def _get_ensemble_process(self, time_left_for_ensembles, task=None, metric=None, precision=None, dataset_name=None, max_iterations=-1, ensemble_nbest=None, ensemble_size=None): if task is None: task = self._task else: self._task = task if metric is None: metric = self._metric else: self._metric = metric if precision is None: precision = self.precision else: self.precision = precision if dataset_name is None: dataset_name = self._dataset_name else: self._dataset_name = dataset_name if ensemble_nbest is None: ensemble_nbest = self._ensemble_nbest else: self._ensemble_nbest = ensemble_nbest if ensemble_size is None: ensemble_size = self._ensemble_size else: self._ensemble_size = ensemble_size return EnsembleBuilder(backend=self._backend, dataset_name=dataset_name, task_type=task, metric=metric, limit=time_left_for_ensembles, ensemble_size=ensemble_size, ensemble_nbest=ensemble_nbest, seed=self._seed, shared_mode=self._shared_mode, precision=precision, max_iterations=max_iterations)
def _get_ensemble_process(self, time_left_for_ensembles, task=None, precision=None, dataset_name=None, max_iterations=None, ensemble_nbest=None, ensemble_size=None): if task is None: task = self._task else: self._task = task if precision is None: precision = self.precision else: self.precision = precision if dataset_name is None: dataset_name = self._dataset_name else: self._dataset_name = dataset_name if ensemble_nbest is None: ensemble_nbest = self._ensemble_nbest else: self._ensemble_nbest = ensemble_nbest if ensemble_size is None: ensemble_size = self._ensemble_size else: self._ensemble_size = ensemble_size return EnsembleBuilder( backend=self._backend, dataset_name=dataset_name, task_type=task, metric=self._metric, limit=time_left_for_ensembles, ensemble_size=ensemble_size, ensemble_nbest=ensemble_nbest, max_models_on_disc=self._max_models_on_disc, seed=self._seed, shared_mode=self._shared_mode, precision=precision, max_iterations=max_iterations, read_at_most=np.inf, memory_limit=self._ensemble_memory_limit, random_state=self._seed, )
def testPerformanceRangeThreshold(ensemble_backend, performance_range_threshold, exp): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=100, performance_range_threshold=performance_range_threshold) ensbuilder.read_scores = { 'A': { 'ens_score': 1, 'num_run': 1, 'loaded': -1, "seed": 1 }, 'B': { 'ens_score': 2, 'num_run': 2, 'loaded': -1, "seed": 1 }, 'C': { 'ens_score': 3, 'num_run': 3, 'loaded': -1, "seed": 1 }, 'D': { 'ens_score': 4, 'num_run': 4, 'loaded': -1, "seed": 1 }, 'E': { 'ens_score': 5, 'num_run': 5, 'loaded': -1, "seed": 1 }, } ensbuilder.read_preds = { key: {key_2: True for key_2 in (Y_ENSEMBLE, Y_VALID, Y_TEST)} for key in ensbuilder.read_scores } sel_keys = ensbuilder.get_n_best_preds() assert len(sel_keys) == exp
def test_main(ensemble_backend): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, ) ensbuilder.SAVE2DISC = False run_history, ensemble_nbest, _, _, _ = ensbuilder.main( time_left=np.inf, iteration=1, return_predictions=False, ) assert len(ensbuilder.read_preds) == 3 assert ensbuilder.last_hash is not None assert ensbuilder.y_true_ensemble is not None # Make sure the run history is ok # We expect at least 1 element to be in the ensemble assert len(run_history) > 0 # As the data loader loads the same val/train/test # we expect 1.0 as score and all keys available expected_performance = { 'ensemble_val_score': 1.0, 'ensemble_test_score': 1.0, 'ensemble_optimization_score': 1.0, } # Make sure that expected performance is a subset of the run history assert all(item in run_history[0].items() for item in expected_performance.items()) assert 'Timestamp' in run_history[0] assert isinstance(run_history[0]['Timestamp'], pd.Timestamp) assert os.path.exists( os.path.join(ensemble_backend.internals_directory, 'ensemble_read_preds.pkl')), os.listdir( ensemble_backend.internals_directory) assert os.path.exists( os.path.join(ensemble_backend.internals_directory, 'ensemble_read_scores.pkl')), os.listdir( ensemble_backend.internals_directory)
