Merge branch 'release/v0.9.2'

Author: schroedk

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.1
+current_version = 0.9.2
 commit = False
 tag = False
 allow_dirty = False

CHANGELOG.md

@@ -1,6 +1,43 @@
 # Changelog
 
-## Unreleased
+## 0.9.2 - 🏗  Bug fixes, logging improvement
+
+### Added
+
+- Add progress bars to the computation of `LazyChunkSequence` and
+  `NestedLazyChunkSequence` 
+  [PR #567](https://github.com/aai-institute/pyDVL/pull/567)
+- Add a device fixture for `pytest`, which depending on the availability and 
+  user input (`pytest --with-cuda`) resolves to cuda device
+  [PR #574](https://github.com/aai-institute/pyDVL/pull/574)
+
+### Fixed
+
+- Fixed logging issue in decorator `log_duration`
+  [PR #567](https://github.com/aai-institute/pyDVL/pull/567)
+- Fixed missing move of tensors to model device in `EkfacInfluence` 
+  implementation [PR #570](https://github.com/aai-institute/pyDVL/pull/570)
+- Missing move to device of `preconditioner` in `CgInfluence` implementation
+  [PR #572](https://github.com/aai-institute/pyDVL/pull/572)
+- Raise a more specific error message, when a `RunTimeError` occurs in 
+  `torch.linalg.eigh`, so the user can check if it is related to a known
+  issue
+  [PR #578](https://github.com/aai-institute/pyDVL/pull/578)
+- Fix an edge case (empty train data) in the test 
+  `test_classwise_scorer_accuracies_manual_derivation`, which resulted
+  in undefined behavior (`np.nan` to `int` conversion with different results
+  depending on OS)
+  [PR #579](https://github.com/aai-institute/pyDVL/pull/579)
+
+### Changed
+
+- Changed logging behavior of iterative methods `LissaInfluence` and
+  `CgInfluence` to warn on not achieving desired tolerance within `maxiter`,
+  add parameter `warn_on_max_iteration` to set the level for this information
+  to `logging.DEBUG`
+  [PR #567](https://github.com/aai-institute/pyDVL/pull/567)
+
+## 0.9.1 - Bug fixes, logging improvement
 
 ### Fixed
 

CONTRIBUTING.md

@@ -131,6 +131,13 @@ There are a few important arguments:
 - `--slow-tests` enables running slow tests. See below for a description
   of slow tests.
 
+- `--with-cuda` sets the device fixture in [tests/influence/torch/conftest.py](
+  tests/influence/torch/conftest.py) to `cuda` if it is available.
+  Using this fixture within tests, you can run parts of your tests on a `cuda` 
+  device. Be aware, that you still have to take care of the usage of the device
+  manually in a specific test. Setting this flag does not result in
+  running all tests on a GPU.
+
 ### Markers
 
 We use a few different markers to differentiate between tests and runs

setup.py

@@ -12,7 +12,7 @@
     package_data={"pydvl": ["py.typed"]},
     packages=find_packages(where="src"),
     include_package_data=True,
-    version="0.9.1",
+    version="0.9.2",
     description="The Python Data Valuation Library",
     install_requires=[
         line

src/pydvl/__init__.py

@@ -7,4 +7,4 @@
 The two main modules you will want to look at are [value][pydvl.value] and
 [influence][pydvl.influence].
 """
-__version__ = "0.9.1"
+__version__ = "0.9.2"

src/pydvl/influence/array.py

@@ -6,13 +6,25 @@
 (chunked in one resp. two dimensions), with support for efficient storage and retrieval
 using the Zarr library.
 """
+from __future__ import annotations
 
 import logging
 from abc import ABC, abstractmethod
-from typing import Callable, Generator, Generic, List, Optional, Tuple, Union
+from typing import (
+    Callable,
+    Generator,
+    Generic,
+    Iterator,
+    List,
+    Optional,
+    Tuple,
+    Union,
+    cast,
+)
 
 import zarr
 from numpy.typing import NDArray
+from tqdm import tqdm
 from zarr.storage import StoreLike
 
 from ..utils import log_duration
@@ -35,9 +47,12 @@ def from_numpy(self, x: NDArray) -> TensorType:
 
 class SequenceAggregator(Generic[TensorType], ABC):
     @abstractmethod
-    def __call__(self, tensor_generator: Generator[TensorType, None, None]):
+    def __call__(
+        self,
+        tensor_sequence: LazyChunkSequence,
+    ):
         """
-        Aggregates tensors from a generator.
+        Aggregates tensors from a sequence.
 
