Cleanup files from merge

Author: eddiebergman

autosklearn/util/single_thread_client.py

@@ -2,7 +2,6 @@
 from typing import Any
 
 from pathlib import Path
-from typing import Any
 
 import dask.distributed
 

test/test_ensemble_builder/ensemble_utils.py

@@ -0,0 +1,116 @@
+import os
+import shutil
+
+import numpy as np
+
+from autosklearn.automl_common.common.ensemble_building.abstract_ensemble import (
+    AbstractEnsemble,
+)
+from autosklearn.ensemble_builder import EnsembleBuilder
+from autosklearn.metrics import make_scorer
+
+import unittest
+import unittest.mock
+
+
+def scorer_function(a, b):
+    return 0.9
+
+
+MockMetric = make_scorer("mock", scorer_function)
+
+
+class BackendMock(object):
+    def __init__(self, target_directory):
+        this_directory = os.path.abspath(os.path.dirname(__file__))
+        shutil.copytree(
+            os.path.join(this_directory, "data"), os.path.join(target_directory)
+        )
+        self.temporary_directory = target_directory
+        self.internals_directory = os.path.join(
+            self.temporary_directory, ".auto-sklearn"
+        )
+
+    def load_datamanager(self):
+        manager = unittest.mock.Mock()
+        manager.__reduce__ = lambda self: (unittest.mock.MagicMock, ())
+        array = np.load(
+            os.path.join(
+                self.temporary_directory,
+                ".auto-sklearn",
+                "runs",
+                "0_3_100.0",
+                "predictions_test_0_3_100.0.npy",
+            )
+        )
+        manager.data.get.return_value = array
+        return manager
+
+    def load_targets_ensemble(self):
+        with open(
+            os.path.join(
+                self.temporary_directory,
+                ".auto-sklearn",
+                "predictions_ensemble_true.npy",
+            ),
+            "rb",
+        ) as fp:
+            y = np.load(fp, allow_pickle=True)
+        return y
+
+    def save_ensemble(self, ensemble, index_run, seed):
+        return
+
+    def save_predictions_as_txt(self, predictions, subset, idx, prefix, precision):
+        return
+
+    def get_runs_directory(self) -> str:
+        return os.path.join(self.temporary_directory, ".auto-sklearn", "runs")
+
+    def get_numrun_directory(self, seed: int, num_run: int, budget: float) -> str:
+        return os.path.join(
+            self.get_runs_directory(), "%d_%d_%s" % (seed, num_run, budget)
+        )
+
+    def get_model_filename(self, seed: int, idx: int, budget: float) -> str:
+        return "%s.%s.%s.model" % (seed, idx, budget)
+
+
+def compare_read_preds(read_preds1, read_preds2):
+    """
+    compares read_preds attribute. An alternative to
+    assert Dict Equal as it contains np arrays, so we have
+    to use np testing utilities accordingly
+    """
+
+    # Both arrays should have the same splits
+    assert set(read_preds1.keys()) == set(read_preds2.keys())
+
+    for k, v in read_preds1.items():
+
+        # Each split should have the same elements
+        assert set(read_preds1[k].keys()) == set(read_preds2[k].keys())
+
+        # This level contains the scores/ensmebles/etc
+        for actual_k, actual_v in read_preds1[k].items():
+
+            # If it is a numpy array, make sure it is the same
+            if type(actual_v) is np.ndarray:
+                np.testing.assert_array_equal(actual_v, read_preds2[k][actual_k])
+            else:
+                assert actual_v == read_preds2[k][actual_k]
+
+
+class EnsembleBuilderMemMock(EnsembleBuilder):
+    def fit_ensemble(self, selected_keys):
+        return True
+
+    def predict(
+        self,
+        set_: str,
+        ensemble: AbstractEnsemble,
+        selected_keys: list,
+        n_preds: int,
+        index_run: int,
+    ):
+        np.ones([10000000, 1000000])

test/test_pipeline/test_regression.py

@@ -247,6 +247,11 @@ def _test_configurations(
                     "identity" in e.args[0]
                 ):
                     continue
+                elif (
+                    "zero-size array to reduction operation maximum which has no "
+                    "identity" in e.args[0]
+                ):
+                    continue
                 else:
                     e.args += (f"config={config}",)
                     raise e