from sklearn.pipeline import Pipeline
from sklearn.base import TransformerMixin
import numpy as np

state = 0

class Cacher(TransformerMixin):
    
    def __init__(self, internal=None, cache=None):
        self.internal = internal
        self.cache = cache or dict()
        assert internal
    
    def fit(self, data, y=None):  
        """Returns `self` unless something different happens in train and test"""
        return self
    
    def save(self):
        '''Save to disk...'''
        return True
    
    def transform(self, X, y=None):
        def cacheado(i):
            hash = str(i)
            if hash not in self.cache:
                self.cache[hash] = self.internal.transform(i)
            return self.cache[hash]
        
        res = np.apply_along_axis(cacheado, 0, X)
        self.save()
        return res
    
class Transformer(TransformerMixin):    
    def fit(self, data, y=None):
        return self
    
    def transform(self, X, y=None):
        print('Original transformer')
        return X+state

# DO IT ONCE

ner_pipe = Pipeline([
    ('trans', Cacher(internal=Transformer()))
    ])

ner_pipe.fit(None)
for i in range(10):
    state = i
    print(ner_pipe.transform(np.array([[0,1], [1, 2]])))
    if i % 4 == 0:
        print('Reentrenado')
        ner_pipe.fit([[0,1], [1, 2]])

```python from sklearn.pipeline import Pipeline from sklearn.base import TransformerMixin import numpy as np state = 0 class Cacher(TransformerMixin): def __init__(self, internal=None, cache=None): self.internal = internal self.cache = cache or dict() assert internal def fit(self, data, y=None): """Returns `self` unless something different happens in train and test""" return self def save(self): '''Save to disk...''' return True def transform(self, X, y=None): def cacheado(i): hash = str(i) if hash not in self.cache: self.cache[hash] = self.internal.transform(i) return self.cache[hash] res = np.apply_along_axis(cacheado, 0, X) self.save() return res class Transformer(TransformerMixin): def fit(self, data, y=None): return self def transform(self, X, y=None): print('Original transformer') return X+state # DO IT ONCE ner_pipe = Pipeline([ ('trans', Cacher(internal=Transformer())) ]) ner_pipe.fit(None) for i in range(10): state = i print(ner_pipe.transform(np.array([[0,1], [1, 2]]))) if i % 4 == 0: print('Reentrenado') ner_pipe.fit([[0,1], [1, 2]]) ```