From 501276832056d86e096475876c21b029fa6fe120 Mon Sep 17 00:00:00 2001
From: artemkuzmenko2501-del <artemkuzmenko2501@gmail.com>
Date: Fri, 13 Mar 2026 18:20:31 +0300
Subject: [PATCH 1/3] Add custom metric functions in Tester and linearization
 in Preprocessor

- Tester now accepts metric_funcs dict mapping metric names to callables,
  enabling ratio and composite metrics without pre-computing columns
- LinearizationTransformer added for ratio metrics (e.g. revenue/orders):
  linearized_i = numerator_i - ratio * denominator_i, where ratio is
  estimated on reference data passed to fit()
- Preprocessor.linearize() integrates linearization into the existing
  chain pattern with full serialization/replay support
- 8 new tests covering metric_funcs constructor/run/override behaviour
  and linearize formula, chaining, serialization

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 CLAUDE.md                              |  88 ++++++++++++++++
 ambrosia/preprocessing/__init__.py     |   3 +-
 ambrosia/preprocessing/preprocessor.py |  46 ++++++++-
 ambrosia/preprocessing/transformers.py | 133 ++++++++++++++++++++++++-
 ambrosia/tester/handlers.py            |  19 +++-
 ambrosia/tester/tester.py              |  20 +++-
 tests/test_preprocessor.py             |  69 +++++++++++++
 tests/test_tester.py                   |  58 +++++++++++
 8 files changed, 427 insertions(+), 9 deletions(-)
 create mode 100644 CLAUDE.md

diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..c322566
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,88 @@
+# Ambrosia
+
+A/B testing framework for experiment design, group splitting, and results evaluation.
+Supports both pandas and Spark DataFrames.
+
+## Commands
+
+```bash
+make install      # create .venv via Poetry (poetry install --all-extras)
+make test         # run pytest with coverage
+make lint         # isort + black + pylint + flake8 (checks only)
+make autoformat   # isort + black (fix in place)
+make clean        # remove .venv, build artifacts, reports/
+```
+
+Single test: `PYTHONPATH=. pytest tests/path/test_file.py::test_fn`
+
+Line length: **120**.
+
+## Architecture
+
+### Three-stage pipeline
+
+`Designer` → `Splitter` → `Tester` are independent, stateless-ish classes.
+No shared state between stages; each takes a DataFrame and parameters.
+
+### Pandas/Spark dispatch
+
+Never subclass for pandas vs. Spark. Instead use `DataframeHandler` or the
+free function `choose_on_table(alternatives, dataframe)` in
+`ambrosia/tools/ab_abstract_component.py`:
+
+```python
+choose_on_table([pandas_func, spark_func], dataframe)
+```
+
+`DataframeHandler._handle_cases` / `_handle_on_table` wrap this pattern for
+method dispatch in handlers (e.g. `TheoryHandler`, `EmpiricHandler`).
+
+### ABMetaClass
+
+`ABMetaClass(ABCMeta, YAMLObjectMetaclass)` in `ab_abstract_component.py`
+resolves the metaclass conflict between `ABCMeta` and PyYAML's
+`YAMLObjectMetaclass`. Any class that inherits from `ABToolAbstract` **and**
+needs YAML serialization must set `metaclass=ABMetaClass`.
+
+### ABToolAbstract._prepare_arguments()
+
+Constructor args are "saved" defaults; `run()` args can override them at
+call time. `_prepare_arguments` resolves the priority:
+run-time arg → constructor arg → `ValueError` if both are None.
+
+```python
+chosen = _prepare_arguments({"alpha": [self._alpha, given_alpha]})
+```
+
+### Stat criteria strategy pattern
+
+Hierarchy: `StatCriterion` (abstract, just `calculate_pvalue`) →
+`ABStatCriterion` (adds `calculate_effect`, `calculate_conf_interval`,
+`get_results`).
+
+Concrete implementations in `ambrosia/tools/stat_criteria.py`:
+`TtestIndCriterion`, `TtestRelCriterion`, `MannWhitneyCriterion`,
+`WilcoxonCriterion`.
+
+`Tester` dispatches by string alias via `AVAILABLE_AB_CRITERIA` dict — duck
+typing, not isinstance checks. To add a criterion: subclass `ABStatCriterion`,
+set `alias` and `implemented_effect_types` class attributes, register in the
+dict.
+
+### Preprocessor chain
+
+`Preprocessor` (pandas only) uses method chaining — each method returns
+`self`. Each step appends a fitted `AbstractFittableTransformer` to
+`self.transformers`. The transformer list supports serialization
+(`store_transformations` / `load_transformations` → JSON) and replay
+(`apply_transformations`) for consistent train/test preprocessing.
+
+### Theoretical vs empirical design
+
+Two design philosophies plug into the same `SimpleDesigner` interface:
+
+- **Theoretical** (`TheoryHandler`): closed-form power/sample-size formulas
+- **Empirical** (`EmpiricHandler`): bootstrap/simulation-based estimates
+
+Both implement `size_design`, `effect_design`, `power_design` and dispatch
+pandas vs. Spark internally via `DataframeHandler`.
diff --git a/ambrosia/preprocessing/__init__.py b/ambrosia/preprocessing/__init__.py
index 82561ab..e6d33e8 100644
--- a/ambrosia/preprocessing/__init__.py
+++ b/ambrosia/preprocessing/__init__.py
@@ -21,7 +21,7 @@
 from .ml_var_reducer import MLVarianceReducer
 from .preprocessor import Preprocessor
 from .robust import IQRPreprocessor, RobustPreprocessor
-from .transformers import BoxCoxTransformer, LogTransformer
+from .transformers import BoxCoxTransformer, LinearizationTransformer, LogTransformer
 
