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7ddc8cb f1ae266 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | import pandas as pd
import plotly.graph_objects as go
from src.analytics import (
BENCHMARK_CATALOG,
benchmark_category,
benchmarks_for_category,
cross_benchmark_ranking_df,
enrich_analysis_df,
filter_category,
matrix_df,
ranking_df,
)
from src.charts import create_coverage_matrix_plot, create_matrix_plot, create_tradeoff_plot
def frame():
return pd.DataFrame(
[
{
"Benchmark": "SWE-Bench Verified", "Model": "a", "Harness": "h", "Run Label": "a / h",
"Category": "FOSS", "Score": .8, "Score (%)": 80, "Tasks": 10, "Errors": 1,
"Input Tokens Per Task": 50, "Cache Tokens Per Task": 10, "Output Tokens Per Task": 20,
"Total Tokens Per Task": 80, "Tokens Per Solved Task": 100, "Cost Per Task": .2,
"Total Time Per Task": 10, "Agent Time Per Task": 8, "Token Data Available": True,
},
{
"Benchmark": "SWE-Bench Verified", "Model": "b", "Harness": "h", "Run Label": "b / h",
"Category": "FOSS", "Score": .4, "Score (%)": 40, "Tasks": 10, "Errors": 2,
"Input Tokens Per Task": 90, "Cache Tokens Per Task": None, "Output Tokens Per Task": 30,
"Total Tokens Per Task": 120, "Tokens Per Solved Task": 300, "Cost Per Task": .1,
"Total Time Per Task": 20, "Agent Time Per Task": 15, "Token Data Available": True,
},
{
"Benchmark": "Terminal Bench 2.0", "Model": "a", "Harness": "h", "Run Label": "a / h",
"Category": "FOSS", "Score": .2, "Score (%)": 20, "Tasks": 10, "Errors": 0,
"Input Tokens Per Task": 30, "Cache Tokens Per Task": 5, "Output Tokens Per Task": 10,
"Total Tokens Per Task": 45, "Tokens Per Solved Task": 225, "Cost Per Task": .3,
"Total Time Per Task": 30, "Agent Time Per Task": 25, "Token Data Available": True,
},
{
"Benchmark": "Terminal Bench 2.0", "Model": "c", "Harness": "h", "Run Label": "c / h",
"Category": "FOSS", "Score": .9, "Score (%)": 90, "Tasks": 10, "Errors": None,
"Input Tokens Per Task": None, "Cache Tokens Per Task": None, "Output Tokens Per Task": None,
"Total Tokens Per Task": None, "Tokens Per Solved Task": None, "Cost Per Task": None,
"Total Time Per Task": None, "Agent Time Per Task": None, "Token Data Available": False,
},
{
"Benchmark": "New Benchmark", "Model": "z", "Harness": "h", "Run Label": "z / h",
"Category": "FOSS", "Score": .5, "Score (%)": 50, "Tasks": 10, "Errors": 0,
"Input Tokens Per Task": 1, "Cache Tokens Per Task": 1, "Output Tokens Per Task": 1,
"Total Tokens Per Task": 3, "Tokens Per Solved Task": 6, "Cost Per Task": .01,
"Total Time Per Task": 1, "Agent Time Per Task": 1, "Token Data Available": True,
},
]
)
def test_benchmark_catalog_and_unknown_fallback():
assert benchmark_category("SWE-Bench Verified") == "Coding"
assert benchmark_category("Terminal Bench 2.0") == "Generalist"
assert benchmark_category("Shellbench") == "Generalist"
assert benchmark_category("New Benchmark") == "Other"
assert "SWE-Bench Pro -- Ansible" in BENCHMARK_CATALOG
def test_category_filtering_keeps_unknown_visible_as_other():
df = frame()
assert set(benchmarks_for_category(df, "Coding")) == {"SWE-Bench Verified"}
assert set(filter_category(df, "Generalist")["Benchmark"]) == {"Terminal Bench 2.0"}
assert set(filter_category(df, "Other")["Benchmark"]) == {"New Benchmark"}
def test_derived_reliability_and_per_success_metrics():
df = enrich_analysis_df(frame())
first = df.iloc[0]
assert first["Execution Error Rate (%)"] == 10
assert first["Tokens Per Successful Task"] == 100
assert first["Cost Per Successful Task"] == .25
assert first["Time Per Successful Task"] == 12.5
assert pd.isna(df.loc[df["Model"].eq("c"), "Execution Error Rate (%)"]).all()
def test_within_benchmark_percentile_and_rank():
df = enrich_analysis_df(frame())
coding = df[df["Benchmark"] == "SWE-Bench Verified"].set_index("Model")
assert coding.loc["a", "Within-Benchmark Rank"] == 1
assert coding.loc["b", "Within-Benchmark Rank"] == 2
assert coding.loc["a", "Within-Benchmark Percentile"] == 100
assert coding.loc["b", "Within-Benchmark Percentile"] == 0
def test_cross_benchmark_ordering_uses_normalization_and_coverage_threshold():
df = frame()
