CHANGELOG.md

Changelog

16.17.8

• dart 3.0 migration (non-breaking changes)

16.17.7

• ml_linalg ^13.11.15 used

16.17.6

• ml_linalg version fixed (13.11.11)
• benchmark info added

16.17.5

• ml_linalg 13.11.1 used

16.17.4

• Documentation:
  • Added links to articles to README

16.17.3

• Log Likelihood Cost function:
  • dtype passed
• Newton optimizer:
  • dtype passed
• Removed package:ml_linalg/linalg.dart and package:ml_algo/ml_algo.dart imports

16.17.2

• Code quality:
  • Strict options turned on
  • Pedantic dependency removed in favour of dart lints package

16.17.1

• e2e tests:
  • more stable tests for LinearRegressor

16.17.0

• LogisticRegressor:
  • Newton method added

16.16.0

• LinearRegressor:
  • Newton method added

16.15.2

• LinearRegressor, LogisticRegressor, SoftmaxRegressor:
  • Set fitIntercept param to true by default

16.15.1

• README: LogisticRegressor example corrected

16.15.0

• LinearRegressor.BGD constructor added

16.14.0

• LinearRegressor.SGD constructor added

16.13.0

• RandomBinaryProjectionSearcher:
  • Distance type considered
• ml_algo export file:
  • Distance type exported
  • DType exported
• kd_tree export file:
  • Distance type exported
  • DType exported

16.12.1

• Corrected link to RandomBinaryProjectionSearcher class in README.md

16.12.0

• RandomBinaryProjectionSearcher class added

16.11.4

• getPimaIndiansDiabetesDataFrame, getIrisDataFrame used

16.11.3

• Toy datasets from ml_dataframe package used

16.11.2

• KDTree:
  • fromIterable constructor, default value for splitting strategy changed
  • Serialization tests added
• README:
  • Example for KDTree persisting added

16.11.1

• KDTree example added to README
• kd_tree exported as a separate library

16.11.0

• ml_preprocessing version upgraded to 7.0.2
• ml_dataframe version upgraded to 1.0.0

16.10.5

• KnnClassifier:
  • Proofreading the documentation

16.10.4

• DecisionTreeClassifier:
  • Proofreading the documentation

16.10.3

• CrossValidator:
  • Proofreading the documentation

16.10.2

• KDTree:
  • Corrected usage example

16.10.1

• README.md:
  • Proofreading the texts

16.10.0

• KDTree:
  • Added queryIterable method

16.9.0

• KDTree:
  • Supported cosine, manhattan and hamming distance

16.8.0

• DecisionTreeClassifier:
  • Added Gini index assessor type

16.7.2

• DecisionTreeClassifier:
  • TreeNode fields renamed
  • Added example of DecisionTreeClassifier usage to README.md

16.7.1

• DecisionTreeClassifier:
  • Fixed greedy splitter in case of a split column consisting of the same values

16.7.0

• DecisionTreeClassifier:
  • Added saveAsSvg method which returns '.svg' file with a graphical representation of a tree

16.6.3

• KDTree:
  • fromIterable constructor added
  • splitStrategy option added to all constructors

16.6.2

• KDTree:
  • KDTree build optimization: split algorithm changed

16.6.1

• KDTree class added to library export file

16.6.0

• Added KDTree algorithm

16.5.2

• Add ecosystem notes to README.md

16.5.1

• Added linear regression examples to README.md

16.5.0

• LinearRegressor:
  • Added LinearRegressor.SGD constructor

16.4.0

• LinearRegressor:
  • Added LinearRegressor.lasso constructor

16.3.2

• LinearRegressor:
  • Coordinated descent optimizer speed up

16.3.1

• README: Added example of linear regression

16.3.0

• Added closed-form solution for linear regression

16.2.4

• Corrected typos and mistakes in README and documentation

16.2.3

• e2e tests: linear regressor's config improved

16.2.2

• Linear optimization-based algorithms: default parameters organised and extracted to separate files

16.2.1

• Documentation for learning rate strategies added

16.2.0

• stepBased learning rate strategy added

16.1.0

• timeBased and exponential learning rate strategies added
• Dart formatting check added to CI pipeline

16.0.4

• README: learning rate examples

16.0.3

• dartfmt applied to the project files

16.0.2

• Retrainable: returning type was fixed

16.0.1

• README updated according to null-safety changes
• All files from lib directory formatted by dartfmt tool

16.0.0

• Null-safety stable release

15.6.7

• README: important notes on data handling added

15.6.6

• LogisticRegressor, SoftmaxRegressor: redundant link function implementations removed

