Python TabularClassifier.from_data примеры использования

Пример #1

Файл: test_data_model_integration.py Проект: aniketmaurya/lightning-flash

def test_classification(backbone, fields, tmpdir):
    train_data_frame = TEST_DF_1.copy()
    val_data_frame = TEST_DF_1.copy()
    test_data_frame = TEST_DF_1.copy()
    data = TabularClassificationData.from_data_frame(
        **fields,
        target_fields="label",
        train_data_frame=train_data_frame,
        val_data_frame=val_data_frame,
        test_data_frame=test_data_frame,
        num_workers=0,
        batch_size=2,
    )
    model = TabularClassifier.from_data(datamodule=data, backbone=backbone)
    trainer = pl.Trainer(fast_dev_run=True, default_root_dir=tmpdir)
    trainer.fit(model, data)

Пример #2

def test_serve():
    train_data = {
        "num_col": [1.4, 2.5],
        "cat_col": ["positive", "negative"],
        "target": [1, 2]
    }
    datamodule = TabularData.from_data_frame(
        "cat_col",
        "num_col",
        "target",
        pd.DataFrame.from_dict(train_data),
    )
    model = TabularClassifier.from_data(datamodule)
    # TODO: Currently only servable once a preprocess has been attached
    model._preprocess = datamodule.preprocess
    model.eval()
    model.serve()

Пример #3

Файл: tabular_classification.py Проект: rrajj/lightning-flash

# 1. Download the data
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "data/")

# 2. Load the data
datamodule = TabularData.from_csv(
    ["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
    ["Fare"],
    target_field="Survived",
    train_file="./data/titanic/titanic.csv",
    test_file="./data/titanic/test.csv",
    val_split=0.25,
)

# 3. Build the model
model = TabularClassifier.from_data(
    datamodule, metrics=[Accuracy(), Precision(),
                         Recall()])

# 4. Create the trainer
trainer = flash.Trainer(fast_dev_run=True)

# 5. Train the model
trainer.fit(model, datamodule=datamodule)

# 6. Test model
trainer.test(model)

# 7. Save it!
trainer.save_checkpoint("tabular_classification_model.pt")

Пример #4

import torch

import flash
from flash.core.data.utils import download_data
from flash.tabular import TabularClassificationData, TabularClassifier

# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "./data")

datamodule = TabularClassificationData.from_csv(
    ["Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"],
    "Fare",
    target_fields="Survived",
    train_file="data/titanic/titanic.csv",
    val_split=0.1,
)

# 2. Build the task
model = TabularClassifier.from_data(datamodule)

# 3. Create the trainer and train the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.fit(model, datamodule=datamodule)

# 4. Generate predictions from a CSV
predictions = model.predict("data/titanic/titanic.csv")
print(predictions)

# 5. Save the model!
trainer.save_checkpoint("tabular_classification_model.pt")

Пример #5

Файл: tabular_classification.py Проект: aniketmaurya/lightning-flash

# 1. Create the DataModule
download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "./data")

datamodule = TabularClassificationData.from_csv(
    categorical_fields=[
        "Sex", "Age", "SibSp", "Parch", "Ticket", "Cabin", "Embarked"
    ],
    numerical_fields="Fare",
    target_fields="Survived",
    train_file="data/titanic/titanic.csv",
    val_split=0.1,
    batch_size=8,
)

# 2. Build the task
model = TabularClassifier.from_data(datamodule, backbone="fttransformer")

# 3. Create the trainer and train the model
trainer = flash.Trainer(max_epochs=3, gpus=torch.cuda.device_count())
trainer.fit(model, datamodule=datamodule)

# 4. Generate predictions from a CSV
datamodule = TabularClassificationData.from_csv(
    predict_file="data/titanic/titanic.csv",
    parameters=datamodule.parameters,
    batch_size=8,
)
predictions = trainer.predict(model, datamodule=datamodule, output="classes")
print(predictions)

# 5. Save the model!