WeltGet startedSubmit, wait, and evaluate a real classifier
Static notebook preview · outputs are cleared. Open in Colab or download the notebook to run the cells. Colab setup
python
!pip install "welt-client[parquet]"
Submit, wait, and evaluate a real classifier
Run the installation cell first, connect your workspace, then run the remaining cells in order. These are real hosted requests: fitting retains a private training dataset and predictor. No extra helper file is needed. Outputs are deliberately not saved in this source.
python
from welt import Client
with Client() as client:
client.connect()
A notebook supports top-level await. We keep the same Iris120/30 seed9 holdout; only submission and waiting change. If waiting times out, retain job_id and reconnect rather than refitting.
python
from pathlib import Path
from tempfile import mkdtemp
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from welt import Client, Classifier, AsyncClient
# 1. Get a real table from Welt's research inventory.
dataset_id = "asset-d839644b6de36fc5cafdda18c5daeca3"
path = Path(mkdtemp(prefix="welt-iris-")) / "iris.parquet"
with Client() as client:
info = client.research_dataset(dataset_id)
print("Dataset:", info["name"])
client.download_research_dataset(dataset_id, path)
data = pd.read_parquet(path)
target = "target" # The outcome column in this table.
python
# 2. Look at the rows before modelling.
print(data.head())
print("Rows and columns:", data.shape)
data.info()
print(data[target].value_counts())
python
# 3. Set aside test rows before fitting; never train on their answers.
train, test = train_test_split(
data, test_size=0.2, random_state=9, stratify=data[target])
query = test.drop(columns=target)
y_test = test[target]
print("Training rows:", len(train), "— Test rows:", len(test))
python
# 4. Create a model and train it on the training table.
model = Classifier(model="tabicl-v2", random_state=9)
job = model.submit_fit(train, target=target)
print("Keep the job ID:", job.id)
job_id = job.id
job.client.close()
# Waiting locally does not cancel durable work on the server.
async def wait_for_fit():
async with AsyncClient() as client:
return await client.job(job_id).result(timeout=600)
predictor = await wait_for_fit()
model = Classifier.from_predictor(predictor["id"])
python
# 5. Predict the held-out rows, without passing their target column.
predictions = model.predict(query)
print(pd.DataFrame({"actual": y_test.to_numpy(), "predicted": predictions}).head())
python
# 6. Compare predictions with the answers we kept aside.
print("Test accuracy:", accuracy_score(y_test, predictions))
print(classification_report(y_test, predictions, zero_division=0))
print("Keep this predictor ID:", model.predictor_id_)