from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import Perceptron
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline

X1 = [{'city':'Gothenburg', 'month':'July'},
      {'city':'Gothenburg', 'month':'December'},
      {'city':'Paris', 'month':'July'},
      {'city':'Paris', 'month':'December'}]
Y1 = ['rain', 'rain', 'sun', 'rain']

X2 = [{'city':'Sydney', 'month':'July'},
      {'city':'Sydney', 'month':'December'},
      {'city':'Paris', 'month':'July'},
      {'city':'Paris', 'month':'December'}]
Y2 = ['rain', 'sun', 'sun', 'rain']

classifier1 = Pipeline([('v', DictVectorizer()),
                        ('c', Perceptron())])
#classifier1 = Pipeline([('v', DictVectorizer()),
#                        ('c', LinearSVC())])
classifier1.fit(X1, Y1)
guesses1 = classifier1.predict(X1)
print(accuracy_score(Y1, guesses1))

classifier2 = Pipeline([('v', DictVectorizer()),
                        ('c', Perceptron())])
#classifier2 = Pipeline([('v', DictVectorizer()),
#                        ('c', LinearSVC())])
classifier2.fit(X2, Y2)
guesses2 = classifier2.predict(X2)
print(accuracy_score(Y2, guesses2))