def setUpClass(cls): cls.model_dir = tempfile.TemporaryDirectory() cls.model = DNNClassifierModel( DNNClassifierModelConfig(directory=cls.model_dir.name, steps=1000, epochs=30, hidden=[10, 20, 10], classification="string", classifications=["a", "not a"], clstype=str)) cls.feature = StartsWithA() cls.features = Features(cls.feature) cls.repos = [ Repo( "a" + str(random.random()), data={"features": { cls.feature.NAME: 1, "string": "a" }}, ) for _ in range(0, 1000) ] cls.repos += [ Repo( "b" + str(random.random()), data={"features": { cls.feature.NAME: 0, "string": "not a" }}, ) for _ in range(0, 1000) ] cls.sources = Sources(MemorySource( MemorySourceConfig(repos=cls.repos)))
def setUpClass(cls): cls.model_dir = tempfile.TemporaryDirectory() cls.feature = Feature("starts_with_a", int, 1) cls.features = Features(cls.feature) cls.records = [ Record( "a" + str(random.random()), data={"features": { cls.feature.name: 1, "string": "a" }}, ) for _ in range(0, 1000) ] cls.records += [ Record( "b" + str(random.random()), data={"features": { cls.feature.name: 0, "string": "not a" }}, ) for _ in range(0, 1000) ] cls.sources = Sources( MemorySource(MemorySourceConfig(records=cls.records))) cls.model = DNNClassifierModel( DNNClassifierModelConfig( directory=cls.model_dir.name, steps=1000, epochs=40, hidden=[50, 20, 10], predict=Feature("string", str, 1), classifications=["a", "not a"], clstype=str, features=cls.features, ))
from dffml import CSVSource, Features, DefFeature from dffml.noasync import train, accuracy, predict from dffml_model_tensorflow.dnnc import DNNClassifierModel model = DNNClassifierModel( features=Features( DefFeature("SepalLength", float, 1), DefFeature("SepalWidth", float, 1), DefFeature("PetalLength", float, 1), DefFeature("PetalWidth", float, 1), ), predict=DefFeature("classification", int, 1), epochs=3000, steps=20000, classifications=[0, 1, 2], clstype=int, ) # Train the model train(model, "iris_training.csv") # Assess accuracy (alternate way of specifying data source) print("Accuracy:", accuracy(model, CSVSource(filename="iris_test.csv"))) # Make prediction for i, features, prediction in predict( model, { "PetalLength": 4.2, "PetalWidth": 1.5, "SepalLength": 5.9,
from dffml import CSVSource, Features, Feature from dffml.noasync import train, accuracy, predict from dffml_model_tensorflow.dnnc import DNNClassifierModel model = DNNClassifierModel( features=Features( Feature("SepalLength", float, 1), Feature("SepalWidth", float, 1), Feature("PetalLength", float, 1), Feature("PetalWidth", float, 1), ), predict=Feature("classification", int, 1), epochs=3000, steps=20000, classifications=[0, 1, 2], clstype=int, directory="tempdir", ) # Train the model train(model, "iris_training.csv") # Assess accuracy (alternate way of specifying data source) print("Accuracy:", accuracy(model, CSVSource(filename="iris_test.csv"))) # Make prediction for i, features, prediction in predict( model, { "PetalLength": 4.2, "PetalWidth": 1.5,