fix: coerce None filter_rate in Label/Int Filter charts

tests/test_cloud_cold_latency_case.py

@@ -55,7 +55,9 @@ def test_cloud_cold_latency_case_accepts_label_filter():
     case = CloudColdLatencyCase(label_percentage=0.9)
 
     assert case.label_percentage == 0.9
+    assert case.filter_rate == pytest.approx(0.1)
     assert case.filters.type == FilterOp.StrEqual
+    assert case.filters.filter_rate == pytest.approx(0.1)
 
 
 def test_cloud_cold_latency_case_rejects_two_filter_types():

tests/test_cloud_payload_case.py

@@ -6,6 +6,7 @@
 from vectordb_bench.backend.clients.api import EmptyDBCaseConfig
 from vectordb_bench.backend.data_source import DatasetSource
 from vectordb_bench.backend.dataset import DatasetWithSizeType
+from vectordb_bench.backend.filter import FilterOp
 from vectordb_bench.backend.payload import PayloadProfile
 from vectordb_bench.backend.runner.mp_runner import MultiProcessingSearchRunner
 from vectordb_bench.backend.runner.serial_runner import SerialSearchRunner
@@ -101,6 +102,17 @@ def test_case_runner_reuse_key_distinguishes_scalar_label_schema_requirement():
     assert hash(ids_only_runner) != hash(scalar_label_runner)
 
 
+def test_cloud_payload_label_filter_sets_filter_rate():
+    case = CloudPayloadSearchCase(
+        dataset_with_size_type=DatasetWithSizeType.CohereMedium.value,
+        label_percentage=0.2,
+    )
+
+    assert case.filter_rate == pytest.approx(0.8)
+    assert case.filters.type == FilterOp.StrEqual
+    assert case.filters.filter_rate == pytest.approx(0.8)
+
+
 def test_cli_propagates_cloud_payload_dataset_selection():
     custom_case = get_custom_case_config(
         {

tests/test_custom_dataset_filter.py

@@ -0,0 +1,49 @@
+import pytest
+
+from vectordb_bench.backend.cases import CaseType, PerformanceCustomDataset
+from vectordb_bench.backend.filter import LabelFilter
+from vectordb_bench.models import CaseConfig
+
+
+def _custom_case_kwargs(**overrides):
+    kwargs = {
+        "name": "custom-ds",
+        "description": "",
+        "load_timeout": 1.0,
+        "optimize_timeout": 1.0,
+        "dataset_config": {
+            "name": "custom-ds",
+            "dir": "/tmp/custom-ds",
+            "size": 1000,
+            "dim": 128,
+            "metric_type": "L2",
+            "file_count": 1,
+        },
+    }
+    kwargs.update(overrides)
+    return kwargs
+
+
+def test_custom_dataset_filter_sets_filter_rate_from_label_percentage():
+    case = PerformanceCustomDataset(**_custom_case_kwargs(use_filter=True, label_percentage=0.01))
+    assert case.filter_rate == pytest.approx(0.99)
+    assert isinstance(case.filters, LabelFilter)
+    assert case.filters.filter_rate == pytest.approx(0.99)
+
+
+def test_custom_dataset_filter_matches_label_filter_formula():
+    case = PerformanceCustomDataset(**_custom_case_kwargs(use_filter=True, label_percentage=0.2))
+    assert case.filter_rate == pytest.approx(LabelFilter(label_percentage=0.2).filter_rate)
+
+
+def test_custom_dataset_without_filter_leaves_filter_rate_unset():
+    case = PerformanceCustomDataset(**_custom_case_kwargs(use_filter=False))
+    assert case.filter_rate is None
+
+
+def test_case_config_builds_custom_dataset_with_filter_rate():
+    case = CaseConfig(
+        case_id=CaseType.PerformanceCustomDataset,
+        custom_case=_custom_case_kwargs(use_filter=True, label_percentage=0.5),
+    ).case
+    assert case.filter_rate == pytest.approx(0.5)

tests/test_filter_charts.py

@@ -0,0 +1,41 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from vectordb_bench.frontend.components.int_filter import charts as int_charts
+from vectordb_bench.frontend.components.label_filter import charts as label_charts
+
+NONE_FILTER_DATA = [
+    {
+        "filter_rate": None,
+        "qps": 10.0,
+        "recall": 0.9,
+        "db_name": "A",
+        "dataset_name": "custom",
+    },
+    {
+        "filter_rate": 0.99,
+        "qps": 20.0,
+        "recall": 0.8,
+        "db_name": "B",
+        "dataset_name": "custom",
+    },
+]
+
+
+@pytest.mark.parametrize("charts", [label_charts, int_charts])
+def test_get_range_coerces_none_filter_rate(charts):
+    data = [{"filter_rate": None}, {"filter_rate": 0.5}]
+    xrange = charts.getRange("filter_rate", data, [0.05, 0.1])
+    assert xrange[0] <= 0
+    assert xrange[1] >= 0.5
+
+
+@pytest.mark.parametrize("charts", [label_charts, int_charts])
+def test_draw_chart_does_not_crash_when_filter_rate_is_none(charts):
+    st = MagicMock()
+    with patch.object(charts, "px") as px:
+        px.line.return_value = MagicMock()
+        charts.drawChart(st, list(NONE_FILTER_DATA), "qps")
+    px.line.assert_called_once()
+    st.plotly_chart.assert_called_once()

