overview

High-quality documentation is a development goal of mlpack. mlpack’s documentation is split into two parts: documentation for the bindings, and documentation for the C++ library. Generally, working with the bindings is a good choice for simple machine learning and data science tasks, and writing C++ is a good idea when complex or custom functionality is desired.

All interfaces are heavily documented, and if you find a documentation issue, please report it.

quickstart

Just getting started with mlpack? Try these quickstart tutorials for the bindings to other languages.

Quickstart Tutorials

These tutorials give very quick “getting started” examples that you can use to get started with mlpack in different languages.

Once you’re comfortable with the quickstart guides for the language of your choice, full documentation for every binding can be found below. Quick links are in the left sidebar.

The C++ interfaces of mlpack are carefully documented and doxygen is used to provide automatically-generated searchable documentation.

tutorials

A number of tutorials are available covering individual algorithms and functionality inside of mlpack, both for bindings to other languages and for the C++ interface.

Introductory Tutorials

These tutorials introduce the basic concepts of working with mlpack, aimed at developers who want to use and contribute to mlpack but are not sure where to start.

Method-specific Tutorials

These tutorials introduce the various methods mlpack offers, aimed at users who want to get started quickly. These tutorials start with simple examples and progress to complex, extensible uses.

Advanced Tutorials

These tutorials discuss some of the more advanced functionality contained in mlpack.

Policy Class Documentation

mlpack uses templates to achieve its genericity and flexibility. Some of the template types used by mlpack are common across multiple machine learning algorithms. The links below provide documentation for some of these common types.

mlpack 3.2.2 binding documentation

data formats

mlpack bindings for Python take and return a restricted set of types, for simplicity. These include primitive types, matrix/vector types, categorical matrix types, and model types. Each type is detailed below.

  • int: An integer (i.e., “1”).
  • float: A floating-point number (i.e., “0.5”).
  • bool: A boolean flag option (True or False).
  • str: A character string (i.e., “hello”).
  • list of ints: A list of integers; i.e., [0, 1, 2].
  • list of strs: A list of strings; i.e., [“hello”, “goodbye”].
  • matrix: A 2-d arraylike containing data. This can be a list of lists, a numpy ndarray, or a pandas DataFrame. If the dtype is not already float64, it will be converted.
  • int matrix: A 2-d arraylike containing data with a uint64 dtype. This can be a list of lists, a numpy ndarray, or a pandas DataFrame. If the dtype is not already uint64, it will be converted.
  • vector: A 1-d arraylike containing data. This can be a 2-d matrix where one dimension has size 1, or it can also be a list, a numpy 1-d ndarray, or a 1-d pandas DataFrame. If the dtype is not already float64, it will be converted.
  • int vector: A 1-d arraylike containing data with a uint64 dtype. This can be a 2-d matrix where one dimension has size 1, or it can also be a list, a numpy 1-d ndarray, or a 1-d pandas DataFrame. If the dtype is not already uint64, it will be converted.
  • categorical matrix: A 2-d arraylike containing data. Like the regular 2-d matrices, this can be a list of lists, a numpy ndarray, or a pandas DataFrame. However, this type can also accept a pandas DataFrame that has columns of type ‘CategoricalDtype’. These categorical values will be converted to numeric indices before being passed to mlpack, and then inside mlpack they will be properly treated as categorical variables, so there is no need to do one-hot encoding for this matrix type. If the dtype of the given matrix is not already float64, it will be converted.
  • mlpackModelType: An mlpack model pointer. This type can be pickled to or from disk, and internally holds a pointer to C++ memory containing the mlpack model. Note that this means that the mlpack model itself cannot be easily inspected in Python; however, the pickled model can be loaded in C++ and inspected there.

adaboost()

AdaBoost

>>> from mlpack import adaboost
>>> d = adaboost(input_model=None, iterations=1000, labels=np.empty([0],
        dtype=np.uint64), test=np.empty([0, 0]), tolerance=1e-10,
        training=np.empty([0, 0]), weak_learner='decision_stump')
>>> output = d['output']
>>> output_model = d['output_model']
>>> predictions = d['predictions']

An implementation of the AdaBoost.MH (Adaptive Boosting) algorithm for classification. This can be used to train an AdaBoost model on labeled data or use an existing AdaBoost model to predict the classes of new points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model AdaBoostModelType Input AdaBoost model. None
iterations int The maximum number of boosting iterations to be run (0 will run until convergence.) 1000
labels int vector Labels for the training set. np.empty([0], dtype=np.uint64)
test matrix Test dataset. np.empty([0, 0])
tolerance float The tolerance for change in values of the weighted error during training. 1e-10
training matrix Dataset for training AdaBoost. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False
weak_learner str The type of weak learner to use: ‘decision_stump’, or ‘perceptron’. 'decision_stump'

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int vector Predicted labels for the test set.
output_model AdaBoostModelType Output trained AdaBoost model.
predictions int vector Predicted labels for the test set.

Detailed documentation

This program implements the AdaBoost (or Adaptive Boosting) algorithm. The variant of AdaBoost implemented here is AdaBoost.MH. It uses a weak learner, either decision stumps or perceptrons, and over many iterations, creates a strong learner that is a weighted ensemble of weak learners. It runs these iterations until a tolerance value is crossed for change in the value of the weighted training error.

For more information about the algorithm, see the paper “Improved Boosting Algorithms Using Confidence-Rated Predictions”, by R.E. Schapire and Y. Singer.

This program allows training of an AdaBoost model, and then application of that model to a test dataset. To train a model, a dataset must be passed with the training option. Labels can be given with the labels option; if no labels are specified, the labels will be assumed to be the last column of the input dataset. Alternately, an AdaBoost model may be loaded with the input_model option.

Once a model is trained or loaded, it may be used to provide class predictions for a given test dataset. A test dataset may be specified with the test parameter. The predicted classes for each point in the test dataset are output to the predictions output parameter. The AdaBoost model itself is output to the output_model output parameter.

Note: the following parameter is deprecated and will be removed in mlpack 4.0.0: output. Use predictions instead of output.

For example, to run AdaBoost on an input dataset 'data' with perceptrons as the weak learner type, storing the trained model in 'model', one could use the following command:

>>> output = adaboost(training=data, weak_learner='perceptron')
>>> model = output['output_model']

Similarly, an already-trained model in 'model' can be used to provide class predictions from test data 'test_data' and store the output in 'predictions' with the following command:

>>> output = adaboost(input_model=model, test=test_data)
>>> predictions = output['predictions']

See also

approx_kfn()

>>> from mlpack import approx_kfn
>>> d = approx_kfn(algorithm='ds', calculate_error=False,
        exact_distances=np.empty([0, 0]), input_model=None, k=0,
        num_projections=5, num_tables=5, query=np.empty([0, 0]),
        reference=np.empty([0, 0]))
>>> distances = d['distances']
>>> neighbors = d['neighbors']
>>> output_model = d['output_model']

An implementation of two strategies for furthest neighbor search. This can be used to compute the furthest neighbor of query point(s) from a set of points; furthest neighbor models can be saved and reused with future query point(s). Detailed documentation.

Input options

name type description default
algorithm str Algorithm to use: ‘ds’ or ‘qdafn’. 'ds'
calculate_error bool If set, calculate the average distance error for the first furthest neighbor only. False
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
exact_distances matrix Matrix containing exact distances to furthest neighbors; this can be used to avoid explicit calculation when –calculate_error is set. np.empty([0, 0])
input_model ApproxKFNModelType File containing input model. None
k int Number of furthest neighbors to search for. 0
num_projections int Number of projections to use in each hash table. 5
num_tables int Number of hash tables to use. 5
query matrix Matrix containing query points. np.empty([0, 0])
reference matrix Matrix containing the reference dataset. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
distances matrix Matrix to save furthest neighbor distances to.
neighbors int matrix Matrix to save neighbor indices to.
output_model ApproxKFNModelType File to save output model to.

Detailed documentation

This program implements two strategies for furthest neighbor search. These strategies are:

  • The ‘qdafn’ algorithm from “Approximate Furthest Neighbor in High Dimensions” by R. Pagh, F. Silvestri, J. Sivertsen, and M. Skala, in Similarity Search and Applications 2015 (SISAP).
  • The ‘DrusillaSelect’ algorithm from “Fast approximate furthest neighbors with data-dependent candidate selection”, by R.R. Curtin and A.B. Gardner, in Similarity Search and Applications 2016 (SISAP).

These two strategies give approximate results for the furthest neighbor search problem and can be used as fast replacements for other furthest neighbor techniques such as those found in the mlpack_kfn program. Note that typically, the ‘ds’ algorithm requires far fewer tables and projections than the ‘qdafn’ algorithm.

Specify a reference set (set to search in) with reference, specify a query set with query, and specify algorithm parameters with num_tables and num_projections (or don’t and defaults will be used). The algorithm to be used (either ‘ds’—the default—or ‘qdafn’) may be specified with algorithm. Also specify the number of neighbors to search for with k.

If no query set is specified, the reference set will be used as the query set. The output_model output parameter may be used to store the built model, and an input model may be loaded instead of specifying a reference set with the input_model option.

Results for each query point can be stored with the neighbors and distances output parameters. Each row of these output matrices holds the k distances or neighbor indices for each query point.

For example, to find the 5 approximate furthest neighbors with 'reference_set' as the reference set and 'query_set' as the query set using DrusillaSelect, storing the furthest neighbor indices to 'neighbors' and the furthest neighbor distances to 'distances', one could call

>>> output = approx_kfn(query=query_set, reference=reference_set, k=5,
  algorithm='ds')
>>> neighbors = output['neighbors']
>>> distances = output['distances']

and to perform approximate all-furthest-neighbors search with k=1 on the set 'data' storing only the furthest neighbor distances to 'distances', one could call

>>> output = approx_kfn(reference=reference_set, k=1)
>>> distances = output['distances']

A trained model can be re-used. If a model has been previously saved to 'model', then we may find 3 approximate furthest neighbors on a query set 'new_query_set' using that model and store the furthest neighbor indices into 'neighbors' by calling

>>> output = approx_kfn(input_model=model, query=new_query_set, k=3)
>>> neighbors = output['neighbors']

See also

cf()

Collaborative Filtering

>>> from mlpack import cf
>>> d = cf(algorithm='NMF', all_user_recommendations=False,
        input_model=None, interpolation='average',
        iteration_only_termination=False, max_iterations=1000,
        min_residue=1e-05, neighbor_search='euclidean', neighborhood=5,
        query=np.empty([0, 0], dtype=np.uint64), rank=0, recommendations=5,
        seed=0, test=np.empty([0, 0]), training=np.empty([0, 0]))
>>> output = d['output']
>>> output_model = d['output_model']

An implementation of several collaborative filtering (CF) techniques for recommender systems. This can be used to train a new CF model, or use an existing CF model to compute recommendations. Detailed documentation.

Input options

name type description default
algorithm str Algorithm used for matrix factorization. 'NMF'
all_user_recommendations bool Generate recommendations for all users. False
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model CFModelType Trained CF model to load. None
interpolation str Algorithm used for weight interpolation. 'average'
iteration_only_termination bool Terminate only when the maximum number of iterations is reached. False
max_iterations int Maximum number of iterations. If set to zero, there is no limit on the number of iterations. 1000
min_residue float Residue required to terminate the factorization (lower values generally mean better fits). 1e-05
neighbor_search str Algorithm used for neighbor search. 'euclidean'
neighborhood int Size of the neighborhood of similar users to consider for each query user. 5
query int matrix List of query users for which recommendations should be generated. np.empty([0, 0], dtype=np.uint64)
rank int Rank of decomposed matrices (if 0, a heuristic is used to estimate the rank). 0
recommendations int Number of recommendations to generate for each query user. 5
seed int Set the random seed (0 uses std::time(NULL)). 0
test matrix Test set to calculate RMSE on. np.empty([0, 0])
training matrix Input dataset to perform CF on. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int matrix Matrix that will store output recommendations.
output_model CFModelType Output for trained CF model.

Detailed documentation

This program performs collaborative filtering (CF) on the given dataset. Given a list of user, item and preferences (the training parameter), the program will perform a matrix decomposition and then can perform a series of actions related to collaborative filtering. Alternately, the program can load an existing saved CF model with the input_model parameter and then use that model to provide recommendations or predict values.

The input matrix should be a 3-dimensional matrix of ratings, where the first dimension is the user, the second dimension is the item, and the third dimension is that user’s rating of that item. Both the users and items should be numeric indices, not names. The indices are assumed to start from 0.

A set of query users for which recommendations can be generated may be specified with the query parameter; alternately, recommendations may be generated for every user in the dataset by specifying the all_user_recommendations parameter. In addition, the number of recommendations per user to generate can be specified with the recommendations parameter, and the number of similar users (the size of the neighborhood) to be considered when generating recommendations can be specified with the neighborhood parameter.

For performing the matrix decomposition, the following optimization algorithms can be specified via the algorithm parameter:

  • ‘RegSVD’ – Regularized SVD using a SGD optimizer
  • ‘NMF’ – Non-negative matrix factorization with alternating least squares update rules
  • ‘BatchSVD’ – SVD batch learning
  • ‘SVDIncompleteIncremental’ – SVD incomplete incremental learning
  • ‘SVDCompleteIncremental’ – SVD complete incremental learning
  • ‘BiasSVD’ – Bias SVD using a SGD optimizer
  • ‘SVDPP’ – SVD++ using a SGD optimizer

The following neighbor search algorithms can be specified via the neighbor_search parameter:

  • ‘cosine’ – Cosine Search Algorithm
  • ‘euclidean’ – Euclidean Search Algorithm
  • ‘pearson’ – Pearson Search Algorithm

The following weight interpolation algorithms can be specified via the interpolation parameter:

  • ‘average’ – Average Interpolation Algorithm
  • ‘regression’ – Regression Interpolation Algorithm
  • ‘similarity’ – Similarity Interpolation Algorithm

A trained model may be saved to with the output_model output parameter.

To train a CF model on a dataset 'training_set' using NMF for decomposition and saving the trained model to 'model', one could call:

>>> output = cf(training=training_set, algorithm='NMF')
>>> model = output['output_model']

Then, to use this model to generate recommendations for the list of users in the query set 'users', storing 5 recommendations in 'recommendations', one could call

>>> output = cf(input_model=model, query=users, recommendations=5)
>>> recommendations = output['output']

See also

dbscan()

DBSCAN clustering

>>> from mlpack import dbscan
>>> d = dbscan(epsilon=1, input=np.empty([0, 0]), min_size=5,
        naive=False, selection_type='ordered', single_mode=False,
        tree_type='kd')
>>> assignments = d['assignments']
>>> centroids = d['centroids']

An implementation of DBSCAN clustering. Given a dataset, this can compute and return a clustering of that dataset. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
epsilon float Radius of each range search. 1
input matrix Input dataset to cluster. required
min_size int Minimum number of points for a cluster. 5
naive bool If set, brute-force range search (not tree-based) will be used. False
selection_type str If using point selection policy, the type of selection to use (‘ordered’, ‘random’). 'ordered'
single_mode bool If set, single-tree range search (not dual-tree) will be used. False
tree_type str If using single-tree or dual-tree search, the type of tree to use (‘kd’, ‘r’, ‘r-star’, ‘x’, ‘hilbert-r’, ‘r-plus’, ‘r-plus-plus’, ‘cover’, ‘ball’). 'kd'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
assignments int vector Output matrix for assignments of each point.
centroids matrix Matrix to save output centroids to.

Detailed documentation

This program implements the DBSCAN algorithm for clustering using accelerated tree-based range search. The type of tree that is used may be parameterized, or brute-force range search may also be used.

The input dataset to be clustered may be specified with the input parameter; the radius of each range search may be specified with the epsilon parameters, and the minimum number of points in a cluster may be specified with the min_size parameter.

The assignments and centroids output parameters may be used to save the output of the clustering. assignments contains the cluster assignments of each point, and centroids contains the centroids of each cluster.

The range search may be controlled with the tree_type, single_mode, and naive parameters. tree_type can control the type of tree used for range search; this can take a variety of values: ‘kd’, ‘r’, ‘r-star’, ‘x’, ‘hilbert-r’, ‘r-plus’, ‘r-plus-plus’, ‘cover’, ‘ball’. The single_mode parameter will force single-tree search (as opposed to the default dual-tree search), and ‘naive will force brute-force range search.

An example usage to run DBSCAN on the dataset in 'input' with a radius of 0.5 and a minimum cluster size of 5 is given below:

>>> dbscan(input=input, epsilon=0.5, min_size=5)

See also

decision_stump()

Decision Stump

>>> from mlpack import decision_stump
>>> d = decision_stump(bucket_size=6, input_model=None,
        labels=np.empty([0], dtype=np.uint64), test=np.empty([0, 0]),
        training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']

An implementation of a decision stump, which is a single-level decision tree. Given labeled data, a new decision stump can be trained; or, an existing decision stump can be used to classify points. Detailed documentation.

Input options

name type description default
bucket_size int The minimum number of training points in each decision stump bucket. 6
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model DSModelType Decision stump model to load. None
labels int vector Labels for the training set. If not specified, the labels are assumed to be the last row of the training data. np.empty([0], dtype=np.uint64)
test matrix A dataset to calculate predictions for. np.empty([0, 0])
training matrix The dataset to train on. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model DSModelType Output decision stump model to save.
predictions int vector The output matrix that will hold the predicted labels for the test set.

Detailed documentation

This program implements a decision stump, which is a single-level decision tree. The decision stump will split on one dimension of the input data, and will split into multiple buckets. The dimension and bins are selected by maximizing the information gain of the split. Optionally, the minimum number of training points in each bin can be specified with the bucket_size parameter.

The decision stump is parameterized by a splitting dimension and a vector of values that denote the splitting values of each bin.

This program enables several applications: a decision tree may be trained or loaded, and then that decision tree may be used to classify a given set of test points. The decision tree may also be saved to a file for later usage.

To train a decision stump, training data should be passed with the training parameter, and their corresponding labels should be passed with the labels option. Optionally, if labels is not specified, the labels are assumed to be the last dimension of the training dataset. The bucket_size parameter controls the minimum number of training points in each decision stump bucket.

For classifying a test set, a decision stump may be loaded with the input_model parameter (useful for the situation where a stump has already been trained), and a test set may be specified with the test parameter. The predicted labels can be saved with the predictions output parameter.

