feat: GenAI Evaluation: Release GenAI Evaluation SDK Autorater Config/Tuning/Evaluation to vertexai.preview module.

PiperOrigin-RevId: 720725088

Author: vertex-sdk-bot

Diff for: setup.py

@@ -151,6 +151,8 @@
 evaluation_extra_require = [
     "pandas >= 1.0.0",
     "tqdm>=4.23.0",
+    "scikit-learn<1.6.0; python_version<='3.10'",
+    "scikit-learn; python_version>'3.10'",
 ]
 
 langchain_extra_require = [

Diff for: tests/unit/vertexai/test_autorater_utils.py

@@ -0,0 +1,349 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+"""Unit tests for autorater utils."""
+
+import copy
+import datetime
+from typing import Any, Dict, List
+from unittest import mock
+import uuid
+
+from google import auth
+from google.auth import credentials as auth_credentials
+import vertexai
+from google.cloud.aiplatform import compat
+from google.cloud.aiplatform import initializer
+from google.cloud.aiplatform import utils as aiplatform_utils
+from google.cloud.aiplatform_v1beta1.services import gen_ai_tuning_service
+from google.cloud.aiplatform_v1beta1.types import job_state
+from google.cloud.aiplatform_v1beta1.types import (
+    tuning_job as gca_tuning_job,
+)
+from vertexai.preview import tuning
+from vertexai.preview.evaluation import autorater_utils
+from vertexai.preview.evaluation.metrics import pairwise_metric
+from vertexai.preview.evaluation.metrics import pointwise_metric
+import numpy as np
+import pandas as pd
+import pytest
+
+
+AutoraterConfig = autorater_utils.AutoraterConfig
+PointwiseMetric = pointwise_metric.PointwiseMetric
+PairwiseMetric = pairwise_metric.PairwiseMetric
+
+_TEST_PROJECT = "test-project"
+_TEST_LOCATION = "us-central1"
+
+
+_global_tuning_jobs: Dict[str, gca_tuning_job.TuningJob] = {}
+_SCORE = "score"
+_METRIC = "metric"
+_PAIRWISE_CHOICE = "pairwise_choice"
+_HUMAN_RATING = "human_rating"
+_HUMAN_PAIRWISE_CHOICE = "human_pairwise_choice"
+_ACCURACY_BALANCED = "accuracy_balanced"
+_F1_SCORE_BALANCED = "f1_score_balanced"
+_CONFUSION_MATRIX = "confusion_matrix"
+_CONFUSION_MATRIX_LABELS = "confusion_matrix_labels"
+
+
+@pytest.fixture
+def google_auth_mock():
+    with mock.patch.object(auth, "default") as google_auth_default_mock:
+        google_auth_default_mock.return_value = (
+            auth_credentials.AnonymousCredentials(),
+            _TEST_PROJECT,
+        )
+        yield google_auth_default_mock
+
+
+class MockGenAiTuningServiceClient(gen_ai_tuning_service.GenAiTuningServiceClient):
+    """Mock GenAiTuningServiceClient."""
+
+    @property
+    def _tuning_jobs(self) -> Dict[str, gca_tuning_job.TuningJob]:
+        return _global_tuning_jobs
+
+    def create_tuning_job(
+        self,
+        *,
+        parent: str,
+        tuning_job: gca_tuning_job.TuningJob,
+        **_,
+    ) -> gca_tuning_job.TuningJob:
+        tuning_job = copy.deepcopy(tuning_job)
+        resource_id = uuid.uuid4().hex
+        resource_name = f"{parent}/tuningJobs/{resource_id}"
+        tuning_job.name = resource_name
+        current_time = datetime.datetime.now(datetime.timezone.utc)
+        tuning_job.tuned_model = gca_tuning_job.TunedModel(
+            model=f"{parent}/models/123",
+            endpoint=f"{parent}/endpoints/456",
+        )
+        tuning_job.state = job_state.JobState.JOB_STATE_SUCCEEDED
+        tuning_job.create_time = current_time
+        tuning_job.update_time = current_time
+        self._tuning_jobs[resource_name] = tuning_job
+        return tuning_job
+
+    def get_tuning_job(self, *, name: str, **_) -> gca_tuning_job.TuningJob:
+        tuning_job = self._tuning_jobs[name]
+        tuning_job = copy.deepcopy(tuning_job)
+        return tuning_job
+
+
+class MockTuningJobClientWithOverride(aiplatform_utils.ClientWithOverride):
+    _is_temporary = False
+    _default_version = compat.V1
+    _version_map = ((compat.V1, MockGenAiTuningServiceClient),)
+
+
+@pytest.mark.usefixtures("google_auth_mock")
+class TestAutoraterUtils:
+    """Unit tests for generative model tuning."""
+
+    def setup_method(self):
+        vertexai.init(
+            project=_TEST_PROJECT,
+            location=_TEST_LOCATION,
+        )
+
+    def teardown_method(self):
+        initializer.global_pool.shutdown(wait=True)
+
+    @mock.patch.object(
+        target=tuning.TuningJob,
+        attribute="client_class",
+        new=MockTuningJobClientWithOverride,
+    )
+    def test_tune_autorater(self):
+        """Test tune_autorater."""
