async def main():
model = LinearRegressionModel(
features=Features(
DefFeature("Years", int, 1),
DefFeature("Expertise", int, 1),
DefFeature("Trust", float, 1),
),
predict=DefFeature("Salary", int, 1),
)
# Train the model
await train(
model,
{"Years": 0, "Expertise": 1, "Trust": 0.1, "Salary": 10},
{"Years": 1, "Expertise": 3, "Trust": 0.2, "Salary": 20},
{"Years": 2, "Expertise": 5, "Trust": 0.3, "Salary": 30},
{"Years": 3, "Expertise": 7, "Trust": 0.4, "Salary": 40},
)
# Assess accuracy
print(
"Accuracy:",
await accuracy(
model,
{"Years": 4, "Expertise": 9, "Trust": 0.5, "Salary": 50},
{"Years": 5, "Expertise": 11, "Trust": 0.6, "Salary": 60},
),
)
# Make prediction
async for i, features, prediction in predict(
model,
{"Years": 6, "Expertise": 13, "Trust": 0.7},
{"Years": 7, "Expertise": 15, "Trust": 0.8},
):
features["Salary"] = prediction["Salary"]["value"]
print(features)
from dffml import Features, DefFeature
from dffml.noasync import train, accuracy, predict
from dffml_model_scikit import LinearRegressionModel
model = LinearRegressionModel(
features=Features(
DefFeature("Years", int, 1),
DefFeature("Expertise", int, 1),
DefFeature("Trust", float, 1),
),
predict=DefFeature("Salary", int, 1),
)
# Train the model
train(
model,
{
"Years": 0,
"Expertise": 1,
"Trust": 0.1,
"Salary": 10
},
{
"Years": 1,
"Expertise": 3,
"Trust": 0.2,
"Salary": 20
},
{
"Years": 2,
"Expertise": 5,
from dffml import CSVSource, Features, Feature
from dffml.noasync import train, accuracy, predict
from dffml_model_scikit import LinearRegressionModel
model = LinearRegressionModel(
features=Features(
Feature("Years", int, 1),
Feature("Expertise", int, 1),
Feature("Trust", float, 1),
),
predict=Feature("Salary", int, 1),
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,
{
"Years": 6,
"Expertise": 13,
"Trust": 0.7
},
{
"Years": 7,
"Expertise": 15,
async def main():
model = LinearRegressionModel(
features=Features(
Feature("Years", int, 1),
Feature("Expertise", int, 1),
Feature("Trust", float, 1),
),
predict=Feature("Salary", int, 1),
location="tempdir",
)
# Train the model
await train(
model,
{
"Years": 0,
"Expertise": 1,
"Trust": 0.1,
"Salary": 10
},
{
"Years": 1,
"Expertise": 3,
"Trust": 0.2,
"Salary": 20
},
{
"Years": 2,
"Expertise": 5,
"Trust": 0.3,
"Salary": 30
},
{
"Years": 3,
"Expertise": 7,
"Trust": 0.4,
"Salary": 40
},
)
# Assess accuracy
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
await score(
model,
scorer,
Feature("Salary", int, 1),
{
"Years": 4,
"Expertise": 9,
"Trust": 0.5,
"Salary": 50
},
{
"Years": 5,
"Expertise": 11,
"Trust": 0.6,
"Salary": 60
},
),
)
# Make prediction
async for i, features, prediction in predict(
model,
{
"Years": 6,
"Expertise": 13,
"Trust": 0.7
},
{
"Years": 7,
"Expertise": 15,
"Trust": 0.8
},
):
features["Salary"] = prediction["Salary"]["value"]
print(features)