Because decision stumps are trained in batch, retraining does not make sense and thus it is not possible to pass both training and input_model; instead, simply build a new decision stump with the training data.

After training, a decision stump can be saved with the output_model output parameter. That stump may later be re-used in subsequent calls to this program (or others).

See also

decision_tree()

Decision tree

>>> from mlpack import decision_tree
>>> d = decision_tree(input_model=None, labels=np.empty([0],
        dtype=np.uint64), maximum_depth=0, minimum_gain_split=1e-07,
        minimum_leaf_size=20, print_training_accuracy=False,
        print_training_error=False, test=np.empty([0, 0]),
        test_labels=np.empty([0], dtype=np.uint64), training=np.empty([0, 0]),
        weights=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of an ID3-style decision tree for classification, which supports categorical data. Given labeled data with numeric or categorical features, a decision tree can be trained and saved; or, an existing decision tree can be used for classification on new points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model DecisionTreeModelType Pre-trained decision tree, to be used with test points. None
labels int vector Training labels. np.empty([0], dtype=np.uint64)
maximum_depth int Maximum depth of the tree (0 means no limit). 0
minimum_gain_split float Minimum gain for node splitting. 1e-07
minimum_leaf_size int Minimum number of points in a leaf. 20
print_training_accuracy bool Print the training accuracy. False
print_training_error bool Print the training error (deprecated; will be removed in mlpack 4.0.0). False
test categorical matrix Testing dataset (may be categorical). np.empty([0, 0])
test_labels int vector Test point labels, if accuracy calculation is desired. np.empty([0], dtype=np.uint64)
training categorical matrix Training dataset (may be categorical). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False
weights matrix The weight of labels np.empty([0, 0])

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model DecisionTreeModelType Output for trained decision tree.
predictions int vector Class predictions for each test point.
probabilities matrix Class probabilities for each test point.

Detailed documentation

Train and evaluate using a decision tree. Given a dataset containing numeric or categorical features, and associated labels for each point in the dataset, this program can train a decision tree on that data.

The training set and associated labels are specified with the training and labels parameters, respectively. The labels should be in the range [0, num_classes - 1]. Optionally, if labels is not specified, the labels are assumed to be the last dimension of the training dataset.

When a model is trained, the output_model output parameter may be used to save the trained model. A model may be loaded for predictions with the input_model parameter. The input_model parameter may not be specified when the training parameter is specified. The minimum_leaf_size parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The minimum_gain_split parameter specifies the minimum gain that is needed for the node to split. The maximum_depth parameter specifies the maximum depth of the tree. If print_training_error is specified, the training error will be printed.

Test data may be specified with the test parameter, and if performance numbers are desired for that test set, labels may be specified with the test_labels parameter. Predictions for each test point may be saved via the predictions output parameter. Class probabilities for each prediction may be saved with the probabilities output parameter.

For example, to train a decision tree with a minimum leaf size of 20 on the dataset contained in 'data' with labels 'labels', saving the output model to 'tree' and printing the training error, one could call

>>> output = decision_tree(training=data, labels=labels, minimum_leaf_size=20,
  minimum_gain_split=0.001, print_training_accuracy=True)
>>> tree = output['output_model']

Then, to use that model to classify points in 'test_set' and print the test error given the labels 'test_labels' using that model, while saving the predictions for each point to 'predictions', one could call

>>> output = decision_tree(input_model=tree, test=test_set,
  test_labels=test_labels)
>>> predictions = output['predictions']

See also

det()

Density Estimation With Density Estimation Trees

>>> from mlpack import det
>>> d = det(folds=10, input_model=None, max_leaf_size=10,
        min_leaf_size=5, path_format='lr', skip_pruning=False, test=np.empty([0,
        0]), training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> tag_counters_file = d['tag_counters_file']
>>> tag_file = d['tag_file']
>>> test_set_estimates = d['test_set_estimates']
>>> training_set_estimates = d['training_set_estimates']
>>> vi = d['vi']

An implementation of density estimation trees for the density estimation task. Density estimation trees can be trained or used to predict the density at locations given by query points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
folds int The number of folds of cross-validation to perform for the estimation (0 is LOOCV) 10
input_model DTree<>Type Trained density estimation tree to load. None
max_leaf_size int The maximum size of a leaf in the unpruned, fully grown DET. 10
min_leaf_size int The minimum size of a leaf in the unpruned, fully grown DET. 5
path_format str The format of path printing: ‘lr’, ‘id-lr’, or ‘lr-id’. 'lr'
skip_pruning bool Whether to bypass the pruning process and output the unpruned tree only. False
test matrix A set of test points to estimate the density of. np.empty([0, 0])
training matrix The data set on which to build a density estimation tree. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model DTree<>Type Output to save trained density estimation tree to.
tag_counters_file str The file to output the number of points that went to each leaf.
tag_file str The file to output the tags (and possibly paths) for each sample in the test set.
test_set_estimates matrix The output estimates on the test set from the final optimally pruned tree.
training_set_estimates matrix The output density estimates on the training set from the final optimally pruned tree.
vi matrix The output variable importance values for each feature.

Detailed documentation

This program performs a number of functions related to Density Estimation Trees. The optimal Density Estimation Tree (DET) can be trained on a set of data (specified by training) using cross-validation (with number of folds specified with the folds parameter). This trained density estimation tree may then be saved with the output_model output parameter.

The variable importances (that is, the feature importance values for each dimension) may be saved with the vi output parameter, and the density estimates for each training point may be saved with the training_set_estimates output parameter.

Enabling path printing for each node outputs the path from the root node to a leaf for each entry in the test set, or training set (if a test set is not provided). Strings like ‘LRLRLR’ (indicating that traversal went to the left child, then the right child, then the left child, and so forth) will be output. If ‘lr-id’ or ‘id-lr’ are given as the path_format parameter, then the ID (tag) of every node along the path will be printed after or before the L or R character indicating the direction of traversal, respectively.

This program also can provide density estimates for a set of test points, specified in the test parameter. The density estimation tree used for this task will be the tree that was trained on the given training points, or a tree given as the parameter input_model. The density estimates for the test points may be saved using the test_set_estimates output parameter.

See also

emst()

Fast Euclidean Minimum Spanning Tree

>>> from mlpack import emst
>>> d = emst(input=np.empty([0, 0]), leaf_size=1, naive=False)
>>> output = d['output']

An implementation of the Dual-Tree Boruvka algorithm for computing the Euclidean minimum spanning tree of a set of input points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Input data matrix. required
leaf_size int Leaf size in the kd-tree. One-element leaves give the empirically best performance, but at the cost of greater memory requirements. 1
naive bool Compute the MST using O(n^2) naive algorithm. False
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Output data. Stored as an edge list.

Detailed documentation

This program can compute the Euclidean minimum spanning tree of a set of input points using the dual-tree Boruvka algorithm.

The set to calculate the minimum spanning tree of is specified with the input parameter, and the output may be saved with the output output parameter.

The leaf_size parameter controls the leaf size of the kd-tree that is used to calculate the minimum spanning tree, and if the naive option is given, then brute-force search is used (this is typically much slower in low dimensions). The leaf size does not affect the results, but it may have some effect on the runtime of the algorithm.

For example, the minimum spanning tree of the input dataset 'data' can be calculated with a leaf size of 20 and stored as 'spanning_tree' using the following command:

>>> output = emst(input=data, leaf_size=20)
>>> spanning_tree = output['output']

The output matrix is a three-dimensional matrix, where each row indicates an edge. The first dimension corresponds to the lesser index of the edge; the second dimension corresponds to the greater index of the edge; and the third column corresponds to the distance between the two points.

See also

fastmks()

>>> from mlpack import fastmks
>>> d = fastmks(bandwidth=1, base=2, degree=2, input_model=None, k=0,
        kernel='linear', naive=False, offset=0, query=np.empty([0, 0]),
        reference=np.empty([0, 0]), scale=1, single=False)
>>> indices = d['indices']
>>> kernels = d['kernels']
>>> output_model = d['output_model']

An implementation of the single-tree and dual-tree fast max-kernel search (FastMKS) algorithm. Given a set of reference points and a set of query points, this can find the reference point with maximum kernel value for each query point; trained models can be reused for future queries. Detailed documentation.

Input options

name type description default
bandwidth float Bandwidth (for Gaussian, Epanechnikov, and triangular kernels). 1
base float Base to use during cover tree construction. 2
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
degree float Degree of polynomial kernel. 2
input_model FastMKSModelType Input FastMKS model to use. None
k int Number of maximum kernels to find. 0
kernel str Kernel type to use: ‘linear’, ‘polynomial’, ‘cosine’, ‘gaussian’, ‘epanechnikov’, ‘triangular’, ‘hyptan’. 'linear'
naive bool If true, O(n^2) naive mode is used for computation. False
offset float Offset of kernel (for polynomial and hyptan kernels). 0
query matrix The query dataset. np.empty([0, 0])
reference matrix The reference dataset. np.empty([0, 0])
scale float Scale of kernel (for hyptan kernel). 1
single bool If true, single-tree search is used (as opposed to dual-tree search. False
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
indices int matrix Output matrix of indices.
kernels matrix Output matrix of kernels.
output_model FastMKSModelType Output for FastMKS model.

Detailed documentation

This program will find the k maximum kernels of a set of points, using a query set and a reference set (which can optionally be the same set). More specifically, for each point in the query set, the k points in the reference set with maximum kernel evaluations are found. The kernel function used is specified with the kernel parameter.

For example, the following command will calculate, for each point in the query set 'query', the five points in the reference set 'reference' with maximum kernel evaluation using the linear kernel. The kernel evaluations may be saved with the 'kernels' output parameter and the indices may be saved with the 'indices' output parameter.

>>> output = fastmks(k=5, reference=reference, query=query, kernel='linear')
>>> indices = output['indices']
>>> kernels = output['kernels']

The output matrices are organized such that row i and column j in the indices matrix corresponds to the index of the point in the reference set that has j’th largest kernel evaluation with the point in the query set with index i. Row i and column j in the kernels matrix corresponds to the kernel evaluation between those two points.

This program performs FastMKS using a cover tree. The base used to build the cover tree can be specified with the base parameter.

See also

gmm_train()

Gaussian Mixture Model (GMM) Training

>>> from mlpack import gmm_train
>>> d = gmm_train(diagonal_covariance=False, gaussians=0,
        input=np.empty([0, 0]), input_model=None, kmeans_max_iterations=1000,
        max_iterations=250, no_force_positive=False, noise=0, percentage=0.02,
        refined_start=False, samplings=100, seed=0, tolerance=1e-10, trials=1)
>>> output_model = d['output_model']

An implementation of the EM algorithm for training Gaussian mixture models (GMMs). Given a dataset, this can train a GMM for future use with other tools. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
diagonal_covariance bool Force the covariance of the Gaussians to be diagonal. This can accelerate training time significantly. False
gaussians int Number of Gaussians in the GMM. required
input matrix The training data on which the model will be fit. required
input_model GMMType Initial input GMM model to start training with. None
kmeans_max_iterations int Maximum number of iterations for the k-means algorithm (used to initialize EM). 1000
max_iterations int Maximum number of iterations of EM algorithm (passing 0 will run until convergence). 250
no_force_positive bool Do not force the covariance matrices to be positive definite. False
noise float Variance of zero-mean Gaussian noise to add to data. 0
percentage float If using –refined_start, specify the percentage of the dataset used for each sampling (should be between 0.0 and 1.0). 0.02
refined_start bool During the initialization, use refined initial positions for k-means clustering (Bradley and Fayyad, 1998). False
samplings int If using –refined_start, specify the number of samplings used for initial points. 100
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
tolerance float Tolerance for convergence of EM. 1e-10
trials int Number of trials to perform in training GMM. 1
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model GMMType Output for trained GMM model.

Detailed documentation

This program takes a parametric estimate of a Gaussian mixture model (GMM) using the EM algorithm to find the maximum likelihood estimate. The model may be saved and reused by other mlpack GMM tools.

The input data to train on must be specified with the input parameter, and the number of Gaussians in the model must be specified with the gaussians parameter. Optionally, many trials with different random initializations may be run, and the result with highest log-likelihood on the training data will be taken. The number of trials to run is specified with the trials parameter. By default, only one trial is run.

The tolerance for convergence and maximum number of iterations of the EM algorithm are specified with the tolerance and max_iterations parameters, respectively. The GMM may be initialized for training with another model, specified with the input_model parameter. Otherwise, the model is initialized by running k-means on the data. The k-means clustering initialization can be controlled with the kmeans_max_iterations, refined_start, samplings, and percentage parameters. If refined_start is specified, then the Bradley-Fayyad refined start initialization will be used. This can often lead to better clustering results.

The ‘diagonal_covariance’ flag will cause the learned covariances to be diagonal matrices. This significantly simplifies the model itself and causes training to be faster, but restricts the ability to fit more complex GMMs.

If GMM training fails with an error indicating that a covariance matrix could not be inverted, make sure that the no_force_positive parameter is not specified. Alternately, adding a small amount of Gaussian noise (using the noise parameter) to the entire dataset may help prevent Gaussians with zero variance in a particular dimension, which is usually the cause of non-invertible covariance matrices.

The no_force_positive parameter, if set, will avoid the checks after each iteration of the EM algorithm which ensure that the covariance matrices are positive definite. Specifying the flag can cause faster runtime, but may also cause non-positive definite covariance matrices, which will cause the program to crash.

As an example, to train a 6-Gaussian GMM on the data in 'data' with a maximum of 100 iterations of EM and 3 trials, saving the trained GMM to 'gmm', the following command can be used:

>>> output = gmm_train(input=data, gaussians=6, trials=3)
>>> gmm = output['output_model']

To re-train that GMM on another set of data 'data2', the following command may be used:

>>> output = gmm_train(input_model=gmm, input=data2, gaussians=6)
>>> new_gmm = output['output_model']

See also

gmm_generate()

GMM Sample Generator

>>> from mlpack import gmm_generate
>>> d = gmm_generate(input_model=None, samples=0, seed=0)
>>> output = d['output']

A sample generator for pre-trained GMMs. Given a pre-trained GMM, this can sample new points randomly from that distribution. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model GMMType Input GMM model to generate samples from. required
samples int Number of samples to generate. required
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to save output samples in.

Detailed documentation

This program is able to generate samples from a pre-trained GMM (use gmm_train to train a GMM). The pre-trained GMM must be specified with the input_model parameter. The number of samples to generate is specified by the samples parameter. Output samples may be saved with the output output parameter.

The following command can be used to generate 100 samples from the pre-trained GMM 'gmm' and store those generated samples in 'samples':

>>> output = gmm_generate(input_model=gmm, samples=100)
>>> samples = output['output']

See also

gmm_probability()

GMM Probability Calculator

>>> from mlpack import gmm_probability
>>> d = gmm_probability(input=np.empty([0, 0]), input_model=None)
>>> output = d['output']

A probability calculator for GMMs. Given a pre-trained GMM and a set of points, this can compute the probability that each point is from the given GMM. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Input matrix to calculate probabilities of. required
input_model GMMType Input GMM to use as model. required
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to store calculated probabilities in.

Detailed documentation

This program calculates the probability that given points came from a given GMM (that is, P(X gmm)). The GMM is specified with the input_model parameter, and the points are specified with the input parameter. The output probabilities may be saved via the output output parameter.

So, for example, to calculate the probabilities of each point in 'points' coming from the pre-trained GMM 'gmm', while storing those probabilities in 'probs', the following command could be used:

>>> output = gmm_probability(input_model=gmm, input=points)
>>> probs = output['output']

See also

hmm_train()

Hidden Markov Model (HMM) Training

>>> from mlpack import hmm_train
>>> d = hmm_train(batch=False, gaussians=0, input_file='',
        input_model=None, labels_file='', seed=0, states=0, tolerance=1e-05,
        type='gaussian')
>>> output_model = d['output_model']

An implementation of training algorithms for Hidden Markov Models (HMMs). Given labeled or unlabeled data, an HMM can be trained for further use with other mlpack HMM tools. Detailed documentation.

Input options

name type description default      
batch bool If true, input_file (and if passed, labels_file) are expected to contain a list of files to use as input observation sequences (and label sequences). False      
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False      
gaussians int Number of gaussians in each GMM (necessary when type is ‘gmm’). 0      
input_file str File containing input observations. required      
input_model HMMModelType Pre-existing HMM model to initialize training with. None      
labels_file str Optional file of hidden states, used for labeled training. ''      
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0      
states int Number of hidden states in HMM (necessary, unless model_file is specified). 0      
tolerance float Tolerance of the Baum-Welch algorithm. 1e-05      
type str Type of HMM: discrete gaussian diag_gmm gmm. 'gaussian'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False      

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model HMMModelType Output for trained HMM.

Detailed documentation

This program allows a Hidden Markov Model to be trained on labeled or unlabeled data. It supports four types of HMMs: Discrete HMMs, Gaussian HMMs, GMM HMMs, or Diagonal GMM HMMs

Either one input sequence can be specified (with –input_file), or, a file containing files in which input sequences can be found (when –input_file and –batch are used together). In addition, labels can be provided in the file specified by –labels_file, and if –batch is used, the file given to –labels_file should contain a list of files of labels corresponding to the sequences in the file given to –input_file.

The HMM is trained with the Baum-Welch algorithm if no labels are provided. The tolerance of the Baum-Welch algorithm can be set with the –tolerance option. By default, the transition matrix is randomly initialized and the emission distributions are initialized to fit the extent of the data.

Optionally, a pre-created HMM model can be used as a guess for the transition matrix and emission probabilities; this is specifiable with –model_file.

See also

hmm_loglik()

Hidden Markov Model (HMM) Sequence Log-Likelihood

>>> from mlpack import hmm_loglik
>>> d = hmm_loglik(input=np.empty([0, 0]), input_model=None)
>>> log_likelihood = d['log_likelihood']

A utility for computing the log-likelihood of a sequence for Hidden Markov Models (HMMs). Given a pre-trained HMM and an observation sequence, this computes and returns the log-likelihood of that sequence being observed from that HMM. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix File containing observations, required
input_model HMMModelType File containing HMM. required
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
log_likelihood float Log-likelihood of the sequence.