15.6.5

• DecisionTreeTrainer: redundant helper for trainer creation removed

15.6.4

• xrange 1.0.0 supported

15.6.3

• ml_dataframe 0.4.0 supported
• README.md: example for flutter developers corrected

15.6.2

• More strict analyser options added

15.6.1

• README.md: example for flutter developers added

15.6.0

• Models retraining functionality added

15.5.0

• KnnClassifier, DecisionTreeClassifier, LogisticRegressor, SoftmaxRegressor, KnnRegressor, LinearRegressor
  • hyperparameters added to the interfaces

15.4.1

• DTypeJsonConverter added
• MatrixJsonConverter added
• VectorJsonConverter added
• DistanceTypeJsonConverter added

15.4.0

• KnnClassifier:
  • serialization/deserialization functionality added with possibility to save the model into a json file
• KnnRegressor:
  • serialization/deserialization functionality added with possibility to save the model into a json file

15.3.6

• ml_dataframe: version 0.3.0 supported
• README.md: build badge corrected

15.3.5

• Github actions set up

15.3.4

• DI logic:
  • conditional dependency registering added

15.3.3

• FUNDING.yml created

15.3.2

• Awfully long identifier SequenceElementsDistributionCalculator renamed to DistributionCalculator

15.3.1

• README:
  • typos corrected
  • LogisticRegressor example corrected

15.3.0

• RSS metric added

15.2.4

• Documentation for classification metrics improved

15.2.3

• Documentation for RMSE metric improved

15.2.2

• Documentation for MAPE metric improved

15.2.1

• classificationMetrics constant list added
• regressionMetrics constant list added

15.2.0

• Recall metric added

15.1.0

• MAPE metric: output range squeezed to [0, 1]

15.0.1

• RegressorAssessor: unit tests added

15.0.0

• Breaking changes:
  • CrossValidator:
    • targetNames argument removed
  • Assessable, assess method: targetNames argument removed
• Precision metric added
• Coordinate descent optimization logic fixed: dtype considered
• LinearClassifier:
  • classNames property replaced with targetNames property in Predictor

14.2.6

• injector lib 1.0.9 supported

14.2.5

• pubspec:
  • injector dependency corrected

14.2.4

• README:
  • File path note for flutter developers added

14.2.3

• README:
  • Kfold constructor renamed to kFold
  • brackets removed from LogisticRegressor constructor arguments
  • file path note added

14.2.2

• ml_dataframe 0.2.0 supported

14.2.1

• README: Examples on prediction and collecting learning data added

14.2.0

• SoftmaxRegressor:
  • Default constructor: collectLearningData parameter added

14.1.1

• README: Advanced usage example on Logistic regression added

14.1.0

• Model selection: splitData helper added

14.0.1

• data splitters renamed and reorganized

14.0.0

• Breaking change:
  • CrossValidator: evalute method's api changed, it returns a Future resolving with scores Vector now instead of a double value

13.10.0

• LinearRegressor:
  • Default constructor: collectLearningData parameter added

13.9.0

• LogisticRegressor:
  • Default constructor: collectLearningData parameter added

13.8.1

• ml_dataframe dependency updated
• xrange dependency constrain removed

13.8.0

• LinkFunction:
  • Float64InverseLogitLinkFunction added
  • Float64SoftmaxLinkFunction added

13.7.0

• LinearRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

13.6.0

• SoftmaxRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

13.5.1

• DecisionTreeClassifier: documentation added for fromJson constructor

13.5.0

• LogisticRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

13.4.0

• DecisionTreeClassifier: serialization/deserialization functionality added with possibility to save the model into a file as json

13.3.7

• TreeLeafLabel: probability validation improvements

13.3.6

• DecisionTreeClassifier: classifier instantiating refactored
• TreeSolver: DI support added

13.3.5

• SoftmaxRegressor: classifier instantiating refactored

13.3.4

• LogisticRegressor: classifier instantiating refactored

13.3.3

• KnnClassifierImpl: unit tests for predictProbability method added

13.3.2

• KnnClassifier: classifier instantiating refactored

13.3.1

• readme: KnnRegressor usage example fixed

13.3.0

• KnnClassifier class added

13.2.0

• KNN algorithm: standardization for distance added
• KnnRegressor:
  • default kernel changed to gaussian
  • k parameter is required now

13.1.1

• KNN regression: documentation for kernel function types added
• KnnRegressor: finding weighted average using kernel function fixed

