| MADlib Release Notes |
| -------------------- |
| |
| These release notes contain the significant changes in each MADlib release, |
| with most recent versions listed at the top. |
| |
| A complete list of changes for each release can be obtained by viewing the git |
| commit history located at https://github.com/madlib/madlib/commits/master. |
| |
| Current list of bugs and issues can be found at http://jira.madlib.net. |
| -------------------------------------------------------------------------------- |
| MADlib v1.8 |
| |
| Release Date: 2015-July-17 |
| |
| New features: |
| * Latent Dirichlet Allocation (LDA) Performance Improvement |
| - Function lda_train() is 1.5X ~ 3X faster |
| - Improve the scalability support (vocabulary size multiplied by the |
| number of topics up to 250 million) |
| * Matrix Operations |
| - |
| * Quotation and International Character Support |
| - Most modules now support table and column names that are quoted and |
| contain international characters, including: |
| - Regression models (GLMs, linear regression, elastic net, etc.) |
| - Decision trees and random forests |
| - Unsupervised learning models (association rules, k-means, LDA, etc.) |
| - Summary, Pearson's correlation, and principal component analysis |
| * Array Norms and Distances |
| - Generic p-norm distance |
| - Jaccard distance |
| - Cosine similarity |
| * Miscellaneous |
| - Text utility for term frequency and vacabulary construction |
| - Low-rank matrix factorization: 32-bit integer aupport (MADLIB-903) |
| - Cross-validation: classification support (MADLIB-908) |
| - Clean up functions for junk tables |
| |
| Note: |
| - LDA models that are trained using MADlib v1.7.1 or earlier need to be |
| re-trained for the use of MADlib v1.8 |
| |
| Known issues: |
| - Performance for decision tree with cross-validation is poor on a HAWQ |
| multi-node system. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.7.1 |
| |
| Release Date: 2015-March-18 |
| |
| New features: |
| * Random Forest Performance Improvement |
| - Function forest_train() is 1.5X ~ 4X faster without variable importance, |
| and up to 100X faster with variable importance |
| - Function forest_predict() is up to 10X faster when type='response' |
| - Allow user-specified sample ratio to train with a small subsample |
| * Gaussian Naive Bayes: allow continuous variables |
| * K-Means: Allow user-specified sample ratio for K-means++ seeding |
| * Miscellaneous |
| - Array functions: array_square() for element-wise square, madlib.sum() |
| for array element-wise aggregation |
| - Madpack does not require password when not necessary (MADLIB-357) |
| - Platform support of PostgreSQL 9.4 and HAWQ 1.3 |
| - Allow views and materialized views for training functions |
| - Support quantile computation in summary functions for HAWQ and PG 9.4 |
| |
| Bug fixes: |
| - Fixed the support of multiple parameter values and NULL in general |
| cross-validation (MADLIB-898, MADLIB-896) |
| - Fixed infinite loop when detecting recursive view-to-view dependencies for |
| upgrading (MADLIB-901) |
| - Allow user-specified column names in PCA and multinom_predict() |
| |
| Known issues: |
| - Performance for decision tree with cross-validation is poor on a HAWQ |
| multi-node system. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.7 |
| |
| Release Date: 2014-December-31 |
| |
| New features: |
| * Generalized Linear Model: |
| - Added a new generic module for GLM functions that allow for response |
| variables that have arbitrary distributions (rather than simply |
| Gaussian distributions), and for an arbitrary function of the response |
| variable (the link function) to vary linearly with the predicted values |
| (rather than assuming that the response itself must vary linearly). |
| - Available distribution families: gaussian (link functions: identity, |
| inverse and log), binomial (link functions: probit and logit), |
| poisson (link functions: log, identity and square-root), gamma (link |
| functions: inverse, identity and log) and inverse gaussian (link functions: |
| square-inverse, inverse, identity and log). |
| - Deprecated 'mlogregr_train' in favor of 'multinom' available as part of |
| the new GLM functionality. |
| - Added a new 'ordinal' function for ordered logit and probit regression. |
| * Decision Tree: Reimplemented the decision tree module which includes following |
| changes: |
| - Improved usability due to a new interface. |
| - Performance enhancements upto 40 times faster than the old interface. |
| - Additional features like pruning methods, surrogate variables for |
| NULL handling, cross validation, and various new tree tuning parameters. |
| - Addition of a new display function to visualize the trained tree and new |
| prediction function for scoring of new datasets. |
| * Random Forest: Reimplemented the random forest module which includes following |
| changes: |
| - New random forest module based on the new decision tree module. |
