From 75c5a7ee6b641fb9f7bb9fb8f1d257278e070e07 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:32:41 +1000
Subject: [PATCH 1/7] chore: replace requirements.txt with pyproject.toml and
 uv

- Add pyproject.toml with hatchling build backend, min-version deps,
  TF/Keras as optional [tf] extra, requires-python>=3.11
- Generate uv.lock (43 packages)
- Pin Python 3.12 via .python-version
- Remove requirements.txt (superseded)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .python-version  |    1 +
 pyproject.toml   |   52 +++
 requirements.txt |   11 -
 uv.lock          | 1024 ++++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 1077 insertions(+), 11 deletions(-)
 create mode 100644 .python-version
 create mode 100644 pyproject.toml
 delete mode 100644 requirements.txt
 create mode 100644 uv.lock

diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..0c0a9b7
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,52 @@
+[project]
+name = "heuristic-hpo"
+version = "0.1.0"
+description = "Heuristic-driven hyperparameter optimization algorithms for deep learning"
+readme = "README.md"
+license = { text = "MIT" }
+authors = [{ name = "outsidermm" }]
+requires-python = ">=3.11"
+dependencies = [
+    "numpy>=2.0",
+    "scipy>=1.10",
+    "scikit-learn>=1.3",
+]
+
+[project.optional-dependencies]
+tf = [
+    "tensorflow>=2.21",
+    "keras>=3.14",
+]
+full = [
+    "heuristic-hpo[tf]",
+]
+
+[dependency-groups]
+dev = [
+    "pytest>=8",
+    "pytest-mock>=3",
+    "ruff>=0.9",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["algorithm", "grid_search", "trend", "utility"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+addopts = "--tb=short"
+
+[tool.ruff]
+target-version = "py310"
+line-length = 100
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "UP"]
+ignore = ["E501"]
+
+[tool.mypy]
+python_version = "3.11"
+ignore_missing_imports = true
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 3106c34..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-absl-py==2.4.0
-h5py==3.16.0
-joblib==1.5.3
-keras==3.14.1
-keras-tuner==1.4.8
-matplotlib-inline==0.2.2
-scikit-learn==1.8.0
-numpy==2.4.5
-scipy==1.17.1
-tensorboard==2.20.0
-tensorflow==2.21.0
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..289f967
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1024 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version < '3.12'",
+]
+
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+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
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+    { url = "https://files.pythonhosted.org/packages/09/af/8e88031a701275b9085c54e64bc88c0b1cd55c77eadd400691c371cd76c4/wrapt-2.2.1-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f6518b94edb9150452e9aba08027d4cc293433753ec1fbefb4629a21cbc74181", size = 192267, upload-time = "2026-05-22T14:49:17.283Z" },
+    { url = "https://files.pythonhosted.org/packages/bf/a8/e657ca876b06710194f243d81c4b0896ade646e244bdbec2d87c8c56a8bd/wrapt-2.2.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ed55af48b3eb28f43228ca2306788892bcb629eb2b5c4876e2a3659872c2f17a", size = 198378, upload-time = "2026-05-22T14:49:18.785Z" },
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+    { url = "https://files.pythonhosted.org/packages/9f/c0/782b86e28d1ceebeb74cccea12d2cd3d2ba0bd68e3dec20b1bc5873f6127/wrapt-2.2.1-cp314-cp314t-win_arm64.whl", hash = "sha256:f70db64e8266d7c45d3b735f2e08eeb434b5e03da9a479ae42b2e2e486a21a00", size = 80722, upload-time = "2026-05-22T14:49:23.59Z" },
+    { url = "https://files.pythonhosted.org/packages/53/46/29ac9daf11a86c22a8c38cd9236c62928ccae83f7ceb06bd3b0467cf9d05/wrapt-2.2.1-py3-none-any.whl", hash = "sha256:3aafea2975caef8ca49400640dde02cc7426e798f24870ed01f490bc3cffd32f", size = 61000, upload-time = "2026-05-22T14:49:41.593Z" },
+]

From f336a7c4108ee7eaf3623c70e5b12e59892e5d09 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:32:52 +1000
Subject: [PATCH 2/7] refactor: convert scripts to proper installable package

- Add __init__.py to algorithm/, grid_search/, trend/ with __all__ exports
- Remove all sys.path.append('.') hacks
- Fix bare imports (from searcher import Searcher) to use relative imports
- Make TF/Keras imports lazy so the package is importable without TF installed
- Replace print() calls with logging.getLogger(__name__) throughout
- Fix hardcoded ./dataset/ and ./algorithm/ paths; use pathlib.Path with
  mkdir(parents=True) and accept dataset_dir constructor arg on DataLoader
- Fix undefined-before-use bug for 'mid' in EpochTuner.binary_search_efficient_epoch
- Fix IndexError on best_epoch[0] in EpochTuner return (was already a scalar)
- Add NumPy-style docstrings to all public classes and key methods
- Add training_fn parameter to EpochTuner, BatchSizeTuner, LrTuner so callers
  can inject a plain callable instead of requiring TF/dataset files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 algorithm/__init__.py          |   6 ++
 algorithm/batch_size.py        |  78 +++++++++++++++---
 algorithm/epoch.py             |  79 +++++++++++++-----
 algorithm/lr.py                |  75 ++++++++++++++----
 algorithm/searcher.py          | 141 +++++++++++++++++++--------------
 grid_search/__init__.py        |   3 +
 grid_search/grid_search.py     |  94 +++++++++-------------
 trend/__init__.py              |   3 +
 trend/hyperparameter_search.py |  70 +++++++---------
 utility/dataloader.py          |  49 +++++++++---
 utility/helper_func.py         |  47 +++++++++++
 utility/model.py               |   4 +-
 12 files changed, 429 insertions(+), 220 deletions(-)
 create mode 100644 algorithm/__init__.py
 create mode 100644 grid_search/__init__.py
 create mode 100644 trend/__init__.py

diff --git a/algorithm/__init__.py b/algorithm/__init__.py
new file mode 100644
index 0000000..7caa8e2
--- /dev/null
+++ b/algorithm/__init__.py
@@ -0,0 +1,6 @@
+from .batch_size import BatchSizeTuner
+from .epoch import EpochTuner
+from .lr import LrTuner
+from .searcher import Searcher
+
+__all__ = ["EpochTuner", "BatchSizeTuner", "LrTuner", "Searcher"]
diff --git a/algorithm/batch_size.py b/algorithm/batch_size.py
index 077949e..facc580 100644
--- a/algorithm/batch_size.py
+++ b/algorithm/batch_size.py
@@ -1,15 +1,47 @@
-import numpy as np
-import sys
+from collections.abc import Callable
 
-sys.path.append(".")
+import numpy as np
 
-from searcher import Searcher
 from utility.dataloader import DataLoader
-from typing import Tuple
-import tensorflow as tf
+
+from .searcher import Searcher
+
+
+def _get_oom_error() -> type:
+    """Return tf.errors.ResourceExhaustedError if TF is available, else a dummy."""
+    try:
+        import tensorflow as tf  # noqa: PLC0415
+
+        return tf.errors.ResourceExhaustedError
+    except ImportError:
+        return type("_NeverRaised", (Exception,), {})
 
 
 class BatchSizeTuner:
+    """Heuristic binary-search tuner for the maximum viable batch size.
+
+    Searches for the largest batch size (as a power of 2) whose accuracy stays
+    within ``acceptable_range / 2`` of the baseline accuracy measured at
+    ``left_bound``.
+
+    Parameters
+    ----------
+    dataset : str
+        Either ``"cifar100"`` or ``"imagenet"``.
+    left_bound : int
+        Log2 of the minimum batch size (e.g. ``4`` → batch size 16).
+    right_bound : int
+        Log2 of the maximum batch size (e.g. ``12`` → batch size 4096).
+    acceptable_range : float
+        Total acceptable accuracy drop relative to the baseline (default 0.30).
+    training_fn : Callable[[int], tuple[float, float]] | None
+        Optional override for the training function. Called as
+        ``training_fn(batch_size)`` (actual batch size, not log2) and must
+        return ``(time, accuracy)``. Pass a callable to run without TensorFlow.
+    dataset_dir : str
+        Root directory for dataset ``.npy`` files (default ``"./dataset"``).
+        Ignored when ``training_fn`` is provided.
+    """
 
     __batch_list = np.array([])
     __accuracy_list = np.array([])
@@ -21,19 +53,36 @@ def __init__(
         left_bound: int,
         right_bound: int,
         acceptable_range: float = 0.30,
+        training_fn: Callable[[int], tuple[float, float]] | None = None,
+        dataset_dir: str = "./dataset",
     ) -> None:
         self.__acceptable_range = acceptable_range / 2
         self.__left_bound = left_bound
         self.__right_bound = right_bound
         self.__dataset = dataset
-        dataset_loader = DataLoader(self.__dataset)
-        self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+        if training_fn is not None:
+            self.__training_fn = training_fn
+            self.__train_ds = self.__val_ds = self.__test_ds = None
+        else:
+            dataset_loader = DataLoader(self.__dataset, dataset_dir=dataset_dir)
+            self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+            self.__training_fn = None
+
+    def search(self) -> tuple[int, float, float]:
+        """Search for the largest acceptable batch size.
 
