MeanClassifier.py

#http://danielhnyk.cz/creating-your-own-estimator-scikit-learn/
from sklearn.base import BaseEstimator, ClassifierMixin

class MeanClassifier(BaseEstimator, ClassifierMixin):  
    """An example of classifier"""

    def __init__(self, intValue=0, stringParam="defaultValue", otherParam=None):
        """
        Called when initializing the classifier
        """
        self.intValue = intValue
        self.stringParam = stringParam

        # THIS IS WRONG! Parameters should have same name as attributes
        self.differentParam = otherParam 


    def fit(self, X, y=None):
        """
        This should fit classifier. All the "work" should be done here.

        Note: assert is not a good choice here and you should rather
        use try/except blog with exceptions. This is just for short syntax.
        """
        print (len(X))
        print (len(y))
        assert (type(self.intValue) == int), "intValue parameter must be integer"
        assert (type(self.stringParam) == str), "stringValue parameter must be string"
        assert (len(X) == 20), "X must be list with numerical values."

        self.treshold_ = (sum(X)/len(X)) + self.intValue  # mean + intValue

        return self

    def _meaning(self, x):
        # returns True/False according to fitted classifier
        # notice underscore on the beginning
        return( True if x >= self.treshold_ else False )

    def predict(self, X, y=None):
        try:
            getattr(self, "treshold_")
        except AttributeError:
            raise RuntimeError("You must train classifer before predicting data!")

        return([self._meaning(x) for x in X])

    def score(self, X, y=None):
        # counts number of values bigger than mean
        return(sum(self.predict(X))) 

from sklearn.grid_search import GridSearchCV
from sklearn.utils.estimator_checks import check_estimator
check_estimator(MeanClassifier)  # passes
trainJZ = [i for i in range(0, 150, 5)]  
testJZ = [i + 3 for i in range(-5, 5, 5)]  
tuned_params = {"intValue" : [-10,-1,0,1,10]}

gs = GridSearchCV(MeanClassifier(), tuned_params)

# for some reason I have to pass y with same shape
# otherwise gridsearch throws an error. Not sure why.

y=[1 for i in range(20)]
gs.fit(trainJZ, y)

print gs.best_params_ # {'intValue': -10} # and that is what we expect :)