def testGetValidTestPreds(self): ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, # Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=1) ensbuilder.score_ensemble_preds() d1 = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1_0.0.npy" ) d2 = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2_0.0.npy" ) d3 = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_3_100.0.npy" ) sel_keys = ensbuilder.get_n_best_preds() self.assertEqual(len(sel_keys), 1) ensbuilder.get_valid_test_preds(selected_keys=sel_keys) # Number of read files should be three and # predictions_ensemble_0_4_0.0.npy must not be in there self.assertEqual(len(ensbuilder.read_preds), 3) self.assertNotIn( os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_4_0.0.npy" ), ensbuilder.read_preds) # not selected --> should still be None self.assertIsNone(ensbuilder.read_preds[d1][Y_VALID]) self.assertIsNone(ensbuilder.read_preds[d1][Y_TEST]) self.assertIsNone(ensbuilder.read_preds[d3][Y_VALID]) self.assertIsNone(ensbuilder.read_preds[d3][Y_TEST]) # selected --> read valid and test predictions self.assertIsNotNone(ensbuilder.read_preds[d2][Y_VALID]) self.assertIsNotNone(ensbuilder.read_preds[d2][Y_TEST])
def testMaxModelsOnDisc(ensemble_backend, test_case, exp): ensemble_nbest = 4 ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=ensemble_nbest, max_models_on_disc=test_case, ) with unittest.mock.patch('os.path.getsize') as mock: mock.return_value = 100 * 1024 * 1024 ensbuilder.score_ensemble_preds() sel_keys = ensbuilder.get_n_best_preds() assert len(sel_keys) == exp, test_case
def testGetValidTestPreds(ensemble_backend): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=1) ensbuilder.compute_loss_per_model() # d1 is a dummt prediction. d2 and d3 have the same prediction with # different name. num_run=2 is selected when doing sorted() d1 = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy") d2 = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy") d3 = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy") sel_keys = ensbuilder.get_n_best_preds() assert len(sel_keys) == 1 ensbuilder.get_valid_test_preds(selected_keys=sel_keys) # Number of read files should be three and # predictions_ensemble_0_4_0.0.npy must not be in there assert len(ensbuilder.read_preds) == 3 assert os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_4_0.0/predictions_ensemble_0_4_0.0.npy" ) not in ensbuilder.read_preds # not selected --> should still be None assert ensbuilder.read_preds[d1][Y_VALID] is None assert ensbuilder.read_preds[d1][Y_TEST] is None assert ensbuilder.read_preds[d3][Y_VALID] is None assert ensbuilder.read_preds[d3][Y_TEST] is None # selected --> read valid and test predictions assert ensbuilder.read_preds[d2][Y_VALID] is not None assert ensbuilder.read_preds[d2][Y_TEST] is not None
def testMain(self): ensbuilder = EnsembleBuilder(backend=self.backend, dataset_name="TEST", task_type=1, #Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=2, max_iterations=1 # prevents infinite loop ) ensbuilder.SAVE2DISC = False ensbuilder.main() self.assertEqual(len(ensbuilder.read_preds), 2) self.assertIsNotNone(ensbuilder.last_hash) self.assertIsNotNone(ensbuilder.y_true_ensemble)
def test_run_end_at(ensemble_backend): with unittest.mock.patch('pynisher.enforce_limits') as pynisher_mock: ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=MULTILABEL_CLASSIFICATION, # Multilabel Classification metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=2, max_models_on_disc=None, ) ensbuilder.SAVE2DISC = False current_time = time.time() ensbuilder.run(end_at=current_time + 10, iteration=1) # 4 seconds left because: 10 seconds - 5 seconds overhead - very little overhead, # but then rounded to an integer assert pynisher_mock.call_args_list[0][1]["wall_time_in_s"], 4
def testNBest(ensemble_backend, ensemble_nbest, max_models_on_disc, exp): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=ensemble_nbest, max_models_on_disc=max_models_on_disc, ) ensbuilder.score_ensemble_preds() sel_keys = ensbuilder.get_n_best_preds() assert len(sel_keys) == exp fixture = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy") assert sel_keys[0] == fixture
