setting a timeout for model_selection combinations in order to prevent some combinations to stuck the model selection

TweetSentQuant/experiments.py

@@ -7,7 +7,6 @@ import os
 import pickle
 import itertools
 from joblib import Parallel, delayed
-import multiprocessing
 import settings
 
 
@@ -78,6 +77,7 @@ def run(experiment):
         return
     else:
         print(f'running dataset={dataset_name} model={model_name} loss={optim_loss}')
+    return
 
     benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
     benchmark_devel.stats()
@@ -91,6 +91,7 @@ def run(experiment):
         n_repetitions=5,
         error=optim_loss,
         refit=False,
+        timeout=60*60,
         verbose=True
     )
     model_selection.fit(benchmark_devel.training, benchmark_devel.test)

TweetSentQuant/gen_tables.py

@@ -144,7 +144,6 @@ for i, eval_func in enumerate(evaluation_measures):
 
     save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
 
-
     # Tables ranks for AE and RAE (two tables)
     # ----------------------------------------------------
     methods = gao_seb_methods

TweetSentQuant/tabular.py

@@ -95,11 +95,6 @@ class Table:
                     normval = 1 - normval
                 self.map['color'][i, col_idx] = color_red2green_01(normval)
 
-    def _addlatex(self):
-        return
-        for i,j in self._getfilled():
-            self.map['latex'][i,j] = self.latex(self.rows[i], self.cols[j])
-
 
     def _run_ttest(self, row, col1, col2):
         mean1 = self.map['mean'][row, col1]
@@ -153,7 +148,6 @@ class Table:
         self._addrank()
         self._addcolor()
         self._addttest()
-        self._addlatex()
         if self.add_average:
             self._addave()
         self.modif = False

quapy/__init__.py

@@ -20,4 +20,4 @@ environ = {
 
 
 def isbinary(x):
-    return data.isbinary(x) or method.isbinary(x)
+    return x.binary

quapy/model_selection.py

@@ -6,6 +6,7 @@ from method.aggregative import BaseQuantifier
 from typing import Union, Callable
 import functional as F
 from copy import deepcopy
+import signal
 
 
 class GridSearchQ(BaseQuantifier):
@@ -21,6 +22,7 @@ class GridSearchQ(BaseQuantifier):
                  refit=False,
                  n_jobs=-1,
                  random_seed=42,
+                 timeout=-1,
                  verbose=False):
         """
         Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation
@@ -48,6 +50,9 @@ class GridSearchQ(BaseQuantifier):
         the best chosen hyperparameter combination
         :param n_jobs: number of parallel jobs
         :param random_seed: set the seed of the random generator to replicate experiments
+        :param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
+        Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
+        being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.
         :param verbose: set to True to get information through the stdout
         """
         self.model = model
@@ -59,8 +64,8 @@ class GridSearchQ(BaseQuantifier):
         self.refit = refit
         self.n_jobs = n_jobs
         self.random_seed = random_seed
+        self.timeout = timeout
         self.verbose = verbose
-
         self.__check_error(error)
 
     def sout(self, msg):
@@ -129,12 +134,23 @@ class GridSearchQ(BaseQuantifier):
         model = self.model
         n_jobs = self.n_jobs
 
+        if self.timeout > 0:
+            def handler(signum, frame):
+                self.sout('timeout reached')
+                raise TimeoutError()
+            signal.signal(signal.SIGALRM, handler)
+
         self.sout(f'starting optimization with n_jobs={n_jobs}')
         self.param_scores_ = {}
         self.best_score_ = None
+        some_timeouts = False
         for values in itertools.product(*params_values):
             params = {k: values[i] for i, k in enumerate(params_keys)}
 
+            if self.timeout > 0:
+                signal.alarm(self.timeout)
+
+            try:
                 # overrides default parameters with the parameters being explored at this iteration
                 model.set_params(**params)
                 model.fit(training)
@@ -152,6 +168,15 @@ class GridSearchQ(BaseQuantifier):
                         self.best_model_ = deepcopy(model)
                 self.param_scores_[str(params)] = score
 
