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Deepbox
22intermediate

Built-in Datasets

Load the 24 built-in datasets and the 6 synthetic generators, and print their shapes and metadata. The datasets ship with the package, so nothing is downloaded.

What you will learn

  • Use deepbox/datasets for loadIris, loadDigits, loadBreastCancer, loadDiabetes, loadLinnerud, loadHousingMini, makeClassification, makeRegression, makeBlobs, makeMoons, makeCircles, makeGaussianQuantiles, and 18 more loaders.
  • Load the 24 built-in datasets and the 6 synthetic generators, and print their shapes and metadata. The datasets ship with the package, so nothing is downloaded.

Source

index.ts
/** * Example 22: Built-in Datasets * * Load each of the 24 built-in datasets and the 6 synthetic generators, and * print their shapes and metadata. The datasets ship with the package, so * nothing is downloaded. Each loader returns an object with `data` (features), * `target` (labels or values) and, for most, `featureNames` and `targetNames`. * The generators return a [X, y] pair. * * For text corpora, fetch20Newsgroups and fetchIMDB download the official * archives. They need network access, so this example does not call them. */ import {  loadBreastCancer,  loadConcentricRings,  loadCropYield,  loadCustomerSegments,  loadDiabetes,  loadDigits,  loadEnergyEfficiency,  loadFitnessScores,  loadFlowersExtended,  loadFruitQuality,  loadGaussianIslands,  loadHousingMini,  loadIris,  loadLeafShapes,  loadLinnerud,  loadMoonsMulti,  loadPerfectlySeparable,  loadPlantGrowth,  loadSeedMorphology,  loadSensorStates,  loadSpiralArms,  loadStudentPerformance,  loadTrafficConditions,  loadWeatherOutcomes,  makeBlobs,  makeCircles,  makeClassification,  makeGaussianQuantiles,  makeMoons,  makeRegression,} from "deepbox/datasets"; console.log("=== Built-in Datasets ===\n"); // ─── Classic Reference Datasets ───────────────────────────────────────────── console.log("--- Classic Reference Datasets ---\n"); console.log("1. Iris Dataset:");console.log("-".repeat(50));const iris = loadIris();console.log(`Samples: ${iris.data.shape[0]}`);console.log(`Features: ${iris.data.shape[1]}`);console.log(`Classes: ${iris.targetNames?.join(", ") || "N/A"}`);console.log(`Features: ${iris.featureNames?.join(", ") || "N/A"}\n`); console.log("2. Digits Dataset:");console.log("-".repeat(50));const digits = loadDigits();console.log(`Samples: ${digits.data.shape[0]}`);console.log(`Features: ${digits.data.shape[1]} (8x8 images flattened)`);console.log(`Classes: 10 (digits 0-9)\n`); console.log("3. Breast Cancer Dataset:");console.log("-".repeat(50));const cancer = loadBreastCancer();console.log(`Samples: ${cancer.data.shape[0]}`);console.log(`Features: ${cancer.data.shape[1]}`);console.log(`Classes: ${cancer.targetNames?.join(", ") || "N/A"}\n`); console.log("4. Diabetes Dataset (Regression):");console.log("-".repeat(50));const diabetes = loadDiabetes();console.log(`Samples: ${diabetes.data.shape[0]}`);console.log(`Features: ${diabetes.data.shape[1]}`);console.log(`Task: Regression (predict disease progression)\n`); console.log("5. Linnerud Dataset (Multi-Output):");console.log("-".repeat(50));const linnerud = loadLinnerud();console.log(`Samples: ${linnerud.data.shape[0]}`);console.log(`Features: ${linnerud.data.shape[1]}`);console.log(`Targets: ${linnerud.target.shape[1]} (multi-output regression)\n`); // ─── Tabular Classification ───────────────────────────────────────────────── console.log("--- Tabular Classification Datasets ---\n"); console.log("6. Flowers