TextAnalytics - Amazon Book Reviews with Word2Vec.md

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TextAnalytics - Amazon Book Reviews with Word2Vec

Yet again, now using the Word2Vec Estimator from Spark. We can use the tree-based learners from spark in this scenario due to the lower dimensionality representation of features.

from pyspark.sql import SparkSession

# Bootstrap Spark Session
spark = SparkSession.builder.getOrCreate()

import pandas as pd

data = spark.read.parquet(
    "wasbs://publicwasb@mmlspark.blob.core.windows.net/BookReviewsFromAmazon10K.parquet"
)
data.limit(10).toPandas()

Modify the label column to predict a rating greater than 3.

processedData = data.withColumn("label", data["rating"] > 3).select(["text", "label"])
processedData.limit(5).toPandas()

Split the dataset into train, test and validation sets.

train, test, validation = processedData.randomSplit([0.60, 0.20, 0.20])

Use Tokenizer and Word2Vec to generate the features.

from pyspark.ml import Pipeline
from pyspark.ml.feature import Tokenizer, Word2Vec

tokenizer = Tokenizer(inputCol="text", outputCol="words")
partitions = train.rdd.getNumPartitions()
word2vec = Word2Vec(
    maxIter=4, seed=42, inputCol="words", outputCol="features", numPartitions=partitions
)
textFeaturizer = Pipeline(stages=[tokenizer, word2vec]).fit(train)

Transform each of the train, test and validation datasets.

ptrain = textFeaturizer.transform(train).select(["label", "features"])
ptest = textFeaturizer.transform(test).select(["label", "features"])
pvalidation = textFeaturizer.transform(validation).select(["label", "features"])
ptrain.limit(5).toPandas()

Generate several models with different parameters from the training data.

from pyspark.ml.classification import (
    LogisticRegression,
    RandomForestClassifier,
    GBTClassifier,
)
from synapse.ml.train import TrainClassifier
import itertools

lrHyperParams = [0.05, 0.2]
logisticRegressions = [
    LogisticRegression(regParam=hyperParam) for hyperParam in lrHyperParams
]
lrmodels = [
    TrainClassifier(model=lrm, labelCol="label").fit(ptrain)
    for lrm in logisticRegressions
]

rfHyperParams = itertools.product([5, 10], [2, 3])
randomForests = [
    RandomForestClassifier(numTrees=hyperParam[0], maxDepth=hyperParam[1])
    for hyperParam in rfHyperParams
]
rfmodels = [
    TrainClassifier(model=rfm, labelCol="label").fit(ptrain) for rfm in randomForests
]

gbtHyperParams = itertools.product([8, 16], [2, 3])
gbtclassifiers = [
    GBTClassifier(maxBins=hyperParam[0], maxDepth=hyperParam[1])
    for hyperParam in gbtHyperParams
]
gbtmodels = [
    TrainClassifier(model=gbt, labelCol="label").fit(ptrain) for gbt in gbtclassifiers
]

trainedModels = lrmodels + rfmodels + gbtmodels

Find the best model for the given test dataset.

from synapse.ml.automl import FindBestModel

bestModel = FindBestModel(evaluationMetric="AUC", models=trainedModels).fit(ptest)
bestModel.getRocCurve().show()
bestModel.getBestModelMetrics().show()
bestModel.getAllModelMetrics().show()

Get the accuracy from the validation dataset.

from synapse.ml.train import ComputeModelStatistics

predictions = bestModel.transform(pvalidation)
metrics = ComputeModelStatistics().transform(predictions)
print(
    "Best model's accuracy on validation set = "
    + "{0:.2f}%".format(metrics.first()["accuracy"] * 100)
)
print(
    "Best model's AUC on validation set = "
    + "{0:.2f}%".format(metrics.first()["AUC"] * 100)
)