Detailed documentation

This utility takes an already-trained HMM, specified with the input_model parameter, and evaluates the log-likelihood of a sequence of observations, given with the input parameter. The computed log-likelihood is given as output.

For example, to compute the log-likelihood of the sequence 'seq' with the pre-trained HMM 'hmm', the following command may be used:

>>> hmm_loglik(input=seq, input_model=hmm)

See also

hmm_viterbi()

Hidden Markov Model (HMM) Viterbi State Prediction

>>> from mlpack import hmm_viterbi
>>> d = hmm_viterbi(input=np.empty([0, 0]), input_model=None)
>>> output = d['output']

A utility for computing the most probable hidden state sequence for Hidden Markov Models (HMMs). Given a pre-trained HMM and an observed sequence, this uses the Viterbi algorithm to compute and return the most probable hidden state sequence. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Matrix containing observations, required
input_model HMMModelType Trained HMM to use. required
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int matrix File to save predicted state sequence to.

Detailed documentation

This utility takes an already-trained HMM, specified as input_model, and evaluates the most probable hidden state sequence of a given sequence of observations (specified as ‘input, using the Viterbi algorithm. The computed state sequence may be saved using the output output parameter.

For example, to predict the state sequence of the observations 'obs' using the HMM 'hmm', storing the predicted state sequence to 'states', the following command could be used:

>>> output = hmm_viterbi(input=obs, input_model=hmm)
>>> states = output['output']

See also

hmm_generate()

Hidden Markov Model (HMM) Sequence Generator

>>> from mlpack import hmm_generate
>>> d = hmm_generate(length=0, model=None, seed=0, start_state=0)
>>> output = d['output']
>>> state = d['state']

A utility to generate random sequences from a pre-trained Hidden Markov Model (HMM). The length of the desired sequence can be specified, and a random sequence of observations is returned. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
length int Length of sequence to generate. required
model HMMModelType Trained HMM to generate sequences with. required
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
start_state int Starting state of sequence. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to save observation sequence to.
state int matrix Matrix to save hidden state sequence to.

Detailed documentation

This utility takes an already-trained HMM, specified as the model parameter, and generates a random observation sequence and hidden state sequence based on its parameters. The observation sequence may be saved with the output output parameter, and the internal state sequence may be saved with the state output parameter.

The state to start the sequence in may be specified with the start_state parameter.

For example, to generate a sequence of length 150 from the HMM 'hmm' and save the observation sequence to 'observations' and the hidden state sequence to 'states', the following command may be used:

>>> output = hmm_generate(model=hmm, length=150)
>>> observations = output['output']
>>> states = output['state']

See also

hoeffding_tree()

Hoeffding trees

>>> from mlpack import hoeffding_tree
>>> d = hoeffding_tree(batch_mode=False, bins=10, confidence=0.95,
        info_gain=False, input_model=None, labels=np.empty([0],
        dtype=np.uint64), max_samples=5000, min_samples=100,
        numeric_split_strategy='binary', observations_before_binning=100,
        passes=1, test=np.empty([0, 0]), test_labels=np.empty([0],
        dtype=np.uint64), training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of Hoeffding trees, a form of streaming decision tree for classification. Given labeled data, a Hoeffding tree can be trained and saved for later use, or a pre-trained Hoeffding tree can be used for predicting the classifications of new points. Detailed documentation.

Input options

name type description default
batch_mode bool If true, samples will be considered in batch instead of as a stream. This generally results in better trees but at the cost of memory usage and runtime. False
bins int If the ‘domingos’ split strategy is used, this specifies the number of bins for each numeric split. 10
confidence float Confidence before splitting (between 0 and 1). 0.95
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
info_gain bool If set, information gain is used instead of Gini impurity for calculating Hoeffding bounds. False
input_model HoeffdingTreeModelType Input trained Hoeffding tree model. None
labels int vector Labels for training dataset. np.empty([0], dtype=np.uint64)
max_samples int Maximum number of samples before splitting. 5000
min_samples int Minimum number of samples before splitting. 100
numeric_split_strategy str The splitting strategy to use for numeric features: ‘domingos’ or ‘binary’. 'binary'
observations_before_binning int If the ‘domingos’ split strategy is used, this specifies the number of samples observed before binning is performed. 100
passes int Number of passes to take over the dataset. 1
test categorical matrix Testing dataset (may be categorical). np.empty([0, 0])
test_labels int vector Labels of test data. np.empty([0], dtype=np.uint64)
training categorical matrix Training dataset (may be categorical). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model HoeffdingTreeModelType Output for trained Hoeffding tree model.
predictions int vector Matrix to output label predictions for test data into.
probabilities matrix In addition to predicting labels, provide rediction probabilities in this matrix.

Detailed documentation

This program implements Hoeffding trees, a form of streaming decision tree suited best for large (or streaming) datasets. This program supports both categorical and numeric data. Given an input dataset, this program is able to train the tree with numerous training options, and save the model to a file. The program is also able to use a trained model or a model from file in order to predict classes for a given test set.

The training file and associated labels are specified with the training and labels parameters, respectively. Optionally, if labels is not specified, the labels are assumed to be the last dimension of the training dataset.

The training may be performed in batch mode (like a typical decision tree algorithm) by specifying the batch_mode option, but this may not be the best option for large datasets.

When a model is trained, it may be saved via the output_model output parameter. A model may be loaded from file for further training or testing with the input_model parameter.

Test data may be specified with the test parameter, and if performance statistics are desired for that test set, labels may be specified with the test_labels parameter. Predictions for each test point may be saved with the predictions output parameter, and class probabilities for each prediction may be saved with the probabilities output parameter.

For example, to train a Hoeffding tree with confidence 0.99 with data 'dataset', saving the trained tree to 'tree', the following command may be used:

>>> output = hoeffding_tree(training=dataset, confidence=0.99)
>>> tree = output['output_model']

Then, this tree may be used to make predictions on the test set 'test_set', saving the predictions into 'predictions' and the class probabilities into 'class_probs' with the following command:

>>> output = hoeffding_tree(input_model=tree, test=test_set)
>>> predictions = output['predictions']
>>> class_probs = output['probabilities']

See also

kde()

Kernel Density Estimation

>>> from mlpack import kde
>>> d = kde(abs_error=0, algorithm='dual-tree', bandwidth=1,
        initial_sample_size=100, input_model=None, kernel='gaussian',
        mc_break_coef=0.4, mc_entry_coef=3, mc_probability=0.95,
        monte_carlo=False, query=np.empty([0, 0]), reference=np.empty([0, 0]),
        rel_error=0.05, tree='kd-tree')
>>> output_model = d['output_model']
>>> predictions = d['predictions']

An implementation of kernel density estimation with dual-tree algorithms. Given a set of reference points and query points and a kernel function, this can estimate the density function at the location of each query point using trees; trees that are built can be saved for later use. Detailed documentation.

Input options

name type description default
abs_error float Relative error tolerance for the prediction. 0
algorithm str Algorithm to use for the prediction.(‘dual-tree’, ‘single-tree’). 'dual-tree'
bandwidth float Bandwidth of the kernel. 1
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
initial_sample_size int Initial sample size for Monte Carlo estimations. 100
input_model KDEModelType Contains pre-trained KDE model. None
kernel str Kernel to use for the prediction.(‘gaussian’, ‘epanechnikov’, ‘laplacian’, ‘spherical’, ‘triangular’). 'gaussian'
mc_break_coef float Controls what fraction of the amount of node’s descendants is the limit for the sample size before it recurses. 0.4
mc_entry_coef float Controls how much larger does the amount of node descendants has to be compared to the initial sample size in order to be a candidate for Monte Carlo estimations. 3
mc_probability float Probability of the estimation being bounded by relative error when using Monte Carlo estimations. 0.95
monte_carlo bool Whether to use Monte Carlo estimations when possible. False
query matrix Query dataset to KDE on. np.empty([0, 0])
reference matrix Input reference dataset use for KDE. np.empty([0, 0])
rel_error float Relative error tolerance for the prediction. 0.05
tree str Tree to use for the prediction.(‘kd-tree’, ‘ball-tree’, ‘cover-tree’, ‘octree’, ‘r-tree’). 'kd-tree'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model KDEModelType If specified, the KDE model will be saved here.
predictions vector Vector to store density predictions.

Detailed documentation

This program performs a Kernel Density Estimation. KDE is a non-parametric way of estimating probability density function. For each query point the program will estimate its probability density by applying a kernel function to each reference point. The computational complexity of this is O(N^2) where there are N query points and N reference points, but this implementation will typically see better performance as it uses an approximate dual or single tree algorithm for acceleration.

Dual or single tree optimization avoids many barely relevant calculations (as kernel function values decrease with distance), so it is an approximate computation. You can specify the maximum relative error tolerance for each query value with rel_error as well as the maximum absolute error tolerance with the parameter abs_error. This program runs using an Euclidean metric. Kernel function can be selected using the kernel option. You can also choose what which type of tree to use for the dual-tree algorithm with tree. It is also possible to select whether to use dual-tree algorithm or single-tree algorithm using the algorithm option.

Monte Carlo estimations can be used to accelerate the KDE estimate when the Gaussian Kernel is used. This provides a probabilistic guarantee on the the error of the resulting KDE instead of an absolute guarantee.To enable Monte Carlo estimations, the monte_carlo flag can be used, and success probability can be set with the mc_probability option. It is possible to set the initial sample size for the Monte Carlo estimation using initial_sample_size. This implementation will only consider a node, as a candidate for the Monte Carlo estimation, if its number of descendant nodes is bigger than the initial sample size. This can be controlled using a coefficient that will multiply the initial sample size and can be set using mc_entry_coef. To avoid using the same amount of computations an exact approach would take, this program recurses the tree whenever a fraction of the amount of the node’s descendant points have already been computed. This fraction is set using mc_break_coef.

For example, the following will run KDE using the data in 'ref_data' for training and the data in 'qu_data' as query data. It will apply an Epanechnikov kernel with a 0.2 bandwidth to each reference point and use a KD-Tree for the dual-tree optimization. The returned predictions will be within 5% of the real KDE value for each query point.

>>> output = kde(reference=ref_data, query=qu_data, bandwidth=0.2,
  kernel='epanechnikov', tree='kd-tree', rel_error=0.05)
>>> out_data = output['predictions']

the predicted density estimations will be stored in 'out_data'. If no query is provided, then KDE will be computed on the reference dataset. It is possible to select either a reference dataset or an input model but not both at the same time. If an input model is selected and parameter values are not set (e.g. bandwidth) then default parameter values will be used.

In addition to the last program call, it is also possible to activate Monte Carlo estimations if a Gaussian kernel is used. This can provide faster results, but the KDE will only have a probabilistic guarantee of meeting the desired error bound (instead of an absolute guarantee). The following example will run KDE using a Monte Carlo estimation when possible. The results will be within a 5% of the real KDE value with a 95% probability. Initial sample size for the Monte Carlo estimation will be 200 points and a node will be a candidate for the estimation only when it contains 700 (i.e. 3.5200) points. If a node contains 700 points and 420 (i.e. 0.6700) have already been sampled, then the algorithm will recurse instead of keep sampling.

>>> output = kde(reference=ref_data, query=qu_data, bandwidth=0.2,
  kernel='gaussian', tree='kd-tree', rel_error=0.05, monte_carlo=,
  mc_probability=0.95, initial_sample_size=200, mc_entry_coef=3.5,
  mc_break_coef=0.6)
>>> out_data = output['predictions']

See also

kernel_pca()

Kernel Principal Components Analysis

>>> from mlpack import kernel_pca
>>> d = kernel_pca(bandwidth=1, center=False, degree=1,
        input=np.empty([0, 0]), kernel='', kernel_scale=1, new_dimensionality=0,
        nystroem_method=False, offset=0, sampling='kmeans')
>>> output = d['output']

An implementation of Kernel Principal Components Analysis (KPCA). This can be used to perform nonlinear dimensionality reduction or preprocessing on a given dataset. Detailed documentation.

Input options

name type description default
bandwidth float Bandwidth, for ‘gaussian’ and ‘laplacian’ kernels. 1
center bool If set, the transformed data will be centered about the origin. False
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
degree float Degree of polynomial, for ‘polynomial’ kernel. 1
input matrix Input dataset to perform KPCA on. required
kernel str The kernel to use; see the above documentation for the list of usable kernels. required
kernel_scale float Scale, for ‘hyptan’ kernel. 1
new_dimensionality int If not 0, reduce the dimensionality of the output dataset by ignoring the dimensions with the smallest eigenvalues. 0
nystroem_method bool If set, the Nystroem method will be used. False
offset float Offset, for ‘hyptan’ and ‘polynomial’ kernels. 0
sampling str Sampling scheme to use for the Nystroem method: ‘kmeans’, ‘random’, ‘ordered’ 'kmeans'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to save modified dataset to.

Detailed documentation

This program performs Kernel Principal Components Analysis (KPCA) on the specified dataset with the specified kernel. This will transform the data onto the kernel principal components, and optionally reduce the dimensionality by ignoring the kernel principal components with the smallest eigenvalues.

For the case where a linear kernel is used, this reduces to regular PCA.

For example, the following command will perform KPCA on the dataset 'input' using the Gaussian kernel, and saving the transformed data to 'transformed':

>>> output = kernel_pca(input=input, kernel='gaussian')
>>> transformed = output['output']

The kernels that are supported are listed below:

  • ‘linear’: the standard linear dot product (same as normal PCA): K(x, y) = x^T y

  • ‘gaussian’: a Gaussian kernel; requires bandwidth: K(x, y) = exp(-(|| x - y || ^ 2) / (2 * (bandwidth ^ 2)))

  • ‘polynomial’: polynomial kernel; requires offset and degree: K(x, y) = (x^T y + offset) ^ degree

  • ‘hyptan’: hyperbolic tangent kernel; requires scale and offset: K(x, y) = tanh(scale * (x^T y) + offset)

  • ‘laplacian’: Laplacian kernel; requires bandwidth: K(x, y) = exp(-(|| x - y ||) / bandwidth)

  • ‘epanechnikov’: Epanechnikov kernel; requires bandwidth: K(x, y) = max(0, 1 - || x - y ||^2 / bandwidth^2)

  • ‘cosine’: cosine distance: K(x, y) = 1 - (x^T y) / (|| x || * || y ||)

The parameters for each of the kernels should be specified with the options bandwidth, kernel_scale, offset, or degree (or a combination of those parameters).

Optionally, the Nystroem method (“Using the Nystroem method to speed up kernel machines”, 2001) can be used to calculate the kernel matrix by specifying the nystroem_method parameter. This approach works by using a subset of the data as basis to reconstruct the kernel matrix; to specify the sampling scheme, the sampling parameter is used. The sampling scheme for the Nystroem method can be chosen from the following list: ‘kmeans’, ‘random’, ‘ordered’.

See also

kmeans()

K-Means Clustering

>>> from mlpack import kmeans
>>> d = kmeans(algorithm='naive', allow_empty_clusters=False,
        clusters=0, in_place=False, initial_centroids=np.empty([0, 0]),
        input=np.empty([0, 0]), kill_empty_clusters=False, labels_only=False,
        max_iterations=1000, percentage=0.02, refined_start=False,
        samplings=100, seed=0)
>>> centroid = d['centroid']
>>> output = d['output']

An implementation of several strategies for efficient k-means clustering. Given a dataset and a value of k, this computes and returns a k-means clustering on that data. Detailed documentation.

Input options

name type description default
algorithm str Algorithm to use for the Lloyd iteration (‘naive’, ‘pelleg-moore’, ‘elkan’, ‘hamerly’, ‘dualtree’, or ‘dualtree-covertree’). 'naive'
allow_empty_clusters bool Allow empty clusters to be persist. False
clusters int Number of clusters to find (0 autodetects from initial centroids). required
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
in_place bool If specified, a column containing the learned cluster assignments will be added to the input dataset file. In this case, –output_file is overridden. (Do not use in Python.) False
initial_centroids matrix Start with the specified initial centroids. np.empty([0, 0])
input matrix Input dataset to perform clustering on. required
kill_empty_clusters bool Remove empty clusters when they occur. False
labels_only bool Only output labels into output file. False
max_iterations int Maximum number of iterations before k-means terminates. 1000
percentage float Percentage of dataset to use for each refined start sampling (use when –refined_start is specified). 0.02
refined_start bool Use the refined initial point strategy by Bradley and Fayyad to choose initial points. False
samplings int Number of samplings to perform for refined start (use when –refined_start is specified). 100
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
centroid matrix If specified, the centroids of each cluster will be written to the given file.
output matrix Matrix to store output labels or labeled data to.

Detailed documentation

This program performs K-Means clustering on the given dataset. It can return the learned cluster assignments, and the centroids of the clusters. Empty clusters are not allowed by default; when a cluster becomes empty, the point furthest from the centroid of the cluster with maximum variance is taken to fill that cluster.

Optionally, the Bradley and Fayyad approach (“Refining initial points for k-means clustering”, 1998) can be used to select initial points by specifying the refined_start parameter. This approach works by taking random samplings of the dataset; to specify the number of samplings, the samplings parameter is used, and to specify the percentage of the dataset to be used in each sample, the percentage parameter is used (it should be a value between 0.0 and 1.0).

There are several options available for the algorithm used for each Lloyd iteration, specified with the algorithm option. The standard O(kN) approach can be used (‘naive’). Other options include the Pelleg-Moore tree-based algorithm (‘pelleg-moore’), Elkan’s triangle-inequality based algorithm (‘elkan’), Hamerly’s modification to Elkan’s algorithm (‘hamerly’), the dual-tree k-means algorithm (‘dualtree’), and the dual-tree k-means algorithm using the cover tree (‘dualtree-covertree’).

The behavior for when an empty cluster is encountered can be modified with the allow_empty_clusters option. When this option is specified and there is a cluster owning no points at the end of an iteration, that cluster’s centroid will simply remain in its position from the previous iteration. If the kill_empty_clusters option is specified, then when a cluster owns no points at the end of an iteration, the cluster centroid is simply filled with DBL_MAX, killing it and effectively reducing k for the rest of the computation. Note that the default option when neither empty cluster option is specified can be time-consuming to calculate; therefore, specifying either of these parameters will often accelerate runtime.

Initial clustering assignments may be specified using the initial_centroids parameter, and the maximum number of iterations may be specified with the max_iterations parameter.