+        autorater_config = autorater_utils.tune_autorater(
+            base_model="gemini-1.0-pro-001",
+            train_dataset="gs://test-bucket/train_dataset.jsonl",
+            validation_dataset="gs://test-bucket/validation_dataset.jsonl",
+            epochs=300,
+            learning_rate_multiplier=1.0,
+            time_out_hours=0,
+        )
+        assert autorater_config.autorater_model == (
+            "projects/test-project/locations/us-central1/endpoints/456"
+        )
+
+    def test_evaluate_autorater(self):
+        """Test evaluate_autorater."""
+        autorater_config = autorater_utils.AutoraterConfig(
+            autorater_model="projects/test-project/locations/us-central1/endpoints/456"
+        )
+        y_true_2_class = [1, 0, 1, 0, 1, 0]
+        y_pred_2_class = [1, 0, 0, 1, 1, 0]
+        y_true_multi_class = ["1", "2", "1", "1", "2", "3"]
+        y_pred_multi_class = [
+            "2",
+            "2",
+            "1",
+            "1",
+            "2",
+            "1",
+        ]
+        metrics = [
+            PairwiseMetric(
+                metric="test_pairwise_2_class",
+                metric_prompt_template="test prompt1",
+            ),
+            PointwiseMetric(
+                metric="test_pointwise_multi_class",
+                metric_prompt_template="test prompt2",
+            ),
+        ]
+        autorater_eval_result = autorater_utils.evaluate_autorater(
+            evaluate_autorater_input=pd.DataFrame(
+                {
+                    f"test_pairwise_2_class/{_PAIRWISE_CHOICE}": y_pred_2_class,
+                    f"test_pairwise_2_class/{_HUMAN_PAIRWISE_CHOICE}": y_true_2_class,
+                    f"test_pointwise_multi_class/{_SCORE}": y_pred_multi_class,
+                    f"test_pointwise_multi_class/{_HUMAN_RATING}": y_true_multi_class,
+                }
+            ),
+            eval_metrics=metrics,
+            autorater_config=autorater_config,
+            eval_dataset_metadata={
+                "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+                "eval_dataset_size": 6,
+            },
+            unused_params=10,
+        )
+        expected_eval_results = [
+            {
+                _METRIC: metrics[0].metric_name,
+                _ACCURACY_BALANCED: 2 / 3,
+                _F1_SCORE_BALANCED: 2 / 3,
+                _CONFUSION_MATRIX: np.array([[2, 1], [1, 2]]),
+                _CONFUSION_MATRIX_LABELS: ["0", "1"],
+            },
+            {
+                _METRIC: metrics[1].metric_name,
+                _ACCURACY_BALANCED: 5 / 9,
+                _F1_SCORE_BALANCED: 3 / 5,
+                _CONFUSION_MATRIX: np.array([[2, 1, 0], [0, 2, 0], [1, 0, 0]]),
+                _CONFUSION_MATRIX_LABELS: ["1.0", "2.0", "3.0"],
+            },
+        ]
+
+        assert _compare_autorater_eval_result(
+            autorater_eval_result.eval_result, expected_eval_results
+        )
+        assert autorater_eval_result.eval_dataset_metadata == {
+            "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+            "eval_dataset_size": 6,
+        }
+        assert autorater_eval_result.autorater_config == autorater_config
+        assert autorater_eval_result.unused_params == 10
+
+    def test_evaluate_autorater_exceed_pointwise_limit(self):
+        """Test evaluate_autorater."""
+        autorater_config = autorater_utils.AutoraterConfig(
+            autorater_model="projects/test-project/locations/us-central1/endpoints/456"
+        )
+        y_true_multi_class = [_ for _ in range(12)]
+        y_pred_multi_class = [_ for _ in range(12)]
+        metrics = [
+            PointwiseMetric(
+                metric="test_pointwise_multi_class",
+                metric_prompt_template="test prompt2",
+            ),
+        ]
+        autorater_eval_result = autorater_utils.evaluate_autorater(
+            evaluate_autorater_input=pd.DataFrame(
+                {
+                    f"test_pointwise_multi_class/{_SCORE}": y_pred_multi_class,
+                    f"test_pointwise_multi_class/{_HUMAN_RATING}": y_true_multi_class,
+                }
+            ),
+            eval_metrics=metrics,
+            autorater_config=autorater_config,
+            eval_dataset_metadata={
+                "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+                "eval_dataset_size": 6,
+            },
+            unused_params=10,
