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"""
Tests for the datasets module.
These tests verify that the dataset loading functions work correctly,
including both the download/cache mechanism and the fallback data generation.
"""
import os
import tempfile
from pathlib import Path
from unittest.mock import patch
import numpy as np
import pandas as pd
import pytest
from diff_diff.datasets import (
_CACHE_DIR,
_construct_card_krueger_data,
_construct_castle_doctrine_data,
_construct_divorce_laws_data,
_construct_mpdta_data,
clear_cache,
list_datasets,
load_card_krueger,
load_castle_doctrine,
load_dataset,
load_divorce_laws,
load_mpdta,
)
class TestListDatasets:
"""Tests for list_datasets function."""
def test_returns_dict(self):
"""list_datasets should return a dictionary."""
result = list_datasets()
assert isinstance(result, dict)
def test_contains_expected_datasets(self):
"""list_datasets should contain all expected datasets."""
result = list_datasets()
expected = {"card_krueger", "castle_doctrine", "divorce_laws", "mpdta"}
assert set(result.keys()) == expected
def test_descriptions_are_strings(self):
"""All descriptions should be non-empty strings."""
result = list_datasets()
for name, desc in result.items():
assert isinstance(desc, str)
assert len(desc) > 0
class TestLoadDataset:
"""Tests for load_dataset function."""
def test_load_by_name(self):
"""load_dataset should load datasets by name."""
# Use fallback data to avoid network dependency
with patch("diff_diff.datasets._download_with_cache") as mock:
mock.side_effect = RuntimeError("No network")
df = load_dataset("card_krueger")
assert isinstance(df, pd.DataFrame)
def test_invalid_name_raises(self):
"""load_dataset should raise ValueError for unknown datasets."""
with pytest.raises(ValueError, match="Unknown dataset"):
load_dataset("nonexistent_dataset")
class TestCardKrueger:
"""Tests for Card-Krueger dataset."""
def test_fallback_data_structure(self):
"""Fallback data should have expected structure."""
df = _construct_card_krueger_data()
# Check required columns
required_cols = {"store_id", "state", "chain", "emp_pre", "emp_post", "treated"}
assert required_cols.issubset(set(df.columns))
# Check states
assert set(df["state"].unique()) == {"NJ", "PA"}
# Check treatment indicator
assert df[df["state"] == "NJ"]["treated"].all() == 1
assert df[df["state"] == "PA"]["treated"].all() == 0
# Check chains
expected_chains = {"bk", "kfc", "roys", "wendys"}
assert set(df["chain"].unique()) == expected_chains
def test_fallback_data_size(self):
"""Fallback data should have reasonable size."""
df = _construct_card_krueger_data()
# Should have roughly 300+ stores total
assert 250 < len(df) < 450
def test_fallback_data_values(self):
"""Fallback data should have reasonable values."""
df = _construct_card_krueger_data()
# Employment should be non-negative
assert (df["emp_pre"] >= 0).all()
assert (df["emp_post"] >= 0).all()
# Wages should be reasonable (around minimum wage range)
assert (df["wage_pre"] > 3).all()
assert (df["wage_pre"] < 7).all()
def test_load_uses_fallback_on_network_error(self):
"""load_card_krueger should use fallback when network fails."""
with patch("diff_diff.datasets._download_with_cache") as mock:
mock.side_effect = RuntimeError("Network error")
df = load_card_krueger()
assert isinstance(df, pd.DataFrame)
assert "treated" in df.columns
class TestCastleDoctrine:
"""Tests for Castle Doctrine dataset."""
def test_fallback_data_structure(self):
"""Fallback data should have expected structure."""
df = _construct_castle_doctrine_data()
# Check required columns
required_cols = {"state", "year", "first_treat", "homicide_rate", "treated"}
assert required_cols.issubset(set(df.columns))
# Check years
assert df["year"].min() == 2000
assert df["year"].max() == 2010
def test_fallback_data_treatment(self):
"""Fallback data should have correct treatment structure."""
df = _construct_castle_doctrine_data()
# Check that never-treated states have first_treat = 0
never_treated = df[df["first_treat"] == 0]
assert len(never_treated) > 0
assert (never_treated["treated"] == 0).all()
# Check that treated indicator matches timing
treated_states = df[df["first_treat"] > 0]
for _, row in treated_states.iterrows():
expected_treated = 1 if row["year"] >= row["first_treat"] else 0
assert row["treated"] == expected_treated
def test_fallback_data_values(self):
"""Fallback data should have reasonable values."""
