VikParuchuri

term<-c("egypt","jordan","israel","saudi")
term_vec<-foreach(i=1:length(all_score_frames),.combine=rbind) %do%
{
  score_row<-rep(0,length(term))
  for(z in 1:length(score_row))
  {
    sel_score<-all_score_frames[[i]][all_score_frames[[i]]$term==term[z],"score"]
    sel_score[is.na(sel_score)]<-0
    if(length(sel_score)==0)
      sel_score<-0

VikParuchuri

class QuerySignatureV1AuthHandler(QuerySignatureHelper, AuthHandler):
    """
Provides Query Signature V1 Authentication.
"""

    SignatureVersion = 1
    capability = ['sign-v1', 'mturk']

    def _calc_signature(self, params, *args):
        boto.log.debug('using _calc_signature_1')

VikParuchuri

class QuerySignatureV1AuthHandler(QuerySignatureHelper, AuthHandler):
    """
    Provides Query Signature V1 Authentication.
    """

    SignatureVersion = 1
    capability = ['sign-v1', 'mturk']

    def _calc_signature(self, params, *args):

VikParuchuri

from copy import deepcopy
def invert(X):
    """
    Invert a matrix X according to gauss-jordan elimination
    In gauss-jordan elimination, we perform basic row operations to turn a matrix into
    row-echelon form.  If we concatenate an identity matrix to our input
    matrix during this process, we will turn the identity matrix into our inverse.
    X - input list of lists where each list is a matrix row
    output - inverse of X
    """

VikParuchuri

import networkx as nx
from nltk.tokenize.punkt import PunktSentenceTokenizer
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer

SENTENCES_IN_SUMMARY = 10
MIN_SENTENCE_LENGTH = 50
MAX_SENTENCE_LENGTH = 200

def f7(seq):
    seen = set()

VikParuchuri

from queue.models import Submission
import queue
subs = Submission.objects.filter(queue_name="open-ended", retired=False)
queue_name="open-ended"
for sub in subs:
    qitem=str(sub.id)
    qcount = queue.producer.push_to_queue(queue_name, qitem)

VikParuchuri

*  Install brew packages (brew-packages.txt)
*  Setup all packages that need it so that they start at startup
*  Install pip (easy_install pip)
*  Install virtualenvwrapper (pip install virtualenvwrapper)
*  export WORKON_HOME=~/Envs
*  mkdir -p $WORKON_HOME
*  source /usr/local/bin/virtualenvwrapper.sh
*  mkvirtualenv turfly
*  export ARCHFLAGS="-Wno-error=unused-command-line-argument-hard-error-in-future";
psql -d postgres

VikParuchuri

import os
import re
import yaml
import textwrap
import html2text

def write_to_yaml(k, v, yaml_data):
    if isinstance(v, str):
        v = v.strip()
        if v.count("\n") == 0 and k not in ["left_text", "initial_display", "hint", "answer", "check_val", "imports", "instructions"]:

VikParuchuri

VikParuchuri

def code_filter(code):
    """
    Filters out low quality training code, with an aim to improving benchmark performance.  Works best with Python.
    """
    # Python keywords
    keywords = ["assert", "async", "await", "break", "class", "continue", "def", "del", "elif", "else", "except",
                "finally", "for", "from", "global", "if", "import", "in", "is", "lambda", "nonlocal", "not", "or",
                "pass", "raise", "return", "try", "while", "with", "yield"]
    # This covers single line comments in python + other common languages
    comment_symbols = ["#", "//"]