Text Explainers - Synapseml

Interpretability - Text Explainers

In this example, we use LIME and Kernel SHAP explainers to explain a text classification model.

First we import the packages and define some UDFs and a plotting function we will need later.

python

from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.functions import vector_to_array
from synapse.ml.explainers import *
from synapse.ml.featurize.text import TextFeaturizer
from synapse.ml.core.platform import *

vec_access = udf(lambda v, i: float(v[i]), FloatType())

Load training data, and convert rating to binary label.

python

data = (
    spark.read.parquet("wasbs://[email protected]/BookReviewsFromAmazon10K.parquet")
    .withColumn("label", (col("rating") > 3).cast(LongType()))
    .select("label", "text")
    .cache()
)

display(data)

We train a text classification model, and randomly sample 10 rows to explain.

python

train, test = data.randomSplit([0.60, 0.40])

pipeline = Pipeline(
    stages=[
        TextFeaturizer(
            inputCol="text",
            outputCol="features",
            useStopWordsRemover=True,
            useIDF=True,
            minDocFreq=20,
            numFeatures=1 << 16,
        ),
        LogisticRegression(maxIter=100, regParam=0.005, labelCol="label", featuresCol="features"),
    ]
)

model = pipeline.fit(train)

prediction = model.transform(test)

explain_instances = prediction.orderBy(rand()).limit(10)

python

def plotConfusionMatrix(df, label, prediction, classLabels):
    from synapse.ml.plot import confusionMatrix
    import matplotlib.pyplot as plt

    fig = plt.figure(figsize=(4.5, 4.5))
    confusionMatrix(df, label, prediction, classLabels)
    if running_on_synapse():
        plt.show()
    else:
        display(fig)


plotConfusionMatrix(model.transform(test), "label", "prediction", [0, 1])

First we use the LIME text explainer to explain the model's predicted probability for a given observation.

python

lime = TextLIME(
    model=model,
    outputCol="weights",
    inputCol="text",
    targetCol="probability",
    targetClasses=[1],
    tokensCol="tokens",
    samplingFraction=0.7,
    numSamples=2000,
)

lime_results = (
    lime.transform(explain_instances)
    .select("tokens", "weights", "r2", "probability", "text")
    .withColumn("probability", vec_access("probability", lit(1)))
    .withColumn("weights", vector_to_array(col("weights").getItem(0)))
    .withColumn("r2", vec_access("r2", lit(0)))
    .withColumn("tokens_weights", arrays_zip("tokens", "weights"))
)

display(lime_results.select("probability", "r2", "tokens_weights", "text").orderBy(col("probability").desc()))

Then we use the Kernel SHAP text explainer to explain the model's predicted probability for a given observation.

Notice that we drop the base value from the SHAP output before displaying the SHAP values. The base value is the model output for an empty string.

python

shap = TextSHAP(
    model=model,
    outputCol="shaps",
    inputCol="text",
    targetCol="probability",
    targetClasses=[1],
    tokensCol="tokens",
    numSamples=5000,
)

shap_results = (
    shap.transform(explain_instances)
    .select("tokens", "shaps", "r2", "probability", "text")
    .withColumn("probability", vec_access("probability", lit(1)))
    .withColumn("shaps", vector_to_array(col("shaps").getItem(0)))
    .withColumn("shaps", slice(col("shaps"), lit(2), size(col("shaps"))))
    .withColumn("r2", vec_access("r2", lit(0)))
    .withColumn("tokens_shaps", arrays_zip("tokens", "shaps"))
)

display(shap_results.select("probability", "r2", "tokens_shaps", "text").orderBy(col("probability").desc()))