MySQL HeatWave User Guide
After training the model, you can run topic modeling on the trained text.
To run topic modeling, use the sample data from the
movies dataset. The dataset has no target
column. When the output table generates, you can review the
generated word groups and expressions for the trained text.
Complete the following tasks:
If not already done, load the model. You can use the
session variable for the model that is valid for the
duration of the connection. Alternatively, you can use
the model handle previously set. For the option to set
the user name, you can set it to
NULL.
The following example uses the session variable.
mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);
The following example uses the model handle.
mysql> CALL sys.ML_MODEL_LOAD('topic_modeling_use_case', NULL);
Run topic modeling on the dataset by using the
ML_PREDICT_TABLE
routine.
mysql> CALL sys.ML_PREDICT_TABLE(table_name, model_handle, output_table_name), [options]);
Replace table_name,
model_handle, and
output_table_name with your
own values. Add options as
needed.
The following example runs
ML_PREDICT_TABLE
on the dataset previously created.
mysql> CALL sys.ML_PREDICT_TABLE('topic_modeling_data.movies', @model, 'topic_modeling_data.topic_modeling_predictions', NULL);
Where:
topic_modeling_data.movies is the
fully qualified name of the input table that
contains the data to generate topic modeling for
(database_name.table_name).
@model is the session variable
for the model handle.
topic_modeling_data.topic_modeling_predictions
is the fully qualified name of the output table with
generated word groups and expressions
(database_name.table_name).
NULL sets no options for the
routine.
Query the ml_results column from the
output table. Review the generated word groups and
expressions for the movie descriptions next to
tags.
mysql> SELECT ml_results FROM topic_modeling_predictions;
+-----------------------------------------------------------------------------------------------------------------------------------------+
| ml_results |
+-----------------------------------------------------------------------------------------------------------------------------------------+
| {"predictions": {"tags": ["dangerous", "future", "join force", "journey", "warrior", "battle", "rebel", "force", "join", "group"]}} |
| {"predictions": {"tags": ["machine", "world", "agent", "group rebel", "human", "real", "real world", "power", "rebel", "fight"]}} |
| {"predictions": {"tags": ["family", "empire", "rival", "criminal", "powerful"]}} |
| {"predictions": {"tags": ["scientist", "astronaut", "attempt", "humanity", "massive", "work", "earth", "ensure", "power", "survival"]}} |
| {"predictions": {"tags": ["include", "mysterious", "strange", "world", "woman", "young woman", "young"]}} |
| {"predictions": {"tags": ["empire", "force", "dark force", "evil", "group rebel", "join force", "wise", "battle", "dark", "help"]}} |
| {"predictions": {"tags": ["alien", "attempt", "humanity", "planet", "battle", "creature", "ensure", "scientist", "survival", "team"]}} |
| {"predictions": {"tags": ["future", "human", "creature", "form", "learn", "secret", "discover", "young"]}} |
| {"predictions": {"tags": ["agent", "deal personal", "murder", "personal", "strange", "victim", "form", "help", "deal", "young"]}} |
| {"predictions": {"tags": ["wizard", "power", "dark", "learn", "school", "secret", "young", "destroy", "discover", "man"]}} |
| {"predictions": {"tags": ["alien", "dangerous", "massive", "mission", "planet", "work", "criminal", "earth", "team", "destroy"]}} |
| {"predictions": {"tags": ["murder", "student", "challenge", "help", "school", "face", "woman", "young woman", "young"]}} |
| {"predictions": {"tags": ["group", "fall", "friend", "place", "victim", "creature", "survival", "travel", "discover", "fight"]}} |
| {"predictions": {"tags": ["fall", "friend", "machine", "secure", "ensure", "scientist", "travel", "deal", "man", "young man"]}} |
| {"predictions": {"tags": ["place", "sister", "young", "fight", "woman", "young woman"]}} |
| {"predictions": {"tags": ["deal personal", "mysterious", "personal", "secure", "criminal", "powerful", "deal", "group"]}} |
| {"predictions": {"tags": ["wizard", "dark force", "evil", "include", "journey", "warrior", "wise", "dark", "powerful", "destroy"]}} |
| {"predictions": {"tags": ["family", "sister", "challenge", "deal", "woman", "young woman", "young"]}} |
| {"predictions": {"tags": ["astronaut", "mission", "earth", "face", "group"]}} |
| {"predictions": {"tags": ["world", "real", "real world", "rival", "challenge", "discover", "face", "join", "man", "young man"]}} |
| {"predictions": {"tags": ["student", "form", "learn", "school", "secret", "force", "group"]}} |
+-----------------------------------------------------------------------------------------------------------------------------------------+
21 rows in set (0.0472 sec)
To modify the number of word groups in the ml_results
column, you can set the topk option. This
option must be an integer greater or equal to one. The
following example uses the same trained table and adds the
option to limit the number of generated word groups to five.
mysql> CALL sys.ML_PREDICT_TABLE('topic_modeling_data.movies', @model, 'topic_modeling_data.topic_modeling_predictions_five', JSON_OBJECT('topk', 5));
Query the ml_results column to review the
top five generated word groups.
mysql> SELECT ml_results FROM topic_modeling_predictions_five;
+----------------------------------------------------------------------------------------------+
| ml_results |
+----------------------------------------------------------------------------------------------+
| {"predictions": {"tags": ["dangerous", "future", "join force", "journey", "warrior"]}} |
| {"predictions": {"tags": ["machine", "world", "agent", "group rebel", "human"]}} |
| {"predictions": {"tags": ["family", "empire", "rival", "criminal", "powerful"]}} |
| {"predictions": {"tags": ["scientist", "astronaut", "attempt", "humanity", "massive"]}} |
| {"predictions": {"tags": ["include", "mysterious", "strange", "world", "woman"]}} |
| {"predictions": {"tags": ["empire", "force", "dark force", "evil", "group rebel"]}} |
| {"predictions": {"tags": ["alien", "attempt", "humanity", "planet", "battle"]}} |
| {"predictions": {"tags": ["future", "human", "creature", "form", "learn"]}} |
| {"predictions": {"tags": ["agent", "deal personal", "murder", "personal", "strange"]}} |
| {"predictions": {"tags": ["wizard", "power", "dark", "learn", "school"]}} |
| {"predictions": {"tags": ["alien", "dangerous", "massive", "mission", "planet"]}} |
| {"predictions": {"tags": ["murder", "student", "challenge", "help", "school"]}} |
| {"predictions": {"tags": ["group", "fall", "friend", "place", "victim"]}} |
| {"predictions": {"tags": ["fall", "friend", "machine", "secure", "ensure"]}} |
| {"predictions": {"tags": ["place", "sister", "young", "fight", "woman"]}} |
| {"predictions": {"tags": ["deal personal", "mysterious", "personal", "secure", "criminal"]}} |
| {"predictions": {"tags": ["wizard", "dark force", "evil", "include", "journey"]}} |
| {"predictions": {"tags": ["family", "sister", "challenge", "deal", "woman"]}} |
| {"predictions": {"tags": ["astronaut", "mission", "earth", "face", "group"]}} |
| {"predictions": {"tags": ["world", "real", "real world", "rival", "challenge"]}} |
| {"predictions": {"tags": ["student", "form", "learn", "school", "secret"]}} |
+----------------------------------------------------------------------------------------------+
21 rows in set (0.0475 sec)To learn more about generating predictions for one or more rows of data, see Generate Predictions for a Row of Data.
Review other Machine Learning Use Cases.