def make_model(data):
data.show()
data = data.dropna()
nb_classes = data.select("label").distinct().count()
input_dim = len(data.select("features").first()[0])
print(nb_classes, input_dim)
model = Sequential()
model.add(Embedding(input_dim=input_dim, output_dim=100))
#model.add(LSTM(64,return_sequences=False,dropout=0.1,recurrent_dropout=0.1))
model.add(Dense(100, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
#sgd = optimizers.SGD(lr=0.1)
#model.compile(sgd, 'categorical_crossentropy', ['acc'])
model.compile(loss='binary_crossentropy', optimizer='adam')
#model.compile(loss='categorical_crossentropy', optimizer='adam')
spark_model = SparkModel(model, frequency='epoch', mode='asynchronous')
adam = optimizers.Adam(lr=0.01)
opt_conf = optimizers.serialize(adam)
estimator = ElephasEstimator()
estimator.setFeaturesCol("features")
estimator.setLabelCol("label")
estimator.set_keras_model_config(model.to_yaml())
estimator.set_categorical_labels(True)
estimator.set_nb_classes(nb_classes)
estimator.set_num_workers(1)
estimator.set_epochs(20)
estimator.set_batch_size(128)
estimator.set_verbosity(1)
estimator.set_validation_split(0.15)
estimator.set_optimizer_config(opt_conf)
estimator.set_mode("synchronous")
estimator.set_loss("categorical_crossentropy")
estimator.set_metrics(['acc'])
#estimator = ElephasEstimator(model, epochs=20, batch_size=32, frequency='batch', mode='asynchronous', nb_classes=1)
pipeline = Pipeline(stages=[estimator])
#fitted_model = estimator.fit(data)
#prediction = fitted_model.transform(data)
fitted_pipeline = pipeline.fit(data) # Fit model to data
prediction = fitted_pipeline.transform(data) # Evaluate on train data.
# prediction = fitted_pipeline.transform(test_df) # <-- The same code evaluates test data.
pnl = prediction.select("text", "prediction")
pnl.show(100)
prediction_and_label = pnl.map(lambda row: (row.text, row.prediction))
metrics = MulticlassMetrics(prediction_and_label)
print(metrics.precision())
pnl = prediction.select("label", "prediction").show()
pnl.show(100)
def test_set_cols_deprecated(spark_context, regression_model,
boston_housing_dataset):
with pytest.deprecated_call():
batch_size = 64
epochs = 10
x_train, y_train, x_test, y_test = boston_housing_dataset
df = to_data_frame(spark_context, x_train, y_train)
df = df.withColumnRenamed('features', 'scaled_features')
df = df.withColumnRenamed('label', 'ground_truth')
test_df = to_data_frame(spark_context, x_test, y_test)
test_df = test_df.withColumnRenamed('features', 'scaled_features')
test_df = test_df.withColumnRenamed('label', 'ground_truth')
sgd = optimizers.SGD(lr=0.00001)
sgd_conf = optimizers.serialize(sgd)
estimator = ElephasEstimator()
estimator.set_keras_model_config(regression_model.to_yaml())
estimator.set_optimizer_config(sgd_conf)
estimator.setFeaturesCol('scaled_features')
estimator.setOutputCol('output')
estimator.setLabelCol('ground_truth')
estimator.set_mode("synchronous")
estimator.set_loss("mae")
estimator.set_metrics(['mae'])
estimator.set_epochs(epochs)
estimator.set_batch_size(batch_size)
estimator.set_validation_split(0.01)
estimator.set_categorical_labels(False)
pipeline = Pipeline(stages=[estimator])
fitted_pipeline = pipeline.fit(df)
prediction = fitted_pipeline.transform(test_df)
pnl = prediction.select("ground_truth", "output")
pnl.show(100)
prediction_and_observations = pnl.rdd.map(
lambda row: (row['ground_truth'], row['output']))
metrics = RegressionMetrics(prediction_and_observations)
print(metrics.r2)
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
