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Text mining at SAP HANA Cloud with Python

The SAP HANA Cloud product team recently announced the availability of out of the box Text mining  capabilities, which you can explore with a Jupyter notebook via the latest  Python machine learning client for SAP HANA


Why you should care about that?

If you are developing in SAP HANA Cloud, good news: you don’t have to move your data in/out of SAP HANA Cloud to operate Text Mining techniques.

Would you like to enhance your advance analytics reports with insights stemmed from text? e.g.

Which is the most frequent spare-part mentioned on High criticality issues reported from Field engineers to the manufacturing plant?

What are the top 3 operations taking place each month from the Maintenance department?

Good news. All you need is one connection to a SAP HANA Cloud instance, consolidate your results in a Calculation View and your BI engineers can enhance their report in SAP Analytics Cloud (SAC).

Last but not least you are able to classify an input document with respect to sets of categories.

This blog will be a series of 2 blog posts, starting with this one to get the very basics of the new functionalities offered. We will use a Jupyter notebook and a sample text with a tiny dataset having 3 columns.

  • Document ID as a unique id per row
  • Free text mocking an S/4 Plant Maintenance ticket notification.
    • If you are not familiar with this S/4 Module, a notification is how a maintenance department is getting a request to check at abnormal or exceptional situation(s) in technical objects of a plant. The ticket among many other fields, includes a free text field with a description of the problem.
  • Priority (High or Low) of a ticket.

The second blog post would be to see how we can make use of another SAP BTP Component (Cloud Foundry or Kyma Runtime) to build an application on top of these functions to make them available to our business users.

Setup your Jupyter notebook

1.Create a Jupyter notebook and import hana_ml library:

import hana_ml
import hana_ml.dataframe as dataframe
import as tmh

import pandas as pd

pd.options.display.max_columns = None
pd.options.display.max_rows = None
pd.set_option('display.max_colwidth', 100)

2.Create 2 csv files at the same directory.

1st file , call it sample-text.csv and add the text below and save it.

DOC1000,repair damaged pipe,HIGH
DOC2000,replace main valve,HIGH
DOC3000,repair oil leak,HIGH
DOC4000,paint doors,LOW
DOC5000,clean filters,LOW
DOC6000,maintain A/C filters,LOW

2nd file , call it new-text.csv and add the text below and save it. (note that we are not including a 3rd Column with Priority. This dataset would be to showcase the text classification function.)

DOC7000,repair main valve
DOC8000,clean debris


Import CSV files to SAP HANA Cloud

table_name = "TM_SAMPLE_TEXT"
file_name = "sample-text.csv"
df_data = pd.read_csv(file_name,sep=',',encoding= 'unicode_escape')
dataframe.create_dataframe_from_pandas(connection_context = conn, 
                                                   pandas_df = df_data, 
                                                   table_name = table_name,
                                                   force = True,
                                                   replace = True)
df_remote = conn.table("TM_SAMPLE_TEXT")

If all worked alright, it should look like this:

Repeat for the second CSV file:

table_name = "TM_NEW_TEXT"
file_name = "new-text.csv"
df_data = pd.read_csv(file_name,sep=',',encoding= 'unicode_escape')
dataframe.create_dataframe_from_pandas(connection_context = conn,
pandas_df = df_data,
table_name = table_name,
force = True,
replace = True)
df_remote_new = conn.table("TM_NEW_TEXT")


Tip 1: Avoid collecting all data as you see above by calling .collect() on the dataframe in real projects. Remember that if you do that you collect all the data at the Jupyter notebook! Instead you should use df_remote.head(5).collect() to check your data and keep the heavy lifting on SAP HANA Cloud.

Now you are holding all the information is this df_remote object. Perform a Term Frequency analysis by simply passing the df_remote object as parameter to the tf_analysis function:

tfidf= tmh.tf_analysis(df_remote)


The object returned is a tuple of hana_ml dataframes and you may receive each one by simply adding an index from 0 to 2 if you are interested to check the TF/IDF values calculated etc.

However, once you have the tfidf object initiated, it can be used as input to other text mining functions. Let’s  see an example:


This function returns the top-ranked related documents for a query document based on Term Frequency – Inverse Document Frequency(TF-IDF) result or reference data.

Get a sample query document from the new dataset. At this case we select just the first one with text “repair main valve”. We pass to get_related_doc this unseen document and the tfidf object.

#get one line from the unseen documents
new_text = conn.sql('SELECT "NOTIFICATION_TEXT" FROM ({}) LIMIT 1 '.format(df_remote_new.select_statement))

#run the get_related_doc
get_related_doc_rs = tmh.get_related_doc(new_text, tfidf)

#display the list of top N related documents with their mathcing score

#join the result table wit the initial table to have text and scores for ambiguity
_result  = get_related_doc_rs.alias('A').join(
    "A.ID = B.ID"



Tip 2 : Please note the latest addition to the hana_ml data frame where you can deselect a column. Never again having to select all the columns one by one and skipping the ones we don’t need! 

The results look like below:

The function has returned three documents, with the  top one being “replace main valve” whereas the new document was “repair main valve”.



This function returns the top-ranked terms that match an initial substring based on Term Frequency – Inverse Document Frequency(TF-IDF) result or reference data.

Let’s hack our way to retrieve one term from a document. What we do below is retrieving just the  the leftmost three characters. The full string is “repair main valve” so we expect to get just “rep” by adding an index of 3.

_index = 3
sub_term = conn.sql('SELECT LEFT(NOTIFICATION_TEXT,{}) as "SUB_STRING_" \
              FROM ({}) LIMIT 1 '.format(_index,df_remote_new.select_statement))


Let’s make use of the get_suggested_term functions to get some results.

_result = tmh.get_suggested_term(sub_term,df_remote)

The results are two other terms “repair” and “replace” with the first one coming on top.


Last but not least, the text classification function. This is where we will need both the dataframes we created at the beginning of the blog. This is based on k-nearest neighbors algorithm used for classification. The default k is 1, so the new document will be assigned to the class of the nearest neighbor. Just pass the two dataframes to the text_classification function.

res = tmh.text_classification(df_remote_new, df_remote)

The returned object consists of two dataframes which can be retrieved by passing the correct index.

The first one has the new documents assigned with a category and the latter one the matching distance and the matched document.

Let’s operate some basic sql using hana_ml join operators to see what was the actual text between the unseen documents and the matched ones.


    "A.TRAIN_ID = B.ID"

    "C.NEW_DOC_ID = D.ID"





From a developer’s point of view is alleviating to know that once you have connected to a SAP HANA Cloud instance with your Jupyter you have scalable and secure access to functions that can handle any data type.

From a business point of view, it is easier than ever forming a business hypotheses that requires data preparation, exploration and visualisation capabilities and get back a report on the spot. (fail fast or make impact faster)

Upgrade to latest SAP HANA Cloud and start formulating business use-cases and improve significantly your traditional analytic approaches by combining all sorts of data (and types).

Thanks for reading!


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