| - Better variable importance metrics and ability to explore each tree |
| in the forest independently. |
| - Ability to get class probabilities of all classes and not just the max |
| class during prediction. |
| - Improved visualization with export capabilities using Graphviz dot format. |
| * PMML: |
| - Upgraded compatible PMML version to 4.1. |
| - Moved PMML export out of early stage development with new functionality |
| available to export GLM, decision tree, and random forest models. |
| * Updated Eigen from 3.1.2 to 3.2.2. |
| * Updated PyXB from 1.2.3 to 1.2.4. |
| * Added finer granularity control for running specific install-check tests. |
| |
| Bug fixes: |
| - Fixed bug in K-means allowing use of user-defined metric functions |
| (MADLIB-874, MADLIB-875). |
| - Fixed issues related to header files included in the build system |
| (MADLIB-855, MADLIB-879, MADLIB-884). |
| |
| Known issues: |
| - Performance for decision tree with cross-validation is poor on a HAWQ |
| multi-node system. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.6 |
| |
| Release Date: 2014-June-30 |
| |
| New features: |
| - Added a new unified 'margins' function that computes marginal effects for |
| linear, logistic, multilogistic, and cox proportional hazards regression. The |
| new function also introduces support for interaction terms in the independent |
| array. |
| - Updated convergence for 'elastic_net_train' by checking the change in the |
| loglikelihood instead of the l2-norm of the change in coefficients. This allows |
| for faster convergence in problems with multiple optimal solutions. |
| The default threshold for convergence has been reduced from 1e-4 to 1e-6. |
| - Added a new helper function to convert categorical variables to indicator |
| variables which can be used directly in regression methods. The function |
| currently only supports dummy encoding. |
| - Improved performance for cox proportional hazards: average improvement of |
| 20 fold on GPDB and 2.5 fold on HAWQ. |
| - Improved performance on ARIMA by 30%. |
| - Added new functionality to export linear and logistic regression models as a |
| PMML object. The new module relies on PyXB to create PMML elements. |
| - Added a function ('array_scalar_add') to 'add' a scalar to an array. |
| - Added 'numeric' type support for all functions that take 'anyarray' as |
| argument. |
| - Made usability and aesthetic enhancements to documentation. |
| |
| Bug Fixes: |
| - Prepended python module name to sys.path before executing madlib function |
| to avoid conflicts with user-defined modules. |
| - Added a check in K-Means to ensure dimensionality of all data points are |
| the same and also equal to the dimensionality of any provided initial centroids |
| (MADLIB-713, MADLIB-789). |
| - Added a check in multinomial regression to quit early and cleanly if model |
| size is greater than the maximum permissible memory (MADLIB-667). |
| - Fixed a minor bug with incorrect column names in the decision trees module |
| (MADLIB-763). |
| - Fixed a bug in Kmeans that resulted in incorrect number of centroids for |
| particular datasets (MADLIB-857). |
| - Fixed bug when grouping columns have same name as one of the output table |
| column names (MADLIB-833). |
| |
| Deprecated Functions: |
| - Modules profile and quantile have been deprecated in favor of the 'summary' |
| function. |
| - Module 'svd_mf' has been deprecated in favor of the improved 'svd' function. |
| - Functions 'margins_logregr' and 'margins_mlogregr' have been deprecated in |
| favor of the 'margins' function. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.5 |
| |
| Release Date: 2014-Mar-05 |
| |
| New features: |
| - Added a new port 'HAWQ'. MADlib can now be used with the Pivotal |
| Distribution of Hadoop (PHD) through HAWQ |
| (see http://www.gopivotal.com/big-data/pivotal-hd for more details). |
| - Implemented performance improvements for linear and logistic predict functions. |
| - Moved Conditional Random Fields (CRFs) out of early stage development, and |
| updated the design and APIs for to enable ease of use and better functionality. |
| API changes include lincrf replaced by lincrf_train, crf_train_fgen and |
| crf_test_fgen with updated arguments, and format of segment tables. |
| - Improved linear support vector machines (SVMs) by enabling iterations, and |
| removed lsvm_predict and svm_predict, which are not useful in GPDB and HAWQ. |
| - Added new functions, with improved performance compared to svec_sfv, for |
| document vectorization into sparse vectors. |
| - Removed the bool-to-text cast and updated all functions depending on it to |
| explicitly convert variable to text. |
| - Added function properties for all SQL functions to allow the database optimizer |
| to make better plans. |
| |
| Bug Fixes: |
| - Set client_min_messages to 'notice' during database installation to ensure |
| that log messages don't get logged to STDERR. |
| - Fixed elastic net prediction to predict using all features instead of just |