As an example, to use Hamerly’s algorithm to perform k-means clustering with k=10 on the dataset 'data', saving the centroids to 'centroids' and the assignments for each point to 'assignments', the following command could be used:

>>> output = kmeans(input=data, clusters=10)
>>> assignments = output['output']
>>> centroids = output['centroid']

To run k-means on that same dataset with initial centroids specified in 'initial' with a maximum of 500 iterations, storing the output centroids in 'final' the following command may be used:

>>> output = kmeans(input=data, initial_centroids=initial, clusters=10,
  max_iterations=500)
>>> final = output['centroid']

See also

lars()

LARS

>>> from mlpack import lars
>>> d = lars(input=np.empty([0, 0]), input_model=None, lambda1=0,
        lambda2=0, responses=np.empty([0, 0]), test=np.empty([0, 0]),
        use_cholesky=False)
>>> output_model = d['output_model']
>>> output_predictions = d['output_predictions']

An implementation of Least Angle Regression (Stagewise/laSso), also known as LARS. This can train a LARS/LASSO/Elastic Net model and use that model or a pre-trained model to output regression predictions for a test set. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Matrix of covariates (X). np.empty([0, 0])
input_model LARSType Trained LARS model to use. None
lambda1 float Regularization parameter for l1-norm penalty. 0
lambda2 float Regularization parameter for l2-norm penalty. 0
responses matrix Matrix of responses/observations (y). np.empty([0, 0])
test matrix Matrix containing points to regress on (test points). np.empty([0, 0])
use_cholesky bool Use Cholesky decomposition during computation rather than explicitly computing the full Gram matrix. False
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model LARSType Output LARS model.
output_predictions matrix If –test_file is specified, this file is where the predicted responses will be saved.

Detailed documentation

An implementation of LARS: Least Angle Regression (Stagewise/laSso). This is a stage-wise homotopy-based algorithm for L1-regularized linear regression (LASSO) and L1+L2-regularized linear regression (Elastic Net).

This program is able to train a LARS/LASSO/Elastic Net model or load a model from file, output regression predictions for a test set, and save the trained model to a file. The LARS algorithm is described in more detail below:

Let X be a matrix where each row is a point and each column is a dimension, and let y be a vector of targets.

The Elastic Net problem is to solve

min_beta 0.5   X * beta - y   _2^2 + lambda_1   beta   _1 +
0.5 lambda_2   beta   _2^2        

If lambda1 > 0 and lambda2 = 0, the problem is the LASSO. If lambda1 > 0 and lambda2 > 0, the problem is the Elastic Net. If lambda1 = 0 and lambda2 > 0, the problem is ridge regression. If lambda1 = 0 and lambda2 = 0, the problem is unregularized linear regression.

For efficiency reasons, it is not recommended to use this algorithm with lambda1 = 0. In that case, use the ‘linear_regression’ program, which implements both unregularized linear regression and ridge regression.

To train a LARS/LASSO/Elastic Net model, the input and responses parameters must be given. The lambda1, lambda2, and use_cholesky parameters control the training options. A trained model can be saved with the output_model. If no training is desired at all, a model can be passed via the input_model parameter.

The program can also provide predictions for test data using either the trained model or the given input model. Test points can be specified with the test parameter. Predicted responses to the test points can be saved with the output_predictions output parameter.

For example, the following command trains a model on the data 'data' and responses 'responses' with lambda1 set to 0.4 and lambda2 set to 0 (so, LASSO is being solved), and then the model is saved to 'lasso_model':

>>> output = lars(input=data, responses=responses, lambda1=0.4, lambda2=0)
>>> lasso_model = output['output_model']

The following command uses the 'lasso_model' to provide predicted responses for the data 'test' and save those responses to 'test_predictions':

>>> output = lars(input_model=lasso_model, test=test)
>>> test_predictions = output['output_predictions']

See also

linear_regression()

Simple Linear Regression and Prediction

>>> from mlpack import linear_regression
>>> d = linear_regression(input_model=None, lambda_=0, test=np.empty([0,
        0]), training=np.empty([0, 0]), training_responses=np.empty([0]))
>>> output_model = d['output_model']
>>> output_predictions = d['output_predictions']

An implementation of simple linear regression and ridge regression using ordinary least squares. Given a dataset and responses, a model can be trained and saved for later use, or a pre-trained model can be used to output regression predictions for a test set. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model LinearRegressionType Existing LinearRegression model to use. None
lambda_ float Tikhonov regularization for ridge regression. If 0, the method reduces to linear regression. 0
test matrix Matrix containing X’ (test regressors). np.empty([0, 0])
training matrix Matrix containing training set X (regressors). np.empty([0, 0])
training_responses vector Optional vector containing y (responses). If not given, the responses are assumed to be the last row of the input file. np.empty([0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model LinearRegressionType Output LinearRegression model.
output_predictions vector If –test_file is specified, this matrix is where the predicted responses will be saved.

Detailed documentation

An implementation of simple linear regression and simple ridge regression using ordinary least squares. This solves the problem

y = X * b + e

where X (specified by training) and y (specified either as the last column of the input matrix training or via the training_responses parameter) are known and b is the desired variable. If the covariance matrix (X’X) is not invertible, or if the solution is overdetermined, then specify a Tikhonov regularization constant (with lambda_) greater than 0, which will regularize the covariance matrix to make it invertible. The calculated b may be saved with the output_predictions output parameter.

Optionally, the calculated value of b is used to predict the responses for another matrix X’ (specified by the test parameter):

y’ = X’ * b

and the predicted responses y’ may be saved with the output_predictions output parameter. This type of regression is related to least-angle regression, which mlpack implements as the ‘lars’ program.

For example, to run a linear regression on the dataset 'X' with responses 'y', saving the trained model to 'lr_model', the following command could be used:

>>> output = linear_regression(training=X, training_responses=y)
>>> lr_model = output['output_model']

Then, to use 'lr_model' to predict responses for a test set 'X_test', saving the predictions to 'X_test_responses', the following command could be used:

>>> output = linear_regression(input_model=lr_model, test=X_test)
>>> X_test_responses = output['output_predictions']

See also

linear_svm()

Linear SVM is an L2-regularized support vector machine.

>>> from mlpack import linear_svm
>>> d = linear_svm(delta=1, epochs=50, input_model=None,
        labels=np.empty([0], dtype=np.uint64), lambda_=0.0001,
        max_iterations=10000, no_intercept=False, num_classes=0,
        optimizer='lbfgs', seed=0, shuffle=False, step_size=0.01,
        test=np.empty([0, 0]), test_labels=np.empty([0], dtype=np.uint64),
        tolerance=1e-10, training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of linear SVM for multiclass classification. Given labeled data, a model can be trained and saved for future use; or, a pre-trained model can be used to classify new points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
delta float Margin of difference between correct class and other classes. 1
epochs int Maximum number of full epochs over dataset for psgd 50
input_model LinearSVMModelType Existing model (parameters). None
labels int vector A matrix containing labels (0 or 1) for the points in the training set (y). np.empty([0], dtype=np.uint64)
lambda_ float L2-regularization parameter for training. 0.0001
max_iterations int Maximum iterations for optimizer (0 indicates no limit). 10000
no_intercept bool Do not add the intercept term to the model. False
num_classes int Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. 0
optimizer str Optimizer to use for training (‘lbfgs’ or ‘psgd’). 'lbfgs'
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
shuffle bool Don’t shuffle the order in which data points are visited for parallel SGD. False
step_size float Step size for parallel SGD optimizer. 0.01
test matrix Matrix containing test dataset. np.empty([0, 0])
test_labels int vector Matrix containing test labels. np.empty([0], dtype=np.uint64)
tolerance float Convergence tolerance for optimizer. 1e-10
training matrix A matrix containing the training set (the matrix of predictors, X). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model LinearSVMModelType Output for trained linear svm model.
predictions int vector If test data is specified, this matrix is where the predictions for the test set will be saved.
probabilities matrix If test data is specified, this matrix is where the class probabilities for the test set will be saved.

Detailed documentation

An implementation of linear SVMs that uses either L-BFGS or parallel SGD (stochastic gradient descent) to train the model.

This program allows loading a linear SVM model (via the input_model parameter) or training a linear SVM model given training data (specified with the training parameter), or both those things at once. In addition, this program allows classification on a test dataset (specified with the test parameter) and the classification results may be saved with the predictions output parameter. The trained linear SVM model may be saved using the output_model output parameter.

The training data, if specified, may have class labels as its last dimension. Alternately, the labels parameter may be used to specify a separate vector of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the lambda_ option, and the number of classes can be manually specified with the num_classesand if an intercept term is not desired in the model, the no_intercept parameter can be specified.Margin of difference between correct class and other classes can be specified with the delta option.The optimizer used to train the model can be specified with the optimizer parameter. Available options are ‘psgd’ (parallel stochastic gradient descent) and ‘lbfgs’ (the L-BFGS optimizer). There are also various parameters for the optimizer; the max_iterations parameter specifies the maximum number of allowed iterations, and the tolerance parameter specifies the tolerance for convergence. For the parallel SGD optimizer, the step_size parameter controls the step size taken at each iteration by the optimizer and the maximum number of epochs (specified with epochs). If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

Optionally, the model can be used to predict the labels for another matrix of data points, if test is specified. The test parameter can be specified without the training parameter, so long as an existing linear SVM model is given with the input_model parameter. The output predictions from the linear SVM model may be saved with the predictions parameter.

As an example, to train a LinaerSVM on the data ‘'data'’ with labels ‘'labels'’ with L2 regularization of 0.1, saving the model to ‘'lsvm_model'’, the following command may be used:

>>> output = linear_svm(training=data, labels=labels, lambda_=0.1, delta=1,
  num_classes=0)
>>> lsvm_model = output['output_model']

Then, to use that model to predict classes for the dataset ‘'test'’, storing the output predictions in ‘'predictions'’, the following command may be used:

>>> output = linear_svm(input_model=lsvm_model, test=test)
>>> predictions = output['predictions']

See also

lmnn()

Large Margin Nearest Neighbors (LMNN)

>>> from mlpack import lmnn
>>> d = lmnn(batch_size=50, center=False, distance=np.empty([0, 0]),
        input=np.empty([0, 0]), k=1, labels=np.empty([0], dtype=np.uint64),
        linear_scan=False, max_iterations=100000, normalize=False,
        optimizer='amsgrad', passes=50, print_accuracy=False, range=1, rank=0,
        regularization=0.5, seed=0, step_size=0.01, tolerance=1e-07)
>>> centered_data = d['centered_data']
>>> output = d['output']
>>> transformed_data = d['transformed_data']

An implementation of Large Margin Nearest Neighbors (LMNN), a distance learning technique. Given a labeled dataset, this learns a transformation of the data that improves k-nearest-neighbor performance; this can be useful as a preprocessing step. Detailed documentation.

Input options

name type description default
batch_size int Batch size for mini-batch SGD. 50
center bool Perform mean-centering on the dataset. It is useful when the centroid of the data is far from the origin. False
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
distance matrix Initial distance matrix to be used as starting point np.empty([0, 0])
input matrix Input dataset to run LMNN on. required
k int Number of target neighbors to use for each datapoint. 1
labels int vector Labels for input dataset. np.empty([0], dtype=np.uint64)
linear_scan bool Don’t shuffle the order in which data points are visited for SGD or mini-batch SGD. False
max_iterations int Maximum number of iterations for L-BFGS (0 indicates no limit). 100000
normalize bool Use a normalized starting point for optimization. Itis useful for when points are far apart, or when SGD is returning NaN. False
optimizer str Optimizer to use; ‘amsgrad’, ‘bbsgd’, ‘sgd’, or ‘lbfgs’. 'amsgrad'
passes int Maximum number of full passes over dataset for AMSGrad, BB_SGD and SGD. 50
print_accuracy bool Print accuracies on initial and transformed dataset False
range int Number of iterations after which impostors needs to be recalculated 1
rank int Rank of distance matrix to be optimized. 0
regularization float Regularization for LMNN objective function 0.5
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
step_size float Step size for AMSGrad, BB_SGD and SGD (alpha). 0.01
tolerance float Maximum tolerance for termination of AMSGrad, BB_SGD, SGD or L-BFGS. 1e-07
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
centered_data matrix Output matrix for mean-centered dataset.
output matrix Output matrix for learned distance matrix.
transformed_data matrix Output matrix for transformed dataset.

Detailed documentation

This program implements Large Margin Nearest Neighbors, a distance learning technique. The method seeks to improve k-nearest-neighbor classification on a dataset. The method employes the strategy of reducing distance between similar labeled data points (a.k.a target neighbors) and increasing distance between differently labeled points (a.k.a impostors) using standard optimization techniques over the gradient of the distance between data points.

To work, this algorithm needs labeled data. It can be given as the last row of the input dataset (specified with input), or alternatively as a separate matrix (specified with labels). Additionally, a starting point for optimization (specified with distancecan be given, having (r x d) dimensionality. Here r should satisfy 1 <= r <= d, Consequently a Low-Rank matrix will be optimized. Alternatively, Low-Rank distance can be learned by specifying the rankparameter (A Low-Rank matrix with uniformly distributed values will be used as initial learning point).

The program also requires number of targets neighbors to work with ( specified with k), A regularization parameter can also be passed, It acts as a trade of between the pulling and pushing terms (specified with regularization), In addition, this implementation of LMNN includes a parameter to decide the interval after which impostors must be re-calculated (specified with range).

Output can either be the learned distance matrix (specified with output), or the transformed dataset (specified with transformed_data), or both. Additionally mean-centered dataset (specified with centered_data) can be accessed given mean-centering (specified with center) is performed on the dataset. Accuracy on initial dataset and final transformed dataset can be printed by specifying the print_accuracyparameter.

This implementation of LMNN uses AdaGrad, BigBatch_SGD, stochastic gradient descent, mini-batch stochastic gradient descent, or the L_BFGS optimizer.

AdaGrad, specified by the value ‘adagrad’ for the parameter optimizer, uses maximum of past squared gradients. It primarily on six parameters: the step size (specified with step_size), the batch size (specified with batch_size), the maximum number of passes (specified with passes). Inaddition, a normalized starting point can be used by specifying the normalize parameter.

BigBatch_SGD, specified by the value ‘bbsgd’ for the parameter optimizer, depends primarily on four parameters: the step size (specified with step_size), the batch size (specified with batch_size), the maximum number of passes (specified with passes). In addition, a normalized starting point can be used by specifying the normalize parameter.

Stochastic gradient descent, specified by the value ‘sgd’ for the parameter optimizer, depends primarily on three parameters: the step size (specified with step_size), the batch size (specified with batch_size), and the maximum number of passes (specified with passes). In addition, a normalized starting point can be used by specifying the normalize parameter. Furthermore, mean-centering can be performed on the dataset by specifying the centerparameter.

The L-BFGS optimizer, specified by the value ‘lbfgs’ for the parameter optimizer, uses a back-tracking line search algorithm to minimize a function. The following parameters are used by L-BFGS: max_iterations, tolerance(the optimization is terminated when the gradient norm is below this value). For more details on the L-BFGS optimizer, consult either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published literature on L-BFGS. In addition, a normalized starting point can be used by specifying the normalize parameter.

By default, the AMSGrad optimizer is used.

Example - Let’s say we want to learn distance on iris dataset with number of targets as 3 using BigBatch_SGD optimizer. A simple call for the same will look like:

>>> output = mlpack_lmnn(input=iris, labels=iris_labels, k=3,
  optimizer='bbsgd')
>>> output = output['output']

An another program call making use of range & regularization parameter with dataset having labels as last column can be made as:

>>> output = mlpack_lmnn(input=letter_recognition, k=5, range=10,
  regularization=0.4)
>>> output = output['output']

See also

local_coordinate_coding()

Local Coordinate Coding

>>> from mlpack import local_coordinate_coding
>>> d = local_coordinate_coding(atoms=0, initial_dictionary=np.empty([0,
        0]), input_model=None, lambda_=0, max_iterations=0, normalize=False,
        seed=0, test=np.empty([0, 0]), tolerance=0.01, training=np.empty([0,
        0]))
>>> codes = d['codes']
>>> dictionary = d['dictionary']
>>> output_model = d['output_model']

An implementation of Local Coordinate Coding (LCC), a data transformation technique. Given input data, this transforms each point to be expressed as a linear combination of a few points in the dataset; once an LCC model is trained, it can be used to transform points later also. Detailed documentation.

Input options

name type description default
atoms int Number of atoms in the dictionary. 0
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
initial_dictionary matrix Optional initial dictionary. np.empty([0, 0])
input_model LocalCoordinateCodingType Input LCC model. None
lambda_ float Weighted l1-norm regularization parameter. 0
max_iterations int Maximum number of iterations for LCC (0 indicates no limit). 0
normalize bool If set, the input data matrix will be normalized before coding. False
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
test matrix Test points to encode. np.empty([0, 0])
tolerance float Tolerance for objective function. 0.01
training matrix Matrix of training data (X). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
codes matrix Output codes matrix.
dictionary matrix Output dictionary matrix.
output_model LocalCoordinateCodingType Output for trained LCC model.

Detailed documentation

An implementation of Local Coordinate Coding (LCC), which codes data that approximately lives on a manifold using a variation of l1-norm regularized sparse coding. Given a dense data matrix X with n points and d dimensions, LCC seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a coding matrix Z with n points in k dimensions. Because of the regularization method used, the atoms in D should lie close to the manifold on which the data points lie.

The original data matrix X can then be reconstructed as D * Z. Therefore, this program finds a representation of each point in X as a sparse linear combination of atoms in the dictionary D.

The coding is found with an algorithm which alternates between a dictionary step, which updates the dictionary D, and a coding step, which updates the coding matrix Z.

To run this program, the input matrix X must be specified (with -i), along with the number of atoms in the dictionary (-k). An initial dictionary may also be specified with the initial_dictionary parameter. The l1-norm regularization parameter is specified with the lambda_ parameter. For example, to run LCC on the dataset 'data' using 200 atoms and an l1-regularization parameter of 0.1, saving the dictionary dictionary and the codes into codes, use

>>> output = local_coordinate_coding(training=data, atoms=200, lambda_=0.1)
>>> dict = output['dictionary']
>>> codes = output['codes']

The maximum number of iterations may be specified with the max_iterations parameter. Optionally, the input data matrix X can be normalized before coding with the normalize parameter.

