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Table of Contents

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  1. Preface
  2. Transformations
  3. Source transformation
  4. Target transformation
  5. Access Policy transformation
  6. Aggregator transformation
  7. B2B transformation
  8. Chunking transformation
  9. Cleanse transformation
  10. Data Masking transformation
  11. Data Services transformation
  12. Deduplicate transformation
  13. Expression transformation
  14. Filter transformation
  15. Hierarchy Builder transformation
  16. Hierarchy Parser transformation
  17. Hierarchy Processor transformation
  18. Input transformation
  19. Java transformation
  20. Java transformation API reference
  21. Joiner transformation
  22. Labeler transformation
  23. Lookup transformation
  24. Machine Learning transformation
  25. Mapplet transformation
  26. Normalizer transformation
  27. Output transformation
  28. Parse transformation
  29. Python transformation
  30. Rank transformation
  31. Router transformation
  32. Rule Specification transformation
  33. Sequence transformation
  34. Sorter transformation
  35. SQL transformation
  36. Structure Parser transformation
  37. Transaction Control transformation
  38. Union transformation
  39. Vector Embedding transformation
  40. Velocity transformation
  41. Verifier transformation
  42. 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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