def setUpClass(cls):
cls.model_dir = tempfile.TemporaryDirectory()
cls.feature1 = Feature_1()
cls.feature2 = Feature_2()
cls.features = Features(cls.feature1, cls.feature2)
cls.model = DNNRegressionModel(
DNNRegressionModelConfig(
directory=cls.model_dir.name,
steps=1000,
epochs=40,
hidden=[50, 20, 10],
predict=DefFeature("TARGET", float, 1),
features=cls.features,
))
# Generating data f(x1,x2) = 2*x1 + 3*x2
_n_data = 2000
_temp_data = np.random.rand(2, _n_data)
cls.repos = [
Repo(
"x" + str(random.random()),
data={
"features": {
cls.feature1.NAME: float(_temp_data[0][i]),
cls.feature2.NAME: float(_temp_data[1][i]),
"TARGET": 2 * _temp_data[0][i] + 3 * _temp_data[1][i],
}
},
) for i in range(0, _n_data)
]
cls.sources = Sources(MemorySource(
MemorySourceConfig(repos=cls.repos)))
from dffml import CSVSource, Features, DefFeature
from dffml.noasync import train, accuracy, predict
from dffml_model_tensorflow.dnnr import DNNRegressionModel
model = DNNRegressionModel(
features=Features(DefFeature("Feature1", float, 1),
DefFeature("Feature2", float, 1)),
predict=DefFeature("TARGET", float, 1),
epochs=300,
steps=2000,
hidden=[8, 16, 8],
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, CSVSource(filename="test.csv")))
# Make prediction
for i, features, prediction in predict(model, {
"Feature1": 0.21,
"Feature2": 0.18,
"TARGET": 0.84
}):
features["TARGET"] = prediction["TARGET"]["value"]
print(features)
from dffml import CSVSource, Features, Feature
from dffml.noasync import train, accuracy, predict
from dffml_model_tensorflow.dnnr import DNNRegressionModel
model = DNNRegressionModel(
features=Features(Feature("Feature1", float, 1),
Feature("Feature2", float, 1)),
predict=Feature("TARGET", float, 1),
epochs=300,
steps=2000,
hidden=[8, 16, 8],
directory="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, CSVSource(filename="test.csv")))
# Make prediction
for i, features, prediction in predict(model, {
"Feature1": 0.21,
"Feature2": 0.18,
"TARGET": 0.84
}):
features["TARGET"] = prediction["TARGET"]["value"]
print(features)
from dffml_model_tensorflow.dnnr import (
DNNRegressionModel,
DNNRegressionModelConfig,
)
training_data = CSVSource(
CSVSourceConfig(filename="training.csv", readonly=True))
test_data = CSVSource(CSVSourceConfig(filename="test.csv", readonly=True))
predict_data = CSVSource(CSVSourceConfig(filename="predict.csv",
readonly=True))
model = DNNRegressionModel(
DNNRegressionModelConfig(
features=Features(
DefFeature("Years", int, 1),
DefFeature("Expertise", int, 1),
DefFeature("Trust", float, 1),
),
predict="Salary",
))
Train(model=model, sources=[training_data])()
accuracy = Accuracy(model=model, sources=[test_data])()
row0, row1 = PredictAll(model=model, sources=[predict_data])()
print("Accuracy", accuracy)
print(row0)
print(row1)