         Implement this method to define how a sequence of tensors, provided by a
         generator, should be combined.
@@ -46,31 +61,37 @@ def __call__(self, tensor_generator: Generator[TensorType, None, None]):
 
 class ListAggregator(SequenceAggregator):
     def __call__(
-        self, tensor_generator: Generator[TensorType, None, None]
+        self,
+        tensor_sequence: LazyChunkSequence,
     ) -> List[TensorType]:
         """
         Aggregates tensors from a single-level generator into a list. This method simply
         collects each tensor emitted by the generator into a single list.
 
         Args:
-            tensor_generator: A generator that yields TensorType objects.
+            tensor_sequence: Object wrapping a generator that yields `TensorType`
+                objects.
 
         Returns:
             A list containing all the tensors provided by the tensor_generator.
         """
-        return [t for t in tensor_generator]
+
+        gen = cast(Iterator[TensorType], tensor_sequence.generator_factory())
+
+        if tensor_sequence.len_generator is not None:
+            gen = cast(
+                Iterator[TensorType],
+                tqdm(gen, total=tensor_sequence.len_generator, desc="Blocks"),
+            )
+
+        return [t for t in gen]
 
 
 class NestedSequenceAggregator(Generic[TensorType], ABC):
     @abstractmethod
-    def __call__(
-        self,
-        nested_generators_of_tensors: Generator[
-            Generator[TensorType, None, None], None, None
-        ],
-    ):
+    def __call__(self, nested_sequence_of_tensors: NestedLazyChunkSequence):
         """
-        Aggregates tensors from a generator of generators.
+        Aggregates tensors from a nested sequence of tensors.
 
         Implement this method to specify how tensors, nested in two layers of
         generators, should be combined. Useful for complex data structures where tensors
@@ -81,27 +102,36 @@ def __call__(
 class NestedListAggregator(NestedSequenceAggregator):
     def __call__(
         self,
-        nested_generators_of_tensors: Generator[
-            Generator[TensorType, None, None], None, None
-        ],
+        nested_sequence_of_tensors: NestedLazyChunkSequence,
     ) -> List[List[TensorType]]:
         """
          Aggregates tensors from a nested generator structure into a list of lists.
          Each inner generator is converted into a list of tensors, resulting in a nested
          list structure.
 
          Args:
-             nested_generators_of_tensors: A generator of generators, where each inner
-                generator yields TensorType objects.
+             nested_sequence_of_tensors: Object wrapping a generator of generators,
+                where each inner generator yields TensorType objects.
 
         Returns:
             A list of lists, where each inner list contains tensors returned from one
                 of the inner generators.
         """
-        return [list(tensor_gen) for tensor_gen in nested_generators_of_tensors]
+        outer_gen = cast(
+            Iterator[Iterator[TensorType]],
+            nested_sequence_of_tensors.generator_factory(),
+        )
+        len_outer_gen = nested_sequence_of_tensors.len_outer_generator
+        if len_outer_gen is not None:
+            outer_gen = cast(
+                Iterator[Iterator[TensorType]],
+                tqdm(outer_gen, total=len_outer_gen, desc="Row blocks"),
+            )
 
+        return [list(tensor_gen) for tensor_gen in outer_gen]
 
-class LazyChunkSequence:
+
+class LazyChunkSequence(Generic[TensorType]):
     """
     A class representing a chunked, and lazily evaluated array,
     where the chunking is restricted to the first dimension
@@ -114,12 +144,18 @@ class LazyChunkSequence:
     Attributes:
         generator_factory: A factory function that returns
             a generator. This generator yields chunks of the large array when called.
+        len_generator: if the number of elements from the generator is
+            known from the context, this optional parameter can be used to improve
+            logging by adding a progressbar.
     """
 
     def __init__(
-        self, generator_factory: Callable[[], Generator[TensorType, None, None]]
+        self,
+        generator_factory: Callable[[], Generator[TensorType, None, None]],
+        len_generator: Optional[int] = None,
     ):
         self.generator_factory = generator_factory
+        self.len_generator = len_generator
 
     @log_duration(log_level=logging.INFO)
     def compute(self, aggregator: Optional[SequenceAggregator] = None):
@@ -140,7 +176,7 @@ def compute(self, aggregator: Optional[SequenceAggregator] = None):
         """
         if aggregator is None:
             aggregator = ListAggregator()
-        return aggregator(self.generator_factory())
+        return aggregator(self)
 