df = _construct_castle_doctrine_data()
# Homicide rates should be positive
assert (df["homicide_rate"] > 0).all()
assert (df["homicide_rate"] < 20).all()
class TestDivorceLaws:
"""Tests for Divorce Laws dataset."""
def test_fallback_data_structure(self):
"""Fallback data should have expected structure."""
df = _construct_divorce_laws_data()
# Check required columns
required_cols = {"state", "year", "first_treat", "divorce_rate", "treated"}
assert required_cols.issubset(set(df.columns))
# Check years
assert df["year"].min() == 1968
assert df["year"].max() == 1988
def test_fallback_data_treatment(self):
"""Fallback data should have correct treatment structure."""
df = _construct_divorce_laws_data()
# Check that treated indicator matches timing
for _, row in df.iterrows():
if row["first_treat"] == 0:
assert row["treated"] == 0
elif row["year"] >= row["first_treat"]:
assert row["treated"] == 1
else:
assert row["treated"] == 0
def test_fallback_data_values(self):
"""Fallback data should have reasonable values."""
df = _construct_divorce_laws_data()
# Divorce rates should be positive
assert (df["divorce_rate"] > 0).all()
assert (df["divorce_rate"] < 15).all()
# Female LFP should be between 0 and 1
assert (df["female_lfp"] >= 0).all()
assert (df["female_lfp"] <= 1).all()
class TestMPDTA:
"""Tests for mpdta dataset."""
def test_fallback_data_structure(self):
"""Fallback data should have expected structure."""
df = _construct_mpdta_data()
# Check required columns
required_cols = {"countyreal", "year", "lpop", "lemp", "first_treat", "treat"}
assert required_cols.issubset(set(df.columns))
# Check years
assert set(df["year"].unique()) == {2003, 2004, 2005, 2006, 2007}
def test_fallback_data_cohorts(self):
"""Fallback data should have expected cohorts."""
df = _construct_mpdta_data()
# Cohorts should be 0, 2004, 2006, 2007
expected_cohorts = {0, 2004, 2006, 2007}
assert set(df["first_treat"].unique()) == expected_cohorts
def test_fallback_data_size(self):
"""Fallback data should have expected size."""
df = _construct_mpdta_data()
# 500 counties * 5 years = 2500 rows
assert len(df) == 2500
assert df["countyreal"].nunique() == 500
class TestClearCache:
"""Tests for cache management."""
def test_clear_cache_creates_directory(self):
"""clear_cache should handle non-existent cache gracefully."""
# This should not raise even if cache doesn't exist
try:
clear_cache()
except Exception as e:
pytest.fail(f"clear_cache raised unexpected exception: {e}")
class TestDatasetIntegration:
"""Integration tests verifying datasets work with estimators."""
def test_card_krueger_with_did(self):
"""Card-Krueger data should work with DifferenceInDifferences."""
from diff_diff import DifferenceInDifferences
# Use fallback data
df = _construct_card_krueger_data()
# Reshape to long format
df_long = df.melt(
id_vars=["store_id", "state", "treated"],
value_vars=["emp_pre", "emp_post"],
var_name="period",
value_name="employment",
)
df_long["post"] = (df_long["period"] == "emp_post").astype(int)
df_long = df_long.dropna(subset=["employment"])
# Should be able to fit DiD
did = DifferenceInDifferences()
results = did.fit(
df_long, outcome="employment", treatment="treated", time="post"
)
assert hasattr(results, "att")
assert hasattr(results, "se")
assert not np.isnan(results.att)
def test_castle_doctrine_with_cs(self):
"""Castle Doctrine data should work with CallawaySantAnna."""
from diff_diff import CallawaySantAnna
# Use fallback data
df = _construct_castle_doctrine_data()
# Should be able to fit CS
cs = CallawaySantAnna(control_group="never_treated")
results = cs.fit(
df,
outcome="homicide_rate",
unit="state",
time="year",
first_treat="first_treat",
)
assert hasattr(results, "group_time_effects")
assert len(results.group_time_effects) > 0
def test_mpdta_with_cs(self):
"""mpdta data should work with CallawaySantAnna."""
from diff_diff import CallawaySantAnna
# Use fallback data
df = _construct_mpdta_data()
# Should be able to fit CS
cs = CallawaySantAnna(control_group="never_treated")
results = cs.fit(
df,
outcome="lemp",
unit="countyreal",
time="year",
first_treat="first_treat",
)
assert hasattr(results, "group_time_effects")
assert len(results.group_time_effects) > 0
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