| the selected features to avoid an error when no feature is selected as relevant |
| in the trained model. |
| - For corner probability values, p=0 and p=1, in bernoulli and binomial |
| distributions, the quantile values should be 0 and num_of_trials (=1 in the case |
| of bernoulli) respectively, independent of the probability of success. |
| - Changed install script to explicitly use /bin/bash instead of /bin/sh to avoid |
| problems in Ubuntu where /bin/sh is linked to 'dash'. |
| - Fixed issue in Elastic Net to take any array expression as input instead of |
| specifically expecting the expression 'ARRAY[...]'. |
| - Fixed wrong output in percentile of count-min (CM) sketches. |
| |
| Known issues: |
| - Elastic net prediction wrapper function elastic_net_prediction is not |
| available in HAWQ. Instead, prediction functionality is available for both |
| families via elastic_net_gaussian_predict and elastic_net_binomial_predict. |
| - Distance metrics functions in K-Means for the HAWQ port are restricted to the |
| in-built functions, specifically squaredDistNorm2, distNorm2, distNorm1, |
| distAngle, and distTanimoto. |
| - Functions in Quantile and Profile modules of Early Stage Development are not |
| available in HAWQ. Replacement of these functions is available as built-in |
| functions (percentile_cont) in HAWQ and Summary module in MADlib, respectively. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.4.1 |
| |
| Release Date: 2013-Dec-13 |
| |
| Bug Fixes: |
| - Fixed problem in Elastic Net for 'binomial' family if an 'integer' column was |
| passed for dependent variable instead of a 'boolean' column. |
| - '*' support in Elastic Net lacked checks for the columns being combined. Now |
| we check if the column for '*' is already an array, in which case we don't wrap |
| it with an 'array' modifier. If there are multiple columns we check that they |
| are of the same numeric type before building an array. |
| - Fixed a software regression in Robust Variance, Clustered Variance and |
| Marginal Effects for multinomial regression introduced in v1.4 when |
| output table name is schema-qualified. |
| - We now also support schema-qualified output table prefixes for SVD and PCA. |
| - Added warning message when deprecated functions are run. Also added a list of |
| deprecated functions in the ReadMe. |
| - Added a Markdown Readme along with the text version for better rendering on |
| Github. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.4 |
| |
| Release Date: 2013-Nov-25 |
| |
| New Features: |
| * Improved interface for Multinomial logistic regression: |
| - Added a new interface that accepts an 'output_table' parameter and |
| stores the model details in the output table instead of returning as a struct |
| data type. The updated function also builds a summary table that includes |
| all parameters and meta-parameters used during model training. |
| - The output table has been reformatted to present the model coefficients |
| and related metrics for each category in a separate row. This replaces the |
| old output format of model stats for all categories combined in a |
| single array. |
| * Variance Estimators |
| - Added Robust Variance estimator for Cox PH models (Lin and Wei, 1989). |
| It is useful in calculating variances in a dataset with potentially |
| noisy outliers. Namely, the standard errors are asymptotically normal even |
| if the model is wrong due to outliers. |
| - Added Clustered Variance estimator for Cox PH models. It is used |
| when data contains extra clustering information besides covariates and |
| are asymptotically normal estimates. |
| * NULL Handling: |
| - Modified behavior of regression modules to 'omit' rows containing NULL |
| values for any of the dependent and independent variables. The number of |
| rows skipped is provided as part of the output table. |
| This release includes NULL handling for following modules: |
| - Linear, Logistic, and Multinomial logistic regression, as well as |
| Cox Proportional Hazards |
| - Huber-White sandwich estimators for linear, logistic, and multinomial |
| logistic regression as well as Cox Proportional Hazards |
| - Clustered variance estimators for linear, logistic, and multinomial |
| logistic regression as well as Cox Proportional Hazards |
| - Marginal effects for logistic and multinomial logistic regression |
| |
| Deprecated functions: |
| - Multinomial logistic regression function has been renamed to |
| 'mlogregr_train'. Old function ('mlogregr') has been deprecated, |
| and will be removed in the next major version update. |
| |
| - For all multinomial regression estimator functions (list given below), |
| changes in the argument list were made to collate all optimizer specific |
| arguments in a single string. An example of the new optimizer parameter is |
| 'max_iter=20, optimizer=irls, precision=0.0001'. |
| This is in contrast to the original argument list that contained 3 arguments: |