13.1.0

• CrossValidator: onDataSplit hook added

13.0.0

• Predictor's API: DataFrame used instead of Matrix
• DecisionTreeSolver: data splitting logic fixed

12.1.2

• xrange package version locked

12.1.1

• ml_linalg 11.0.0 supported
• Unit tests: iterable2dAlmostEqualTo and iterableAlmostEqualTo matchers used from ml_tech

12.1.0

• Decision tree classifier added

12.0.2

• ScoreToProbMapperFactory removed
• ScoreToProbMapperType enum removed
• ScoreToProbMapper: the entity renamed to LinkFunction

12.0.1

• Cost function factory removed
• Cost function type removed

12.0.0

• Breaking change: GradientType enum removed
• Breaking change: OptimizerType enum removed
• Breaking change, Predictor: fit method removed, fitting is happening while a model is being created
• Breaking change, Predictor: interface replaced with Assessable, redundant properties removed
• Breaking change: LinearClassifier reorganized
• Optimizers now have immutable state
• InterceptPreprocessor replaced with a helper function addInterceptIf

11.0.1

• Cross validator refactored
• Data splitters refactored
• Unit tests for cross validator added

11.0.0

• Added immutable state to all the predictor subclasses

10.3.0

• kernels added:
  • uniform
  • epanechnikov
  • cosine
  • gaussian
• NoNParametricRegressor.nearestNeighbour: added possibility to specify the kernel function

10.2.1

• test coverage restored

10.2.0

• NoNParametricRegressor class added
• KNNRegressor class added
• ml_linalg v9.0.0 supported

10.1.0

• ml_linalg v7.0.0 support

10.0.0

• Data preprocessing: all the entities moved to separate repo - ml_preprocessing

9.2.4

• Data preprocessing: All categorical values are now converted to String type

9.2.3

• Examples for Linear regression and Logistic regression updated (vector's normalize method used)
• CategoricalDataEncoderType: one-hot encoding documentation corrected

9.2.2

• Softmax regression example added to README

9.2.1

• README corrected

9.2.0

• LinearClassifier.logisticRegressor: numerical stability improved
• LinearClassifier.logisticRegressor: probabilityThreshold parameter added
• DataFrame.fromCsv: parameter fieldDelimiter added

9.1.0

• DataFrame: labelName parameter added

9.0.0

• ml_linalg v6.0.2 supported
• Classifier: type of weightsByClasses changed from Map to Matrix
• SoftmaxRegressor: more detailed unit tests for softmax regression added
• Data preprocessing: DataFrame introduced (former MLData)

8.0.0

• LinearClassifier.softmaxRegressor implemented
• Metric interface refactored (getError renamed to getScore)

7.2.0

• SoftmaxMapper added (aka Softmax activation function)

7.1.0

• ConvergenceDetector added (this entity stops the optimizer when it is needed)

7.0.0

• All the exports packed into ml_algo entry

6.2.0

• Coefficients in optimizers now are a matrix
• InitialWeightsGenerator instantiating fixed: dtype is passed now

6.1.0

• LinkFunction renamed to ScoreToProbMapper
• ScoreToProbMapper accepts vector and returns vector instead of a scalar

6.0.6

• Pedantic package integration added
• Some linter issues fixed

6.0.5

• Coveralls integration added
• dartfm check task added

6.0.4

• Documentation for linear regression corrected
• Documentation for MLData corrected

6.0.3

• Documentation for logistic regression corrected

6.0.2

• Tests corrected: removed import test_api.dart

6.0.1

• Readme corrected

6.0.0

• Library fully refactored:
  • add possibility to set certain data type for numeric computations
  • all algorithms now are more generic
  • a lot of unit tests added
  • bug fixes

5.2.0

• Ordinal encoder added
• Float32x4CsvMlData significantly extended

5.1.0

• Real-life example added (black friday dataset)
• rows parameter added to Float32x4CsvMlData
• Unknown categorical values handling strategy types added

5.0.0

• One hot encoder integrated into CSV ML data

4.3.3

• Performance test for one hot encoder added

4.3.2

• One hot encoder implemented

4.3.1

• enum for categorical data encoding added

4.3.0

• Cross validator factory added
• README updated

4.2.0

• csv-parser added

4.1.0

• ml_linalg removed from export file
• README refreshed
• General datasets directory created