 __all__ = [
     "AggregatePreprocessor",
@@ -32,5 +32,6 @@
     "RobustPreprocessor",
     "IQRPreprocessor",
     "BoxCoxTransformer",
+    "LinearizationTransformer",
     "LogTransformer",
 ]
diff --git a/ambrosia/preprocessing/preprocessor.py b/ambrosia/preprocessing/preprocessor.py
index c865008..6d7b205 100644
--- a/ambrosia/preprocessing/preprocessor.py
+++ b/ambrosia/preprocessing/preprocessor.py
@@ -34,7 +34,7 @@
 from ambrosia.preprocessing.aggregate import AggregatePreprocessor
 from ambrosia.preprocessing.cuped import Cuped, MultiCuped
 from ambrosia.preprocessing.robust import IQRPreprocessor, RobustPreprocessor
-from ambrosia.preprocessing.transformers import BoxCoxTransformer, LogTransformer
+from ambrosia.preprocessing.transformers import BoxCoxTransformer, LinearizationTransformer, LogTransformer
 
 
 class Preprocessor:
@@ -378,6 +378,50 @@ def multicuped(
         self.transformers.append(transformer)
         return self
 
+    def linearize(
+        self,
+        numerator: types.ColumnNameType,
+        denominator: types.ColumnNameType,
+        transformed_name: Optional[types.ColumnNameType] = None,
+        load_path: Optional[Path] = None,
+    ) -> Preprocessor:
+        """
+        Linearize a ratio metric for use in A/B testing.
+
+        Computes a per-unit linearized value that is approximately normally
+        distributed, enabling correct t-test usage for ratio metrics:
+
+            linearized_i = numerator_i - ratio * denominator_i
+
+        where ratio = mean(numerator) / mean(denominator) is estimated on
+        the data passed to this ``Preprocessor`` instance (reference / control data).
+
+        Parameters
+        ----------
+        numerator : ColumnNameType
+            Column name of the ratio numerator (e.g. ``"revenue"``).
+        denominator : ColumnNameType
+            Column name of the ratio denominator (e.g. ``"orders"``).
+        transformed_name : ColumnNameType, optional
+            Name for the new linearized column. Defaults to
+            ``"{numerator}_lin"``.
+        load_path : Path, optional
+            Path to a json file with pre-fitted parameters.
+
+        Returns
+        -------
+        self : Preprocessor
+            Instance object.
+        """
+        transformer = LinearizationTransformer()
+        if load_path is None:
+            transformer.fit_transform(self.dataframe, numerator, denominator, transformed_name, inplace=True)
+        else:
+            transformer.load_params(load_path)
+            transformer.transform(self.dataframe, inplace=True)
+        self.transformers.append(transformer)
+        return self
+
     def transformations(self) -> List:
         """
         List of all transformations which were called.
diff --git a/ambrosia/preprocessing/transformers.py b/ambrosia/preprocessing/transformers.py
index 6397641..1649e86 100644
--- a/ambrosia/preprocessing/transformers.py
+++ b/ambrosia/preprocessing/transformers.py
@@ -16,7 +16,7 @@
 Module contains tools for metrics transformations during a
 preprocessing task.
 """
-from typing import Dict, Union
+from typing import Dict, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -386,3 +386,134 @@ def inverse_transform(self, dataframe: pd.DataFrame, inplace: bool = False) -> U
         transformed: pd.DataFrame = dataframe if inplace else dataframe.copy()
         transformed[self.column_names] = np.exp(transformed[self.column_names].values)
         return None if inplace else transformed
+
+
+class LinearizationTransformer(AbstractFittableTransformer):
+    """
+    Linearization transformer for ratio metrics.
+
+    Converts a ratio metric (numerator / denominator) into a per-unit linearized
+    metric that is approximately normally distributed, enabling correct t-test usage:
+
+        linearized_i = numerator_i - ratio * denominator_i
+
+    where ratio = mean(numerator) / mean(denominator), estimated on the reference
+    (control group / historical) data passed to fit().
+
+    Parameters
+    ----------
+    numerator : str
+        Column name of the ratio numerator (e.g. "revenue").
+    denominator : str
+        Column name of the ratio denominator (e.g. "orders").
+    transformed_name : str, optional
+        Name for the new column. Defaults to ``"{numerator}_lin"``.
+
+    Examples
+    --------
+    >>> transformer = LinearizationTransformer()
+    >>> transformer.fit(control_df, "revenue", "orders", "arpu_lin")
+    >>> transformer.transform(experiment_df, inplace=True)
+    """
+
+    def __str__(self) -> str:
+        return "Linearization transformation"
+
+    def __init__(self) -> None:
+        self.numerator: Optional[str] = None
+        self.denominator: Optional[str] = None
+        self.transformed_name: Optional[str] = None
+        self.ratio: Optional[float] = None
+        super().__init__()
+
+    def get_params_dict(self) -> Dict:
+        self._check_fitted()
+        return {
+            "numerator": self.numerator,
+            "denominator": self.denominator,
+            "transformed_name": self.transformed_name,
+            "ratio": self.ratio,
+        }
+
+    def load_params_dict(self, params: Dict) -> None:
+        for key in ("numerator", "denominator", "transformed_name", "ratio"):
+            if key not in params:
+                raise TypeError(f"params argument must contain: {key}")
+            setattr(self, key, params[key])
+        self.fitted = True
+
+    def fit(
+        self,
+        dataframe: pd.DataFrame,
+        numerator: str,
+        denominator: str,
+        transformed_name: Optional[str] = None,
+    ):
+        """
+        Estimate ratio = mean(numerator) / mean(denominator) on reference data.
+
+        Parameters
+        ----------
+        dataframe : pd.DataFrame
+            Reference dataframe (typically control group or historical data).
+        numerator : str
+            Column name of the ratio numerator.
+        denominator : str
+            Column name of the ratio denominator.
+        transformed_name : str, optional
+            Name for the linearized column. Defaults to ``"{numerator}_lin"``.
+        """
+        self._check_cols(dataframe, [numerator, denominator])
+        denom_mean = dataframe[denominator].mean()
+        if denom_mean == 0:
+            raise ValueError(f"Mean of denominator column '{denominator}' is zero; cannot compute ratio.")
+        self.numerator = numerator
+        self.denominator = denominator
+        self.transformed_name = transformed_name if transformed_name is not None else f"{numerator}_lin"
+        self.ratio = dataframe[numerator].mean() / denom_mean
+        self.fitted = True
+        return self
+
+    def transform(self, dataframe: pd.DataFrame, inplace: bool = False) -> Union[pd.DataFrame, None]:
+        """
+        Apply linearization: transformed = numerator - ratio * denominator.
+
+        Parameters
+        ----------
+        dataframe : pd.DataFrame
+            Dataframe to transform.
+        inplace : bool, default: ``False``
+            If ``True`` modifies dataframe in place, otherwise returns a copy.
+        """
+        self._check_fitted()
+        self._check_cols(dataframe, [self.numerator, self.denominator])
+        df = dataframe if inplace else dataframe.copy()
+        df[self.transformed_name] = df[self.numerator] - self.ratio * df[self.denominator]
+        return None if inplace else df
+
+    def fit_transform(
+        self,
+        dataframe: pd.DataFrame,
+        numerator: str,
+        denominator: str,
+        transformed_name: Optional[str] = None,
+        inplace: bool = False,
+    ) -> Union[pd.DataFrame, None]:
+        """
+        Fit and transform in one step.
+
+        Parameters
+        ----------
+        dataframe : pd.DataFrame
+            Reference dataframe for fitting and transformation.
+        numerator : str
+            Column name of the ratio numerator.
+        denominator : str
+            Column name of the ratio denominator.
+        transformed_name : str, optional
+            Name for the linearized column.
+        inplace : bool, default: ``False``
+            If ``True`` modifies dataframe in place.
+        """
+        self.fit(dataframe, numerator, denominator, transformed_name)
+        return self.transform(dataframe, inplace)
diff --git a/ambrosia/tester/handlers.py b/ambrosia/tester/handlers.py
index 7d65c35..5c97779 100644
--- a/ambrosia/tester/handlers.py
+++ b/ambrosia/tester/handlers.py
@@ -51,7 +51,15 @@ class SparkCriteria(enum.Enum):
 