-    def search(self) -> Tuple[int, float, float]:
+        Returns
+        -------
+        Tuple[int, float, float]
+            ``(best_batch_size, accuracy, time_taken_seconds)``.
+            Returns ``(-1.0, -1.0, -1.0)`` if even the minimum batch size
+            exceeds available GPU memory.
+        """
         left = self.__left_bound
+        _oom_error: type = _get_oom_error()
         try:
             _, acc_bound = self.batch_size_runner(left)
-        except tf.errors.ResourceExhaustedError:
+        except _oom_error:
             return -1.0, -1.0, -1.0
 
         right = self.__right_bound
@@ -55,7 +104,7 @@ def search(self) -> Tuple[int, float, float]:
                 else:
                     right = mid - 1
 
-            except tf.errors.ResourceExhaustedError:
+            except _oom_error:
                 right = mid - 1
 
             mid = left + (right - left) // 2
@@ -66,11 +115,14 @@ def search(self) -> Tuple[int, float, float]:
             self.__time_list[self.__batch_list == best_batch][0],
         )
 
-    def batch_size_runner(self, batch_size: int) -> Tuple[float, float]:
+    def batch_size_runner(self, batch_size: int) -> tuple[float, float]:
+        actual = 2**batch_size
+        if self.__training_fn is not None:
+            return self.__training_fn(actual)
         return Searcher(
             dataset=self.__dataset,
             train_ds=self.__train_ds,
             val_ds=self.__val_ds,
             test_ds=self.__test_ds,
             verbose=1,
-        ).training(batch_size=2**batch_size)
+        ).training(batch_size=actual)
diff --git a/algorithm/epoch.py b/algorithm/epoch.py
index d89a6d0..ce067d3 100644
--- a/algorithm/epoch.py
+++ b/algorithm/epoch.py
@@ -1,16 +1,39 @@
-import numpy as np
 import math
-import sys
+from collections.abc import Callable
 
-sys.path.append(".")
+import numpy as np
 
-from utility.helper_func import min_max_scalar, weighted_avg
-from searcher import Searcher
 from utility.dataloader import DataLoader
-from typing import Tuple
+from utility.helper_func import min_max_scalar, weighted_avg
+
+from .searcher import Searcher
 
 
 class EpochTuner:
+    """Heuristic binary-search tuner for the optimal number of training epochs.
+
+    Uses a weighted efficiency metric combining normalized training time and
+    (1 - accuracy) to find the epoch count with the best accuracy/time trade-off.
+
+    Parameters
+    ----------
+    dataset : str
+        Either ``"cifar100"`` or ``"imagenet"``.
+    left_bound : int
+        Minimum epoch count to consider.
+    right_bound : int
+        Maximum epoch count to consider.
+    exploration_factor : int
+        Stops the search when ``right - left <= exploration_factor``.
+    training_fn : Callable[[int], tuple[float, float]] | None
+        Optional override for the training function. Called as
+        ``training_fn(epoch)`` and must return ``(time, accuracy)``.
+        When ``None`` (default) a real ``Searcher`` + ``DataLoader`` is used.
+        Pass a custom callable to run without TensorFlow or dataset files.
+    dataset_dir : str
+        Root directory for dataset ``.npy`` files (default ``"./dataset"``).
+        Ignored when ``training_fn`` is provided.
+    """
 
     __epoch_list = np.array([])
     __time_list = np.array([])
@@ -25,24 +48,34 @@ def __init__(
         left_bound: int,
         right_bound: int,
         exploration_factor: int = 1,
+        training_fn: Callable[[int], tuple[float, float]] | None = None,
+        dataset_dir: str = "./dataset",
     ) -> None:
         self.__left_bound = left_bound
         self.__right_bound = right_bound
         self.__exploration_factor = exploration_factor
         self.__dataset = dataset
-        dataset_loader = DataLoader(self.__dataset)
-        self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+        if training_fn is not None:
+            self.__training_fn = training_fn
+            self.__train_ds = self.__val_ds = self.__test_ds = None
+        else:
+            dataset_loader = DataLoader(self.__dataset, dataset_dir=dataset_dir)
+            self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+            self.__training_fn = None
 
     def epoch_run(self, epoch: int) -> None:
         # run the epoch
         if epoch not in self.__epoch_list:
-            time, acc = Searcher(
-                dataset=self.__dataset,
-                train_ds=self.__train_ds,
-                val_ds=self.__val_ds,
-                test_ds=self.__test_ds,
-                verbose=1,
-            ).training(epoch=epoch)
+            if self.__training_fn is not None:
+                time, acc = self.__training_fn(epoch)
+            else:
+                time, acc = Searcher(
+                    dataset=self.__dataset,
+                    train_ds=self.__train_ds,
+                    val_ds=self.__val_ds,
+                    test_ds=self.__test_ds,
+                    verbose=1,
+                ).training(epoch=epoch)
 
             self.__time_list = np.append(self.__time_list, time)
             self.__accuracy_list = np.append(self.__accuracy_list, acc)
@@ -56,7 +89,14 @@ def epoch_run(self, epoch: int) -> None:
                 self.__time_normalized = min_max_scalar(self.__time_list)
                 self.__accuracy_normalized = min_max_scalar(self.__accuracy_list)
 
-    def binary_search_efficient_epoch(self) -> Tuple[int, float, float]:
+    def binary_search_efficient_epoch(self) -> tuple[int, float, float]:
+        """Search for the most efficient epoch count.
+
+        Returns
+        -------
+        Tuple[int, float, float]
+            ``(best_epoch, accuracy_at_best_epoch, time_at_best_epoch)``
+        """
         left = self.__left_bound
         right = self.__right_bound
         best_epoch = self.__left_bound
@@ -66,6 +106,7 @@ def binary_search_efficient_epoch(self) -> Tuple[int, float, float]:
         left_efficiency = self.weighted_avg_from_epoch(left)
         right_efficiency = self.weighted_avg_from_epoch(right)
 
+        mid = left  # sentinel: mid is unused on the first pass when interval is already small
         mid_efficiency = best_efficiency = math.inf
 
         while left <= right:
@@ -102,9 +143,9 @@ def binary_search_efficient_epoch(self) -> Tuple[int, float, float]:
                 right = mid
 
         return (
-            best_epoch[0],
-            self.__accuracy_list[self.__epoch_list == best_epoch][0],
-            self.__time_list[self.__epoch_list == best_epoch][0],
+            int(best_epoch),
+            float(self.__accuracy_list[self.__epoch_list == best_epoch][0]),
+            float(self.__time_list[self.__epoch_list == best_epoch][0]),
         )
 
     def weighted_avg_from_epoch(self, epoch: int) -> float:
diff --git a/algorithm/lr.py b/algorithm/lr.py
index 5bc6ccb..0970f79 100644
--- a/algorithm/lr.py
+++ b/algorithm/lr.py
@@ -1,15 +1,37 @@
-import numpy as np
-import sys
+from collections.abc import Callable
 
-sys.path.append(".")
+import numpy as np
 
-from searcher import Searcher
 from utility.dataloader import DataLoader
-from typing import Tuple
-import tensorflow as tf
+
+from .searcher import Searcher
 
 
 class LrTuner:
+    """Heuristic binary-search tuner for the optimal learning rate.
+
+    Searches the log-linear space ``[10^-left_bound, 10^-right_bound]`` for a
+    local accuracy maximum, stopping when a point exceeds both its neighbours
+    by at least ``local_extrema_allowance``.
+
+    Parameters
+    ----------
+    dataset : str
+        Either ``"cifar100"`` or ``"imagenet"``.
+    left_bound : int
+        Negative exponent lower bound (e.g. ``1`` → lr starts at ``0.1``).
+    right_bound : int
+        Negative exponent upper bound (e.g. ``7`` → lr ends at ``1e-7``).
+    local_extrema_allowance : float
+        Minimum accuracy gap required to declare a local maximum (default 0.05).
+    training_fn : Callable[[float], tuple[float, float]] | None
+        Optional override for the training function. Called as
+        ``training_fn(lr)`` (actual learning rate value) and must return
+        ``(time, accuracy)``. Pass a callable to run without TensorFlow.
+    dataset_dir : str
+        Root directory for dataset ``.npy`` files (default ``"./dataset"``).
+        Ignored when ``training_fn`` is provided.
+    """
 
