Pipeline requires both fit and transform method to be available instead of only fit_transform

Describe the bug

Calling a pipeline with a nonparametric function causes an error since the function transform() is missing. The pipeline itself calls the function fit_transform() if it’s present. For nonparametric functions (the most prominent being t-SNE) a regular transform() method does not exist since there is no projection or mapping that is learned. It could still be used for dimensionality reduction.

Steps/Code to Reproduce

Example:

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.pipeline import make_pipeline

make_pipeline(TSNE(), PCA())

Expected Results

A pipeline is created.

Actual Results

Output:

TypeError: All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'TSNE(angle=0.5,...

Possible Solution

Editing this https://github.com/scikit-learn/scikit-learn/blob/95d4f0841/sklearn/pipeline.py#L179 to the following in order to reflect the change:

if (not hasattr(t, "fit_transform")) or not (hasattr(t, "fit") and hasattr(t, "transform")):

Versions

import sklearn; sklearn.show_versions()
System:
    python: 3.8.2 (default, Feb 26 2020, 22:21:03)  [GCC 9.2.1 20200130]
executable: /usr/bin/python3
   machine: Linux-5.5.9-arch1-2-x86_64-with-glibc2.2.5

Python dependencies:
       pip: 20.0.2
setuptools: 46.0.0
   sklearn: 0.22.2.post1
     numpy: 1.18.1
     scipy: 1.4.1
    Cython: 0.29.15
    pandas: 1.0.1
matplotlib: 3.2.0
    joblib: 0.14.1

Built with OpenMP: True

[jnothman]

if_delegate_has_method does not check the intermediate estimators, only the final one.

FeatureUnion._validate_transformers handles all the transformers identically. Pipeline._validate_steps has to handle the last step differently.

[raisa1921]

I’m having this error, can you please tell me how to solve it.