Difference between revisions of "Industry Classifier"

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{{McNair Projects
+
{{Project
−
|Project Title=Industry Classifier,
+
|Has project output=Tool
−
|Start Term=Spring 2017,
+
|Has sponsor=McNair Center
 +
|Has title=Industry Classifier
 +
|Has owner=Christy Warden,
 +
|Has start date=Spring 2017
 +
|Has keywords=Tool
 +
|Has project status=Subsume
 
}}
 
}}
 +
The objective of this project is to build a neural network that can classify a firm's industry based on text of its business description.
  
 +
The following projects are dependent on this projects:  {{#ask: [[Category:McNair Projects]] [[Is dependent on::{{PAGENAME}}]]}}
 +
 +
=Summer 2018 Work=
 +
Test data will come from crunchbase.
 +
Database is called crunchbase2 and is located in:
 +
/bulk/crunchbase2
 +
The pulled information is in:
 +
E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\Our companies with other info.xlsx
 +
The code to build tables to pull all info is in:
 +
E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\BuildTestData.sql
 +
 +
==MLP Classifier==
 +
The new version that I am editing on is:
 +
E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\IndustryClassifierCONDENSED-USETHIS.py
 +
Small training and testing data is called:
 +
2018traindata.txt
 +
NewTestData2018.txt
 +
Larger training and testing data is called:
 +
bigtrain2018.txt
 +
bigtest2018.txt
 +
This file modifies the Classifier.pkl file which stores the components of the model. Eventually, we should be able to run this through FinalIndustryClassifier.py.
 +
 +
The crunchbase data in my training data has almost 40 labels and I could not get the accuracy rate of this model to go up past 30%. However, if you assign only 3 labels, the accuracy rate goes up to 50%
 +
 +
==LSTM Model==
 +
See old page here [[Deep Text Classifier]]. I updated the preprocessing file to run on python3.
 +
 +
I tried updating this code to run on the new data from Crunchbase. Files used are located in:
 +
E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\Yang's Code
 +
 +
You should first run the preprocessing file and then use the classification file. I could not figure out why the accuracy on this model was only 10% with 40 labels and around 30% with 5-8 labels. The accuracy of this one should be higher than the MLP classifier.
 +
 +
=New Notes=
 +
 +
We're rebuilding the [[Industry Classifier]] using better technology and better inputs.
 +
 +
For the inputs:
 +
*Run LoadLongDescription.sql in Z:\VentureCapitalData\SDCVCData\vcdb2
 +
*With sdccompanybase1 table already loaded, load the commented code in that file too
 +
*This outputs longdescriptionindu.txt
 +
 +
=Final Product and Use=
 +
 +
==Description==
 +
 +
The final product (as of 2/27/17) is FinalIndustryClassifier.py which is located in McNair/Projects/Accelerators/Industry_Classifier.
 +
It takes in an input file of the format Company tab Description and outputs a file called inputfile + Classified.txt. (So if you input Myfile.txt, your output file will be
 +
MyfileClassified.txt). This file will be located in the same folder as the FinalIndustry.py code (McNair/Projects/Accelerators/Industry_Classifier).
 +
 +
==Use==
 +
 +
1) Create a file of the format Company [tab] Description. The description must all be on one line.
 +
 +
2) Copy your file into the folder McNair/Projects/Accelerators/Industry_Classifier
 +
 +
3) Open the file FinalIndustryClassifier.py in Komodo
 +
 +
4) On line 7 of the code, change the words inside the quotation marks to the name of your file. For example, if your file is called MyFile.txt, line 7 should read myfile = "MyFile.txt"
 +
 +
5) Press the play button and wait for "Done!" to print in the output window of Komodo.
 +
 +
6) Open McNair/Projects/Accelerators/Industry_Classifier and find the file called "(the name of your file)Classified.txt" (aka MyFileClassified.txt)
 +
 +
7) Open this file (IN TEXTPAD). It should be your output of the format Company [tab] Classification.
 +
 +
==Command Line Use==
 +
A command line program exists for this tool. To use it, open the Command Prompt and change directories to:
 +
E:\McNair\Projects\Accelerators\Industry_Classifier
 +
To run the program, enter:
 +
python FinalIndustryClassifier_command.py
 +
A prompt will appear asking you to enter an F or S. F stands for File Input, and S stands for Single Use.
 +
If you select F, a prompt will appear asking you to enter an input filename, and an output filename, separated by a space.
  
 
=Possible Tools=
 
=Possible Tools=
Line 23: Line 101:
  
 
It's complexity is listed as: Suppose there are n training samples, m features, k hidden layers, each containing h neurons - for simplicity, and o output neurons. The time complexity of backpropagation is O(n * m * h^k * o * i), where i is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training.
 
