#205 Add stochastic heuristics from Kirsch et al.

baal/active/heuristics/stochastics.py

@@ -0,0 +1,140 @@
+import types
+
+import numpy as np
+import structlog
+from scipy.special import softmax
+from scipy.stats import rankdata
+
+from baal.active.heuristics import AbstractHeuristic, Sequence
+
+log = structlog.get_logger(__name__)
+EPSILON = 1e-8
+
+
+class StochasticHeuristic(AbstractHeuristic):
+    def __init__(self, base_heuristic: AbstractHeuristic, query_size):
+        """Heuristic that is stochastic to improve diversity.
+
+        Common acquisition functions are heavily impacted by duplicates.
+        When using a `top-k` approache where the most
+        uncertain examples are selected, the acquisition function can select many duplicates.
+        Techniques such as BADGE (Ash et al, 2019) or BatchBALD (Kirsh et al. 2019)
+        are common solutions to this problem, but they are quite expensive.
+
+        Stochastic acquisitions are cheap to compute and get similar performances.
+
+        References:
+            Stochastic Batch Acquisition for Deep Active Learning, Kirsch et al. (2022)
+            https://arxiv.org/abs/2106.12059
+
+        Args:
+            base_heuristic: Heuristic to get uncertainty from before sampling.
+            query_size: These heuristics will return `query_size` items.
+        """
+        # TODO handle reverse
+        super().__init__(reverse=False)
+        self._bh = base_heuristic
+        self.query_size = query_size
+
+    def get_ranks(self, predictions):
+        # Get the raw uncertainty from the base heuristic.
+        scores = self.get_scores(predictions)
+        # Create the distribution to sample from.
+        distributions = self._make_distribution(scores)
+        # Force normalization for np.random.choice
+        distributions = np.clip(distributions, 0)
+        distributions /= distributions.sum()
+
+        # TODO Seed?
+        if (distributions > 0).sum() < self.query_size:
+            log.warnings("Not enough values, return random")
+            distributions = np.ones_like(distributions) / len(distributions)
+        return (
+            np.random.choice(len(distributions), self.query_size, replace=False, p=distributions),
+            distributions,
+        )
+
+    def get_scores(self, predictions):
+        if isinstance(predictions, types.GeneratorType):
+            scores = self._bh.get_uncertainties_generator(predictions)
+        else:
+            scores = self._bh.get_uncertainties(predictions)
+        if isinstance(scores, Sequence):
+            scores = np.concatenate(scores)
+        return scores
+
+    def _make_distribution(self, scores: np.ndarray) -> np.ndarray:
+        raise NotImplementedError
+
+
+class PowerSampling(StochasticHeuristic):
+    def __init__(self, base_heuristic: AbstractHeuristic, query_size, temperature=1.0):
+        """Samples from the uncertainty distribution without modification beside
+        temperature scaling and normalization.
+
+        Stochastic heuristic that assumes that the uncertainty distribution
+        is positive and that items with near-zero uncertainty are uninformative.
+        Empirically worked the best in the paper.
+
+        References:
+            Stochastic Batch Acquisition for Deep Active Learning, Kirsch et al. (2022)
+            https://arxiv.org/abs/2106.12059
+
+        Args:
+            base_heuristic: Heuristic to get uncertainty from before sampling.
+            query_size: These heuristics will return `query_size` items.
+            temperature: Value to temper the uncertainty distribution before sampling.
+        """
+        super().__init__(base_heuristic=base_heuristic, query_size=query_size)
+        self.temperature = temperature
+
+    def _make_distribution(self, scores: np.ndarray) -> np.ndarray:
+        scores = scores ** (1 / self.temperature)
+        scores = scores / scores.sum()
+        return scores
+
+
+class GibbsSampling(StochasticHeuristic):
+    def __init__(self, base_heuristic: AbstractHeuristic, query_size, temperature=1.0):
+        """Samples from the uncertainty distribution after applying softmax.
+
+        References:
+            Stochastic Batch Acquisition for Deep Active Learning, Kirsch et al. (2022)
+            https://arxiv.org/abs/2106.12059
+
+        Args:
+            base_heuristic: Heuristic to get uncertainty from before sampling.
+            query_size: These heuristics will return `query_size` items.
+            temperature: Value to temper the uncertainty distribution before sampling.
+        """
+        super().__init__(base_heuristic=base_heuristic, query_size=query_size)
+        self.temperature = temperature
+
+    def _make_distribution(self, scores: np.ndarray) -> np.ndarray:
+        scores /= self.temperature
+        # scores dimensions is [N]
+        scores = softmax(scores)
+        return scores
+
+
+class RankBasedSampling(StochasticHeuristic):
+    def __init__(self, base_heuristic: AbstractHeuristic, query_size, temperature=1.0):
+        """Samples from the ranks of the uncertainty distribution.
+
+        References:
+            Stochastic Batch Acquisition for Deep Active Learning, Kirsch et al. (2022)
+            https://arxiv.org/abs/2106.12059
+
+        Args:
+            base_heuristic: Heuristic to get uncertainty from before sampling.
+            query_size: These heuristics will return `query_size` items.
+            temperature: Value to temper the uncertainty distribution before sampling.
+        """
+        super().__init__(base_heuristic=base_heuristic, query_size=query_size)
+        self.temperature = temperature
+
+    def _make_distribution(self, scores: np.ndarray) -> np.ndarray:
+        rank = rankdata(-scores)
+        weights = rank ** (-1 / self.temperature)
+        normalized_weights: np.ndarray = weights / weights.sum()
+        return normalized_weights

