# Add a split
training_pipeline.add_split(RandomSplit(split_map={'eval': 0.3, 'train': 0.7}))
# Add a preprocessing unit
training_pipeline.add_preprocesser(
StandardPreprocesser(features=[
'times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi', 'pedigree',
'age'
],
labels=['has_diabetes'],
overwrite={
'has_diabetes': {
'transform': [{
'method': 'no_transform',
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(
TorchFeedForwardTrainer(loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epoch=100))
# Run the pipeline locally
training_pipeline.run()
path='gs://zenml_quickstart/diabetes.csv')
except AlreadyExistsException:
ds = Repository.get_instance().get_datasource_by_name(
'Pima Indians Diabetes')
training_pipeline.add_datasource(ds)
# Add a split
training_pipeline.add_split(RandomSplit(split_map={'eval': 0.3, 'train': 0.7}))
# Add a preprocessing unit
training_pipeline.add_preprocesser(
StandardPreprocesser(features=[
'times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi', 'pedigree',
'age'
],
labels=['has_diabetes'],
overwrite={
'has_diabetes': {
'transform': [{
'method': 'no_transform',
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(MyPyTorchLightningTrainer(epoch=100))
# Run the pipeline locally
training_pipeline.run()
# Add a split
training_pipeline.add_split(RandomSplit(split_map={'train': 0.7, 'eval': 0.3}))
# Add a preprocessing unit
training_pipeline.add_preprocesser(
StandardPreprocesser(features=[
'times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi', 'pedigree',
'age'
],
labels=['has_diabetes'],
overwrite={
'has_diabetes': {
'transform': [{
'method': 'no_transform',
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(MyScikitTrainer(
C=1.0,
kernel='rbf',
))
# Run the pipeline locally
training_pipeline.run()
# See statistics of train and eval
training_pipeline.view_statistics()
