jnothman

/*

On the coursera lecture index, execute this Javascript (via greasemonkey script, bookmarklet, etc) to show 'quizzes' links next to each lecture. Clicking it will open the lecture, but instead of showing the video, will:
1. Pause it
2. Show the first quiz
3. Upon clicking "skip" or "continue" on the quiz, proceed to the next
4. Repeat from 3.
5. Continue showing the video

Having watched a downloaded video, you can now easily do the in-lecture quizzes separately.

jnothman

function! SourceGitVimrc(dir)
  let gitroot = system("cd " . fnameescape(a:dir) . "; git rev-parse --show-toplevel 2>/dev/null")
  " Strip trailing newline and escape
  let gitroot = substitute(gitroot, "\\n*$","","")
  if strlen(gitroot) && filereadable(gitroot . '/.vimrc')
    execute "source " . fnameescape(gitroot) . '/.vimrc'
  endif
endfunction 

jnothman

import datetime
import re

class TimeDeltaType(object):
    """
    Interprets a string as a timedelta for argument parsing.

    With no default unit:
    >>> tdtype = TimeDeltaType()

jnothman

import re, sys

num_re = re.compile(r'(?<=^\[)[0-9]+')

in_n = ''
out_n = 0

def sub_cb(match):
    global in_n, out_n

jnothman

from __future__ import print_function

from abc import ABCMeta, abstractmethod
from functools import partial

import numpy as np
from sklearn.metrics import precision_recall_fscore_support
from sklearn.base import BaseEstimator

jnothman

"""
Tool to examine the output of model selection search results from scikit-learn (assuming #1787).
Pandas might be more appropriate, but I haven't worked out how to do group_best there...

For example:
    >>> my_search = GridSearchCV(est, param_dict={'a': [...], 'b': [...], 'c': [...]})
    >>> my_search.fit(X, y)
    >>> rw = ResultsWrangler(my_search.grid_results_, my_search.fold_results_)
    >>> grouped = rw.group_best(['a', 'b'])
    >>> print(zip(grouped.parameters, grouped.scores))

jnothman

#!/bin/bash

branch="$1"

if [ -z "$branch" -o -n "$2" ]
then
	echo Usage: $0 gh_user:branch_name
	exit 2
fi

jnothman

from collections import defaultdict
from six import iteritems


def group_params(items, key_name=lambda x: True):
    """bin by sub-dicts where values are compared by id if not hashable
    
    >>> a = ('boo', 6)
    >>> b = ('boo', 6)
    >>> id(a) == id(b)

jnothman

from sklearn import pipeline, grid_search, linear_model, feature_selection, decomposition, datasets, preprocessing

iris = datasets.load_iris()

clf = pipeline.Pipeline([
    ('scale', preprocessing.StandardScaler()),
    ('sel', feature_selection.SelectKBest()),
    ('clf', linear_model.ElasticNet(warm_start=False)),
])

jnothman

"""An extension of numpy.testing.assert_warns

Handles checking of message and multiple warnings.
"""

from __future__ import absolute_import, print_function

import sys
import re
import warnings