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Configuration error
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942b115 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import pandas as pd
from app.features import FEATURE_COLUMNS, engineer_features
def _raw_frame() -> pd.DataFrame:
return pd.DataFrame(
[
# step, type, amount, nameOrig, oldOrig, newOrig, nameDest, oldDest, newDest
[1, "TRANSFER", 100.0, "C1", 100.0, 0.0, "C2", 0.0, 0.0],
[2, "PAYMENT", 50.0, "C1", 0.0, 0.0, "M1", 0.0, 0.0],
[3, "CASH_OUT", 200.0, "C3", 500.0, 300.0, "C2", 1000.0, 1200.0],
],
columns=[
"step",
"type",
"amount",
"nameOrig",
"oldbalanceOrg",
"newbalanceOrig",
"nameDest",
"oldbalanceDest",
"newbalanceDest",
],
)
def test_engineer_features_adds_all_feature_columns():
out = engineer_features(_raw_frame())
for col in FEATURE_COLUMNS:
assert col in out.columns
def test_type_one_hot_is_mutually_exclusive():
out = engineer_features(_raw_frame())
type_cols = [c for c in out.columns if c.startswith("type_")]
assert (out[type_cols].sum(axis=1) == 1).all()
def test_orig_zero_after_flag_detects_fraud_like_pattern():
out = engineer_features(_raw_frame())
row = out[out["nameOrig"] == "C1"].iloc[0]
assert row["orig_zero_after_flag"] == 1
def test_dest_is_merchant_flag():
out = engineer_features(_raw_frame())
row = out[out["nameDest"] == "M1"].iloc[0]
assert row["dest_is_merchant"] == 1
row2 = out[out["nameDest"] == "C2"].iloc[0]
assert row2["dest_is_merchant"] == 0
def test_velocity_only_counts_prior_transactions():
df = pd.DataFrame(
[
[1, "PAYMENT", 10.0, "C1", 100.0, 90.0, "M1", 0.0, 0.0],
[2, "PAYMENT", 10.0, "C1", 90.0, 80.0, "M1", 0.0, 0.0],
[3, "PAYMENT", 10.0, "C1", 80.0, 70.0, "M1", 0.0, 0.0],
],
columns=[
"step",
"type",
"amount",
"nameOrig",
"oldbalanceOrg",
"newbalanceOrig",
"nameDest",
"oldbalanceDest",
"newbalanceDest",
],
)
out = engineer_features(df)
counts = out.sort_values("step")["orig_prior_txn_count"].tolist()
assert counts == [0, 1, 2]
def test_balance_consistency_flags_true_when_arithmetic_matches():
df = pd.DataFrame(
[[1, "CASH_OUT", 100.0, "C1", 500.0, 400.0, "C2", 200.0, 300.0]],
columns=[
"step",
"type",
"amount",
"nameOrig",
"oldbalanceOrg",
"newbalanceOrig",
"nameDest",
"oldbalanceDest",
"newbalanceDest",
],
)
out = engineer_features(df)
assert out.iloc[0]["orig_balance_consistent"] == 1
assert out.iloc[0]["dest_balance_consistent"] == 1
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