slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy', 'binary_accuracy']}))
# Run the pipeline locally
training_pipeline.run()
######################
# DO SOME EVALUATION #
#####################
# Sample data
df = training_pipeline.sample_transformed_data()
print(df.shape)
print(df.describe())
# See schema of data and detect drift
print(training_pipeline.view_schema())
##########################
# CREATE SECOND PIPELINE #
#########################
training_pipeline_2 = training_pipeline.copy('Experiment 2')
training_pipeline_2.add_trainer(
FeedForwardTrainer(loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=15))
training_pipeline_2.run()
############################
# DO SOME REPOSITORY STUFF #
'parameters': {}
}]
}
}))
# Add a trainer
training_pipeline.add_trainer(
FeedForwardTrainer(loss='binary_crossentropy',
last_activation='sigmoid',
output_units=1,
metrics=['accuracy'],
epochs=20))
# Add an evaluator
training_pipeline.add_evaluator(
TFMAEvaluator(
slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy', 'binary_accuracy']}))
# Run the pipeline locally
training_pipeline.run()
# See schema of data
training_pipeline.view_schema()
# See statistics of train and eval
training_pipeline.view_statistics()
# Creates a notebook for evaluation
training_pipeline.evaluate()