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Prathap PandGitHub
c2756964a7
* fix(channels): wire channel→agent handler and fix Telegram send pipeline * format code * add supported tests
152 lines
4.3 KiB
Python
152 lines
4.3 KiB
Python
"""Tests for HeuristicRewardFunction."""
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from __future__ import annotations
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import pytest
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from openjarvis.learning._stubs import RoutingContext
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from openjarvis.learning.routing.heuristic_reward import HeuristicRewardFunction
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class TestHeuristicRewardFunction:
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def test_perfect_score(self) -> None:
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rf = HeuristicRewardFunction()
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score = rf.compute(
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RoutingContext(),
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"model-a",
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"response",
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latency_seconds=0.0,
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cost_usd=0.0,
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prompt_tokens=10,
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completion_tokens=10,
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)
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# latency=1.0, cost=1.0, efficiency=0.5 → 0.4*1 + 0.3*1 + 0.3*0.5 = 0.85
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assert score == pytest.approx(0.85)
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def test_worst_score(self) -> None:
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rf = HeuristicRewardFunction()
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score = rf.compute(
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RoutingContext(),
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"model-a",
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"response",
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latency_seconds=60.0,
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cost_usd=0.1,
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prompt_tokens=100,
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completion_tokens=0,
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)
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# latency clamped to 0, cost clamped to 0, efficiency=0/100=0
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assert score == pytest.approx(0.0)
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def test_latency_only_weight(self) -> None:
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rf = HeuristicRewardFunction(
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weight_latency=1.0,
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weight_cost=0.0,
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weight_efficiency=0.0,
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)
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=15.0,
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cost_usd=0.0,
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prompt_tokens=10,
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completion_tokens=10,
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)
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# 1 - 15/30 = 0.5
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assert score == pytest.approx(0.5)
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def test_cost_only_weight(self) -> None:
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rf = HeuristicRewardFunction(
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weight_latency=0.0,
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weight_cost=1.0,
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weight_efficiency=0.0,
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)
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=0.0,
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cost_usd=0.005,
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prompt_tokens=10,
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completion_tokens=10,
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)
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# 1 - 0.005/0.01 = 0.5
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assert score == pytest.approx(0.5)
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def test_efficiency_only_weight(self) -> None:
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rf = HeuristicRewardFunction(
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weight_latency=0.0,
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weight_cost=0.0,
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weight_efficiency=1.0,
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)
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=0.0,
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cost_usd=0.0,
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prompt_tokens=25,
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completion_tokens=75,
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)
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# 75/100 = 0.75
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assert score == pytest.approx(0.75)
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def test_clamped_to_unit_interval(self) -> None:
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rf = HeuristicRewardFunction()
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# Even with extreme values, score should be in [0, 1]
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=-10.0,
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cost_usd=-0.1,
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prompt_tokens=0,
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completion_tokens=100,
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)
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assert 0.0 <= score <= 1.0
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def test_custom_max_values(self) -> None:
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rf = HeuristicRewardFunction(max_latency=10.0, max_cost=1.0)
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=5.0,
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cost_usd=0.5,
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prompt_tokens=50,
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completion_tokens=50,
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)
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# latency: 1-5/10=0.5, cost: 1-0.5/1.0=0.5, efficiency: 50/100=0.5
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# 0.4*0.5 + 0.3*0.5 + 0.3*0.5 = 0.5
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assert score == pytest.approx(0.5)
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def test_zero_total_tokens_default(self) -> None:
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rf = HeuristicRewardFunction()
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=0.0,
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cost_usd=0.0,
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prompt_tokens=0,
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completion_tokens=0,
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)
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# latency=1, cost=1, efficiency default=0.5
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# 0.4*1 + 0.3*1 + 0.3*0.5 = 0.85
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assert score == pytest.approx(0.85)
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def test_extra_kwargs_ignored(self) -> None:
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rf = HeuristicRewardFunction()
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# Should not raise even with extra kwargs
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score = rf.compute(
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RoutingContext(),
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"m",
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"",
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latency_seconds=1.0,
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cost_usd=0.001,
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prompt_tokens=10,
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completion_tokens=10,
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random_extra="ignored",
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)
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assert 0.0 <= score <= 1.0
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