Honor complete attribution beta semantics labels
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@@ -3,6 +3,7 @@ from __future__ import annotations
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import json
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import http.client
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import io
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import re
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import socket
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import ssl
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import warnings
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@@ -472,25 +473,33 @@ def _resolve_beta_semantics(row: pd.Series) -> dict[str, str]:
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else:
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if isinstance(parsed, dict):
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parsed_mapping = {str(key): str(value) for key, value in parsed.items()}
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if set(parsed_mapping) == set(SEMANTIC_BETA_COLUMNS) and parsed_mapping == canonical:
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if set(parsed_mapping) == set(SEMANTIC_BETA_COLUMNS) and all(
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value.strip() for value in parsed_mapping.values()
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):
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return parsed_mapping
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return canonical
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def _section_beta_header_map(summary_df: pd.DataFrame) -> dict[str, str]:
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if summary_df.empty:
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return {}
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def _beta_header_name(factor_name: str) -> str:
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suffix = factor_name.strip().lower()
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suffix = re.sub(r"[^a-z0-9]+", "_", suffix).strip("_")
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if suffix == "mkt_rf":
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suffix = "mkt"
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return f"beta_{suffix}"
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semantics = _resolve_beta_semantics(summary_df.iloc[0])
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def _section_beta_header_map(semantics: dict[str, str]) -> dict[str, str]:
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header_map: dict[str, str] = {}
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for beta_column, factor_name in semantics.items():
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suffix = factor_name.lower()
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if suffix == "mkt_rf":
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suffix = "mkt"
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header_map[beta_column] = f"beta_{suffix}"
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header_map[beta_column] = _beta_header_name(factor_name)
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return header_map
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def _section_key(row: pd.Series) -> tuple[bool, tuple[tuple[str, str], ...]]:
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semantics = _resolve_beta_semantics(row)
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return bool(row.get("proxy_only", False)), tuple((key, semantics[key]) for key in SEMANTIC_BETA_COLUMNS)
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def attribute_strategies(
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results_df: pd.DataFrame,
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benchmark_label: str,
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@@ -651,7 +660,7 @@ def _top_loading_descriptions(row: pd.Series, limit: int = 2) -> str:
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return ", ".join(f"{name} {value:.2f}" for name, value in top_loadings)
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def _print_attribution_section(summary_df: pd.DataFrame, title: str, proxy_labels: bool) -> None:
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def _print_attribution_section(summary_df: pd.DataFrame, title: str, semantics: dict[str, str]) -> None:
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display_columns = [
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"strategy",
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"market",
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@@ -671,8 +680,7 @@ def _print_attribution_section(summary_df: pd.DataFrame, title: str, proxy_label
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"beta_recovery",
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]
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table = summary_df.reindex(columns=display_columns).copy()
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del proxy_labels
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table = table.rename(columns=_section_beta_header_map(summary_df))
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table = table.rename(columns=_section_beta_header_map(semantics))
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numeric_columns = [
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column
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for column in table.columns
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@@ -689,22 +697,18 @@ def print_attribution_summary(summary_df: pd.DataFrame) -> None:
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print("Factor attribution: no usable regressions were produced.")