4.0.0

• ml_linal ^4.0.0 supported

3.5.4

• README.md updated
• build_runner dependency updated

3.5.3

• dartfmt tool applied to all necessary files

3.5.2

• Travis configuration file name corrected

3.5.1

• Travis integration added

3.5.0

• Vectorized cost functions applied

3.4.0

• ml_linalg 2.0.0 supported

3.3.0

• Matrix-based gradient calculation added for log likelihood cost function

3.2.0

• Matrix-based gradient calculation added for squared cost function

3.1.2

• Description corrected

3.1.1

• dartfm tool applied

3.1.0

• Get rid of MLVector's deprecated methods

3.0.0

• Library public release

2.0.0

• ml_linalg supported

1.2.1

• subVector -> subvector

1.2.0

• Matrices support added

1.1.1

• Examples fixed, dependencies fixed

1.1.0

• Support of updated linalg package

1.0.1

• Readme updated, dependencies fixed

1.0.0

• Migration to dart 2.0

0.38.1

• issue solved

0.38.0

• Lasso solution refactored

0.37.0

• Support of linalg package (former simd_vector)

0.36.0

• Intercept term considered (fitIntercept and interceptScale parameters)

0.35.1

• Logistic regression tests improved

0.35.0

• One versus all refactored, tests for logistic regression added

0.34.0

• One versus all classifier

0.33.0

• Gradient descent regressor type enum added

0.32.1

• Gradient optimizer unit tests

0.32.0

• Get rid of derivative computation

0.31.0

• Get rid of di package usage

0.30.1

• File structure flattened

0.30.0

• Redundant gradient optimizers removed

0.29.0

• part ... part of directives removed

0.28.0

• Coordinate descent optimizer added
• Lasso regressor added

0.27.0

• Gradient calculation changed

0.26.1

• Code was optimized (removed unnecessary)
• Refactoring

0.26.0

• More distinct modularity was added to the library
• Unit tests were fixed

0.25.0

• Tests for gradient optimizers were added
• Gradient calculator was created as a separate entity
• Initial weights generator was created as a separate entity
• Learning rate generator was created as a separate entity

0.24.0

• All implementations were hidden

0.23.0

• findMaxima and findMinima methods were added to Optimizer interface

0.22.0

• File structure reorganized, predictor classes refactored
• README.md updated

0.21.0

• Logistic regression model added (with example)

0.20.2

• README.md updated

0.20.1

• simd_vector dependency url fixed

0.20.0

• Repository dependency corrected (dart_vector -> simd_vector)

0.19.0

• Support for Float32x4Vector class was added (from dart_vector library)
• Type List for label (target) list replaced with Float32List (in Predictor.train() and Optimizer.optimize())

0.18.0

• class Vector and enum Norm were extracted to separate library (https://github.com/gyrdym/dart_vector.git)

0.17.0

• Common interface for loss function was added
• Derivative calculation was fixed (common canonical method was used)
• Squared loss function was added as a separate class

0.16.0

• README.md was actualized

0.15.0

• Tests for gradient optimizers were added
• Interfaces (almost for all entities) for DI and IOC mechanism were added
• Randomizer class was added
• Removed separate classes for k-fold cross validation and lpo cross validation, now it resides in CrossValidation class

0.14.0

• L1 and L2 regularization added

0.13.0

• Script for running all unit tests added

0.12.0

• Vector interface removed
• Regular vector implementation removed
• TypedVector -> Vector
• Implicit vectors constructing replaced with explicit new-instantiation

0.11.0

• Entity names correction

0.10.0

• K-fold cross validation added (KFoldCrossValidation)
• Leave P out cross validation added (LpoCrossValidation)
• DataTrainTestSplitter was removed

0.9.0

• copy, fill methods were added to Vector

0.8.0

• Reflection was removed for all cases (Vector instantiation, Optimizer instantiation)

0.7.0

• Abstract Vector-class was added as a base for typed and regular vector classes

0.6.0

• Manhattan norm support was added

0.5.2

• README file was extended and clarified

0.5.1

• Random interval obtaining for the mini-batch gradient descent was fixed

0.5.0

• BGDOptimizer, MBGDOptimizer and GradientOptimizer were added

0.4.0

• OptimizerInterface was added
• Stochastic gradient descent optimizer was extracted from the linear regressor class
• Line separators changed for all files (CRLF -> LF)

0.3.1

• tests for sum, abs, fromRange methods of the TypedVector were added
• tests for DataTrainTestSplitter was added

0.3.0

• MAPE cost function was added

0.2.0

• SGD Regressor refactored (rmse on training removed, estimator added) + example extended

0.1.0

• Implementation of -, *, / operators and all vectors methods added to the TypedVector

0.0.1

• Initial version