Skip to main content
Version: 0.11.3

Build your first SynapseML models

This tutorial provides a brief introduction to SynapseML. In particular, we use SynapseML to create two different pipelines for sentiment analysis. The first pipeline combines a text featurization stage with LightGBM regression to predict ratings based on review text from a dataset containing book reviews from Amazon. The second pipeline shows how to use prebuilt models through the Azure Cognitive Services to solve this problem without training data.

Load a dataset​

Load your dataset and split it into train and test sets.

train, test = (
    spark.read.parquet(
        "wasbs://publicwasb@mmlspark.blob.core.windows.net/BookReviewsFromAmazon10K.parquet"
    )
    .limit(1000)
    .cache()
    .randomSplit([0.8, 0.2])
)

display(train)

Create the training pipeline​

Create a pipeline that featurizes data using TextFeaturizer from the synapse.ml.featurize.text library and derives a rating using the LightGBMRegressor function.

from pyspark.ml import Pipeline
from synapse.ml.featurize.text import TextFeaturizer
from synapse.ml.lightgbm import LightGBMRegressor

model = Pipeline(
    stages=[
        TextFeaturizer(inputCol="text", outputCol="features"),
        LightGBMRegressor(featuresCol="features", labelCol="rating"),
    ]
).fit(train)

Predict the output of the test data​

Call the transform function on the model to predict and display the output of the test data as a dataframe.

display(model.transform(test))

Use Cognitive Services to transform data in one step​

Alternatively, for these kinds of tasks that have a prebuilt solution, you can use SynapseML's integration with Cognitive Services to transform your data in one step.

from synapse.ml.cognitive import TextSentiment
from synapse.ml.core.platform import find_secret

model = TextSentiment(
    textCol="text",
    outputCol="sentiment",
    subscriptionKey=find_secret(
        "cognitive-api-key"
    ),  # Replace the call to find_secret with your key as a python string.
).setLocation("eastus")

display(model.transform(test))