"""Improved pipelines for data science projects.
SKLearn pipelines easy to declare and Pandas-compatible.
"""
from sklearn.impute import MissingIndicator
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Binarizer
from recipipe.core import Recipipe
from recipipe.core import RecipipeTransformer
from recipipe.transformers import AsTypeTransformer
from recipipe.transformers import CategoryEncoder
from recipipe.transformers import ColumnTransformer
from recipipe.transformers import ColumnGroupsTransformer
from recipipe.transformers import ColumnsTransformer
from recipipe.transformers import ConcatTransformer
from recipipe.transformers import DropTransformer
from recipipe.transformers import DropNARowsTransformer
from recipipe.transformers import ExtractTransformer
from recipipe.transformers import GroupByTransformer
from recipipe.transformers import PandasScaler
from recipipe.transformers import QueryTransformer
from recipipe.transformers import ReduceMemoryTransformer
from recipipe.transformers import ReplaceTransformer
from recipipe.transformers import SelectTransformer
from recipipe.transformers import SklearnCreator
from recipipe.transformers import SklearnColumnsWrapper
from recipipe.transformers import SklearnColumnWrapper
from recipipe.transformers import SklearnFitOneWrapper
from recipipe.transformers import SumTransformer
from recipipe.transformers import TargetEncoderTransformer
from recipipe.utils import fit_columns
from recipipe.utils import flatten_list
from recipipe._version import __version__
__author__ = "guiferviz"
##############################################
# Define aliases to make it easier to use. #
##############################################
recipipe = Recipipe
astype = AsTypeTransformer
category = CategoryEncoder
concat = ConcatTransformer
extract = ExtractTransformer
groupby = GroupByTransformer
select = SelectTransformer
drop = DropTransformer
dropna = DropNARowsTransformer
dropna_rows = DropNARowsTransformer
query = QueryTransformer
replace = ReplaceTransformer
reduce_memory = ReduceMemoryTransformer
sum = SumTransformer
target_encoder = TargetEncoderTransformer
# SKLearn recipipe transformer.
from_sklearn = SklearnCreator
binarizer = from_sklearn(Binarizer())
impute = from_sklearn(SimpleImputer(strategy="constant"))
indicator = from_sklearn(MissingIndicator(), col_format="INDICATOR({})")
minmax = from_sklearn(MinMaxScaler())
onehot = from_sklearn(OneHotEncoder(sparse=False, handle_unknown="ignore"))
robust_scale = from_sklearn(RobustScaler())
scale = from_sklearn(StandardScaler())
standarize = from_sklearn(StandardScaler())
# Alias for SKLearn transformers.
sk_binarizer = Binarizer
sk_indicator = MissingIndicator
sk_inputer = SimpleImputer
sk_onehot = OneHotEncoder
sk_scale = StandardScaler
[docs]def greet(): # pragma: no cover
"""Print a silly sentence to stdout. """
# https://upjoke.com/recipe-jokes
print("I've found the recipe for happiness.\n"
"Can someone just send me some money so I can buy the ingredients?")