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@@ -3,6 +3,13 @@ from dataclasses import replace
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import pandas as pd
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+from roi_control.fission_multiplier import (
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+ DISPLAY_TOTAL_TO_FIRST_COLUMN,
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+ MATCH_QIWEI_PARTNER,
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+ QIWEI_REFERENCE_RUN_SUFFIX,
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+ QIWEI_REFERENCE_VERSION,
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+ load_fission_multiplier_parameters,
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+)
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from roi_control.metrics import (
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ENTITY_GZH,
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ENTITY_QIWEI,
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@@ -11,25 +18,19 @@ from roi_control.metrics import (
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QIWEI_CHANNEL,
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SELF_CHANNEL,
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)
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-from roi_control.fission_multiplier import (
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- DISPLAY_TOTAL_TO_FIRST_COLUMN,
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- MATCH_QIWEI_PARTNER,
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- QIWEI_REFERENCE_RUN_SUFFIX,
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- QIWEI_REFERENCE_VERSION,
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- load_fission_multiplier_parameters,
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-)
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from roi_control.reporting import (
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- BASE_COLUMNS,
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FINAL_ROI_COLUMN,
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T0_FISSION_MULTIPLIER_COLUMN,
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TOTAL_FISSION_TO_FIRST_UV_COLUMN,
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_sheet_frame,
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+ _visible_columns,
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)
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+from roi_control.data_source import build_daily_sql
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from roi_control.rules import evaluate_rules as _evaluate_rules
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from roi_control.service import _run_identity
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-DATES = ["20260720", "20260721", "20260722"]
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+DATES = ["20260721", "20260722"]
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FISSION_PARAMETERS = load_fission_multiplier_parameters()
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@@ -38,7 +39,7 @@ def evaluate_rules(*args, **kwargs):
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return _evaluate_rules(*args, **kwargs)
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-def row(entity_type, channel, entity_id, dt, roi, uv=600, age=10, cost=200.0):
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+def row(entity_type, channel, entity_id, dt, roi, uv=600, cost=200.0):
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common = {
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"dt": dt,
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"entity_type": entity_type,
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@@ -63,7 +64,7 @@ def row(entity_type, channel, entity_id, dt, roi, uv=600, age=10, cost=200.0):
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common.update(
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{
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"代理名称": "代理",
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- "账号id": "account",
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+ "账号id": "84502354",
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"账号名称": "账户",
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"广告id": entity_id,
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"广告名称": f"广告{entity_id}",
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@@ -80,10 +81,43 @@ def row(entity_type, channel, entity_id, dt, roi, uv=600, age=10, cost=200.0):
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class RoiRulesTest(unittest.TestCase):
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- def test_run_identity_tracks_qiwei_parameter_source(self):
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- temporary_id, temporary_key = _run_identity("20260727", FISSION_PARAMETERS)
