def test_pipeline(self, bag):
from pyspark.ml.pipeline import Pipeline
# create and save and load
pth = "/tmp/spatial-join"
new_p = Pipeline().setStages([bag["transformer"]])
new_p.write().overwrite().save(pth)
saved_p = Pipeline.load(pth)
# check transformations
inp = bag["input"]
exp = bag["expected"]
check(new_p.fit(inp), inp, exp)
check(saved_p.fit(inp), inp, exp)
def test_save_pipeline(self):
processed = self.generate_random_data()
mg = build_graph(SparkFlowTests.create_random_model)
spark_model = SparkAsyncDL(inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='outer/Sigmoid:0',
tfOptimizer='adam',
tfLearningRate=.1,
iters=20,
partitions=2,
predictionCol='predicted',
labelCol='label')
p = Pipeline(stages=[spark_model]).fit(processed)
p.write().overwrite().save('example_pipeline')
p = PysparkPipelineWrapper.unwrap(
PipelineModel.load('example_pipeline'))
data = p.transform(processed).take(10)
nb_errors = SparkFlowTests.calculate_errors(data)
self.assertTrue(nb_errors < len(data))
mg = build_graph(small_model)
#Assemble and one hot encode
va = VectorAssembler(inputCols=final_df.columns[1:151],
outputCol='features')
encoded = OneHotEncoder(inputCol='result',
outputCol='labels',
dropLast=False)
adam_config = build_adam_config(learning_rate=0.001,
beta1=0.9,
beta2=0.999)
spark_model = SparkAsyncDL(inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='out:0',
tfLearningRate=.001,
iters=20,
predictionCol='predicted',
labelCol='labels',
verbose=1,
optimizerOptions=adam_config)
ckptpath = os.path.join(ckptdir, task)
print('save model in ckptpath')
p = Pipeline(stages=[va, encoded, spark_model]).fit(final_df)
p.write().overwrite().save(ckptpath)
print('===task all done===')
outputCol='features')
# Demonstration of some options. Not all are required
# Note: This uses the barrier execution mode, which is sensitive to the number of partitions
spark_model = SparkTorch(inputCol='features',
labelCol='_c0',
predictionCol='predictions',
torchObj=torch_obj,
iters=50,
verbose=1,
validationPct=0.2,
miniBatch=128)
# Create and save the Pipeline
p = Pipeline(stages=[vector_assembler, spark_model]).fit(df)
p.write().overwrite().save('simple_cnn')
# Example of loading the pipeline
loaded_pipeline = PysparkPipelineWrapper.unwrap(
PipelineModel.load('simple_cnn'))
# Run predictions and evaluation
predictions = loaded_pipeline.transform(df).persist()
evaluator = MulticlassClassificationEvaluator(labelCol="_c0",
predictionCol="predictions",
metricName="accuracy")
accuracy = evaluator.evaluate(predictions)
print("Train accuracy = %g" % accuracy)
df = df.withColumn(f, df[f].cast('int'))
if f == 'class':
df = df.withColumn(f, df[f].cast('string'))
df = df.dropna()
train, test = df.randomSplit([0.8, 0.2], seed=0)
class_index = StringIndexer(inputCol='class', outputCol='label')
vector = VectorAssembler(inputCols=feature_cols, outputCol='feature')
model = LinearSVC(featuresCol='feature', labelCol='label')
pipeline = Pipeline(stages=[class_index, vector, model])
pipeline = pipeline.fit(train)
if os.path.exists(MODEL_SAVE_PATH):
shutil.rmtree(MODEL_SAVE_PATH)
pipeline.write().overwrite().save(pipeline) # pipeline.save('/to/path')
load_pipeline = PipelineModel.load('pipeline')
test_predict = load_pipeline.transform(test)
evaluator = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction',
labelCol='label')
print(evaluator.evaluate(test_predict, {evaluator.metricName: 'areaUnderROC'}))
print(evaluator.evaluate(test_predict, {evaluator.metricName: 'areaUnderPR'}))
origin_test_df = df.select(feature_cols)
predict_df = load_pipeline.transform(origin_test_df)
print(predict_df.show(20))
def small_model():
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
y = tf.placeholder(tf.float32, shape=[None, 10], name='y')
layer1 = tf.layers.dense(x, 256, activation=tf.nn.relu)
layer2 = tf.layers.dense(layer1, 256, activation=tf.nn.relu)
out = tf.layers.dense(layer2, 10)
z = tf.argmax(out, 1, name='out')
loss = tf.losses.softmax_cross_entropy(y, out)
return loss
df = spark.read.option("inferSchema", "true").csv('mnist_train.csv')
mg = build_graph(small_model)
# Assemble and one hot encode
va = VectorAssembler(inputCols=df.columns[1:785], outputCol='features')
encoded = OneHotEncoder(inputCol='_c0', outputCol='labels', dropLast=False)
spark_model = SparkAsyncDL(inputCol='features',
tensorflowGraph=mg,
tfInput='x:0',
tfLabel='y:0',
tfOutput='out:0',
tfLearningRate=.001,
iters=20,
predictionCol='predicted',
labelCol='labels',
verbose=1)
p = Pipeline(stages=[va, encoded, spark_model]).fit(df)
p.write().overwrite().save("location")
tfOutput='out:0',
tfLearningRate=.001,
iters=10,
predictionCol='predicted',
labelCol='labels',
verbose=1)
if __name__ == "__main__":
#Pipeline definition
#pipe = [va, encoded, spark_model]
#Train the model
try:
import time
start_time = time.time()
p = Pipeline(stages=[va, encoded, spark_model]).fit(df)
print("--- %s seconds ---" % (time.time() - start_time))
p.write().save("dnn_model")
except Exception as e:
print ("Error --> ", e)
#Save the model
#p.write().overwrite().save("dnn_model")
#exec(open("dnn_model.py").read())
