Put customized functions in Sklearn pipeline

scikit created a FunctionTransformer as part of the preprocessing class in version 0.17. It can be used in a similar manner as David's implementation of the class Fisher in the answer above - but with less flexibility. If the input/output of the function is configured properly, the transformer can implement the fit/transform/fit_transform methods for the function and thus allow it to be used in the scikit pipeline.

For example, if the input to a pipeline is a series, the transformer would be as follows:

def trans_func(input_series):
    return output_series

from sklearn.preprocessing import FunctionTransformer
transformer = FunctionTransformer(trans_func)

sk_pipe = Pipeline([("trans", transformer), ("vect", tf_1k), ("clf", clf_1k)])
sk_pipe.fit(train.desc, train.tag)

where vect is a tf_idf transformer, clf is a classifier and train is the training dataset. "train.desc" is the series text input to the pipeline.

You actually can put all of these functions into a single pipeline!

In the accepted answer, @David wrote that your functions

transform your target in addition to your training data (i.e. both X and y). Pipeline does not support transformations to your target so you will have do them prior as you originally were.

It is true that sklearn's pipeline does not support this. However imblearn's pipeline here supports this. The imblearn pipeline is just like that of sklearn but it allows you to call transformations separately on the training and testing data via sample methods. Moreover, these sample methods are actually designed so that you can change both the data X and the labels y. This is important because many times you want to include smote in your pipeline but you want to smote just the training data, not the testing data. And with the imblearn pipeline, you can call smote in the pipeline to transform just X_train and y_train and not X_test and y_test.

So you can create an imblearn pipeline that has a smote sampler, pre-processing step, and svc.

For more details check out this stack overflow post here and machine learning mastery article here.

I don't know where your SMOTE() and Fisher() functions are coming from, but the answer is yes you can definitely do this. In order to do so you will need to write a wrapper class around those functions though. The easiest way to this is inherit sklearn's BaseEstimator and TransformerMixin classes, see this for an example: http://scikit-learn.org/stable/auto_examples/hetero_feature_union.html

If this isn't making sense to you, post the details of at least one of your functions (the library it comes from or your code if you wrote it yourself) and we can go from there.

EDIT:

I apologize, I didn't look at your functions closely enough to realize that they transform your target in addition to your training data (i.e. both X and y). Pipeline does not support transformations to your target so you will have do them prior as you originally were. For your reference, here is what it would look like to write your custom class for your Fisher process which would work if the function itself did not need to affect your target variable.

>>> from sklearn.base import BaseEstimator, TransformerMixin
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.svm import SVC
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.grid_search import GridSearchCV
>>> from sklearn.datasets import load_iris
>>> 
>>> class Fisher(BaseEstimator, TransformerMixin):
...     def __init__(self,percentile=0.95):
...             self.percentile = percentile
...     def fit(self, X, y):
...             from numpy import shape, argsort, ceil
...             X_pos, X_neg = X[y==1], X[y==0]
...             X_mean = X.mean(axis=0)
...             X_pos_mean, X_neg_mean = X_pos.mean(axis=0), X_neg.mean(axis=0)
...             deno = (1.0/(shape(X_pos)[0]-1))*X_pos.var(axis=0) + (1.0/(shape(X_neg)[0]-1))*X_neg.var(axis=0)
...             num = (X_pos_mean - X_mean)**2 + (X_neg_mean - X_mean)**2
...             F = num/deno
...             sort_F = argsort(F)[::-1]
...             n_feature = (float(self.percentile)/100)*shape(X)[1]
...             self.ind_feature = sort_F[:ceil(n_feature)]
...             return self
...     def transform(self, x):
...             return x[self.ind_feature,:]
... 
>>> 
>>> data = load_iris()
>>> 
>>> pipeline = Pipeline([
...     ('fisher', Fisher()),
...     ('normal',StandardScaler()),
...     ('svm',SVC(class_weight='auto'))
... ])
>>> 
>>> grid = {
...     'fisher__percentile':[0.75,0.50],
...     'svm__C':[1,2]
... }
>>> 
>>> model = GridSearchCV(estimator = pipeline, param_grid=grid, cv=2)
>>> model.fit(data.data,data.target)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py", line 596, in fit
    return self._fit(X, y, ParameterGrid(self.param_grid))
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py", line 378, in _fit
    for parameters in parameter_iterable
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 653, in __call__
    self.dispatch(function, args, kwargs)
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 400, in dispatch
    job = ImmediateApply(func, args, kwargs)
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 138, in __init__
    self.results = func(*args, **kwargs)
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py", line 1239, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/pipeline.py", line 130, in fit
    self.steps[-1][-1].fit(Xt, y, **fit_params)
  File "/Users/dmcgarry/anaconda/lib/python2.7/site-packages/sklearn/svm/base.py", line 149, in fit
    (X.shape[0], y.shape[0]))
ValueError: X and y have incompatible shapes.
X has 1 samples, but y has 75.