Quickstart - Train Classifier.md

title	hide_title	status
Quickstart - Train Classifier	true	stable

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),

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

data = spark.read.parquet(
    "wasbs://[email protected]/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://[email protected]/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")