human
Write me a the Python program you requested given the following:
I am working on the file "HandsOnML/ch02/ex02.ipynb".
The first task is:
2.5.5 Transformation Pipeline
Can you write Python program for it?
assistant
#Here is the Python program you requested.

from sklearn.pipeline import Pipeline

num_pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy="median")),
    ('attr_adder', CombinedAttributesAdder()),
    ('std_scaler', StandardScaler())
])

# I want to verify the pipelined version
# doest the same thing as the separated steps

num_pipeline_stage1 = Pipeline([
    ('imputer', SimpleImputer(strategy="median")),
])

X_pipeline = num_pipeline_stage1.fit_transform(housing_num)
X = imputer.transform(housing_num)
X_pipeline
np.array_equal(X, X_pipeline)

num_pipeline_stage2 = Pipeline([
    ('imputer', SimpleImputer(strategy="median")),
    ('attr_adder', CombinedAttributesAdder()),
])

Y = attr_adder.fit_transform(X)
Y_pipeline = num_pipeline_stage2.fit_transform(housing_num)
np.array_equal(Y, Y_pipeline)

num_pipeline_stage3 = Pipeline([
    ('imputer', SimpleImputer(strategy="median")),
    ('attr_adder', CombinedAttributesAdder()),
    ('std_scaler', StandardScaler())
])

Z = scalar.fit_transform(Y)
Z.std(), Z.mean()
Z_pipeline = num_pipeline_stage3.fit_transform(housing_num)
np.array_equal(Z, Z_pipeline)

from sklearn.base import BaseEstimator, TransformerMixin

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

class CustomizedLabelBinarizer(BaseEstimator, TransformerMixin):
    def __init__(self, sparse_output=False):
        self.encode = LabelBinarizer(sparse_output = sparse_output)
    def fit(self, X, y=None):
        return self.encode.fit(X)
    def transform(self, X):
        return self.encode.transform(X)

num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]

num_pipeline = Pipeline([
    ('selector', DataFrameSelector(num_attribs)),
    ('imputer', SimpleImputer(strategy="median")),
    ('attr_adder', CombinedAttributesAdder()),
    ('std_scaler', StandardScaler()),
]
)

cat_pipeline = Pipeline([
   ...