def iris_data():
iris = datasets.load_iris()
X = pd.DataFrame(iris.data[:, :2], columns=iris.feature_names[:2])
y = pd.Series(iris.target, name="label", dtype=np.float32)
return TabularDataLoaders.from_df(df=pd.concat([X, y], axis=1),
cont_names=list(X.columns),
y_names="label")
def fastai_model():
iris = datasets.load_iris()
X = pd.DataFrame(iris.data[:, :2], columns=iris.feature_names[:2])
y = pd.Series(iris.target, name="label")
dl = TabularDataLoaders.from_df(df=pd.concat([X, y], axis=1),
cont_names=list(X.columns),
y_names="label")
model = tabular_learner(dl, metrics=accuracy, layers=[3])
model.fit(1)
return ModelWithData(model=model, inference_dataframe=X)
def data_loader():
path = untar_data(URLs.ADULT_SAMPLE)
dls = TabularDataLoaders.from_csv(
path / "adult.csv",
path=path,
y_names="salary",
cat_names=[
"workclass",
"education",
"marital-status",
"occupation",
"relationship",
"race",
],
cont_names=["age", "fnlwgt", "education-num"],
procs=[Categorify, FillMissing, Normalize],
)
return dls
def get_data_loaders():
X, y = load_iris(return_X_y=True, as_frame=True)
y = y.astype(np.float32)
return TabularDataLoaders.from_df(X.assign(target=y),
cont_names=list(X.columns),
y_names=y.name)