 class TheoreticalTesterHandler:
     def __init__(
-        self, group_a, group_b, column: str, alpha: np.ndarray, effect_type: str, criterion: StatCriterion, **kwargs
+        self,
+        group_a,
+        group_b,
+        column: str,
+        alpha: np.ndarray,
+        effect_type: str,
+        criterion: StatCriterion,
+        metric_func=None,
+        **kwargs,
     ):
         self.group_a = group_a
         self.group_b = group_b
@@ -59,6 +67,7 @@ def __init__(
         self.alpha = alpha
         self.effect_type = effect_type
         self.criterion = criterion
+        self.metric_func = metric_func
         self.kwargs = kwargs
 
     def _correct_criterion(self, criterion: tp.Any) -> bool:
@@ -79,8 +88,12 @@ def get_criterion(self, criterion: str, data_example: types.SparkOrPandas):
 
     def _set_kwargs(self):
         if isinstance(self.group_a, pd.DataFrame):
-            self.group_a = self.group_a[self.column].values
-            self.group_b = self.group_b[self.column].values
+            if self.metric_func is not None:
+                self.group_a = np.asarray(self.metric_func(self.group_a))
+                self.group_b = np.asarray(self.metric_func(self.group_b))
+            else:
+                self.group_a = self.group_a[self.column].values
+                self.group_b = self.group_b[self.column].values
         elif isinstance(self.group_a, types.SparkDataFrame):
             self.kwargs["column"] = self.column
         self.kwargs["alpha"] = self.alpha
diff --git a/ambrosia/tester/tester.py b/ambrosia/tester/tester.py
index 386d6a5..304cdbe 100644
--- a/ambrosia/tester/tester.py
+++ b/ambrosia/tester/tester.py
@@ -29,7 +29,7 @@
 """
 import itertools
 from copy import deepcopy
-from typing import Dict, List, Optional, Union
+from typing import Callable, Dict, List, Optional, Union
 from warnings import warn
 