| 'max_iter', 'optimizer', and 'precision'. This change allows adding new |
| optimizer-specific parameters without changing the argument list. |
| Affected functions: |
| - robust_variance_mlogregr |
| - clustered_variance_mlogregr |
| - margins_mlogregr |
| |
| Bug Fixes: |
| - Fixed an overflow problem in LDA by using INT64 instead of INT32. |
| - Fixed integer to boolean cast bug in clustered variance for logistic |
| regression. After this fix, integer columns are accepted for binary |
| dependent variable using the 'integer to bool' cast rules. |
| - Fixed two bugs in SVD: |
| - The 'example' option for online help has been fixed |
| - Column names for sparse input tables in the 'svd_sparse' and |
| 'svd_sparse_native' functions are no longer restricted to 'row_id', |
| 'col_id' and 'value'. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.3 |
| |
| Release Date: 2013-October-03 |
| |
| New Features: |
| * Cox Proportional Hazards: |
| - Added stratification support for Cox PH models. Stratification is used as |
| shorthand for building a Cox model that allows for more than one stratum, |
| and hence, allows for more than one baseline hazard function. |
| Stratification provides two pieces of key, flexible functionality for the |
| end user of Cox models: |
| -- Allows a categorical variable Z to be appropriately accounted for in |
| the model without estimating its predictive impact on the response |
| variable. |
| -- Categorical variable Z is predictive/associated with the response |
| variable, but Z may not satisfy the proportional hazards assumption |
| - Added a new function (cox_zph) that tests the proportional hazards |
| assumption of a Cox model. This allows the user to build Cox models and then |
| verify the relevance of the model. |
| * NULL Handling: |
| - Modified behavior of linear and logistic regression to 'omit' rows |
| containing NULL values for any of the dependent and independent variables. |
| The number of rows skipped is provided as part of the output table. |
| |
| Deprecated functions: |
| - Cox Proportional Hazard function has been renamed to 'coxph_train'. |
| Old function names ('cox_prop_hazards' and 'cox_prop_hazards_regr') |
| have been deprecated, and will be removed in the next major version update. |
| - The aggregate form of linear regression ('linregr') has been deprecated. |
| The stored-procedure form ('linregr_train') should be used instead. |
| |
| Bug Fixes: |
| - Fixed a memory leak in the Apriori algorithm. |
| |
| |
| -------------------------------------------------------------------------------- |
| MADlib v1.2 |
| |
| Release Date: 2013-September-06 |
| |
| New Features: |
| * ARIMA Timeseries modeling |
| - Added auto-regressive integrated moving average (ARIMA) modeling for |
| non-seasonal, univariate timeseries data. |
| - Module includes a training function to compute an ARIMA model and a |
| forecasting function to predict future values in the timeseries |
| - Training function employs the Levenberg-Marquardt algorithm (LMA) to |
| compute a numerical solution for the parameters of the model. The |
| observations and innovations for time before the first timestamp |
| are assumed to be zero leading to minimization of the conditional sum of |
| squares. This produces estimates referred to as conditional maximum likelihood |
| estimates (also referred as 'CSS' in some statistical packages). |
| * Documentation updates: |
| - Introduced a new format for documentation improving usability. |
| - Upgraded to Doxygen v1.84. |
| - Updated documentation improving consistency for multiple modules including |
| Regression methods, SVD, PCA, Summary function, and Linear systems. |
| Bug fixes: |
| - Checking out-of-bounds access of a 'svec' even if the size of svec is zero. |
| - Fixed a minor bug allowing use of GCC 4.7 and higher to build from source. |
| -------------------------------------------------------------------------------- |
| MADlib v1.1 |
| |
| Release Date: 2013-August-09 |
| |
| New Features: |
| * Singular Value Decomposition: |
| - Added Singular Value Decomposition using the Lanczos bidiagonalization |
| iterative method to decompose the original matrix into PBQ^t, where B is |
| a bidiagonalized matrix. We assume that the original matrix is too big to |
| load into memory but B can be loaded into the memory. B is then further |
| decomposed into XSY^T using Eigen's JacobiSVD function. This restricts the |
| number of features in the data matrix to about 5000. |
| - This implementation provides SVD (for dense matrix), SVD_BLOCK (also for |
| dense matrix but faster), SVD_SPARSE (convert a sparse matrix into a |
| dense one, slower) and SVD_SPARSE_NATIVE (directly operate on the sparse |
| matrix, much faster for really sparse matrices). |
| |
| * Principal Component Analysis: |
| - Added a PCA training function that generates the top-K principal |
| components for an input matrix. The original data is mean-centered by the |
| function with the mean matrix returned by the function as a separate table. |
