def test_targetencoder_multi_column():
"""
Test jointly encoding multiple columns
"""
train = cudf.DataFrame({
'cat_1': ['a', 'b', 'b', 'a', 'a', 'b'],
'cat_2': [1, 1, 2, 2, 1, 2],
'label': [1, 0, 1, 1, 0, 1]
})
test = cudf.DataFrame({
'cat_1': ['b', 'b', 'a', 'b'],
'cat_2': [1, 2, 1, 2]
})
encoder = TargetEncoder()
train_encoded = encoder.fit_transform(train[['cat_1', 'cat_2']],
train.label)
test_encoded = encoder.transform(test[['cat_1', 'cat_2']])
train_answer = np.array([2. / 3, 2. / 3, 1., 2. / 3, 2. / 3, 1.])
test_answer = np.array([0., 1., 0.5, 1.])
assert array_equal(train_encoded, train_answer)
assert array_equal(test_encoded, test_answer)
encoder = TargetEncoder()
encoder.fit(train[['cat_1', 'cat_2']], train.label)
train_encoded = encoder.transform(train[['cat_1', 'cat_2']])
test_encoded = encoder.transform(test[['cat_1', 'cat_2']])
assert array_equal(train_encoded, train_answer)
assert array_equal(test_encoded, test_answer)
def test_get_params():
params = {
'n_folds': 5,
'smooth': 1,
'seed': 49,
'split_method': 'customize'
}
encoder = TargetEncoder(**params)
p2 = encoder.get_params()
for k, v in params.items():
assert v == p2[k]
def test_targetencoder_cupy():
"""
Note that there are newly-encountered values in x_test,
namely, 3 and 4.
"""
x_train = cp.array([1, 2, 2, 1])
y_train = cp.array([1, 0, 1, 1])
x_test = cp.array([1, 2, 3, 4])
encoder = TargetEncoder()
encoder.fit_transform(x_train, y_train)
test_encoded = encoder.transform(x_test)
answer = np.array([1., 0.5, 0.75, 0.75])
assert array_equal(test_encoded, answer)
print(type(test_encoded))
assert isinstance(test_encoded, cp.ndarray)
def test_one_category():
train = cudf.DataFrame({
'category': ['a', 'a', 'a', 'a'],
'label': [3, 0, 0, 3]
})
test = cudf.DataFrame({'category': ['c', 'b', 'a', 'd']})
encoder = TargetEncoder()
train_encoded = encoder.fit_transform(train.category, train.label)
answer = np.array([1., 2., 2., 1.])
assert array_equal(train_encoded, answer)
test_encoded = encoder.transform(test.category)
answer = np.array([1.5, 1.5, 1.5, 1.5])
assert array_equal(test_encoded, answer)
def test_targetencoder_transform():
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'a'],
'label': [1, 0, 1, 1]
})
test = cudf.DataFrame({'category': ['b', 'b', 'a', 'b']})
encoder = TargetEncoder()
encoder.fit_transform(train.category, train.label)
test_encoded = encoder.transform(test.category)
answer = np.array([0.5, 0.5, 1., 0.5])
assert array_equal(test_encoded, answer)
encoder = TargetEncoder()
encoder.fit(train.category, train.label)
test_encoded = encoder.transform(test.category)
assert array_equal(test_encoded, answer)
def test_targetencoder_pandas():
"""
Note that there are newly-encountered values in test,
namely, 'c' and 'd'.
