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Published 3 months ago • verified publisherml-algo.com
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Machine learning algorithms, Machine learning models performance evaluation functionality

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Changelog #

16.18.0 #

  • Added MetricType.logLoss and LogLossMetric for evaluating probabilistic binary classifiers

16.17.13 #

  • Added Decision Tree web demo using Web Assembly

16.17.12 #

  • update in README
    • mention that web is possible
    • fix the getIrisDataFrame and Pipeline example

16.17.11 #

  • Update contacts in README

16.17.10 #

  • Upgrade injector dependency

16.17.9 #

  • Gradient descent example corrected

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 #

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

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Publisher

verified publisherml-algo.com

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Metadata

Machine learning algorithms, Machine learning models performance evaluation functionality

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Documentation

API reference

License

BSD-2-Clause (license)

Dependencies

collection, injector, json_annotation, ml_dataframe, ml_linalg, ml_preprocessing, quiver, xrange

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