     __lr_list = np.array([])
     __accuracy_list = np.array([])
@@ -21,15 +43,30 @@ def __init__(
         left_bound: int,
         right_bound: int,
         local_extrema_allowance: float = 0.05,
+        training_fn: Callable[[float], tuple[float, float]] | None = None,
+        dataset_dir: str = "./dataset",
     ) -> None:
         self.__left_bound = left_bound
         self.__right_bound = right_bound
         self.__dataset = dataset
-        dataset_loader = DataLoader(self.__dataset)
-        self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+        if training_fn is not None:
+            self.__training_fn = training_fn
+            self.__train_ds = self.__val_ds = self.__test_ds = None
+        else:
+            dataset_loader = DataLoader(self.__dataset, dataset_dir=dataset_dir)
+            self.__train_ds, self.__val_ds, self.__test_ds = dataset_loader.load_dataset()
+            self.__training_fn = None
         self.__local_extrema_allowance = local_extrema_allowance
 
-    def search(self) -> Tuple[int, float, float]:
+    def search(self) -> tuple[int, float, float]:
+        """Search for the learning rate with a local accuracy maximum.
+
+        Returns
+        -------
+        Tuple[int, float, float]
+            ``(lr_linear_exponent, accuracy, time_taken_seconds)``.
+            Returns ``(-1, -1, -1)`` if no local maximum is found.
+        """
         left = self.__left_bound
         right = self.__right_bound
 
@@ -52,15 +89,19 @@ def search(self) -> Tuple[int, float, float]:
 
         return -1, -1, -1
 
-    def data_stat_search(self, lr_linear: float) -> Tuple[float, float]:
+    def data_stat_search(self, lr_linear: float) -> tuple[float, float]:
         if lr_linear not in self.__lr_list:
-            time, acc = Searcher(
-                dataset=self.__dataset,
-                train_ds=self.__train_ds,
-                val_ds=self.__val_ds,
-                test_ds=self.__test_ds,
-                verbose=1,
-            ).training(lr=10 ** (-lr_linear))
+            lr = 10 ** (-lr_linear)
+            if self.__training_fn is not None:
+                time, acc = self.__training_fn(lr)
+            else:
+                time, acc = Searcher(
+                    dataset=self.__dataset,
+                    train_ds=self.__train_ds,
+                    val_ds=self.__val_ds,
+                    test_ds=self.__test_ds,
+                    verbose=1,
+                ).training(lr=lr)
             self.__lr_list = np.append(self.__lr_list, lr_linear)
             self.__accuracy_list = np.append(self.__accuracy_list, acc)
             self.__time_list = np.append(self.__time_list, time)
diff --git a/algorithm/searcher.py b/algorithm/searcher.py
index 314c3ca..da7a6ff 100644
--- a/algorithm/searcher.py
+++ b/algorithm/searcher.py
@@ -1,34 +1,47 @@
-import sys
-
-sys.path.append(".")
+import logging
+import time
+from pathlib import Path
 
-from keras import optimizers, losses, metrics, backend
-from utility.model import VGG16
 from utility.helper_func import write_csv, write_header
-import time
-import tensorflow as tf
-from typing import Tuple
 
-TOP_1 = metrics.TopKCategoricalAccuracy(k=1, name="Top_1")
-TOP_5 = metrics.TopKCategoricalAccuracy(k=5, name="Top_5")
-TOP_1_SPARSE = metrics.SparseTopKCategoricalAccuracy(k=1, name="Top_1")
-TOP_5_SPARSE = metrics.SparseTopKCategoricalAccuracy(k=5, name="Top_5")
-LOSS = losses.CategoricalCrossentropy()
-LOSS_SPARSE = losses.SparseCategoricalCrossentropy()
-AUTOTUNE = tf.data.experimental.AUTOTUNE
+logger = logging.getLogger(__name__)
+
+CSV_HEADER = [
+    "test_epoch", "test_batch_size", "test_lr", "test_momentum",
+    "Time", "Accuracy", "Top1", "Top5",
+]
 
 
 class Searcher:
+    """Single-run trainer for a VGG16 model on CIFAR-100 or ImageNet.
+
+    Parameters
+    ----------
+    dataset : str
+        Either ``"cifar100"`` or ``"imagenet"``.
+    train_ds : tf.data.Dataset
+        Unbatched training dataset of ``(image, label)`` pairs.
+    val_ds : tf.data.Dataset
+        Unbatched validation dataset.
+    test_ds : tf.data.Dataset
+        Unbatched test dataset.
+    verbose : int
+        Keras verbosity level passed to ``model.fit``.
+    """
+
     def __init__(
         self,
         dataset: str,
-        train_ds: tf.data.Dataset,
-        val_ds: tf.data.Dataset,
-        test_ds: tf.data.Dataset,
+        train_ds,
+        val_ds,
+        test_ds,
         verbose: int = 2,
     ) -> None:
+        import tensorflow as tf  # lazy import — TF is an optional dependency
+        from keras import backend
 
         self.__dataset = dataset
+        self.__output_dir = Path("./algorithm") / dataset
         self.__distributed_strategy = tf.distribute.MirroredStrategy()
         self.__num_device = self.__distributed_strategy.num_replicas_in_sync
         self.__verbose = verbose
@@ -37,6 +50,7 @@ def __init__(
         self.__test_ds = test_ds
         self.__input_shape = (64, 64, 3) if dataset == "imagenet" else (32, 32, 3)
         self.__num_classes = 200 if self.__dataset == "imagenet" else 100
+        self.__output_dir.mkdir(parents=True, exist_ok=True)
         backend.clear_session()
 
     def training(
@@ -45,41 +59,61 @@ def training(
         batch_size: int = 128,
         lr: float = 0.01,
         momentum: float = 0.9,
-    ) -> Tuple[float, float]:
-        batch_size = batch_size * self.__num_device
+    ) -> tuple[float, float]:
+        """Train the model and return elapsed time and test accuracy.
+
+        Parameters
+        ----------
+        epoch : int
+            Number of training epochs.
+        batch_size : int
+            Per-device batch size (automatically scaled by the number of GPUs).
+        lr : float
+            SGD learning rate.
+        momentum : float
+            SGD momentum.
+
+        Returns
+        -------
+        tuple[float, float]
+            ``(time_taken_seconds, test_accuracy)``
+        """
+        import tensorflow as tf  # noqa: PLC0415
+        from keras import backend, losses, metrics, optimizers  # noqa: PLC0415
+
+        from utility.model import VGG16  # noqa: PLC0415
+
+        top_1 = metrics.TopKCategoricalAccuracy(k=1, name="Top_1")
+        top_5 = metrics.TopKCategoricalAccuracy(k=5, name="Top_5")
+        top_1_sparse = metrics.SparseTopKCategoricalAccuracy(k=1, name="Top_1")
+        top_5_sparse = metrics.SparseTopKCategoricalAccuracy(k=5, name="Top_5")
+
+        autotune = tf.data.experimental.AUTOTUNE
+        top_1_metrics = top_1 if self.__dataset == "imagenet" else top_1_sparse
+        top_5_metrics = top_5 if self.__dataset == "imagenet" else top_5_sparse
+        loss_function = (
+            losses.CategoricalCrossentropy()
+            if self.__dataset == "imagenet"
+            else losses.SparseCategoricalCrossentropy()
+        )
 
-        top_1_metrics = TOP_1 if self.__dataset == "imagenet" else TOP_1_SPARSE
-        top_5_metrics = TOP_5 if self.__dataset == "imagenet" else TOP_5_SPARSE
-        loss_function = LOSS if self.__dataset == "imagenet" else LOSS_SPARSE
+        batch_size = batch_size * self.__num_device
 
         self.__train_ds = self.__train_ds.batch(batch_size)
         self.__val_ds = self.__val_ds.batch(batch_size)
         self.__test_ds = self.__test_ds.batch(batch_size)
 
-        self.__train_ds = self.__train_ds.cache().prefetch(buffer_size=AUTOTUNE)
-        self.__val_ds = self.__val_ds.cache().prefetch(buffer_size=AUTOTUNE)
-        self.__test_ds = self.__test_ds.cache().prefetch(buffer_size=AUTOTUNE)
+        self.__train_ds = self.__train_ds.cache().prefetch(buffer_size=autotune)
+        self.__val_ds = self.__val_ds.cache().prefetch(buffer_size=autotune)
+        self.__test_ds = self.__test_ds.cache().prefetch(buffer_size=autotune)
 
-        write_header(
-            [
-                "test_epoch",
-                "test_batch_size",
-                "test_lr",
-                "test_momentum",
-                "Time",
-                "Accuracy",
-                "Top1",
-                "Top5",
-            ],
-            "./algorithm/" + self.__dataset + "/lr.csv",
-        )
+        write_header(CSV_HEADER, str(self.__output_dir / "lr.csv"))
 
         backend.clear_session()
 
         with self.__distributed_strategy.scope():
             cnn = VGG16(input_shape=self.__input_shape, num_class=self.__num_classes)
             cnn.summary()
-
             cnn.compile(
                 optimizer=optimizers.SGD(learning_rate=lr, momentum=momentum),
                 loss=loss_function,
@@ -97,17 +131,17 @@ def training(
         end = time.time()
         time_taken = end - start
 
