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Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Aggregator transformation
  6. Cleanse transformation
  7. Data Masking transformation
  8. Data Services transformation
  9. Deduplicate transformation
  10. Expression transformation
  11. Filter transformation
  12. Hierarchy Builder transformation
  13. Hierarchy Parser transformation
  14. Hierarchy Processor transformation
  15. Input transformation
  16. Java transformation
  17. Java transformation API reference
  18. Joiner transformation
  19. Labeler transformation
  20. Lookup transformation
  21. Machine Learning transformation
  22. Mapplet transformation
  23. Normalizer transformation
  24. Output transformation
  25. Parse transformation
  26. Python transformation
  27. Rank transformation
  28. Router transformation
  29. Rule Specification transformation
  30. Sequence Generator transformation
  31. Sorter transformation
  32. SQL transformation
  33. Structure Parser transformation
  34. Transaction Control transformation
  35. Union transformation
  36. Velocity transformation
  37. Verifier transformation
  38. Web Services transformation

Transformations

Transformations

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Example: Operationalize a pre-trained model

Example: Operationalize a pre-trained model

You work for a pharmaceutical company and you are studying data on flower formation in foxgloves to provide a better treatment for heart diseases. You want to find out whether the common foxglove
Digitalis purpurea
 or the woolly foxglove
Digitalis lanata
 can provide a better prognosis.
To perform your research, you must classify data on the length and width of the flower sepals and petals by flower species. To classify the data, you developed a pre-trained model outside of
Data Integration
.
To operationalize the pre-trained model, complete the following tasks:
  1. Create a mapping that contains a passive Python transformation and list the pre-trained model as a resource file.
  2. Write a Python script that accesses the pre-trained model.
  3. Pass the data on flower sepals and petals to the Python transformation to classify the data by foxglove species.
The following table shows sample sepals and petals data that you can pass to the Python transformation:
Name
Type
Precision
 
sepal_length
decimal
10
sepal_width
decimal
10
petal_length
decimal
10
petal_width
decimal
10
true_class
string
50
The passive Python transformation uses the following components:
Resource File
Specify the path of the pre-trained model as the resource file.
For example, you might use a pre-trained model that is stored in the file
foxgloveDataMLmodel.pkl
 in the following path:
  • Path that is relative to the location on the Secure Agent machine.
    For example, if the resource file is under
    <Secure Agent installation directory>/ext/python/folder1/foxgloveDataMLmodel.pkl
    , then the relative path would be
    /folder1/foxgloveDataMLmodel.pkl
    .
  • The supplementary file location for a serverless runtime environment. 
/data/home/dtmqa/data/foxgloveDataMLmodel.pkl
Python Code
Specify the Python code in the
Pre-Partition Python Code
 and
Main Python Code
 sections.
Use the
Pre-Partition Python Code
 section to import libraries, load the resource file, and initialize variables.
For example, you might enter the following code in the
Pre-Partition Python Code
 section: 
from sklearn import svm
from sklearn.externals import joblib
import numpy as np
clf = joblib.load(resourceFileArrays[0])
classes = ['common', 'woolly']
Use the
Main Python Code
 section to define how the Python transformation uses the pre-trained model to evaluate each row of data.
For example, you might enter the following code in the
Main Python Code
 section: 
input = [sepal_length, sepal_width, petal_length, petal_width]
input = np.array(input).reshape(1,-1)
pred = clf.predict(input)
predicted_class = classes[pred[0]]
sepal_length_out = sepal_length
sepal_width_out = sepal_width
petal_length_out = petal_length
petal_width_out = petal_width
true_class_out = true_class
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