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feat: add strategy="quantile" in KBinsDiscretizer #654

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1 change: 1 addition & 0 deletions 1 bigframes/ml/compose.py
Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@
"ML.MAX_ABS_SCALER": preprocessing.MaxAbsScaler,
"ML.MIN_MAX_SCALER": preprocessing.MinMaxScaler,
"ML.BUCKETIZE": preprocessing.KBinsDiscretizer,
"ML.QUANTILE_BUCKETIZE": preprocessing.KBinsDiscretizer,
"ML.LABEL_ENCODER": preprocessing.LabelEncoder,
}
)
Expand Down
51 changes: 35 additions & 16 deletions 51 bigframes/ml/preprocessing.py
Original file line number Diff line number Diff line change
Expand Up @@ -290,10 +290,6 @@ def __init__(
n_bins: int = 5,
strategy: Literal["uniform", "quantile"] = "quantile",
):
if strategy != "uniform":
raise NotImplementedError(
f"Only strategy = 'uniform' is supported now, input is {strategy}."
)
if n_bins < 2:
raise ValueError(
f"n_bins has to be larger than or equal to 2, input is {n_bins}."
Expand Down Expand Up @@ -337,30 +333,53 @@ def _compile_to_sql(
min_value + i * bin_size for i in range(self.n_bins - 1)
]

return [
(
self._base_sql_generator.ml_bucketize(
column, array_split_points[column], f"kbinsdiscretizer_{column}"
),
f"kbinsdiscretizer_{column}",
return [
(
self._base_sql_generator.ml_bucketize(
column, array_split_points[column], f"kbinsdiscretizer_{column}"
),
f"kbinsdiscretizer_{column}",
)
for column in columns
]

elif self.strategy == "quantile":

return [
(
self._base_sql_generator.ml_quantile_bucketize(
column, self.n_bins, f"kbinsdiscretizer_{column}"
),
f"kbinsdiscretizer_{column}",
)
for column in columns
]

else:
raise ValueError(
f"strategy should be set 'quantile' or 'uniform', but your input is {self.strategy}."
)
for column in columns
]

@classmethod
def _parse_from_sql(cls, sql: str) -> tuple[KBinsDiscretizer, str]:
"""Parse SQL to tuple(KBinsDiscretizer, column_label).

Args:
sql: SQL string of format "ML.BUCKETIZE({col_label}, array_split_points, FALSE) OVER()"
sql: SQL string of format "ML.BUCKETIZE({col_label}, array_split_points, FALSE)"
or ML.QUANTILE_BUCKETIZE({col_label}, num_bucket) OVER()"

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Returns:
tuple(KBinsDiscretizer, column_label)"""
s = sql[sql.find("(") + 1 : sql.find(")")]
array_split_points = s[s.find("[") + 1 : s.find("]")]
col_label = s[: s.find(",")]
n_bins = array_split_points.count(",") + 2
return cls(n_bins, "uniform"), col_label

if sql.startswith("ML.QUANTILE_BUCKETIZE"):
num_bins = s.split(",")[1]
return cls(int(num_bins), "quantile"), col_label
else:
array_split_points = s[s.find("[") + 1 : s.find("]")]
n_bins = array_split_points.count(",") + 2
return cls(n_bins, "uniform"), col_label

def fit(
self,
Expand Down
11 changes: 10 additions & 1 deletion 11 bigframes/ml/sql.py
Original file line number Diff line number Diff line change
Expand Up @@ -109,9 +109,18 @@ def ml_bucketize(
array_split_points: Iterable[Union[int, float]],
name: str,
) -> str:
"""Encode ML.MIN_MAX_SCALER for BQML"""
"""Encode ML.BUCKETIZE for BQML"""
return f"""ML.BUCKETIZE({numeric_expr_sql}, {array_split_points}, FALSE) AS {name}"""

def ml_quantile_bucketize(
self,
numeric_expr_sql: str,
num_bucket: int,
name: str,
) -> str:
"""Encode ML.QUANTILE_BUCKETIZE for BQML"""
return f"""ML.QUANTILE_BUCKETIZE({numeric_expr_sql}, {num_bucket}) OVER() AS {name}"""