-        metrics = cnn.evaluate(
+        eval_metrics = cnn.evaluate(
             self.__test_ds,
             batch_size=batch_size,
             return_dict=True,
         )
 
-        accuracy = metrics["accuracy"]
-        top_1 = metrics["Top_1"]
-        top_5 = metrics["Top_5"]
+        accuracy = eval_metrics["accuracy"]
+        top_1_score = eval_metrics["Top_1"]
+        top_5_score = eval_metrics["Top_5"]
 
-        print(f"Time taken: {time_taken}, Accuracy: {accuracy}")
+        logger.info("Time taken: %.2f s, Accuracy: %.4f", time_taken, accuracy)
 
         write_csv(
             [
@@ -118,21 +152,12 @@ def training(
                     "test_momentum": momentum,
                     "Time": time_taken,
                     "Accuracy": accuracy,
-                    "Top1": top_1,
-                    "Top5": top_5,
+                    "Top1": top_1_score,
+                    "Top5": top_5_score,
                 }
             ],
-            [
-                "test_epoch",
-                "test_batch_size",
-                "test_lr",
-                "test_momentum",
-                "Time",
-                "Accuracy",
-                "Top1",
-                "Top5",
-            ],
-            "./algorithm/" + self.__dataset + "/lr.csv",
+            CSV_HEADER,
+            str(self.__output_dir / "lr.csv"),
         )
 
         return time_taken, accuracy
diff --git a/grid_search/__init__.py b/grid_search/__init__.py
new file mode 100644
index 0000000..ccbc378
--- /dev/null
+++ b/grid_search/__init__.py
@@ -0,0 +1,3 @@
+from .grid_search import GridSearch
+
+__all__ = ["GridSearch"]
diff --git a/grid_search/grid_search.py b/grid_search/grid_search.py
index aacc7ff..9f5690e 100644
--- a/grid_search/grid_search.py
+++ b/grid_search/grid_search.py
@@ -1,12 +1,14 @@
-import sys
+import logging
+import time
+from pathlib import Path
 
-sys.path.append(".")
+import tensorflow as tf
+from keras import backend, losses, metrics, optimizers
 
-from keras import optimizers, losses, metrics, backend
-from utility.model import VGG16
 from utility.helper_func import write_csv, write_header
-import time
-import tensorflow as tf
+from utility.model import VGG16
+
+logger = logging.getLogger(__name__)
 
 TOP_1 = metrics.TopKCategoricalAccuracy(k=1, name="Top_1")
 TOP_5 = metrics.TopKCategoricalAccuracy(k=5, name="Top_5")
@@ -33,6 +35,7 @@ def __init__(
     ) -> None:
         self.__input_shape = (64, 64, 3) if dataset == "imagenet" else (32, 32, 3)
         self.__dataset = dataset
+        self.__output_dir = Path("./grid_search") / dataset
         self.__num_classes = 200 if self.__dataset == "imagenet" else 100
         self.__distributed_strategy = tf.distribute.MirroredStrategy()
         self.__num_device = self.__distributed_strategy.num_replicas_in_sync
@@ -44,26 +47,36 @@ def __init__(
         self.__train_ds = train_ds
         self.__val_ds = val_ds
         self.__test_ds = test_ds
+        self.__output_dir.mkdir(parents=True, exist_ok=True)
 
     def training(self) -> None:
         top_1_metrics = TOP_1 if self.__dataset == "imagenet" else TOP_1_SPARSE
         top_5_metrics = TOP_5 if self.__dataset == "imagenet" else TOP_5_SPARSE
         loss_function = LOSS if self.__dataset == "imagenet" else LOSS_SPARSE
 
-        print(
+        logger.info(
+            "Grid search space: epochs=%s, batch_sizes=%s, lrs=%s, momentums=%s",
             self.__epoch_range,
             self.__batch_size_range,
             self.__lr_range,
             self.__momentum_range,
         )
 
+        CSV_HEADER = [
+            "test_epoch", "test_batch_size", "test_lr", "test_momentum",
+            "Time", "Accuracy", "Top1", "Top5",
+        ]
+
         for test_epoch in self.__epoch_range:
             for test_batch_size in self.__batch_size_range:
                 for test_lr in self.__lr_range:
                     for test_momentum in self.__momentum_range:
                         backend.clear_session()
                         test_batch_size *= self.__num_device
-                        print(test_epoch, test_batch_size, test_lr, test_momentum)
+                        logger.info(
+                            "Training: epoch=%d, batch_size=%d, lr=%g, momentum=%g",
+                            test_epoch, test_batch_size, test_lr, test_momentum,
+                        )
                         train_ds = self.__train_ds.batch(test_batch_size)
                         val_ds = self.__val_ds.batch(test_batch_size)
                         test_ds = self.__test_ds.batch(test_batch_size)
@@ -72,29 +85,14 @@ def training(self) -> None:
                         val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
                         test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)
 
-                        write_header(
-                            [
-                                "test_epoch",
-                                "test_batch_size",
-                                "test_lr",
-                                "test_momentum",
-                                "Time",
-                                "Accuracy",
-                                "Top1",
-                                "Top5",
-                            ],
-                            "./grid_search/"
-                            + self.__dataset
-                            + "/epoch"
-                            + str(test_epoch)
-                            + "batch_size"
-                            + str(test_batch_size / self.__num_device)
-                            + "lr"
-                            + str(test_lr)
-                            + "momentum"
-                            + str(test_momentum)
-                            + ".csv",
+                        csv_name = (
+                            f"epoch{test_epoch}"
+                            f"batch_size{test_batch_size / self.__num_device}"
+                            f"lr{test_lr}"
+                            f"momentum{test_momentum}.csv"
                         )
+                        csv_path = str(self.__output_dir / csv_name)
+                        write_header(CSV_HEADER, csv_path)
 
                         with self.__distributed_strategy.scope():
                             cnn = VGG16(
@@ -122,23 +120,22 @@ def training(self) -> None:
                         end = time.time()
                         time_taken = end - start
 
-                        metrics = cnn.evaluate(
+                        eval_metrics = cnn.evaluate(
                             test_ds,
                             batch_size=test_batch_size,
                             return_dict=True,
                         )
 
-                        accuracy = metrics["accuracy"]
-                        top_1 = metrics["Top_1"]
-                        top_5 = metrics["Top_5"]
+                        accuracy = eval_metrics["accuracy"]
+                        top_1 = eval_metrics["Top_1"]
+                        top_5 = eval_metrics["Top_5"]
 
-                        print(f"Time taken: {time_taken}, Accuracy: {accuracy}")
+                        logger.info("Time taken: %.2f s, Accuracy: %.4f", time_taken, accuracy)
                         write_csv(
                             [
                                 {
                                     "test_epoch": test_epoch,
-                                    "test_batch_size": test_batch_size
-                                    / self.__num_device,
+                                    "test_batch_size": test_batch_size / self.__num_device,
                                     "test_lr": test_lr,
                                     "test_momentum": test_momentum,
                                     "Time": time_taken,
@@ -147,25 +144,6 @@ def training(self) -> None:
                                     "Top5": top_5,
                                 }
                             ],
-                            [
-                                "test_epoch",
-                                "test_batch_size",
-                                "test_lr",
-                                "test_momentum",
-                                "Time",
-                                "Accuracy",
-                                "Top1",
-                                "Top5",
-                            ],
-                            "./grid_search/"
-                            + self.__dataset
-                            + "/epoch"
-                            + str(test_epoch)
-                            + "batch_size"
-                            + str(test_batch_size / self.__num_device)
-                            + "lr"
-                            + str(test_lr)
-                            + "momentum"
-                            + str(test_momentum)
-                            + ".csv",
+                            CSV_HEADER,
+                            csv_path,
                         )
diff --git a/trend/__init__.py b/trend/__init__.py
new file mode 100644
index 0000000..0a74d1c
--- /dev/null
+++ b/trend/__init__.py
@@ -0,0 +1,3 @@
+from .hyperparameter_search import HyperParameterSearch
+
+__all__ = ["HyperParameterSearch"]
diff --git a/trend/hyperparameter_search.py b/trend/hyperparameter_search.py
index 20b170a..2c3db4e 100644
--- a/trend/hyperparameter_search.py
+++ b/trend/hyperparameter_search.py
@@ -1,13 +1,15 @@
-import sys
-
-sys.path.append(".")
-
-from keras import optimizers, losses, metrics, callbacks, backend
-from utility.model import VGG16
-from utility.helper_func import write_csv, write_header
+import logging
 import time
+from pathlib import Path
+
 import numpy as np
 import tensorflow as tf
+from keras import backend, callbacks, losses, metrics, optimizers
+
+from utility.helper_func import write_csv, write_header
+from utility.model import VGG16
+
+logger = logging.getLogger(__name__)
 
