from dffml import CSVSource, Features, Feature
from dffml.noasync import train, accuracy, predict
from dffml_model_scratch.logisticregression import LogisticRegression
model = LogisticRegression(
features=Features(Feature("f1", float, 1)),
predict=Feature("ans", int, 1),
)
# Train the model
train(model, "dataset.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, 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)
from dffml import CSVSource, DefFeature
from dffml.noasync import train, accuracy, predict
from dffml_model_transformers.ner.ner_model import NERModel
model = NERModel(
sid=DefFeature("SentenceId", int, 1),
words=DefFeature("Words", str, 1),
predict=DefFeature("Tag", str, 1),
model_architecture_type="distilbert",
model_name_or_path="distilbert-base-cased",
epochs=1,
no_cuda=True,
)
# Train the model
train(model, "train.csv")
# Assess accuracy (alternate way of specifying data source)
print("Accuracy:", accuracy(model, CSVSource(filename="train.csv")))
# Make prediction
for i, features, prediction in predict(
model,
{"SentenceID": 1, "Words": "DFFML models can do NER",},
{"SentenceID": 2, "Words": "DFFML models can do regression",},
):
features["Tag"] = prediction["Tag"]["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)
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,
"SepalWidth": 3.0,
},
{
"PetalLength": 5.4,
"PetalWidth": 2.1,
"SepalLength": 6.9,
"SepalWidth": 3.1,
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,
"Trust": 0.8
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,
"PetalWidth": 2.1,