aronwc

# show that max_depth affects floating point precision of predict_proba in RandomForest

from collections import Counter
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_features=20,
                                    n_informative=5, n_redundant=10,
                                    random_state=42)

aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

aronwc

import numpy as np

from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer


def print_features(coef, names):
    """ Print sorted list of non-zero features/weights. """
    print "\n".join('%s/%.2f' % (names[j], coef[j]) for j in np.argsort(coef)[::-1] if coef[j] != 0)

aronwc

''' An example of how to use Celery to manage a mix of serial and parallel
tasks.  This depends on a running instance of a rabbitmq messaging server to
keep track of task statuses. This can be launched on our ec2 instance with:
~/rabbitmq/rabbitmq_server-3.1.3/sbin/rabbitmq-server

For this script to work, you first need to run a celery worker process to
await orders:
$ celery -A tasks worker --loglevel=info

Then, you can call any of the functions below (see main for an example).

aronwc

Here are the steps I took to make Emacs have code-completion and PEP8 checking:

1. Install [Marmalade](http://marmalade-repo.org) by putting this in your .emacs

(require 'package)
(add-to-list 'package-archives 
    '("marmalade" .
      "http://marmalade-repo.org/packages/"))
(package-initialize)

aronwc

# cluster users based on feature vectors
import argparse
import io
import numpy as np
import pickle
import re
import string
import sys

from sklearn.cluster import MiniBatchKMeans