accs=true_accs[acc_name],
|
|
estim_accs=estim_accs,
|
|
t_train=t_train,
|
|
t_test_ave=t_test_ave,
|
|
).save_json(basedir)
|
|
|
|
# instances of CAPContingencyTable instead are generic, and the evaluation measure can
|
|
# be nested to the predictions to speed up things
|
|
for method_name, method in gen_CAP_cont_table(h):
|
|
if not any_missing(basedir, cls_name, dataset_name, method_name):
|
|
print(
|
|
f"\t\tmethod {method_name} has all results already computed. Skipping."
|
|
)
|
|
continue
|
|
|
|
print(f"\t\tmethod {method_name} computing...")
|
|
|
|
method, t_train = fit_method(method, V)
|
|
estim_accs_dict, t_test_ave = predictionsCAPcont_table(
|
|
method, test_prot, gen_acc_measure, ORACLE
|
|
)
|
|
for acc_name in estim_accs_dict.keys():
|
|
report = TestReport(cls_name, acc_name, dataset_name, method_name)
|
|
report.add_result(
|
|
true_accs=true_accs[acc_name],
|
|
estim_accs=estim_accs,
|
|
t_train=t_train,
|
|
t_test_ave=t_test_ave,
|
|
).save_json(basedir)
|
|
|
|
print()
|
|
|
|
# generate diagonal plots
|
|
print("generating plots")
|
|
for (cls_name, _), (acc_name, _) in itertools.product(
|
|
gen_classifiers(), gen_acc_measure()
|
|
):
|
|
plot_diagonal(basedir, cls_name, acc_name)
|
|
for dataset_name, _ in gen_datasets(only_names=True):
|
|
plot_diagonal(basedir, cls_name, acc_name, dataset_name=dataset_name)
|
|
|
|
print("generating tables")
|
|
# gen_tables(basedir, datasets=[d for d, _ in gen_datasets(only_names=True)])
|