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- self.assertIn(QIWEI_REFERENCE_RUN_SUFFIX, temporary_id)
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- self.assertIn(QIWEI_REFERENCE_VERSION, temporary_key)
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+ def build_daily(self):
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+ rows = []
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+ for dt in DATES:
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+ for index, roi in enumerate(
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+ [0.1, 0.5, 0.8, 1.0, 1.2, 1.5, 2.0, 3.0, 4.0, 5.0]
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+ ):
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+ rows.append(
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+ row(ENTITY_SELF, SELF_CHANNEL, f"ad-{index}", dt, roi)
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+ )
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+ for index, roi in enumerate([0.1, 0.5, 1.0, 2.0, 4.0]):
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+ rows.append(
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+ row(
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+ ENTITY_GZH,
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+ GZH_CHANNEL,
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+ f"公众号-{index}",
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+ dt,
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+ roi,
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+ uv=300,
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+ )
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+ )
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+ rows.append(
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+ row(ENTITY_QIWEI, QIWEI_CHANNEL, "企微合作方", dt, 2.0, uv=300)
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+ )
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+ return pd.DataFrame(rows)
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+
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+ def test_run_identity_tracks_metric_policy_and_qiwei_versions(self):
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+ run_id, run_key = _run_identity("20260727", FISSION_PARAMETERS)
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+ self.assertIn("m7", run_id)
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+ self.assertIn("p6", run_id)
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+ self.assertIn("r15", run_id)
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+ self.assertIn(QIWEI_REFERENCE_RUN_SUFFIX, run_id)
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+ self.assertIn(QIWEI_REFERENCE_VERSION, run_key)
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+
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+ def test_daily_sql_uses_root_deduplicated_t0_fission_uv(self):
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+ sql = build_daily_sql(DATES[0], DATES[1])
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+ self.assertEqual(sql.count("SUM(NVL(t0_fission_uv_root, 0))"), 3)
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+ self.assertNotIn("t0裂变人数", sql)
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formal = replace(
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FISSION_PARAMETERS,
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@@ -95,240 +129,179 @@ class RoiRulesTest(unittest.TestCase):
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qiwei_exact_rows=1,
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qiwei_exact_available_rows=1,
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)
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- formal_id, formal_key = _run_identity("20260727", formal)
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+ formal_id, _ = _run_identity("20260727", formal)
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self.assertIn(QIWEI_REFERENCE_RUN_SUFFIX, formal_id)
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- self.assertIn(QIWEI_REFERENCE_VERSION, formal_key)
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-
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- def test_run_identity_isolates_corrected_source_revision(self):
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- default_id, default_key = _run_identity("20260729", FISSION_PARAMETERS)
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- revised_id, revised_key = _run_identity(
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- "20260729",
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- FISSION_PARAMETERS,
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- "datafix_20260730_1",
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- )
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-
