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