Microsoft Azureマシンのラーニングと認知サービスAPI

Question

Azure MLスタジオのCognitive Services APIをビルドモデルで呼び出すことは可能ですか？サンプルの実験で参照できるドキュメントはありますか？

ありがとうございます。

Answer 1

execute Python snippets inside Azure MLに可能です。そこから、Pythonインターフェイスを使用してMicrosoft認知サービスAPIを呼び出すことができます（Face APIの例をPythonから見てください）。ここで

Answer 2

はあなたが試すことができますサンプルコードです：

import urllib2 
import urllib 
import sys 
import base64 
import json 
import numpy as np 
import pandas as pd 

# The entry point function can contain up to two input arguments: 
# Param<dataframe1>: a pandas.DataFrame 
# Param<dataframe2>: a pandas.DataFrame 
def azureml_main(dataframe1 = None, dataframe2 = None): 

    # Execution logic goes here 
    #print('Input pandas.DataFrame #1:\r\n\r\n{0}'.format(dataframe1)) 

# Account key is for Ted Way 
    account_key = str(dataframe2['Col1'][0]) 
    #account_key = 'api_key' 

    #base_url = 'https://api.datamarket.azure.com/data.ashx/amla/text-analytics/v1' 
    #base_url = str(dataframe2['Col2'][0])   
    base_url = 'https://westus.api.cognitive.microsoft.com/' 

    headers = {'Content-Type':'application/json', 'Ocp-Apim-Subscription-Key':account_key} 

    #input_text = sys.argv[2] 
    sentiment_scores = [] 
    num_examples = len(dataframe1.index) 
    input_texts = '{"documents":[' 
#for each record 
    for i in range(0,num_examples): 
     input_text = str(dataframe1['Text'][i])   
     input_text = input_text.replace("\"", "'") 

     #params = { 'Text': input_text}   
     input_texts = input_texts + '{"id":"' + str(i) + '","text":"'+ input_text + '"},'   

    input_texts = input_texts + ']}' 
    print input_texts 

    # Detect sentiment. 
    batch_sentiment_url = base_url + 'text/analytics/v2.0/sentiment'   

    req = urllib2.Request(batch_sentiment_url, input_texts, headers) 
    response = urllib2.urlopen(req) 
    result = response.read() 
    obj = json.loads(result) 

    for sentiment_analysis in obj['documents']:    
     sentiment_scores.append(str(sentiment_analysis['score'])) 
     #print('Sentiment score: ' + str(obj['Score'])) 

    sentiment_scores = pd.Series(np.array(sentiment_scores))   

    df1 = pd.DataFrame({'SentimentScore':sentiment_scores}) 

# Don't return the original text' 
    #frames = [dataframe1, df1] 

    #dataframe1 = pd.concat(frames, axis=1) 

    # Return value must be of a sequence of pandas.DataFrame 
    return df1