 TOP_1 = metrics.TopKCategoricalAccuracy(k=1, name="Top_1")
 TOP_5 = metrics.TopKCategoricalAccuracy(k=5, name="Top_5")
@@ -35,6 +37,7 @@ def __init__(
     ) -> None:
         self.__dataset = dataset
         self.__hyperparameter = hyperparameter
+        self.__output_dir = Path("./trend") / dataset
         self.__epoch = epoch
         self.__batch_size = batch_size
         self.__momentum = momentum
@@ -48,6 +51,7 @@ def __init__(
         self.__test_ds = test_ds
         self.__input_shape = (64, 64, 3) if dataset == "imagenet" else (32, 32, 3)
         self.__num_classes = 200 if self.__dataset == "imagenet" else 100
+        self.__output_dir.mkdir(parents=True, exist_ok=True)
         backend.clear_session()
 
     def training(self) -> None:
@@ -66,17 +70,17 @@ def training(self) -> None:
             linear_search_space = np.linspace(-7, -1, 7, endpoint=True)
             search_space = np.power(10, linear_search_space)
         else:
-            print("Wrong Hyperparameter Input!")
+            logger.error("Unknown hyperparameter: %s", self.__hyperparameter)
+            return
 
         test_epoch = self.__epoch
         test_batch_size = self.__batch_size
         test_lr = self.__lr
         test_momentum = self.__momentum
 
-        write_header(
-            [self.__hyperparameter, "Time", "Accuracy", "Top1", "Top5"],
-            "./trend/" + self.__dataset + "/" + self.__hyperparameter + ".csv",
-        )
+        hp_csv = str(self.__output_dir / f"{self.__hyperparameter}.csv")
+        CSV_HEADER = [self.__hyperparameter, "Time", "Accuracy", "Top1", "Top5"]
+        write_header(CSV_HEADER, hp_csv)
 
         for changing_hp in search_space:
             backend.clear_session()
@@ -89,7 +93,8 @@ def training(self) -> None:
             elif self.__hyperparameter == "momentum":
                 test_momentum = changing_hp
             else:
-                print("Wrong Hyperparameter Input!")
+                logger.error("Unknown hyperparameter: %s", self.__hyperparameter)
+                continue
 
             train_ds = self.__train_ds.batch(test_batch_size)
             val_ds = self.__val_ds.batch(test_batch_size)
@@ -113,27 +118,10 @@ def training(self) -> None:
                     metrics=["accuracy", top_1_metrics, top_5_metrics],
                 )
 
-            log = open(
-                "./trend/"
-                + self.__dataset
-                + "/"
-                + self.__hyperparameter
-                + "_"
-                + str(changing_hp)
-                + "_log.csv",
-                "a",
-            )
-            log.close()
-
-            logger = callbacks.CSVLogger(
-                "./trend/"
-                + self.__dataset
-                + "/"
-                + self.__hyperparameter
-                + "_"
-                + str(changing_hp)
-                + "_log.csv"
+            epoch_log_path = str(
+                self.__output_dir / f"{self.__hyperparameter}_{changing_hp}_log.csv"
             )
+            csv_logger = callbacks.CSVLogger(epoch_log_path)
 
             start = time.time()
             cnn.fit(
@@ -142,22 +130,22 @@ def training(self) -> None:
                 epochs=test_epoch,
                 verbose=self.__verbose,
                 validation_data=(val_ds),
-                callbacks=[logger],
+                callbacks=[csv_logger],
             )
             end = time.time()
             time_taken = end - start
 
-            metrics = cnn.evaluate(
+            eval_metrics = cnn.evaluate(
                 test_ds,
                 batch_size=test_batch_size,
                 return_dict=True,
             )
 
-            accuracy = metrics["accuracy"]
-            top_1 = metrics["Top_1"]
-            top_5 = metrics["Top_5"]
+            accuracy = eval_metrics["accuracy"]
+            top_1 = eval_metrics["Top_1"]
+            top_5 = eval_metrics["Top_5"]
 
-            print(f"Time taken: {time_taken}, Accuracy: {accuracy}")
+            logger.info("Time taken: %.2f s, Accuracy: %.4f", time_taken, accuracy)
             write_csv(
                 [
                     {
@@ -168,6 +156,6 @@ def training(self) -> None:
                         "Top5": top_5,
                     }
                 ],
-                [self.__hyperparameter, "Time", "Accuracy", "Top1", "Top5"],
-                "./trend/" + self.__dataset + "/" + self.__hyperparameter + ".csv",
+                CSV_HEADER,
+                hp_csv,
             )
diff --git a/utility/dataloader.py b/utility/dataloader.py
index 635eeb7..10aba0f 100644
--- a/utility/dataloader.py
+++ b/utility/dataloader.py
@@ -1,23 +1,48 @@
-import sys
-
-sys.path.append(".")
+from __future__ import annotations
 
 import numpy as np
 from sklearn.model_selection import train_test_split
-from utility.model import normalisation
-import tensorflow as tf
-from typing import Tuple
 
 
 class DataLoader:
-    def __init__(self, dataset: str) -> None:
+    """Loads pre-saved NumPy arrays from disk and returns TF datasets.
+
+    Expects four ``.npy`` files under ``{dataset_dir}/{dataset}/``:
+    ``X_train.npy``, ``y_train.npy``, ``X_test.npy``, ``y_test.npy``.
+
+    Parameters
+    ----------
+    dataset : str
+        Dataset name, e.g. ``"cifar100"`` or ``"imagenet"``.
+    dataset_dir : str
+        Root directory containing dataset subdirectories (default ``"./dataset"``).
+    """
+
+    def __init__(self, dataset: str, dataset_dir: str = "./dataset") -> None:
         self.__dataset = dataset
+        self.__dataset_dir = dataset_dir
+
+    def load_dataset(self):
+        """Load and preprocess the dataset.
+
+        Splits the original training set 60/40 into train/validation, applies
+        pixel rescaling via the normalisation layer, and duplicates the training
+        data once to augment the effective dataset size.
+
+        Returns
+        -------
+        tuple[tf.data.Dataset, tf.data.Dataset, tf.data.Dataset]
+            Unbatched ``(train_ds, val_ds, test_ds)`` datasets.
+        """
+        import tensorflow as tf  # lazy import — TF is an optional dependency
+
+        from utility.model import normalisation  # noqa: PLC0415
 
-    def load_dataset(self) -> Tuple[tf.data.Dataset, tf.data.Dataset, tf.data.Dataset]:
-        X_train: np.ndarray = np.load("./dataset/" + self.__dataset + "/X_train.npy")
-        y_train: np.ndarray = np.load("./dataset/" + self.__dataset + "/y_train.npy")
-        X_test: np.ndarray = np.load("./dataset/" + self.__dataset + "/X_test.npy")
-        y_test: np.ndarray = np.load("./dataset/" + self.__dataset + "/y_test.npy")
+        base = f"{self.__dataset_dir}/{self.__dataset}"
+        X_train: np.ndarray = np.load(f"{base}/X_train.npy")
+        y_train: np.ndarray = np.load(f"{base}/y_train.npy")
+        X_test: np.ndarray = np.load(f"{base}/X_test.npy")
+        y_test: np.ndarray = np.load(f"{base}/y_test.npy")
 
         X_train, X_val, y_train, y_val = train_test_split(
             X_train,
diff --git a/utility/helper_func.py b/utility/helper_func.py
index 4c307cb..d9c499d 100644
--- a/utility/helper_func.py
+++ b/utility/helper_func.py
@@ -1,24 +1,71 @@
 import csv
+
 import numpy as np
 
 
 def write_csv(data: list[dict], header: list[str], filename: str) -> None:
+    """Append rows to a CSV file.
+
+    Parameters
+    ----------
+    data : list[dict]
+        Rows to write, each keyed by the column names in ``header``.
+    header : list[str]
+        Column names (must match keys in ``data``).
+    filename : str
+        Path to the target CSV file.
+    """
     with open(filename, mode="a+") as file:
         writer = csv.DictWriter(file, fieldnames=header)
         writer.writerows(data)
 
 
 def write_header(header: list[str], filename: str) -> None:
+    """Append a CSV header row to a file.
+
+    Parameters
+    ----------
+    header : list[str]
+        Column names to write as the header.
+    filename : str
+        Path to the target CSV file.
+    """
     with open(filename, mode="a+") as file:
         writer = csv.DictWriter(file, fieldnames=header)
         writer.writeheader()
 
 
 def min_max_scalar(data: np.array) -> np.array:
+    """Normalise an array to the [0, 1] range using min-max scaling.
+
+    Parameters
+    ----------
+    data : np.array
+        1-D array of numeric values.
+
+    Returns
+    -------
+    np.array
+        Scaled array with values in ``[0, 1]``.
+    """
     return (data - np.min(data)) / (np.max(data) - np.min(data))
 
 
 def weighted_avg(
     val1: float, val2: float, weight1: float = 0.5, weight2: float = 0.5
 ) -> float:
+    """Compute a weighted average of two values.
+
+    Parameters
+    ----------
+    val1, val2 : float
+        Values to average.
+    weight1, weight2 : float
+        Weights for ``val1`` and ``val2`` respectively (default 0.5 each).
+
+    Returns
+    -------
+    float
+        ``val1 * weight1 + val2 * weight2``
+    """
     return val1 * weight1 + val2 * weight2
diff --git a/utility/model.py b/utility/model.py
index 8fbcb41..3332894 100644
--- a/utility/model.py
+++ b/utility/model.py
@@ -1,4 +1,4 @@
-from typing import Tuple
+
 from keras import layers, models, regularizers
 