| - The module also includes the projection function that projects a test data |
| set to the principal components returned by the train function. |
| |
| * Linear Systems: |
| - Added a module to solve linear system of equations (Ax = b). |
| - The module utilizes various direct methods from the Eigen library for |
| dense systems. Given below is a summary of the methods (more details at |
| http://eigen.tuxfamily.org/dox-devel/group__TutorialLinearAlgebra.html): |
| - Householder QR |
| - Partial Pivoting LU |
| - Full Pivoting LU |
| - Column Pivoting Householder QR |
| - Full Pivoting Householder QR |
| - Standard Cholesky decomposition (LLT) |
| - Robust Cholesky decomposition (LDLT) |
| - The module also includes direct and iterative methods for sparse linear |
| systems: |
| Direct: |
| - Standard Cholesky decomposition (LLT) |
| - Robust Cholesky decomposition (LDLT) |
| Iterative: |
| - In-memory Conjugate gradient |
| - In-memory Conjugate gradient with diagonal preconditioners |
| - In-memory Bi-conjugate gradient |
| - In-memory Bi-conjugate gradient with incomplete LU preconditioners |
| |
| Bug fixes and other changes: |
| * Robust input validation: |
| - Validation of input parameters to various functions has been improved to |
| ensure that it does not fail if double quotes are included as part of the |
| table name. |
| * Random Forest |
| - The ID field in rf_train has been expanded from INT to BIGINT (MADLIB-764) |
| * Various documentation updates: |
| - Documentation updated for various modules including elastic net, linear |
| and logistic regression. |
| -------------------------------------------------------------------------------- |
| MADlib v1.0 |
| |
| Release Date: 2013-July-03 |
| |
| New Features: |
| * Cox Proportional Hazards: |
| - Added Right Censoring support for Cox Prop Hazards |
| * Robust Variance Tests - Huber White: |
| - Added a method of calculating robust variance statistic by utilizing the |
| Huber-White sandwich estimator for linear regression, logistic regression, |
| and multinomial logistic regression |
| - Robust variance for linear and logistic regression also includes |
| grouping support |
| * Clustered Sandwich Estimators: |
| - Added clustered robust variance statistic by utilizing a clustered sandwich |
| estimator for linear regression, logistic regression, and multinomial |
| logistic regression |
| - Grouping is currently not implemented for clustered and parameter is only |
| a placeholder at present |
| * Marginal Effects Estimator: |
| - Added a method for computing the marginal effects for logistic regression |
| and multinomial logistic regression |
| - Grouping is currently not implemented for marginal effects and the |
| parameter is only a placeholder at present |
| * Multinomial logistic regression: |
| - Added a parameter in multinomial logistic regression, to enable picking |
| the reference category. Input for number of categories has been removed |
| due to redundancy |
| * Linear regression: |
| - Updated grouping columns to input as a comma delimited string rather |
| than as an array |
| - Resolved an issue with highly collinear data to produce results consistent |
| with other statistical packages. Threshold on condition number to use an |
| approximation for computing the pseudo-inverse was increased. |
| * Logistic regression: |
| - Changed behavior to error-out if the ouput table already exists |
| |
| Bug fixes: |
| * Summary: |
| - Summary function (when used with quartiles) used high memory when number |
| of column is large. This has been fixed by computing quartiles in an |
| iterative manner for a fixed number of columns (Pivotal-170) |
| - Fixed a problem with incorrect number of rows returned for Summary when |
| all values in a column are NULL (Pivotal-171) |
| -------------------------------------------------------------------------------- |
| MADlib v0.7 |
| |
| Release Date: 2013-May-01 |
| |
| New Features: |
| * Correlation function: |
| - Function to compute Pearson's cross-correlation for numeric columns in a |
| relational table |
| * Upgrade capability: |
| - All new versions since v0.7 are installed in a version-specific folder |
| (/usr/local/madlib/Versions/) |
| - Upgrade from v0.5/v0.6 to v0.7 on the database is now supported without |
| uninstalling previous MADlib database installation. |
| - Dependencies on updated functions, types, and other operators are caught |
| and upgrade is aborted with an appropriate message |
| |
| Bug fixes: |
| * Linear Regression: |
| - Improved matrix inversion method to compute coefficients comparable to R |
| for regression problems with high multicollinearity (MADLIB-790) |
| * Logistic Regression: |
| - Fixed a problem in logistic regression with grouping on 'text' datatype |
| columns (MADLIB-791) |
| |
| Known issues: |
| * Upgrade: |
| - Views dependent on MADlib functions being updated will be dropped during |
| the upgrade and restored after finishing upgrade. If upgrade fails for |