sgd = optimizers.SGD(lr=0.01)
sgd_conf = optimizers.serialize(sgd)
# Initialize Elephas Spark ML Estimator
estimator = ElephasEstimator()
estimator.set_keras_model_config(model.to_yaml())
estimator.set_optimizer_config(sgd_conf)
estimator.set_mode("synchronous")
estimator.set_loss("categorical_crossentropy")
estimator.set_metrics(['acc'])
estimator.setFeaturesCol("scaled_features")
estimator.setLabelCol("index_category")
estimator.set_epochs(10)
estimator.set_batch_size(128)
estimator.set_num_workers(1)
estimator.set_verbosity(0)
estimator.set_validation_split(0.15)
estimator.set_categorical_labels(True)
estimator.set_nb_classes(nb_classes)
# Fitting a model returns a Transformer
pipeline = Pipeline(stages=[string_indexer, scaler, estimator])
fitted_pipeline = pipeline.fit(train_df)
# Evaluate Spark model
prediction = fitted_pipeline.transform(train_df)
activity_regularizer=regularizers.l2(0.01)))
model.add(Activation('relu'))
model.add(Dropout(rate=0.3))
model.add(Dense(256, activity_regularizer=regularizers.l2(0.01)))
model.add(Activation('relu'))
model.add(Dropout(rate=0.3))
model.add(Dense(nb_classes))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd')
# Model Summary
model.summary()
# Initialize SparkML Estimator and Get Settings
estimator = ElephasEstimator()
estimator.setFeaturesCol("features")
estimator.setLabelCol("label_index")
estimator.set_keras_model_config(model.to_yaml())
estimator.set_categorical_labels(True)
estimator.set_nb_classes(nb_classes)
estimator.set_num_workers(1)
estimator.set_epochs(25)
estimator.set_batch_size(64)
estimator.set_verbosity(1)
estimator.set_validation_split(0.10)
estimator.set_optimizer_config(sgd)
estimator.set_mode("synchronous")
estimator.set_loss("binary_crossentropy")
estimator.set_metrics(['acc'])
# Create Deep Learning Pipeline
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
# Initialize Elephas Spark ML Estimator
adagrad = elephas_optimizers.Adagrad()
estimator = ElephasEstimator()
estimator.setFeaturesCol("scaled_features")
estimator.setLabelCol("index_category")
estimator.set_keras_model_config(model.to_yaml())
estimator.set_optimizer_config(adagrad.get_config())
estimator.set_nb_epoch(10)
estimator.set_batch_size(128)
estimator.set_num_workers(4)
estimator.set_verbosity(0)
estimator.set_validation_split(0.15)
estimator.set_categorical_labels(True)
estimator.set_nb_classes(nb_classes)
# Fitting a model returns a Transformer
pipeline = Pipeline(stages=[string_indexer, scaler, estimator])
fitted_pipeline = pipeline.fit(train_df)
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
from elephas.ml_model import ElephasEstimator
from elephas import optimizers as elephas_optimizers
# Define elephas optimizer (which tells the model how to aggregate updates on the Spark master)
adadelta = elephas_optimizers.Adadelta()
# Initialize SparkML Estimator and set all relevant properties
estimator = ElephasEstimator()
estimator.setFeaturesCol("scaled_features") # These two come directly from pyspark,
estimator.setLabelCol("index_category") # hence the camel case. Sorry :)
estimator.set_keras_model_config(model.to_yaml()) # Provide serialized Keras model
estimator.set_optimizer_config(adadelta.get_config()) # Provide serialized Elephas optimizer
estimator.set_categorical_labels(True)
estimator.set_nb_classes(nb_classes)
estimator.set_num_workers(1) # We just use one worker here. Feel free to adapt it.
estimator.set_nb_epoch(20)
estimator.set_batch_size(128)
estimator.set_verbosity(1)
estimator.set_validation_split(0.15)
from pyspark.ml import Pipeline
