Machine learning algorithms with dart
Table of contents
What is the ml_algo for?
The main purpose of the library  to give developers, interested both in Dart language and data science, native Dart implementation of machine learning algorithms. This library targeted to dart vm, so, to get smoothest experience with the lib, please, do not use it in a browser.
Following algorithms are implemented:

Linear regression:
 Gradient descent algorithm (batch, minibatch, stochastic) with ridge regularization
 Lasso regression

Linear classifier:
 Logistic regression (with "onevsall" multiclass classification)
 Softmax regression

Nonparametric regression:
 KNN regression
The library's structure

CrossValidator. Factory, that creates instances of a cross validator. In a few words, this entity allows researchers to fit different hyperparameters of machine learning algorithms, assessing prediction quality on different parts of a dataset.

LinearClassifier.logisticRegressor. An algorithm, that performs simplest linear classification. If you want to use this classifier for your data, please, make sure, that your data is linearly separable. Multiclass classification is also supported (see ovr classification)

LinearClassifier.softmaxRegressor. An algorithm, that performs simplest linear multiclass classification. As well as for logistic regression, if you want to use this classifier for your data, please, make sure, that your data is linearly separable.

LinearRegressor.gradient. A wellknown algorithm, that performs linear regression using gradient vector of a cost function.

LinearRegressor.lasso An algorithm, that performs feature selection along with regression process. The heart of the algorithm  coordinate descent optimization. If you want to decide, which features are less important  go ahead and use this regressor.

NoNParametricRegressor.nearestNeighbor An algorithm, that makes prediction for each new observation based on first
k
closest observations from training data. It has quite high computational complexity, but in the same time it may easily catch nonlinear pattern of the data.
Examples
Logistic regression
Let's classify records from wellknown dataset  Pima Indians Diabets Database via Logistic regressor
Import all necessary packages. First, it's needed to ensure, if you have ml_preprocessing
package in your
dependencies:
dependencies:
ml_preprocessing: ^3.2.0
We need this repo to parse raw data in order to use it farther. For more details, please, visit ml_preprocessing repository page.
import 'dart:async';
import 'package:ml_algo/ml_algo.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';
Download dataset from Pima Indians Diabets Database and read it (of course, you should provide a proper path to your downloaded file):
final data = DataFrame.fromCsv('datasets/pima_indians_diabetes_database.csv',
labelName: 'class variable (0 or 1)');
final features = await data.features;
final labels = await data.labels;
Data in this file is represented by 768 records and 8 features. 9th column is a label column, it contains either 0 or 1
on each row. This column is our target  we should predict a class label for each observation. Therefore, we
should point, where to get label values. Let's use labelName
parameter for that (labels column name, 'class variable
(0 or 1)' in our case).
Processed features and labels are contained in data structures of Matrix
type. To get more information about
Matrix
type, please, visit ml_linal repo
Then, we should create an instance of CrossValidator
class for fitting hyperparameters
of our model
final validator = CrossValidator.KFold(numberOfFolds: 5);
All are set, so, we can do our classification.
Let's create a logistic regression classifier instance with fullbatch gradient descent optimizer:
final model = LinearClassifier.logisticRegressor(
initialLearningRate: .8,
iterationsLimit: 500,
gradientType: GradientType.batch,
fitIntercept: true,
interceptScale: 0.1,
learningRateType: LearningRateType.constant);
Evaluate our model via accuracy metric:
final accuracy = validator.evaluate(model, featuresMatrix, labels, MetricType.accuracy);
Let's print score:
print('accuracy on classification: ${accuracy.toStringAsFixed(2)}');
We will see something like this:
acuracy on classification: 0.77
All the code above all together:
import 'dart:async';
import 'package:ml_algo/ml_algo.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';
Future main() async {
final data = DataFrame.fromCsv('datasets/pima_indians_diabetes_database.csv',
labelName: 'class variable (0 or 1)');
final features = await data.features;
final labels = await data.labels;
final validator = CrossValidator.kFold(numberOfFolds: 5);
final model = LinearClassifier.logisticRegressor(
initialLearningRate: .8,
iterationsLimit: 500,
gradientType: GradientType.batch,
fitIntercept: true,
interceptScale: .1,
learningRateType: LearningRateType.constant);
final accuracy = validator.evaluate(model, features, labels, MetricType.accuracy);
print('accuracy on classification: ${accuracy.toStringFixed(2)}');
}
Softmax regression
Let's classify another famous dataset  Iris dataset. Data in this csv is separated into 3 classes  therefore we need to use different approach to data classification  Softmax regression.
As usual, start with data preparation. Before we start, we should update our pubspec's dependencies with xrange` library:
dependencies:
...
xrange: ^0.0.5
...
Download the file and read it:
final data = DataFrame.fromCsv('datasets/iris.csv',
labelName: 'Species',
columns: [ZRange.closed(1, 5)],
categories: {
'Species': CategoricalDataEncoderType.oneHot,
},
);
final features = await data.features;
final labels = await data.labels;
The csv database has 6 columns, but we need to get rid of the first column, because it contains just ID of every observation  it's absolutely useless data. So, as you may notice, we provided a columns range to exclude IDcolumn:
columns: [ZRange.closed(1, 5)]
Also, since the label column 'Species' has categorical data, we encoded it to numerical format:
categories: {
'Species': CategoricalDataEncoderType.oneHot,
},
Next step  create a cross validator instance:
final validator = CrossValidator.kFold(numberOfFolds: 5);
And finally, create an instance of the classifier:
final softmaxRegressor = LinearClassifier.softmaxRegressor(
initialLearningRate: 0.03,
iterationsLimit: null,
minWeightsUpdate: 1e6,
randomSeed: 46,
learningRateType: LearningRateType.constant);
Evaluate quality of prediction:
final accuracy = validator.evaluate(softmaxRegressor, features, labels, MetricType.accuracy);
print('Iris dataset, softmax regression: accuracy is '
'${accuracy.toStringAsFixed(2)}'); // It yields 0.93
Gather all the code above all together:
import 'dart:async';
import 'package:ml_algo/ml_algo.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';
import 'package:xrange/zrange.dart';
Future main() async {
final data = DataFrame.fromCsv('datasets/iris.csv',
labelName: 'Species',
columns: [ZRange.closed(1, 5)],
categories: {
'Species': CategoricalDataEncoderType.oneHot,
},
);
final features = await data.features;
final labels = await data.labels;
final validator = CrossValidator.kFold(numberOfFolds: 5);
final softmaxRegressor = LinearClassifier.softmaxRegressor(
initialLearningRate: 0.03,
iterationsLimit: null,
minWeightsUpdate: 1e6,
randomSeed: 46,
learningRateType: LearningRateType.constant);
final accuracy = validator.evaluate(
softmaxRegressor, features, labels, MetricType.accuracy);
print('Iris dataset, softmax regression: accuracy is '
'${accuracy.toStringAsFixed(2)}');
}
For more examples, please, visit ml_algo_examples repository
Contacts
If you have questions, feel free to write me on