| any reason, these views and the original MADlib schema will *not* be |
| restored. Before initiating upgrade, we recommend taking a backup of |
| the MADlib schema and move all views dependent on MADlib to separate |
| schema and perform a backup with: |
| pg_dump -n 'schema_name' |
| |
| - Upgrade is currently not supported for the PostgreSQL platform and will |
| abort with an error |
| |
| - Upgrade currently does not detect functions defined by the user that |
| depend upon MADlib functions. Semantic/API changes to these MADlib |
| functions could lead to undefined results in such user-defined functions |
| |
| - Some important changes for the upgrade from v0.5 to v0.7 are given below |
| (Upgrade will raise an error and abort if there exist user-defined views |
| that depend on these changes. User-defined functions are not validated |
| with this check. An aborted upgrade does not affect the installed version |
| of MADlib.) |
| -- Logistic regression renamed from 'logregr' to 'logregr_train' |
| -- All internal and external aggregates in logistic regression |
| have been updated |
| -- PLDA module replaced with a refactored LDA module. Due to the |
| renaming all functions using PLDA need to be updated |
| -- Updated MADlib types: |
| logregr_result, plda_topics_t, plda_word_distrn, |
| plda_word_weight |
| -------------------------------------------------------------------------------- |
| MADlib v0.6 |
| |
| Release Date: 2013-Apr-01 |
| |
| New Features / Improvements: |
| * Generic cross-validation: |
| - Support for k-fold cross-validation of any supervised learning |
| algorithm |
| * Heteroskedasticity of linear regression |
| - Support for calculating heteroskedasticity via Breusch-Pagan test |
| * Grouping support for linear regression |
| - Support for linear regression on each group of data grouped by |
| one or multiple columns |
| * Grouping support for logistic regression |
| - Refactor of logistic regression code |
| - Support for logistic regression on each group of data grouped by |
| one or multiple columns |
| - Grouping support is added to the convex optimization framework |
| * LDA: |
| - Improved performance and scalability (MADLIB-480) |
| * Elastic net regularization for both linear and logistic regressions |
| - Support FISTA and IGD optimizers |
| * Summary function |
| - Support for an overview of data table |
| * Eigen package upgrade |
| - Now Eigen 3.1.2 is used by MADlib v0.6 |
| * Unit testing framework: |
| - A new unit testing framework is added for C++ abstraction layer |
| |
| Bug Fixes: |
| * C++ abstraction layer: |
| - Improved handling of NULL values in the input array (MADLIB-773) |
| * Naive Bayes: |
| - Improved the handling of NULL values. (MADLIB-749) |
| |
| Known Issues: |
| |
| * K-means: |
| - K-means crashes on some datasets, when the dimensionality of the points |
| is not uniform on the data set. (MADLIB-789) |
| |
| * Distribution Functions: |
| - Certain quantile functions will abort their session on invalid input |
| (MADLIB-786) |
| |
| * Multinomial Logistic Regression: |
| - Signs of coefficient outputs are inconsistent with other tools like R and |
| Stata (MADLIB-785) |
| |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.5 |
| |
| Release Date: 2012-Nov-15 |
| |
| Bug Fixes: |
| * K-means: |
| - Improved handling of invalid arguments (MADLIB-359, 361) |
| * Sketch-based estimators: |
| - Addressed security vulnerability (MADLIB-630) |
| |
| New Features / Improvements: |
| * Association Rules (Apriori): |
| - Improved reporting output format for better usability (MADLIB-411) |
| - Significant improvement in performance (MADLIB-638) |
| * C++ (Database) Abstraction Layer: |
| - Extension to support modular transition states (MADLIB-499) |
| - Extension to support functions returning set of values (MADLIB-638) |
| * Conditional Random fields: |
| - Support for Linear Chain Conditional Random Fields for NLP (MADLIB-628) |
| * Decision Tree: |
| - Improved performance for C4.5 and Random forests (MADLIB-605) |
| - Improved encoding (MADLIB-590) |
| * Infrastructure: |
| - Convex optimization framework |
| * K-means: |
| - Code refactoring and Improved performance |
| (MADLIB-454, MADLIB-522, MADLIB-678) |
| - Silhouette function for k-means (MADLIB-681) |
| * Low-rank Matrix Factorization |
| - New module |
| * Logistic Regression: |
| - Support for Multinomial Logistic Regression (MADLIB-575) |
| * Naive Bayes |
| - Significant improvement in performance (MADLIB-611, 619, 626) |
| * Regression Analysis: |
| - Support for Cox Proportional Hazards test (MADLIB-576) |
| * Sampling |
| - Added weighted sampling of a single row (MADLIB-584) |
| * SVD Matrix Factorization: |
| - Improved performance (MADLIB-578) |
| |
| Documentation: |
| * Conditional Random Fields: |
| - Example added for CRF module (MADLIB-731) |
| * SVD Matrix Factorization: |
| - Incremental-gradient SVD algorithm (MADLIB-572) |
| |
| Known issues: |
| * Multinomial Logistic Regression: |
