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:
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): |
I hereby claim:
To claim this, I am signing this object:
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)) |
# 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', |
library(data.table) | |
library(stringr) | |
OE_PATH = '~/medsl/openelections' | |
`%=%` = function(string, pattern) { | |
str_detect(string, stringr::regex(pattern, TRUE)) | |
} | |
`%-%` = function(string, pattern) { |
""" | |
Deduplicate organization names. | |
""" | |
import json | |
import logging | |
import pickle | |
import random | |
from io import StringIO | |
from itertools import combinations |
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 |
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) | |
} |
rm(list = ls()) | |
library(dgo) | |
library(dplyr) | |
data(toy_dgirtfit) | |
data(targets) | |
targets = targets %>% | |
group_by(year, state, race3) %>% | |
summarize(proportion = sum(proportion)) %>% | |
ungroup() %>% |
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 |