Merge branch 'main' into GSK-1567-add-number-to-word-transformation

Author: mattbit

.github/workflows/build-python.yml

@@ -60,11 +60,11 @@ jobs:
         langchain_minimal: [false]
         # https://docs.github.com/en/actions/using-github-hosted-runners/about-github-hosted-runners#supported-runners-and-hardware-resources
         include:
-          - python-version: "3.10"
-            os: windows-2019
-            pydantic_v1: false
-            pandas_v1: false
-            langchain_minimal: false
+          # - python-version: "3.10" # Deactivating windows-2019, since it's trying to use python 3.7 to install PDM. Maybe try to reactivate later ?
+          #   os: windows-2019
+          #   pydantic_v1: false
+          #   pandas_v1: false
+          #   langchain_minimal: false
           - python-version: "3.10"
             os: windows-2022
             pydantic_v1: false
@@ -250,6 +250,7 @@ jobs:
         uses: pdm-project/setup-pdm@v3
         with:
           python-version: '3.10'
+          version: '2.10.4' # Fix to repair the CI, use latest version when fixed on pdm
           cache: false
       - name: Build wheel
         run: pdm build

giskard/scanner/robustness/text_transformations.py

@@ -1,6 +1,5 @@
 import itertools
 import json
-import random
 import re
 from pathlib import Path
 
@@ -71,7 +70,7 @@ def execute(self, data: pd.DataFrame) -> pd.DataFrame:
 class TextTypoTransformation(TextTransformation):
     name = "Add typos"
 
-    def __init__(self, column, rate=0.05, min_length=10, rng_seed=None):
+    def __init__(self, column, rate=0.05, min_length=10, rng_seed=1729):
         super().__init__(column)
         from .entity_swap import typos
 
@@ -151,10 +150,11 @@ def make_perturbation(self, text):
 class TextLanguageBasedTransformation(TextTransformation):
     needs_dataset = True
 
-    def __init__(self, column):
+    def __init__(self, column, rng_seed=1729):
         super().__init__(column)
         self._lang_dictionary = dict()
         self._load_dictionaries()
+        self.rng = np.random.default_rng(seed=rng_seed)
 
     def _load_dictionaries(self):
         raise NotImplementedError()
@@ -253,7 +253,7 @@ def make_perturbation(self, row):
                 mask_value = f"__GSK__ENT__RELIGION__{n_list}__{n_term}__"
                 text, num_rep = re.subn(rf"\b{re.escape(term)}(s?)\b", rf"{mask_value}\1", text, flags=re.IGNORECASE)
                 if num_rep > 0:
-                    i = (n_term + 1 + random.randrange(len(term_list) - 1)) % len(term_list)
+                    i = (n_term + 1 + self.rng.choice(len(term_list) - 1)) % len(term_list)
                     replacement = term_list[i]
                     replacements.append((mask_value, replacement))
 
@@ -295,7 +295,7 @@ def make_perturbation(self, row):
                 )
                 if num_rep > 0:
                     r_income_type = "low-income" if income_type == "high-income" else "high-income"
-                    replacement = random.choice(nationalities_word_dict[entity_type][r_income_type])
+                    replacement = self.rng.choice(nationalities_word_dict[entity_type][r_income_type])
                     replacements.append((mask_value, replacement))
 
         # Replace masks

tests/scan/test_text_transformations.py

@@ -1,4 +1,3 @@
-import random
 import re
 
 import pandas as pd
@@ -202,9 +201,8 @@ def test_religion_based_transformation():
     )
     from giskard.scanner.robustness.text_transformations import TextReligionTransformation
 
-    t = TextReligionTransformation(column="text")
+    t = TextReligionTransformation(column="text", rng_seed=10)
 
-    random.seed(0)
     transformed = dataset.transform(t)
     transformed_text = transformed.df.text.values
 
@@ -213,12 +211,12 @@ def test_religion_based_transformation():
         "mois de ramadan."
     )
     assert (
-        transformed_text[1] == "Une partie des chrétiens commémorent ce vendredi 5 mai la naissance, l’éveil et la "
-        "mort de muhammad, dit « le Bouddha »"
+        transformed_text[1] == "Une partie des hindous commémorent ce vendredi 5 mai la naissance, l’éveil et la "
+        "mort de abraham, dit « le Bouddha »"
     )
     assert (
         transformed_text[2] == "Signs have also been placed in the direction of kumbh mela along one of the Peak "
-        "District’s most popular hiking routes, Cave Dale, to help christians combine prayer "
+        "District’s most popular hiking routes, Cave Dale, to help jews combine prayer "
         "with enjoying the outdoors."
     )
     assert (
@@ -228,9 +226,6 @@ def test_religion_based_transformation():
 
 
 def test_country_based_transformation():
-    import random
-
-    random.seed(10)
     dataset = _dataset_from_dict(
         {
             "text": [
@@ -244,31 +239,30 @@ def test_country_based_transformation():
     )
     from giskard.scanner.robustness.text_transformations import TextNationalityTransformation
 
-    t = TextNationalityTransformation(column="text")
+    t = TextNationalityTransformation(column="text", rng_seed=0)
 
     transformed = dataset.transform(t)
     transformed_text = transformed.df.text.values
 
     assert (
-        transformed_text[0] == "Les musulmans de Eswatini fêtent vendredi 21 avril la fin du "
+        transformed_text[0] == "Les musulmans de Saint Thomas et Prince fêtent vendredi 21 avril la fin du "
         "jeûne pratiqué durant le mois de ramadan."
     )
-    assert transformed_text[1] == "Des incendies ravagent l'Congo depuis la fin août 2019."
+    assert transformed_text[1] == "Des incendies ravagent l'Liban depuis la fin août 2019."
     assert (
-        transformed_text[2] == "Bali is an Libyan island known for its forested volcanic mountains, iconic"
+        transformed_text[2] == "Bali is an Singaporean island known for its forested volcanic mountains, iconic"
         " rice paddies, beaches and coral reefs. The island is home to religious sites "
         "such as cliffside Uluwatu Temple"
     )
     assert (
         transformed_text[3]
-        == "President Joe Biden visited U.S.'s capital for the first time since Nigeria invaded the country"
+        == "President Joe Biden visited UAE's capital for the first time since Syria invaded the country"
     )
 
 
 def test_country_based_transformation_edge_cases():
     from giskard.scanner.robustness.text_transformations import TextNationalityTransformation
 
-    random.seed(0)
     df = pd.DataFrame(
         {
             "text": [
@@ -281,7 +275,7 @@ def test_country_based_transformation_edge_cases():
         }
     )
 
-    t = TextNationalityTransformation(column="text")
+    t = TextNationalityTransformation(column="text", rng_seed=0)
 
     t1 = t.make_perturbation(df.iloc[0])
     t2 = t.make_perturbation(df.iloc[1])