This is the class and function reference of Mars learn.
cluster.KMeans([n_clusters, init, n_init, …])
cluster.KMeans
K-Means clustering.
cluster.k_means(X, n_clusters[, …])
cluster.k_means
K-means clustering algorithm.
datasets.make_blobs([n_samples, n_features, …])
datasets.make_blobs
Generate isotropic Gaussian blobs for clustering.
datasets.make_classification([n_samples, …])
datasets.make_classification
Generate a random n-class classification problem.
datasets.make_low_rank_matrix([n_samples, …])
datasets.make_low_rank_matrix
Generate a mostly low rank matrix with bell-shaped singular values
decomposition.PCA([n_components, copy, …])
decomposition.PCA
Principal component analysis (PCA)
decomposition.TruncatedSVD([n_components, …])
decomposition.TruncatedSVD
Dimensionality reduction using truncated SVD (aka LSA).
metrics.accuracy_score(y_true, y_pred[, …])
metrics.accuracy_score
Accuracy classification score.
metrics.auc(x, y[, session, run_kwargs])
metrics.auc
Compute Area Under the Curve (AUC) using the trapezoidal rule
metrics.roc_curve(y_true, y_score[, …])
metrics.roc_curve
Compute Receiver operating characteristic (ROC)
metrics.pairwise.cosine_similarity(X[, Y, …])
metrics.pairwise.cosine_similarity
Compute cosine similarity between samples in X and Y.
metrics.pairwise.cosine_distances(X[, Y])
metrics.pairwise.cosine_distances
Compute cosine distance between samples in X and Y.
metrics.pairwise.euclidean_distances(X[, Y, …])
metrics.pairwise.euclidean_distances
Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.
metrics.pairwise.haversine_distances(X[, Y])
metrics.pairwise.haversine_distances
Compute the Haversine distance between samples in X and Y
metrics.pairwise.manhattan_distances(X[, Y, …])
metrics.pairwise.manhattan_distances
Compute the L1 distances between the vectors in X and Y.
metrics.pairwise.rbf_kernel(X[, Y, gamma])
metrics.pairwise.rbf_kernel
Compute the rbf (gaussian) kernel between X and Y.
metrics.pairwise_distances(X[, Y, metric])
metrics.pairwise_distances
model_selection.train_test_split(*arrays, …)
model_selection.train_test_split
Split arrays or matrices into random train and test subsets
neighbors.NearestNeighbors([n_neighbors, …])
neighbors.NearestNeighbors
preprocessing.MinMaxScaler([feature_range, …])
preprocessing.MinMaxScaler
Transform features by scaling each feature to a given range.
preprocessing.minmax_scale(X[, …])
preprocessing.minmax_scale
preprocessing.normalize(X[, norm, axis, …])
preprocessing.normalize
Scale input vectors individually to unit norm (vector length).
semi_supervised.LabelPropagation([kernel, …])
semi_supervised.LabelPropagation
Label Propagation classifier
utils.assert_all_finite(X[, allow_nan, …])
utils.assert_all_finite
utils.check_X_y(X, y[, accept_sparse, …])
utils.check_X_y
Input validation for standard estimators.
utils.check_array(array[, accept_sparse, …])
utils.check_array
Input validation on a tensor, list, sparse matrix or similar.
utils.check_consistent_length(*arrays[, …])
utils.check_consistent_length
Check that all arrays have consistent first dimensions.
utils.multiclass.type_of_target(y)
utils.multiclass.type_of_target
Determine the type of data indicated by the target.
utils.multiclass.is_multilabel(y)
utils.multiclass.is_multilabel
Check if y is in a multilabel format.
y
utils.shuffle(*arrays, **options)
utils.shuffle
utils.validation.check_is_fitted(estimator)
utils.validation.check_is_fitted
Perform is_fitted validation for estimator.
utils.validation.column_or_1d(y[, warn])
utils.validation.column_or_1d
Ravel column or 1d numpy array, else raises an error
contrib.tensorflow.run_tensorflow_script(…)
contrib.tensorflow.run_tensorflow_script
Run TensorFlow script in Mars cluster.
contrib.pytorch.run_pytorch_script(script, …)
contrib.pytorch.run_pytorch_script
Run PyTorch script in Mars cluster.
contrib.pytorch.MarsDataset
contrib.pytorch.MarsDistributedSampler
contrib.pytorch.MarsRandomSampler(data_source)
contrib.pytorch.MarsRandomSampler
contrib.xgboost.MarsDMatrix(data[, label, …])
contrib.xgboost.MarsDMatrix
contrib.xgboost.train(params, dtrain[, evals])
contrib.xgboost.train
Train XGBoost model in Mars manner.
contrib.xgboost.predict(model, data[, …])
contrib.xgboost.predict
contrib.xgboost.XGBClassifier
contrib.xgboost.XGBRegressor
contrib.lightgbm.LGBMClassifier
contrib.lightgbm.LGBMRegressor
contrib.lightgbm.LGBMRanker