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Version: 0.10.2

Classification - Adult Census

In this example, we try to predict incomes from the Adult Census dataset.

First, we import the packages (use help(synapse) to view contents),

from pyspark.sql import SparkSession

# Bootstrap Spark Session
spark = SparkSession.builder.getOrCreate()
import numpy as np
import pandas as pd

Now let's read the data and split it to train and test sets:

data = spark.read.parquet(
"wasbs://publicwasb@mmlspark.blob.core.windows.net/AdultCensusIncome.parquet"
)
data = data.select(["education", "marital-status", "hours-per-week", "income"])
train, test = data.randomSplit([0.75, 0.25], seed=123)
train.limit(10).toPandas()

TrainClassifier can be used to initialize and fit a model, it wraps SparkML classifiers. You can use help(synapse.ml.train.TrainClassifier) to view the different parameters.

Note that it implicitly converts the data into the format expected by the algorithm: tokenize and hash strings, one-hot encodes categorical variables, assembles the features into a vector and so on. The parameter numFeatures controls the number of hashed features.

from synapse.ml.train import TrainClassifier
from pyspark.ml.classification import LogisticRegression

model = TrainClassifier(
model=LogisticRegression(), labelCol="income", numFeatures=256
).fit(train)

Finally, we save the model so it can be used in a scoring program.

from synapse.ml.core.platform import *

if running_on_synapse():
model.write().overwrite().save(
"abfss://synapse@mmlsparkeuap.dfs.core.windows.net/models/AdultCensus.mml"
)
elif running_on_synapse_internal():
model.write().overwrite().save("Files/models/AdultCensus.mml")
elif running_on_databricks():
model.write().overwrite().save("dbfs:/AdultCensus.mml")
elif running_on_binder():
model.write().overwrite().save("/tmp/AdultCensus.mml")
else:
print(f"{current_platform()} platform not supported")