Build and train classifier

Scikit-Learn

from superduperdb.ext.sklearn import Estimator, SklearnTrainer
from sklearn.svm import SVC

model = Estimator(
    identifier="my-model",
    object=SVC(),
    trainer=SklearnTrainer(
        "my-trainer",
        key=(input_key, "label"),
        select=training_select,
    ),
)        

Torch

import torch
from torch import nn
from superduperdb.ext.torch.model import TorchModel
from superduperdb.ext.torch.training import TorchTrainer
from torch.nn.functional import cross_entropy


class SimpleModel(nn.Module):
    def __init__(self, input_size=16, hidden_size=32, num_classes=3):
        super(SimpleModel, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

preprocess = lambda x: torch.tensor(x)

# Postprocess function for the model output    
def postprocess(x):
    return int(x.topk(1)[1].item())

def data_transform(features, label):
    return torch.tensor(features), label

# Create a Logistic Regression model
# feature_length is the input feature size
model = SimpleModel(feature_size, num_classes=num_classes)
model = TorchModel(
    identifier='my-model',
    object=model,         
    preprocess=preprocess,
    postprocess=postprocess,
    trainer=TorchTrainer(
        key=(input_key, 'label'),
        identifier='my_trainer',
        objective=cross_entropy,
        loader_kwargs={'batch_size': 10},
        max_iterations=1000,
        validation_interval=100,
        select=select,
        transform=data_transform,
    ),
)