tests/active/heuristic_test.py

@@ -17,64 +17,22 @@
     Precomputed,
     CombineHeuristics,
 )
+from tests.test_utils import make_fake_dist, make_3d_fake_dist, make_5d_fake_dist
 
-N_ITERATIONS = 50
-IMG_SIZE = 3
 N_CLASS = 10
 
-
 def chunks(l, n):
     """Yield successive n-sized chunks from l."""
     for i in range(0, len(l), n):
         yield l[i: i + n]
 
 
-def _make_3d_fake_dist(means, stds, dims=10):
-    d = np.stack(
-        [_make_fake_dist(means, stds, dims=dims) for _ in range(N_ITERATIONS)]
-    )  # 50 iterations
-    d = np.rollaxis(d, 0, 3)
-    # [n_sample, n_class, n_iter]
-    return d
-
-
-def _make_5d_fake_dist(means, stds, dims=10):
-    d = np.stack(
-        [_make_3d_fake_dist(means, stds, dims=dims) for _ in range(IMG_SIZE ** 2)], -1
-    )  # 3x3 image
-    b, c, i, hw = d.shape
-    d = np.reshape(d, [b, c, i, IMG_SIZE, IMG_SIZE])
-    d = np.rollaxis(d, 2, 5)
-    # [n_sample, n_class, H, W, iter]
-    return d
-
-
-def _make_fake_dist(means, stds, dims=10):
-    """
-    Create some fake discrete distributions
-    Args:
-        means: List of means
-        stds: List of standard deviations
-        dims: Dimensions of the distributions
-
-    Returns:
-        List of distributions
-    """
-    n_trials = 100
-    distributions = []
-    for m, std in zip(means, stds):
-        dist = np.zeros([dims])
-        for i in range(n_trials):
-            dist[
-                np.round(np.clip(np.random.normal(m, std, 1), 0, dims - 1)).astype(int).item()
-            ] += 1
-        distributions.append(dist / n_trials)
-    return np.array(distributions)
-
-
-distribution_2d = _make_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
-distributions_3d = _make_3d_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
-distributions_5d = _make_5d_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
+
+
+
+distribution_2d = make_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
+distributions_3d = make_3d_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
+distributions_5d = make_5d_fake_dist([5, 6, 9], [0.1, 4, 2], dims=N_CLASS)
 
