Fix two bugs w/ kwargs and preprocess_batch_fn in preprocess_fn s…

…erialisation (#752)

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ repos:
     hooks:
       - id: flake8
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.990
+    rev: v1.0.1
     hooks:
       - id: mypy
         additional_dependencies: [

CHANGELOG.md

@@ -1,8 +1,16 @@
 # Change Log
 
 ## v0.12.0dev
-[Full Changelog](https://github.com/SeldonIO/alibi-detect/compare/v0.11.0...master)
+[Full Changelog](https://github.com/SeldonIO/alibi-detect/compare/v0.11.1...master)
 
+## v0.11.1
+[Full Changelog](https://github.com/SeldonIO/alibi-detect/compare/v0.11.0...v0.11.1)
+
+### Fixed
+
+- Fixed two bugs with the saving/loading of drift detector `preprocess_fn`'s [#752](https://github.com/SeldonIO/alibi-detect/pull/752)):
+  - When `preprocess_fn` was a custom Python function wrapped in a partial, included kwarg's were not serialized. This has now been fixed.
+  - When saving drift detector `preprocess_fn`'s, for kwargs saved to `.dill` files, the filenames are now prepended with the kwarg name, so that files aren't overwritten if multiple kwargs are saved to `.dill`.
 
 ## v0.11.0
 [Full Changelog](https://github.com/SeldonIO/alibi-detect/compare/v0.10.5...v0.11.0)

alibi_detect/saving/loading.py

@@ -253,7 +253,10 @@ def _load_preprocess_config(cfg: dict) -> Optional[Callable]:
         logger.warning('Unable to process preprocess_fn. No preprocessing function is defined.')
         return None
 
-    return partial(preprocess_fn, **kwargs)
+    if kwargs == {}:
+        return preprocess_fn
+    else:
+        return partial(preprocess_fn, **kwargs)
 
 
 def _load_model_config(cfg: dict) -> Callable:

alibi_detect/saving/saving.py

@@ -4,7 +4,7 @@
 import warnings
 from functools import partial
 from pathlib import Path
-from typing import Callable, Optional, Tuple, Union, Any, TYPE_CHECKING
+from typing import Callable, Optional, Tuple, Union, Any, Dict, TYPE_CHECKING
 import dill
 import numpy as np
 import toml
@@ -264,7 +264,7 @@ def _save_preprocess_config(preprocess_fn: Callable,
     The config dictionary, containing references to the serialized artefacts. The format if this dict matches that
     of the `preprocess` field in the drift detector specification.
     """
-    preprocess_cfg = {}
+    preprocess_cfg: Dict[str, Any] = {}
     local_path = Path('preprocess_fn')
 
     # Serialize function
@@ -292,7 +292,7 @@ def _save_preprocess_config(preprocess_fn: Callable,
 
         # Arbitrary function
         elif callable(v):
-            src, _ = _serialize_object(v, filepath, local_path)
+            src, _ = _serialize_object(v, filepath, local_path.joinpath(k))
             kwargs.update({k: src})
 
         # Put remaining kwargs directly into cfg
@@ -302,7 +302,7 @@ def _save_preprocess_config(preprocess_fn: Callable,
     if 'preprocess_drift' in func:
         preprocess_cfg.update(kwargs)
     else:
-        kwargs.update({'kwargs': kwargs})
+        preprocess_cfg.update({'kwargs': kwargs})
 
     return preprocess_cfg
 

alibi_detect/saving/tests/models.py

@@ -89,7 +89,7 @@ def encoder_dropout_model(backend, current_cases):
 
 
 @fixture
-def preprocess_custom(encoder_model):
+def preprocess_uae(encoder_model):
     """
     Preprocess function with Untrained Autoencoder.
     """
@@ -263,6 +263,14 @@ def preprocess_simple(x: np.ndarray):
     return x*2.0
 
