Add documentation for criterion (#297)

baal-org · May 27, 2024 · a4ceccd · a4ceccd
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diff --git a/docs/api/index.md b/docs/api/index.md
@@ -6,12 +6,13 @@
 * [baal.bayesian](./bayesian.md)
 * [baal.active](./dataset_management.md)
 * [baal.active.heuristics](./heuristics.md)
+* [baal.active.stopping_criteria](./stopping_criteria.md)
 * [baal.calibration](./calibration.md)
 * [baal.utils](./utils.md)
 
 ### :material-file-tree: Compatibility
 
-* [baal.utils.pytorch_lightning] (./compatibility/pytorch-lightning)
+* [baal.utils.pytorch_lightning](./compatibility/pytorch-lightning)
 * [baal.transformers_trainer_wrapper](./compatibility/huggingface)
 
 

diff --git a/docs/api/stopping_criteria.md b/docs/api/stopping_criteria.md
@@ -0,0 +1,35 @@
+# Stopping Criteria
+
+Stopping criterion are used to determine when to stop your active learning experiment.
+
+Their usage are simple, but best put in practice with `ActiveExperiment`.
+
+**Example**
+```python
+from baal.active.stopping_criteria import LabellingBudgetStoppingCriterion
+from baal.active.dataset import ActiveLearningDataset
+
+al_dataset: ActiveLearningDataset = ... # len(al_dataset) == 10
+criterion = LabellingBudgetStoppingCriterion(al_dataset, labelling_budget=100)
+
+assert not criterion.should_stop({}, [])
+
+# len(al_dataset) == 60
+al_dataset.label_randomly(50)
+assert not criterion.should_stop({}, [])
+
+# len(al_dataset) == 110, budget exhausted! We've labelled 100 items.
+al_dataset.label_randomly(50)
+assert criterion.should_stop({}, [])
+```
+
+
+### API
+
+### baal.active.stopping_criteria
+
+::: baal.active.stopping_criteria.LabellingBudgetStoppingCriterion
+
+::: baal.active.stopping_criteria.LowAverageUncertaintyStoppingCriterion
+
+::: baal.active.stopping_criteria.EarlyStoppingCriterion
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -78,6 +78,7 @@ nav:
       - api/calibration.md
       - api/dataset_management.md
       - api/heuristics.md
+      - api/stopping_criteria.md
       - api/modelwrapper.md
       - api/utils.md
       - Compatibility:

