Alfred Francis alfredfrancis

## bitbucket-pipelines.yml
image: python:3.7.4
pipelines:
 default:
    - step:
        script:
          - pip install -r requirements.txt
          - python -m unittest discover tests/
    - step:
        caches:
          - sonar

## dimple.html
<!DOCTYPE html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <title>Lyca Mobiles - Social Media Sentiment analysis </title>
  <script src="//d3js.org/d3.v4.min.js"></script>
  <script src="//cdnjs.cloudflare.com/ajax/libs/dimple/2.3.0/dimple.latest.js"></script>

## install_spacy.sh
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz

python -m spacy link en_core_web_sm en

## parse_table.py
import pandas as pd
import requests
from lxml import etree
import urllib

url = 'https://www.sec.gov/Archives/edgar/data/1090872/000156459018001685/a-def14a_20180321.htm#BENEFICIAL_OWNERSHIP'
html = requests.get(url).content
html = etree.HTML(html)
tr_nodes = html.xpath('.//table[contains(.,"Name and Address of Beneficial Owner")]')

## w2v_gen.py
# import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)

## ml_flow.ipynb

      
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                alfredfrancis
                / ml_flow.ipynb
            
            
              Created
              July 3, 2018 18:51
            
              
                ml_flow.ipynb
              
          
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## opencv_line_dectection.py
https://www.hackster.io/kemfic/simple-lane-detection-c3db2f?utm_source=Hackster.io+newsletter&utm_campaign=8cd41332a3-EMAIL_CAMPAIGN_2018_05_23&utm_medium=email&utm_term=0_6ff81e3e5b-8cd41332a3-140260637&mc_cid=8cd41332a3&mc_eid=0555616438

https://www.hackster.io/kemfic/curved-lane-detection-34f771

## requirements.txt
cloudpickle
tensorflow
spacy
numpy
sklearn

## question_class
http://cogcomp.org/Data/QA/QC/

## superviosrd.conf
[program:mlmodule]
command = /root/anaconda2/bin/gunicorn launcher:app -w 4  -b 0.0.0.0:6666
directory = /datavolume/manuels_module
user = root
stdout_logfile = /datavolume/manuels_module/gunicorn_stdout.log
stderr_logfile = /datavolume/manuels_module/gunicorn_stderr.log
redirect_stderr = True
	image: python:3.7.4
	pipelines:
	default:
	- step:
	script:
	- pip install -r requirements.txt
	- python -m unittest discover tests/
	- step:
	caches:
	- sonar
	<!DOCTYPE html>
	<html lang="en">

	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>Lyca Mobiles - Social Media Sentiment analysis </title>
	<script src="//d3js.org/d3.v4.min.js"></script>
	<script src="//cdnjs.cloudflare.com/ajax/libs/dimple/2.3.0/dimple.latest.js"></script>
	pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz

	python -m spacy link en_core_web_sm en
	import pandas as pd
	import requests
	from lxml import etree
	import urllib

	url = 'https://www.sec.gov/Archives/edgar/data/1090872/000156459018001685/a-def14a_20180321.htm#BENEFICIAL_OWNERSHIP'
	html = requests.get(url).content
	html = etree.HTML(html)
	tr_nodes = html.xpath('.//table[contains(.,"Name and Address of Beneficial Owner")]')
	# import modules & set up logging
	import gensim, logging
	logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

	sentences = [['first', 'sentence'], ['second', 'sentence']]
	# train word2vec on the two sentences
	model = gensim.models.Word2Vec(sentences, min_count=1)
	https://www.hackster.io/kemfic/simple-lane-detection-c3db2f?utm_source=Hackster.io+newsletter&utm_campaign=8cd41332a3-EMAIL_CAMPAIGN_2018_05_23&utm_medium=email&utm_term=0_6ff81e3e5b-8cd41332a3-140260637&mc_cid=8cd41332a3&mc_eid=0555616438

	https://www.hackster.io/kemfic/curved-lane-detection-34f771
	[program:mlmodule]
	command = /root/anaconda2/bin/gunicorn launcher:app -w 4 -b 0.0.0.0:6666
	directory = /datavolume/manuels_module
	user = root
	stdout_logfile = /datavolume/manuels_module/gunicorn_stdout.log
	stderr_logfile = /datavolume/manuels_module/gunicorn_stderr.log
	redirect_stderr = True