 
@@ -30,7 +30,7 @@ def add_layer(
 
 
 def VGG16(
-    input_shape: Tuple[int, int, int], num_class: int, weight_decay: float = 0.0005
+    input_shape: tuple[int, int, int], num_class: int, weight_decay: float = 0.0005
 ):
 
     model = models.Sequential()

From c501d36fb51fc02fe3482e42972d70d8f9c18307 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:32:59 +1000
Subject: [PATCH 3/7] test: add pytest suite for pure-logic and algorithm
 search code
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- tests/test_helper_func.py: 11 tests for min_max_scalar, weighted_avg,
  write_csv, write_header — no mocking required
- tests/test_epoch_tuner.py: binary search convergence and efficiency scoring
- tests/test_batch_size_tuner.py: search bounds, OOM sentinel, accuracy range
- tests/test_lr_tuner.py: local maximum detection and training call caching
- TF-dependent test files skip cleanly via pytest.importorskip when TF is absent
- Add conftest.py with shared mock_dataloader and mock_searcher_training fixtures

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 tests/__init__.py              |   0
 tests/conftest.py              |  28 +++++++++
 tests/test_batch_size_tuner.py |  74 +++++++++++++++++++++++
 tests/test_epoch_tuner.py      | 103 +++++++++++++++++++++++++++++++++
 tests/test_helper_func.py      |  71 +++++++++++++++++++++++
 tests/test_lr_tuner.py         |  90 ++++++++++++++++++++++++++++
 6 files changed, 366 insertions(+)
 create mode 100644 tests/__init__.py
 create mode 100644 tests/conftest.py
 create mode 100644 tests/test_batch_size_tuner.py
 create mode 100644 tests/test_epoch_tuner.py
 create mode 100644 tests/test_helper_func.py
 create mode 100644 tests/test_lr_tuner.py

diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..36c5c58
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,28 @@
+"""Shared pytest fixtures."""
+
+import pytest
+
+
+@pytest.fixture
+def mock_searcher_training(mocker):
+    """Patch Searcher so it never touches TF/GPU.
+
+    Returns a factory: call it with a list of (time, accuracy) pairs that will
+    be returned in order on successive Searcher().training() calls.
+    """
+
+    def _factory(return_values: list[tuple[float, float]]):
+        side_effects = list(return_values)
+        mock = mocker.patch("algorithm.searcher.Searcher.training", side_effect=side_effects)
+        return mock
+
+    return _factory
+
+
+@pytest.fixture
+def mock_dataloader(mocker):
+    """Patch DataLoader.load_dataset so no .npy files are needed."""
+    mocker.patch(
+        "utility.dataloader.DataLoader.load_dataset",
+        return_value=(None, None, None),
+    )
diff --git a/tests/test_batch_size_tuner.py b/tests/test_batch_size_tuner.py
new file mode 100644
index 0000000..275a781
--- /dev/null
+++ b/tests/test_batch_size_tuner.py
@@ -0,0 +1,74 @@
+"""Tests for BatchSizeTuner search logic.
+
+Searcher.training() and DataLoader.load_dataset() are mocked so no GPU or
+dataset files are needed.  Requires the ``tf`` optional extra.
+"""
+
+import pytest
+
+pytest.importorskip("tensorflow", reason="tensorflow not installed — run: uv sync --extra tf")
+
+from algorithm.batch_size import BatchSizeTuner
+
+
+@pytest.fixture(autouse=True)
+def _no_dataloader(mocker):
+    mocker.patch(
+        "utility.dataloader.DataLoader.load_dataset",
+        return_value=(None, None, None),
+    )
+
+
+@pytest.fixture(autouse=True)
+def _no_tf_strategy(mocker):
+    strategy = mocker.MagicMock()
+    strategy.num_replicas_in_sync = 1
+    mocker.patch("tensorflow.distribute.MirroredStrategy", return_value=strategy)
+
+
+def make_tuner(left=4, right=6, acceptable_range=0.30):
+    return BatchSizeTuner("cifar100", left_bound=left, right_bound=right,
+                          acceptable_range=acceptable_range)
+
+
+class TestBatchSizeTunerSearch:
+    def test_returns_three_values(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner()
+        result = tuner.search()
+        assert len(result) == 3
+
+    def test_best_batch_within_bounds(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner(left=4, right=6)
+        best_batch, acc, elapsed = tuner.search()
+        # batch size is 2**left to 2**right (16 to 64)
+        assert 2**4 <= best_batch <= 2**6
+
+    def test_oom_at_minimum_returns_sentinel(self, mocker):
+        """ResourceExhaustedError at left_bound → returns (-1, -1, -1)."""
+        import tensorflow as tf
+
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=tf.errors.ResourceExhaustedError(None, None, "OOM"),
+        )
+        tuner = make_tuner()
+        result = tuner.search()
+        assert result == (-1.0, -1.0, -1.0)
+
+    def test_accuracy_stays_within_range(self, mocker):
+        """If all accuracies are identical, the largest batch should be chosen."""
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(3.0, 0.75),
+        )
+        tuner = make_tuner(left=4, right=5, acceptable_range=0.50)
+        best_batch, acc, _ = tuner.search()
+        assert acc == pytest.approx(0.75)
diff --git a/tests/test_epoch_tuner.py b/tests/test_epoch_tuner.py
new file mode 100644
index 0000000..f68cea0
--- /dev/null
+++ b/tests/test_epoch_tuner.py
@@ -0,0 +1,103 @@
+"""Tests for EpochTuner binary search logic.
+
+Searcher.training() and DataLoader.load_dataset() are mocked so no GPU or
+dataset files are needed.  Requires the ``tf`` optional extra (TensorFlow
+is imported at module level in algorithm/).
+"""
+
+import pytest
+
+pytest.importorskip("tensorflow", reason="tensorflow not installed — run: uv sync --extra tf")
+
+from algorithm.epoch import EpochTuner
+
+
+@pytest.fixture(autouse=True)
+def _no_dataloader(mocker):
+    mocker.patch(
+        "utility.dataloader.DataLoader.load_dataset",
+        return_value=(None, None, None),
+    )
+
+
+@pytest.fixture(autouse=True)
+def _no_tf_strategy(mocker):
+    """Prevent MirroredStrategy from requiring a GPU."""
+    strategy = mocker.MagicMock()
+    strategy.num_replicas_in_sync = 1
+    mocker.patch("tensorflow.distribute.MirroredStrategy", return_value=strategy)
+
+
+def make_tuner(left=1, right=8, exploration_factor=1):
+    return EpochTuner("cifar100", left_bound=left, right_bound=right,
+                      exploration_factor=exploration_factor)
+
+
+class TestEpochTunerWeightedAvg:
+    """weighted_avg_from_epoch uses the cached epoch/accuracy/time lists."""
+
+    def test_returns_midpoint_for_equal_weights(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=[(10.0, 0.5), (20.0, 0.8)],
+        )
+        tuner = make_tuner(left=1, right=2)
+        tuner.epoch_run(1)
+        tuner.epoch_run(2)
+        # After two runs, normalized time=[0,1], accuracy=[0,1]
+        # weighted_avg for epoch 1: time_norm=0, (1-acc_norm)=1 → avg=0.5
+        result = tuner.weighted_avg_from_epoch(1)
+        assert result == pytest.approx(0.5)
+
+    def test_more_efficient_epoch_has_lower_score(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=[(10.0, 0.9), (20.0, 0.5)],
+        )
+        tuner = make_tuner(left=1, right=2)
+        tuner.epoch_run(1)
+        tuner.epoch_run(2)
+        # epoch 1: fast + high accuracy → lower score (better)
+        assert tuner.weighted_avg_from_epoch(1) < tuner.weighted_avg_from_epoch(2)
+
+
+class TestEpochTunerBinarySearch:
+    def test_returns_tuple_of_three(self, mocker):
+        # Provide enough return values for multiple training calls
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner(left=2, right=4, exploration_factor=2)
+        result = tuner.binary_search_efficient_epoch()
+        assert len(result) == 3
+
+    def test_best_epoch_within_bounds(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner(left=2, right=4, exploration_factor=2)
+        best_epoch, acc, elapsed = tuner.binary_search_efficient_epoch()
+        assert 2 <= best_epoch <= 4
+        assert 0.0 <= acc <= 1.0
+        assert elapsed >= 0.0
+
+    def test_prefers_faster_high_accuracy_epoch(self, mocker):
+        """When left is fast+accurate and right is slow+low, left should win."""
+        # epoch_run is called with left=1 and right=4 first, then mid=2
+        # We want epoch 1 to look best (fast, high acc)
+        def training_side_effect(**kwargs):
+            epoch = kwargs.get("epoch", 250)
+            if epoch == 1:
+                return (1.0, 0.9)
+            return (100.0, 0.1)
+
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=lambda **kw: training_side_effect(**kw),
+        )
+        tuner = make_tuner(left=1, right=4, exploration_factor=2)
+        best_epoch, acc, elapsed = tuner.binary_search_efficient_epoch()
+        # epoch 1 should have been selected as most efficient
+        assert best_epoch == 1
diff --git a/tests/test_helper_func.py b/tests/test_helper_func.py
new file mode 100644
index 0000000..f5f5ebf
--- /dev/null
+++ b/tests/test_helper_func.py
@@ -0,0 +1,71 @@
+"""Tests for utility/helper_func.py — all pure functions, no mocking needed."""
+
+import csv
+
+import numpy as np
+import pytest
+
+from utility.helper_func import min_max_scalar, weighted_avg, write_csv, write_header
+
+
+class TestMinMaxScalar:
+    def test_range_is_zero_to_one(self):
+        data = np.array([2.0, 4.0, 6.0, 8.0, 10.0])
+        result = min_max_scalar(data)
+        assert result.min() == pytest.approx(0.0)
+        assert result.max() == pytest.approx(1.0)
+
+    def test_midpoint(self):
+        data = np.array([0.0, 5.0, 10.0])
+        result = min_max_scalar(data)
+        assert result[1] == pytest.approx(0.5)
+
+    def test_single_element_is_nan(self):
+        # (x - x) / (x - x) = 0/0 → NaN; document this known edge case
+        data = np.array([42.0])
+        result = min_max_scalar(data)
+        assert np.isnan(result[0])
+
+    def test_preserves_array_length(self):
+        data = np.array([1.0, 3.0, 5.0, 7.0])
+        assert len(min_max_scalar(data)) == 4
+
+
+class TestWeightedAvg:
+    def test_equal_weights(self):
+        assert weighted_avg(0.0, 1.0) == pytest.approx(0.5)
+
+    def test_custom_weights(self):
+        assert weighted_avg(1.0, 0.0, weight1=0.8, weight2=0.2) == pytest.approx(0.8)
+
+    def test_same_values(self):
+        assert weighted_avg(0.3, 0.3, 0.4, 0.6) == pytest.approx(0.3)
+
+    def test_zero_and_one(self):
+        assert weighted_avg(0.0, 0.0) == pytest.approx(0.0)
+        assert weighted_avg(1.0, 1.0) == pytest.approx(1.0)
+
+
+class TestWriteCsvAndHeader:
+    def test_write_header_creates_row(self, tmp_path):
+        csv_file = tmp_path / "out.csv"
+        write_header(["a", "b", "c"], str(csv_file))
+        rows = csv_file.read_text().splitlines()
+        assert rows[0] == "a,b,c"
+
+    def test_write_csv_appends_rows(self, tmp_path):
+        csv_file = tmp_path / "out.csv"
+        write_header(["x", "y"], str(csv_file))
+        write_csv([{"x": 1, "y": 2}, {"x": 3, "y": 4}], ["x", "y"], str(csv_file))
+        rows = list(csv.DictReader(csv_file.open()))
+        assert len(rows) == 2
+        assert rows[0]["x"] == "1"
+        assert rows[1]["y"] == "4"
+
+    def test_write_csv_multiple_calls_append(self, tmp_path):
+        csv_file = tmp_path / "out.csv"
+        write_header(["v"], str(csv_file))
+        write_csv([{"v": 10}], ["v"], str(csv_file))
+        write_csv([{"v": 20}], ["v"], str(csv_file))
+        rows = list(csv.DictReader(csv_file.open()))
+        assert len(rows) == 2
diff --git a/tests/test_lr_tuner.py b/tests/test_lr_tuner.py
new file mode 100644
index 0000000..e8ee8bd
--- /dev/null
+++ b/tests/test_lr_tuner.py
@@ -0,0 +1,90 @@
+"""Tests for LrTuner search logic.
+
+Searcher.training() and DataLoader.load_dataset() are mocked so no GPU or
+dataset files are needed.  Requires the ``tf`` optional extra.
+"""
+
+import math
+
+import pytest
+
+pytest.importorskip("tensorflow", reason="tensorflow not installed — run: uv sync --extra tf")
+
+from algorithm.lr import LrTuner
+
+
+@pytest.fixture(autouse=True)
+def _no_dataloader(mocker):
+    mocker.patch(
+        "utility.dataloader.DataLoader.load_dataset",
+        return_value=(None, None, None),
+    )
+
+
+@pytest.fixture(autouse=True)
+def _no_tf_strategy(mocker):
+    strategy = mocker.MagicMock()
+    strategy.num_replicas_in_sync = 1
+    mocker.patch("tensorflow.distribute.MirroredStrategy", return_value=strategy)
+
+
+def make_tuner(left=1, right=5, allowance=0.05):
+    return LrTuner("cifar100", left_bound=left, right_bound=right,
+                   local_extrema_allowance=allowance)
+
+
+class TestLrTunerSearch:
+    def test_returns_three_values(self, mocker):
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner()
+        result = tuner.search()
+        assert len(result) == 3
+
+    def test_finds_local_maximum(self, mocker):
+        """Mid accuracy clearly exceeds left and right → mid should be returned."""
+        def training_by_lr(**kwargs):
+            lr = kwargs.get("lr", 0.01)
+            # mid exponent is 3 → lr=1e-3 → high accuracy
+            if abs(lr - 1e-3) < 1e-9:
+                return (5.0, 0.90)
+            return (5.0, 0.50)
+
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=lambda **kw: training_by_lr(**kw),
+        )
+        tuner = make_tuner(left=1, right=5, allowance=0.05)
+        lr_exp, acc, _ = tuner.search()
+        # Should find the exponent whose lr=1e-3 (exponent=3)
+        assert lr_exp == 3
+        assert acc == pytest.approx(0.90)
+
+    def test_no_local_max_returns_sentinel(self, mocker):
+        """Monotonically increasing accuracy → no local max → return (-1,-1,-1)."""
+        def monotone(**kwargs):
+            lr = kwargs.get("lr", 0.01)
+            # Higher lr (smaller exponent) → better accuracy; no local maximum
+            exp = -math.log10(lr) if lr > 0 else 7
+            return (5.0, 1.0 - exp * 0.1)
+
+        mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            side_effect=lambda **kw: monotone(**kw),
+        )
+        tuner = make_tuner(left=1, right=3, allowance=0.50)
+        result = tuner.search()
+        assert result == (-1, -1, -1)
+
+    def test_caches_training_calls(self, mocker):
+        """The same lr_linear value should only trigger one Searcher.training call."""
+        training_mock = mocker.patch(
+            "algorithm.searcher.Searcher.training",
+            return_value=(5.0, 0.8),
+        )
+        tuner = make_tuner(left=2, right=2, allowance=0.05)
+        tuner.data_stat_search(2)
+        tuner.data_stat_search(2)  # second call should hit cache
+        assert training_mock.call_count == 1

From 7f6e8df5d159d649150442e6a7fe8c4eee7b60c3 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:33:05 +1000
Subject: [PATCH 4/7] ci: add GitHub Actions workflow for lint and tests

Runs on every push and PR across Python 3.11 and 3.12:
- Install uv and sync dev dependencies (no TF)
- ruff check for linting
- pytest (TF-dependent tests skip automatically)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .github/workflows/ci.yml | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 .github/workflows/ci.yml

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..a33ea54
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,35 @@
+name: CI
+
+on:
+  push:
+    branches: ["**"]
+  pull_request:
+    branches: ["**"]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11", "3.12"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
+      - name: Set up Python ${{ matrix.python-version }}
+        run: uv python install ${{ matrix.python-version }}
+
+      - name: Install dev dependencies
+        run: uv sync --group dev
+
+      - name: Lint with ruff
+        run: uv run ruff check .
+
+      - name: Run tests
+        run: uv run pytest tests/ -v