 
+@fixture
+def preprocess_simple_with_kwargs():
+    """
+    Simple function to test serialization of generic Python function with kwargs, within preprocess_fn.
+    """
+    return partial(preprocess_simple, kwarg1=42, kwarg2=True)
+
+
 @fixture
 def preprocess_nlp(embedding, tokenizer, max_len, backend):
     """

alibi_detect/saving/tests/test_saving.py

@@ -5,6 +5,7 @@
 Internal functions such as save_kernel/load_kernel_config etc are also tested.
 """
 from functools import partial
+import os
 from pathlib import Path
 from typing import Callable
 
@@ -19,7 +20,8 @@
 import torch.nn as nn
 
 from .datasets import BinData, CategoricalData, ContinuousData, MixedData, TextData
-from .models import (encoder_model, preprocess_custom, preprocess_hiddenoutput, preprocess_simple,  # noqa: F401
+from .models import (encoder_model, preprocess_uae, preprocess_hiddenoutput, preprocess_simple,  # noqa: F401
+                     preprocess_simple_with_kwargs,
                      preprocess_nlp, LATENT_DIM, classifier_model, kernel, deep_kernel, nlp_embedding_and_tokenizer,
                      embedding, tokenizer, max_len, enc_dim, encoder_dropout_model, optimizer)
 
@@ -105,7 +107,7 @@ def test_load_simple_config(cfg, tmp_path):
             assert v == cfg_new[k]
 
 
-@parametrize('preprocess_fn', [preprocess_custom, preprocess_hiddenoutput])
+@parametrize('preprocess_fn', [preprocess_uae, preprocess_hiddenoutput])
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
 def test_save_ksdrift(data, preprocess_fn, tmp_path):
     """
@@ -171,7 +173,7 @@ def test_save_ksdrift_nlp(data, preprocess_fn, enc_dim, tmp_path):  # noqa: F811
 @pytest.mark.skipif(version.parse(scipy.__version__) < version.parse('1.7.0'),
                     reason="Requires scipy version >= 1.7.0")
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
-def test_save_cvmdrift(data, preprocess_custom, tmp_path):
+def test_save_cvmdrift(data, preprocess_uae, tmp_path):
     """
     Test CVMDrift on continuous datasets, with UAE as preprocess_fn.
 
@@ -181,14 +183,14 @@ def test_save_cvmdrift(data, preprocess_custom, tmp_path):
     X_ref, X_h0 = data
     cd = CVMDrift(X_ref,
                   p_val=P_VAL,
-                  preprocess_fn=preprocess_custom,
+                  preprocess_fn=preprocess_uae,
                   preprocess_at_init=True,
                   )
     save_detector(cd, tmp_path)
     cd_load = load_detector(tmp_path)
 
     # Assert
-    np.testing.assert_array_equal(preprocess_custom(X_ref), cd_load.x_ref)
+    np.testing.assert_array_equal(preprocess_uae(X_ref), cd_load.x_ref)
     assert cd_load.n_features == LATENT_DIM
     assert cd_load.p_val == P_VAL
     assert isinstance(cd_load.preprocess_fn, Callable)
@@ -203,7 +205,7 @@ def test_save_cvmdrift(data, preprocess_custom, tmp_path):
     ], indirect=True
 )
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
-def test_save_mmddrift(data, kernel, preprocess_custom, backend, tmp_path, seed):  # noqa: F811
+def test_save_mmddrift(data, kernel, preprocess_uae, backend, tmp_path, seed):  # noqa: F811
     """
     Test MMDDrift on continuous datasets, with UAE as preprocess_fn.
 