+        )
+        assert autorater_eval_result.eval_result == [
+            {
+                _METRIC: metrics[0].metric_name,
+                _ACCURACY_BALANCED: 1.0,
+                _F1_SCORE_BALANCED: 1.0,
+            },
+        ]
+        assert autorater_eval_result.eval_dataset_metadata == {
+            "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+            "eval_dataset_size": 6,
+        }
+        assert autorater_eval_result.autorater_config == autorater_config
+        assert autorater_eval_result.unused_params == 10
+
+    @mock.patch.object(
+        target=tuning.TuningJob,
+        attribute="client_class",
+        new=MockTuningJobClientWithOverride,
+    )
+    def test_evaluate_autorater_with_skipped_results(self):
+        """Test evaluate_autorater."""
+        autorater_config = autorater_utils.AutoraterConfig(
+            autorater_model="projects/test-project/locations/us-central1/endpoints/456"
+        )
+        y_true_2_class = ["1", "0", "1", "0", "1", "0", "Error", "1"]
+        y_pred_2_class = ["1", "0", "0", "1", "1", "0", "0", "ERROR"]
+        y_true_multi_class = ["1", "2", "1", 1, "2", "3", "1", "NaN"]
+        y_pred_multi_class = ["2", "2.0", "1", 1.0, "2", "1", "NaN", "1"]
+        metrics = [
+            PairwiseMetric(
+                metric="test_pairwise_2_class",
+                metric_prompt_template="test prompt1",
+            ),
+            PointwiseMetric(
+                metric="test_pointwise_multi_class",
+                metric_prompt_template="test prompt2",
+            ),
+        ]
+        autorater_eval_result = autorater_utils.evaluate_autorater(
+            evaluate_autorater_input=pd.DataFrame(
+                {
+                    f"test_pairwise_2_class/{_PAIRWISE_CHOICE}": y_pred_2_class,
+                    f"test_pairwise_2_class/{_HUMAN_PAIRWISE_CHOICE}": y_true_2_class,
+                    f"test_pointwise_multi_class/{_SCORE}": y_pred_multi_class,
+                    f"test_pointwise_multi_class/{_HUMAN_RATING}": y_true_multi_class,
+                }
+            ),
+            eval_metrics=metrics,
+            autorater_config=autorater_config,
+            eval_dataset_metadata={
+                "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+                "eval_dataset_size": 6,
+            },
+            unused_params=10,
+        )
+        expected_eval_results = [
+            {
+                _METRIC: metrics[0].metric_name,
+                _ACCURACY_BALANCED: 2 / 3,
+                _F1_SCORE_BALANCED: 2 / 3,
+                _CONFUSION_MATRIX: np.array([[2, 1], [1, 2]]),
+                _CONFUSION_MATRIX_LABELS: ["0", "1"],
+            },
+            {
+                _METRIC: metrics[1].metric_name,
+                _ACCURACY_BALANCED: 5 / 9,
+                _F1_SCORE_BALANCED: 3 / 5,
+                _CONFUSION_MATRIX: np.array([[2, 1, 0], [0, 2, 0], [1, 0, 0]]),
+                _CONFUSION_MATRIX_LABELS: ["1.0", "2.0", "3.0"],
+            },
+        ]
+        assert _compare_autorater_eval_result(
+            autorater_eval_result.eval_result, expected_eval_results
+        )
+        assert autorater_eval_result.eval_dataset_metadata == {
+            "eval_dataset_path": "gs://test-bucket/eval_dataset.jsonl",
+            "eval_dataset_size": 6,
+        }
+        assert autorater_eval_result.autorater_config == autorater_config
+        assert autorater_eval_result.unused_params == 10
+
+
+def _compare_autorater_eval_result(
+    actual_eval_results: List[Dict[str, Any]],
+    expected_eval_results: List[Dict[str, Any]],
+) -> bool:
+    """Compare autorater eval result."""
+    for actual, expected in zip(actual_eval_results, expected_eval_results):
+        if actual[_METRIC] != expected[_METRIC]:
+            return False
+        if not _almost_equal(actual[_ACCURACY_BALANCED], expected[_ACCURACY_BALANCED]):
+            return False
+        if not _almost_equal(actual[_F1_SCORE_BALANCED], expected[_F1_SCORE_BALANCED]):
+            return False
+        if not (actual[_CONFUSION_MATRIX] == expected[_CONFUSION_MATRIX]).all():
+            return False
+        if actual[_CONFUSION_MATRIX_LABELS] != expected[_CONFUSION_MATRIX_LABELS]:
+            return False
+    return True
+
+
+def _almost_equal(a: Any, b: Any) -> bool:
+    """Compare two numbers with a small tolerance."""
+    return abs(a - b) <= 1e-6