It's complexity is listed as: Suppose there are n training samples, m features, k hidden layers, each containing h neurons - for simplicity, and o output neurons. The time complexity of backpropagation is O(n * m * h^k * o * i), where i is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training.
 +
 +
------WE ENDED UP USING THIS ONE
 +
------
  
  
Line 38: Line 119:
 
An example is given at:
 
An example is given at:
 
  https://www.packtpub.com/books/content/training-and-visualizing-neural-network-r
 
  https://www.packtpub.com/books/content/training-and-visualizing-neural-network-r
 +
 +
=Scripts=
 +
 +
Scripts and data for this project are located in:
 +
E:\McNair\Projects\Accelerators\Code+Final_Data\ChristyCode
 +
 +
===Industry Classifier===
 +
This is a neural net built in python that trains on industry designation data from the SDC Platinum database. It serves as a predictive model to predict the industry allocation of given companies.
 +
The file is located in the directory listed above.
 +
 +
===FindTrainData.py===
 +
Builds a tab-delimited text file containing 200 companies with each Industry classification (i.e. 200 biotech, 200 media etc). Hopefully if we use this as our training data, we will get more accurate classifications.
 +
 +
==FixDescriptions.py==
 +
Deals with the problem that by output files from SDC are poorly formatted when the description goes beyond 1 line. Outputs a tab-delimited text file where the whole description is on the same line and can be read.
 +
 +
===Addresses.txt===
 +
This text file contains investment info, name, address, city, state of Portfolio companies.
 +
 +
 +
===Descriptions.txt===
 +
This text file contains company, short description, major industry, minor industry of Portfolio companies.
 +
 +
 +
 +
 +
 +
=Statistics=
 +
 +
Stastical methods for analyzing results from a neural network.
 +
 +
[https://en.wikipedia.org/wiki/Precision_and_recall Precision and Recall]
 +
 +
Quick Check using excel; Finding number of correct matches between two columns:
 +
 +
=SUMPRODUCT(--(range1=range2))
 +
 +
See an example [https://exceljet.net/formula/count-matches-between-two-columns here].
 +
 +
 +
=Comments and Thoughts=
 +
 +
'''2/17/17'''
 +
 +
Christy: No matter what parameters I change in the NN, I can't get the accuracy to go up above around 30%. Looking at the descriptions that the classifier fails on, I realized that it pretty much guesses randomly a lot of the time when the descriptions are terrible like "We provide services to our customers." I think we need to be training and classifying based on the longer description, which is why I started working on the FixDescriptions.txt script.
 +
 +
 +
'''2/27/17'''
 +
 +
Christy: The pickle library is vital and we should remember to use it when we use black boxish libraries like the sklearn classifier.

Latest revision as of 13:47, 21 September 2020


Project
Industry Classifier
Project logo 02.png
Project Information
Has title Industry Classifier
Has owner Christy Warden
Has start date Spring 2017
Has deadline date
Has keywords Tool
Has project status Subsume
Dependent(s): Accelerator Seed List (Data), U.S. Seed Accelerators
Subsumed by: Deep Text Classifier
Has sponsor McNair Center
Has project output Tool
Copyright © 2019 edegan.com. All Rights Reserved.

The objective of this project is to build a neural network that can classify a firm's industry based on text of its business description.

The following projects are dependent on this projects:

Summer 2018 Work

Test data will come from crunchbase. Database is called crunchbase2 and is located in: 

/bulk/crunchbase2

The pulled information is in: 

E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\Our companies with other info.xlsx

The code to build tables to pull all info is in: 

E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\BuildTestData.sql

MLP Classifier

The new version that I am editing on is: 

E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\IndustryClassifierCONDENSED-USETHIS.py

Small training and testing data is called: 

2018traindata.txt
NewTestData2018.txt

Larger training and testing data is called: 

bigtrain2018.txt
bigtest2018.txt

This file modifies the Classifier.pkl file which stores the components of the model. Eventually, we should be able to run this through FinalIndustryClassifier.py.

The crunchbase data in my training data has almost 40 labels and I could not get the accuracy rate of this model to go up past 30%. However, if you assign only 3 labels, the accuracy rate goes up to 50%

LSTM Model

See old page here Deep Text Classifier. I updated the preprocessing file to run on python3.

I tried updating this code to run on the new data from Crunchbase. Files used are located in: 

E:\McNair\Projects\Accelerators\Summer 2018\Industry Classifier update\Yang's Code

You should first run the preprocessing file and then use the classification file. I could not figure out why the accuracy on this model was only 10% with 40 labels and around 30% with 5-8 labels. The accuracy of this one should be higher than the MLP classifier.

