test fix

cognitive/src/test/python/synapsemltest/services/langchain/test_LangchainTransform.py

@@ -127,19 +127,22 @@ class LangchainTransformTest(unittest.TestCase):
         # construct langchain transformer using the chain defined above. And test if the generated
         # column has the expected result.
         dataframes_to_test = spark.createDataFrame(
-            [(0, "people on disability don't deserve the money")]
+            [(0, "people on disability don't deserve the money")], ["label", "technology"]
         )
         self._assert_chain_output_invalid_case(
             self.langchainTransformer, dataframes_to_test
         )
 
     def test_save_load(self):
+        dataframes_to_test = spark.createDataFrame(
+            [(0, "docker"), (0, "spark"), (1, "python")], ["label", "technology"]
+        )
         temp_dir = "tmp"
         os.mkdir(temp_dir)
         path = os.path.join(temp_dir, "langchainTransformer")
         self.langchainTransformer.save(path)
         loaded_transformer = LangchainTransformer.load(path)
-        self._assert_chain_output(loaded_transformer)
+        self._assert_chain_output(loaded_transformer, dataframes_to_test)
 
 
 if __name__ == "__main__":

docs/Explore Algorithms/AI Services/Quickstart - Document Question and Answering with PDFs.ipynb

@@ -886,7 +886,6 @@
    "source": [
     "# Define a Question Answering chain function using LangChain\n",
     "def qa_chain_func():\n",
-    "\n",
     "    # Define llm model\n",
     "    llm = AzureOpenAI(\n",
     "        deployment_name=aoai_deployment_name_query,\n",

docs/Explore Algorithms/Hyperparameter Tuning/HyperOpt.ipynb

@@ -246,7 +246,6 @@
     "    # Use MLflow to track training.\n",
     "    # Specify \"nested=True\" since this single model will be logged as a child run of Hyperopt's run.\n",
     "    with mlflow.start_run(nested=True):\n",
-    "\n",
     "        lgr = LightGBMRegressor(\n",
     "            objective=\"quantile\",\n",
     "            alpha=alpha,\n",

docs/Explore Algorithms/Responsible AI/Image Explainers.ipynb

@@ -56,9 +56,9 @@
     "    green_value = np.percentile(weights, green_threshold)\n",
     "    img = Image.fromarray(image_rgb_array, mode=\"RGB\").convert(\"RGBA\")\n",
     "    image_array = np.asarray(img).copy()\n",
-    "    for (sp, v) in zip(superpixels, weights):\n",
+    "    for sp, v in zip(superpixels, weights):\n",
     "        if v > green_value:\n",
-    "            for (x, y) in sp:\n",
+    "            for x, y in sp:\n",
     "                image_array[y, x, 1] = 255\n",
     "                image_array[y, x, 3] = 200\n",
     "    plt.clf()\n",

docs/Explore Algorithms/Responsible AI/Quickstart - Snow Leopard Detection.ipynb

@@ -325,9 +325,9 @@
     "    mean_weight = np.percentile(weights, 90)\n",
     "    img = (PIL.Image.open(io.BytesIO(image_bytes))).convert(\"RGBA\")\n",
     "    image_array = np.asarray(img).copy()\n",
-    "    for (sp, w) in zip(superpixels, weights):\n",
+    "    for sp, w in zip(superpixels, weights):\n",
     "        if w > mean_weight:\n",
-    "            for (x, y) in sp:\n",
+    "            for x, y in sp:\n",
     "                image_array[y, x, 1] = 255\n",
     "                image_array[y, x, 3] = 200\n",
     "    plt.clf()\n",