An LCC model may be saved using the output_model output parameter. Then, to encode new points from the dataset 'points' with the previously saved model 'lcc_model', saving the new codes to 'new_codes', the following command can be used:

>>> output = local_coordinate_coding(input_model=lcc_model, test=points)
>>> new_codes = output['codes']

See also

logistic_regression()

L2-regularized Logistic Regression and Prediction

>>> from mlpack import logistic_regression
>>> d = logistic_regression(batch_size=64, decision_boundary=0.5,
        input_model=None, labels=np.empty([0], dtype=np.uint64), lambda_=0,
        max_iterations=10000, optimizer='lbfgs', step_size=0.01,
        test=np.empty([0, 0]), tolerance=1e-10, training=np.empty([0, 0]))
>>> output = d['output']
>>> output_model = d['output_model']
>>> output_probabilities = d['output_probabilities']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of L2-regularized logistic regression for two-class classification. Given labeled data, a model can be trained and saved for future use; or, a pre-trained model can be used to classify new points. Detailed documentation.

Input options

name type description default
batch_size int Batch size for SGD. 64
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
decision_boundary float Decision boundary for prediction; if the logistic function for a point is less than the boundary, the class is taken to be 0; otherwise, the class is 1. 0.5
input_model LogisticRegression<>Type Existing model (parameters). None
labels int vector A matrix containing labels (0 or 1) for the points in the training set (y). np.empty([0], dtype=np.uint64)
lambda_ float L2-regularization parameter for training. 0
max_iterations int Maximum iterations for optimizer (0 indicates no limit). 10000
optimizer str Optimizer to use for training (‘lbfgs’ or ‘sgd’). 'lbfgs'
step_size float Step size for SGD optimizer. 0.01
test matrix Matrix containing test dataset. np.empty([0, 0])
tolerance float Convergence tolerance for optimizer. 1e-10
training matrix A matrix containing the training set (the matrix of predictors, X). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int vector If test data is specified, this matrix is where the predictions for the test set will be saved.
output_model LogisticRegression<>Type Output for trained logistic regression model.
output_probabilities matrix If test data is specified, this matrix is where the class probabilities for the test set will be saved.
predictions int vector If test data is specified, this matrix is where the predictions for the test set will be saved.
probabilities matrix If test data is specified, this matrix is where the class probabilities for the test set will be saved.

Detailed documentation

An implementation of L2-regularized logistic regression using either the L-BFGS optimizer or SGD (stochastic gradient descent). This solves the regression problem

y = (1 / 1 + e^-(X * b))

where y takes values 0 or 1.

This program allows loading a logistic regression model (via the input_model parameter) or training a logistic regression model given training data (specified with the training parameter), or both those things at once. In addition, this program allows classification on a test dataset (specified with the test parameter) and the classification results may be saved with the predictions output parameter. The trained logistic regression model may be saved using the output_model output parameter.

The training data, if specified, may have class labels as its last dimension. Alternately, the labels parameter may be used to specify a separate matrix of labels.

When a model is being trained, there are many options. L2 regularization (to prevent overfitting) can be specified with the lambda_ option, and the optimizer used to train the model can be specified with the optimizer parameter. Available options are ‘sgd’ (stochastic gradient descent) and ‘lbfgs’ (the L-BFGS optimizer). There are also various parameters for the optimizer; the max_iterations parameter specifies the maximum number of allowed iterations, and the tolerance parameter specifies the tolerance for convergence. For the SGD optimizer, the step_size parameter controls the step size taken at each iteration by the optimizer. The batch size for SGD is controlled with the batch_size parameter. If the objective function for your data is oscillating between Inf and 0, the step size is probably too large. There are more parameters for the optimizers, but the C++ interface must be used to access these.

For SGD, an iteration refers to a single point. So to take a single pass over the dataset with SGD, max_iterations should be set to the number of points in the dataset.

Optionally, the model can be used to predict the responses for another matrix of data points, if test is specified. The test parameter can be specified without the training parameter, so long as an existing logistic regression model is given with the input_model parameter. The output predictions from the logistic regression model may be saved with the predictions parameter.

Note : The following parameters are deprecated and will be removed in mlpack 4: output, output_probabilities Use predictions instead of output Use probabilities instead of output_probabilities

This implementation of logistic regression does not support the general multi-class case but instead only the two-class case. Any labels must be either 0 or 1. For more classes, see the softmax_regression program.

As an example, to train a logistic regression model on the data ‘'data'’ with labels ‘'labels'’ with L2 regularization of 0.1, saving the model to ‘'lr_model'’, the following command may be used:

>>> output = logistic_regression(training=data, labels=labels, lambda_=0.1)
>>> lr_model = output['output_model']

Then, to use that model to predict classes for the dataset ‘'test'’, storing the output predictions in ‘'predictions'’, the following command may be used:

>>> output = logistic_regression(input_model=lr_model, test=test)
>>> predictions = output['output']

See also

lsh()

K-Approximate-Nearest-Neighbor Search with LSH

>>> from mlpack import lsh
>>> d = lsh(bucket_size=500, hash_width=0, input_model=None, k=0,
        num_probes=0, projections=10, query=np.empty([0, 0]),
        reference=np.empty([0, 0]), second_hash_size=99901, seed=0, tables=30,
        true_neighbors=np.empty([0, 0], dtype=np.uint64))
>>> distances = d['distances']
>>> neighbors = d['neighbors']
>>> output_model = d['output_model']

An implementation of approximate k-nearest-neighbor search with locality-sensitive hashing (LSH). Given a set of reference points and a set of query points, this will compute the k approximate nearest neighbors of each query point in the reference set; models can be saved for future use. Detailed documentation.

Input options

name type description default
bucket_size int The size of a bucket in the second level hash. 500
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
hash_width float The hash width for the first-level hashing in the LSH preprocessing. By default, the LSH class automatically estimates a hash width for its use. 0
input_model LSHSearch<>Type Input LSH model. None
k int Number of nearest neighbors to find. 0
num_probes int Number of additional probes for multiprobe LSH; if 0, traditional LSH is used. 0
projections int The number of hash functions for each table 10
query matrix Matrix containing query points (optional). np.empty([0, 0])
reference matrix Matrix containing the reference dataset. np.empty([0, 0])
second_hash_size int The size of the second level hash table. 99901
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
tables int The number of hash tables to be used. 30
true_neighbors int matrix Matrix of true neighbors to compute recall with (the recall is printed when -v is specified). np.empty([0, 0], dtype=np.uint64)
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
distances matrix Matrix to output distances into.
neighbors int matrix Matrix to output neighbors into.
output_model LSHSearch<>Type Output for trained LSH model.

Detailed documentation

This program will calculate the k approximate-nearest-neighbors of a set of points using locality-sensitive hashing. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

For example, the following will return 5 neighbors from the data for each point in 'input' and store the distances in 'distances' and the neighbors in 'neighbors':

>>> output = lsh(k=5, reference=input)
>>> distances = output['distances']
>>> neighbors = output['neighbors']

The output is organized such that row i and column j in the neighbors output corresponds to the index of the point in the reference set which is the j’th nearest neighbor from the point in the query set with index i. Row j and column i in the distances output file corresponds to the distance between those two points.

Because this is approximate-nearest-neighbors search, results may be different from run to run. Thus, the seed parameter can be specified to set the random seed.

This program also has many other parameters to control its functionality; see the parameter-specific documentation for more information.

See also

mean_shift()

Mean Shift Clustering

>>> from mlpack import mean_shift
>>> d = mean_shift(force_convergence=False, in_place=False,
        input=np.empty([0, 0]), labels_only=False, max_iterations=1000,
        radius=0)
>>> centroid = d['centroid']
>>> output = d['output']

A fast implementation of mean-shift clustering using dual-tree range search. Given a dataset, this uses the mean shift algorithm to produce and return a clustering of the data. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
force_convergence bool If specified, the mean shift algorithm will continue running regardless of max_iterations until the clusters converge. False
in_place bool If specified, a column containing the learned cluster assignments will be added to the input dataset file. In this case, –output_file is overridden. (Do not use with Python.) False
input matrix Input dataset to perform clustering on. required
labels_only bool If specified, only the output labels will be written to the file specified by –output_file. False
max_iterations int Maximum number of iterations before mean shift terminates. 1000
radius float If the distance between two centroids is less than the given radius, one will be removed. A radius of 0 or less means an estimate will be calculated and used for the radius. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
centroid matrix If specified, the centroids of each cluster will be written to the given matrix.
output matrix Matrix to write output labels or labeled data to.

Detailed documentation

This program performs mean shift clustering on the given dataset, storing the learned cluster assignments either as a column of labels in the input dataset or separately.

The input dataset should be specified with the input parameter, and the radius used for search can be specified with the radius parameter. The maximum number of iterations before algorithm termination is controlled with the max_iterations parameter.

The output labels may be saved with the output output parameter and the centroids of each cluster may be saved with the centroid output parameter.

For example, to run mean shift clustering on the dataset 'data' and store the centroids to 'centroids', the following command may be used:

>>> output = mean_shift(input=data)
>>> centroids = output['centroid']

See also

nbc()

Parametric Naive Bayes Classifier

>>> from mlpack import nbc
>>> d = nbc(incremental_variance=False, input_model=None,
        labels=np.empty([0], dtype=np.uint64), test=np.empty([0, 0]),
        training=np.empty([0, 0]))
>>> output = d['output']
>>> output_model = d['output_model']
>>> output_probs = d['output_probs']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of the Naive Bayes Classifier, used for classification. Given labeled data, an NBC model can be trained and saved, or, a pre-trained model can be used for classification. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
incremental_variance bool The variance of each class will be calculated incrementally. False
input_model NBCModelType Input Naive Bayes model. None
labels int vector A file containing labels for the training set. np.empty([0], dtype=np.uint64)
test matrix A matrix containing the test set. np.empty([0, 0])
training matrix A matrix containing the training set. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int vector The matrix in which the predicted labels for the test set will be written (deprecated).
output_model NBCModelType File to save trained Naive Bayes model to.
output_probs matrix The matrix in which the predicted probability of labels for the test set will be written (deprecated).
predictions int vector The matrix in which the predicted labels for the test set will be written.
probabilities matrix The matrix in which the predicted probability of labels for the test set will be written.

Detailed documentation

This program trains the Naive Bayes classifier on the given labeled training set, or loads a model from the given model file, and then may use that trained model to classify the points in a given test set.

The training set is specified with the training parameter. Labels may be either the last row of the training set, or alternately the labels parameter may be specified to pass a separate matrix of labels.

If training is not desired, a pre-existing model may be loaded with the input_model parameter.

The incremental_variance parameter can be used to force the training to use an incremental algorithm for calculating variance. This is slower, but can help avoid loss of precision in some cases.

If classifying a test set is desired, the test set may be specified with the test parameter, and the classifications may be saved with the predictionspredictions parameter. If saving the trained model is desired, this may be done with the output_model output parameter.

Note: the output and output_probs parameters are deprecated and will be removed in mlpack 4.0.0. Use predictions and probabilities instead.

For example, to train a Naive Bayes classifier on the dataset 'data' with labels 'labels' and save the model to 'nbc_model', the following command may be used:

>>> output = nbc(training=data, labels=labels)
>>> nbc_model = output['output_model']

Then, to use 'nbc_model' to predict the classes of the dataset 'test_set' and save the predicted classes to 'predictions', the following command may be used:

>>> output = nbc(input_model=nbc_model, test=test_set)
>>> predictions = output['output']

See also

nca()

Neighborhood Components Analysis (NCA)

>>> from mlpack import nca
>>> d = nca(armijo_constant=0.0001, batch_size=50, input=np.empty([0,
        0]), labels=np.empty([0], dtype=np.uint64), linear_scan=False,
        max_iterations=500000, max_line_search_trials=50, max_step=1e+20,
        min_step=1e-20, normalize=False, num_basis=5, optimizer='sgd', seed=0,
        step_size=0.01, tolerance=1e-07, wolfe=0.9)
>>> output = d['output']

An implementation of neighborhood components analysis, a distance learning technique that can be used for preprocessing. Given a labeled dataset, this uses NCA, which seeks to improve the k-nearest-neighbor classification, and returns the learned distance metric. Detailed documentation.

Input options

name type description default
armijo_constant float Armijo constant for L-BFGS. 0.0001
batch_size int Batch size for mini-batch SGD. 50
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Input dataset to run NCA on. required
labels int vector Labels for input dataset. np.empty([0], dtype=np.uint64)
linear_scan bool Don’t shuffle the order in which data points are visited for SGD or mini-batch SGD. False
max_iterations int Maximum number of iterations for SGD or L-BFGS (0 indicates no limit). 500000
max_line_search_trials int Maximum number of line search trials for L-BFGS. 50
max_step float Maximum step of line search for L-BFGS. 1e+20
min_step float Minimum step of line search for L-BFGS. 1e-20
normalize bool Use a normalized starting point for optimization. This is useful for when points are far apart, or when SGD is returning NaN. False
num_basis int Number of memory points to be stored for L-BFGS. 5
optimizer str Optimizer to use; ‘sgd’ or ‘lbfgs’. 'sgd'
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
step_size float Step size for stochastic gradient descent (alpha). 0.01
tolerance float Maximum tolerance for termination of SGD or L-BFGS. 1e-07
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False
wolfe float Wolfe condition parameter for L-BFGS. 0.9

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Output matrix for learned distance matrix.

Detailed documentation

This program implements Neighborhood Components Analysis, both a linear dimensionality reduction technique and a distance learning technique. The method seeks to improve k-nearest-neighbor classification on a dataset by scaling the dimensions. The method is nonparametric, and does not require a value of k. It works by using stochastic (“soft”) neighbor assignments and using optimization techniques over the gradient of the accuracy of the neighbor assignments.

To work, this algorithm needs labeled data. It can be given as the last row of the input dataset (specified with input), or alternatively as a separate matrix (specified with labels).

This implementation of NCA uses stochastic gradient descent, mini-batch stochastic gradient descent, or the L_BFGS optimizer. These optimizers do not guarantee global convergence for a nonconvex objective function (NCA’s objective function is nonconvex), so the final results could depend on the random seed or other optimizer parameters.

Stochastic gradient descent, specified by the value ‘sgd’ for the parameter optimizer, depends primarily on three parameters: the step size (specified with step_size), the batch size (specified with batch_size), and the maximum number of iterations (specified with max_iterations). In addition, a normalized starting point can be used by specifying the normalize parameter, which is necessary if many warnings of the form ‘Denominator of p_i is 0!’ are given. Tuning the step size can be a tedious affair. In general, the step size is too large if the objective is not mostly uniformly decreasing, or if zero-valued denominator warnings are being issued. The step size is too small if the objective is changing very slowly. Setting the termination condition can be done easily once a good step size parameter is found; either increase the maximum iterations to a large number and allow SGD to find a minimum, or set the maximum iterations to 0 (allowing infinite iterations) and set the tolerance (specified by tolerance) to define the maximum allowed difference between objectives for SGD to terminate. Be careful—setting the tolerance instead of the maximum iterations can take a very long time and may actually never converge due to the properties of the SGD optimizer. Note that a single iteration of SGD refers to a single point, so to take a single pass over the dataset, set the value of the max_iterations parameter equal to the number of points in the dataset.

The L-BFGS optimizer, specified by the value ‘lbfgs’ for the parameter optimizer, uses a back-tracking line search algorithm to minimize a function. The following parameters are used by L-BFGS: num_basis (specifies the number of memory points used by L-BFGS), max_iterations, armijo_constant, wolfe, tolerance (the optimization is terminated when the gradient norm is below this value), max_line_search_trials, min_step, and max_step (which both refer to the line search routine). For more details on the L-BFGS optimizer, consult either the mlpack L-BFGS documentation (in lbfgs.hpp) or the vast set of published literature on L-BFGS.

By default, the SGD optimizer is used.

See also

knn()

>>> from mlpack import knn
>>> d = knn(algorithm='dual_tree', epsilon=0, input_model=None, k=0,
        leaf_size=20, query=np.empty([0, 0]), random_basis=False,
        reference=np.empty([0, 0]), rho=0.7, seed=0, tau=0, tree_type='kd',
        true_distances=np.empty([0, 0]), true_neighbors=np.empty([0, 0],
        dtype=np.uint64))
>>> distances = d['distances']
>>> neighbors = d['neighbors']
>>> output_model = d['output_model']

An implementation of k-nearest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use. Detailed documentation.

Input options

name type description default
algorithm str Type of neighbor search: ‘naive’, ‘single_tree’, ‘dual_tree’, ‘greedy’. 'dual_tree'
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
epsilon float If specified, will do approximate nearest neighbor search with given relative error. 0
input_model KNNModelType Pre-trained kNN model. None
k int Number of nearest neighbors to find. 0
leaf_size int Leaf size for tree building (used for kd-trees, vp trees, random projection trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, spill trees, and octrees). 20
query matrix Matrix containing query points (optional). np.empty([0, 0])
random_basis bool Before tree-building, project the data onto a random orthogonal basis. False
reference matrix Matrix containing the reference dataset. np.empty([0, 0])
rho float Balance threshold (only valid for spill trees). 0.7
seed int Random seed (if 0, std::time(NULL) is used). 0
tau float Overlapping size (only valid for spill trees). 0
tree_type str Type of tree to use: ‘kd’, ‘vp’, ‘rp’, ‘max-rp’, ‘ub’, ‘cover’, ‘r’, ‘r-star’, ‘x’, ‘ball’, ‘hilbert-r’, ‘r-plus’, ‘r-plus-plus’, ‘spill’, ‘oct’. 'kd'
true_distances matrix Matrix of true distances to compute the effective error (average relative error) (it is printed when -v is specified). np.empty([0, 0])
true_neighbors int matrix Matrix of true neighbors to compute the recall (it is printed when -v is specified). np.empty([0, 0], dtype=np.uint64)
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
distances matrix Matrix to output distances into.
neighbors int matrix Matrix to output neighbors into.
output_model KNNModelType If specified, the kNN model will be output here.

