Merge branch 'main' into onnx

CHANGELOG.rst

@@ -12,14 +12,17 @@ Changelog
 
 **New features**
 
-* Added ``scoring`` parameter to :meth:`metalearners.metalearner.MetaLearner.evaluate` and
-  implemented the abstract method for the :class:`metalearners.XLearner` and
+* Add ``scoring`` parameter to :meth:`metalearners.metalearner.MetaLearner.evaluate` and
+  implement the abstract method for the :class:`metalearners.XLearner` and
   :class:`metalearners.DRLearner`.
 
 **Other changes**
 
 * Increase lower bound on ``scikit-learn`` from 1.3 to 1.4.
 
+* Drop the run dependency on ``git_root``.
+
+
 0.5.0 (2024-06-18)
 ------------------
 

benchmarks/benchmark.py

@@ -115,7 +115,7 @@ def _twins_data(rng, use_numpy=False, test_fraction=0.2):
         feature_columns,
         categorical_feature_columns,
         true_cate_column,
-    ) = load_twins_data(rng)
+    ) = load_twins_data(Path(git_root()) / "data" / "twins.zip", rng)
 
     covariates = chosen_df[feature_columns]
     observed_outcomes = chosen_df[outcome_column]

metalearners/_utils.py

@@ -4,10 +4,10 @@
 from collections.abc import Callable
 from inspect import signature
 from operator import le, lt
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
-from git_root import git_root
 from sklearn.base import check_array, check_X_y, is_classifier, is_regressor
 from sklearn.ensemble import (
     HistGradientBoostingClassifier,
@@ -326,10 +326,12 @@ def validate_valid_treatment_variant_not_control(
         )
 
 
-def load_mindset_data() -> tuple[pd.DataFrame, str, str, list[str], list[str]]:
+def load_mindset_data(
+    path: Path,
+) -> tuple[pd.DataFrame, str, str, list[str], list[str]]:
     # TODO: Optionally make this function work with a URL instead of a file system reference.
     # That way, we don't need to package the data for someone to be able to use this function.
-    df = pd.read_csv(git_root("data/learning_mindset.zip"))
+    df = pd.read_csv(path)
     outcome_column = "achievement_score"
     treatment_column = "intervention"
     feature_columns = [
@@ -363,11 +365,12 @@ def load_mindset_data() -> tuple[pd.DataFrame, str, str, list[str], list[str]]:
 
 
 def load_twins_data(
+    path: Path,
     rng: np.random.Generator,
 ) -> tuple[pd.DataFrame, str, str, list[str], list[str], str]:
     # TODO: Optionally make this function work with a URL instead of a file system reference.
     # That way, we don't need to package the data for someone to be able to use this function.
-    df = pd.read_csv(git_root("data/twins.zip"))
+    df = pd.read_csv(path)
     drop_columns = [
         "bord",
         "brstate_reg",

pyproject.toml

@@ -26,7 +26,6 @@ dependencies = [
     "pandas",
     "numpy",
     "typing-extensions",
-    "git_root",
     "shap",
     "joblib>=1.2.0"
 ]

tests/conftest.py

@@ -1,9 +1,12 @@
 # Copyright (c) QuantCo 2024-2024
 # SPDX-License-Identifier: BSD-3-Clause
 
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
 import pytest
+from git_root import git_root
 from sklearn.calibration import CalibratedClassifierCV
 from sklearn.discriminant_analysis import (
     LinearDiscriminantAnalysis,
@@ -194,7 +197,7 @@ def rng():
 
 @pytest.fixture(scope="session")
 def mindset_data():
-    return load_mindset_data()
+    return load_mindset_data(Path(git_root()) / "data" / "learning_mindset.zip")
 
 
 @pytest.fixture(scope="session")
@@ -207,7 +210,7 @@ def twins_data():
         feature_columns,
         categorical_feature_columns,
         _,
-    ) = load_twins_data(rng)
+    ) = load_twins_data(Path(git_root()) / "data" / "twins.zip", rng)
     return (
         chosen_df,
         outcome_column,