Add transform submodule, parameter compression transform

docs/guides/transforms.md

@@ -4,7 +4,7 @@ After a successful benchmark run execution, you end up with your metrics, contex
 In general, this data is a best-effort representation of the environment and configuration the benchmarks are run in.
 
 However, in some situations, manual editing and transformation of these records is required.
-nnbench exposes the `nnbench.io.transforms` module to facilitate these transforms.
+nnbench exposes the `nnbench.transforms` module to facilitate these transforms.
 
 ## Types of transforms: 1->1 vs. N->1 vs. N->N
 

examples/transforms/transforms.py

@@ -4,8 +4,8 @@
 import numpy as np
 
 import nnbench
-from nnbench.io.transforms import OneToOneTransform
 from nnbench.reporter.file import FileIO
+from nnbench.transforms import OneToOneTransform
 from nnbench.types import BenchmarkRecord
 
 

src/nnbench/transforms/__init__.py

@@ -0,0 +1 @@
+from .base import ManyToManyTransform, ManyToOneTransform, OneToOneTransform

src/nnbench/transforms/params.py

@@ -0,0 +1,48 @@
+from typing import Any, Sequence
+
+from nnbench.transforms import ManyToManyTransform, OneToOneTransform
+from nnbench.types import BenchmarkRecord
+
+
+class CompressionMixin:
+    def compress(self, params: dict[str, Any]) -> dict[str, Any]:
+        containers = (tuple, list, set, frozenset)
+        natives = (float, int, str, bool, bytes, complex)
+        compressed: dict[str, Any] = {}
+
+        def _compress_impl(val):
+            if isinstance(val, natives):
+                # save native types without modification...
+                return val
+            else:
+                # ... or return the string repr.
+                # TODO: Allow custom representations for types with formatters.
+                return repr(val)
+
+        for k, v in params.items():
+            if isinstance(v, containers):
+                container_type = type(v)
+                compressed[k] = container_type(_compress_impl(vv) for vv in v)
+            elif isinstance(v, dict):
+                compressed[k] = self.compress(v)
+            else:
+                compressed[k] = _compress_impl(v)
+
+        return compressed
+
+
+class ParameterCompression1to1(OneToOneTransform, CompressionMixin):
+    def apply(self, record: BenchmarkRecord) -> BenchmarkRecord:
+        for bm in record.benchmarks:
+            bm["params"] = self.compress(bm["params"])
+
+        return record
+
+
+class ParameterCompressionNtoN(ManyToManyTransform, CompressionMixin):
+    def apply(self, record: Sequence[BenchmarkRecord]) -> Sequence[BenchmarkRecord]:
+        for rec in record:
+            for bm in rec.benchmarks:
+                bm["params"] = self.compress(bm["params"])
+
+        return record