AA_151

Author: franchuterivera

autoPyTorch/api/base_task.py

@@ -486,11 +486,14 @@ def _load_best_individual_model(self) -> SingleBest:
 
         return ensemble
 
-    def _do_dummy_prediction(self, num_run: int) -> None:
+    def _do_dummy_prediction(self) -> None:
 
         assert self._metric is not None
         assert self._logger is not None
 
+        # For dummy estimator, we always expect the num_run to be 1
+        num_run = 1
+
         self._logger.info("Starting to create dummy predictions.")
 
         memory_limit = self._memory_limit
@@ -551,29 +554,20 @@ def _do_dummy_prediction(self, num_run: int) -> None:
                     % (str(status), str(additional_info))
                 )
 
-    def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_time_limit_secs: int
-                                   ) -> int:
+    def _do_traditional_prediction(self, time_left: int, func_eval_time_limit_secs: int) -> None:
         """
         Fits traditional machine learning algorithms to the provided dataset, while
         complying with time resource allocation.
 
         This method currently only supports classification.
 
         Args:
-            num_run: (int)
-                An identifier to indicate the current machine learning algorithm
-                being processed
             time_left: (int)
                 Hard limit on how many machine learning algorithms can be fit. Depending on how
                 fast a traditional machine learning algorithm trains, it will allow multiple
                 models to be fitted.
             func_eval_time_limit_secs: (int)
                 Maximum training time each algorithm is allowed to take, during training
-
-        Returns:
-            num_run: (int)
-                The incremented identifier index. This depends on how many machine learning
-                models were fitted.
         """
 
         # Mypy Checkings -- Traditional prediction is only called for search
@@ -588,8 +582,8 @@ def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_tim
         available_classifiers = get_available_classifiers()
         dask_futures = []
 
-        total_number_classifiers = len(available_classifiers) + num_run
-        for n_r, classifier in enumerate(available_classifiers, start=num_run):
+        total_number_classifiers = len(available_classifiers)
+        for n_r, classifier in enumerate(available_classifiers):
 
             # Only launch a task if there is time
             start_time = time.time()
@@ -608,7 +602,7 @@ def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_tim
                     logger_port=self._logger_port,
                     cost_for_crash=get_cost_of_crash(self._metric),
                     abort_on_first_run_crash=False,
-                    initial_num_run=n_r,
+                    initial_num_run=self._backend.get_next_num_run(),
                     stats=stats,
                     memory_limit=memory_limit,
                     disable_file_output=True if len(self._disable_file_output) > 0 else False,
@@ -622,9 +616,6 @@ def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_tim
                     )
                 ])
 
-                # Increment the launched job index
-                num_run = n_r
-
             # When managing time, we need to take into account the allocated time resources,
             # which are dependent on the number of cores. 'dask_futures' is a proxy to the number
             # of workers /n_jobs that we have, in that if there are 4 cores allocated, we can run at most
@@ -677,7 +668,7 @@ def _do_traditional_prediction(self, num_run: int, time_left: int, func_eval_tim
                                      "Please consider increasing the run time to further improve performance.")
                 break
 
-        return num_run
+        return
 
     def _search(
         self,
@@ -847,10 +838,9 @@ def _search(
             )
 
         # ============> Run dummy predictions
-        num_run = 1
         dummy_task_name = 'runDummy'
         self._stopwatch.start_task(dummy_task_name)
-        self._do_dummy_prediction(num_run)
+        self._do_dummy_prediction()
         self._stopwatch.stop_task(dummy_task_name)
 