def testNBest(self): ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, #Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=1, ) ensbuilder.read_ensemble_preds() sel_keys = ensbuilder.get_n_best_preds() self.assertEquals(len(sel_keys), 1) fixture = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2.npy") self.assertEquals(sel_keys[0], fixture)
def testFallBackNBest(ensemble_backend): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ensemble_nbest=1) ensbuilder.score_ensemble_preds() print() print(ensbuilder.read_preds.keys()) print(ensbuilder.read_scores.keys()) print(ensemble_backend.temporary_directory) filename = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy") ensbuilder.read_scores[filename]["ens_score"] = -1 filename = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_3_100.0/predictions_ensemble_0_3_100.0.npy") ensbuilder.read_scores[filename]["ens_score"] = -1 filename = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy") ensbuilder.read_scores[filename]["ens_score"] = -1 sel_keys = ensbuilder.get_n_best_preds() fixture = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy") assert len(sel_keys) == 1 assert sel_keys[0] == fixture
def testMain(self): ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=3, # Multilabel Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=2, max_iterations=1, # prevents infinite loop max_models_on_disc=None, ) ensbuilder.SAVE2DISC = False ensbuilder.main() self.assertEqual(len(ensbuilder.read_preds), 3) self.assertIsNotNone(ensbuilder.last_hash) self.assertIsNotNone(ensbuilder.y_true_ensemble) # Make sure the run history is ok run_history = ensbuilder.get_ensemble_history() # We expect 1 element to be the ensemble self.assertEqual(len(run_history), 1) # As the data loader loads the same val/train/test # we expect 1.0 as score and all keys available expected_performance = { 'ensemble_val_score': 1.0, 'ensemble_test_score': 1.0, 'ensemble_optimization_score': 1.0, } self.assertDictContainsSubset(expected_performance, run_history[0]) self.assertIn('Timestamp', run_history[0]) self.assertIsInstance(run_history[0]['Timestamp'], pd.Timestamp)
def testRead(ensemble_backend): ensbuilder = EnsembleBuilder( backend=ensemble_backend, dataset_name="TEST", task_type=BINARY_CLASSIFICATION, metric=roc_auc, seed=0, # important to find the test files ) success = ensbuilder.score_ensemble_preds() assert success, str(ensbuilder.read_preds) assert len(ensbuilder.read_preds) == 3, ensbuilder.read_preds.keys() assert len(ensbuilder.read_scores) == 3, ensbuilder.read_scores.keys() filename = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_1_0.0/predictions_ensemble_0_1_0.0.npy") assert ensbuilder.read_scores[filename]["ens_score"] == 0.5 filename = os.path.join( ensemble_backend.temporary_directory, ".auto-sklearn/runs/0_2_0.0/predictions_ensemble_0_2_0.0.npy") assert ensbuilder.read_scores[filename]["ens_score"] == 1.0
def testPerformanceRangeThreshold(self): to_test = ((0.0, 4), (0.1, 4), (0.3, 3), (0.5, 2), (0.6, 2), (0.8, 1), (1.0, 1), (1, 1)) for performance_range_threshold, exp in to_test: ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, # Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=100, performance_range_threshold=performance_range_threshold ) ensbuilder.read_preds = { 'A': {'ens_score': 1, 'num_run': 1, 0: True, 'loaded': -1, "seed": 1}, 'B': {'ens_score': 2, 'num_run': 2, 0: True, 'loaded': -1, "seed": 1}, 'C': {'ens_score': 3, 'num_run': 3, 0: True, 'loaded': -1, "seed": 1}, 'D': {'ens_score': 4, 'num_run': 4, 0: True, 'loaded': -1, "seed": 1}, 'E': {'ens_score': 5, 'num_run': 5, 0: True, 'loaded': -1, "seed": 1}, } sel_keys = ensbuilder.get_n_best_preds() self.assertEqual(len(sel_keys), exp)
def testRead(self): ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, #Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ) success = ensbuilder.read_ensemble_preds() self.assertTrue(success, str(ensbuilder.read_preds)) self.assertEqual(len(ensbuilder.read_preds), 2) filename = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_1.npy") self.assertEqual(ensbuilder.read_preds[filename]["ens_score"], 0.5) filename = os.path.join( self.backend.temporary_directory, ".auto-sklearn/predictions_ensemble/predictions_ensemble_0_2.npy") self.assertEqual(ensbuilder.read_preds[filename]["ens_score"], 1.0)