 import numpy as np
@@ -241,6 +241,7 @@ def __init__(
         id_column: Optional[types.ColumnNameType] = None,
         first_type_errors: types.StatErrorType = 0.05,
         metrics: Optional[types.MetricNamesType] = None,
+        metric_funcs: Optional[Dict[str, Callable]] = None,
     ):
         """
         Tester class constructor to initialize the object.
@@ -257,6 +258,7 @@ def __init__(
             self.set_experiment_results(experiment_results=experiment_results)
         self.set_errors(first_type_errors)
         self.set_metrics(metrics)
+        self.__metric_funcs = metric_funcs or {}
 
     @staticmethod
     def __filter_data(
@@ -372,9 +374,15 @@ def __pre_run(method: str, args: types._UsageArgumentsType, **kwargs) -> types.T
         if method not in accepted_methods:
             raise ValueError(f'Choose method from {", ".join(accepted_methods)}')
         result: types.TesterResult = {}
+        metric_funcs: Dict = args.get("metric_funcs", {})
         for metric in args["metrics"]:
-            a_values: np.ndarray = args["data_a_group"][metric].values
-            b_values: np.ndarray = args["data_b_group"][metric].values
+            metric_func = metric_funcs.get(metric)
+            if metric_func is not None:
+                a_values: np.ndarray = np.asarray(metric_func(args["data_a_group"]))
+                b_values: np.ndarray = np.asarray(metric_func(args["data_b_group"]))
+            else:
+                a_values = args["data_a_group"][metric].values
+                b_values = args["data_b_group"][metric].values
             if method == "theory":
                 # TODO: Make it SolverClass ~ method
                 # solver = SolverClass(...)
@@ -386,6 +394,7 @@ def __pre_run(method: str, args: types._UsageArgumentsType, **kwargs) -> types.T
                     alpha=np.array(args["alpha"]),
                     effect_type=args["effect_type"],
                     criterion=args["criterion"],
+                    metric_func=metric_func,
                     **kwargs,
                 )
                 sub_result = solver.solve()
@@ -473,6 +482,7 @@ def run(
         criterion: Optional[ABStatCriterion] = None,
         correction_method: Union[str, None] = "bonferroni",
         as_table: bool = True,
+        metric_funcs: Optional[Dict[str, Callable]] = None,
         **kwargs,
     ) -> types.TesterResult:
         """
@@ -556,6 +566,8 @@ def run(
         chosen_args: types._UsageArgumentsType = Tester._prepare_arguments(arguments_choice)
         chosen_args["effect_type"] = effect_type
         chosen_args["criterion"] = criterion
+        effective_metric_funcs = {**self.__metric_funcs, **(metric_funcs or {})}
+        chosen_args["metric_funcs"] = effective_metric_funcs
 