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- self.assertEqual(f"{default_id}_datafix_20260730_1", revised_id)
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- self.assertNotEqual(default_key, revised_key)
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- self.assertNotIn("datafix_20260730_1", revised_key)
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- self.assertLessEqual(len(revised_id), 64)
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- self.assertLessEqual(len(revised_key), 128)
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- self.assertEqual(
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- (revised_id, revised_key),
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- _run_identity(
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- "20260729",
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- FISSION_PARAMETERS,
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- "datafix_20260730_1",
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- ),
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- )
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- with self.assertRaisesRegex(ValueError, "source_revision"):
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- _run_identity("20260729", FISSION_PARAMETERS, "Invalid Revision")
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- with self.assertRaisesRegex(ValueError, "run_id"):
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- _run_identity("20260729", FISSION_PARAMETERS, "a" * 40)
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- def build_daily(self):
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- rows = []
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- # 每日形成稳定分布，低值实体连续低于后20%，高值实体连续高于前20%。
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- for dt in DATES:
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- for index, roi in enumerate([0.1, 0.5, 0.8, 1.0, 1.2, 1.5, 2.0, 3.0, 4.0, 5.0]):
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- rows.append(row(ENTITY_SELF, SELF_CHANNEL, f"ad-{index}", dt, roi))
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- rows.append(row(ENTITY_GZH, GZH_CHANNEL, "低ROI公众号", dt, 0.05, uv=300))
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- rows.append(row(ENTITY_GZH, GZH_CHANNEL, "高ROI公众号", dt, 6.0, uv=300))
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- rows.append(row(ENTITY_QIWEI, QIWEI_CHANNEL, "低ROI企微", dt, 0.02, uv=300))
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- rows.append(row(ENTITY_QIWEI, QIWEI_CHANNEL, "高ROI企微", dt, 7.0, uv=300))
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- return pd.DataFrame(rows)
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-
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- def test_stop_and_up_actions(self):
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- daily = self.build_daily()
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+ def test_two_daily_thresholds_drive_consecutive_actions(self):
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ages = pd.DataFrame(
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{
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"广告id": [f"ad-{index}" for index in range(10)],
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"广告age": [10] * 10,
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}
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)
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- candidates, thresholds, summary = evaluate_rules(daily, DATES, ages)
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+ candidates, thresholds, summary = evaluate_rules(
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+ self.build_daily(), DATES, ages
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+ )
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- actions = set(candidates["动作"])
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- self.assertIn("关停", actions)
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- self.assertIn("扩量", actions)
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- self.assertEqual(len(thresholds), 1)
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+ self.assertEqual(len(thresholds), 4)
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self.assertEqual(
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- thresholds.iloc[0]["阈值样本数"],
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- int(summary["entity_type"].ne(ENTITY_QIWEI).sum()),
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+ set(zip(thresholds["统计日期"], thresholds["entity_type"])),
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+ {
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+ (DATES[0], ENTITY_SELF),
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+ (DATES[1], ENTITY_SELF),
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+ (DATES[0], ENTITY_GZH),
