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| """Tests for src.analyzer — core aggregation and analysis logic.""" | |
| import sys | |
| from pathlib import Path | |
| import pandas as pd | |
| import numpy as np | |
| sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) | |
| from src.analyzer import _agg_group, _safe_pct_change, compute_wow | |
| def _make_df(rows: list[dict]) -> pd.DataFrame: | |
| return pd.DataFrame(rows) | |
| class TestSafePctChange: | |
| def test_normal(self): | |
| assert _safe_pct_change(150, 100) == 50.0 | |
| def test_decrease(self): | |
| assert _safe_pct_change(80, 100) == -20.0 | |
| def test_zero_prev(self): | |
| assert _safe_pct_change(100, 0) is None | |
| def test_nan_prev(self): | |
| assert _safe_pct_change(100, float("nan")) is None | |
| class TestAggGroup: | |
| def test_single_row(self): | |
| df = _make_df([{ | |
| "Cost": 100, "Ads.Sales": 500, "Impressions": 10000, | |
| "CTR": 0.02, "GA4.Sessions": 50, "CVR": 0.05, | |
| }]) | |
| result = _agg_group(df, ["Cost", "Ads.Sales", "ROAS", "CTR", "CVR", "CPC"]) | |
| assert result["Cost"] == 100 | |
| assert result["Ads.Sales"] == 500 | |
| assert result["ROAS"] == 5.0 | |
| assert abs(result["CTR"] - 0.02) < 1e-6 | |
| assert abs(result["CVR"] - 0.05) < 1e-6 | |
| def test_multi_row_weighted_ctr(self): | |
| df = _make_df([ | |
| {"Cost": 100, "Ads.Sales": 300, "Impressions": 10000, "CTR": 0.01, "GA4.Sessions": 20, "CVR": 0.1}, | |
| {"Cost": 200, "Ads.Sales": 400, "Impressions": 20000, "CTR": 0.03, "GA4.Sessions": 80, "CVR": 0.05}, | |
| ]) | |
| result = _agg_group(df, ["Cost", "Ads.Sales", "ROAS", "CTR", "CVR", "CPC"]) | |
| assert result["Cost"] == 300 | |
| assert result["Ads.Sales"] == 700 | |
| expected_clicks = 10000 * 0.01 + 20000 * 0.03 # 100 + 600 = 700 | |
| expected_ctr = expected_clicks / 30000 | |
| assert abs(result["CTR"] - expected_ctr) < 1e-6 | |
| assert abs(result["CPC"] - 300 / expected_clicks) < 1e-6 | |
| def test_empty_df(self): | |
| df = _make_df([]) | |
| df = pd.DataFrame(columns=["Cost", "Ads.Sales", "Impressions", "CTR"]) | |
| result = _agg_group(df, ["Cost", "Ads.Sales"]) | |
| assert result.get("Cost", 0) == 0 | |
| assert result.get("Ads.Sales", 0) == 0 | |
| class TestComputeWow: | |
| def test_normal_change(self): | |
| curr = {"Cost": 200, "ROAS": 3.0} | |
| prev = {"Cost": 100, "ROAS": 2.0} | |
| result = compute_wow(curr, prev, ["Cost", "ROAS"]) | |
| assert result["Cost"] == 100.0 | |
| assert result["ROAS"] == 50.0 | |
| def test_zero_prev(self): | |
| curr = {"Cost": 100} | |
| prev = {"Cost": 0} | |
| result = compute_wow(curr, prev, ["Cost"]) | |
| assert result["Cost"] is None | |