@@ -217,7 +219,7 @@ def test_save_mmddrift(data, kernel, preprocess_custom, backend, tmp_path, seed)
     kwargs = {
         'p_val': P_VAL,
         'backend': backend,
-        'preprocess_fn': preprocess_custom,
+        'preprocess_fn': preprocess_uae,
         'n_permutations': N_PERMUTATIONS,
         'preprocess_at_init': True,
         'kernel': kernel,
@@ -237,7 +239,7 @@ def test_save_mmddrift(data, kernel, preprocess_custom, backend, tmp_path, seed)
         preds_load = cd_load.predict(X_h0)
 
     # assertions
-    np.testing.assert_array_equal(preprocess_custom(X_ref), cd_load._detector.x_ref)
+    np.testing.assert_array_equal(preprocess_uae(X_ref), cd_load._detector.x_ref)
     assert not cd_load._detector.infer_sigma
     assert cd_load._detector.n_permutations == N_PERMUTATIONS
     assert cd_load._detector.p_val == P_VAL
@@ -248,7 +250,7 @@ def test_save_mmddrift(data, kernel, preprocess_custom, backend, tmp_path, seed)
     assert preds['data']['p_val'] == preds_load['data']['p_val']
 
 
-# @parametrize('preprocess_fn', [preprocess_custom, preprocess_hiddenoutput])
+# @parametrize('preprocess_fn', [preprocess_uae, preprocess_hiddenoutput])
 @parametrize('preprocess_at_init', [True, False])
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
 def test_save_lsdddrift(data, preprocess_at_init, backend, tmp_path, seed):
@@ -553,7 +555,7 @@ def test_save_contextmmddrift(data, kernel, backend, tmp_path, seed):  # noqa: F
     assert cd_load._detector.n_permutations == N_PERMUTATIONS
     assert cd_load._detector.p_val == P_VAL
     assert isinstance(cd_load._detector.preprocess_fn, Callable)
-    assert cd_load._detector.preprocess_fn.func.__name__ == 'preprocess_simple'
+    assert cd_load._detector.preprocess_fn.__name__ == 'preprocess_simple'
     assert cd._detector.x_kernel.sigma == cd_load._detector.x_kernel.sigma
     assert cd._detector.c_kernel.sigma == cd_load._detector.c_kernel.sigma
     assert cd._detector.x_kernel.init_sigma_fn == cd_load._detector.x_kernel.init_sigma_fn
@@ -629,7 +631,7 @@ def test_save_regressoruncertaintydrift(data, regressor, backend, tmp_path, seed
     ], indirect=True
 )
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
-def test_save_onlinemmddrift(data, kernel, preprocess_custom, backend, tmp_path, seed):  # noqa: F811
+def test_save_onlinemmddrift(data, kernel, preprocess_uae, backend, tmp_path, seed):  # noqa: F811
     """
     Test MMDDriftOnline on continuous datasets, with UAE as preprocess_fn.
 
@@ -645,7 +647,7 @@ def test_save_onlinemmddrift(data, kernel, preprocess_custom, backend, tmp_path,
         cd = MMDDriftOnline(X_ref,
                             ert=ERT,
                             backend=backend,
-                            preprocess_fn=preprocess_custom,
+                            preprocess_fn=preprocess_uae,
                             n_bootstraps=N_BOOTSTRAPS,
                             kernel=kernel,
                             window_size=WINDOW_SIZE
@@ -667,7 +669,7 @@ def test_save_onlinemmddrift(data, kernel, preprocess_custom, backend, tmp_path,
                 stats_load.append(pred['data']['test_stat'])
 
     # assertions
-    np.testing.assert_array_equal(preprocess_custom(X_ref), cd_load._detector.x_ref)
+    np.testing.assert_array_equal(preprocess_uae(X_ref), cd_load._detector.x_ref)
     assert cd_load._detector.n_bootstraps == N_BOOTSTRAPS
     assert cd_load._detector.ert == ERT
     assert isinstance(cd_load._detector.preprocess_fn, Callable)
@@ -678,7 +680,7 @@ def test_save_onlinemmddrift(data, kernel, preprocess_custom, backend, tmp_path,
 
 
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
-def test_save_onlinelsdddrift(data, preprocess_custom, backend, tmp_path, seed):
+def test_save_onlinelsdddrift(data, preprocess_uae, backend, tmp_path, seed):
     """
     Test LSDDDriftOnline on continuous datasets, with UAE as preprocess_fn.
 