Detailed documentation

This program will calculate the k-nearest-neighbors of a set of points using kd-trees or cover trees (cover tree support is experimental and may be slow). You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

For example, the following command will calculate the 5 nearest neighbors of each point in 'input' and store the distances in 'distances' and the neighbors in 'neighbors':

>>> output = knn(k=5, reference=input)
>>> neighbors = output['neighbors']

The output is organized such that row i and column j in the neighbors output matrix corresponds to the index of the point in the reference set which is the j’th nearest neighbor from the point in the query set with index i. Row j and column i in the distances output matrix corresponds to the distance between those two points.

See also

kfn()

>>> from mlpack import kfn
>>> d = kfn(algorithm='dual_tree', epsilon=0, input_model=None, k=0,
        leaf_size=20, percentage=1, query=np.empty([0, 0]), random_basis=False,
        reference=np.empty([0, 0]), seed=0, tree_type='kd',
        true_distances=np.empty([0, 0]), true_neighbors=np.empty([0, 0],
        dtype=np.uint64))
>>> distances = d['distances']
>>> neighbors = d['neighbors']
>>> output_model = d['output_model']

An implementation of k-furthest-neighbor search using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k furthest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use. Detailed documentation.

Input options

name type description default
algorithm str Type of neighbor search: ‘naive’, ‘single_tree’, ‘dual_tree’, ‘greedy’. 'dual_tree'
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
epsilon float If specified, will do approximate furthest neighbor search with given relative error. Must be in the range [0,1). 0
input_model KFNModelType Pre-trained kFN model. None
k int Number of furthest neighbors to find. 0
leaf_size int Leaf size for tree building (used for kd-trees, vp trees, random projection trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, and octrees). 20
percentage float If specified, will do approximate furthest neighbor search. Must be in the range (0,1] (decimal form). Resultant neighbors will be at least (p*100) % of the distance as the true furthest neighbor. 1
query matrix Matrix containing query points (optional). np.empty([0, 0])
random_basis bool Before tree-building, project the data onto a random orthogonal basis. False
reference matrix Matrix containing the reference dataset. np.empty([0, 0])
seed int Random seed (if 0, std::time(NULL) is used). 0
tree_type str Type of tree to use: ‘kd’, ‘vp’, ‘rp’, ‘max-rp’, ‘ub’, ‘cover’, ‘r’, ‘r-star’, ‘x’, ‘ball’, ‘hilbert-r’, ‘r-plus’, ‘r-plus-plus’, ‘oct’. 'kd'
true_distances matrix Matrix of true distances to compute the effective error (average relative error) (it is printed when -v is specified). np.empty([0, 0])
true_neighbors int matrix Matrix of true neighbors to compute the recall (it is printed when -v is specified). np.empty([0, 0], dtype=np.uint64)
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
distances matrix Matrix to output distances into.
neighbors int matrix Matrix to output neighbors into.
output_model KFNModelType If specified, the kFN model will be output here.

Detailed documentation

This program will calculate the k-furthest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set.

For example, the following will calculate the 5 furthest neighbors of eachpoint in 'input' and store the distances in 'distances' and the neighbors in 'neighbors':

>>> output = kfn(k=5, reference=input)
>>> distances = output['distances']
>>> neighbors = output['neighbors']

The output files are organized such that row i and column j in the neighbors output matrix corresponds to the index of the point in the reference set which is the j’th furthest neighbor from the point in the query set with index i. Row i and column j in the distances output file corresponds to the distance between those two points.

See also

nmf()

Non-negative Matrix Factorization

>>> from mlpack import nmf
>>> d = nmf(initial_h=np.empty([0, 0]), initial_w=np.empty([0, 0]),
        input=np.empty([0, 0]), max_iterations=10000, min_residue=1e-05, rank=0,
        seed=0, update_rules='multdist')
>>> h = d['h']
>>> w = d['w']

An implementation of non-negative matrix factorization. This can be used to decompose an input dataset into two low-rank non-negative components. Detailed documentation.

Input options

name type description default    
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False    
initial_h matrix Initial H matrix. np.empty([0, 0])    
initial_w matrix Initial W matrix. np.empty([0, 0])    
input matrix Input dataset to perform NMF on. required    
max_iterations int Number of iterations before NMF terminates (0 runs until convergence. 10000    
min_residue float The minimum root mean square residue allowed for each iteration, below which the program terminates. 1e-05    
rank int Rank of the factorization. required    
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0    
update_rules str Update rules for each iteration; ( multdist multdiv als ). 'multdist'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False    

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
h matrix Matrix to save the calculated H to.
w matrix Matrix to save the calculated W to.

Detailed documentation

This program performs non-negative matrix factorization on the given dataset, storing the resulting decomposed matrices in the specified files. For an input dataset V, NMF decomposes V into two matrices W and H such that

V = W * H

where all elements in W and H are non-negative. If V is of size (n x m), then W will be of size (n x r) and H will be of size (r x m), where r is the rank of the factorization (specified by the rank parameter).

Optionally, the desired update rules for each NMF iteration can be chosen from the following list:

  • multdist: multiplicative distance-based update rules (Lee and Seung 1999)
  • multdiv: multiplicative divergence-based update rules (Lee and Seung 1999)
  • als: alternating least squares update rules (Paatero and Tapper 1994)

The maximum number of iterations is specified with max_iterations, and the minimum residue required for algorithm termination is specified with the min_residue parameter.

For example, to run NMF on the input matrix 'V' using the ‘multdist’ update rules with a rank-10 decomposition and storing the decomposed matrices into 'W' and 'H', the following command could be used:

>>> output = nmf(input=V, rank=10, update_rules='multdist')
>>> W = output['w']
>>> H = output['h']

See also

pca()

Principal Components Analysis

>>> from mlpack import pca
>>> d = pca(decomposition_method='exact', input=np.empty([0, 0]),
        new_dimensionality=0, scale=False, var_to_retain=0)
>>> output = d['output']

An implementation of several strategies for principal components analysis (PCA), a common preprocessing step. Given a dataset and a desired new dimensionality, this can reduce the dimensionality of the data using the linear transformation determined by PCA. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
decomposition_method str Method used for the principal components analysis: ‘exact’, ‘randomized’, ‘randomized-block-krylov’, ‘quic’. 'exact'
input matrix Input dataset to perform PCA on. required
new_dimensionality int Desired dimensionality of output dataset. If 0, no dimensionality reduction is performed. 0
scale bool If set, the data will be scaled before running PCA, such that the variance of each feature is 1. False
var_to_retain float Amount of variance to retain; should be between 0 and 1. If 1, all variance is retained. Overrides -d. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to save modified dataset to.

Detailed documentation

This program performs principal components analysis on the given dataset using the exact, randomized, randomized block Krylov, or QUIC SVD method. It will transform the data onto its principal components, optionally performing dimensionality reduction by ignoring the principal components with the smallest eigenvalues.

Use the input parameter to specify the dataset to perform PCA on. A desired new dimensionality can be specified with the new_dimensionality parameter, or the desired variance to retain can be specified with the var_to_retain parameter. If desired, the dataset can be scaled before running PCA with the scale parameter.

Multiple different decomposition techniques can be used. The method to use can be specified with the decomposition_method parameter, and it may take the values ‘exact’, ‘randomized’, or ‘quic’.

For example, to reduce the dimensionality of the matrix 'data' to 5 dimensions using randomized SVD for the decomposition, storing the output matrix to 'data_mod', the following command can be used:

>>> output = pca(input=data, new_dimensionality=5,
  decomposition_method='randomized')
>>> data_mod = output['output']

See also

perceptron()

Perceptron

>>> from mlpack import perceptron
>>> d = perceptron(input_model=None, labels=np.empty([0],
        dtype=np.uint64), max_iterations=1000, test=np.empty([0, 0]),
        training=np.empty([0, 0]))
>>> output = d['output']
>>> output_model = d['output_model']
>>> predictions = d['predictions']

An implementation of a perceptron—a single level neural network–=for classification. Given labeled data, a perceptron can be trained and saved for future use; or, a pre-trained perceptron can be used for classification on new points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model PerceptronModelType Input perceptron model. None
labels int vector A matrix containing labels for the training set. np.empty([0], dtype=np.uint64)
max_iterations int The maximum number of iterations the perceptron is to be run 1000
test matrix A matrix containing the test set. np.empty([0, 0])
training matrix A matrix containing the training set. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output int vector The matrix in which the predicted labels for the test set will be written.
output_model PerceptronModelType Output for trained perceptron model.
predictions int vector The matrix in which the predicted labels for the test set will be written.

Detailed documentation

This program implements a perceptron, which is a single level neural network. The perceptron makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The perceptron learning rule is able to converge, given enough iterations (specified using the max_iterations parameter), if the data supplied is linearly separable. The perceptron is parameterized by a matrix of weight vectors that denote the numerical weights of the neural network.

This program allows loading a perceptron from a model (via the input_model parameter) or training a perceptron given training data (via the training parameter), or both those things at once. In addition, this program allows classification on a test dataset (via the test parameter) and the classification results on the test set may be saved with the predictions output parameter. The perceptron model may be saved with the output_model output parameter.

Note: the following parameter is deprecated and will be removed in mlpack 4.0.0: output. Use predictions instead of output.

The training data given with the training option may have class labels as its last dimension (so, if the training data is in CSV format, labels should be the last column). Alternately, the labels parameter may be used to specify a separate matrix of labels.

All these options make it easy to train a perceptron, and then re-use that perceptron for later classification. The invocation below trains a perceptron on 'training_data' with labels 'training_labels', and saves the model to 'perceptron_model'.

>>> output = perceptron(training=training_data, labels=training_labels)
>>> perceptron_model = output['output_model']

Then, this model can be re-used for classification on the test data 'test_data'. The example below does precisely that, saving the predicted classes to 'predictions'.

>>> output = perceptron(input_model=perceptron_model, test=test_data)
>>> predictions = output['predictions']

Note that all of the options may be specified at once: predictions may be calculated right after training a model, and model training can occur even if an existing perceptron model is passed with the input_model parameter. However, note that the number of classes and the dimensionality of all data must match. So you cannot pass a perceptron model trained on 2 classes and then re-train with a 4-class dataset. Similarly, attempting classification on a 3-dimensional dataset with a perceptron that has been trained on 8 dimensions will cause an error.

See also

preprocess_split()

Split Data

>>> from mlpack import preprocess_split
>>> d = preprocess_split(input=np.empty([0, 0]),
        input_labels=np.empty([0, 0], dtype=np.uint64), seed=0, test_ratio=0.2)
>>> test = d['test']
>>> test_labels = d['test_labels']
>>> training = d['training']
>>> training_labels = d['training_labels']

A utility to split data into a training and testing dataset. This can also split labels according to the same split. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Matrix containing data. required
input_labels int matrix Matrix containing labels. np.empty([0, 0], dtype=np.uint64)
seed int Random seed (0 for std::time(NULL)). 0
test_ratio float Ratio of test set; if not set,the ratio defaults to 0.2 0.2
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
test matrix Matrix to save test data to.
test_labels int matrix Matrix to save test labels to.
training matrix Matrix to save training data to.
training_labels int matrix Matrix to save train labels to.

Detailed documentation

This utility takes a dataset and optionally labels and splits them into a training set and a test set. Before the split, the points in the dataset are randomly reordered. The percentage of the dataset to be used as the test set can be specified with the test_ratio parameter; the default is 0.2 (20%).

The output training and test matrices may be saved with the training and test output parameters.

Optionally, labels can be also be split along with the data by specifying the input_labels parameter. Splitting labels works the same way as splitting the data. The output training and test labels may be saved with the training_labels and test_labels output parameters, respectively.

So, a simple example where we want to split the dataset 'X' into 'X_train' and 'X_test' with 60% of the data in the training set and 40% of the dataset in the test set, we could run

>>> output = preprocess_split(input=X, test_ratio=0.4)
>>> X_train = output['training']
>>> X_test = output['test']

If we had a dataset 'X' and associated labels 'y', and we wanted to split these into 'X_train', 'y_train', 'X_test', and 'y_test', with 30% of the data in the test set, we could run

>>> output = preprocess_split(input=X, input_labels=y, test_ratio=0.3)
>>> X_train = output['training']
>>> y_train = output['training_labels']
>>> X_test = output['test']
>>> y_test = output['test_labels']

See also

preprocess_binarize()

Binarize Data

>>> from mlpack import preprocess_binarize
>>> d = preprocess_binarize(dimension=0, input=np.empty([0, 0]),
        threshold=0)
>>> output = d['output']

A utility to binarize a dataset. Given a dataset, this utility converts each value in the desired dimension(s) to 0 or 1; this can be a useful preprocessing step. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
dimension int Dimension to apply the binarization. If not set, the program will binarize every dimension by default. 0
input matrix Input data matrix. required
threshold float Threshold to be applied for binarization. If not set, the threshold defaults to 0.0. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix in which to save the output.

Detailed documentation

This utility takes a dataset and binarizes the variables into either 0 or 1 given threshold. User can apply binarization on a dimension or the whole dataset. The dimension to apply binarization to can be specified using the dimension parameter; if left unspecified, every dimension will be binarized. The threshold for binarization can also be specified with the threshold parameter; the default threshold is 0.0.

The binarized matrix may be saved with the output output parameter.

For example, if we want to set all variables greater than 5 in the dataset 'X' to 1 and variables less than or equal to 5.0 to 0, and save the result to 'Y', we could run

>>> output = preprocess_binarize(input=X, threshold=5)
>>> Y = output['output']

But if we want to apply this to only the first (0th) dimension of 'X', we could instead run

>>> output = preprocess_binarize(input=X, threshold=5, dimension=0)
>>> Y = output['output']

See also

preprocess_describe()

Descriptive Statistics

>>> from mlpack import preprocess_describe
>>> preprocess_describe(dimension=0, input=np.empty([0, 0]),
        population=False, precision=4, row_major=False, width=8)

A utility for printing descriptive statistics about a dataset. This prints a number of details about a dataset in a tabular format. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
dimension int Dimension of the data. Use this to specify a dimension 0
input matrix Matrix containing data, required
population bool If specified, the program will calculate statistics assuming the dataset is the population. By default, the program will assume the dataset as a sample. False
precision int Precision of the output statistics. 4
row_major bool If specified, the program will calculate statistics across rows, not across columns. (Remember that in mlpack, a column represents a point, so this option is generally not necessary.) False
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False
width int Width of the output table. 8

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

| name | type | description | |————|————|——————-|

Detailed documentation

This utility takes a dataset and prints out the descriptive statistics of the data. Descriptive statistics is the discipline of quantitatively describing the main features of a collection of information, or the quantitative description itself. The program does not modify the original file, but instead prints out the statistics to the console. The printed result will look like a table.

Optionally, width and precision of the output can be adjusted by a user using the width and precision parameters. A user can also select a specific dimension to analyze if there are too many dimensions. The population parameter can be specified when the dataset should be considered as a population. Otherwise, the dataset will be considered as a sample.

So, a simple example where we want to print out statistical facts about the dataset 'X' using the default settings, we could run

>>> preprocess_describe(input=X, verbose=True)

If we want to customize the width to 10 and precision to 5 and consider the dataset as a population, we could run

>>> preprocess_describe(input=X, width=10, precision=5, verbose=True)

See also

Impute Data

This utility provides several imputation strategies for missing data. Given a dataset with missing values, this can impute according to several strategies, including user-defined values. .

preprocess_scale()

Scale Data

>>> from mlpack import preprocess_scale
>>> d = preprocess_scale(epsilon=1e-06, input=np.empty([0, 0]),
        input_model=None, inverse_scaling=False, max_value=1, min_value=0,
        scaler_method='standard_scaler', seed=0)
>>> output = d['output']
>>> output_model = d['output_model']

A utility to perform feature scaling on datasets using one of sixtechniques. Both scaling and inverse scaling are supported, andscalers can be saved and then applied to other datasets. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
epsilon float regularization Parameter for pcawhitening, or zcawhitening, should be between -1 to 1. 1e-06
input matrix Matrix containing data. required
input_model ScalingModelType Input Scaling model. None
inverse_scaling bool Inverse Scaling to get original dataset False
max_value int Ending value of range for min_max_scaler. 1
min_value int Starting value of range for min_max_scaler. 0
scaler_method str method to use for scaling, the default is standard_scaler. 'standard_scaler'
seed int Random seed (0 for std::time(NULL)). 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output matrix Matrix to save scaled data to.
output_model ScalingModelType Output scaling model.

Detailed documentation

This utility takes a dataset and performs feature scaling using one of the six scaler methods namely: ‘max_abs_scaler’, ‘mean_normalization’, ‘min_max_scaler’ ,’standard_scaler’, ‘pca_whitening’ and ‘zca_whitening’. The function takes a matrix as input and a scaling method type which you can specify using scaler_method parameter; the default is standard scaler, and outputs a matrix with scaled feature.

The output scaled feature matrix may be saved with the output output parameters.

The model to scale features can be saved using output_model and later can be loaded back usinginput_model.

So, a simple example where we want to scale the dataset 'X' into 'X_scaled' with standard_scaler as scaler_method, we could run

>>> output = preprocess_scale(input=X, scaler_method='standard_scaler')
>>> X_scaled = output['output']

A simple example where we want to whiten the dataset 'X' into 'X_whitened' with PCA as whitening_method and use 0.01 as regularization parameter, we could run

>>> output = preprocess_scale(input=X, scaler_method='pca_whitening',
  epsilon=0.01)
>>> X_scaled = output['output']

You can also retransform the scaled dataset back usinginverse_scaling. An example to rescale : 'X_scaled' into 'X'using the saved model input_model is:

>>> output = preprocess_scale(input=X_scaled, inverse_scaling=True,
  input_model=saved)
>>> X = output['output']

Another simple example where we want to scale the dataset 'X' into 'X_scaled' with min_max_scaler as scaler method, where scaling range is 1 to 3 instead of default 0 to 1. We could run

>>> output = preprocess_scale(input=X, scaler_method='min_max_scaler',
  min_value=1, max_value=3)
>>> X_scaled = output['output']

See also

radical()

RADICAL

>>> from mlpack import radical
>>> d = radical(angles=150, input=np.empty([0, 0]), noise_std_dev=0.175,
        objective=False, replicates=30, seed=0, sweeps=0)
>>> output_ic = d['output_ic']
>>> output_unmixing = d['output_unmixing']

An implementation of RADICAL, a method for independent component analysis (ICA). Given a dataset, this can decompose the dataset into an unmixing matrix and an independent component matrix; this can be useful for preprocessing. Detailed documentation.

