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42029e4 | 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 | """Tests for data loading, validation and labelling."""
from __future__ import annotations
import pandas as pd
import pytest
from src.data import LendingClubLoader, build_fraud_labels, LabelConfig
from src.data.splitter import stratified_split, time_based_split
def test_synthetic_loader_shape():
df = LendingClubLoader._synthetic(n=1000, seed=1)
assert len(df) == 1000
assert "loan_amnt" in df.columns
assert df["loan_amnt"].between(500, 50_000).all()
def test_synthetic_loader_no_nan_in_target_fields():
df = LendingClubLoader._synthetic(n=500, seed=2)
# Critical fields used downstream cannot be all-NaN
for col in ("loan_amnt", "annual_inc", "grade", "issue_d"):
assert df[col].notna().any()
def test_fraud_labels_positive_rate_in_range(synthetic_df):
rate = synthetic_df["is_fraud"].mean()
# Defensible bounds for a synthetic 5k sample
assert 0.0 < rate < 0.5
def test_fraud_labels_columns_present(synthetic_df):
for col in ("rule_fpd", "rule_income_anomaly", "rule_debt_inconsist",
"rule_address_ring", "n_anomalies", "is_fraud"):
assert col in synthetic_df.columns
def test_time_split_chronological(synthetic_df):
train, val, test = time_based_split(synthetic_df, "issue_d", 0.15, 0.15)
assert len(train) + len(val) + len(test) == len(synthetic_df)
assert train["issue_d"].max() <= val["issue_d"].max()
assert val["issue_d"].max() <= test["issue_d"].max()
def test_stratified_split_preserves_rate(synthetic_df):
train, val, test = stratified_split(synthetic_df, "is_fraud", 0.15, 0.15, random_seed=1)
rates = [d["is_fraud"].mean() for d in (train, val, test)]
assert max(rates) - min(rates) < 0.02 # within 2pp
def test_label_config_from_dict():
cfg = LabelConfig.from_dict(
{"rules": {"first_payment_default": {"enabled": False, "max_days_to_default": 60}}}
)
assert cfg.fpd_enabled is False
assert cfg.fpd_max_days == 60
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