In [1]:

    

# write out some toy data
from sklearn.datasets import load_digits
import cPickle

digits = load_digits()

X, y = digits.data, digits.target

for i in range(10):
    cPickle.dump((X[i::10], y[i::10]), open("data/batch_%02d.pickle" % i, "w"), -1)


    

In [2]:

    

from sklearn.linear_model import SGDClassifier


    

In [3]:

    

sgd = SGDClassifier()

for i in range(9):
    X_batch, y_batch = cPickle.load(open("data/batch_%02d.pickle" % i))
    sgd.partial_fit(X_batch, y_batch, classes=range(10))


    

In [4]:

    

X_test, y_test = cPickle.load(open("data/batch_09.pickle"))

sgd.score(X_test, y_test)


    

    
Out[4]:





0.88268156424581001

In [5]:

    

import pandas as pd
from sklearn.feature_extraction.text import HashingVectorizer

sgd = SGDClassifier()
hashing_vectorizer = HashingVectorizer()

for i in range(10):
    data_batch = pd.read_csv("data/train_%d.csv" % i)
    text_batch = data_batch.Comment.tolist()
    y_batch = data_batch.Insult.values
    X_batch = hashing_vectorizer.transform(text_batch)
    sgd.partial_fit(X_batch, y_batch, classes=range(10))


    

In [6]:

    

data_test = pd.read_csv("data/test_with_solutions.csv")
X_test = hashing_vectorizer.transform(data_test.Comment.tolist())
y_test = data_test.Insult.values
sgd.score(X_test, y_test)


    

    
Out[6]:





0.82546278806195694

In [7]:

    

from sklearn.kernel_approximation import RBFSampler

sgd = SGDClassifier()
kernel_approximation = RBFSampler(gamma=.001, n_components=400)

for i in range(9):
    X_batch, y_batch = cPickle.load(open("data/batch_%02d.pickle" % i))
    if i == 0:
        kernel_approximation.fit(X_batch)
    X_transformed = kernel_approximation.transform(X_batch)
    sgd.partial_fit(X_transformed, y_batch, classes=range(10))


    

In [8]:

    

X_test, y_test = cPickle.load(open("data/batch_09.pickle"))

sgd.score(kernel_approximation.transform(X_test), y_test)


    

    
Out[8]:





0.94413407821229045