Input options

name type description default
angles int Number of angles to consider in brute-force search during Radical2D. 150
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input matrix Input dataset for ICA. required
noise_std_dev float Standard deviation of Gaussian noise. 0.175
objective bool If set, an estimate of the final objective function is printed. False
replicates int Number of Gaussian-perturbed replicates to use (per point) in Radical2D. 30
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
sweeps int Number of sweeps; each sweep calls Radical2D once for each pair of dimensions. 0
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_ic matrix Matrix to save independent components to.
output_unmixing matrix Matrix to save unmixing matrix to.

Detailed documentation

An implementation of RADICAL, a method for independent component analysis (ICA). Assuming that we have an input matrix X, the goal is to find a square unmixing matrix W such that Y = W * X and the dimensions of Y are independent components. If the algorithm is running particularly slowly, try reducing the number of replicates.

The input matrix to perform ICA on should be specified with the input parameter. The output matrix Y may be saved with the output_ic output parameter, and the output unmixing matrix W may be saved with the output_unmixing output parameter.

For example, to perform ICA on the matrix 'X' with 40 replicates, saving the independent components to 'ic', the following command may be used:

>>> output = radical(input=X, replicates=40)
>>> ic = output['output_ic']

See also

random_forest()

Random forests

>>> from mlpack import random_forest
>>> d = random_forest(input_model=None, labels=np.empty([0],
        dtype=np.uint64), maximum_depth=0, minimum_gain_split=0,
        minimum_leaf_size=1, num_trees=10, print_training_accuracy=False,
        seed=0, subspace_dim=0, test=np.empty([0, 0]), test_labels=np.empty([0],
        dtype=np.uint64), training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']
>>> probabilities = d['probabilities']

An implementation of the standard random forest algorithm by Leo Breiman for classification. Given labeled data, a random forest can be trained and saved for future use; or, a pre-trained random forest can be used for classification. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model RandomForestModelType Pre-trained random forest to use for classification. None
labels int vector Labels for training dataset. np.empty([0], dtype=np.uint64)
maximum_depth int Maximum depth of the tree (0 means no limit). 0
minimum_gain_split float Minimum gain needed to make a split when building a tree. 0
minimum_leaf_size int Minimum number of points in each leaf node. 1
num_trees int Number of trees in the random forest. 10
print_training_accuracy bool If set, then the accuracy of the model on the training set will be predicted (verbose must also be specified). False
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
subspace_dim int Dimensionality of random subspace to use for each split. ‘0’ will autoselect the square root of data dimensionality. 0
test matrix Test dataset to produce predictions for. np.empty([0, 0])
test_labels int vector Test dataset labels, if accuracy calculation is desired. np.empty([0], dtype=np.uint64)
training matrix Training dataset. np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model RandomForestModelType Model to save trained random forest to.
predictions int vector Predicted classes for each point in the test set.
probabilities matrix Predicted class probabilities for each point in the test set.

Detailed documentation

This program is an implementation of the standard random forest classification algorithm by Leo Breiman. A random forest can be trained and saved for later use, or a random forest may be loaded and predictions or class probabilities for points may be generated.

The training set and associated labels are specified with the training and labels parameters, respectively. The labels should be in the range [0, num_classes - 1]. Optionally, if labels is not specified, the labels are assumed to be the last dimension of the training dataset.

When a model is trained, the output_model output parameter may be used to save the trained model. A model may be loaded for predictions with the input_modelparameter. The input_model parameter may not be specified when the training parameter is specified. The minimum_leaf_size parameter specifies the minimum number of training points that must fall into each leaf for it to be split. The num_trees controls the number of trees in the random forest. The minimum_gain_split parameter controls the minimum required gain for a decision tree node to split. Larger values will force higher-confidence splits. The maximum_depth parameter specifies the maximum depth of the tree. The subspace_dim parameter is used to control the number of random dimensions chosen for an individual node’s split. If print_training_accuracy is specified, the calculated accuracy on the training set will be printed.

Test data may be specified with the test parameter, and if performance measures are desired for that test set, labels for the test points may be specified with the test_labels parameter. Predictions for each test point may be saved via the predictionsoutput parameter. Class probabilities for each prediction may be saved with the probabilities output parameter.

For example, to train a random forest with a minimum leaf size of 20 using 10 trees on the dataset contained in 'data'with labels 'labels', saving the output random forest to 'rf_model' and printing the training error, one could call

>>> output = random_forest(training=data, labels=labels, minimum_leaf_size=20,
  num_trees=10, print_training_accuracy=True)
>>> rf_model = output['output_model']

Then, to use that model to classify points in 'test_set' and print the test error given the labels 'test_labels' using that model, while saving the predictions for each point to 'predictions', one could call

>>> output = random_forest(input_model=rf_model, test=test_set,
  test_labels=test_labels)
>>> predictions = output['predictions']

See also

An implementation of range search with single-tree and dual-tree algorithms. Given a set of reference points and a set of query points and a range, this can find the set of reference points within the desired range for each query point, and any trees built during the computation can be saved for reuse with future range searches. .

krann()

K-Rank-Approximate-Nearest-Neighbors (kRANN)

>>> from mlpack import krann
>>> d = krann(alpha=0.95, first_leaf_exact=False, input_model=None, k=0,
        leaf_size=20, naive=False, query=np.empty([0, 0]), random_basis=False,
        reference=np.empty([0, 0]), sample_at_leaves=False, seed=0,
        single_mode=False, single_sample_limit=20, tau=5, tree_type='kd')
>>> distances = d['distances']
>>> neighbors = d['neighbors']
>>> output_model = d['output_model']

An implementation of rank-approximate k-nearest-neighbor search (kRANN) using single-tree and dual-tree algorithms. Given a set of reference points and query points, this can find the k nearest neighbors in the reference set of each query point using trees; trees that are built can be saved for future use. Detailed documentation.

Input options

name type description default
alpha float The desired success probability. 0.95
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
first_leaf_exact bool The flag to trigger sampling only after exactly exploring the first leaf. False
input_model RANNModelType Pre-trained kNN model. None
k int Number of nearest neighbors to find. 0
leaf_size int Leaf size for tree building (used for kd-trees, UB trees, R trees, R* trees, X trees, Hilbert R trees, R+ trees, R++ trees, and octrees). 20
naive bool If true, sampling will be done without using a tree. False
query matrix Matrix containing query points (optional). np.empty([0, 0])
random_basis bool Before tree-building, project the data onto a random orthogonal basis. False
reference matrix Matrix containing the reference dataset. np.empty([0, 0])
sample_at_leaves bool The flag to trigger sampling at leaves. False
seed int Random seed (if 0, std::time(NULL) is used). 0
single_mode bool If true, single-tree search is used (as opposed to dual-tree search. False
single_sample_limit int The limit on the maximum number of samples (and hence the largest node you can approximate). 20
tau float The allowed rank-error in terms of the percentile of the data. 5
tree_type str Type of tree to use: ‘kd’, ‘ub’, ‘cover’, ‘r’, ‘x’, ‘r-star’, ‘hilbert-r’, ‘r-plus’, ‘r-plus-plus’, ‘oct’. 'kd'
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
distances matrix Matrix to output distances into.
neighbors int matrix Matrix to output neighbors into.
output_model RANNModelType If specified, the kNN model will be output here.

Detailed documentation

This program will calculate the k rank-approximate-nearest-neighbors of a set of points. You may specify a separate set of reference points and query points, or just a reference set which will be used as both the reference and query set. You must specify the rank approximation (in %) (and optionally the success probability).

For example, the following will return 5 neighbors from the top 0.1% of the data (with probability 0.95) for each point in 'input' and store the distances in 'distances' and the neighbors in 'neighbors.csv':

>>> output = krann(reference=input, k=5, tau=0.1)
>>> distances = output['distances']
>>> neighbors = output['neighbors']

Note that tau must be set such that the number of points in the corresponding percentile of the data is greater than k. Thus, if we choose tau = 0.1 with a dataset of 1000 points and k = 5, then we are attempting to choose 5 nearest neighbors out of the closest 1 point – this is invalid and the program will terminate with an error message.

The output matrices are organized such that row i and column j in the neighbors output file corresponds to the index of the point in the reference set which is the i’th nearest neighbor from the point in the query set with index j. Row i and column j in the distances output file corresponds to the distance between those two points.

See also

softmax_regression()

Softmax Regression

>>> from mlpack import softmax_regression
>>> d = softmax_regression(input_model=None, labels=np.empty([0],
        dtype=np.uint64), lambda_=0.0001, max_iterations=400,
        no_intercept=False, number_of_classes=0, test=np.empty([0, 0]),
        test_labels=np.empty([0], dtype=np.uint64), training=np.empty([0, 0]))
>>> output_model = d['output_model']
>>> predictions = d['predictions']

An implementation of softmax regression for classification, which is a multiclass generalization of logistic regression. Given labeled data, a softmax regression model can be trained and saved for future use, or, a pre-trained softmax regression model can be used for classification of new points. Detailed documentation.

Input options

name type description default
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
input_model SoftmaxRegressionType File containing existing model (parameters). None
labels int vector A matrix containing labels (0 or 1) for the points in the training set (y). The labels must order as a row. np.empty([0], dtype=np.uint64)
lambda_ float L2-regularization constant 0.0001
max_iterations int Maximum number of iterations before termination. 400
no_intercept bool Do not add the intercept term to the model. False
number_of_classes int Number of classes for classification; if unspecified (or 0), the number of classes found in the labels will be used. 0
test matrix Matrix containing test dataset. np.empty([0, 0])
test_labels int vector Matrix containing test labels. np.empty([0], dtype=np.uint64)
training matrix A matrix containing the training set (the matrix of predictors, X). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
output_model SoftmaxRegressionType File to save trained softmax regression model to.
predictions int vector Matrix to save predictions for test dataset into.

Detailed documentation

This program performs softmax regression, a generalization of logistic regression to the multiclass case, and has support for L2 regularization. The program is able to train a model, load an existing model, and give predictions (and optionally their accuracy) for test data.

Training a softmax regression model is done by giving a file of training points with the training parameter and their corresponding labels with the labels parameter. The number of classes can be manually specified with the number_of_classes parameter, and the maximum number of iterations of the L-BFGS optimizer can be specified with the max_iterations parameter. The L2 regularization constant can be specified with the lambda_ parameter and if an intercept term is not desired in the model, the no_intercept parameter can be specified.

The trained model can be saved with the output_model output parameter. If training is not desired, but only testing is, a model can be loaded with the input_model parameter. At the current time, a loaded model cannot be trained further, so specifying both input_model and training is not allowed.

The program is also able to evaluate a model on test data. A test dataset can be specified with the test parameter. Class predictions can be saved with the predictions output parameter. If labels are specified for the test data with the test_labels parameter, then the program will print the accuracy of the predictions on the given test set and its corresponding labels.

For example, to train a softmax regression model on the data 'dataset' with labels 'labels' with a maximum of 1000 iterations for training, saving the trained model to 'sr_model', the following command can be used:

>>> output = softmax_regression(training=dataset, labels=labels)
>>> sr_model = output['output_model']

Then, to use 'sr_model' to classify the test points in 'test_points', saving the output predictions to 'predictions', the following command can be used:

>>> output = softmax_regression(input_model=sr_model, test=test_points)
>>> predictions = output['predictions']

See also

sparse_coding()

Sparse Coding

>>> from mlpack import sparse_coding
>>> d = sparse_coding(atoms=15, initial_dictionary=np.empty([0, 0]),
        input_model=None, lambda1=0, lambda2=0, max_iterations=0,
        newton_tolerance=1e-06, normalize=False, objective_tolerance=0.01,
        seed=0, test=np.empty([0, 0]), training=np.empty([0, 0]))
>>> codes = d['codes']
>>> dictionary = d['dictionary']
>>> output_model = d['output_model']

An implementation of Sparse Coding with Dictionary Learning. Given a dataset, this will decompose the dataset into a sparse combination of a few dictionary elements, where the dictionary is learned during computation; a dictionary can be reused for future sparse coding of new points. Detailed documentation.

Input options

name type description default
atoms int Number of atoms in the dictionary. 15
copy_all_inputs bool If specified, all input parameters will be deep copied before the method is run. This is useful for debugging problems where the input parameters are being modified by the algorithm, but can slow down the code. Only exists in Python binding. False
initial_dictionary matrix Optional initial dictionary matrix. np.empty([0, 0])
input_model SparseCodingType File containing input sparse coding model. None
lambda1 float Sparse coding l1-norm regularization parameter. 0
lambda2 float Sparse coding l2-norm regularization parameter. 0
max_iterations int Maximum number of iterations for sparse coding (0 indicates no limit). 0
newton_tolerance float Tolerance for convergence of Newton method. 1e-06
normalize bool If set, the input data matrix will be normalized before coding. False
objective_tolerance float Tolerance for convergence of the objective function. 0.01
seed int Random seed. If 0, ‘std::time(NULL)’ is used. 0
test matrix Optional matrix to be encoded by trained model. np.empty([0, 0])
training matrix Matrix of training data (X). np.empty([0, 0])
verbose bool Display informational messages and the full list of parameters and timers at the end of execution. False

Output options

Results are returned in a Python dictionary. The keys of the dictionary are the names of the output parameters.

name type description
codes matrix Matrix to save the output sparse codes of the test matrix (–test_file) to.
dictionary matrix Matrix to save the output dictionary to.
output_model SparseCodingType File to save trained sparse coding model to.

Detailed documentation

An implementation of Sparse Coding with Dictionary Learning, which achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer on the codes (the Elastic Net). Given a dense data matrix X with d dimensions and n points, sparse coding seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a sparse coding matrix Z with n points in k dimensions.

The original data matrix X can then be reconstructed as Z * D. Therefore, this program finds a representation of each point in X as a sparse linear combination of atoms in the dictionary D.

The sparse coding is found with an algorithm which alternates between a dictionary step, which updates the dictionary D, and a sparse coding step, which updates the sparse coding matrix.

Once a dictionary D is found, the sparse coding model may be used to encode other matrices, and saved for future usage.

To run this program, either an input matrix or an already-saved sparse coding model must be specified. An input matrix may be specified with the training option, along with the number of atoms in the dictionary (specified with the atoms parameter). It is also possible to specify an initial dictionary for the optimization, with the initial_dictionary parameter. An input model may be specified with the input_model parameter.

As an example, to build a sparse coding model on the dataset 'data' using 200 atoms and an l1-regularization parameter of 0.1, saving the model into 'model', use

>>> output = sparse_coding(training=data, atoms=200, lambda1=0.1)
>>> model = output['output_model']

Then, this model could be used to encode a new matrix, 'otherdata', and save the output codes to 'codes':

>>> output = sparse_coding(input_model=model, test=otherdata)
>>> codes = output['codes']

See also

changelog/history

mlpack 3.2.2

2019-11-26
  • Add valid and same padding option in Convolution and Atrous Convolution layer #1988.

  • Add Model() to the FFN class to access individual layers #2043.

  • Update documentation for pip and conda installation packages #2044.

  • Add bindings for linear SVM #1935; mlpack_linear_svm from the command-line, linear_svm() from Python.

  • Add support to return the layer name as std::string #1987.

  • Speed and memory improvements for the Transposed Convolution layer #1493.

  • Fix Windows Python build configuration #1885.

  • Validate md5 of STB library after download #2087.

  • Add __version__ to __init__.py #2092.

mlpack 3.2.1

2019-10-01
  • Enforce CMake version check for ensmallen #2032.

  • Fix CMake check for Armadillo version #2029.

  • Better handling of when STB is not installed #2033.

  • Fix Naive Bayes classifier computations in high dimensions #2022.

mlpack 3.2.0

2019-09-25
  • Fix some potential infinity errors in Naive Bayes Classifier #2022.

  • Fix occasionally-failing RADICAL test #1924.

  • Fix gcc 9 OpenMP compilation issue #1970.

  • Added support for loading and saving of images #1903.

  • Add Multiple Pole Balancing Environment (#1901, #1951).

  • Added functionality for scaling of data #1876; see the command-line binding mlpack_preprocess_scale or Python binding preprocess_scale().

  • Add new parameter maximum_depth to decision tree and random forest bindings #1916.

  • Fix prediction output of softmax regression when test set accuracy is calculated #1922.

  • Pendulum environment now checks for termination. All RL environments now have an option to terminate after a set number of time steps (no limit by default) #1941.

  • Add support for probabilistic KDE (kernel density estimation) error bounds when using the Gaussian kernel #1934.

  • Fix negative distances for cover tree computation #1979.

  • Fix cover tree building when all pairwise distances are 0 #1986.

  • Improve KDE pruning by reclaiming not used error tolerance (#1954, #1984).

  • Optimizations for sparse matrix accesses in z-score normalization for CF #1989.

  • Add kmeans_max_iterations option to GMM training binding gmm_train_main.

  • Bump minimum Armadillo version to 8.400.0 due to ensmallen dependency requirement #2015.

mlpack 3.1.1

2019-05-26
  • Fix random forest bug for numerical-only data #1887.

  • Significant speedups for random forest #1887.

  • Random forest now has minimum_gain_split and subspace_dim parameters #1887.

  • Decision tree parameter print_training_error deprecated in favor of print_training_accuracy.

  • output option changed to predictions for adaboost and perceptron binding. Old options are now deprecated and will be preserved until mlpack 4.0.0 #1882.

  • Concatenated ReLU layer #1843.

  • Accelerate NormalizeLabels function using hashing instead of linear search (see src/mlpack/core/data/normalize_labels_impl.hpp) #1780.

  • Add ConfusionMatrix() function for checking performance of classifiers #1798.

  • Install ensmallen headers when it is downloaded during build #1900.

mlpack 3.1.0

2019-04-25
  • Add DiagonalGaussianDistribution and DiagonalGMM classes to speed up the diagonal covariance computation and deprecate DiagonalConstraint #1666.

  • Add kernel density estimation (KDE) implementation with bindings to other languages #1301.

  • Where relevant, all models with a Train() method now return a double value representing the goodness of fit (i.e. final objective value, error, etc.) #1678.

  • Add implementation for linear support vector machine (see src/mlpack/methods/linear_svm).