New Notes

We're rebuilding the Industry Classifier using better technology and better inputs.

For the inputs:

  • Run LoadLongDescription.sql in Z:\VentureCapitalData\SDCVCData\vcdb2
  • With sdccompanybase1 table already loaded, load the commented code in that file too
  • This outputs longdescriptionindu.txt

Final Product and Use

Description

The final product (as of 2/27/17) is FinalIndustryClassifier.py which is located in McNair/Projects/Accelerators/Industry_Classifier. It takes in an input file of the format Company tab Description and outputs a file called inputfile + Classified.txt. (So if you input Myfile.txt, your output file will be MyfileClassified.txt). This file will be located in the same folder as the FinalIndustry.py code (McNair/Projects/Accelerators/Industry_Classifier).

Use

1) Create a file of the format Company [tab] Description. The description must all be on one line.

2) Copy your file into the folder McNair/Projects/Accelerators/Industry_Classifier

3) Open the file FinalIndustryClassifier.py in Komodo

4) On line 7 of the code, change the words inside the quotation marks to the name of your file. For example, if your file is called MyFile.txt, line 7 should read myfile = "MyFile.txt"

5) Press the play button and wait for "Done!" to print in the output window of Komodo.

6) Open McNair/Projects/Accelerators/Industry_Classifier and find the file called "(the name of your file)Classified.txt" (aka MyFileClassified.txt)

7) Open this file (IN TEXTPAD). It should be your output of the format Company [tab] Classification.

Command Line Use

A command line program exists for this tool. To use it, open the Command Prompt and change directories to: 

E:\McNair\Projects\Accelerators\Industry_Classifier

To run the program, enter: 

python FinalIndustryClassifier_command.py

A prompt will appear asking you to enter an F or S. F stands for File Input, and S stands for Single Use. If you select F, a prompt will appear asking you to enter an input filename, and an output filename, separated by a space.

Possible Tools

Python Tools

SciKit Learn SVM

http://scikit-learn.org/stable/modules/svm.html#svm

It's complexity is between O(n^2) and O(n^3). Seems easy to use. This is not a neural net; it is a support vector machine.


SciKit Learn Neural Net

http://scikit-learn.org/stable/modules/neural_networks_supervised.html

This IS a neural net using back propagation.

It's complexity is listed as: Suppose there are n training samples, m features, k hidden layers, each containing h neurons - for simplicity, and o output neurons. The time complexity of backpropagation is O(n * m * h^k * o * i), where i is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training.

WE ENDED UP USING THIS ONE


SK Neural Network Package

This is a separate package than listed above. It requires a separate installation. Documentation is provided at: 

https://scikit-neuralnetwork.readthedocs.io/en/latest/index.html

We ran into deprecation warnings, and the program would not execute due to a missing g++ drive.

R Tools

R seems to have a built in package called "neuralnet".

An example is given at: 

https://www.packtpub.com/books/content/training-and-visualizing-neural-network-r

Scripts

Scripts and data for this project are located in: 

E:\McNair\Projects\Accelerators\Code+Final_Data\ChristyCode

Industry Classifier

This is a neural net built in python that trains on industry designation data from the SDC Platinum database. It serves as a predictive model to predict the industry allocation of given companies. The file is located in the directory listed above.

FindTrainData.py

Builds a tab-delimited text file containing 200 companies with each Industry classification (i.e. 200 biotech, 200 media etc). Hopefully if we use this as our training data, we will get more accurate classifications.

FixDescriptions.py

Deals with the problem that by output files from SDC are poorly formatted when the description goes beyond 1 line. Outputs a tab-delimited text file where the whole description is on the same line and can be read.

Addresses.txt

This text file contains investment info, name, address, city, state of Portfolio companies.


Descriptions.txt

This text file contains company, short description, major industry, minor industry of Portfolio companies.



Statistics

Stastical methods for analyzing results from a neural network.

Precision and Recall

Quick Check using excel; Finding number of correct matches between two columns: 

=SUMPRODUCT(--(range1=range2))

See an example here.


Comments and Thoughts

2/17/17

Christy: No matter what parameters I change in the NN, I can't get the accuracy to go up above around 30%. Looking at the descriptions that the classifier fails on, I realized that it pretty much guesses randomly a lot of the time when the descriptions are terrible like "We provide services to our customers." I think we need to be training and classifying based on the longer description, which is why I started working on the FixDescriptions.txt script.


2/27/17

Christy: The pickle library is vital and we should remember to use it when we use black boxish libraries like the sklearn classifier.

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