"""
train = pandas.DataFrame({
'category': ['a', 'b', 'b', 'a'],
'label': [1, 0, 1, 1]
})
test = pandas.DataFrame({'category': ['c', 'b', 'a', 'd']})
encoder = TargetEncoder()
encoder.fit_transform(train.category, train.label)
test_encoded = encoder.transform(test.category)
answer = np.array([0.75, 0.5, 1., 0.75])
assert array_equal(test_encoded, answer)
print(type(test_encoded))
assert isinstance(test_encoded, np.ndarray)
def test_targetencoder_random(n_samples, dtype):
x = cp.random.randint(0, 1000, n_samples).astype(dtype)
y = cp.random.randint(0, 2, n_samples).astype(dtype)
xt = cp.random.randint(0, 1000, n_samples).astype(dtype)
encoder = TargetEncoder()
encoder.fit_transform(x, y)
test_encoded = encoder.transform(xt)
df_train = cudf.DataFrame({'x': x, 'y': y})
dg = df_train.groupby('x', as_index=False).agg({'y': 'mean'})
df_test = cudf.DataFrame({'x': xt})
df_test['row_id'] = cp.arange(len(df_test))
df_test = df_test.merge(dg, on='x', how='left')
df_test = df_test.sort_values('row_id')
answer = df_test['y'].fillna(cp.mean(y).item()).values
assert array_equal(test_encoded, answer)
def test_targetencoder_var():
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'b'],
'label': [1, 0, 1, 1]
})
encoder = TargetEncoder(stat='var')
train_encoded = encoder.fit_transform(train.category, train.label)
answer = np.array([.25, 0., .5, .5])
assert array_equal(train_encoded, answer)
encoder = TargetEncoder(stat='var')
encoder.fit(train.category, train.label)
train_encoded = encoder.transform(train.category)
assert array_equal(train_encoded, answer)
def test_targetencoder_smooth():
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'a'],
'label': [1, 0, 1, 1]
})
answers = np.array([[1., 1., 0., 1.], [0.875, 0.875, 0.375, 0.875],
[0.8333, 0.8333, 0.5, 0.8333],
[0.75, 0.75, 0.75, 0.75]])
smooths = [0, 1, 2, 10000]
for smooth, answer in zip(smooths, answers):
encoder = TargetEncoder(smooth=smooth)
train_encoded = encoder.fit_transform(train.category, train.label)
assert array_equal(train_encoded, answer)
encoder = TargetEncoder(smooth=smooth)
encoder.fit(train.category, train.label)
train_encoded = encoder.transform(train.category)
assert array_equal(train_encoded, answer)
def test_transform_with_index():
df = cudf.DataFrame({
"a": [1, 1, 2, 3],
"b": [True, False, False, True]
},
index=[9, 4, 5, 3])
t_enc = TargetEncoder()
t_enc.fit(df.a, y=df.b)
train_encoded = t_enc.transform(df.a)
ans = cp.asarray([0, 1, 0.5, 0.5])
assert array_equal(train_encoded, ans)
train_encoded = t_enc.transform(df[["a"]])
assert array_equal(train_encoded, ans)
def test_targetencoder_customized_fold_id():
"""
use customized `fold_ids` array to split data.
in this example, the 1st sample belongs to `fold 0`
the 2nd and 3rd sample belongs to `fold 1`
and the 4th sample belongs to `fold 2`
"""
train = cudf.DataFrame({
'category': ['a', 'b', 'b', 'a'],
'label': [1, 0, 1, 1]
})
fold_ids = [0, 1, 1, 2]
encoder = TargetEncoder(split_method='customize')
train_encoded = encoder.fit_transform(train.category,
train.label,
fold_ids=fold_ids)
answer = np.array([1., 0.75, 0.75, 1.])