| - Number of independent variables cannot exceed 65535 (MADLIB-665) |
| * Naive Bayes: |
| - Current implementation of Naive Bayes is only suitable for |
| categorical attributes (MADLIB-679) |
| - NULL input values not accepted for attributes (MADLIB-614) |
| - NULL probabilities given for test set values not seen in |
| training set (MADLIB-523) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.4.1 |
| |
| Release Date: 2012-Aug-9 |
| |
| Bug Fixes: |
| * PGXN: |
| - Fixed installation problem that could occur on some platforms (MADLIB-589) |
| |
| New Features/Improvements: |
| * C++ Abstraction Layer: |
| - Increased ABI compatibility across multiple Greenplum versions |
| (MADLIB-606) |
| * Hypothesis Tests: |
| - Tests that are not implemented as ordered aggregates are now also |
| installed on PostgreSQL 8.4 and Greenplum 4.0. |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.4 |
| |
| Release Date: 2012-Jun-18 |
| |
| Bug Fixes: |
| * Association Rules: |
| - assoc_rules() now uses schema-qualified function calls (MADLIB-435) |
| * Decision Trees: |
| - Enhanced correctness (MADLIB-409, 502, 503) |
| - Improved handling of invalid arguments (MADLIB-331) |
| * k-Means: |
| - Improved handling of invalid arguments (MADLIB-336, 364, 459) |
| * PLDA: |
| - Improved robustness (MADLIB-474) |
| * Sparse Vectors: |
| - svec_sfv() now uses locale-aware sorting (MADLIB-457) |
| - Operators now install to MADlib schema (MADLIB-470) |
| |
| New Features/Improvements: |
| * C++ Abstraction Layer: |
| - Support for "function pointers" (MADLIB-370) |
| - Support for sparse vectors (MADLIB-371) |
| - Support for more Eigen (linear algebra) types (MADLIB-533) |
| * Decision Trees: |
| - Code refactoring and optimization (MADLIB-410, 476, 504, 509) |
| - Documentation improvments (MADLIB-507) |
| - Output table now contains unencoded information (MADLIB-434) |
| - Enhance the missing value handling for continuous features (MADLIB-493) |
| * Hypothesis Tests: |
| - Pearson chi-square test (MADLIB-390) |
| - One- and two-sample t-Tests (MADLIB-391) |
| - F-test (MADLIB-392) |
| - Mann-Whitney U-test (MADLIB-393) |
| - Kolmogorov-Smirnov test (MADLIB-394) |
| - Wilcoxon-Signed-Rank test (MADLIB-405) |
| - One-way ANOVA (MADLIB-406) |
| * PostgreSQL Extensibility: |
| - Support for CREATE EXTENSION in PostgreSQL >= 9.1 (MADLIB-316) |
| - Availability on PGXN (MADLIB-334) |
| * Probability Functions: |
| - Wrap all distribution functions implemented by Boost (MADLIB-412) |
| - Wrap Kolmogorov distribution function from CERN ROOT project (MADLIB-413) |
| * Random Forests: |
| - New module (MADLIB-419) |
| * Support: |
| - Add elementary matrix/vector functions (e.g., norm/distances etc.) |
| (MADLIB-532) |
| * Viterbi Feature Extraction: |
| - New module (MADLIB-478) |
| |
| Known issues: |
| - svec_sfv() does not support collations, as introduced with PostgreSQL 9.1 |
| (MADLIB-558) |
| - Invalid arguments are not always guaranteed to be handled gracefully and |
| may lead to confusing error messages (MADLIB-28, 359, 361, 363) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.3 |
| |
| Release Date: 2012-Feb-9 |
| |
| New features: |
| * Installer: |
| - Single installer package targeting all supported DBMSs per OS (MADLIB-218) |
| * C++ Abstraction Layer: |
| - Switched from using Armadillo to using Eigen for linear-algebra |
| operations, thereby eliminating the dependency on LAPACK/BLAS (MADLIB-275) |
| - Reimplemented as a template library for performance improvements |
| (MADLIB-295) |
| * Decision Trees: |
| - Major update |
| - Now supports multiple split criteria (information gain, gini, gain ratio) |
| - Now supports tree pruning using a validation set to address over fitting |
| - Now supports additional functions for tree output |
| - Now supports continuous features in addition to categorical features |
| - Additional support for handling null values |
| - Improved scalability and performance |
| * k-Means Clustering: |
| - Now handles any input that is convertible to SVEC. (MADLIB-42) |
| - Multiple distance functions (L1-norm, L2-norm, cosine similarity, Tanimoto |
| similarity) (MADLIB-43) |
| - Supports multiple seedings methods (kmeans++, random, user-specified list |
| of centroids) |
| - Replaced goodness of fit with the (simplified) Silhouette coefficient |
| (MADLIB-45) |
| - New run-time parameters (MADLIB-47) |
| * Linear Regression: |
| - Major speed improvement |
| * Logistic Regression: |
| - Major speed improvement |
| - Now handles any input that is convertible to BOOLEAN (dependent variable) |
| or DOUBLE PRECISION[] (independent variables). (MADLIB-283) |
| - An under-/overflow safe version to evaluate the (usual) logistic function, |
| for scoring logistic regression (MADLIB-271) |
| - A third optimizer: Incremental-gradient-descent (MADLIB-303) |
| * Support: |
| - For Greenplum <= 4.2.0, added a workaround for INSERT INTO in the same way |
| as the existing CREATE TABLE AS workaround. This workaround is not needed |
