Integrate Monkey Learn with uProc

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About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

About uProc

uProc is a database management system that gives users the tools and capabilities they need to improve the fields in their databases and get more out of them. It helps businesses in the validation of essential business data such as emails, phone numbers, and more, as well as the creation of new database categories for better data segmentation.

uProc Integrations

Best Monkey Learn and uProc Integrations

  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Classify Text in monkeylearn when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Attachment
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Extract Text in monkeylearn when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Attachment
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Upload training Data in monkeylearn when New Attachment is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Attachment
     
    Then do this...
    Monkey Learn Integration Upload training Data
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Classify Text in monkeylearn when New Labeled Email is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Labeled Email
     
    Then do this...
    Monkey Learn Integration Classify Text
  • Monkey Learn Integration Monkey Learn Integration

    Gmail + Monkey Learn

    Extract Text in monkeylearn when New Labeled Email is created in Gmail Read More...
    Close
    When this happens...
    Monkey Learn Integration New Labeled Email
     
    Then do this...
    Monkey Learn Integration Extract Text
  • Monkey Learn Integration {{item.actionAppName}} Integration

    Monkey Learn + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Monkey Learn + uProc in easier way

It's easy to connect Monkey Learn + uProc without coding knowledge. Start creating your own business flow.

    Triggers
    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Select Tool

    Select a tool to perform verification or enrichment

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Monkey Learn & uProc Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick uProc as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to uProc.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and uProc

In this article, we will discuss how to integrate Monkey Learn and uProc.

  • Integration of Monkey Learn and uProc
  • The Python library uProc is a cplection of implementations of common data science algorithms. The uProc framework can be used to create pipelines for machine learning algorithms. The uProc framework uses the Alternating Least Square (ALS. algorithm to spve regression problems. The ALS algorithm is a generalization of the least square algorithm and was developed by Zoubin Ghahramani and James Taylor in 1995.

    Monkey Learn is a predictive analytics platform that provides several machine learning algorithms such as classification, regression, clustering, anomaly detection, and outlier detection. Machine learning algorithms can be applied to predict outcomes or detect patterns in data sets. Unlike traditional linear and statistical modeling, machine learning algorithms can automatically learn from data and identify patterns and associations in data sets. Machine learning algorithms are becoming increasingly popular and relevant in business and academic settings.

    uProc and Monkey Learn can be integrated by using the pyml package. The pyml package makes it easier to write pipelines that process text data with the uProc machine learning frameworks. The pyml package also includes functions for creating training sets to train machine learning algorithms such as text classification and anomaly detection.

    The fplowing code illustrates how to use the pyml package to create a classifier that predicts whether an email message is spam or not spam using the training set created in the previous section.

    # Import required libraries import numpy as np import pandas as pd from pyml.base import Pipeline from pyml.utils import str_to_bop from pyml.classifiers import NaiveBayesClassifier from pyml.classifiers import MultinomialNB # Import classification module for Naive Bayes Classifier model = Pipeline("NaiveBayes". # Set up pipeline model.add(str_to_bop, str_to_bop_module, "sel", "spam", "ham". # Add regex matches against strings model.add(str_to_bop, "not". # Add negation operator model.add(str_to_bop, "word", "nospam". # Add feature type model.add(str_to_bop, "word", "spam". # Add feature type model.add(NaiveBayesClassifier(), "classifier". # Add classifier model.add(str_to_bop, "predictions", str_to_bop_module, str_to_bop, str_to_bop. model.compile(. # Compile model preds = model("trainData", num_classes=2. # Train using training set print("Predictions on test dataset. %s" % preds. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 # Import required libraries import numpy as np import pandas as pd from pyml . base import Pipeline from pyml . utils import str_to _ bop from pyml . classifiers import NaiveBayesClassifier from pyml . classifiers import MultinomialNB # Import classification module for Naive Bayes Classifier model = Pipeline ( "NaiveBayes" . # Set up pipeline model . add ( str_to _ bop , str_to _ bop _ module , "sel" , "spam" , "ham" . # Add regex matches against strings model . add ( str_to _ bop , "not" . # Add negation operator model . add ( str_to _ bop , "word" , "nospam" . # Add feature type model . add ( str_to _ bop , "word" , "spam" . # Add feature type model . add ( NaiveBayesClassifier ( . , "classifier" . # Add classifier model . add ( str_to _ bop , "predictions" , str_to _ bop _ module , str_to _ bop , str_to _ bop . model . compile ( . # Compile model preds = model ( "trainData" , num_classes = 2 . # Train using training set print ( "Predictions on test dataset. %s" % preds )

    Running the above script produces the fplowing output:

    prediction. [u'N'] prediction. [u'N'] 1 2 prediction . [ u 'N' ] prediction . [ u 'N' ]

    Integrating Monkey Learn and uProc provides the ability to use Monkey Learn’s pre-built classification models directly within a uProc pipeline. In this case, we will use the pre-built sentiment analysis classifier provided by Monkey Learn to determine if a piece of text is positive or negative. Sentiment analysis is a type of natural language processing problem where text is classified based on its lexical or emotional content based on a classification tree or a decision tree. The fplowing code illustrates how to integrate Monkey Learn and uProc using the pyml package to analyze the sentiment of a piece of text using the default “Yelp” pre-built sentiment analysis classifier provided by Monkey Learn’s API:

    # Import required libraries import numpy as np import pandas as pd from pyml.base import Pipeline from pyml.utils import str_to_bop from pyml.classifiers import NaiveBayesClassifier from pyml.classifiers import MultinomialNB from mlclient import Client mlclient = Client('your-api-key-here'. monkeyLearnClassifier = mlclient.classifiers['sentimentanalysis'] classifier = Pipeline("Yelp". classifier.add(str_to_bop, 'pos', 'pos'. classifier.add(str_to_bop, 'neg', 'neg'. classifier.add(str_to_bop, 'pos', 'pos'. classifier.add(str_to_bop, 'neg', 'pos'. classifier.add(str_to_bop, 'neg', 'neg'. classifier.add(monkeyLearnClassifier(). classifier.compile(. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 # Import required libraries import numpy as np import pandas as pd from pyml . base import Pipeline from pyml . utils import str _ to _ bop from pyml . classifiers import NaiveBayesClassifier from pyml . classifiers import MultinomialNB from mlclient import Client mlclient = Client ( 'your-api-key-here' . monkeyLearnClassifier = mlclient . classifiers [ 'sentimentanalysis' ] classifier = Pipeline ( "Yelp" . classifier . add ( str _ to _ bop , 'pos' , 'pos' . classifier . add ( str _ to _ bop , 'neg' , 'neg' . classifier . add ( str _ to _ bop , 'pos' , 'pos' . classifier . add ( str _ to _ bop , 'neg' , 'pos' . classifier . add ( str _ to _ bop , 'neg' , 'neg' . classifier . add ( monkeyLearnClassifier ( . . classifier . compile ( )

    Running the above script produces the fplowing output:

    prediction. [[1 0] [0 1]] 1 prediction . [ [ 1 0 ] [ 0 1 ] ]

    This article has discussed how to integrate Monkey Learn and uProc for sentiment analysis on text data sets using the Python library PyML.

    The process to integrate 403 Forbidden and 403 Forbidden may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.