  • Change DBSCAN to use PointSelectionPolicy and add OrderedPointSelection #1625.

  • Residual block support #1594.

  • Bidirectional RNN #1626.

  • Dice loss layer (#1674, #1714) and hard sigmoid layer #1776.

  • output option changed to predictions and output_probabilities to probabilities for Naive Bayes binding (mlpack_nbc/nbc()). Old options are now deprecated and will be preserved until mlpack 4.0.0 #1616.

  • Add support for Diagonal GMMs to HMM code (#1658, #1666). This can provide large speedup when a diagonal GMM is acceptable as an emission probability distribution.

  • Python binding improvements: check parameter type #1717, avoid copying Pandas dataframes #1711, handle Pandas Series objects #1700.

mlpack 3.0.4

2018-11-13
  • Bump minimum CMake version to 3.3.2.

  • CMake fixes for Ninja generator by Marc Espie.

mlpack 3.0.3

2018-07-27
  • Fix Visual Studio compilation issue #1443.

  • Allow running local_coordinate_coding binding with no initial_dictionary parameter when input_model is not specified #1457.

  • Make use of OpenMP optional via the CMake ‘USE_OPENMP’ configuration variable #1474.

  • Accelerate FNN training by 20-30% by avoiding redundant calculations #1467.

  • Fix math::RandomSeed() usage in tests (#1462, #1440).

  • Generate better Python setup.py with documentation #1460.

mlpack 3.0.2

2018-06-08
  • Documentation generation fixes for Python bindings #1421.

  • Fix build error for man pages if command-line bindings are not being built #1424.

  • Add ‘shuffle’ parameter and Shuffle() method to KFoldCV #1412. This will shuffle the data when the object is constructed, or when Shuffle() is called.

  • Added neural network layers: AtrousConvolution #1390, Embedding #1401, and LayerNorm (layer normalization) #1389.

  • Add Pendulum environment for reinforcement learning #1388 and update Mountain Car environment #1394.

mlpack 3.0.1

2018-05-10
  • Fix intermittently failing tests #1387.

  • Add big-batch SGD (BBSGD) optimizer in src/mlpack/core/optimizers/bigbatch_sgd/ #1131.

  • Fix simple compiler warnings (#1380, #1373).

  • Simplify NeighborSearch constructor and Train() overloads #1378.

  • Add warning for OpenMP setting differences (#1358/#1382). When mlpack is compiled with OpenMP but another application is not (or vice versa), a compilation warning will now be issued.

  • Restructured loss functions in src/mlpack/methods/ann/ #1365.

  • Add environments for reinforcement learning tests (#1368, #1370, #1329).

  • Allow single outputs for multiple timestep inputs for recurrent neural networks #1348.

  • Add He and LeCun normal initializations for neural networks #1342. Neural networks: add He and LeCun normal initializations #1342, add FReLU and SELU activation functions (#1346, #1341), add alpha-dropout #1349.

mlpack 3.0.0

2018-03-30
  • Speed and memory improvements for DBSCAN. –single_mode can now be used for situations where previously RAM usage was too high.

  • Bump minimum required version of Armadillo to 6.500.0.

  • Add automatically generated Python bindings. These have the same interface as the command-line programs.

  • Add deep learning infrastructure in src/mlpack/methods/ann/.

  • Add reinforcement learning infrastructure in src/mlpack/methods/reinforcement_learning/.

  • Add optimizers: AdaGrad, CMAES, CNE, FrankeWolfe, GradientDescent, GridSearch, IQN, Katyusha, LineSearch, ParallelSGD, SARAH, SCD, SGDR, SMORMS3, SPALeRA, SVRG.

  • Add hyperparameter tuning infrastructure and cross-validation infrastructure in src/mlpack/core/cv/ and src/mlpack/core/hpt/.

  • Fix bug in mean shift.

  • Add random forests (see src/mlpack/methods/random_forest).

  • Numerous other bugfixes and testing improvements.

  • Add randomized Krylov SVD and Block Krylov SVD.

mlpack 2.2.5

2017-08-25
  • Compilation fix for some systems #1082.

  • Fix PARAM_INT_OUT() #1100.

mlpack 2.2.4

2017-07-18
  • Speed and memory improvements for DBSCAN. –single_mode can now be used for situations where previously RAM usage was too high.

  • Fix bug in CF causing incorrect recommendations.

mlpack 2.2.3

2017-05-24
  • Bug fix for –predictions_file in mlpack_decision_tree program.

mlpack 2.2.2

2017-05-04
  • Install backwards-compatibility mlpack_allknn and mlpack_allkfn programs; note they are deprecated and will be removed in mlpack 3.0.0 #992.

  • Fix RStarTree bug that surfaced on OS X only #964.

  • Small fixes for MiniBatchSGD and SGD and tests.

mlpack 2.2.1

2017-04-13
  • Compilation fix for mlpack_nca and mlpack_test on older Armadillo versions #984.

mlpack 2.2.0

2017-03-21
  • Bugfix for mlpack_knn program #816.

  • Add decision tree implementation in methods/decision_tree/. This is very similar to a C4.5 tree learner.

  • Add DBSCAN implementation in methods/dbscan/.

  • Add support for multidimensional discrete distributions (#810, #830).

  • Better output for Log::Debug/Log::Info/Log::Warn/Log::Fatal for Armadillo objects (#895, #928).

  • Refactor categorical CSV loading with boost::spirit for faster loading #681.

mlpack 2.1.1

2016-12-22
  • HMMs now use random initialization; this should fix some convergence issues #828.

  • HMMs now initialize emissions according to the distribution of observations #833.

  • Minor fix for formatted output #814.

  • Fix DecisionStump to properly work with any input type.

mlpack 2.1.0

2016-10-31
  • Fixed CoverTree to properly handle single-point datasets.

  • Fixed a bug in CosineTree (and thus QUIC-SVD) that caused split failures for some datasets #717.

  • Added mlpack_preprocess_describe program, which can be used to print statistics on a given dataset #742.

  • Fix prioritized recursion for k-furthest-neighbor search (mlpack_kfn and the KFN class), leading to orders-of-magnitude speedups in some cases.

  • Bump minimum required version of Armadillo to 4.200.0.

  • Added simple Gradient Descent optimizer, found in src/mlpack/core/optimizers/gradient_descent/ #792.

  • Added approximate furthest neighbor search algorithms QDAFN and DrusillaSelect in src/mlpack/methods/approx_kfn/, with command-line program mlpack_approx_kfn.

mlpack 2.0.3

2016-07-21
  • Added multiprobe LSH #691. The parameter ‘T’ to LSHSearch::Search() can now be used to control the number of extra bins that are probed, as can the -T (–num_probes) option to mlpack_lsh.

  • Added the Hilbert R tree to src/mlpack/core/tree/rectangle_tree/ #664. It can be used as the typedef HilbertRTree, and it is now an option in the mlpack_knn, mlpack_kfn, mlpack_range_search, and mlpack_krann command-line programs.

  • Added the mlpack_preprocess_split and mlpack_preprocess_binarize programs, which can be used for preprocessing code (#650, #666).

  • Added OpenMP support to LSHSearch and mlpack_lsh #700.

mlpack 2.0.2

2016-06-20
  • Added the function LSHSearch::Projections(), which returns an arma::cube with each projection table in a slice #663. Instead of Projection(i), you should now use Projections().slice(i).

  • A new constructor has been added to LSHSearch that creates objects using projection tables provided in an arma::cube #663.

  • Handle zero-variance dimensions in DET #515.

  • Add MiniBatchSGD optimizer (src/mlpack/core/optimizers/minibatch_sgd/) and allow its use in mlpack_logistic_regression and mlpack_nca programs.

  • Add better backtrace support from Grzegorz Krajewski for Log::Fatal messages when compiled with debugging and profiling symbols. This requires libbfd and libdl to be present during compilation.

  • CosineTree test fix from Mikhail Lozhnikov #358.

  • Fixed HMM initial state estimation #600.

  • Changed versioning macros __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH to MLPACK_VERSION_MAJOR, MLPACK_VERSION_MINOR, and MLPACK_VERSION_PATCH. The old names will remain in place until mlpack 3.0.0.

  • Renamed mlpack_allknn, mlpack_allkfn, and mlpack_allkrann to mlpack_knn, mlpack_kfn, and mlpack_krann. The mlpack_allknn, mlpack_allkfn, and mlpack_allkrann programs will remain as copies until mlpack 3.0.0.

  • Add –random_initialization option to mlpack_hmm_train, for use when no labels are provided.

  • Add –kill_empty_clusters option to mlpack_kmeans and KillEmptyClusters policy for the KMeans class (#595, #596).

mlpack 2.0.1

2016-02-04
  • Fix CMake to properly detect when MKL is being used with Armadillo.

  • Minor parameter handling fixes to mlpack_logistic_regression (#504, #505).

  • Properly install arma_config.hpp.

  • Memory handling fixes for Hoeffding tree code.

  • Add functions that allow changing training-time parameters to HoeffdingTree class.

  • Fix infinite loop in sparse coding test.

  • Documentation spelling fixes #501.

  • Properly handle covariances for Gaussians with large condition number #496, preventing GMMs from filling with NaNs during training (and also HMMs that use GMMs).

  • CMake fixes for finding LAPACK and BLAS as Armadillo dependencies when ATLAS is used.

  • CMake fix for projects using mlpack’s CMake configuration from elsewhere #512.

mlpack 2.0.0

2015-12-24
  • Removed overclustering support from k-means because it is not well-tested, may be buggy, and is (I think) unused. If this was support you were using, open a bug or get in touch with us; it would not be hard for us to reimplement it.

  • Refactored KMeans to allow different types of Lloyd iterations.

  • Added implementations of k-means: Elkan’s algorithm, Hamerly’s algorithm, Pelleg-Moore’s algorithm, and the DTNN (dual-tree nearest neighbor) algorithm.

  • Significant acceleration of LRSDP via the use of accu(a % b) instead of trace(a * b).

  • Added MatrixCompletion class (matrix_completion), which performs nuclear norm minimization to fill unknown values of an input matrix.

  • No more dependence on Boost.Random; now we use C++11 STL random support.

  • Add softmax regression, contributed by Siddharth Agrawal and QiaoAn Chen.

  • Changed NeighborSearch, RangeSearch, FastMKS, LSH, and RASearch API; these classes now take the query sets in the Search() method, instead of in the constructor.

  • Use OpenMP, if available. For now OpenMP support is only available in the DET training code.

  • Add support for predicting new test point values to LARS and the command-line ‘lars’ program.

  • Add serialization support for Perceptron and LogisticRegression.

  • Refactor SoftmaxRegression to predict into an arma::Row object, and add a softmax_regression program.

  • Refactor LSH to allow loading and saving of models.

  • ToString() is removed entirely #487.

  • Add –input_model_file and –output_model_file options to appropriate machine learning algorithms.

  • Rename all executables to start with an “mlpack” prefix #229.

  • Add HoeffdingTree and mlpack_hoeffding_tree, an implementation of the streaming decision tree methodology from Domingos and Hulten in 2000.

mlpack 1.0.12

2015-01-07
  • Switch to 3-clause BSD license (from LGPL).

mlpack 1.0.11

2014-12-11
  • Proper handling of dimension calculation in PCA.

  • Load parameter vectors properly for LinearRegression models.

  • Linker fixes for AugLagrangian specializations under Visual Studio.

  • Add support for observation weights to LinearRegression.

  • MahalanobisDistance<> now takes the root of the distance by default and therefore satisfies the triangle inequality (TakeRoot now defaults to true).

  • Better handling of optional Armadillo HDF5 dependency.

  • Fixes for numerous intermittent test failures.

  • math::RandomSeed() now sets the random seed for recent (>=3.930) Armadillo versions.

  • Handle Newton method convergence better for SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.

  • Known bug: CosineTree construction may fail in some cases on i386 systems #358.

mlpack 1.0.10

2014-08-29
  • Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are now restored to approximately 1.0.8 speeds, with significant improvement for the cover tree #347.

  • Detect dependencies correctly when ARMA_USE_WRAPPER is not being defined (i.e., libarmadillo.so does not exist).

  • Bugfix for compilation under Visual Studio #348.

mlpack 1.0.9

2014-07-28
  • GMM initialization is now safer and provides a working GMM when constructed with only the dimensionality and number of Gaussians #301.

  • Check for division by 0 in Forward-Backward Algorithm in HMMs #301.

  • Fix MaxVarianceNewCluster (used when re-initializing clusters for k-means) #301.

  • Fixed implementation of Viterbi algorithm in HMM::Predict() #303.

  • Significant speedups for dual-tree algorithms using the cover tree (#235, #314) including a faster implementation of FastMKS.

  • Fix for LRSDP optimizer so that it compiles and can be used #312.

  • CF (collaborative filtering) now expects users and items to be zero-indexed, not one-indexed #311.

  • CF::GetRecommendations() API change: now requires the number of recommendations as the first parameter. The number of users in the local neighborhood should be specified with CF::NumUsersForSimilarity().

  • Removed incorrect PeriodicHRectBound #58.

  • Refactor LRSDP into LRSDP class and standalone function to be optimized #305.

  • Fix for centering in kernel PCA #337.

  • Added simulated annealing (SA) optimizer, contributed by Zhihao Lou.

  • HMMs now support initial state probabilities; these can be set in the constructor, trained, or set manually with HMM::Initial() #302.

  • Added Nyström method for kernel matrix approximation by Marcus Edel.

  • Kernel PCA now supports using Nyström method for approximation.

  • Ball trees now work with dual-tree algorithms, via the BallBound<> bound structure #307; fixed by Yash Vadalia.

  • The NMF class is now AMF<>, and supports far more types of factorizations, by Sumedh Ghaisas.

  • A QUIC-SVD implementation has returned, written by Siddharth Agrawal and based on older code from Mudit Gupta.

  • Added perceptron and decision stump by Udit Saxena (these are weak learners for an eventual AdaBoost class).

  • Sparse autoencoder added by Siddharth Agrawal.

mlpack 1.0.8

2014-01-06
  • Memory leak in NeighborSearch index-mapping code fixed #298.

  • GMMs can be trained using the existing model as a starting point by specifying an additional boolean parameter to GMM::Estimate() #296.

  • Logistic regression implementation added in methods/logistic_regression (see also #293).

  • L-BFGS optimizer now returns its function via Function().

  • Version information is now obtainable via mlpack::util::GetVersion() or the __MLPACK_VERSION_MAJOR, __MLPACK_VERSION_MINOR, and __MLPACK_VERSION_PATCH macros #297.

  • Fix typos in allkfn and allkrann output.

mlpack 1.0.7

2013-10-04
  • Cover tree support for range search (range_search), rank-approximate nearest neighbors (allkrann), minimum spanning tree calculation (emst), and FastMKS (fastmks).

  • Dual-tree FastMKS implementation added and tested.

  • Added collaborative filtering package (cf) that can provide recommendations when given users and items.

  • Fix for correctness of Kernel PCA (kernel_pca) #270.

  • Speedups for PCA and Kernel PCA #198.

  • Fix for correctness of Neighborhood Components Analysis (NCA) #279.

  • Minor speedups for dual-tree algorithms.

  • Fix for Naive Bayes Classifier (nbc) #269.

  • Added a ridge regression option to LinearRegression (linear_regression) #286.

  • Gaussian Mixture Models (gmm::GMM<>) now support arbitrary covariance matrix constraints #283.

  • MVU (mvu) removed because it is known to not work #183.

  • Minor updates and fixes for kernels (in mlpack::kernel).

mlpack 1.0.6

2013-06-13
  • Minor bugfix so that FastMKS gets built.

mlpack 1.0.5

2013-05-01
  • Speedups of cover tree traversers #235.

  • Addition of rank-approximate nearest neighbors (RANN), found in src/mlpack/methods/rann/.

  • Addition of fast exact max-kernel search (FastMKS), found in src/mlpack/methods/fastmks/.

  • Fix for EM covariance estimation; this should improve GMM training time.

  • More parameters for GMM estimation.

  • Force GMM and GaussianDistribution covariance matrices to be positive definite, so that training converges much more often.

  • Add parameter for the tolerance of the Baum-Welch algorithm for HMM training.

  • Fix for compilation with clang compiler.

  • Fix for k-furthest-neighbor-search.

mlpack 1.0.4

2013-02-08
  • Force minimum Armadillo version to 2.4.2.

  • Better output of class types to streams; a class with a ToString() method implemented can be sent to a stream with operator«.

  • Change return type of GMM::Estimate() to double #257.

  • Style fixes for k-means and RADICAL.

  • Handle size_t support correctly with Armadillo 3.6.2 #258.

  • Add locality-sensitive hashing (LSH), found in src/mlpack/methods/lsh/.

  • Better tests for SGD (stochastic gradient descent) and NCA (neighborhood components analysis).

mlpack 1.0.3

2012-09-16
  • Remove internal sparse matrix support because Armadillo 3.4.0 now includes it. When using Armadillo versions older than 3.4.0, sparse matrix support is not available.

  • NCA (neighborhood components analysis) now support an arbitrary optimizer #245, including stochastic gradient descent #249.

mlpack 1.0.2

2012-08-15
  • Added density estimation trees, found in src/mlpack/methods/det/.

  • Added non-negative matrix factorization, found in src/mlpack/methods/nmf/.

  • Added experimental cover tree implementation, found in src/mlpack/core/tree/cover_tree/ #157.

  • Better reporting of boost::program_options errors #225.

  • Fix for timers on Windows (#212, #211).

  • Fix for allknn and allkfn output #204.

  • Sparse coding dictionary initialization is now a template parameter #220.

mlpack 1.0.1

2012-03-03
  • Added kernel principal components analysis (kernel PCA), found in src/mlpack/methods/kernel_pca/ #74.

  • Fix for Lovasz-Theta AugLagrangian tests #182.

  • Fixes for allknn output (#185, #186).

  • Added range search executable #192.

  • Adapted citations in documentation to BibTeX; no citations in -h output #195.

  • Stop use of ‘const char*’ and prefer ‘std::string’ #176.

  • Support seeds for random numbers #177.

mlpack 1.0.0

2011-12-17
  • Initial release. See any resolved tickets numbered less than #196 or execute this query: http://www.mlpack.org/trac/query?status=closed&milestone=mlpack+1.0.0