{"library":"pypots","title":"PyPOTS","description":"A Python toolbox for machine learning on partially-observed time series. Current version 1.4, released with bug fixes. Active development with frequent releases.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install pypots"],"cli":null},"imports":["import pypots"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pypots\nfrom pypots.utils.random import set_random_seed\nimport numpy as np\n\n# Generate random data with missing values\nX = np.random.randn(100, 10, 5)\nX[X < -1] = np.nan  # introduce missing values\n\n# Impute using a simple model\nfrom pypots.imputation import SAITS\n\nmodel = SAITS(\n    n_steps=10,\n    n_features=5,\n    n_layers=2,\n    d_model=32,\n    d_ffn=64,\n    n_heads=4,\n    d_k=16,\n    d_v=16,\n    epochs=10,\n    batch_size=32,\n    loss_fn='mse',\n    optimizer='adam',\n    lr=1e-3,\n    verbose=True\n)\nmodel.fit(X)\nimputed_X = model.impute(X)\nprint(imputed_X.shape)","lang":"python","description":"Impute missing values in 3D time series data using SAITS.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}