| in Greenplum >= 4.2.1 any more. (MADLIB-265) |
| - Function version() returns Madlib build information (MADLIB-309) |
| |
| Bug fixes: |
| * Sparse vectors: |
| - Fixed sparse-vector type case problems (MADLIB-282, MADLIB-305) |
| - Fixed a situation where using svec_svf() could cause a segmentation fault |
| (MADLIB-350) |
| - Increased compatibility with internal PostgreSQL conventions (MADLIB-257) |
| * Logistic regression: |
| - Handle numerical instability more gracefully (MADLIB-343, MADLIB-345) |
| - Handle unexpected inputs more gracefully (MADLIB-284, MADLIB-344) |
| - Fixed "Random variate x is nan, but must be finite" issue (MADLIB-356) |
| |
| Known issues: |
| - Decision Trees not supported on Greenplum 4.0 (MADLIB-346, MADLIB-347) |
| - K-means: the error '"nan" does not exist' may be raised when input vectors |
| contain NaN. (MADLIB-364) |
| - Association Rules require the madlib schema to be in the search path |
| (MADLIB-353) |
| - Invalid arguments are not always guaranteed to be handled gracefully and |
| may lead to confusing error messages (MADLIB-28, 336, 359, 361, 363, 364) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.2.1beta |
| |
| Release Date: 2011-Sep-14 |
| |
| General changes: |
| * numerous improvements to the C++ abstraction layer: |
| - code clean-up |
| - fixed issue where incorrect values were returned when used with |
| debug builds of PostgreSQL/Greenplum (MADLIB-253) |
| - fixed issue where returning arrays to PostgreSQL/Greenplum could lead |
| to a crash (MADLIB-250) |
| - allocated memory is now 16-byte aligned for improved stability and |
| performance (MADLIB-236) |
| * compiling with advanced warnings enabled by default now |
| * all C/C++ code now free of warnings. On gcc <= 4.6, there might still be |
| warnings due to "unclean" macros in DBMS header files (MADLIB-228) |
| * prepared Solaris support in a later release (MADLIB-204) |
| - added support for Sun Compiler in CMake build script |
| - fixed all compilation errors with Sun compiler |
| * added UDF to mimic "CREATE TABLE AS ...", as a workaround for a Greenplum |
| issue (MADLIB-241). Included this as GP Compatibility module. |
| * madpack utility: |
| - dropped madpack dependency on PygreSQL (MADLIB-217) |
| - improved security in madpack install-check (MADLIB-229) |
| - fixed bashism in madpack (MADLIB-222) |
| - fixed install-check not running on non-default schema (MADLIB-251) |
| |
| Modules/methods: |
| * SVM (kernel_machines): |
| - fixed cumulative error count in svm_cls_update() function |
| - improved memory management in SVM module |
| * Linear regression (regress): |
| - fixed unexpected behavior for some edge cases (MADLIB-214) |
| - fixed crashing with huge number of independent vars (MADLIB-250) |
| * Logistic regression (regress): |
| - added support for arbitrary expressions for dep./indep. variables, not |
| just column names (MADLIB-255) |
| * Quantile: |
| - fixed quantile() function to be exact |
| - added simple version for small data sets |
| * Sparse Vectors: |
| - added check for sorted dictionary to svec_sfv (MADLIB-187) |
| * Decision Tree (decision_tree): |
| - now can be run multiple times in one session (MADLIB-156) |
| |
| Known issues: |
| * non-unified API for several SQL UDFs (MADLIB-208) |
| * performance of the conjugate-gradient optimizer in logistic regression |
| can be very poor (MADLIB-164) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.2.0beta |
| |
| Release Date: 2011-Jul-8 |
| |
| General changes: |
| * new build and installation framework based on CMake |
| * new C++ abstraction layer for easy and secure method development |
| * new database installation utility (madpack) |
| |
| Modules/methods: |
| * new: Association Rules (assoc_rules) |
| * new: Array Operators (array_ops) |
| * new: Decision Tree (decision_tree) |
| * new: Conjugate Gradient (conjugate_gradient) |
| * new: Parallel LDA (plda) |
| * improved: all methods from previous release |
| |
| Known issues: |
| * non-unified API for several SQL UDFs (MADLIB-208) |
| * running decision tree more than once in one session fails (MADLIB-156) |
| * performance of the conjugate-gradient optimizer in logistic regression |
| can be very poor (MADLIB-164) |
| * svec_sfv function doesn't check for sorted dictionary (MADLIB-187) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.1.0alpha |
| |
| Release Date: 2011-Jan-31 |
| |
| Initial release. |
| |
| Included modules/methods: |
| * Naive-Bayes Classification (bayes) |
| * k-Means Clustering (kmeans) |
| * Support Vector Machines (kernel_machines) |
| * Sketch-based Estimators (sketch) |
| * Sketch-based Profile (data_profile) |
| * Quantile (quantile) |
| * Linear & Logistic Regression (regress) |
| * SVD Matrix Factorisation (svdmf) |
| * Sparse Vectors (svec) |
| |
| -------------------------------------------------------------------------------- |
| MADlib v0.1.0prerelease |
| |
| Release date: 2011-Jan-25 |
| |
| Demo release. |