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+ (DATES[1], ENTITY_GZH),
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+ },
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)
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- self.assertTrue((summary["覆盖天数"] == 3).all())
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- self.assertTrue((summary["t_stop"] == thresholds.iloc[0]["t_stop"]).all())
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- self.assertTrue((summary["t_up"] == thresholds.iloc[0]["t_up"]).all())
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- qiwei = summary[summary["entity_type"].eq(ENTITY_QIWEI)]
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- self.assertEqual(len(qiwei), 2)
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+ self.assertIn("关停", set(candidates["动作"]))
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+ self.assertIn("扩量", set(candidates["动作"]))
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+ self.assertTrue((summary["覆盖天数"] == 2).all())
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self.assertTrue(
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- qiwei["调控参与状态"].eq("仅展示_不进入阈值和调控").all()
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+ summary[
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+ summary["entity_type"].isin([ENTITY_SELF, ENTITY_GZH])
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+ ]["阈值样本状态"].eq("进入连续两日阈值样本池").all()
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)
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- def test_qiwei_is_excluded_before_metric_and_action_evaluation(self):
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- daily = self.build_daily()
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- candidates, _, summary = evaluate_rules(daily, DATES)
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- self.assertTrue(summary["entity_type"].eq(ENTITY_QIWEI).any())
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- self.assertFalse(candidates["合作方名"].str.contains("企微").any())
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-
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- def test_no_complete_three_day_entity_fails_clearly(self):
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- daily = self.build_daily()
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- incomplete = daily[daily["dt"].ne(DATES[-1])]
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- with self.assertRaisesRegex(ValueError, "连续三日数据"):
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- evaluate_rules(incomplete, DATES)
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-
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- def test_daily_fission_income_is_scaled_once_without_duplicate_addition(self):
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+ def test_inconsistent_daily_direction_is_observe(self):
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daily = self.build_daily()
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- mask = (
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- daily["entity_type"].eq(ENTITY_GZH)
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- & daily["公众号名"].eq("高ROI公众号")
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- )
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- daily.loc[mask, "效率收入"] = 100
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- daily.loc[mask, "裂变效率收入"] = 200
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- daily.loc[mask, "成本"] = 100
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-
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- _, _, summary = evaluate_rules(daily, DATES)
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- target = summary[
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- summary["entity_type"].eq(ENTITY_GZH)
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- & summary["公众号名"].eq("高ROI公众号")
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- ].iloc[0]
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- expected = (
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- 100 + 200 * FISSION_PARAMETERS.gzh_channel
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- ) / 100
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- self.assertEqual(target["裂变效率收入"], 600)
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- self.assertEqual(target["T0实际裂变收入"], 600)
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- self.assertAlmostEqual(target["ROI"], expected)
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- self.assertAlmostEqual(target["实际ROI"], 3.0)
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+ mask = daily["广告id"].eq("ad-0")
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+ daily.loc[mask & daily["dt"].eq(DATES[1]), "效率收入"] = 1000
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+ ages = pd.DataFrame({"广告id": ["ad-0"], "广告age": [10]})