From a456eb114b5b73c3af6a46fb6baba17a38b5deb6 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:33:09 +1000
Subject: [PATCH 5/7] feat: add demo.py to run all tuners without TF or dataset
 files

Uses synthetic accuracy/time curves so the full search logic is exercisable
on any machine straight after cloning. Run with: uv run python demo.py

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 demo.py | 87 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 demo.py

diff --git a/demo.py b/demo.py
new file mode 100644
index 0000000..7af4edf
--- /dev/null
+++ b/demo.py
@@ -0,0 +1,87 @@
+"""Demo: run all three tuners without TensorFlow or dataset files.
+
+Uses a synthetic training function that simulates realistic accuracy/time curves
+so the search logic can be exercised end-to-end on any machine.
+
+Usage:
+    uv run python demo.py
+"""
+
+import math
+import time
+
+
+def synthetic_epoch_fn(epoch: int) -> tuple[float, float]:
+    """Accuracy grows logarithmically; time grows linearly."""
+    accuracy = 0.05 + 0.45 * math.log(epoch + 1) / math.log(251)
+    elapsed = epoch * 0.01  # 10 ms per epoch (simulated)
+    return elapsed, accuracy
+
+
+def synthetic_batch_size_fn(batch_size: int) -> tuple[float, float]:
+    """Accuracy stays flat up to batch 256 then degrades; time decreases."""
+    # batch_size here is 2**log2_bs; acceptable range is 0.30 so flat region is wide
+    log2_bs = math.log2(batch_size)
+    accuracy = max(0.70 - max(log2_bs - 8, 0) * 0.15, 0.0)
+    elapsed = 100.0 / log2_bs
+    return elapsed, accuracy
+
+
+def synthetic_lr_fn(lr: float) -> tuple[float, float]:
+    """Accuracy peaks sharply at lr=1e-2 (exponent=2) — well above neighbours."""
+    log_lr = -math.log10(lr)  # exponent: 1→lr=0.1, 2→lr=0.01, ...
+    accuracy = max(0.80 - (log_lr - 2) ** 2 * 0.12, 0.0)
+    elapsed = 5.0
+    return elapsed, accuracy
+
+
+def main() -> None:
+    from algorithm import BatchSizeTuner, EpochTuner, LrTuner
+
+    print("=== EpochTuner demo ===")
+    t0 = time.perf_counter()
+    tuner = EpochTuner(
+        "cifar100",
+        left_bound=10,
+        right_bound=200,
+        exploration_factor=5,
+        training_fn=synthetic_epoch_fn,
+    )
+    best_epoch, acc, elapsed = tuner.binary_search_efficient_epoch()
+    print(f"  Best epoch : {best_epoch}")
+    print(f"  Accuracy   : {acc:.4f}")
+    print(f"  Simulated  : {elapsed:.2f}s  (real wall time: {time.perf_counter()-t0:.3f}s)")
+
+    print()
+    print("=== BatchSizeTuner demo ===")
+    t0 = time.perf_counter()
+    bs_tuner = BatchSizeTuner(
+        "cifar100",
+        left_bound=4,   # 2^4  = 16
+        right_bound=12, # 2^12 = 4096
+        acceptable_range=0.30,
+        training_fn=synthetic_batch_size_fn,
+    )
+    best_bs, acc, elapsed = bs_tuner.search()
+    print(f"  Best batch size : 2^{best_bs} = {2**best_bs}")
+    print(f"  Accuracy        : {acc:.4f}")
+    print(f"  Simulated       : {elapsed:.2f}s  (real wall time: {time.perf_counter()-t0:.3f}s)")
+
+    print()
+    print("=== LrTuner demo ===")
+    t0 = time.perf_counter()
+    lr_tuner = LrTuner(
+        "cifar100",
+        left_bound=1,  # 10^-1 = 0.1
+        right_bound=7, # 10^-7
+        local_extrema_allowance=0.05,
+        training_fn=synthetic_lr_fn,
+    )
+    lr_exp, acc, elapsed = lr_tuner.search()
+    print(f"  Best lr exponent : {lr_exp}  (lr = 1e-{lr_exp} = {10**-lr_exp:.0e})")
+    print(f"  Accuracy         : {acc:.4f}")
+    print(f"  Simulated        : {elapsed:.2f}s  (real wall time: {time.perf_counter()-t0:.3f}s)")
+
+
+if __name__ == "__main__":
+    main()

From 85a4c129b7460a72f51743ca2020f4cbeb4cd795 Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:33:14 +1000
Subject: [PATCH 6/7] docs: update README with install instructions, quick
 start, and CI badge

- Add CI status badge
- Replace clone+pip instructions with uv/pip install sections
- Add Quick Start code snippet covering all three tuners
- Add Development section (pytest, ruff, uv build commands)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 README.md | 66 ++++++++++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 56 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 5e69a83..9828936 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 # Heuristic-Driven Hyperparameter Optimization Algorithms
 
+![CI](https://github.com/outsidermm/Heuristic-driven-Hyperparameter-Optimization-Algorithms/actions/workflows/ci.yml/badge.svg)
+
 Welcome to the GitHub repository for the paper "Heuristic-driven Hyperparameter Optimization Algorithms." This project explores heuristic-based hyperparameter optimization (HPO) algorithms, offering an efficient alternative to traditional methods like Grid Search and Bayesian Optimization. The proposed algorithms are designed to balance computational efficiency and model performance, making them accessible to both novice and experienced machine learning practitioners.
 
 ## Table of Contents
@@ -105,13 +107,57 @@ We would like to thank Professor Seda Memik from the University of Northwestern
 
 ## Installation
 
-To install and run the heuristic-driven HPO algorithms, follow these steps:
-
-1. Clone the repository:
-    ```bash
-    git clone https://github.com/outsidermm/Optimisation-of-Grid-Search-for-CNN-Hyperparameter-Tuning.git
-    ```
-2. Install the required dependencies:
-    ```bash
-    pip install -r requirements.txt
-    ```
+**With uv (recommended):**
+```bash
+uv add heuristic-hpo
+# TensorFlow / Keras are optional — install when you need to run training:
+uv add "heuristic-hpo[tf]"
+```
+
+**With pip:**
+```bash
+pip install heuristic-hpo
+pip install "heuristic-hpo[tf]"  # includes TensorFlow + Keras
+```
+
+**From source:**
+```bash
+git clone https://github.com/outsidermm/Heuristic-driven-Hyperparameter-Optimization-Algorithms.git
+cd Heuristic-driven-Hyperparameter-Optimization-Algorithms
+uv sync --extra tf
+```
+
+## Quick Start
+
+```python
+from utility.dataloader import DataLoader
+from algorithm import EpochTuner, BatchSizeTuner, LrTuner
+
+# Load your dataset (expects ./dataset/cifar100/{X,y}_{train,test}.npy)
+loader = DataLoader("cifar100")
+train_ds, val_ds, test_ds = loader.load_dataset()
+
+# Find the most cost-efficient epoch count between 10 and 250
+tuner = EpochTuner("cifar100", left_bound=10, right_bound=250, exploration_factor=5)
+best_epoch, accuracy, time_taken = tuner.binary_search_efficient_epoch()
+print(f"Best epoch: {best_epoch}, accuracy: {accuracy:.4f}, time: {time_taken:.1f}s")
+
+# Find the largest safe batch size (log2 scale: 4=16, 12=4096)
+bs_tuner = BatchSizeTuner("cifar100", left_bound=4, right_bound=12, acceptable_range=0.30)
+best_bs, accuracy, time_taken = bs_tuner.search()
+print(f"Best batch size: {best_bs}")
+
+# Find the optimal learning rate (searches 10^-1 to 10^-7)
+lr_tuner = LrTuner("cifar100", left_bound=1, right_bound=7, local_extrema_allowance=0.05)
+lr_exp, accuracy, time_taken = lr_tuner.search()
+print(f"Best learning rate: 1e-{lr_exp}, accuracy: {accuracy:.4f}")
+```
+
+## Development
+
+```bash
+uv sync --group dev       # install dev tools (pytest, ruff)
+uv run pytest tests/ -v   # run tests (TF-independent tests run without --extra tf)
+uv run ruff check .       # lint
+uv build                  # build wheel
+```

From b29a43127069290019689e8f430f45d8d011da9d Mon Sep 17 00:00:00 2001
From: outsidermm <71582657+outsidermm@users.noreply.github.com>
Date: Mon, 25 May 2026 15:37:21 +1000
Subject: [PATCH 7/7] chore: switch Dependabot from pip to uv ecosystem

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .github/dependabot.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 9d866e3..34c1a3a 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -5,7 +5,7 @@
 
 version: 2
 updates:
-  - package-ecosystem: "pip" # See documentation for possible values
-    directory: "/" # Location of package manifests
+  - package-ecosystem: "uv"
+    directory: "/"
     schedule:
       interval: "weekly"