ranked = cross_benchmark_ranking_df(df, minimum_coverage=0.5)
a = ranked[ranked["Model"] == "a"].iloc[0]
assert a["Benchmarks Covered"] == 2
assert a["Eligible Benchmarks"] == 3
assert a["Normalized Performance"] == 50
strict = cross_benchmark_ranking_df(df, minimum_coverage=1.0)
assert strict.empty
def test_token_ranking_excludes_missing_and_orders_lower_first():
ranked = ranking_df(frame(), "Total tokens", benchmark="Terminal Bench 2.0")
assert ranked["Model"].tolist() == ["a"]
def test_score_rank_and_metric_matrices_preserve_missing_cells():
df = frame()
score = matrix_df(df, "Score")
rank = matrix_df(df, "Within-benchmark rank")
cost = matrix_df(df, "Cost")
assert pd.isna(score.loc["b / h", "Terminal Bench 2.0"])
assert rank.loc["a / h", "SWE-Bench Verified"] == 1
assert pd.isna(cost.loc["c / h", "Terminal Bench 2.0"])
def test_coverage_matrix_uses_missing_not_zero_score():
matrix = matrix_df(frame(), "Coverage")
assert matrix.loc["a / h", "SWE-Bench Verified"] == 1
assert pd.isna(matrix.loc["b / h", "Terminal Bench 2.0"])
figure = create_coverage_matrix_plot(matrix)
assert isinstance(figure, go.Figure)
assert "Available" in figure.data[0].text[0] or "Missing" in figure.data[0].text[0]
def test_generic_tradeoff_and_matrix_charts_construct():
df = enrich_analysis_df(frame())
figure = create_tradeoff_plot(
df[df["Benchmark"] == "SWE-Bench Verified"],
"Total Tokens Per Task", "Score (%)",
"Total tokens per task", "Score (%)",
show_pareto_frontier=True,
)
matrix_figure = create_matrix_plot(matrix_df(df, "Score"), "Score matrix", "Score (%)")
assert isinstance(figure, go.Figure)
assert isinstance(matrix_figure, go.Figure)
assert any(trace.name == "Pareto frontier" for trace in figure.data)
def test_tradeoff_pareto_supports_lower_is_better_on_both_axes():
df = pd.DataFrame(
{
"Model": ["a", "b", "c"],
"Harness": ["h", "h", "h"],
"Benchmark": ["bench", "bench", "bench"],
"Total Tokens Per Task": [100, 200, 300],
"Cost Per Task": [0.3, 0.2, 0.4],
}
)
figure = create_tradeoff_plot(
df,
"Total Tokens Per Task",
"Cost Per Task",
"Tokens",
"Cost",
show_pareto_frontier=True,
lower_x_is_better=True,
higher_y_is_better=False,
)
frontier = next(trace for trace in figure.data if trace.name == "Pareto frontier")
assert list(frontier.x) == [100, 200]
assert list(frontier.y) == [0.3, 0.2]
def test_matrix_can_color_by_percentile_but_display_raw_values():
color_matrix = pd.DataFrame([[100.0, 0.0]], index=["a / h"], columns=["b1", "b2"] )
raw_matrix = pd.DataFrame([[82.5, 41.25]], index=["a / h"], columns=["b1", "b2"] )
figure = create_matrix_plot(
color_matrix,
"Score matrix",
"Within-benchmark percentile",
display_matrix=raw_matrix,
display_metric_label="Benchmark score (%)",
)
assert list(figure.data[0].z[0]) == [100.0, 0.0]
assert list(figure.data[0].text[0]) == ["82.5", "41.25"]
assert "Benchmark score (%)" in figure.data[0].hovertemplate
def test_ranking_plot_order_can_be_value_or_alphabetical():
from src.charts import create_ranking_plot
df = pd.DataFrame(
{
"Model": ["b", "a", "c"],
"Harness": ["h", "h", "h"],
"Benchmark": ["bench", "bench", "bench"],
"Score (%)": [20, 10, 30],
}
)
largest = create_ranking_plot(
df, "Score (%)", "Score", True, sort_order="Largest value first"
)
lowest = create_ranking_plot(
df, "Score (%)", "Score", True, sort_order="Lowest value first"
)
alpha = create_ranking_plot(
df, "Score (%)", "Score", True, sort_order="Alphabetical (A–Z)"
)
assert list(largest.layout.yaxis.categoryarray) == ["c / h", "b / h", "a / h"]
assert list(lowest.layout.yaxis.categoryarray) == ["a / h", "b / h", "c / h"]
assert list(alpha.layout.yaxis.categoryarray) == ["a / h", "b / h", "c / h"]
def test_matrix_height_scales_with_rows_and_keeps_all_y_labels():
rows = 18
matrix = pd.DataFrame(
{"bench-a": range(rows), "bench-b": range(rows)},
index=[f"model-{i} / harness" for i in range(rows)],
)
figure = create_matrix_plot(matrix, "Dense matrix", "Score (%)")
assert figure.layout.height >= 900
assert figure.layout.yaxis.tickmode == "array"
assert len(figure.layout.yaxis.tickvals) == rows
assert len(figure.layout.yaxis.ticktext) == rows
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