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Apache Spark Deep Learning Cookbook

You're reading from   Apache Spark Deep Learning Cookbook Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781788474221
Length 474 pages
Edition 1st Edition
Languages
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Authors (2):
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Ahmed Sherif Ahmed Sherif
Author Profile Icon Ahmed Sherif
Ahmed Sherif
 Ravindra Ravindra
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Ravindra
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Toc

Table of Contents (21) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
1. Setting Up Spark for Deep Learning Development FREE CHAPTER 2. Creating a Neural Network in Spark 3. Pain Points of Convolutional Neural Networks 4. Pain Points of Recurrent Neural Networks 5. Predicting Fire Department Calls with Spark ML 6. Using LSTMs in Generative Networks 7. Natural Language Processing with TF-IDF 8. Real Estate Value Prediction Using XGBoost 9. Predicting Apple Stock Market Cost with LSTM 10. Face Recognition Using Deep Convolutional Networks 11. Creating and Visualizing Word Vectors Using Word2Vec 12. Creating a Movie Recommendation Engine with Keras 13. Image Classification with TensorFlow on Spark 1. Other Books You May Enjoy Index

Training the TF-IDF model


We are now ready to train our TF-IDF NLP model and see if we can classify these transactions as either escalate or do_not_escalate.

Getting ready

This section will require importing from spark.ml.feature and spark.ml.classification.

How to do it...

The following section walks through the steps to train the TF-IDF model.

  1. Create a new user-defined function, udf, to define numerical values for the label column using the following script:
label = F.udf(lambda x: 1.0 if x == 'escalate' else 0.0, FloatType())
df = df.withColumn('label', label('label'))
  1. Execute the following script to set the TF and IDF columns for the vectorization of the words:
import pyspark.ml.feature as feat
TF_ = feat.HashingTF(inputCol="words without stop", 
                     outputCol="rawFeatures", numFeatures=100000)
IDF_ = feat.IDF(inputCol="rawFeatures", outputCol="features")
  1. Set up a pipeline, pipelineTFIDF, to set the sequence of stages for TF_ and IDF_ using the following script:
pipelineTFIDF...
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