Mars Learn

This is the class and function reference of Mars learn.

Clustering

Classes

cluster.KMeans([n_clusters, init, n_init, ...])

K-Means clustering.

Functions

cluster.k_means(X, n_clusters[, ...])

K-means clustering algorithm.

Datasets

Samples generator

datasets.make_blobs([n_samples, n_features, ...])	Generate isotropic Gaussian blobs for clustering.
datasets.make_classification([n_samples, ...])	Generate a random n-class classification problem.
datasets.make_low_rank_matrix([n_samples, ...])	Generate a mostly low rank matrix with bell-shaped singular values
datasets.make_regression([n_samples, ...])	Generate a random regression problem.

Matrix Decomposition

decomposition.PCA([n_components, copy, ...])	Principal component analysis (PCA)
decomposition.TruncatedSVD([n_components, ...])	Dimensionality reduction using truncated SVD (aka LSA).

Ensemble Methods

ensemble.BaggingClassifier([base_estimator, ...])	A Bagging classifier.
ensemble.BaggingRegressor([base_estimator, ...])	A Bagging regressor.
ensemble.BlockwiseVotingClassifier(estimator)	Blockwise training and ensemble voting classifier.
ensemble.BlockwiseVotingRegressor(estimator)	Blockwise training and ensemble voting regressor.
ensemble.IsolationForest(*[, n_estimators, ...])	Isolation Forest Algorithm.

Linear Models

Classical linear regressors

linear_model.LinearRegression(*[, ...])

Ordinary least squares Linear Regression.

Metrics

Classification metrics

metrics.accuracy_score(y_true, y_pred[, ...])	Accuracy classification score.
metrics.auc(x, y[, session, run_kwargs])	Compute Area Under the Curve (AUC) using the trapezoidal rule
metrics.f1_score(y_true, y_pred, *[, ...])	Compute the F1 score, also known as balanced F-score or F-measure
metrics.fbeta_score(y_true, y_pred, *, beta)	Compute the F-beta score
metrics.log_loss(y_true, y_pred, *[, eps, ...])	Log loss, aka logistic loss or cross-entropy loss.
metrics.multilabel_confusion_matrix(y_true, ...)	Compute a confusion matrix for each class or sample.
metrics.precision_score(y_true, y_pred, *[, ...])	Compute the precision
metrics.precision_recall_fscore_support(...)	Compute precision, recall, F-measure and support for each class
metrics.recall_score(y_true, y_pred, *[, ...])	Compute the recall
metrics.roc_auc_score(y_true, y_score, *[, ...])	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
metrics.roc_curve(y_true, y_score[, ...])	Compute Receiver operating characteristic (ROC)

Regression metrics

metrics.r2_score(y_true, y_pred, *[, ...])

\(R^2\) (coefficient of determination) regression score function.

Pairwise metrics

metrics.pairwise.cosine_similarity(X[, Y, ...])	Compute cosine similarity between samples in X and Y.
metrics.pairwise.cosine_distances(X[, Y])	Compute cosine distance between samples in X and Y.
metrics.pairwise.euclidean_distances(X[, Y, ...])	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])	Compute the Haversine distance between samples in X and Y
metrics.pairwise.manhattan_distances(X[, Y, ...])	Compute the L1 distances between the vectors in X and Y.
metrics.pairwise.rbf_kernel(X[, Y, gamma])	Compute the rbf (gaussian) kernel between X and Y.
metrics.pairwise_distances(X[, Y, metric])

Model Selection

Splitter Classes

model_selection.KFold([n_splits, shuffle, ...])

K-Folds cross-validator

Splitter Functions

model_selection.train_test_split(*arrays, ...)

Split arrays or matrices into random train and test subsets

Nearest Neighbors

neighbors.NearestNeighbors([n_neighbors, ...])

Preprocessing and Normalization

preprocessing.LabelBinarizer(*[, neg_label, ...])	Binarize labels in a one-vs-all fashion.
preprocessing.LabelEncoder()	Encode target labels with value between 0 and n_classes-1.
preprocessing.MinMaxScaler([feature_range, ...])	Transform features by scaling each feature to a given range.
preprocessing.minmax_scale(X[, ...])	Transform features by scaling each feature to a given range.
preprocessing.label_binarize(y, *, classes)	Binarize labels in a one-vs-all fashion.
preprocessing.normalize(X[, norm, axis, ...])	Scale input vectors individually to unit norm (vector length).

Semi-Supervised Learning

semi_supervised.LabelPropagation([kernel, ...])

Label Propagation classifier

Utilities

utils.assert_all_finite(X[, allow_nan, ...])
utils.check_X_y(X, y[, accept_sparse, ...])	Input validation for standard estimators.
utils.check_array(array[, accept_sparse, ...])	Input validation on a tensor, list, sparse matrix or similar.
utils.check_consistent_length(*arrays[, ...])	Check that all arrays have consistent first dimensions.
utils.multiclass.type_of_target(y)	Determine the type of data indicated by the target.
utils.multiclass.is_multilabel(y)	Check if y is in a multilabel format.
utils.shuffle(*arrays, **options)
utils.validation.check_is_fitted(estimator)	Perform is_fitted validation for estimator.
utils.validation.column_or_1d(y[, warn])	Ravel column or 1d numpy array, else raises an error

Misc

wrappers.ParallelPostFit([estimator, scoring])

Meta-estimator for parallel predict and transform.

LightGBM Integration

contrib.lightgbm.LGBMClassifier(*args, **kwargs)
contrib.lightgbm.LGBMRegressor(*args, **kwargs)
contrib.lightgbm.LGBMRanker(*args, **kwargs)

PyTorch Integration

contrib.pytorch.run_pytorch_script(script, ...)	Run PyTorch script in Mars cluster.
contrib.pytorch.MarsDataset
contrib.pytorch.SequentialSampler
contrib.pytorch.RandomSampler
contrib.pytorch.SubsetRandomSampler
contrib.pytorch.DistributedSampler

StatsModels Integration

contrib.statsmodels.MarsDistributedModel([...])
contrib.statsmodels.MarsResults(model)

TensorFlow Integration

contrib.tensorflow.run_tensorflow_script(...)	Run TensorFlow script in Mars cluster.
contrib.tensorflow.gen_tensorflow_dataset(tensors)	convert mars data type to tf.data.Dataset.

XGBoost Integration

contrib.xgboost.MarsDMatrix(data[, label, ...])
contrib.xgboost.train(params, dtrain[, evals])	Train XGBoost model in Mars manner.
contrib.xgboost.predict(model, data[, ...])
contrib.xgboost.XGBClassifier([max_depth, ...])	Implementation of the scikit-learn API for XGBoost classification.
contrib.xgboost.XGBRegressor([max_depth, ...])	Implementation of the scikit-learn API for XGBoost regressor.