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c0608db a01d7ac 3285f4c a01d7ac c0608db | 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 | import numpy as np
import pandas as pd
import pytest
from tabicl_mcp import server
from tabicl_mcp.report import build_report
CSV = "x1,x2,label\n" + "\n".join(
f"{i % 10},{(i * 7) % 5},{'yes' if i % 10 > 4 else 'no'}" for i in range(60)
)
def test_load_data_inline():
result = server.load_data(csv_content=CSV, target_column="label")
assert result["dataset_id"].startswith("ds_")
assert result["n_rows"] == 60
assert result["target"]["suggested_task"] == "classification"
def test_load_data_requires_one_source():
assert "error" in server.load_data()
assert "error" in server.load_data(csv_content=CSV, url="https://x.com/a.csv")
def test_load_data_bad_target_warns():
result = server.load_data(csv_content=CSV, target_column="nope")
assert "target_warning" in result
def test_evaluate_unknown_dataset_id_is_friendly():
result = server.evaluate(target_column="label", dataset_id="ds_missing")
assert "not found" in result["error"]
def test_export_predictions_pages():
df = pd.DataFrame({"a": range(10), "p": range(10)})
result_id = server.D.CACHE.put(df, prefix="pred")
page = server.export_predictions(result_id, offset=8, limit=5)
assert page["total_rows"] == 10
assert page["returned_rows"] == 2
assert "csv" in page
@pytest.mark.slow
def test_full_flow_and_report(tmp_path, monkeypatch):
monkeypatch.setattr(server, "REPORTS_DIR", str(tmp_path))
loaded = server.load_data(csv_content=CSV)
ds = loaded["dataset_id"]
evaluation = server.evaluate(target_column="label", dataset_id=ds)
assert "metrics" in evaluation, evaluation
report = server.create_report(target_column="label", dataset_id=ds)
assert "report_file" in report, report
html = open(report["report_file"], encoding="utf-8").read()
assert "<svg" in html and "Accuracy" in html
def test_upload_page_roundtrip():
from starlette.testclient import TestClient
app = server.mcp.streamable_http_app()
with TestClient(app) as client:
assert "Upload a CSV" in client.get("/").text
resp = client.post("/upload", files={"file": ("t.csv", b"a,b\n1,2\n3,4\n", "text/csv")})
assert "ds_" in resp.text
assert "✔ Uploaded" in resp.text and "2 rows × 2 columns" in resp.text
import re
ds_id = re.search(r"ds_\w+", resp.text).group(0)
assert server.D.CACHE.get(ds_id).df.shape == (2, 2)
bad = client.post("/upload", files={"file": ("t.csv", b"", "text/csv")})
assert "✘" in bad.text
def test_build_report_smoke_without_model():
evaluation = {
"task_type": "classification",
"target_column": "label",
"note": "test",
"metrics": {
"accuracy": 0.9,
"balanced_accuracy": 0.88,
"f1_macro": 0.89,
"roc_auc": 0.95,
"confusion_matrix": {"labels": ["no", "yes"], "matrix": [[40, 5], [4, 51]]},
},
"class_distribution": {"no": 45, "yes": 55},
}
importance = {
"importances": [
{"feature": "income", "importance": 0.31, "std": 0.02},
{"feature": "age", "importance": 0.12, "std": 0.01},
]
}
html = build_report(
title="Test report",
dataset_summary={"n_rows": 100, "n_columns": 3},
evaluation=evaluation,
importance=importance,
)
assert "Test report" in html
assert "income" in html
assert "pred: yes" in html
assert "<script" not in html # self-contained, no JS
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