jamesdunham

from io import StringIO

import psycopg2
from tqdm import tqdm


def copy_from(df: pd.DataFrame,
              table: str,
              connection: psycopg2.extensions.connection,
              chunk_size: int = 10000):

jamesdunham

Keybase proof

I hereby claim:

• I am jamesdunham on github.
• I am jdunham (https://keybase.io/jdunham) on keybase.
• I have a public key ASBfo4fghAbkFJnfASnep_tSPk_gKGjT1Gx-PnY8SxgcfQo

To claim this, I am signing this object:

jamesdunham

library(testthat)
devtools::load_all()

names_to_levels = function(item_names, gt_names) {
  vapply(item_names, function(item_name) {
    sum(grepl(paste0('^', item_name, '_gt[0-9]+$'), gt_names))
  }, integer(1))
}
test_that('names_to_levels recovers number of choices per item given item names', {
  expect_equal(names_to_levels('abortion', 'abortion_gt1'), c('abortion' = 1))

jamesdunham

# Define a minimal Ctrl object
Ctrl <- setClass("Ctrl", slots = c(item_data = "data.frame", item_names = "ANY",
    time_name = "character", geo_name = "character", group_names = "ANY",
    weight_name = "ANY", time_filter='numeric'))

# Create minimal test data
test_data = data.table(
  abortion = c(0, 0, 1, 2),
  year = 2018,
  state = 'MA',

jamesdunham

library(data.table)
library(stringr)

OE_PATH = '~/medsl/openelections'

`%=%` = function(string, pattern) {
  str_detect(string, stringr::regex(pattern, TRUE))
}

`%-%` = function(string, pattern) {

jamesdunham

"""
Deduplicate organization names.
"""
import json
import logging
import pickle
import random
from io import StringIO
from itertools import combinations

jamesdunham

from copy import copy

import spacy
from spacy.tokens import Doc, Span


class Template(object):
    """Create synthetic NER training data from a template document.

    Provide a template NER-annotated spacy Doc when instantiating the class. Passing text to the `render` method 

jamesdunham

require(dataverse)
require(readr)

read_presidential <- function() {
  f <- dataverse::get_file("1976-2016-president.tab", "doi:10.7910/DVN/42MVDX",
    server="dataverse.harvard.edu")
  tab <- readr::read_csv(readBin(f, "character"))
  return(tab)
}

jamesdunham

rm(list = ls())
library(dgo)
library(dplyr)

data(toy_dgirtfit)
data(targets)
targets = targets %>%
  group_by(year, state, race3) %>%
  summarize(proportion = sum(proportion)) %>%
  ungroup() %>%

jamesdunham

library(rstan)
stan_model <- readLines("https://raw.githubusercontent.com/jamesdunham/dgo/master/exec/2017_01_04.stan")
stan_data <- structure(list(NNl2=structure(c(0L,0L,0L,0L),.Dim=c(2L,2L,1L)),SSl2=structure(c(0L,0L,0L,0L),.Dim=c(2L,2L,1L)),XX=structure(c(0,1,0,1,0,1,0,0,1,1,0,0,0,0,0,0,1,1),.Dim=c(6L,3L),.Dimnames=list(c("SC__black","VA__black","SC__other","VA__other","SC__white","VA__white"),c("VA","race3other","race3white"))),ZZ=structure(c(0,0,0,0,0,0),.Dim=c(2L,3L,1L),.Dimnames=list(c("2009","2010"),c("VA","race3other","race3white"),"")),ZZ_prior=structure(c(0,0,0,0,0,0),.Dim=c(2L,3L,1L),.Dimnames=list(c("2009","2010"),c("VA","race3other","race3white"),"")),G=6L,Q=2L,T=2L,N=24L,P=3L,S=1,H=1L,D=1L,Hprior=1L,WT=structure(c(1,1,1,1,1,1,1,1,1,1,1,1),.Dim=c(2L,1L,6L)),l2_only=structure(c(0L,0L,0L,0L),.Dim=c(2L,2L)),G_hier=1L,constant_item=TRUE,n_vec=structure(c(20,36,11,13,102,190,0,0,0,0,0,0,31,40,5,15,95,150,31,40,5,15,94,150),.Names=c("2009__SC__black__affirmative_action_gt1","2009__VA__black__affirmati