def testMaxModelsOnDisc(self): ensemble_nbest = 4 for (test_case, exp) in [ # If None, no reduction (None, 2), # If Int, limit only on exceed (4, 2), (1, 1), # If Float, translate float to # models. # below, mock of each file is 100 Mb and # 4 files .model and .npy (test/val/pred) exist (700.0, 1), (800.0, 2), (9999.0, 2), ]: ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, # Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=ensemble_nbest, max_models_on_disc=test_case, ) with unittest.mock.patch('os.path.getsize') as mock: mock.return_value = 100 * 1024 * 1024 ensbuilder.score_ensemble_preds() sel_keys = ensbuilder.get_n_best_preds() self.assertEqual(len(sel_keys), exp) # Test for Extreme scenarios # Make sure that the best predictions are kept ensbuilder = EnsembleBuilder( backend=self.backend, dataset_name="TEST", task_type=1, # Binary Classification metric=roc_auc, limit=-1, # not used, seed=0, # important to find the test files ensemble_nbest=50, max_models_on_disc=10000.0, ) ensbuilder.read_preds = {} for i in range(50): ensbuilder.read_preds['pred' + str(i)] = { 'ens_score': i * 10, 'num_run': i, 0: True, 'loaded': 1, "seed": 1, "disc_space_cost_mb": 50 * i, } sel_keys = ensbuilder.get_n_best_preds() self.assertListEqual(['pred49', 'pred48', 'pred47', 'pred46'], sel_keys) # Make sure at least one model is kept alive ensbuilder.max_models_on_disc = 0.0 sel_keys = ensbuilder.get_n_best_preds() self.assertListEqual(['pred49'], sel_keys)
def main(task_id, ensemble_dir, performance_range_threshold, ensemble_size, max_keep_best, seed, only_portfolio_runs, call_from_cmd): if max_keep_best > 1: assert max_keep_best == int(max_keep_best) max_keep_best = int(max_keep_best) memory_limit = 4000 precision = 32 metric = make_scorer('balanced_accuracy_fast', BalancedAccuracy()) if not os.path.exists(ensemble_dir): raise NotADirectoryError("%s does not exist") if call_from_cmd: assert str(task_id) in ensemble_dir fl_name = "ensemble_results_%fthresh_%dsize_%fbest" % \ (performance_range_threshold, ensemble_size, max_keep_best) if only_portfolio_runs: fl_name += "_only_portfolio" fl_name = os.path.join(ensemble_dir, fl_name) if os.path.isfile(fl_name): raise ValueError("Nothing left to do, %s already exists" % fl_name) # figure out how many prediction files are in dir if call_from_cmd: pred_dir = os.path.join(ensemble_dir, "auto-sklearn-output", ".auto-sklearn", "predictions_ensemble") n_models = glob.glob(pred_dir + "/predictions_ensemble_%d_*.npy.gz" % seed) else: pred_dir = os.path.join(ensemble_dir, ".auto-sklearn", "predictions_ensemble") n_models = glob.glob(pred_dir + "/predictions_ensemble_%d_*.npy" % seed) n_models.sort(key=lambda x: int(float(x.split("_")[-2]))) print("\n".join(n_models)) print("Found %d ensemble predictions" % len(n_models)) if len(n_models) == 0: raise ValueError("%s has no ensemble predictions" % pred_dir) # Get start time of ensemble building: 1) load json 2) find key 3) get creation times if call_from_cmd: timestamps_fl = os.path.join(ensemble_dir, "auto-sklearn-output", "timestamps.json") else: timestamps_fl = os.path.join(ensemble_dir, "timestamps.json") with open(timestamps_fl, "r") as fh: timestamps = json.load(fh) model_timestamps = None overall_start_time = None for k in timestamps: if "predictions_ensemble" in k: model_timestamps = timestamps[k] if "start_time_%d" % seed in timestamps[k]: overall_start_time = timestamps[k]["start_time_%d" % seed] timestamp_keys = list(model_timestamps.keys()) for timestamp_key in timestamp_keys: if timestamp_key.endswith( 'lock') or 'predictions_ensemble' not in timestamp_key: del model_timestamps[timestamp_key] assert model_timestamps is not None and overall_start_time is not None assert len(model_timestamps) == len(n_models), (len(model_timestamps), len(n_models)) # Get overall timelimit vanilla_results_fl = os.path.join(ensemble_dir, "result.json") with open(vanilla_results_fl, "r") as fh: vanilla_results = json.load(fh) # If only