         hypothesis_num: int = len(list(itertools.combinations(chosen_args["experiment_results"], 2))) * len(
             chosen_args["metrics"]
@@ -602,6 +614,7 @@ def test(
     criterion: Optional[ABStatCriterion] = None,
     correction_method: Union[str, None] = "bonferroni",
     as_table: bool = True,
+    metric_funcs: Optional[Dict[str, Callable]] = None,
     **kwargs,
 ) -> types.TesterResult:
     """
@@ -673,5 +686,6 @@ def test(
         criterion=criterion,
         correction_method=correction_method,
         as_table=as_table,
+        metric_funcs=metric_funcs,
         **kwargs,
     )
diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py
index 9106336..e2954b9 100644
--- a/tests/test_preprocessor.py
+++ b/tests/test_preprocessor.py
@@ -1,5 +1,6 @@
 import os
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -117,3 +118,71 @@ def test_store_load_config(data_for_agg):
     transformed_by_config: pd.DataFrame = loaded_preprocessor.apply_transformations()
     os.remove(store_path)
     assert (transformed == transformed_by_config).all(None)
+
+
+@pytest.mark.smoke()
+def test_linearize_basic(data_nonlin_var):
+    """
+    Test that linearize creates new column and returns self.
+    """
+    preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    result = preprocessor.linearize("target", "feature_1", transformed_name="target_lin")
+    assert result is preprocessor  # method chaining
+    assert "target_lin" in preprocessor.data().columns
+
+
+@pytest.mark.unit()
+def test_linearize_formula(data_nonlin_var):
+    """
+    Test that linearized values satisfy: linearized = num - ratio * denom.
+    """
+    preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    preprocessor.linearize("target", "feature_1", transformed_name="target_lin")
+    df = preprocessor.data()
+    transformer = preprocessor.transformations()[-1]
+    ratio = transformer.ratio
+    expected = data_nonlin_var["target"] - ratio * data_nonlin_var["feature_1"]
+    np.testing.assert_allclose(df["target_lin"].values, expected.values, rtol=1e-10)
+
+
+@pytest.mark.unit()
+def test_linearize_in_chain(data_nonlin_var):
+    """
+    Test linearize as part of a preprocessing chain.
+    """
+    preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    result = (
+        preprocessor.robust("feature_1", alpha=0.01)
+        .linearize("target", "feature_1", transformed_name="target_lin")
+        .data()
+    )
+    assert "target_lin" in result.columns
+
+
+@pytest.mark.unit()
+def test_linearize_load_store(data_nonlin_var):
+    """
+    Test that linearization transformer can be serialized and replayed.
+    """
+    store_path = "tests/configs/linearize_config.json"
+    preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    preprocessor.linearize("target", "feature_1", transformed_name="target_lin")
+    preprocessor.store_transformations(store_path)
+
+    loaded_preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    loaded_preprocessor.load_transformations(store_path)
+
+    os.remove(store_path)
+
+    for t, lt in zip(preprocessor.transformations(), loaded_preprocessor.transformations()):
+        assert t.get_params_dict() == lt.get_params_dict()
+
+
+@pytest.mark.unit()
+def test_linearize_default_name(data_nonlin_var):
+    """
+    Test that default transformed_name is '{numerator}_lin'.
+    """
+    preprocessor = Preprocessor(data_nonlin_var, verbose=False)
+    preprocessor.linearize("target", "feature_1")
+    assert "target_lin" in preprocessor.data().columns
diff --git a/tests/test_tester.py b/tests/test_tester.py
index c4ba892..a1597e8 100644
--- a/tests/test_tester.py
+++ b/tests/test_tester.py
@@ -384,3 +384,61 @@ def test_paired_bootstrap(effect_type, alternative):
     )
     assert test_results_dep[0]["pvalue"] < test_results_ind[0]["pvalue"]
     assert test_results_dep[0]["confidence_interval"][0] > test_results_ind[0]["confidence_interval"][0]
+
+
+@pytest.mark.unit
+def test_metric_func_constructor(results_ltv_retention_conversions):
+    """
+    Test that metric_funcs passed to constructor are used when metric name matches.
+    """
+    # ratio metric: ltv / retention (arbitrary, just to test callable path)
+    ratio_func = lambda df: (df["ltv"] / (df["retention"] + 1e-6)).values
+    tester = Tester(
+        dataframe=results_ltv_retention_conversions,
+        column_groups="group",
+        metrics=["ratio_metric"],
+        metric_funcs={"ratio_metric": ratio_func},
+    )
+    result = tester.run(as_table=False)
+    assert len(result) == 1
+    assert "pvalue" in result[0]
+
+
+@pytest.mark.unit
+@pytest.mark.parametrize("method", ["theory", "empiric"])
+def test_metric_func_run(method, results_ltv_retention_conversions):
+    """
+    Test that metric_funcs passed to run() work for theory and empiric methods.
+    """
+    double_ltv = lambda df: (df["ltv"] * 2).values
+    tester = Tester(
+        dataframe=results_ltv_retention_conversions,
+        column_groups="group",
+        metrics=["ltv"],
+    )
+    result_normal = tester.run(method=method, metrics=["ltv"], as_table=False)
+    result_func = tester.run(
+        method=method,
+        metrics=["custom"],
+        metric_funcs={"custom": double_ltv},
+        as_table=False,
+    )
+    # Doubling values doesn't change pvalue for ttest (same scale), but effect should be doubled
+    assert abs(result_func[0]["effect"]) == pytest.approx(abs(result_normal[0]["effect"]) * 2, rel=1e-4)
+
+
+@pytest.mark.unit
+def test_metric_func_overrides_constructor(results_ltv_retention_conversions):
+    """
+    Test that metric_funcs in run() override those set in constructor.
+    """
+    func_a = lambda df: df["ltv"].values
+    func_b = lambda df: (df["ltv"] * 3).values
+    tester = Tester(
+        dataframe=results_ltv_retention_conversions,
+        column_groups="group",
+        metric_funcs={"my_metric": func_a},
+    )
+    result_a = tester.run(metrics=["my_metric"], as_table=False)
+    result_b = tester.run(metrics=["my_metric"], metric_funcs={"my_metric": func_b}, as_table=False)
+    assert abs(result_b[0]["effect"]) == pytest.approx(abs(result_a[0]["effect"]) * 3, rel=1e-4)

From c83f93d0d74f5e70203696b62f1fec40aa4b4392 Mon Sep 17 00:00:00 2001
From: artemkuzmenko2501-del <artemkuzmenko2501@gmail.com>
Date: Fri, 13 Mar 2026 19:17:07 +0300
Subject: [PATCH 2/3] Fix CI: remove EOL Python 3.7, make nmslib optional

- Remove Python 3.7 from CI matrix (GitHub Actions dropped it, EOL)
- Replace Python 3.10 with 3.11/3.12 (nmslib has unfixable C++ build
  failures on modern gcc; 3.11+ already passes in scheduled runs)
- Make nmslib an optional dependency so missing it doesn't break install
- Lazy-import nmslib in knn.py with a clear ImportError message