     @log_duration(log_level=logging.INFO)
     def to_zarr(
@@ -171,7 +207,15 @@ def to_zarr(
         """
         row_idx = 0
         z = None
-        for block in self.generator_factory():
+
+        gen = cast(Iterator[TensorType], self.generator_factory())
+
+        if self.len_generator is not None:
+            gen = cast(
+                Iterator[TensorType], tqdm(gen, total=self.len_generator, desc="Blocks")
+            )
+
+        for block in gen:
             numpy_block = converter.to_numpy(block)
 
             if z is None:
@@ -204,7 +248,7 @@ def _initialize_zarr_array(block: NDArray, path_or_url: str, overwrite: bool):
         )
 
 
-class NestedLazyChunkSequence:
+class NestedLazyChunkSequence(Generic[TensorType]):
     """
     A class representing chunked, and lazily evaluated array, where the chunking is
     restricted to the first two dimensions.
@@ -216,16 +260,21 @@ class NestedLazyChunkSequence:
 
     Attributes:
         generator_factory: A factory function that returns a generator of generators.
-            Each inner generator yields chunks.
+            Each inner generator yields chunks
+        len_outer_generator: if the number of elements from the outer generator is
+            known from the context, this optional parameter can be used to improve
+            logging by adding a progressbar.
     """
 
     def __init__(
         self,
         generator_factory: Callable[
             [], Generator[Generator[TensorType, None, None], None, None]
         ],
+        len_outer_generator: Optional[int] = None,
     ):
         self.generator_factory = generator_factory
+        self.len_outer_generator = len_outer_generator
 
     @log_duration(log_level=logging.INFO)
     def compute(self, aggregator: Optional[NestedSequenceAggregator] = None):
@@ -247,7 +296,7 @@ def compute(self, aggregator: Optional[NestedSequenceAggregator] = None):
         """
         if aggregator is None:
             aggregator = NestedListAggregator()
-        return aggregator(self.generator_factory())
+        return aggregator(self)
 
     @log_duration(log_level=logging.INFO)
     def to_zarr(
@@ -280,7 +329,17 @@ def to_zarr(
         row_idx = 0
         z = None
         numpy_block = None
-        for row_blocks in self.generator_factory():
+        block_generator = cast(Iterator[Iterator[TensorType]], self.generator_factory())
+
+        if self.len_outer_generator is not None:
+            block_generator = cast(
+                Iterator[Iterator[TensorType]],
+                tqdm(
+                    block_generator, total=self.len_outer_generator, desc="Row blocks"
+                ),
+            )
+
+        for row_blocks in block_generator:
             col_idx = 0
             for block in row_blocks:
                 numpy_block = converter.to_numpy(block)

src/pydvl/influence/base_influence_function_model.py

@@ -4,6 +4,8 @@
 from enum import Enum
 from typing import Collection, Generic, Iterable, Optional, Type, TypeVar
 
+__all__ = ["InfluenceMode"]
+
 
 class InfluenceMode(str, Enum):
     """

src/pydvl/influence/influence_calculator.py

@@ -7,7 +7,7 @@
 
 import logging
 from functools import partial
-from typing import Generator, Iterable, Optional, Tuple, Type, Union
+from typing import Generator, Iterable, Optional, Sized, Tuple, Type, Union, cast
 
 import distributed
 from dask import array as da
@@ -619,8 +619,14 @@ def influence_factors(
         Returns:
             A lazy data structure representing the chunks of the resulting tensor
         """
+        try:
+            len_iterable = len(cast(Sized, data_iterable))
+        except Exception as e:
+            logger.debug(f"Failed to retrieve len of data iterable: {e}")
+            len_iterable = None
+
         tensors_gen_factory = partial(self._influence_factors_gen, data_iterable)
-        return LazyChunkSequence(tensors_gen_factory)
+        return LazyChunkSequence(tensors_gen_factory, len_generator=len_iterable)
 
     def _influences_gen(
         self,
@@ -677,7 +683,15 @@ def influences(
             mode,
         )
 
-        return NestedLazyChunkSequence(nested_tensor_gen_factory)
+        try:
+            len_iterable = len(cast(Sized, test_data_iterable))
+        except Exception as e:
+            logger.debug(f"Failed to retrieve len of test data iterable: {e}")
+            len_iterable = None
+
+        return NestedLazyChunkSequence(
+            nested_tensor_gen_factory, len_outer_generator=len_iterable
+        )
 
     def _influences_from_factors_gen(
         self,
@@ -735,4 +749,13 @@ def influences_from_factors(
             train_data_iterable,
             mode,
         )
-        return NestedLazyChunkSequence(nested_tensor_gen)
+
+        try:
+            len_iterable = len(cast(Sized, z_test_factors))
+        except Exception as e:
+            logger.debug(f"Failed to retrieve len of factors iterable: {e}")
+            len_iterable = None
+
+        return NestedLazyChunkSequence(
+            nested_tensor_gen, len_outer_generator=len_iterable
+        )