@@ -694,7 +696,7 @@ def test_save_onlinelsdddrift(data, preprocess_custom, backend, tmp_path, seed):
         cd = LSDDDriftOnline(X_ref,
                              ert=ERT,
                              backend=backend,
-                             preprocess_fn=preprocess_custom,
+                             preprocess_fn=preprocess_uae,
                              n_bootstraps=N_BOOTSTRAPS,
                              window_size=WINDOW_SIZE
                              )
@@ -715,7 +717,7 @@ def test_save_onlinelsdddrift(data, preprocess_custom, backend, tmp_path, seed):
                 stats_load.append(pred['data']['test_stat'])
 
     # assertions
-    np.testing.assert_array_almost_equal(preprocess_custom(X_ref), cd_load.get_config()['x_ref'], 5)
+    np.testing.assert_array_almost_equal(preprocess_uae(X_ref), cd_load.get_config()['x_ref'], 5)
     assert cd_load._detector.n_bootstraps == N_BOOTSTRAPS
     assert cd_load._detector.ert == ERT
     assert isinstance(cd_load._detector.preprocess_fn, Callable)
@@ -726,7 +728,7 @@ def test_save_onlinelsdddrift(data, preprocess_custom, backend, tmp_path, seed):
 
 
 @parametrize_with_cases("data", cases=ContinuousData, prefix='data_')
-def test_save_onlinecvmdrift(data, preprocess_custom, tmp_path, seed):
+def test_save_onlinecvmdrift(data, preprocess_uae, tmp_path, seed):
     """
     Test CVMDriftOnline on continuous datasets, with UAE as preprocess_fn.
 
@@ -738,7 +740,7 @@ def test_save_onlinecvmdrift(data, preprocess_custom, tmp_path, seed):
     with fixed_seed(seed):
         cd = CVMDriftOnline(X_ref,
                             ert=ERT,
-                            preprocess_fn=preprocess_custom,
+                            preprocess_fn=preprocess_uae,
                             n_bootstraps=N_BOOTSTRAPS,
                             window_sizes=[WINDOW_SIZE]
                             )
@@ -759,7 +761,7 @@ def test_save_onlinecvmdrift(data, preprocess_custom, tmp_path, seed):
                 stats_load.append(pred['data']['test_stat'])
 
     # assertions
-    np.testing.assert_array_almost_equal(preprocess_custom(X_ref), cd_load.get_config()['x_ref'], 5)
+    np.testing.assert_array_almost_equal(preprocess_uae(X_ref), cd_load.get_config()['x_ref'], 5)
     assert cd_load.n_bootstraps == N_BOOTSTRAPS
     assert cd_load.ert == ERT
     assert isinstance(cd_load.preprocess_fn, Callable)
@@ -1100,15 +1102,12 @@ def test_save_deepkernel(data, deep_kernel, backend, tmp_path):  # noqa: F811
     assert kernel_loaded.kernel_b.sigma == deep_kernel.kernel_b.sigma
 
 
-@parametrize('preprocess_fn', [preprocess_custom, preprocess_hiddenoutput])
+@parametrize('preprocess_fn', [preprocess_uae, preprocess_hiddenoutput])
 @parametrize_with_cases("data", cases=ContinuousData.data_synthetic_nd, prefix='data_')
-def test_save_preprocess(data, preprocess_fn, tmp_path, backend):
+def test_save_preprocess_drift(data, preprocess_fn, tmp_path, backend):
     """
-    Unit test for _save_preprocess_config and _load_preprocess_config, with continuous data.
-
-    preprocess_fn's are saved (serialized) and then loaded, with assertions to check equivalence.
-    Note: _save_model_config, _save_embedding_config, _save_tokenizer_config, _load_model_config,
-     _load_embedding_config, _load_tokenizer_config and _prep_model_and_embedding are all well covered by this test.
+    Test saving/loading of the inbuilt `preprocess_drift` preprocessing functions when containing a `model`, with the
+    `model` either being a simple tf/torch model, or a `HiddenOutput` class.
     """
     registry_str = 'tensorflow' if backend == 'tensorflow' else 'pytorch'
     # Save preprocess_fn to config
@@ -1132,14 +1131,40 @@ def test_save_preprocess(data, preprocess_fn, tmp_path, backend):
         assert isinstance(preprocess_fn_load.keywords['model'], nn.Module)
 
