129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
import multiprocessing
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import time
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from traceback import print_exception as traceback
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from typing import List
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import numpy as np
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import pandas as pd
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import quapy as qp
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from quacc.dataset import Dataset
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from quacc.environment import env
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from quacc.evaluation import baseline, method
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from quacc.evaluation.report import CompReport, DatasetReport, EvaluationReport
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from quacc.evaluation.worker import estimate_worker
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from quacc.logger import Logger
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pd.set_option("display.float_format", "{:.4f}".format)
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qp.environ["SAMPLE_SIZE"] = env.SAMPLE_SIZE
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class CompEstimatorName_:
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def __init__(self, ce):
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self.ce = ce
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def __getitem__(self, e: str | List[str]):
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if isinstance(e, str):
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return self.ce._CompEstimator__get(e)[0]
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elif isinstance(e, list):
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return list(self.ce._CompEstimator__get(e).keys())
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class CompEstimatorFunc_:
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def __init__(self, ce):
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self.ce = ce
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def __getitem__(self, e: str | List[str]):
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if isinstance(e, str):
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return self.ce._CompEstimator__get(e)[1]
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elif isinstance(e, list):
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return list(self.ce._CompEstimator__get(e).values())
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class CompEstimator:
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__dict = method._methods | baseline._baselines
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def __get(cls, e: str | List[str]):
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if isinstance(e, str):
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try:
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return (e, cls.__dict[e])
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except KeyError:
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raise KeyError(f"Invalid estimator: estimator {e} does not exist")
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elif isinstance(e, list):
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_subtr = np.setdiff1d(e, list(cls.__dict.keys()))
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if len(_subtr) > 0:
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raise KeyError(
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f"Invalid estimator: estimator {_subtr[0]} does not exist"
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)
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e_fun = {k: fun for k, fun in cls.__dict.items() if k in e}
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if "ref" not in e:
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e_fun["ref"] = cls.__dict["ref"]
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return e_fun
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@property
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def name(self):
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return CompEstimatorName_(self)
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@property
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def func(self):
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return CompEstimatorFunc_(self)
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CE = CompEstimator()
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def evaluate_comparison(dataset: Dataset, estimators=None) -> EvaluationReport:
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log = Logger.logger()
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# with multiprocessing.Pool(1) as pool:
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with multiprocessing.Pool(len(estimators)) as pool:
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dr = DatasetReport(dataset.name)
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log.info(f"dataset {dataset.name}")
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for d in dataset():
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log.info(
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f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} started"
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)
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tstart = time.time()
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tasks = [
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(estim, d.train, d.validation, d.test) for estim in CE.func[estimators]
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]
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results = [
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pool.apply_async(estimate_worker, t, {"_env": env, "q": Logger.queue()})
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for t in tasks
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]
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results_got = []
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for _r in results:
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try:
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r = _r.get()
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if r["result"] is not None:
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results_got.append(r)
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except Exception as e:
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log.warning(
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f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}"
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)
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tend = time.time()
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times = {r["name"]: r["time"] for r in results_got}
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times["tot"] = tend - tstart
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log.info(
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f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} finished [took {times['tot']:.4f}s]"
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)
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try:
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cr = CompReport(
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[r["result"] for r in results_got],
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name=dataset.name,
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train_prev=d.train_prev,
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valid_prev=d.validation_prev,
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times=times,
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)
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except Exception as e:
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log.warning(
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f"Dataset sample {d.train_prev[1]:.2f} of dataset {dataset.name} failed. Exception: {e}"
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)
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traceback(e)
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cr = None
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dr += cr
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return dr
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