 
 @pytest.mark.parametrize(

tests/active/stochastic_heuristic_test.py

@@ -0,0 +1,37 @@
+import numpy as np
+import pytest
+from scipy.stats import entropy
+
+from baal.active.heuristics import BALD, Entropy
+from baal.active.heuristics.stochastics import GibbsSampling, RankBasedSampling, PowerSampling
+
+NUM_CLASSES = 10
+NUM_ITERATIONS = 20
+BATCH_SIZE = 32
+
+
+@pytest.fixture
+def sampled_predictions():
+    predictions = np.stack(
+        [np.histogram(np.random.rand(5), bins=np.linspace(-.5, .5, NUM_CLASSES + 1))[0] for _ in
+         range(BATCH_SIZE * NUM_ITERATIONS)]).reshape(
+        [BATCH_SIZE, NUM_ITERATIONS, NUM_CLASSES])
+    return np.rollaxis(predictions, -1, 1)
+
+
+@pytest.mark.parametrize("stochastic_heuristic", [GibbsSampling, RankBasedSampling, PowerSampling])
+@pytest.mark.parametrize("base_heuristic", [BALD, Entropy])
+def test_stochastic_heuristic(stochastic_heuristic, base_heuristic, sampled_predictions):
+    heur_temp_1 = stochastic_heuristic(base_heuristic(), query_size=100, temperature=1.0)
+    heur_temp_10 = stochastic_heuristic(base_heuristic(), query_size=100, temperature=10.0)
+    heur_temp_05 = stochastic_heuristic(base_heuristic(), query_size=100, temperature=0.01)
+
+    scores = heur_temp_1.get_scores(sampled_predictions)
+
+    dist_temp_1, dist_temp_10, dist_temp_05 = (heur_temp_1._make_distribution(scores),
+                                               heur_temp_10._make_distribution(scores),
+                                               heur_temp_05._make_distribution(scores))
+
+    assert entropy(dist_temp_1) < entropy(dist_temp_10)
+    # NOTE: it is possible that this fails, as temp_1 can already have minimal entropy. This is unlikely.
+    assert entropy(dist_temp_1) > entropy(dist_temp_05)

tests/conftest.py

@@ -11,3 +11,8 @@ def fn(module: nn.Module, input_shape):
         pred1 = module(inp).detach().cpu().numpy()
         return all(np.allclose(pred1, module(inp).detach().cpu().numpy()) for _ in range(5))
     return fn
+
+
+@pytest.fixture
+def sampled_predictions():
+    return np.random.randn(100, 10, 20)

tests/test_utils.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+N_ITERATIONS = 50
+IMG_SIZE = 3
+
+
+def make_3d_fake_dist(means, stds, dims=10):
+    d = np.stack(
+        [make_fake_dist(means, stds, dims=dims) for _ in range(N_ITERATIONS)]
+    )  # 50 iterations
+    d = np.rollaxis(d, 0, 3)
+    # [n_sample, n_class, n_iter]
+    return d
+
+
+def make_5d_fake_dist(means, stds, dims=10):
+    d = np.stack(
+        [make_3d_fake_dist(means, stds, dims=dims) for _ in range(IMG_SIZE ** 2)], -1
+    )  # 3x3 image
+    b, c, i, hw = d.shape
+    d = np.reshape(d, [b, c, i, IMG_SIZE, IMG_SIZE])
+    d = np.rollaxis(d, 2, 5)
+    # [n_sample, n_class, H, W, iter]
+    return d
+
+
+def make_fake_dist(means, stds, dims=10):
+    """
+    Create some fake discrete distributions
+    Args:
+        means: List of means
+        stds: List of standard deviations
+        dims: Dimensions of the distributions
+
+    Returns:
+        List of distributions
+    """
+    n_trials = 100
+    distributions = []
+    for m, std in zip(means, stds):
+        dist = np.zeros([dims])
+        for i in range(n_trials):
+            dist[
+                np.round(np.clip(np.random.normal(m, std, 1), 0, dims - 1)).astype(int).item()
+            ] += 1
+        distributions.append(dist / n_trials)
+    return np.array(distributions)