Algorithmia¶

Reference: Algorithmia Documentation

Table of Contents:

Installation
Authentication
Face Detection
Content Summarizer
Latent Dirichlet Allocation
Optical Character Recognition

1. Installation¶

You need to have the algorithmia package (version 0.9.3) installed for this notebook.

You can install the package using the pip package manager:

In [ ]:

pip install algorithmia==0.9.3

In [1]:

import Algorithmia
import pprint
pp = pprint.PrettyPrinter(indent=2)

2. Authentication¶

You only need your Algorithmia API Key to run the following commands.

In [2]:

API_KEY = 'YOUR_API_KEY'
# Create a client instance
client = Algorithmia.client(API_KEY)

3. Face Detection¶

Uses a pretrained model to detect faces in a given image.

4. Content Summarizer¶

SummarAI is an advanced content summarizer with the option of generating context-controlled summaries. It is based on award-winning patented methods related to artificial intelligence and vector space developed at Lawrence Berkeley National Laboratory.

In [6]:

# Get a Wikipedia article as content
wiki_article_name = 'Technological Singularity'
client = Algorithmia.client(API_KEY)
algo = client.algo('web/WikipediaParser/0.1.0')
wiki_page_content = algo.pipe(wiki_article_name)['content']
print 'Wikipedia article length: ' + str(len(wiki_page_content))

Wikipedia article length: 39683

In [7]:

# Summarize the Wikipedia article
client = Algorithmia.client(API_KEY)
algo = client.algo('SummarAI/Summarizer/0.1.2')
summary = algo.pipe(wiki_page_content.encode('utf-8'))
print 'Wikipedia generated summary length: ' + str(len(summary['summarized_data']))
print summary['summarized_data']

Wikipedia generated summary length: 406
The term was popularized by mathematician, computer scientist and science fiction author Vernor Vinge, who argues that artificial intelligence, human biological enhancement, or brain computer interfaces could be possible causes of the singularity...The technological singularity is a hypothetical event related to the advent of genuine artificial general intelligence also known as quote_tokenstrong AI"...

5. Latent Dirichlet Allocation¶

This algorithm takes a group of documents (anything that is made of up text), and returns a number of topics (which are made up of a number of words) most relevant to these documents.

Read more about Latent Dirichlet Allocation here

In [8]:

# Get up to 20 random Wikipedia articles
client = Algorithmia.client(API_KEY)
algo = client.algo('web/WikipediaParser/0.1.0')
random_wiki_article_names = algo.pipe({"random":20})
random_wiki_articles = []
for article_name in random_wiki_article_names:
 try:
 article_content = algo.pipe(article_name)['content']
 random_wiki_articles.append(article_content)
 except:
 pass
print 'Number of Wikipedia articles scraped: ' + str(len(random_wiki_articles))

Number of Wikipedia articles scraped: 17

In [9]:

# Find topics from 20 random Wikipedia articles
algo = client.algo('nlp/LDA/0.1.0')
input = {"docsList": random_wiki_articles, "mode": "quality"}
topics = algo.pipe(input)
pp.pprint(topics)

[ { u'album': 9,
 u'bomfunk': 10,
 u'kurepa': 13,
 u'mathematics': 9,
 u"mc's": 9,
 u'music': 9,
 u'university': 21,
 u'zagreb': 9},
 { u'british': 23,
 u'hindu': 115,
 u'india': 47,
 u'indian': 33,
 u'movement': 24,
 u'national': 22,
 u'political': 28,
 u'rss': 29},
 { u'berlin': 9,
 u'film': 26,
 u'gotcha': 12,
 u'jonathan': 33,
 u'sasha': 23,
 u'states': 8,
 u'township': 15,
 u'united': 8},
 { u'belfast': 8,
 u'building': 6,
 u'church': 12,
 u'history': 7,
 u'incumbent': 7,
 u'james': 5,
 u"rev'd": 7,
 u'worship': 5}]

6. Optical Character Recognition¶

Recognize text in your images.

Read more about Optical Character Recognition here

In [10]:

from IPython.display import Image
businesscard_url = 'https://s3.amazonaws.com/algorithmia-assets/data-science-ipython-notebooks/businesscard.jpg'
# Sample Image
Image(url=businesscard_url)

Out[10]:

In [11]:

input = {"src": businesscard_url,
"hocr":{
"tessedit_create_hocr":1,
"tessedit_pageseg_mode":1,
"tessedit_char_whitelist":"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-@/.,:()"}}
algo = client.algo('tesseractocr/OCR/0.1.0')
pp.pprint(algo.pipe(input))

{ u'compound': { u'': 95,
 u'206.552.9054': 84,
 u'@doppenhe': 85,
 u'AALGORITHMIA': 53,
 u'CEO': 88,
 u'DIEGO': 88,
 u'OPPENHEIMER': 88,
 u'Q': 55,
 u'diego@algorithmia.com': 83,
 u'doppenheimer': 79,
 u'o': 77},
 u'result': u' \n \n \n \nAALGORITHMIA \nDIEGO \nOPPENHEIMER \nCEO \ndiego@algorithmia.com \no \n@doppenhe \n206.552.9054 \nQ \ndoppenheimer \n \n \n'}