125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
import multiprocessing
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import time
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from typing import List
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import pandas as pd
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import quapy as qp
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from quapy.protocol import APP
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from sklearn.linear_model import LogisticRegression
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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.logger import Logger, SubLogger
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qp.environ["SAMPLE_SIZE"] = env.SAMPLE_SIZE
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pd.set_option("display.float_format", "{:.4f}".format)
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class CompEstimator:
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__dict = method._methods | baseline._baselines
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def __class_getitem__(cls, e: str | List[str]):
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if isinstance(e, str):
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try:
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return 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 = [k for k in e if k not in cls.__dict]
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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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return [fun for k, fun in cls.__dict.items() if k in e]
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CE = CompEstimator
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def fit_and_estimate(_estimate, train, validation, test, _env=None, q=None):
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_env = env if _env is None else _env
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SubLogger.setup(q)
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log = SubLogger.logger()
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model = LogisticRegression()
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model.fit(*train.Xy)
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protocol = APP(
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test,
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n_prevalences=_env.PROTOCOL_N_PREVS,
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repeats=_env.PROTOCOL_REPEATS,
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return_type="labelled_collection",
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)
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start = time.time()
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try:
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result = _estimate(model, validation, protocol)
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except Exception as e:
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log.error(f"Method {_estimate.__name__} failed. Exception: {e}")
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return {
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"name": _estimate.__name__,
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"result": None,
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"time": 0,
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}
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end = time.time()
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log.info(f"{_estimate.__name__} finished [took {end-start:.4f}s]")
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return {
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"name": _estimate.__name__,
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"result": result,
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"time": end - start,
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}
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def evaluate_comparison(
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dataset: Dataset, estimators=["OUR_BIN_SLD", "OUR_MUL_SLD"]
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) -> 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 = [(estim, d.train, d.validation, d.test) for estim in CE[estimators]]
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results = [
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pool.apply_async(
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fit_and_estimate, t, {"_env": env, "q": Logger.queue()}
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)
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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.error(
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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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dr += 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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return dr
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