Add compatibility for python 3.12

.github/workflows/build-python.yml

@@ -54,7 +54,7 @@ jobs:
     strategy:
       fail-fast: false # Do not stop when any job fails
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest]
         pydantic_v1: [false]
         pandas_v1: [false]
@@ -72,7 +72,7 @@ jobs:
             pandas_v1: false
             langchain_minimal: false
           - python-version: "3.10"
-            os: macos-12
+            os: macos-latest
             pydantic_v1: false
             pandas_v1: false
             langchain_minimal: false
@@ -134,7 +134,14 @@ jobs:
         run: |
           df -h
 
+      - name: Install dependencies (Windows)
+        if: ${{ matrix.os == 'windows-2022' }}
+        run: |
+          pdm install -G :all --without tensorflow
+          pdm list
+
       - name: Install dependencies
+        if: ${{ matrix.os != 'windows-2022' }}
         run: |
           pdm install -G :all
           pdm list
@@ -144,10 +151,12 @@ jobs:
           df -h
 
       - name: Re-install lightgbm from sources for MacOS
-        if: ${{ matrix.os == 'macos-12' }}
+        if: ${{ matrix.os == 'macos-latest' }}
         run: |
           pdm run pip uninstall lightgbm -y
           pdm run pip install --no-binary lightgbm lightgbm --config-settings=cmake.define.USE_OPENMP=OFF
+          brew install libomp
+
       - name: Install pydantic v1
         if: ${{ matrix.pydantic_v1 }}
         run: |

docs/reference/notebooks/LLM_QA_IPCC.ipynb

@@ -368,7 +368,7 @@
    },
    "outputs": [],
    "source": [
-    "results = scan(giskard_model, giskard_dataset, only=\"hallucination\")"
+    "results = scan(giskard_model, giskard_dataset, only=[\"hallucination\"])"
    ]
   },
   {

giskard/models/automodel.py

@@ -21,7 +21,7 @@
     ("giskard.models.langchain", "LangchainModel"): [("langchain.chains.base", "Chain")],
     ("giskard.models.catboost", "CatboostModel"): [("catboost", "CatBoost")],
     ("giskard.models.pytorch", "PyTorchModel"): [("torch.nn", "Module")],
-    ("giskard.models.tensorflow", "TensorFlowModel"): [("tensorflow", "Module")],
+    ("giskard.models.tensorflow", "TensorFlowModel"): [("tensorflow", "Module"), ("keras", "Model")],
 }
 
 

giskard/testing/tests/statistic.py

@@ -1,4 +1,5 @@
 """Statistical tests"""
+
 from typing import Optional
 
 import numbers
@@ -498,7 +499,7 @@ def test_nominal_association(
 def test_cramer_v(
     model: BaseModel, dataset: Dataset, slicing_function: SlicingFunction, threshold: float = 0.5
 ) -> TestResult:
-    """Tests for nominal association between a dataset slice and the model predictions using Cramer's V.
+    r"""Tests for nominal association between a dataset slice and the model predictions using Cramer's V.
 
     Cramer's V is a statistical measure used to assess the strength and nature of association between two
     categorical variables. It is an extension of the chi-squared test for independence and takes into account the
@@ -513,7 +514,7 @@ def test_cramer_v(
 
     .. math::
 
-       V = \sqrt{\\frac{\chi^2}{n \cdot \min(k-1, r-1)}}
+       V = \sqrt{\frac{\chi^2}{n \cdot \min(k-1, r-1)}}
 
     where: :math:`\chi^2` is the chi-squared statistic for the two variables. n is the total number of observations.
     :math:`k` is the
@@ -550,7 +551,7 @@ def test_cramer_v(
 def test_mutual_information(
     model: BaseModel, dataset: Dataset, slicing_function: SlicingFunction, threshold: float = 0.5
 ) -> TestResult:
-    """Tests for nominal association between a dataset slice and the model predictions using Mutual Information.
+    r"""Tests for nominal association between a dataset slice and the model predictions using Mutual Information.
 
     The mutual information statistical test is a measure used to quantify the degree of association between two
     categorical variables. It assesses how much information about one variable can be gained from knowing the other
@@ -565,7 +566,7 @@ def test_mutual_information(
 
     .. math::
 
-       I(X;Y) = \sum_{x \in X} \sum_{y \in Y} p(x, y) \cdot \log ( \\frac{p(x, y)}{p(x) \cdot p(y)})
+       I(X;Y) = \sum_{x \in X} \sum_{y \in Y} p(x, y) \cdot \log ( \frac{p(x, y)}{p(x) \cdot p(y)})
 
     where: :math:`p(x,y)` is the joint probability mass function of variables :math:`X` and :math:`Y`. :math:`p(x)` and
     :math:`p(y)` are the marginal probability mass functions of variables :math:`X` and :math:`Y` respectively.
@@ -601,7 +602,7 @@ def test_mutual_information(
 def test_theil_u(
     model: BaseModel, dataset: Dataset, slicing_function: SlicingFunction, threshold: float = 0.5
 ) -> TestResult:
-    """Tests for nominal association between a dataset slice and the model predictions using Theil's U.
+    r"""Tests for nominal association between a dataset slice and the model predictions using Theil's U.
 
     Theil's U statistical test for nominal association is a measure used to assess the strength and direction
     of association between two categorical variables. It quantifies the inequality in the distribution of one variable
@@ -616,7 +617,7 @@ def test_theil_u(
 
     .. math::
 
-       U = \\frac{H(x|y) - H(y|x)}{H(x)}
+       U = \frac{H(x|y) - H(y|x)}{H(x)}
 
     where :math:`H(x|y)`, :math:`H(y|x)` are the conditional entropies of the two variables and :math:`H(x)` is the
     entropy of the first variable.