chore: Remove deprecated static methods from TrajectoryEvaluator

This change removes the `evaluate`, `_evaluate_row`, `are_tools_equal`, `_remove_tool_outputs`, `_report_failures`, and `_print_results` static methods from `TrajectoryEvaluator`, along with their corresponding unit tests. These methods were previously marked as deprecated.

PiperOrigin-RevId: 817477494

Author: ankursharmas

src/google/adk/evaluation/trajectory_evaluator.py

@@ -14,13 +14,9 @@
 
 from __future__ import annotations
 
-from typing import Any
 from typing import Optional
 
 from google.genai import types as genai_types
-import pandas as pd
-from tabulate import tabulate
-from typing_extensions import deprecated
 from typing_extensions import override
 
 from .eval_case import get_all_tool_calls
@@ -30,7 +26,6 @@
 from .eval_metrics import MetricInfo
 from .eval_metrics import MetricValueInfo
 from .eval_metrics import PrebuiltMetrics
-from .evaluation_constants import EvalConstants
 from .evaluator import EvalStatus
 from .evaluator import EvaluationResult
 from .evaluator import Evaluator
@@ -129,170 +124,3 @@ def _are_tool_calls_equal(
 
   def _get_eval_status(self, score: float):
     return EvalStatus.PASSED if score >= self._threshold else EvalStatus.FAILED
-
-  @staticmethod
-  @deprecated(
-      "This method has been deprecated and will be removed soon. Please use"
-      " evaluate_invocations instead."
-  )
-  def evaluate(
-      eval_dataset: list[list[dict[str, Any]]],
-      *,
-      print_detailed_results: bool = False,
-  ):
-    r"""Returns the mean tool use accuracy of the eval dataset.
-
-    Tool use accuracy is calculated by comparing the expected and the actual
-    tool use trajectories. An exact match scores a 1, 0 otherwise. The final
-    number is an average of these individual scores.
-
-    Value range: [0, 1], where 0 means none of the tool use entries aligned,
-    and 1 would mean all of them aligned. Higher value is good.
-
-    Args:
-      eval_dataset: The dataset that will be evaluated.
-      print_detailed_results: Prints detailed results on the console. This is
-        usually helpful during debugging.
-
-    A note on eval_dataset:
-      The dataset should be a list session, where each session is represented
-      as a list of interaction that need evaluation. Each evaluation is
-      represented as a dictionary that is expected to have values for the
-      following keys:
-        1) query
-        2) response
-        3) acutal_tool_use
-        4) expected_tool_use
-
-      Here is a sample eval_dataset value with one entry:
-
-      [
-        [
-          {
-            "query": "Roll a 16 sided dice for me",
-            "response": "I rolled a 16 sided die and got 13.\n",
-            "expected_tool_use": [
-              {
-                "tool_name": "roll_die",
-                "tool_input": {
-                  "sides": 16
-                }
-              }
-            ],
-            "acutal_tool_use": [
-              {
-                "tool_name": "roll_die",
-                "tool_input": {
-                  "sides": 16
-                }
-              }
-            ]
-          }
-        ]
-      ]
-    """
-    if not eval_dataset:
-      raise ValueError("The evaluation dataset is empty.")
-
-    results_df = pd.DataFrame(
-        columns=[
-            "query",
-            "response",
-            "actual_tool_use",
-            "expected_tool_use",
-            "tool_use_accuracy",
-        ]
-    )
-    failures = []
-
-    for conversation in eval_dataset:
-      for index, row in enumerate(conversation):
-        new_row, failure = TrajectoryEvaluator._evaluate_row(row)
-        results_df = pd.concat(
-            [results_df, pd.DataFrame([new_row])], ignore_index=True
-        )
-        if failure:
-          failure["turn"] = index + 1
-          failures.append(failure)
-
-    TrajectoryEvaluator._report_failures(failures)
-
-    if print_detailed_results:
-      TrajectoryEvaluator._print_results(results_df)
-
-    return results_df["tool_use_accuracy"].mean()
-
-  @staticmethod
-  def _evaluate_row(row):
-    # We don't evaluate the mock tool outputs.
-    expected = TrajectoryEvaluator._remove_tool_outputs(
-        row["expected_tool_use"]
-    )
-    actual = row["actual_tool_use"]
-    tool_use_accuracy = (
-        1.0 if TrajectoryEvaluator.are_tools_equal(actual, expected) else 0.0
-    )
-
-    new_row = {
-        "query": row["query"],
-        "response": row["response"],
-        "actual_tool_use": actual,
-        "expected_tool_use": expected,
-        "tool_use_accuracy": tool_use_accuracy,
-    }
-    failure = (
-        None
-        if tool_use_accuracy == 1.0
-        else {"query": row["query"], "actual": actual, "expected": expected}
-    )
-    return new_row, failure
-
-  @staticmethod
-  @deprecated(
-      "are_tools_equal is deprecated and will be removed soon. Please use"
-      " TrajectoryEvaluator._are_tool_calls_equal instead."
-  )
-  def are_tools_equal(list_a_original, list_b_original):
-    # Remove other entries that we don't want to evaluate
-    list_a = [
-        {"tool_name": tool["tool_name"], "tool_input": tool["tool_input"]}
-        for tool in list_a_original
-    ]
-
-    list_b = [
-        {"tool_name": tool["tool_name"], "tool_input": tool["tool_input"]}
-        for tool in list_b_original
-    ]
-
-    return list_a == list_b
-
-  @staticmethod
-  def _remove_tool_outputs(tool_use_list):
-    """Removes 'mock_tool_output' from each dictionary in the list."""
-    result = []
-    for tool_use in tool_use_list:
-      new_tool_use = (
-          tool_use.copy()
-      )  # Create a copy to avoid modifying the original
-      new_tool_use.pop(
-          EvalConstants.MOCK_TOOL_OUTPUT, None
-      )  # Remove 'tool_output' if it exists
-      result.append(new_tool_use)
-    return result
-
-  @staticmethod
-  def _report_failures(failures):
-    if failures:
-      print("Failures:")
-      for failure in failures:
-        print(f"""{{
-  "turn": {failure["turn"]},
-  "query": '{failure["query"]}',
-  "actual": {failure["actual"]},
-  "expected_tool_use": {failure["expected"]},
-}}
-""")
-
-  @staticmethod
-  def _print_results(results_df):
-    print(tabulate(results_df, headers="keys", tablefmt="grid"))