assert array_equal(train_encoded, answer)
encoder = TargetEncoder(split_method='customize')
encoder.fit(train.category, train.label, fold_ids=fold_ids)
train_encoded = encoder.transform(train.category)
assert array_equal(train_encoded, answer)
def cv(
self,
y_train: AoS,
train_features: XDataFrame,
test_features: XDataFrame,
y_valid: Optional[AoS],
valid_features: Optional[XDataFrame],
feature_name: List[str],
folds_ids: List[Tuple[np.ndarray, np.ndarray]],
target_scaler: Optional[MinMaxScaler],
config: dict,
log: bool = True,
) -> Tuple[
List[Model], np.ndarray, np.ndarray, Optional[np.ndarray], pd.DataFrame, dict
]:
# initialize
valid_exists = True if valid_features is not None else False
test_preds = np.zeros(len(test_features))
oof_preds = np.zeros(len(train_features))
if valid_exists:
valid_preds = np.zeros(len(valid_features))
else:
valid_preds = None
best_iteration = 0.0
cv_score_list: List[dict] = []
models: List[Model] = []
with timer("make X"):
X_train = train_features.copy()
X_test = test_features.copy()
X_valid = valid_features.copy() if valid_features is not None else None
with timer("make y"):
y = y_train.values if isinstance(y_train, pd.Series) else y_train
y_valid = y_valid.values if isinstance(y_valid, pd.Series) else y_valid
if config["target_encoding"]:
with timer("target encoding for test"):
cat_cols = config["categorical_cols"]
for cat_col in cat_cols:
encoder = TargetEncoder(n_folds=4, smooth=0.3)
encoder.fit(X_train[cat_col], y)
X_test[cat_col + "_TE"] = encoder.transform(X_test[cat_col])
feature_name.append((cat_col + "_TE"))
importances = pd.DataFrame(index=feature_name)
for i_fold, (trn_idx, val_idx) in enumerate(folds_ids):
with timer(f"fold {i_fold}"):
self.fold = i_fold
with timer("get train data and valid data"):
# get train data and valid data
x_trn = X_train.iloc[trn_idx]
y_trn = y[trn_idx]
x_val = X_train.iloc[val_idx]
y_val = y[val_idx]
if config["target_encoding"]:
with timer("target encoding"):
cat_cols = config["categorical_cols"]
for cat_col in cat_cols:
encoder = TargetEncoder(n_folds=4, smooth=0.3)
x_trn[cat_col + "_TE"] = encoder.fit_transform(
x_trn[cat_col], y_trn
)
x_val[cat_col + "_TE"] = encoder.transform(x_val[cat_col])
logging.info(f"train size: {x_trn.shape}, valid size: {x_val.shape}")
print(f"train size: {x_trn.shape}, valid size: {x_val.shape}")
with timer("get sampling"):
x_trn, y_trn = get_sampling(x_trn, y_trn, config)
with timer("train model"):
# train model
model, best_score = self.fit(x_trn, y_trn, x_val, y_val, config)
cv_score_list.append(best_score)
models.append(model)
best_iteration += self.get_best_iteration(model) / len(folds_ids)
with timer("predict oof and test"):
# predict oof and test
oof_preds[val_idx] = self.predict(model, x_val).reshape(-1)
test_preds += self.predict(model, X_test).reshape(-1) / len(
folds_ids
)
if valid_exists:
valid_preds += self.predict(model, valid_features).reshape(
-1
) / len(folds_ids)
with timer("get feature importance"):
# get feature importances
importances_tmp = pd.DataFrame(
self.get_feature_importance(model),
columns=[f"gain_{i_fold+1}"],
index=feature_name,
)
importances = importances.join(importances_tmp, how="inner")
# summary of feature importance
feature_importance = importances.mean(axis=1)
# save raw prediction
self.raw_oof_preds = oof_preds
self.raw_test_preds = test_preds
self.raw_valid_preds = valid_preds
# post_process (if you have any)
y, oof_preds, test_preds, y_valid, valid_preds = self.post_process(
oof_preds=oof_preds,
test_preds=test_preds,
valid_preds=valid_preds,
y_train=y_train,
y_valid=y_valid,
train_features=train_features,
test_features=test_features,
valid_features=valid_features,
target_scaler=target_scaler,
config=config,
)
# print oof score
oof_score = calc_metric(y, oof_preds)
print(f"oof score: {oof_score:.5f}")
if valid_exists:
valid_score = calc_metric(y_valid, valid_preds)
print(f"valid score: {valid_score:.5f}")
if log:
logging.info(f"oof score: {oof_score:.5f}")
if valid_exists:
logging.info(f"valid score: {valid_score:.5f}")
evals_results = {
"evals_result": {
"oof_score": oof_score,
"cv_score": {
f"cv{i + 1}": cv_score for i, cv_score in enumerate(cv_score_list)
},
"n_data": np.shape(X_train)[0],
"best_iteration": best_iteration,
"n_features": np.shape(X_train)[1],
"feature_importance": feature_importance.sort_values(
ascending=False
).to_dict(),
}
}
if valid_exists:
evals_results["valid_score"] = valid_score
return (
models,
oof_preds,
test_preds,
valid_preds,
feature_importance,
evals_results,
)
