"""Woodwork utility methods."""
import numpy as np
import pandas as pd
import woodwork as ww
from evalml.utils.gen_utils import is_all_numeric
numeric_and_boolean_ww = [
ww.logical_types.Integer.type_string,
ww.logical_types.Double.type_string,
ww.logical_types.Boolean.type_string,
ww.logical_types.Age.type_string,
ww.logical_types.AgeFractional.type_string,
ww.logical_types.IntegerNullable.type_string,
ww.logical_types.BooleanNullable.type_string,
ww.logical_types.AgeNullable.type_string,
]
def _numpy_to_pandas(array):
if len(array.shape) == 1:
data = pd.Series(array)
else:
data = pd.DataFrame(array)
return data
def _list_to_pandas(list):
return _numpy_to_pandas(np.array(list))
[docs]def infer_feature_types(data, feature_types=None):
"""Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns. If a column's type is not specified, it will be inferred by Woodwork.
Args:
data (pd.DataFrame, pd.Series): Input data to convert to a Woodwork data structure.
feature_types (string, ww.logical_type obj, dict, optional): If data is a 2D structure, feature_types must be a dictionary
mapping column names to the type of data represented in the column. If data is a 1D structure, then feature_types must be
a Woodwork logical type or a string representing a Woodwork logical type ("Double", "Integer", "Boolean", "Categorical", "Datetime", "NaturalLanguage")
Returns:
A Woodwork data structure where the data type of each column was either specified or inferred.
Raises:
ValueError: If there is a mismatch between the dataframe and the woodwork schema.
"""
if isinstance(data, list):
data = _list_to_pandas(data)
elif isinstance(data, np.ndarray):
data = _numpy_to_pandas(data)
if data.ww.schema is not None:
if isinstance(data, pd.DataFrame) and not ww.is_schema_valid(
data,
data.ww.schema,
):
ww_error = ww.get_invalid_schema_message(data, data.ww.schema)
if "dtype mismatch" in ww_error:
ww_error = (
"Dataframe types are not consistent with logical types. This usually happens "
"when a data transformation does not go through the ww accessor. Call df.ww.init() to "
f"get rid of this message. This is a more detailed message about the mismatch: {ww_error}"
)
else:
ww_error = f"{ww_error}. Please initialize ww with df.ww.init() to get rid of this message."
raise ValueError(ww_error)
return data
if isinstance(data, pd.Series):
if all(data.isna()):
data = data.replace(pd.NA, np.nan)
feature_types = "Double"
return ww.init_series(data, logical_type=feature_types)
else:
ww_data = data.copy()
ww_data.ww.init(logical_types=feature_types)
return ww_data
def _convert_numeric_dataset_pandas(X, y):
"""Convert numeric and non-null data to pandas datatype. Raises ValueError if there is null or non-numeric data. Used with data sampler strategies.
Args:
X (pd.DataFrame, np.ndarray): Data to transform.
y (pd.Series, np.ndarray): Target data.
Returns:
Tuple(pd.DataFrame, pd.Series): Transformed X and y.
"""
X_ww = infer_feature_types(X)
if not is_all_numeric(X_ww):
raise ValueError(
"Values not all numeric or there are null values provided in the dataset",
)
y_ww = infer_feature_types(y)
return X_ww, y_ww
def _schema_is_equal(first, other):
"""Loosely check whether or not the Woodwork schemas are equivalent. This only checks that the string values for the schemas are equal and doesn't take the actual type objects into account.
Args:
first (ww.Schema): The schema of the first woodwork datatable
other (ww.Schema): The schema of the second woodwork datatable
Returns:
bool: Whether or not the two schemas are equal
"""
if first.types.index.tolist() != other.types.index.tolist():
return False
logical = [
x if x != "Integer" else "Double"
for x in first.types["Logical Type"].astype(str).tolist()
] == [
x if x != "Integer" else "Double"
for x in other.types["Logical Type"].astype(str).tolist()
]
semantic = first.semantic_tags == other.semantic_tags
return logical and semantic
[docs]def downcast_nullable_types(data, ignore_null_cols=True):
"""Downcasts IntegerNullable, BooleanNullable types to Double, Boolean in order to support certain estimators like ARIMA, CatBoost, and LightGBM.
Args:
data (pd.DataFrame, pd.Series): Feature data.
ignore_null_cols (bool): Whether to ignore downcasting columns with null values or not. Defaults to True.
Returns:
data: DataFrame or Series initialized with logical type information where BooleanNullable are cast as Double.
"""
if data.ww.schema is None:
data.ww.init()
if isinstance(data, pd.Series):
if ignore_null_cols and data.isna().any():
return data
if isinstance(data.ww.logical_type, ww.logical_types.BooleanNullable):
data = data.ww.set_logical_type("Boolean")
if isinstance(
data.ww.logical_type,
ww.logical_types.IntegerNullable,
) or isinstance(data.ww.logical_type, ww.logical_types.AgeNullable):
data = data.ww.set_logical_type("Double")
return data
bool_nullable_cols = data.ww.select("BooleanNullable")
int_nullable_cols = data.ww.select(["IntegerNullable", "AgeNullable"])
non_null_columns = (
set(data.dropna(axis=1).columns) if ignore_null_cols else set(data.columns)
)
new_ltypes = {
col: "Boolean" for col in bool_nullable_cols if col in non_null_columns
}
new_ltypes.update(
{col: "BooleanNullable" for col in bool_nullable_cols if col not in new_ltypes},
)
new_ltypes.update(
{col: "Double" for col in int_nullable_cols if col in non_null_columns},
)
if new_ltypes:
data.ww.set_types(logical_types=new_ltypes)
return data