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- def test_self_age_blocks_actions(self):
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- daily = self.build_daily()
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- ages = pd.DataFrame({"广告id": ["ad-0", "ad-9"], "广告age": [2, 2]})
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candidates, _, _ = evaluate_rules(daily, DATES, ages)
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- blocked_ids = set(candidates.loc[candidates["entity_type"].eq(ENTITY_SELF), "广告id"])
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- self.assertNotIn("ad-0", blocked_ids)
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- self.assertNotIn("ad-9", blocked_ids)
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-
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- def test_pyodps_lowercase_aliases_are_normalized(self):
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- daily = self.build_daily().rename(
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- columns={"首层UV": "首层uv", "T0裂变数": "t0裂变数"}
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- )
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- candidates, thresholds, _ = evaluate_rules(daily, DATES)
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- self.assertFalse(candidates.empty)
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- self.assertEqual(len(thresholds), 1)
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+ target = candidates[candidates["广告id"].eq("ad-0")].iloc[0]
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+ self.assertEqual(target["动作"], "观察")
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+ self.assertIn("表现不一致", target["动作原因"])
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- def test_zero_roi_at_stop_boundary_is_stopped(self):
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- rows = []
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- for dt in DATES:
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- for index, roi in enumerate([0, 0, 0, 0, 0, 1, 2, 3, 4, 5]):
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- rows.append(row(ENTITY_SELF, SELF_CHANNEL, f"boundary-{index}", dt, roi))
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- ages = pd.DataFrame(
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- {
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- "广告id": [f"boundary-{index}" for index in range(10)],
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- "广告age": [10] * 10,
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- }
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+ def test_latest_day_uv_over_100_is_reported_as_observe(self):
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+ daily = self.build_daily()
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+ extra = row(
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+ ENTITY_SELF,
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+ SELF_CHANNEL,
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+ "latest-only",
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+ DATES[1],
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+ 1.5,
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+ uv=150,
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)
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- candidates, thresholds, _ = evaluate_rules(pd.DataFrame(rows), DATES, ages)
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- self.assertEqual(thresholds.iloc[0]["t_stop"], 0)
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- stopped = candidates[candidates["动作"].eq("关停")]
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- self.assertTrue(stopped["广告id"].eq("boundary-0").any())
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+ daily = pd.concat([daily, pd.DataFrame([extra])], ignore_index=True)
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- def test_display_name_change_does_not_break_three_day_identity(self):
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+ candidates, _, summary = evaluate_rules(daily, DATES)
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+ target = summary[summary["广告id"].eq("latest-only")].iloc[0]
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+ self.assertEqual(target["覆盖天数"], 1)
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+ self.assertEqual(target["动作"], "观察")
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+ self.assertEqual(target["阈值样本状态"], "观察_最新日UV达标")
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+ self.assertAlmostEqual(target["最新日效率ROI"], 1.5)
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+ self.assertTrue(candidates["广告id"].eq("latest-only").any())
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+
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+ def test_both_days_must_exceed_daily_uv_gate(self):