tests/test_multitenant_case.py

@@ -65,7 +65,9 @@ def test_case_config_constructs_multitenant_case():
 
     assert isinstance(case, CloudMultiTenantSearchCase)
     assert case.payload_profile == PayloadProfile.SCALAR_LABEL
+    assert case.filter_rate == pytest.approx(0.99)
     assert case.filters.type == FilterOp.StrEqual
+    assert case.filters.filter_rate == pytest.approx(0.99)
     assert case.tenant_labels() == ["tenant_0000", "tenant_0001", "tenant_0002", "tenant_0003", "tenant_0004"]
 
 

vectordb_bench/backend/cases.py

@@ -439,6 +439,11 @@ def __init__(
             gt_neighbors_field=dataset_config.gt_col_name,
             scalar_labels_file=f"{dataset_config.scalar_labels_name}.parquet",
         )
+        filter_rate = (
+            LabelFilter(label_percentage=label_percentage).filter_rate
+            if (use_filter and label_percentage is not None)
+            else None
+        )
         super().__init__(
             name=name,
             description=description,
@@ -448,6 +453,7 @@ def __init__(
             dataset=DatasetManager(data=dataset),
             use_filter=use_filter,
             label_percentage=label_percentage,
+            filter_rate=filter_rate,
         )
 
     @property
@@ -670,6 +676,8 @@ def __init__(
             "Cloud leaderboard search envelope case with explicit response payload profile. "
             f"Payload profile: {payload_profile.value}; dataset: {dataset_name}."
         )
+        if label_percentage is not None:
+            filter_rate = LabelFilter(label_percentage=label_percentage).filter_rate
         super().__init__(
             name=name,
             description=description,
@@ -739,6 +747,8 @@ def __init__(
             "Cloud leaderboard cold/warm serial latency case with explicit response payload profile. "
             f"Payload profile: {payload_profile.value}; dataset: {dataset_name}; query count: {query_count}."
         )
+        if label_percentage is not None:
+            filter_rate = LabelFilter(label_percentage=label_percentage).filter_rate
         super().__init__(
             name=name,
             description=description,
@@ -840,6 +850,8 @@ def __init__(
             raise ValueError(msg)
 
         dataset = dataset_with_size_type.get_manager()
+        if label_percentage is not None:
+            filter_rate = LabelFilter(label_percentage=label_percentage).filter_rate
         super().__init__(
             name=f"Cloud Multi-Tenant Search - {dataset_with_size_type.value}, {tenant_count} tenants",
             description=(

vectordb_bench/frontend/components/int_filter/charts.py

@@ -21,8 +21,10 @@ def drawChartByMetric(st, data, metrics=("qps", "recall"), **kwargs):
 
 
 def getRange(metric, data, padding_multipliers):
-    minV = min([d.get(metric, 0) for d in data])
-    maxV = max([d.get(metric, 0) for d in data])
+    # dict.get(metric, 0) still returns None when the key is present with a None value.
+    values = [d.get(metric) or 0 for d in data]
+    minV = min(values)
+    maxV = max(values)
     padding = maxV - minV
     rangeV = [
         minV - padding * padding_multipliers[0],
@@ -39,7 +41,7 @@ def drawChart(st, data: list[object], metric):
     y = metric
     yrange = getRange(y, data, [0.2, 0.1])
 
-    data.sort(key=lambda a: a[x])
+    data.sort(key=lambda a: a.get(x) or 0)
 
     fig = px.line(
         data,

vectordb_bench/frontend/components/label_filter/charts.py

@@ -21,8 +21,10 @@ def drawChartByMetric(st, data, metrics=("qps", "recall"), **kwargs):
 
 
 def getRange(metric, data, padding_multipliers):
-    minV = min([d.get(metric, 0) for d in data])
-    maxV = max([d.get(metric, 0) for d in data])
+    # dict.get(metric, 0) still returns None when the key is present with a None value.
+    values = [d.get(metric) or 0 for d in data]
+    minV = min(values)
+    maxV = max(values)
     padding = maxV - minV
     rangeV = [
         minV - padding * padding_multipliers[0],
@@ -39,7 +41,7 @@ def drawChart(st, data: list[object], metric):
     y = metric
     yrange = getRange(y, data, [0.2, 0.1])
 
-    data.sort(key=lambda a: a[x])
+    data.sort(key=lambda a: a.get(x) or 0)
 
     fig = px.line(
         data,