 
+@parametrize('preprocess_fn', [preprocess_simple, preprocess_simple_with_kwargs])
+def test_save_preprocess_custom(preprocess_fn, tmp_path):
+    """
+    Test saving/loading of custom preprocessing functions, without and with kwargs.
+    """
+    # Save preprocess_fn to config
+    filepath = tmp_path
+    cfg_preprocess = _save_preprocess_config(preprocess_fn, input_shape=None, filepath=filepath)
+    cfg_preprocess = _path2str(cfg_preprocess)
+    cfg_preprocess = PreprocessConfig(**cfg_preprocess).dict()  # pydantic validation
+
+    assert tmp_path.joinpath(cfg_preprocess['src']).is_file()
+    assert cfg_preprocess['src'] == os.path.join('preprocess_fn', 'function.dill')
+    if isinstance(preprocess_fn, partial):  # kwargs expected
+        assert cfg_preprocess['kwargs'] == preprocess_fn.keywords
+    else:  # no kwargs expected
+        assert cfg_preprocess['kwargs'] == {}
+
+    # Resolve and load preprocess config
+    cfg = {'preprocess_fn': cfg_preprocess}
+    preprocess_fn_load = resolve_config(cfg, tmp_path)['preprocess_fn']  # tests _load_preprocess_config implicitly
+    if isinstance(preprocess_fn, partial):
+        assert preprocess_fn_load.func == preprocess_fn.func
+        assert preprocess_fn_load.keywords == preprocess_fn.keywords
+    else:
+        assert preprocess_fn_load == preprocess_fn
+
+
 @parametrize('preprocess_fn', [preprocess_nlp])
 @parametrize_with_cases("data", cases=TextData.movie_sentiment_data, prefix='data_')
 def test_save_preprocess_nlp(data, preprocess_fn, tmp_path, backend):
     """
-    Unit test for _save_preprocess_config and _load_preprocess_config, with text data.
-
-    Note: _save_model_config, _save_embedding_config, _save_tokenizer_config, _load_model_config,
-     _load_embedding_config, _load_tokenizer_config and _prep_model_and_embedding are all covered by this test.
+    Test saving/loading of the inbuilt `preprocess_drift` preprocessing functions when containing a `model`, text
+    `tokenizer` and text `embedding` model.
     """
     registry_str = 'tensorflow' if backend == 'tensorflow' else 'pytorch'
     # Save preprocess_fn to config
@@ -1152,6 +1177,8 @@ def test_save_preprocess_nlp(data, preprocess_fn, tmp_path, backend):
     assert cfg_preprocess['src'] == '@cd.' + registry_str + '.preprocess.preprocess_drift'
     assert cfg_preprocess['embedding']['src'] == 'preprocess_fn/embedding'
     assert cfg_preprocess['tokenizer']['src'] == 'preprocess_fn/tokenizer'
+    assert tmp_path.joinpath(cfg_preprocess['preprocess_batch_fn']).is_file()
+    assert cfg_preprocess['preprocess_batch_fn'] == os.path.join('preprocess_fn', 'preprocess_batch_fn.dill')
 
     if isinstance(preprocess_fn.keywords['model'], (TransformerEmbedding_tf, TransformerEmbedding_pt)):
         assert cfg_preprocess['model'] is None