rsync (Everyone seems to like -z, but it is much slower for me)
| ## Configure eth0 | |
| # | |
| # vi /etc/sysconfig/network-scripts/ifcfg-eth0 | |
| DEVICE="eth0" | |
| NM_CONTROLLED="yes" | |
| ONBOOT=yes | |
| HWADDR=A4:BA:DB:37:F1:04 | |
| TYPE=Ethernet | |
| BOOTPROTO=static |
| #!/bin/bash | |
| ################################ | |
| # OS X Install ISO Creater # | |
| # # | |
| # Author: shela # | |
| ################################ | |
| ####################################### | |
| # Declarations |
| # installs to /opt/gradle | |
| # existing versions are not overwritten/deleted | |
| # seamless upgrades/downgrades | |
| # $GRADLE_HOME points to latest *installed* (not released) | |
| gradle_version=2.9 | |
| wget -N https://services.gradle.org/distributions/gradle-${gradle_version}-all.zip | |
| sudo unzip -foq gradle-${gradle_version}-all.zip -d /opt/gradle | |
| sudo ln -sfn gradle-${gradle_version} /opt/gradle/latest | |
| sudo printf "export GRADLE_HOME=/opt/gradle/latest\nexport PATH=\$PATH:\$GRADLE_HOME/bin" > /etc/profile.d/gradle.sh | |
| . /etc/profile.d/gradle.sh |
| CREATE OR REPLACE FUNCTION search_street(IN street text) | |
| RETURNS TABLE(aoguid uuid, fulladdress text, postalcode integer) AS | |
| $BODY$ | |
| WITH RECURSIVE child_to_parents AS ( | |
| SELECT aoguid, format('{"%s": %s}', fias_addr_obj.shortname, formalname) AS fulladdress, | |
| postalcode, parentguid, aolevel | |
| FROM fias_addr_obj | |
| WHERE currstatus = 0 | |
| AND (regioncode = '50' OR regioncode = '77') | |
| AND formalname ILIKE '%' || format('%s', street) || '%' |
| SELECT search_street.way, format('%s, %s, %s', mo_m.fullname, all_other.fullname, search_street.fullname) as fullname | |
| FROM | |
| ( | |
| SELECT way, name as fullname | |
| FROM planet_osm_polygon | |
| WHERE admin_level = '4' AND (name = 'Москва' OR name = 'Московская область') | |
| AND boundary = 'administrative' | |
| ) as mo_m | |
| INNER JOIN | |
| ( |
| #!/usr/bin/env python -S | |
| # -*- coding: utf-8 -*- | |
| import sys | |
| import re | |
| import xml.sax | |
| import io # for 2.6 | |
| import StringIO # for 3.0 | |
| # | |
| # ** If your python is 2.x and xml-cording is utf-8 set follows. |
| # Get root: | |
| sudo su | |
| # Install prerequisites: | |
| apt-get install build-essential pkg-config checkinstall git avahi-daemon libavahi-client-dev libcrack2-dev libwrap0-dev autotools-dev automake libtool libdb-dev libacl1-dev libdb5.1-dev db-util db5.1-util libgcrypt11 libgcrypt11-dev | |
| # Build libevent from source: | |
| cd /usr/local/src |
| #!/usr/bin/python | |
| import os | |
| import hashlib | |
| import getpass | |
| import base64 | |
| password1 = None | |
| password2 = None |
To demonstrate text classification with Scikit Learn, we'll build a simple spam filter. While the filters in production for services like Gmail will obviously be vastly more sophisticated, the model we'll have by the end of this chapter is effective and surprisingly accurate.
Spam filtering is the "hello world" of document classification, but something to be aware of is that we aren't limited to two classes. The classifier we will be using supports multi-class classification, which opens up vast opportunities like author identification, support email routing, etc… However, in this example we'll just stick to two classes: SPAM and HAM.
For this exercise, we'll be using a combination of the Enron-Spam data sets and the SpamAssassin public corpus. Both are publicly available for download and are retreived from the internet during the setup phase of the example code that goes with this chapter.