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return
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proxy_mask = summary_df["proxy_only"].fillna(False).astype(bool)
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standard_rows = summary_df.loc[~proxy_mask]
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proxy_rows = summary_df.loc[proxy_mask]
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print("\nFactor attribution")
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if not standard_rows.empty:
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sections: dict[tuple[bool, tuple[tuple[str, str], ...]], list[int]] = {}
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for index, row in summary_df.iterrows():
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sections.setdefault(_section_key(row), []).append(index)
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for (is_proxy, semantics_items), row_indexes in sections.items():
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section_rows = summary_df.loc[row_indexes]
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title = "Proxy factor attribution" if is_proxy else "Standard factor attribution"
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_print_attribution_section(
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standard_rows,
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title="Standard factor attribution",
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proxy_labels=False,
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)
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if not proxy_rows.empty:
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_print_attribution_section(
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proxy_rows,
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title="Proxy factor attribution",
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proxy_labels=True,
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section_rows,
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title=title,
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semantics=dict(semantics_items),
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)
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print("\nInterpretation")
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for _, row in summary_df.iterrows():
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@@ -923,6 +923,100 @@ class AttributionIntegrationTests(unittest.TestCase):
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self.assertIn("SMB_PROXY", output)
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self.assertNotIn(" beta_smb ", output)
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def test_print_attribution_summary_honors_complete_noncanonical_beta_semantics(self):
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summary = pd.DataFrame(
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[
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{
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"strategy": "US Strategy",
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"market": "us",
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"model": "ff5",
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"factor_source": "external+local",
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"proxy_only": False,
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"beta_semantics": json.dumps(
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{
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"beta_mkt": "MARKET_EXCESS",
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"beta_smb": "SIZE",
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"beta_hml": "VALUE",
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"beta_rmw": "QUALITY",
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"beta_cma": "INVESTMENT",
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"beta_mom": "MOMENTUM",
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"beta_lowvol": "MINVOL",
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"beta_recovery": "BOUNCE",
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}
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),
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"start_date": "2025-01-02",
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"end_date": "2026-03-24",
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"n_obs": 319,
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"alpha_daily": 0.0004,
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"alpha_ann": 0.1008,
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"alpha_t_stat": 2.1,
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"alpha_p_value": 0.04,
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"r_squared": 0.82,
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"adj_r_squared": 0.81,
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"residual_vol_ann": 0.12,
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"beta_mkt": 1.05,
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"beta_smb": -0.20,
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"beta_hml": 0.30,
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"beta_rmw": 0.05,
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"beta_cma": np.nan,
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"beta_mom": np.nan,
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"beta_lowvol": np.nan,
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"beta_recovery": np.nan,
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},
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{
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"strategy": "CN Strategy",
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"market": "cn",
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"model": "proxy",
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"factor_source": "proxy_only",
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"proxy_only": True,
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"beta_semantics": json.dumps(
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{
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"beta_mkt": "LOCAL_MARKET",
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"beta_smb": "SIZE_PROXY_CUSTOM",
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"beta_hml": "VALUE_PROXY_CUSTOM",
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"beta_rmw": "QUALITY_PROXY_CUSTOM",
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"beta_cma": "INVEST_PROXY_CUSTOM",
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"beta_mom": "TREND",
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"beta_lowvol": "DEFENSIVE",
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"beta_recovery": "RECOVERY_PROXY",
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}
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),
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"start_date": "2025-01-02",
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"end_date": "2026-03-24",
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"n_obs": 319,
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"alpha_daily": 0.0002,
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"alpha_ann": 0.0504,
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"alpha_t_stat": 1.5,
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"alpha_p_value": 0.12,
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"r_squared": 0.72,
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"adj_r_squared": 0.70,
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"residual_vol_ann": 0.14,
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"beta_mkt": 0.85,
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"beta_smb": -0.30,
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"beta_hml": 0.25,
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"beta_rmw": 0.10,
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"beta_cma": -0.05,
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"beta_mom": 0.20,
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"beta_lowvol": np.nan,
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"beta_recovery": np.nan,
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},
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]
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)
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buffer = io.StringIO()
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with contextlib.redirect_stdout(buffer):
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print_attribution_summary(summary)
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output = buffer.getvalue()
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self.assertIn("Standard factor attribution", output)
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self.assertIn("Proxy factor attribution", output)
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self.assertIn("beta_market_excess", output)
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self.assertIn("beta_size_proxy_custom", output)
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self.assertIn("MARKET_EXCESS 1.05", output)
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self.assertIn("SIZE_PROXY_CUSTOM -0.30", output)
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self.assertNotIn("MKT_RF 1.05", output)
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self.assertNotIn("SMB_PROXY -0.30", output)
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def test_print_attribution_summary_splits_standard_and_proxy_sections_for_mixed_frames(self):
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summary = pd.DataFrame(
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[
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