Increase maxmimum allowed

CHANGELOG.md

@@ -3,7 +3,6 @@
 ## [4.0.0rc1] - 2024-05-22
 - **Enhancement**
   - Add support for python 3.11 and 3.10
-  - Decrease maxmimum allowed `scikit-learn`
 
 ## [3.0.0] - 2023-11-08
 - **Enhancement**

install_all_deps.sh

@@ -0,0 +1,16 @@
+
+
+REQUIREMENTS_ARR="requirements.txt requirements_catboost.txt requirements_demos.txt requirements_lgbm.txt requirements_tools.txt requirements_xgboost.txt requirements_test.txt"
+
+install_requirements() {
+    echo "Installing requirements..."
+    for req in $REQUIREMENTS_ARR; do
+        echo "Installing $req..."
+        pip install -r $req --force-reinstall
+    done
+
+    echo "Installing project in development mode..."
+    pip install -e . --force-reinstall
+}
+
+install_requirements

requirements.txt

@@ -1,6 +1,6 @@
 joblib>=1.3.2,<2
 numpy>=1.24.4,<2
 pandas>=2,<3
-scikit-learn>=1,<1.4
+scikit-learn>=1,<2
 statsmodels>=0.14.0,<1
 toolz>=0.12.0,<1

src/fklearn/validation/evaluators.py

@@ -1065,7 +1065,7 @@ def logistic_coefficient_evaluator(test_data: pd.DataFrame,
     if eval_name is None:
         eval_name = "logistic_coefficient_evaluator__" + target_column
 
-    score = LogisticRegression(penalty='none', multi_class="ovr").fit(test_data[[prediction_column]],
+    score = LogisticRegression(penalty=None, multi_class="ovr").fit(test_data[[prediction_column]],
                                                                       test_data[target_column]).coef_[0][0]
 
     return {eval_name: score}

tests/causal/test_effects.py

@@ -62,4 +62,9 @@ def test_logistic_coefficient_effect():
     ))
 
     result = logistic_coefficient_effect(df, treatment_column="t", outcome_column="y")
-    assert round(result, 3) == 20.645
+
+    SKLEARN_GTE_1_4_RESULT = 17.922
+    SKLEARN_LT_1_4_RESULT = 20.645
+    expected_result_range = {SKLEARN_GTE_1_4_RESULT, SKLEARN_LT_1_4_RESULT}
+
+    assert round(result, 3) in expected_result_range

tests/validation/test_evaluators.py

@@ -509,4 +509,8 @@ def test_logistic_coefficient_evaluator():
 
     result = logistic_coefficient_evaluator(predictions)
 
-    assert round(result['logistic_coefficient_evaluator__target'], 3) == 20.645
+    SKLEARN_GTE_1_4_RESULT = 17.922
+    SKLEARN_LT_1_4_RESULT = 20.645
+    expected_result_range = {SKLEARN_GTE_1_4_RESULT, SKLEARN_LT_1_4_RESULT}
+
+    assert round(result['logistic_coefficient_evaluator__target'], 3) in expected_result_range