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 .github/workflows/test.yaml |  2 +-
 ambrosia/tools/knn.py       | 11 ++++++++++-
 pyproject.toml              |  3 ++-
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 54c2785..0fb1eac 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -36,7 +36,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v2
diff --git a/ambrosia/tools/knn.py b/ambrosia/tools/knn.py
index 3a90129..c6a8131 100644
--- a/ambrosia/tools/knn.py
+++ b/ambrosia/tools/knn.py
@@ -14,9 +14,13 @@
 
 from typing import Any, List
 
-import nmslib
 import numpy as np
 
+try:
+    import nmslib
+except ImportError as _nmslib_import_error:
+    nmslib = None  # type: ignore[assignment]
+
 
 class NMTree:
     """
@@ -24,6 +28,11 @@ class NMTree:
     """
 
     def __init__(self, points: np.ndarray, payload: np.ndarray, ef_search: int):
+        if nmslib is None:
+            raise ImportError(
+                "nmslib is required for KNN-based group matching. "
+                "Install it with: pip install nmslib"
+            )
         self.__was = set()
         self.__index = nmslib.init(space="l2")
         self.__index.addDataPointBatch(points)
diff --git a/pyproject.toml b/pyproject.toml
index 2512ef5..323e7a6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,7 +42,7 @@ packages = [
 python = ">=3.7.2, <3.11.0"
 jinja2 = "^3.0.0"
 joblib = "^1.1.0"
-nmslib = "^2.0.4"
+nmslib = { version = "^2.0.4", optional = true }
 numpy = ">=1.19.5, <2.0.0"
 pandas = ">=0.25.3, <2.0.0"
 pyspark = { version = "^3.2", optional = true }
@@ -67,6 +67,7 @@ pytest-lazy-fixture = "0.6.3"
 
 [tool.poetry.extras]
 spark = ["pyspark"]
+knn = ["nmslib"]
 
 [tool.black]
 line-length = 120

From 7dbac31face6aedb165182e29914bde7a4109f77 Mon Sep 17 00:00:00 2001
From: artemkuzmenko2501-del <artemkuzmenko2501@gmail.com>
Date: Fri, 13 Mar 2026 19:34:51 +0300
Subject: [PATCH 3/3] Fix poetry.lock and python version constraint

- Extend python constraint to >=3.8, <3.13 to support 3.11/3.12 in CI
- Restrict nmslib optional dep to python <3.11 (build fails on newer gcc)
- Regenerate poetry.lock to fix 'Extra [knn] is not specified' error

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 poetry.lock    | 277 +++++++++++++++----------------------------------
 pyproject.toml |   4 +-
 2 files changed, 86 insertions(+), 195 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index a40864f..3760b59 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -15,7 +15,6 @@ files = [
 [package.dependencies]
 lazy-object-proxy = ">=1.4.0"
 setuptools = ">=20.0"
-typed-ast = {version = ">=1.4.0,<2.0", markers = "implementation_name == \"cpython\" and python_version < \"3.8\""}
 typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
 wrapt = ">=1.11,<2"
 
@@ -42,9 +41,6 @@ files = [
     {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
 ]
 
-[package.dependencies]
-importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
-
 [package.extras]
 cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
 dev = ["attrs[docs,tests]", "pre-commit"]
@@ -107,7 +103,6 @@ mypy-extensions = ">=0.4.3"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
 tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
-typed-ast = {version = ">=1.4.2", markers = "python_version < \"3.8\" and implementation_name == \"cpython\""}
 typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
@@ -164,7 +159,6 @@ files = [
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
-importlib-metadata = {version = "*", markers = "python_version < \"3.8\""}
 
 [[package]]
 name = "cloudpickle"
@@ -297,7 +291,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-metadata = {version = "<4.3", markers = "python_version < \"3.8\""}
 mccabe = ">=0.6.0,<0.7.0"
 pycodestyle = ">=2.8.0,<2.9.0"
 pyflakes = ">=2.4.0,<2.5.0"
@@ -400,26 +393,6 @@ dev = ["black", "nose", "pre-commit", "pytest"]
 mongotrials = ["pymongo"]
 sparktrials = ["pyspark"]
 
-[[package]]
-name = "importlib-metadata"
-version = "4.2.0"
-description = "Read metadata from Python packages"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "importlib_metadata-4.2.0-py3-none-any.whl", hash = "sha256:057e92c15bc8d9e8109738a48db0ccb31b4d9d5cfbee5a8670879a30be66304b"},
-    {file = "importlib_metadata-4.2.0.tar.gz", hash = "sha256:b7e52a1f8dec14a75ea73e0891f3060099ca1d8e6a462a4dff11c3e119ea1b31"},
-]
-
-[package.dependencies]
-typing-extensions = {version = ">=3.6.4", markers = "python_version < \"3.8\""}
-zipp = ">=0.5"
-
-[package.extras]
-docs = ["jaraco.packaging (>=8.2)", "rst.linker (>=1.9)", "sphinx"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pep517", "pyfakefs", "pytest (>=4.6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.0.1)", "pytest-flake8", "pytest-mypy"]
-
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -558,9 +531,6 @@ files = [
     {file = "kiwisolver-1.4.4.tar.gz", hash = "sha256:d41997519fcba4a1e46eb4a2fe31bc12f0ff957b2b81bac28db24744f333e955"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = "*", markers = "python_version < \"3.8\""}
-
 [[package]]
 name = "lazy-object-proxy"
 version = "1.9.0"
@@ -721,7 +691,6 @@ packaging = ">=20.0"
 pillow = ">=6.2.0"
 pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=4,<7"
 
