Evaluate tests (#33977)

* Evaluate tests

* Update setup.py

* Update setup.py

* Update test_evaluate_e2e.py

Author: singankit

sdk/ai/azure-ai-generative/setup.py

@@ -75,7 +75,7 @@
     install_requires=[
         # NOTE: To avoid breaking changes in a major version bump, all dependencies should pin an upper bound if possible.
         "azure-ai-resources<2.0.0,>=1.0.0b1",
-        "mlflow-skinny<3",
+        "mlflow-skinny<3,>=1.27.0",
         "opencensus-ext-azure~=1.0",
         "opencensus-ext-logging<=0.1.1",
     ],

sdk/ai/azure-ai-generative/tests/evaluate/e2etests/test_evaluate_e2e.py

@@ -0,0 +1,58 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# --------------------------------------------------------
+
+import logging
+import os
+
+import numpy as np
+import pandas as pd
+import pytest
+from devtools_testutils import AzureRecordedTestCase, recorded_by_proxy
+from azure.ai.generative.evaluate import evaluate
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.mark.e2etest
+@pytest.mark.live_test_only
+@pytest.mark.usefixtures("recorded_test")
+class TestEvaluate(AzureRecordedTestCase):
+
+    def test_evaluate_built_in_metrics(self, e2e_openai_api_base, e2e_openai_api_key, e2e_openai_completion_deployment_name, tmpdir):
+        test_data = [
+            {"question": "How do you create a run?", "context": "AML API only",
+             "answer": "To create a run using the Azure Machine Learning API, you first need to create an Experiment. Once you have an experiment, you can create a Run object that is associated with that experiment. Here is some Python code that demonstrates this process:\n\n```\nfrom azureml.core import Experiment, Run\nfrom azureml.core.workspace import Workspace\n\n# Define workspace and experiment\nws = Workspace.from_config()\nexp = Experiment(workspace=ws, name='my_experiment')\n\n# Create a new run\nrun = exp.start_logging()\n```\n\nIn this code, the `from_config()` method reads the configuration file that you created when you set up your Azure Machine Learning workspace. The `Experiment` constructor creates an Experiment object that is associated with your workspace, and the `start_logging()` method creates a new Run object that is associated with the Experiment. Now you can use the `run` object to log metrics, upload files, and track other information related to your machine learning experiment."},
+            {"question": "How do you log a model?", "context": "Logging can be done using any OSS Sdk",
+             "answer": "There are a few ways to log models in Azure Machine Learning. \n\nOne way is to use the `register_model()` method of the `Run` object. The `register_model()` method logs a model file in the Azure Machine Learning service workspace and makes it available for deployment. Here's an example:\n\n```python\nfrom azureml.core import Model\n\nmodel_path = '.\/outputs\/my_model.pkl'\nmodel = Model.register(workspace=ws, model_path=model_path, model_name='my_model')\n```\n\nThis code registers the model file located at `model_path` to the Azure Machine Learning service workspace with the name `my_model`. \n\nAnother way to log a model is to save it as an output of a `Run`. If your model generation code is part of a script or Jupyter notebook that runs as an Azure Machine Learning experiment, you can save the model file as an output of the `Run` object. Here's an example:\n\n```python\nfrom sklearn.linear_model import LogisticRegression\nfrom azureml.core.run import Run\n\n# Initialize a run object\nrun = Run.get_context()\n\n# Train your model\nX_train, y_train = ...\nclf = LogisticRegression().fit(X_train, y_train)\n\n# Save the model to the Run object's outputs directory\nmodel_path = 'outputs\/model.pkl'\njoblib.dump(value=clf, filename=model_path)\n\n# Log the model as a run artifact\nrun.upload_file(name=model_path, path_or_stream=model_path)\n```\n\nIn this code, `Run.get_context()` retrieves the current run context object, which you can use to track metadata and metrics for the run. After training your model, you can use `joblib.dump()` to save the model to a file, and then log the file as an artifact of the run using `run.upload_file()`."},
+        ]
+
+        with tmpdir.as_cwd():
+            output_path = tmpdir + "/evaluation_output"
+
+            result = evaluate(  # This will log metric/artifacts using mlflow
+                evaluation_name="rag-chat-1",
+                data=test_data,
+                task_type="qa",
+                metrics_list=["gpt_groundedness"],
+                model_config={
+                    "api_version": "2023-07-01-preview",
+                    "api_base": e2e_openai_api_base,
+                    "api_type": "azure",
+                    "api_key": e2e_openai_api_key,
+                    "deployment_id": e2e_openai_completion_deployment_name,
+                },
+                data_mapping={
+                    "questions": "question",
+                    "contexts": "context",
+                    "y_pred": "answer",
+                    "y_test": "truth",
+                },
+                output_path=output_path
+            )
+
+            metrics_summary = result.metrics_summary
+            tabular_result = pd.read_json(os.path.join(output_path, "eval_results.jsonl"), lines=True)
+
+            assert "mean_gpt_groundedness" in metrics_summary.keys()
+            assert metrics_summary.get("mean_gpt_groundedness") == np.nanmean(tabular_result["gpt_groundedness"])