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daily = self.build_daily()
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mask = daily["广告id"].eq("ad-9")
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- daily.loc[mask & daily["dt"].eq(DATES[1]), "广告名称"] = "改名后的广告"
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+ daily.loc[mask & daily["dt"].eq(DATES[0]), "首层UV"] = 180
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ages = pd.DataFrame({"广告id": ["ad-9"], "广告age": [10]})
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- candidates, _, summary = evaluate_rules(daily, DATES, ages)
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- target = summary[
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- summary["entity_type"].eq(ENTITY_SELF)
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- & summary["广告id"].eq("ad-9")
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- ]
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- self.assertEqual(len(target), 1)
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- self.assertTrue(candidates["广告id"].eq("ad-9").any())
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- def test_global_threshold_pool_only_uses_miniapp_and_gzh(self):
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+ _, _, summary = evaluate_rules(daily, DATES, ages)
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+ target = summary[summary["广告id"].eq("ad-9")].iloc[0]
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+ self.assertEqual(target["动作"], "观察")
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+ self.assertEqual(target["阈值样本状态"], "观察_最新日UV达标")
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+
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+ def test_stop_threshold_uses_cost_weighted_p30(self):
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rows = []
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specifications = [
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- (ENTITY_SELF, SELF_CHANNEL, "self-qualified", 1.0, 300),
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- (ENTITY_GZH, GZH_CHANNEL, "gzh-qualified", 2.0, 300),
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- (ENTITY_QIWEI, QIWEI_CHANNEL, "qiwei-qualified", 3.0, 300),
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- (ENTITY_GZH, GZH_CHANNEL, "gzh-low-uv-excluded", 100.0, 100),
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+ ("weighted-0", 0.1, 10),
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+ ("weighted-1", 0.2, 10),
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+ ("weighted-2", 1.0, 1000),
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+ ("weighted-3", 2.0, 1000),
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]
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- for entity_type, channel, entity_id, roi, uv in specifications:
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- for dt in DATES:
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- rows.append(row(entity_type, channel, entity_id, dt, roi, uv=uv))
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-
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- _, thresholds, summary = evaluate_rules(pd.DataFrame(rows), DATES)
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- threshold = thresholds.iloc[0]
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- expected = pd.Series([1.0, 2.0])
|
|
|
- self.assertAlmostEqual(threshold["t_stop"], expected.quantile(0.20))
|
|
|
- self.assertAlmostEqual(threshold["t_up"], expected.quantile(0.80))
|
|
|
- self.assertEqual(threshold["阈值样本数"], 2)
|
|
|
- self.assertEqual(threshold["小程序样本数"], 1)
|
|
|
- self.assertEqual(threshold["公众号样本数"], 1)
|
|
|
- self.assertEqual(threshold["企微参考实体数"], 1)
|
|
|
- self.assertEqual(len(summary), 4)
|
|
|
-
|
|
|
- def test_actions_use_three_day_aggregate_not_each_daily_roi(self):
|
|
|
- rows = []
|
|
|
- daily_rois = [0.0, 0.0, 6.0]
|
|
|
- for dt, roi in zip(DATES, daily_rois):
|
|
|
- rows.append(row(ENTITY_GZH, GZH_CHANNEL, "波动公众号", dt, roi, uv=300))
|
|
|
for dt in DATES:
|
|
|
- rows.append(row(ENTITY_SELF, SELF_CHANNEL, "基准创意", dt, 1.0, uv=600))
|
|
|
- rows.append(row(ENTITY_GZH, GZH_CHANNEL, "基准公众号", dt, 3.0, uv=300))
|
|
|
-
|
|
|
- candidates, thresholds, summary = evaluate_rules(pd.DataFrame(rows), DATES)
|
|
|
- target = summary[summary["公众号名"].eq("波动公众号")].iloc[0]
|
|
|
- self.assertAlmostEqual(target["ROI"], 2.0)
|
|
|
- self.assertAlmostEqual(target["t_stop"], thresholds.iloc[0]["t_stop"])
|
|
|
- self.assertAlmostEqual(target["t_up"], thresholds.iloc[0]["t_up"])
|
|
|
- self.assertNotEqual(
|
|
|
- candidates[candidates["公众号名"].eq("波动公众号")]["动作"].tolist(),
|
|
|
- ["关停"],
|
|
|
+ for entity_id, roi, cost in specifications:
|
|
|
+ rows.append(
|
|
|
+ row(
|
|
|
+ ENTITY_SELF,
|
|
|