 [[package]]
 name = "mccabe"
@@ -771,7 +740,7 @@ name = "nmslib"
 version = "2.1.1"
 description = "Non-Metric Space Library (NMSLIB)"
 category = "main"
-optional = false
+optional = true
 python-versions = "*"
 files = [
     {file = "nmslib-2.1.1-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:495ace1146bb1e89ef585a490fa65de19a87fa0d5bb029f3156402a431fc3558"},
@@ -818,43 +787,40 @@ pybind11 = "<2.6.2"
 
 [[package]]
 name = "numpy"
-version = "1.21.6"
-description = "NumPy is the fundamental package for array computing with Python."
+version = "1.24.4"
+description = "Fundamental package for array computing in Python"
 category = "main"
 optional = false
-python-versions = ">=3.7,<3.11"
-files = [
-    {file = "numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8737609c3bbdd48e380d463134a35ffad3b22dc56295eff6f79fd85bd0eeeb25"},
-    {file = "numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:fdffbfb6832cd0b300995a2b08b8f6fa9f6e856d562800fea9182316d99c4e8e"},
-    {file = "numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3820724272f9913b597ccd13a467cc492a0da6b05df26ea09e78b171a0bb9da6"},
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 ]
 
 [[package]]
@@ -1040,9 +1006,6 @@ files = [
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-[package.dependencies]
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-
 [package.extras]
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 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.2.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
@@ -1075,9 +1038,6 @@ files = [
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-[package.dependencies]
-importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
-
 [package.extras]
 dev = ["pre-commit", "tox"]
 testing = ["pytest", "pytest-benchmark"]
@@ -1087,7 +1047,7 @@ name = "psutil"
 version = "5.9.4"
 description = "Cross-platform lib for process and system monitoring in Python."
 category = "main"
-optional = false
+optional = true
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
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@@ -1138,7 +1098,7 @@ name = "pybind11"
 version = "2.6.1"
 description = "Seamless operability between C++11 and Python"
 category = "main"
-optional = false
+optional = true
 python-versions = "!=3.0,!=3.1,!=3.2,!=3.3,!=3.4,>=2.7"
 files = [
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@@ -1173,7 +1133,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-metadata = {version = ">=2.0.0,<5.0.0", markers = "python_version < \"3.8\""}
 snowballstemmer = ">=2.2.0"
 
 [package.extras]
@@ -1270,7 +1229,6 @@ files = [
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-importlib-metadata = {version = ">=0.12", markers = "python_version < \"3.8\""}
 iniconfig = "*"
 packaging = "*"
 pluggy = ">=0.12,<2.0"
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 [[package]]
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-description = "SciPy: Scientific Library for Python"
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 category = "main"
 optional = false
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 ]
 
 [package.dependencies]
-numpy = ">=1.16.5,<1.23.0"
+numpy = ">=1.18.5,<1.26.0"
+
+[package.extras]
+dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
+doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
+test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "setuptools"
 version = "67.6.1"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1497,27 +1459,6 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-g
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
-[[package]]
-name = "setuptools-scm"
-version = "6.4.2"
-description = "the blessed package to manage your versions by scm tags"
-category = "main"
-optional = false
-python-versions = ">=3.6"
-files = [
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-]
-
-[package.dependencies]
-packaging = ">=20.0"
-setuptools = "*"
-tomli = ">=1.0.0"
-
-[package.extras]
-test = ["pytest (>=6.2)", "virtualenv (>20)"]
-toml = ["setuptools (>=42)"]
-
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1623,15 +1564,14 @@ files = [
 ]
 
 [package.dependencies]
-numpy = {version = ">=1.17", markers = "python_version != \"3.10\" or platform_system != \"Windows\" or platform_python_implementation == \"PyPy\""}
+numpy = [
+    {version = ">=1.17", markers = "python_version != \"3.10\" or platform_system != \"Windows\" or platform_python_implementation == \"PyPy\""},
+    {version = ">=1.22.3", markers = "python_version == \"3.10\" and platform_system == \"Windows\" and platform_python_implementation != \"PyPy\""},
+]
 packaging = ">=21.3"
 pandas = ">=0.25"
 patsy = ">=0.5.2"
-scipy = [
-    {version = ">=1.3", markers = "(python_version > \"3.9\" or platform_system != \"Windows\" or platform_machine != \"x86\") and python_version < \"3.12\""},
-    {version = ">=1.3,<1.8", markers = "python_version == \"3.7\""},
-    {version = ">=1.3,<1.9", markers = "python_version == \"3.8\" and platform_system == \"Windows\" and platform_machine == \"x86\" or python_version == \"3.9\" and platform_system == \"Windows\" and platform_machine == \"x86\""},
-]
+scipy = {version = ">=1.3", markers = "(python_version > \"3.9\" or platform_system != \"Windows\" or platform_machine != \"x86\") and python_version < \"3.12\""}
 
