Compositions return individual steps as part of get_params

Describe the bug

When calling get_params on an Pipeline or a ColumnTransformer, it returns the steps as parameters as well. However, the docs for get_params says that only the arguments to __init__ will be returned. By having additional entries here, the output of get_params can no longer be used to call __init__.

Steps/Code to Reproduce

import sklearn.preprocessing
from sklearn.compose import ColumnTransformer

model_original = sklearn.pipeline.Pipeline(
    steps=[("transform", 'passthrough')]
)
print(model_original.get_params().keys())
ct = ColumnTransformer(
    [("continous", "passthrough", [])]
)
print(ct.get_params().keys())

params = ct.get_params()
ColumnTransformer(**params)

Expected Results

dict_keys(['memory', 'steps', 'verbose'])
dict_keys(['n_jobs', 'remainder', 'sparse_threshold', 'transformer_weights', 'transformers', 'verbose'])

Actual Results

dict_keys(['memory', 'steps', 'verbose', 'transform'])
dict_keys(['n_jobs', 'remainder', 'sparse_threshold', 'transformer_weights', 'transformers', 'verbose', 'continous'])
Traceback (most recent call last):
  File "/home/feurerm/sync_dir/projects/openml/python/local/bug3.py", line 14, in <module>
    ColumnTransformer(**params)
  File "/home/feurerm/miniconda/3-4.5.4/envs/openml/lib/python3.7/site-packages/sklearn/utils/validation.py", line 72, in inner_f
    return f(**kwargs)
TypeError: __init__() got an unexpected keyword argument 'continous'

Versions

Python 3.7.3 (default, Mar 27 2019, 22:11:17) [GCC 7.3.0] NumPy 1.17.0 SciPy 1.3.0 Scikit-Learn 0.23.2

[glemaitre]

I made a proposal there: https://github.com/scikit-learn/scikit-learn/pull/18285

• it gives an example in the developer guide of what deep is useful for
• update the docstring of get_params and set_params to be more explicit regarding what will be returned or set.

[mfeurer]

I am thinking that the docstring of get_params and set_params of the estimator inheriting from _BaseComposition could be more explicit regarding the fact that you can set or get parameters from the steps or transformers, which are not given in init.

I think that would be great.