+ SELF_CHANNEL,
|
|
|
+ entity_id,
|
|
|
+ dt,
|
|
|
+ roi,
|
|
|
+ cost=cost,
|
|
|
+ )
|
|
|
+ )
|
|
|
+ _, thresholds, _ = evaluate_rules(pd.DataFrame(rows), DATES)
|
|
|
+ self_thresholds = thresholds[
|
|
|
+ thresholds["entity_type"].eq(ENTITY_SELF)
|
|
|
+ ]
|
|
|
+ self.assertTrue(self_thresholds["t_stop"].eq(1.0).all())
|
|
|
+ self.assertTrue(
|
|
|
+ self_thresholds["关停线口径"].eq("消耗加权P25").all()
|
|
|
)
|
|
|
|
|
|
- def test_uv_gate_uses_three_day_average_not_each_day(self):
|
|
|
- daily = self.build_daily()
|
|
|
- target_mask = daily["广告id"].eq("ad-9")
|
|
|
- daily.loc[target_mask, "首层UV"] = [100, 250, 400]
|
|
|
- ages = pd.DataFrame({"广告id": ["ad-9"], "广告age": [10]})
|
|
|
-
|
|
|
- candidates, thresholds, summary = evaluate_rules(daily, DATES, ages)
|
|
|
- target = summary[summary["广告id"].eq("ad-9")].iloc[0]
|
|
|
- self.assertEqual(target["日均首层UV"], 250)
|
|
|
- self.assertEqual(
|
|
|
- candidates[candidates["广告id"].eq("ad-9")]["动作"].tolist(),
|
|
|
- ["扩量"],
|
|
|
+ def test_qiwei_is_display_only(self):
|
|
|
+ candidates, _, summary = evaluate_rules(self.build_daily(), DATES)
|
|
|
+ self.assertFalse(candidates["entity_type"].eq(ENTITY_QIWEI).any())
|
|
|
+ qiwei = summary[summary["entity_type"].eq(ENTITY_QIWEI)]
|
|
|
+ self.assertTrue(
|
|
|
+ qiwei["调控参与状态"].eq("仅展示_不进入阈值和调控").all()
|
|
|
)
|
|
|
- self.assertEqual(thresholds.iloc[0]["小程序样本数"], 10)
|
|
|
|
|
|
- def test_every_day_cost_must_be_above_100(self):
|
|
|
+ def test_report_uses_daily_rows_and_only_latest_day_is_actionable(self):
|
|
|
daily = self.build_daily()
|
|
|
- target_mask = daily["广告id"].eq("ad-9")
|
|
|
- daily.loc[target_mask, "成本"] = [100, 200, 200]
|
|
|
- daily.loc[target_mask, "效率收入"] = [500, 1000, 1000]
|
|
|
- ages = pd.DataFrame({"广告id": ["ad-9"], "广告age": [10]})
|
|
|
-
|
|
|
- candidates, thresholds, summary = evaluate_rules(daily, DATES, ages)
|
|
|
- target = summary[summary["广告id"].eq("ad-9")].iloc[0]
|
|
|
- self.assertEqual(target["三日最小单日成本"], 100)
|
|
|
- self.assertFalse(candidates["广告id"].eq("ad-9").any())
|
|
|
- self.assertEqual(thresholds.iloc[0]["小程序样本数"], 9)
|
|
|
-
|
|
|
- def test_report_exposes_final_roi_and_both_fission_coefficients(self):
|
|
|
- candidates, _, _ = evaluate_rules(self.build_daily(), DATES)
|
|
|
- frame = _sheet_frame(candidates, "小程序投流")
|
|
|
-
|
|
|
- visible_columns = list(BASE_COLUMNS["小程序投流"])
|
|
|
- self.assertIn(FINAL_ROI_COLUMN, visible_columns)
|
|
|
- self.assertIn(T0_FISSION_MULTIPLIER_COLUMN, visible_columns)
|
|
|
- self.assertIn(TOTAL_FISSION_TO_FIRST_UV_COLUMN, visible_columns)
|
|
|
- self.assertIn("T0裂变效率收入", visible_columns)
|
|
|
- self.assertIn("总预估效率收入", visible_columns)
|
|
|
- self.assertNotIn("LTV预测效率收入", visible_columns)
|
|
|
- self.assertIn("裂变效率收入", frame.columns)
|
|
|
- self.assertNotIn("裂变效率收入", visible_columns)
|
|
|
+ extra = row(
|
|
|
+ ENTITY_SELF,
|
|
|
+ SELF_CHANNEL,
|
|
|
+ "latest-only",
|
|
|
+ DATES[1],
|
|
|
+ 0.01,
|
|
|
+ uv=150,
|
|
|
+ )
|
|
|
+ daily = pd.concat([daily, pd.DataFrame([extra])], ignore_index=True)
|
|
|
+ candidates, _, _ = evaluate_rules(daily, DATES)
|
|
|
+ frame = _sheet_frame(candidates, "小程序投流", DATES, 0.25)
|
|
|
+ visible = _visible_columns("小程序投流", DATES, 0.25)
|
|
|
+
|
|
|
+ for column in (
|
|
|
+ "dt",
|
|
|
+ "首层UV",
|
|
|
+ "T0裂变人数",
|
|
|
+ "T0裂变率",
|
|
|
+ "首层效率收入",
|
|
|
+ "T0裂变效率收入",
|
|
|
+ "预测总效率收入",
|
|
|
+ "成本",
|
|
|
+ "效率ROI",
|
|
|
+ T0_FISSION_MULTIPLIER_COLUMN,
|
|
|
+ TOTAL_FISSION_TO_FIRST_UV_COLUMN,
|
|
|
+ "消耗加权分位",
|
|
|
+ "消耗加权P25线",
|
|
|
+ "动作原因",
|
|
|
+ "阈值样本状态",
|
|
|
+ ):
|
|
|
+ self.assertIn(column, visible)
|
|
|
+ self.assertNotIn(FINAL_ROI_COLUMN, visible)
|
|
|
+ self.assertIn(FINAL_ROI_COLUMN, frame.columns)
|
|
|
+ latest_row = frame[
|
|
|
+ frame["广告id"].eq("ad-0") & frame["dt"].eq(DATES[1])
|
|
|
+ ].iloc[0]
|
|
|
+ self.assertEqual(latest_row["T0裂变人数"], 120)
|
|
|
+ self.assertAlmostEqual(latest_row["T0裂变率"], 0.2)
|
|
|
+ self.assertEqual(latest_row["首层效率收入"], 20)
|
|
|
+ self.assertEqual(latest_row["预测总效率收入"], 20)
|
|
|
+ self.assertEqual(set(frame["dt"]), set(DATES))
|
|
|
+ self.assertTrue((frame.groupby("广告id").size() == 2).all())
|
|
|
+
|
|
|
+ history = frame[frame["dt"].eq(DATES[0])]
|
|
|
+ latest = frame[frame["dt"].eq(DATES[1])]
|
|
|
+ self.assertTrue(history["动作"].fillna("").eq("").all())
|
|
|
+ self.assertTrue(history["审批选择"].eq("历史日").all())
|
|
|
+ self.assertTrue(history["动作幂等键"].fillna("").eq("").all())
|
|
|
+ self.assertTrue(latest["动作"].ne("").any())
|
|
|
self.assertTrue(
|
|
|
- frame[FINAL_ROI_COLUMN].equals(frame["ROI"])
|
|
|
+ latest["阈值样本状态"].eq("观察_最新日UV达标").any()
|
|
|
+ )
|
|
|
+ self.assertEqual(
|
|
|
+ frame.iloc[0]["dt"],
|
|
|
+ DATES[1],
|
|
|
+ )
|
|
|
+ self.assertEqual(
|
|
|
+ frame.iloc[-1]["dt"],
|
|
|
+ DATES[0],
|
|
|
)
|
|
|
pd.testing.assert_series_equal(
|
|
|
frame[TOTAL_FISSION_TO_FIRST_UV_COLUMN],
|
刘立冬