 [package.extras]
 build = ["cython (>=0.29.32)"]
@@ -1681,7 +1621,7 @@ files = [
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1722,45 +1662,11 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
-[[package]]
-name = "typed-ast"
-version = "1.5.4"
-description = "a fork of Python 2 and 3 ast modules with type comment support"
-category = "dev"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "typed_ast-1.5.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:669dd0c4167f6f2cd9f57041e03c3c2ebf9063d0757dc89f79ba1daa2bfca9d4"},
-    {file = "typed_ast-1.5.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:211260621ab1cd7324e0798d6be953d00b74e0428382991adfddb352252f1d62"},
-    {file = "typed_ast-1.5.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:267e3f78697a6c00c689c03db4876dd1efdfea2f251a5ad6555e82a26847b4ac"},
-    {file = "typed_ast-1.5.4-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:c542eeda69212fa10a7ada75e668876fdec5f856cd3d06829e6aa64ad17c8dfe"},
-    {file = "typed_ast-1.5.4-cp310-cp310-win_amd64.whl", hash = "sha256:a9916d2bb8865f973824fb47436fa45e1ebf2efd920f2b9f99342cb7fab93f72"},
-    {file = "typed_ast-1.5.4-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:79b1e0869db7c830ba6a981d58711c88b6677506e648496b1f64ac7d15633aec"},
-    {file = "typed_ast-1.5.4-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a94d55d142c9265f4ea46fab70977a1944ecae359ae867397757d836ea5a3f47"},
-    {file = "typed_ast-1.5.4-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:183afdf0ec5b1b211724dfef3d2cad2d767cbefac291f24d69b00546c1837fb6"},
-    {file = "typed_ast-1.5.4-cp36-cp36m-win_amd64.whl", hash = "sha256:639c5f0b21776605dd6c9dbe592d5228f021404dafd377e2b7ac046b0349b1a1"},
-    {file = "typed_ast-1.5.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:cf4afcfac006ece570e32d6fa90ab74a17245b83dfd6655a6f68568098345ff6"},
-    {file = "typed_ast-1.5.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed855bbe3eb3715fca349c80174cfcfd699c2f9de574d40527b8429acae23a66"},
-    {file = "typed_ast-1.5.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6778e1b2f81dfc7bc58e4b259363b83d2e509a65198e85d5700dfae4c6c8ff1c"},
-    {file = "typed_ast-1.5.4-cp37-cp37m-win_amd64.whl", hash = "sha256:0261195c2062caf107831e92a76764c81227dae162c4f75192c0d489faf751a2"},
-    {file = "typed_ast-1.5.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2efae9db7a8c05ad5547d522e7dbe62c83d838d3906a3716d1478b6c1d61388d"},
-    {file = "typed_ast-1.5.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7d5d014b7daa8b0bf2eaef684295acae12b036d79f54178b92a2b6a56f92278f"},
-    {file = "typed_ast-1.5.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:370788a63915e82fd6f212865a596a0fefcbb7d408bbbb13dea723d971ed8bdc"},
-    {file = "typed_ast-1.5.4-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4e964b4ff86550a7a7d56345c7864b18f403f5bd7380edf44a3c1fb4ee7ac6c6"},
-    {file = "typed_ast-1.5.4-cp38-cp38-win_amd64.whl", hash = "sha256:683407d92dc953c8a7347119596f0b0e6c55eb98ebebd9b23437501b28dcbb8e"},
-    {file = "typed_ast-1.5.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4879da6c9b73443f97e731b617184a596ac1235fe91f98d279a7af36c796da35"},
-    {file = "typed_ast-1.5.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3e123d878ba170397916557d31c8f589951e353cc95fb7f24f6bb69adc1a8a97"},
-    {file = "typed_ast-1.5.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ebd9d7f80ccf7a82ac5f88c521115cc55d84e35bf8b446fcd7836eb6b98929a3"},
-    {file = "typed_ast-1.5.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:98f80dee3c03455e92796b58b98ff6ca0b2a6f652120c263efdba4d6c5e58f72"},
-    {file = "typed_ast-1.5.4-cp39-cp39-win_amd64.whl", hash = "sha256:0fdbcf2fef0ca421a3f5912555804296f0b0960f0418c440f5d6d3abb549f3e1"},
-    {file = "typed_ast-1.5.4.tar.gz", hash = "sha256:39e21ceb7388e4bb37f4c679d72707ed46c2fbf2a5609b8b8ebc4b067d977df2"},
-]
-
 [[package]]
 name = "typing-extensions"
 version = "4.5.0"
 description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1853,26 +1759,11 @@ files = [
     {file = "wrapt-1.15.0.tar.gz", hash = "sha256:d06730c6aed78cee4126234cf2d071e01b44b915e725a6cb439a879ec9754a3a"},
 ]
 
-[[package]]
-name = "zipp"
-version = "3.15.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"},
-    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
-
 [extras]
+knn = ["nmslib"]
 spark = ["pyspark"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.7.2, <3.11.0"
-content-hash = "02758672434b7dd88b696da7545793eec9ead1eff5357161c3afabedd76b9f7c"
+python-versions = ">=3.8, <3.13"
+content-hash = "6e9ca734137c93abc80756be23ebc670efb11c122c4fc3ddfef26e5c07db963f"
diff --git a/pyproject.toml b/pyproject.toml
index 323e7a6..6675646 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,10 +39,10 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.7.2, <3.11.0"
+python = ">=3.8, <3.13"
 jinja2 = "^3.0.0"
 joblib = "^1.1.0"
-nmslib = { version = "^2.0.4", optional = true }
+nmslib = { version = "^2.0.4", optional = true, python = "<3.11" }
 numpy = ">=1.19.5, <2.0.0"
 pandas = ">=0.25.3, <2.0.0"
 pyspark = { version = "^3.2", optional = true }