diff --git a/notebooks/production/baal_prod_cls.ipynb b/notebooks/production/baal_prod_cls.ipynb
@@ -35,15 +35,6 @@
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train: 5174, Valid: 1725, Num. classes : 8\n"
-     ]
-    }
-   ],
    "source": [
     "from glob import glob\n",
     "import os\n",
@@ -52,7 +43,8 @@
     "classes = os.listdir('/tmp/natural_images')\n",
     "train, test = train_test_split(files, random_state=1337)  # Split 75% train, 25% validation\n",
     "print(f\"Train: {len(train)}, Valid: {len(test)}, Num. classes : {len(classes)}\")\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -79,7 +71,6 @@
      "is_executing": false
     }
    },
-   "outputs": [],
    "source": [
     "from baal.active import FileDataset, ActiveLearningDataset\n",
     "from torchvision import transforms\n",
@@ -101,7 +92,8 @@
     "# We use -1 to specify that the data is unlabeled.\n",
     "test_dataset = FileDataset(test, [-1] * len(test), test_transform)\n",
     "active_learning_ds = ActiveLearningDataset(train_dataset, pool_specifics={'transform': test_transform})\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -129,7 +121,6 @@
      "is_executing": false
     }
    },
-   "outputs": [],
    "source": [
     "import torch\n",
     "from torch import nn, optim\n",
@@ -149,7 +140,8 @@
     "# ModelWrapper is an object similar to keras.Model.\n",
     "baal_model = ModelWrapper(model, criterion)\n",
     "\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -170,11 +162,11 @@
      "is_executing": false
     }
    },
-   "outputs": [],
    "source": [
     "from baal.active.heuristics import BALD\n",
     "heuristic = BALD(shuffle_prop=0.1)\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -193,13 +185,13 @@
      "is_executing": false
     }
    },
-   "outputs": [],
    "source": [
     "# This function would do the work that a human would do.\n",
     "def get_label(img_path):\n",
     "  return classes.index(img_path.split('/')[-2])\n",
     "\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -223,15 +215,6 @@
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Num. labeled: 100/5174\n"
-     ]
-    }
-   ],
    "source": [
     "import numpy as np\n",
     "# 1. Label all the test set and some samples from the training set.\n",
@@ -246,7 +229,8 @@
     "active_learning_ds.label(train_idxs, labels)\n",
     "\n",
     "print(f\"Num. labeled: {len(active_learning_ds)}/{len(train_dataset)}\")\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -256,56 +240,19 @@
      "is_executing": false
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[103-MainThread  ] [baal.modelwrapper:train_on_dataset:109] 2021-07-28T14:47:48.133213Z [\u001B[32minfo     ] Starting training              dataset=100 epoch=5\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.9/site-packages/torch/utils/data/dataloader.py:478: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 1, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
-      "  warnings.warn(_create_warning_msg(\n",
-      "/opt/conda/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
-      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[103-MainThread  ] [baal.modelwrapper:train_on_dataset:119] 2021-07-28T14:48:07.477011Z [\u001B[32minfo     ] Training complete              train_loss=2.058176279067993\n",
-      "[103-MainThread  ] [baal.modelwrapper:test_on_dataset:147] 2021-07-28T14:48:07.479793Z [\u001B[32minfo     ] Starting evaluating            dataset=1725\n",
-      "[103-MainThread  ] [baal.modelwrapper:test_on_dataset:156] 2021-07-28T14:48:21.277716Z [\u001B[32minfo     ] Evaluation complete            test_loss=2.0671451091766357\n",
-      "Metrics: {'test_loss': 2.0671451091766357, 'train_loss': 2.058176279067993}\n"
-     ]
-    }
-   ],
    "source": [
     "# 2. Train the model for a few epoch on the training set.\n",
     "baal_model.train_on_dataset(active_learning_ds, optimizer, batch_size=16, epoch=5, use_cuda=USE_CUDA)\n",
     "baal_model.test_on_dataset(test_dataset, batch_size=16, use_cuda=USE_CUDA)\n",
     "\n",
     "print(\"Metrics:\", {k:v.avg for k,v in baal_model.metrics.items()})\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[103-MainThread  ] [baal.modelwrapper:predict_on_dataset_generator:241] 2021-07-28T14:48:21.291851Z [\u001B[32minfo     ] Start Predict                  dataset=5074\n"
-     ]
-    }
-   ],
    "source": [
     "# 3. Select the K-top uncertain samples according to the heuristic.\n",
     "pool = active_learning_ds.pool\n",
@@ -316,29 +263,22 @@
     "predictions = baal_model.predict_on_dataset(pool, batch_size=16, iterations=15, use_cuda=USE_CUDA, verbose=False)\n",
     "# We will label the 10 most uncertain samples.\n",
     "top_uncertainty = heuristic(predictions)[:10]\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[(3, 1429), (4, 2971), (2, 1309), (4, 5), (3, 3761), (4, 2708), (6, 4679), (7, 160), (7, 1638), (6, 73)]\n"
-     ]
-    }
-   ],
    "source": [
     "# 4. Label those samples.\n",
     "oracle_indices = active_learning_ds._pool_to_oracle_index(top_uncertainty)\n",
     "labels = [get_label(train_dataset.files[idx]) for idx in oracle_indices]\n",
     "print(list(zip(labels, oracle_indices)))\n",
     "active_learning_ds.label(top_uncertainty, labels)\n",
     "\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "code",
@@ -348,7 +288,6 @@
      "is_executing": true
     }
    },
-   "outputs": [],
    "source": [
     "# 5. If not done, go back to 2.\n",
     "for step in range(5): # 5 Active Learning step!\n",
@@ -372,7 +311,8 @@
     "  active_learning_ds.label(top_uncertainty, labels)\n",
     "  \n",
     "  "
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -386,14 +326,14 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
    "source": [
     "torch.save({\n",
     "  'active_dataset': active_learning_ds.state_dict(),\n",
     "  'model': baal_model.state_dict(),\n",
     "  'metrics': {k:v.avg for k,v in baal_model.metrics.items()}\n",
     "}, '/tmp/baal_output.pth')\n"
-   ]
+   ],
+   "outputs": []
   },
   {
    "cell_type": "markdown",