$python boston.py
 RF: R2: 0.736624, RMSE:4.631010
SVM: R2: 0.515467, RMSE:6.281300
TFL: R2: 0.647992, RMSE:5.353827
 MY R2: -1.007581, RMSE:12.785702

import numpy as np
import tensorflow as tf
from sklearn import cross_validation
from sklearn import datasets
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from math import sqrt
import random

tf.logging.set_verbosity(tf.logging.ERROR)

def test_svm(x_train, x_test, y_train, y_test):
    clf = SVR(kernel='linear').fit(x_train, y_train)
    y_pred = clf.predict(x_test)
    r2 = r2_score(y_test, y_pred)
    rmse = sqrt(mean_squared_error(y_test, y_pred))
    print('SVM: R2: {0:f}, RMSE:{1:f}'.format(r2, rmse))

def test_rf(x_train, x_test, y_train, y_test):
    rlf = RandomForestRegressor().fit(x_train, y_train)
    y_pred = rlf.predict(x_test)
    r2 = r2_score(y_test, y_pred)
    rmse = sqrt(mean_squared_error(y_test, y_pred))
    print(' RF: R2: {0:f}, RMSE:{1:f}'.format(r2, rmse))

def test_tf_learn_dnn(x_train, x_test, y_train, y_test, steps=10000, hidden=[20, 20]):
    feature_columns = [tf.contrib.layers.real_valued_column("", dimension=13)]
    tfl = tf.contrib.learn.DNNRegressor(hidden_units=hidden, feature_columns=feature_columns)
    tfl.fit(x=x_train, y=y_train, steps=steps)
    y_pred = tfl.predict(x_test)
    r2 = r2_score(y_test, y_pred)
    rmse = sqrt(mean_squared_error(y_test, y_pred))
    print('TFL: R2: {0:f}, RMSE:{1:f}'.format(r2, rmse))

def inference(x):
    hidden1 = 20
    hidden2 = 20

    with tf.name_scope("l1") as scope:
        w1 = tf.Variable(tf.truncated_normal([13, hidden1]), name="w1")
        b1 = tf.Variable(tf.zeros([hidden1]), name="b1")
        h1 = tf.nn.relu(tf.matmul(x, w1) + b1)
        #h1 = tf.nn.dropout(h1, 0.9)

    with tf.name_scope("l2") as scope:
        w2 = tf.Variable(tf.truncated_normal([hidden1, hidden2]), name="w1")
        b2 = tf.Variable(tf.zeros([hidden2]), name="b2")
        h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
        #h2 = tf.nn.dropout(h2, 0.9)

    with tf.name_scope("l3") as scope:
        w3 = tf.Variable(tf.truncated_normal([hidden2, 1]), name="w2")
        b3 = tf.Variable(tf.zeros([1]), name="b3")
        y = tf.matmul(h2, w3) + b3
    return y

def loss(model, y_):
    return tf.reduce_mean(tf.square(tf.sub(model, y_)))

def training(loss, rate):
    return tf.train.AdagradOptimizer(rate).minimize(loss)

def test_my_dnn(x_train, x_test, y_train, y_test, batch_size=32, epoch=10000, shuffle=True):
    max_size = x_train.shape[0]
    n = max_size - batch_size
    idx = list(range(x_train.shape[0]))
    x = tf.placeholder(tf.float32, shape=[None, 13])
    y_ = tf.placeholder(tf.float32, shape=[None])

    model = inference(x)
    loss_value = loss(model, y_)
    train_op = training(loss_value, 0.1)

    init = tf.initialize_all_variables()
    sess = tf.Session()
    sess.run(init)

    for e in range(epoch):
        if shuffle:
            random.shuffle(idx)
            x_train = x_train[idx]
            y_train = y_train[idx]
        for i in range(n / batch_size):
            batch = batch_size * i
            x_train_b = x_train[batch:batch + batch_size]
            y_train_b = y_train[batch:batch + batch_size]
            _, l = sess.run([train_op, loss_value], feed_dict={x: x_train_b, y_: y_train_b})
        #if e % 100 == 0:
        #    print e, l

    y_pred = sess.run(model, feed_dict={x: x_test})
    y_pred = y_pred.T[0]
    r2 = r2_score(y_test, y_pred)
    rmse = sqrt(mean_squared_error(y_test, y_pred))
    print(' MY R2: {0:f}, RMSE:{1:f}'.format(r2, rmse))

if __name__ == "__main__":
    boston = datasets.load_boston()
    x_train, x_test, y_train, y_test = cross_validation.train_test_split(
        boston.data, boston.target, test_size=0.2, random_state=0)

    test_rf(x_train, x_test, y_train, y_test)
    test_svm(x_train, x_test, y_train, y_test)
    test_tf_learn_dnn(x_train, x_test, y_train, y_test)
    test_my_dnn(x_train, x_test, y_train, y_test, shuffle=True, epoch=30000)

import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation

iris = datasets.load_iris()
x_train, x_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)

classifier = tf.contrib.learn.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(x=x_train, y=y_train, steps=200)
dnn_accuracy_score = classifier.evaluate(x=x_test, y=y_test)["accuracy"]
print('DNN Accuracy: {0:f}'.format(dnn_accuracy_score))

clf = svm.SVC(kernel='linear').fit(x_train, y_train)
svm_accuracy_score = clf.score(x_test, y_test)
print('SVM Accuracy: {0:f}'.format(svm_accuracy_score))

rlf = RandomForestClassifier().fit(x_train, y_train)
rf_accuracy_score = rlf.score(x_test, y_test)
print('RF Accuracy: {0:f}'.format(rf_accuracy_score))

import pickle
test_dict = {"one": 1, "two": 2, "three": 3}

with open("test.dump2", mode='wb') as f:
    pickle.dump(test_dict, f)

with open("test.dump2", mode='rb') as f:
    print pickle.load(f)

import joblib
test_dict = {"one": 1, "two": 2, "three": 3}

joblib.dump(test_dict, "test.dump")
print joblib.load("test.dump")

>>> pq(url="http://www.transgenic.co.jp/pressrelease/")
[<html>]
>>> d2 = pq(url="http://www.transgenic.co.jp/pressrelease/")
>>> len(d2("a"))
71

>>> d3 = pq(url="http://www.transgenic.co.jp/pressrelease/", parser="xml")
>>> d3
[<{http://www.w3.org/1999/xhtml}html>]
>>> len(d3("a"))
0