Embedding layer

examples/example_tmp_for_debug.py

@@ -0,0 +1,150 @@
+"""
+Example file to be deleted
+"""
+import os
+
+import sklearn.datasets
+
+from autoPyTorch.api.tabular_classification import TabularClassificationTask
+from autoPyTorch.datasets.resampling_strategy import (
+    CrossValTypes,
+)
+
+from pathlib import Path
+
+
+class DisplayablePath(object):
+    display_filename_prefix_middle = '├──'
+    display_filename_prefix_last = '└──'
+    display_parent_prefix_middle = '    '
+    display_parent_prefix_last = '│   '
+
+    def __init__(self, path, parent_path, is_last):
+        self.path = Path(str(path))
+        self.parent = parent_path
+        self.is_last = is_last
+        if self.parent:
+            self.depth = self.parent.depth + 1
+        else:
+            self.depth = 0
+
+    @property
+    def displayname(self):
+        if self.path.is_dir():
+            return self.path.name + '/'
+        return self.path.name
+
+    @classmethod
+    def make_tree(cls, root, parent=None, is_last=False, criteria=None):
+        root = Path(str(root))
+        criteria = criteria or cls._default_criteria
+
+        displayable_root = cls(root, parent, is_last)
+        yield displayable_root
+
+        children = sorted(list(path
+                               for path in root.iterdir()
+                               if criteria(path)),
+                          key=lambda s: str(s).lower())
+        count = 1
+        for path in children:
+            is_last = count == len(children)
+            if path.is_dir():
+                yield from cls.make_tree(path,
+                                         parent=displayable_root,
+                                         is_last=is_last,
+                                         criteria=criteria)
+            else:
+                yield cls(path, displayable_root, is_last)
+            count += 1
+
+    @classmethod
+    def _default_criteria(cls, path):
+        return True
+
+    @property
+    def displayname(self):
+        if self.path.is_dir():
+            return self.path.name + '/'
+        return self.path.name
+
+    def displayable(self):
+        if self.parent is None:
+            return self.displayname
+
+        _filename_prefix = (self.display_filename_prefix_last
+                            if self.is_last
+                            else self.display_filename_prefix_middle)
+
+        parts = ['{!s} {!s}'.format(_filename_prefix,
+                                    self.displayname)]
+
+        parent = self.parent
+        while parent and parent.parent is not None:
+            parts.append(self.display_parent_prefix_middle
+                         if parent.is_last
+                         else self.display_parent_prefix_last)
+            parent = parent.parent
+
+        return ''.join(reversed(parts))
+
+
+openml_id = 40981
+resampling_strategy = CrossValTypes.k_fold_cross_validation
+X, y = sklearn.datasets.fetch_openml(
+    data_id=int(openml_id),
+    return_X_y=True, as_frame=True
+)
+X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
+    X, y, random_state=1)
+
+
+if __name__ == '__main__':
+    # Search for a good configuration
+    estimator = TabularClassificationTask(
+        temporary_directory='./tmp',
+        delete_tmp_folder_after_terminate=False,
+        resampling_strategy=resampling_strategy,
+    )
+
+    estimator.search(
+        X_train=X_train, y_train=y_train,
+        X_test=X_test, y_test=y_test,
+        optimize_metric='accuracy',
+        total_walltime_limit=150,
+        func_eval_time_limit=50,
+        traditional_per_total_budget=0
+    )
+
+    # Search for an existing run key in disc. A individual model might have
+    # a timeout and hence was not written to disc
+    for i, (run_key, value) in enumerate(estimator.run_history.data.items()):
+        if i == 0:
+            # Ignore dummy run
+            continue
+        if 'SUCCESS' not in str(value.status):
+            continue
+
+        run_key_model_run_dir = estimator._backend.get_numrun_directory(
+            estimator.seed, run_key.config_id, run_key.budget)
+        if os.path.exists(run_key_model_run_dir):
+            break
+
+    model_file = os.path.join(
+        run_key_model_run_dir,
+        f"{estimator.seed}.{run_key.config_id}.{run_key.budget}.cv_model"
+    )
+
+    print(model_file)
+
+    paths = DisplayablePath.make_tree(os.path.dirname(run_key_model_run_dir))
+    for path in paths:
+        print(path.displayable())
+
+    # printing log file
+    tmp_dir = estimator._backend.temporary_directory
+    log_file = os.path.join(tmp_dir, "AutoPyTorch:{}:{}.log".format(estimator.dataset_name, estimator.seed))
+    f = open(log_file, 'r')
+    lines = f.readlines()
+    for line in lines:
+        print(line)

test/test_pipeline/components/setup/test_setup_networks.py

@@ -27,7 +27,7 @@ def embedding(request):
                                                     'classification_categorical_only',
                                                     'classification_numerical_and_categorical'], indirect=True)
 class TestNetworks:
-    def test_pipeline_fit(self, fit_dictionary_tabular, backbone, head):
+    def test_pipeline_fit(self, fit_dictionary_tabular, embedding, backbone, head):
         """This test makes sure that the pipeline is able to fit
         every combination of network embedding, backbone, head"""
 

test/utils.py

@@ -0,0 +1,71 @@
+from pathlib import Path
+
+
+class DisplayablePath(object):
+    display_filename_prefix_middle = '├──'
+    display_filename_prefix_last = '└──'
+    display_parent_prefix_middle = '    '
+    display_parent_prefix_last = '│   '
+
+    def __init__(self, path, parent_path, is_last):
+        self.path = Path(str(path))
+        self.parent = parent_path
+        self.is_last = is_last
+        if self.parent:
+            self.depth = self.parent.depth + 1
+        else:
+            self.depth = 0
+
+    @property
+    def displayname(self):
+        if self.path.is_dir():
+            return self.path.name + '/'
+        return self.path.name
+
+    @classmethod
+    def make_tree(cls, root, parent=None, is_last=False, criteria=None):
+        root = Path(str(root))
+        criteria = criteria or cls._default_criteria
+
+        displayable_root = cls(root, parent, is_last)
+        yield displayable_root
+
+        children = sorted(list(path
+                               for path in root.iterdir()
+                               if criteria(path)),
+                          key=lambda s: str(s).lower())
+        count = 1
+        for path in children:
+            is_last = count == len(children)
+            if path.is_dir():
+                yield from cls.make_tree(path,
+                                         parent=displayable_root,
+                                         is_last=is_last,
+                                         criteria=criteria)
+            else:
+                yield cls(path, displayable_root, is_last)
+            count += 1
+
+    @classmethod
+    def _default_criteria(cls, path):
+        return True
+
+    def displayable(self):
+        if self.parent is None:
+            return self.displayname
+
+        _filename_prefix = (self.display_filename_prefix_last
+                            if self.is_last
+                            else self.display_filename_prefix_middle)
+
+        parts = ['{!s} {!s}'.format(_filename_prefix,
+                                    self.displayname)]
+
+        parent = self.parent
+        while parent and parent.parent is not None:
+            parts.append(self.display_parent_prefix_middle
+                         if parent.is_last
+                         else self.display_parent_prefix_last)
+            parent = parent.parent
+
+        return ''.join(reversed(parts))