from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

from aida_data.dense import SafeL2Normalizer, load

# Raw (cleaned) data
ds = load("glove-100-angular")

# Preprocess with any sklearn transformer/pipeline: it is fitted on the
# train set and applied to the test set.
pipeline = make_pipeline(StandardScaler(with_std=False), PCA(64), SafeL2Normalizer())
ds = load("glove-100-angular", pipeline, load_queries=True)
print(pipeline.named_steps["pca"].explained_variance_ratio_)

Datasets are downloaded to datasets/ (or $AIDA_DATA_DIR). Large datasets are parsed once into a zstd-compressed <name>.parquet cache next to the download. The raw download is then deleted, since it is not needed anymore; set AIDA_DATA_KEEP_RAW=1 to keep it. Downloads and caches are written to a temporary file with a unique name (<name>.<random>.part or <name>.parquet.<random>.parquet.tmp) and renamed once complete, so concurrent loads of the same dataset do not clash. A process that is killed can leave such a *.part or *.tmp file behind; it is safe to delete, and prune_raw removes the ones older than 24 hours. Raw files left over from earlier versions can be removed with prune_raw too:

from aida_data import dense

dense.prune_raw()               # dry run: list the raw files that can go
dense.prune_raw(dry_run=False)  # delete them

Several dense datasets (fair-clustering, streaming-fair and silhouette datasets: adult, athlete, diabetes, creditcard, census1990, 4area, reuter_50_50, victorian, bank, covertype, phones, higgs, higgs-highlevel, breast, wine, shuttle, rt-iot2022, biokdd) come with categorical attributes, returned as Dataset.colors and kept aligned with the rows through deduplication, NaN filtering and the pipeline. Rows are duplicates only if both features and colors are equal.

from aida_data import dense

ds = dense.load("adult")
ds.dataset            # (n, 5) float32
ds.colors.names       # ("sex", "race", "marital-status")
sex = ds.colors.column("sex")  # (n,) int64 codes
ds.colors.labels[0]   # ("Female", "Male"), so labels[0][code] decodes sex
ds.colors.n_colors("race")

shuttle needs the optional unlzw3 package (pip install aida-data[shuttle]).

Graphs from Sirius, loaded as undirected simple graphs (self loops and duplicate edges dropped, each edge stored once as (u, v) with u < v).

from aida_data import graph

print(graph.available_datasets())
g = graph.load_edge_list("brexit")
g.edges   # (m, 2) int64 array
g.colors  # (n,) int64 array, g.colors[v] is the color of node v (-1 if missing)
g.n_nodes, g.n_edges, g.n_colors

# Keep edge orientation, and remap colors to 0..k-1
g = graph.load_edge_list("brexit", directed=True, remap_colors=True)

Each graph is parsed once into datasets/graphs/<name>.parquet (or under $AIDA_DATA_DIR), a zstd-compressed adjacency list with one row per node (nbrs: sorted out-neighbors, color), roughly a tenth of the size of the TSV files. The TSV files are then deleted; set AIDA_DATA_KEEP_RAW=1 to keep them, or call graph.prune_raw(dry_run=False) to remove the ones left over from earlier versions, along with stale *.part/*.tmp files (graph.prune_raw() is a dry run). The options of load_edge_list are applied when reading the cache.

Series used by ATTIMO and MOMENTI: astro, ecg, freezer, gap, humany (from the ATTIMO figshare article, CC BY 4.0) and the four-dimensional steamgen (Zenodo 4273921, CC BY 4.0). From the Motiflets repository: dishwasher, npo141 (sleep EEG) and arrhythmia. From the MOMENTI repository: foetal-ecg (8 dimensions), evaporator (6) and ruth (32), and oikolab-weather (8 hourly weather series, with ts.time). fl010 (6 channels at 100 Hz, 25 million samples, from the PhysioNet LTMM database) needs the optional wfdb package: pip install aida-data[wfdb]. The files of the last two groups come from GitHub at a pinned commit and have their own origin and terms, see the comments in timeseries.py.

from aida_data import timeseries

print(timeseries.available_datasets())
ts = timeseries.load("ecg")
ts.values      # (n, d) float64, one row per time step, in the order of the source
ts.univariate  # (n,) view, only for series with d == 1
ts.dim_names   # ("drum pressure", ...) for steamgen, None if the source has no names
ts.time        # (n,) datetime64[ms], None if the source has no time stamps

values is always two-dimensional, so univariate and multivariate series share one type; transpose it if your library expects (d, n). Rows are never reordered, deduplicated or normalized, and missing values stay NaN. The one exception is ecg, whose file has 46991 blank lines between runs of values: they are skipped, as pyattimo.load_dataset("ecg") does.

Each series is parsed once into datasets/timeseries/<name>.parquet (or under $AIDA_DATA_DIR), a zstd-compressed float64 file, about the size of the gzipped text and roughly ten times faster to read than to parse. The raw files are then deleted; set AIDA_DATA_KEEP_RAW=1 to keep them, or call timeseries.prune_raw(dry_run=False) to remove the ones left over, along with stale *.part/*.tmp files (timeseries.prune_raw() is a dry run).