Extended (4-class Iris variant):");console.log("-".repeat(50));const flowers = loadFlowersExtended();console.log(`Samples: ${flowers.data.shape[0]}, Features: ${flowers.data.shape[1]}`);console.log(`Classes: ${flowers.targetNames?.join(", ") || "N/A"}\n`); console.log("7. Leaf Shapes (5-class morphology):");console.log("-".repeat(50));const leaves = loadLeafShapes();console.log(`Samples: ${leaves.data.shape[0]}, Features: ${leaves.data.shape[1]}`);console.log(`Classes: ${leaves.targetNames?.join(", ") || "N/A"}\n`); console.log("8. Fruit Quality:");console.log("-".repeat(50));const fruit = loadFruitQuality();console.log(`Samples: ${fruit.data.shape[0]}, Features: ${fruit.data.shape[1]}`);console.log(`Classes: ${fruit.targetNames?.join(", ") || "N/A"}\n`); console.log("9. Seed Morphology:");console.log("-".repeat(50));const seeds = loadSeedMorphology();console.log(`Samples: ${seeds.data.shape[0]}, Features: ${seeds.data.shape[1]}`);console.log(`Classes: ${seeds.targetNames?.join(", ") || "N/A"}\n`); // ─── Non-Linear Classification ────────────────────────────────────────────── console.log("--- Non-Linear Classification Datasets ---\n"); console.log("10. Moons-Multi (3 interleaving crescents):");console.log("-".repeat(50));const moons = loadMoonsMulti();console.log(`Samples: ${moons.data.shape[0]}, Features: ${moons.data.shape[1]}\n`); console.log("11. Concentric Rings:");console.log("-".repeat(50));const rings = loadConcentricRings();console.log(`Samples: ${rings.data.shape[0]}, Features: ${rings.data.shape[1]}\n`); console.log("12. Spiral Arms:");console.log("-".repeat(50));const spirals = loadSpiralArms();console.log(`Samples: ${spirals.data.shape[0]}, Features: ${spirals.data.shape[1]}\n`); console.log("13. Gaussian Islands (3D clusters):");console.log("-".repeat(50));const islands = loadGaussianIslands();console.log(`Samples: ${islands.data.shape[0]}, Features: ${islands.data.shape[1]}\n`); // ─── Regression Datasets ──────────────────────────────────────────────────── console.log("--- Regression Datasets ---\n"); console.log("14. Plant Growth:");console.log("-".repeat(50));const plant = loadPlantGrowth();console.log(`Samples: ${plant.data.shape[0]}, Features: ${plant.data.shape[1]}`);console.log(`Features: ${plant.featureNames.join(", ")}\n`); console.log("15. Housing-Mini:");console.log("-".repeat(50));const housing = loadHousingMini();console.log(`Samples: ${housing.data.shape[0]}, Features: ${housing.data.shape[1]}`);console.log(`Features: ${housing.featureNames.join(", ")}\n`); console.log("16. Energy Efficiency:");console.log("-".repeat(50));const energy = loadEnergyEfficiency();console.log(`Samples: ${energy.data.shape[0]}, Features: ${energy.data.shape[1]}\n`); console.log("17. Crop Yield:");console.log("-".repeat(50));const crop = loadCropYield();console.log(`Samples: ${crop.data.shape[0]}, Features: ${crop.data.shape[1]}\n`); // ─── Clustering Datasets ──────────────────────────────────────────────────── console.log("--- Clustering Datasets ---\n"); console.log("18. Customer Segments:");console.log("-".repeat(50));const customers = loadCustomerSegments();console.log(`Samples: ${customers.data.shape[0]}, Features: ${customers.data.shape[1]}`);console.log(`Clusters: ${customers.targetNames?.join(", ") || "N/A"}\n`); console.log("19. Sensor States:");console.log("-".repeat(50));const sensors = loadSensorStates();console.log(`Samples: ${sensors.data.shape[0]}, Features: ${sensors.data.shape[1]}`);console.log(`Modes: ${sensors.targetNames?.join(", ") || "N/A"}\n`); // ─── Integer-Heavy