def test_predict(self):
self.required_plugins("dffml-model-scikit")
# Import SciKit modules
dffml_model_scikit = importlib.import_module("dffml_model_scikit")
# Instantiate the model
model = dffml_model_scikit.LinearRegressionModel(
location=self.mktempdir(),
predict=Feature("Salary", int, 1),
features=Features(
Feature("Years", int, 1),
Feature("Expertise", int, 1),
Feature("Trust", float, 1),
),
)
training_data = CSVSource(filename=self.train_filename)
test_data = CSVSource(filename=self.test_filename)
predict_data = CSVSource(filename=self.predict_filename)
# Train the model
train(model, training_data)
# Assess accuracy
scorer = MeanSquaredErrorAccuracy()
score(model, scorer, Feature("Salary", int, 1), test_data)
# Make prediction
predictions = [
prediction for prediction in predict(model, predict_data)
]
self.assertEqual(round(predictions[0][2]["Salary"]["value"]), 70)
self.assertEqual(round(predictions[1][2]["Salary"]["value"]), 80)
def main():
# Train the model
train(model, "train.csv")
# Assess accuracy
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
score(model, scorer, Feature("TARGET", float, 1), "test.csv"),
)
# Make prediction
for i, features, prediction in predict(model, "predict.csv"):
features["TARGET"] = prediction["TARGET"]["value"]
print(features)
from dffml import CSVSource, Features, Feature
from dffml.noasync import train, score, predict
from dffml.accuracy import MeanSquaredErrorAccuracy
from dffml_model_scratch.logisticregression import LogisticRegression
model = LogisticRegression(
features=Features(Feature("f1", float, 1)),
predict=Feature("ans", int, 1),
location="tempdir",
)
# Train the model
train(model, "dataset.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
score(
model,
scorer,
Feature("ans", int, 1),
CSVSource(filename="dataset.csv"),
),
)
# Make prediction
for i, features, prediction in predict(model, {"f1": 0.8, "ans": 0}):
features["ans"] = prediction["ans"]["value"]
print(features)
},
)
# Assess accuracy
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
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
for i, features, prediction in predict(
model,
{
"Years": 6,
Feature("Trust", float, 1),
),
predict=Feature("Salary", int, 1),
location="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
score(
model,
scorer,
Feature("Salary", int, 1),
CSVSource(filename="test.csv"),
),
)
# Make prediction
for i, features, prediction in predict(
model,
{
"Years": 6,
"Expertise": 13,
"Trust": 0.7
},
{
"Years": 7,
"Expertise": 15,
from dffml import Feature, Features
from dffml.noasync import train, score, predict
from dffml.accuracy import MeanSquaredErrorAccuracy
from REPLACE_IMPORT_PACKAGE_NAME.myslr import MySLRModel
model = MySLRModel(
features=Features(Feature("x", float, 1)),
predict=Feature("y", int, 1),
location="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print("Accuracy:", score(model, scorer, Feature("y", int, 1), "test.csv"))
# Make prediction
for i, features, prediction in predict(model, "predict.csv"):
features["y"] = prediction["y"]["value"]
print(features)
from dffml_model_scratch.anomalydetection import AnomalyModel
from dffml_model_scratch.anomaly_detection_scorer import (
AnomalyDetectionAccuracy,
)
# Configure the model
model = AnomalyModel(
features=Features(Feature("A", int, 2),),
predict=Feature("Y", int, 1),
location="model",
)
# Train the model
train(model, "trainex.csv")
# Assess accuracy for test set
scorer = AnomalyDetectionAccuracy()
print(
"Test set F1 score :",
score(model, scorer, Feature("Y", int, 1), "testex.csv"),
)
# Assess accuracy for training set
print(
"Training set F1 score :",
score(model, scorer, Feature("Y", int, 1), "trainex.csv"),
)
colsample_bytree=0,
subsample=1,
))
# Train the model
train(model, *[{"data": x, "target": y} for x, y in zip(trainX, trainy)])
# Assess accuracy
scorer = ClassificationAccuracy()
print(
"Test accuracy:",
score(
model,
scorer,
Feature("target", float, 1),
*[{
"data": x,
"target": y
} for x, y in zip(testX, testy)],
),
)
print(
"Training accuracy:",
score(
model,
scorer,
Feature("target", float, 1),
*[{
"data": x,
"target": y
} for x, y in zip(trainX, trainy)],
steps=20000,
classifications=[0, 1, 2],
clstype=int,
location="tempdir",
)
# Train the model
train(model, "iris_training.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = ClassificationAccuracy()
print(
"Accuracy:",
score(
model,
scorer,
Feature("classification", int, 1),
CSVSource(filename="iris_test.csv"),
),
)
# Make prediction
for i, features, prediction in predict(
model,
{
"PetalLength": 4.2,
"PetalWidth": 1.5,
"SepalLength": 5.9,
"SepalWidth": 3.0,
},
{
"PetalLength": 5.4,
predict=Feature("TARGET", float, 1),
epochs=300,
steps=2000,
hidden=[8, 16, 8],
location="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
score(
model,
scorer,
Feature("TARGET", float, 1),
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 Features, Feature, SLRModel
from dffml.noasync import score, train
from dffml.accuracy import MeanSquaredErrorAccuracy
model = SLRModel(
features=Features(Feature("f1", float, 1)),
predict=Feature("ans", int, 1),
location="tempdir",
)
# Train the model
train(model, "dataset.csv")
# Choose the accuracy plugin
mse_accuracy = MeanSquaredErrorAccuracy()
# Assess accuracy (alternate way of specifying data source)
print(
"Accuracy:",
score(model, mse_accuracy, Feature("ans", int, 1), "dataset.csv"),
)
from dffml import CSVSource, Features, Feature
from dffml.noasync import train, score, predict
from dffml_model_daal4py.daal4pylr import DAAL4PyLRModel
from dffml.accuracy import MeanSquaredErrorAccuracy
model = DAAL4PyLRModel(
features=Features(Feature("f1", float, 1)),
predict=Feature("ans", int, 1),
location="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print(
"Accuracy:",
score(model, scorer, Feature("ans", int, 1),
CSVSource(filename="test.csv")),
)
# Make prediction
for i, features, prediction in predict(model, {"f1": 0.8, "ans": 0}):
features["ans"] = prediction["ans"]["value"]
print(features)
model = TextClassificationModel(
features=Features(Feature("sentence", str, 1)),
predict=Feature("sentiment", int, 1),
classifications=[0, 1, 2],
clstype=int,
location="tempdir",
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = TextClassifierAccuracy()
print(
"Accuracy:",
score(
model,
scorer,
Feature("sentiment", int, 1),
CSVSource(filename="test.csv"),
),
)
# Make prediction
for i, features, prediction in predict(
model, {"sentence": "This track is horrible"},
):
features["sentiment"] = prediction["sentiment"]["value"]
print(features)
from dffml import Features, Feature, SLRModel
from dffml.noasync import train, score, predict
from dffml.accuracy import MeanSquaredErrorAccuracy
model = SLRModel(
features=Features(Feature("f1", float, 1)),
predict=Feature("ans", int, 1),
location="tempdir",
)
# Train the model
train(model, "dataset.csv")
# Assess accuracy (alternate way of specifying data source)
scorer = MeanSquaredErrorAccuracy()
print("Accuracy:", score(model, scorer, Feature("ans", int, 1), "dataset.csv"))
# Make prediction
for i, features, prediction in predict(model, {"f1": 0.8, "ans": 0}):
features["ans"] = prediction["ans"]["value"]
print(features)