glamp

Packing version 98c8d04-master

Deploying to Scrapy Cloud project "373200"

Deploy log last 3 lines:

{"message": "500 Server Error: Internal Server Error for url: https://kumo-builder-prod.dc21.scrapinghub.com:2376/v1.27/auth", "error": "internal_error"}

glamp

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
target_variable = 'does-make-more-than-50k'
columns = ['age', 'education', 'hours-worked-per-week']
clf.fit(df[columns], df[target_variable])

glamp

version: '3.3'

services:
 db:
   image: mysql:5.7
   volumes:
     - dbdata:/var/lib/mysql
   restart: always
   environment:
     MYSQL_ROOT_PASSWORD: somewordpress

glamp

" Generated by Color Theme Generator at Sweyla
" http://sweyla.com/themes/seed/690000/

set background=dark

hi clear

if exists("syntax_on")
  syntax reset
endif

glamp

" Generated by Color Theme Generator at Sweyla
" http://sweyla.com/themes/seed/690000/

set background=dark

hi clear

if exists("syntax_on")
  syntax reset
endif

glamp

#!/usr/bin/env bash

# make sure the user running this script has permission to run docker
docker ps > /dev/null

# remove "<none>" images
docker rmi -f $(docker images | grep none | awk '{ print $3 }')
# cleanup exited containers
docker rm -f $(docker ps -f status=exited -q)

glamp

from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.layers import Embedding
from keras.layers import AveragePooling1D
from keras.datasets import imdb

glamp

library(XML)
library(uuid)
library(stringr)
library(plyr)
library(reshape2)
library(ggplot2)

f <- "https://raw.githubusercontent.com/chris-taylor/USElection/master/data/electoral-college-votes.csv"
electoral.college <- read.csv(f, header=FALSE)
names(electoral.college) <- c("state", "electoral_votes")

glamp

library(plyr)
library(XML)
library(uuid)
library(reshape2)

results <- ldply(states, function(state) {
  url <- "http://www.electionprojection.com/latest-polls/%s-presidential-polls-trump-vs-clinton-vs-johnson-vs-stein.php"
  state.fmt <- gsub(" ", "-", tolower(state))

  url.state <- sprintf(url, state.fmt)

glamp

from yhat import Yhat, YhatModel
import numpy as np


class SplitTestExample(YhatModel):
    def execute(self, data):
        # randomly select model to use. `p` defines the split percentage
        endpoints = ["ModelA", "ModelB"]
        endpoint = np.random.choice(endpoints, p=[0.2, 0.8])
        # make prediction to a "sub-model". this will give you back the full API response