Datasets ───────────────────────────────────────────────── console.log("--- Integer-Heavy Datasets ---\n"); console.log("20. Student Performance:");console.log("-".repeat(50));const students = loadStudentPerformance();console.log(`Samples: ${students.data.shape[0]}, Features: ${students.data.shape[1]}\n`); console.log("21. Traffic Conditions:");console.log("-".repeat(50));const traffic = loadTrafficConditions();console.log(`Samples: ${traffic.data.shape[0]}, Features: ${traffic.data.shape[1]}\n`); // ─── Multi-Output Datasets ────────────────────────────────────────────────── console.log("--- Multi-Output Datasets ---\n"); console.log("22. Fitness Scores (3 targets):");console.log("-".repeat(50));const fitness = loadFitnessScores();console.log(`Samples: ${fitness.data.shape[0]}, Features: ${fitness.data.shape[1]}`);console.log(`Targets: ${fitness.target.shape[1]} (${fitness.targetNames?.join(", ") || "N/A"})\n`); console.log("23. Weather Outcomes (2 targets):");console.log("-".repeat(50));const weather = loadWeatherOutcomes();console.log(`Samples: ${weather.data.shape[0]}, Features: ${weather.data.shape[1]}`);console.log(`Targets: ${weather.target.shape[1]} (${weather.targetNames?.join(", ") || "N/A"})\n`); // ─── Benchmark / Sanity-Check ─────────────────────────────────────────────── console.log("--- Benchmark / Sanity-Check ---\n"); console.log("24. Perfectly Separable:");console.log("-".repeat(50));const perfect = loadPerfectlySeparable();console.log(`Samples: ${perfect.data.shape[0]}, Features: ${perfect.data.shape[1]}`);console.log(`Classes: ${perfect.targetNames?.join(", ") || "N/A"}\n`); // ─── Synthetic Dataset Generators ─────────────────────────────────────────── console.log("=== Synthetic Dataset Generators ===\n"); console.log("25. Make Classification:");console.log("-".repeat(50));const classData = makeClassification({  nSamples: 100,  nFeatures: 4,  nClasses: 2,  randomState: 42,});const [XClass, yClass] = classData;console.log(`Generated ${XClass.shape[0]} samples with ${XClass.shape[1]} features`);console.log(`Labels: shape [${yClass.shape}], dtype ${yClass.dtype}\n`); console.log("26. Make Regression:");console.log("-".repeat(50));const regData = makeRegression({  nSamples: 100,  nFeatures: 3,  noise: 0.1,  randomState: 42,});const XReg = regData[0];console.log(`Generated ${XReg.shape[0]} samples with ${XReg.shape[1]} features\n`); console.log("27. Make Blobs:");console.log("-".repeat(50));const blobsData = makeBlobs({  nSamples: 150,  nFeatures: 2,  centers: 3,  randomState: 42,});const XBlobs = blobsData[0];console.log(`Generated ${XBlobs.shape[0]} samples in 3 clusters\n`); console.log("28. Make Moons:");console.log("-".repeat(50));const moonsData = makeMoons({  nSamples: 100,  noise: 0.1,  randomState: 42,});const XMoons = moonsData[0];console.log(`Generated ${XMoons.shape[0]} samples (2 interleaving half circles)\n`); console.log("29. Make Circles:");console.log("-".repeat(50));const circlesData = makeCircles({  nSamples: 100,  noise: 0.05,  randomState: 42,});const XCircles = circlesData[0];console.log(`Generated ${XCircles.shape[0]} samples (concentric circles)\n`); console.log("30. Make Gaussian Quantiles:");console.log("-".repeat(50));const gaussData = makeGaussianQuantiles({  nSamples: 100,  nFeatures: 2,  nClasses: 3,  randomState: 42,});const XGauss = gaussData[0];console.log(`Generated ${XGauss.shape[0]} samples in 3 quantile-based classes\n`); console.log("Listed 24 built-in datasets and 6 synthetic generators.");

Output

Console output
Console output only, about 160 lines.