def ml_one_hot_encoder(
self,
numeric_expr_sql: str,
Expand Down
58 changes: 58 additions & 0 deletions 58 tests/system/small/ml/test_preprocessing.py
Original file line number Diff line number Diff line change
Expand Up @@ -373,6 +373,27 @@ def test_k_bins_discretizer_normalized_fit_transform_default_params(new_penguins
pd.testing.assert_frame_equal(result, expected, rtol=0.1)


def test_k_bins_discretizer_normalized_fit_transform_default_params_quantile(
new_penguins_df,
):
discretizer = preprocessing.KBinsDiscretizer(strategy="quantile")
result = discretizer.fit_transform(
new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
).to_pandas()

expected = pd.DataFrame(
{
"kbinsdiscretizer_culmen_length_mm": ["bin_2", "bin_2", "bin_1"],
"kbinsdiscretizer_culmen_depth_mm": ["bin_2", "bin_1", "bin_2"],
"kbinsdiscretizer_flipper_length_mm": ["bin_2", "bin_1", "bin_2"],
},
dtype="string[pyarrow]",
index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
)

pd.testing.assert_frame_equal(result, expected, rtol=0.1)


def test_k_bins_discretizer_series_normalizes(
penguins_df_default_index, new_penguins_df
):
Expand All @@ -395,6 +416,28 @@ def test_k_bins_discretizer_series_normalizes(
pd.testing.assert_frame_equal(result, expected, rtol=0.1)


def test_k_bins_discretizer_series_normalizes_quantile(
penguins_df_default_index, new_penguins_df
):
discretizer = preprocessing.KBinsDiscretizer(strategy="quantile")
discretizer.fit(penguins_df_default_index["culmen_length_mm"])

result = discretizer.transform(
penguins_df_default_index["culmen_length_mm"]
).to_pandas()
result = discretizer.transform(new_penguins_df).to_pandas()

expected = pd.DataFrame(
{
"kbinsdiscretizer_culmen_length_mm": ["bin_2", "bin_2", "bin_1"],
},
dtype="string[pyarrow]",
index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
)

pd.testing.assert_frame_equal(result, expected, rtol=0.1)


def test_k_bins_discretizer_normalizes(penguins_df_default_index, new_penguins_df):
# TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.KBinsDiscretizer, when BQML's change is in prod.
discretizer = preprocessing.KBinsDiscretizer(strategy="uniform")
Expand Down Expand Up @@ -488,6 +531,21 @@ def test_k_bins_discretizer_save_load(new_penguins_df, dataset_id):
pd.testing.assert_frame_equal(result, expected, rtol=0.1)


def test_k_bins_discretizer_save_load_quantile(new_penguins_df, dataset_id):
transformer = preprocessing.KBinsDiscretizer(n_bins=6, strategy="quantile")
transformer.fit(
new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
)

reloaded_transformer = transformer.to_gbq(
f"{dataset_id}.temp_configured_model", replace=True
)
assert isinstance(reloaded_transformer, preprocessing.KBinsDiscretizer)
assert reloaded_transformer.n_bins == transformer.n_bins
assert reloaded_transformer.strategy == transformer.strategy
assert reloaded_transformer._bqml_model is not None


def test_one_hot_encoder_default_params(new_penguins_df):
encoder = preprocessing.OneHotEncoder()
encoder.fit(new_penguins_df[["species", "sex"]])
Expand Down
7 changes: 7 additions & 0 deletions 7 tests/unit/ml/test_sql.py
Original file line number Diff line number Diff line change
Expand Up @@ -113,6 +113,13 @@ def test_k_bins_discretizer_correct(
assert sql == "ML.BUCKETIZE(col_a, [1, 2, 3, 4], FALSE) AS scaled_col_a"


def test_k_bins_discretizer_quantile_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
sql = base_sql_generator.ml_quantile_bucketize("col_a", 5, "scaled_col_a")
assert sql == "ML.QUANTILE_BUCKETIZE(col_a, 5) OVER() AS scaled_col_a"


def test_one_hot_encoder_correct(
base_sql_generator: ml_sql.BaseSqlGenerator,
):
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):
strategy ({'uniform', 'quantile'}, default='quantile'):
Strategy used to define the widths of the bins. 'uniform': All bins
in each feature have identical widths. 'quantile': All bins in each
feature have the same number of points. Only `uniform` is supported.
feature have the same number of points.
"""

def fit(self, X, y=None):
Expand Down
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