         # ============> Run traditional ml
@@ -866,8 +856,8 @@ def _search(
                 time_for_traditional = int(
                     self._time_for_task - elapsed_time - func_eval_time_limit_secs
                 )
-                num_run = self._do_traditional_prediction(
-                    num_run=num_run + 1, func_eval_time_limit_secs=func_eval_time_limit_secs,
+                self._do_traditional_prediction(
+                    func_eval_time_limit_secs=func_eval_time_limit_secs,
                     time_left=time_for_traditional,
                 )
                 self._stopwatch.stop_task(traditional_task_name)
@@ -943,7 +933,9 @@ def _search(
                 pipeline_config={**self.pipeline_options, **budget_config},
                 ensemble_callback=proc_ensemble,
                 logger_port=self._logger_port,
-                start_num_run=num_run,
+                # We do not increase the num_run here, this is something
+                # smac does internally
+                start_num_run=self._backend.get_next_num_run(peek=True),
                 search_space_updates=self.search_space_updates
             )
             try:
@@ -1048,7 +1040,7 @@ def refit(
                                   'train_indices': dataset.splits[split_id][0],
                                   'val_indices': dataset.splits[split_id][1],
                                   'split_id': split_id,
-                                  'num_run': 0
+                                  'num_run': self._backend.get_next_num_run(),
                                   })
         X.update({**self.pipeline_options, **budget_config})
         if self.models_ is None or len(self.models_) == 0 or self.ensemble_ is None:
@@ -1125,7 +1117,7 @@ def fit(self,
                                   'train_indices': dataset.splits[split_id][0],
                                   'val_indices': dataset.splits[split_id][1],
                                   'split_id': split_id,
-                                  'num_run': 0
+                                  'num_run': self._backend.get_next_num_run(),
                                   })
         X.update({**self.pipeline_options, **budget_config})
 

autoPyTorch/evaluation/abstract_evaluator.py

@@ -134,7 +134,9 @@ def __init__(self, config: Configuration,
                  random_state: Optional[Union[int, np.random.RandomState]] = None,
                  init_params: Optional[Dict] = None
                  ) -> None:
-        self.configuration = config
+        self.config = config
+        self.init_params = init_params
+        self.random_state = random_state
         if config == 1:
             super(DummyClassificationPipeline, self).__init__(strategy="uniform")
         else:
@@ -198,7 +200,9 @@ class DummyRegressionPipeline(DummyRegressor):
     def __init__(self, config: Configuration,
                  random_state: Optional[Union[int, np.random.RandomState]] = None,
                  init_params: Optional[Dict] = None) -> None:
-        self.configuration = config
+        self.config = config
+        self.init_params = init_params
+        self.random_state = random_state
         if config == 1:
             super(DummyRegressionPipeline, self).__init__(strategy='mean')
         else:

autoPyTorch/utils/backend.py

@@ -169,6 +169,10 @@ def __init__(self, context: BackendContext):
         self._logger = None  # type: Optional[PicklableClientLogger]
         self.context = context
 
+        # Track the number of configurations launched
+        # num_run == 1 means a dummy estimator run
+        self.active_num_run = 1
+
         # Create the temporary directory if it does not yet exist
         try:
             os.makedirs(self.temporary_directory)
@@ -329,6 +333,25 @@ def get_runs_directory(self) -> str:
     def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str:
         return os.path.join(self.internals_directory, 'runs', '%d_%d_%s' % (seed, num_run, budget))
 
+    def get_next_num_run(self, peek: bool = False) -> int:
+
+        # If there are other num_runs, their name would be runs/<seed>_<num_run>_<budget>
+        other_num_runs = [int(os.path.basename(run_dir).split('_')[1])
+                          for run_dir in glob.glob(os.path.join(self.internals_directory,
+                                                                'runs',
+                                                                '*'))]
+        if len(other_num_runs) > 0:
+            # We track the number of runs from two forefronts:
+            # The physically available num_runs (which might be deleted or a crash could happen)
+            # From a internally kept attribute. The later should be sufficient, but we
+            # want to be robust against multiple backend copies on different workers
+            self.active_num_run = max([self.active_num_run] + other_num_runs)
+
+        # We are interested in the next run id
+        if not peek:
+            self.active_num_run += 1
+        return self.active_num_run
+
     def get_model_filename(self, seed: int, idx: int, budget: float) -> str:
         return '%s.%s.%s.model' % (seed, idx, budget)
 

test/test_api/test_api.py

@@ -12,8 +12,10 @@
 
 import sklearn
 import sklearn.datasets
+from sklearn.base import clone
 from sklearn.ensemble import VotingClassifier, VotingRegressor
 