portfolio configurations, read runhistory if only_portfolio_runs: if call_from_cmd: runhistory_fl = os.path.join(ensemble_dir, "auto-sklearn-output", "smac3-output", "run*", "runhistory.json") else: runhistory_fl = os.path.join(ensemble_dir, "smac3-output", "run*", "runhistory.json") runhistory_fl = glob.glob(runhistory_fl) assert len(runhistory_fl) == 1 with open(runhistory_fl[0], "r") as fh: runhistory = json.load(fh) init_design_num_runs = [] for i in runhistory["data"]: if i[1][3]["configuration_origin"] == "Initial design": if "error" in i[1][3]: continue init_design_num_runs.append(i[1][3]["num_run"]) print("Portfolio stopped after %s runs" % str(init_design_num_runs)) last_run = max(init_design_num_runs) print("Cut down to only portfolio runs fom %d" % len(n_models)) for i, n in enumerate(n_models): if int(float(n.split("_")[-2])) > last_run: n_models = n_models[:i] break print("... to %d" % len(n_models)) # load data X_train, y_train, X_test, y_test, cat = load_task(task_id) if len(np.unique(y_test)) == 2: task_type = BINARY_CLASSIFICATION elif len(np.unique(y_test)) > 2: task_type = MULTICLASS_CLASSIFICATION else: raise ValueError("Unknown task type for task %d" % task_id) tmp_dir = tempfile.TemporaryDirectory() loss_trajectory = [] # Construct ensemble builder context = BackendContextMock( temporary_directory=(ensemble_dir + "/auto-sklearn-output/" if call_from_cmd else ensemble_dir), output_directory=tmp_dir.name, delete_tmp_folder_after_terminate=False, delete_output_folder_after_terminate=False, shared_mode=False) backend = Backend(context) ens_builder = EnsembleBuilder( backend=backend, dataset_name=str(task_id), task_type=task_type, metric=metric, limit=np.inf, ensemble_size=ensemble_size, ensemble_nbest=max_keep_best, performance_range_threshold=performance_range_threshold, max_models_on_disc=None, seed=seed, shared_mode=False, precision=precision, max_iterations=1, read_at_most=1, memory_limit=memory_limit, random_state=1, sleep_duration=0) try: # iterate over all models, take construction time into account when creating new trajectory current_ensemble_timestamp = 0 skipped = 1 for midx, model_path in enumerate(n_models): tstamp = model_timestamps[model_path.split("/")[-1].replace( '.gz', '')] - overall_start_time if current_ensemble_timestamp > tstamp: # while this model was built, the ensemble script was not yet done skipped += 1 continue # Do one ensemble building step start = time.time() ens_builder.random_state = check_random_state(1) print("############## %d: Working on %s (skipped %d)" % (midx + 1, model_path, skipped - 1)) logging.basicConfig(level=logging.DEBUG) ens_builder.read_at_most = skipped valid_pred, test_pred = ens_builder.main(return_pred=True) last_dur = time.time() - start current_ensemble_timestamp = tstamp + last_dur if current_ensemble_timestamp >= vanilla_results["0"]["time_limit"]: print("############## Went over time %f > %f; Stop here" % (current_ensemble_timestamp, vanilla_results["0"]["time_limit"])) break # Reset, since we have just read model files skipped = 1 if test_pred is None: # Adding this model did not change the ensemble, no new prediction continue if task_type == BINARY_CLASSIFICATION: # Recreate nx2 array test_pred = np.concatenate([ 1 - test_pred.reshape([-1, 1]), test_pred.reshape([-1, 1]) ], axis=1) # Build trajectory entry score = 1 - balanced_accuracy(y_true=y_test, y_pred=test_pred) loss_trajectory.append((current_ensemble_timestamp, score)) print("############## Round %d took %g sec" % (midx, time.time() - start)) except: raise finally: tmp_dir.cleanup() # Store results result = dict() result[ensemble_size] = { 'task_id': task_id, 'time_limit': vanilla_results["0"]["time_limit"], 'loss': loss_trajectory[-1][1], 'configuration': { "n_models": n_models, "performance_range_threshold": performance_range_threshold, "ensemble_size": ensemble_size, "max_keep_best": max_keep_best, "seed": seed, "memory_limit": memory_limit, "precision": precision, }, 'n_models': len(n_models), 'trajectory': loss_trajectory, } with open(fl_name, 'wt') as fh: json.dump(result, fh, indent=4) print("Dumped to %s" % fl_name)