+                if self.timeout > 0:
+                    signal.alarm(0)
+            except TimeoutError:
+                print(f'timeout reached for config {params}')
+                some_timeouts = True
+
+        if self.best_score_ is None and some_timeouts:
+            raise TimeoutError('all jobs took more than the timeout time to end')
+
         self.sout(f'optimization finished: best params {self.best_params_} (score={self.best_score_:.5f})')
         model.set_params(**self.best_params_)
         self.best_model_ = deepcopy(model)

test.py

@@ -20,7 +20,7 @@ param_grid = {'C': np.logspace(0,3,4), 'class_weight': ['balanced']}
 max_evaluations = 5000
 
 sample_size = qp.environ['SAMPLE_SIZE']
-binary = True
+binary = False
 svmperf_home = './svm_perf_quantification'
 
 if binary:
@@ -29,7 +29,7 @@ if binary:
 
 else:
     dataset = qp.datasets.fetch_twitter('hcr', for_model_selection=False, min_df=10, pickle=True)
-    dataset.training = dataset.training.sampling(sample_size, 0.2, 0.5, 0.3)
+    #dataset.training = dataset.training.sampling(sample_size, 0.2, 0.5, 0.3)
 
 print(f'dataset loaded: #training={len(dataset.training)} #test={len(dataset.test)}')
 
@@ -52,14 +52,15 @@ print(f'dataset loaded: #training={len(dataset.training)} #test={len(dataset.tes
 
 #learner = GridSearchCV(LogisticRegression(max_iter=1000), param_grid=param_grid, n_jobs=-1, verbose=1)
 learner = LogisticRegression(max_iter=1000)
-model = qp.method.meta.ECC(learner, size=20, red_size=10, param_grid=None, optim=None, policy='ds')
+model = qp.method.aggregative.ClassifyAndCount(learner)
+#model = qp.method.meta.ECC(learner, size=20, red_size=10, param_grid=None, optim=None, policy='ds')
 #model = qp.method.meta.EHDy(learner, param_grid=param_grid, optim='mae',
 #                           sample_size=sample_size, eval_budget=max_evaluations//10, n_jobs=-1)
 #model = qp.method.aggregative.ClassifyAndCount(learner)
 
 
-#if qp.isbinary(model) and not qp.isbinary(dataset):
-#    model = qp.method.aggregative.OneVsAll(model)
+if qp.isbinary(model) and not qp.isbinary(dataset):
+    model = qp.method.aggregative.OneVsAll(model)
 
 
 # Model fit and Evaluation on the test data
@@ -91,7 +92,6 @@ print(f'mae={error:.3f}')
 # Model fit and Evaluation according to the artificial sampling protocol
 # ----------------------------------------------------------------------------
 
-
 n_prevpoints = F.get_nprevpoints_approximation(combinations_budget=max_evaluations, n_classes=dataset.n_classes)
 n_evaluations = F.num_prevalence_combinations(n_prevpoints, dataset.n_classes)
 print(f'the prevalence interval [0,1] will be split in {n_prevpoints} prevalence points for each class, so that\n'
@@ -109,8 +109,6 @@ for error in qp.error.QUANTIFICATION_ERROR:
 
 # Model selection and Evaluation according to the artificial sampling protocol
 # ----------------------------------------------------------------------------
-sys.exit(0)
-
 
 model_selection = GridSearchQ(model,
                               param_grid=param_grid,
@@ -118,7 +116,8 @@ model_selection = GridSearchQ(model,
                               eval_budget=max_evaluations//10,
                               error='mae',
                               refit=True,
-                              verbose=True)
+                              verbose=True,
+                              timeout=4)
 
 model = model_selection.fit(dataset.training, validation=0.3)
 #model = model_selection.fit(train, validation=val)