+
 import torch
 
 from autoPyTorch.api.tabular_classification import TabularClassificationTask
@@ -23,6 +25,7 @@
     HoldoutValTypes,
 )
 from autoPyTorch.optimizer.smbo import AutoMLSMBO
+from autoPyTorch.pipeline.components.training.metrics.metrics import accuracy
 
 
 # Fixtures
@@ -394,3 +397,48 @@ def test_tabular_input_support(openml_id, backend):
             enable_traditional_pipeline=False,
             load_models=False,
         )
+
+
+@pytest.mark.parametrize("fit_dictionary_tabular", ['classification_categorical_only'], indirect=True)
+def test_do_dummy_prediction(dask_client, fit_dictionary_tabular):
+    backend = fit_dictionary_tabular['backend']
+    estimator = TabularClassificationTask(
+        backend=backend,
+        resampling_strategy=HoldoutValTypes.holdout_validation,
+        ensemble_size=0,
+    )
+
+    # Setup pre-requisites normally set by search()
+    estimator._create_dask_client()
+    estimator._metric = accuracy
+    estimator._logger = estimator._get_logger('test')
+    estimator._memory_limit = 5000
+    estimator._time_for_task = 60
+    estimator._disable_file_output = []
+    estimator._all_supported_metrics = False
+
+    estimator._do_dummy_prediction()
+
+    # Ensure that the dummy predictions are not in the current working
+    # directory, but in the temporary directory.
+    assert not os.path.exists(os.path.join(os.getcwd(), '.autoPyTorch'))
+    assert os.path.exists(os.path.join(
+        backend.temporary_directory, '.autoPyTorch', 'runs', '1_1_1.0',
+        'predictions_ensemble_1_1_1.0.npy')
+    )
+
+    model_path = os.path.join(backend.temporary_directory,
+                              '.autoPyTorch',
+                              'runs', '1_1_1.0',
+                              '1.1.1.0.model')
+
+    # Make sure the dummy model complies with scikit learn
+    # get/set params
+    assert os.path.exists(model_path)
+    with open(model_path, 'rb') as model_handler:
+        clone(pickle.load(model_handler))
+
+    estimator._close_dask_client()
+    estimator._clean_logger()
+
+    del estimator

test/test_utils/test_backend.py

@@ -1,8 +1,11 @@
 # -*- encoding: utf-8 -*-
 import builtins
+import logging.handlers
 import unittest
 import unittest.mock
 
+import numpy as np
+
 import pytest
 
 from autoPyTorch.utils.backend import Backend
@@ -81,3 +84,23 @@ def test_loads_models_by_identifiers(exists_mock, openMock, pickleLoadMock, back
 
     assert isinstance(actual_dict, dict)
     assert expected_dict == actual_dict
+
+
+def test_get_next_num_run(backend):
+    # Asking for a num_run increases the tracked num_run
+    assert backend.get_next_num_run() == 2
+    assert backend.get_next_num_run() == 3
+    # Then test that we are robust against new files being generated
+    backend.setup_logger('Test', logging.handlers.DEFAULT_TCP_LOGGING_PORT)
+    backend.save_numrun_to_dir(
+        seed=0,
+        idx=12,
+        budget=0.0,
+        model=dict(),
+        cv_model=None,
+        ensemble_predictions=np.zeros(10),
+        valid_predictions=None,
+        test_predictions=None,
+    )
+    assert backend.get_next_num_run() == 13
+    assert backend.get_next_num_run(peek=True) == 13