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### Summary Click decorated functions cannot (properly) be called outside of the click interface. This makes it difficult to reuse the setup functionality in measure_text_edit_distance or measure_element_type_accuracy. This PR removes the click decoration and separates it into a wrapper function purely to execute the command. ### Technical Details - Changed as suggested in [this StackOverflow post](https://stackoverflow.com/questions/40091347/call-another-click-command-from-a-click-command) response - The locations of these now distinct functions are separate: the `_command` click-decorated functions stay in ingest/evaluate.py, and the core functions measure_text_edit_distance and measure_element_type_accuracy are moved into the unstructured/metrics/ folder (which is a more logical location for them). - Initial test added for measure_text_edit_distance ### Test `sh ./test_unstructured_ingest/evaluation-metrics.sh text-extraction` functionality is unchanged. --------- Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com> Co-authored-by: shreyanid <shreyanid@users.noreply.github.com> Co-authored-by: Trevor Bossert <37596773+tabossert@users.noreply.github.com>
37 lines
1.5 KiB
Plaintext
37 lines
1.5 KiB
Plaintext
Bank Good Credit
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Accredited with IABACTM
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(International Association of Business Analytics Certifications)
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IABAC International Association of
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Business Analytics Certification
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DataMitesTM. All Right Reserved
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Objective & Background
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Classify credit card customers as good / bad, based on information from internal and external sources.
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Data provided
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Demographic: Base file of with credit card history details. Only one record for every customer.
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Account: Contians data for various loans availed by the customer. Not related to credit card. Multiple records for every customer.
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Enquiries: Enquired made by customers for different loan purposes. Multiple records for every customer.
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DataMitesTM. All Right Reserved
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Design
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Data to be downloaded using SQL queries.
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Required information to be extracted from Account and Enquiry files and converted to one-to-one files.
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The columns from the two files should be merged with Demographic file using Left Join with customer no as key column, to create a final file. The final file should contain all the records in demographic and additional columns/features from Account and Enquiry files will get added to Demographic file.
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There will be many customers in account and enquiry file who will get left out. This is fine as we anyway dont know their good/bad label for training purpose.
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DataMitesTM. All Right Reserved
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Analysis of Data
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Show using Excel File
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DataMitesTM. All Right Reserved
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Explain Coding / outcomes
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Show using Jupyter
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DataMitesTM. All Right Reserved
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Thank You
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DataMitesTM. All Right Reserved
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