I'd like to pass additional data to a transformer in scikit-learn:
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
import numpy as np
from sklearn.model_selection import GridSearchCV
class myTransformer(BaseEstimator, TransformerMixin):
def __init__(self, my_np_array):
self.data = my_np_array
print self.data
def transform(self, X):
return X
def fit(self, X, y=None):
return self
data = np.random.rand(20,20)
data2 = np.random.rand(6,6)
y = np.array([1, 2, 3, 1, 2, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3])
pipe = Pipeline(steps=[('myt', myTransformer(data2)), ('randforest', RandomForestClassifier())])
params = {"randforest__n_estimators": [100, 1000]}
estimators = GridSearchCV(pipe, param_grid=params, verbose=True)
estimators.fit(data, y)
However, when used in a scikit-learn pipeline, it seems to disappear
I'm getting None from the print inside the init method. How do I fix it?
This happens because sklearn handles estimators in a very specific way. In general it will create a new instance of the class for things like grid searching, and will pass a parameters to the constructor. This happens because sklearn has its own clone operation (defined in base.py) which takes your estimator class, gets parameters (returned by get_params) and passes it to the constructor of your class
klass = estimator.__class__
new_object_params = estimator.get_params(deep=False)
for name, param in six.iteritems(new_object_params):
new_object_params[name] = clone(param, safe=False)
new_object = klass(**new_object_params)
In order to support that your object has to override get_params(deep=False) method, which should return dictionary, which will be passed to constructor
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
class myTransformer(BaseEstimator, TransformerMixin):
def __init__(self, my_np_array):
self.data = my_np_array
print self.data
def transform(self, X):
return X
def fit(self, X, y=None):
return self
def get_params(self, deep=False):
return {'my_np_array': self.data}
will work as expected.
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