From 6663b4c91d01c0edb5735da637cb2416433d031c Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Mon, 20 Nov 2023 22:05:26 +0100
Subject: [PATCH] context timeout

---
 quapy/model_selection.py | 54 ++++++++++++++++++++++++++++++++--------
 quapy/util.py            | 34 +++++++++++++++++++++++++
 2 files changed, 78 insertions(+), 10 deletions(-)

diff --git a/quapy/model_selection.py b/quapy/model_selection.py
index 9bd0985..6637d62 100644
--- a/quapy/model_selection.py
+++ b/quapy/model_selection.py
@@ -13,6 +13,7 @@ from quapy import evaluation
 from quapy.protocol import AbstractProtocol, OnLabelledCollectionProtocol
 from quapy.data.base import LabelledCollection
 from quapy.method.aggregative import BaseQuantifier, AggregativeQuantifier
+from quapy.util import timeout
 from time import time
 
 
@@ -127,7 +128,6 @@ class GridSearchQ(BaseQuantifier):
             raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
                              f'the name of an error function in {qp.error.QUANTIFICATION_ERROR_NAMES}')
 
-    @check_status
     def _prepare_classifier(self, args):
         cls_params = args['cls-params']
         training = args['training']
@@ -136,9 +136,8 @@ class GridSearchQ(BaseQuantifier):
         predictions = model.classifier_fit_predict(training)
         return {'model': model, 'predictions': predictions, 'cls-params': cls_params}
 
-    @check_status
     def _prepare_aggregation(self, args):
-        # (partial_setup, q_params), training = args
+
         model = args['model']
         predictions = args['predictions']
         cls_params = args['cls-params']
@@ -147,15 +146,32 @@ class GridSearchQ(BaseQuantifier):
 
         params = {**cls_params, **q_params}
 
-        model = deepcopy(model)
-        # overrides default parameters with the parameters being explored at this iteration
-        model.set_params(**q_params)
-        model.aggregation_fit(predictions, training)
-        score = evaluation.evaluate(model, protocol=self.protocol, error_metric=self.error)
+        def job(model):
+            tinit = time()
+            model = deepcopy(model)
+            # overrides default parameters with the parameters being explored at this iteration
+            model.set_params(**q_params)
+            model.aggregation_fit(predictions, training)
+            score = evaluation.evaluate(model, protocol=self.protocol, error_metric=self.error)
+            ttime = time()-tinit
+
+            return {
+                'model': model,
+                'cls-params':cls_params,
+                'q-params': q_params,
+                'params': params,
+                'score': score,
+                'ttime':ttime
+            }
+
+        out, status = self._error_handler(job, args)
+        if status == Status.SUCCESS:
+            self._sout(f'hyperparams=[{params}]\t got {self.error.__name__} = {out["score"]:.5f} [took {out["time"]:.4f}s]')
+        elif status == Status.INVALID:
+            self._sout(f'the combination of hyperparameters {params} is invalid')
+        elif status == Status.
 
-        return {'model': model, 'cls-params':cls_params, 'q-params': q_params, 'params': params, 'score': score}
 
-    @check_status
     def _prepare_model(self, args):
         params, training = args
         model = deepcopy(self.model)
@@ -309,6 +325,24 @@ class GridSearchQ(BaseQuantifier):
         raise ValueError('best_model called before fit')
 
 
+    def _error_handler(self, func, *args, **kwargs):
+
+        try:
+            with timeout(self.timeout):
+                output = func(*args, **kwargs)
+                return output, Status.SUCCESS
+
+        except TimeoutError:
+            return None, Status.TIMEOUT
+
+        except ValueError:
+            return None, Status.INVALID
+
+        except Exception:
+            return None, Status.ERROR
+
+
+
 def cross_val_predict(quantifier: BaseQuantifier, data: LabelledCollection, nfolds=3, random_state=0):
     """
     Akin to `scikit-learn's cross_val_predict <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html>`_
diff --git a/quapy/util.py b/quapy/util.py
index 51c2a41..de5c131 100644
--- a/quapy/util.py
+++ b/quapy/util.py
@@ -10,6 +10,8 @@ import quapy as qp
 
 import numpy as np
 from joblib import Parallel, delayed
+from time import time
+import signal
 
 
 def _get_parallel_slices(n_tasks, n_jobs):
@@ -257,3 +259,35 @@ class EarlyStop:
             if self.patience <= 0:
                 self.STOP = True
 
+
+@contextlib.contextmanager
+def timeout(seconds):
+    """
+    Opens a context that will launch an exception if not closed after a given number of seconds
+
+    >>> def func(start_msg, end_msg):
+    >>>     print(start_msg)
+    >>>     sleep(2)
+    >>>     print(end_msg)
+    >>>
+    >>> with timeout(1):
+    >>>     func('begin function', 'end function')
+    >>> Out[]
+    >>> begin function
+    >>> TimeoutError
+
+
+    :param seconds: number of seconds, set to <=0 to ignore the timer
+    """
+    if seconds > 0:
+        def handler(signum, frame):
+            raise TimeoutError()
+
+        signal.signal(signal.SIGALRM, handler)
+        signal.alarm(seconds)
+
+    yield
+
+    if seconds > 0:
+        signal.alarm(0)
+