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@gyrdym
gyrdym committed Sep 8, 2020
2 parents ed1769e + 54fd305 commit ed51475
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10 changes: 10 additions & 0 deletions CHANGELOG.md
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@@ -1,5 +1,15 @@
# Changelog

## 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

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24 changes: 12 additions & 12 deletions README.md
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Expand Up @@ -8,7 +8,7 @@
## What is the ml_algo for?

The main purpose of the library is to give native Dart implementation of machine learning algorithms to those who are
interested both in Dart language and data science. This library targeted to dart vm, so to get smoothest experience with
interested both in Dart language and data science. This library targeted to the dart vm, so to get smoothest experience with
the lib, please do not use it in a browser.

## The library's content
Expand Down Expand Up @@ -57,8 +57,8 @@ in your dependencies:

````
dependencies:
ml_dataframe: ^0.1.1
ml_preprocessing: ^5.1.0
ml_dataframe: ^0.2.0
ml_preprocessing: ^5.2.0
````

We need these repos to parse raw data in order to use it farther. For more details, please
Expand Down Expand Up @@ -103,18 +103,18 @@ final testData = splits[1];
data as a validation set and 30% as a test set for evaluating generalization error.

Then we may create an instance of `CrossValidator` class to fit [hyperparameters](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning))
of our model. We should pass validation data (our `validationData` variable), a list of target column names (in our case it's
just a name stored in `targetColumnName` variable) and a number of folds into CrossValidator constructor.
of our model. We should pass validation data (our `validationData` variable), and a number of folds into CrossValidator
constructor.

````dart
final validator = CrossValidator.kFold(validationData, [targetColumnName], numberOfFolds: 5);
final validator = CrossValidator.kFold(validationData, numberOfFolds: 5);
````

Let's create a factory for the classifier with desired hyperparameters. We have to decide after the cross validation,
if the selected hyperparametrs are good enough or not:

```dart
final createClassifier = (DataFrame samples, _) =>
final createClassifier = (DataFrame samples) =>
LogisticRegressor(
samples
targetColumnName,
Expand All @@ -141,7 +141,7 @@ If we want to evaluate the learning process more thoroughly, we may pass `collec
constructor:

```dart
final createClassifier = (DataFrame samples, _) =>
final createClassifier = (DataFrame samples) =>
LogisticRegressor(
...,
collectLearningData: true,
Expand Down Expand Up @@ -180,8 +180,8 @@ Let's assess our hyperparameters on test set in order to evaluate the model's ge

```dart
final testSplits = splitData(testData, [0.8]);
final classifier = createClassifier(testSplits[0], targetNames);
final finalScore = classifier.assess(testSplits[1], targetNames, MetricType.accuracy);
final classifier = createClassifier(testSplits[0]);
final finalScore = classifier.assess(testSplits[1], MetricType.accuracy);
```

The final score is like:
Expand Down Expand Up @@ -239,8 +239,8 @@ void main() async {
final splits = splitData(samples, [0.7]);
final validationData = splits[0];
final testData = splits[1];
final validator = CrossValidator.kFold(validationData, [targetColumnName], numberOfFolds: 5);
final createClassifier = (DataFrame samples, _) =>
final validator = CrossValidator.kFold(validationData, numberOfFolds: 5);
final createClassifier = (DataFrame samples) =>
LogisticRegressor(
samples
targetColumnName,
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8 changes: 3 additions & 5 deletions benchmark/cross_validator.dart
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Expand Up @@ -19,20 +19,18 @@ class CrossValidatorBenchmark extends BenchmarkBase {

@override
void run() {
crossValidator.evaluate((trainSamples, targetFeatureNames) =>
KnnRegressor(trainSamples, targetFeatureNames.first, 7),
crossValidator.evaluate((trainSamples) =>
KnnRegressor(trainSamples, 'col_20', 7),
MetricType.mape);
}

@override
void setup() {
final samples = Matrix.fromRows(List.generate(observationsNum,
(i) => Vector.randomFilled(columnsNum)));

final dataFrame = DataFrame.fromMatrix(samples);

crossValidator = CrossValidator.kFold(dataFrame, ['col_20'],
numberOfFolds: 5);
crossValidator = CrossValidator.kFold(dataFrame, numberOfFolds: 5);
}

void tearDown() {}
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201 changes: 201 additions & 0 deletions e2e/datasets/advertising.csv
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@@ -0,0 +1,201 @@
Num,TV,Radio,Newspaper,Sales
1 ,230.1,37.8 ,69.2 ,22.1
2 ,44.5 ,39.3 ,45.1 ,10.4
3 ,17.2 ,45.9 ,69.3 ,9.3
4 ,151.5,41.3 ,58.5 ,18.5
5 ,180.8,10.8 ,58.4 ,12.9
6 ,8.7 ,48.9 ,75 ,7.2
7 ,57.5 ,32.8 ,23.5 ,11.8
8 ,120.2,19.6 ,11.6 ,13.2
9 ,8.6 ,2.1 ,1 ,4.8
10 ,199.8,2.6 ,21.2 ,10.6
11 ,66.1 ,5.8 ,24.2 ,8.6
12 ,214.7,24 ,4 ,17.4
13 ,23.8 ,35.1 ,65.9 ,9.2
14 ,97.5 ,7.6 ,7.2 ,9.7
15 ,204.1,32.9 ,46 ,19
16 ,195.4,47.7 ,52.9 ,22.4
17 ,67.8 ,36.6 ,114 ,12.5
18 ,281.4,39.6 ,55.8 ,24.4
19 ,69.2 ,20.5 ,18.3 ,11.3
20 ,147.3,23.9 ,19.1 ,14.6
21 ,218.4,27.7 ,53.4 ,18
22 ,237.4,5.1 ,23.5 ,12.5
23 ,13.2 ,15.9 ,49.6 ,5.6
24 ,228.3,16.9 ,26.2 ,15.5
25 ,62.3 ,12.6 ,18.3 ,9.7
26 ,262.9,3.5 ,19.5 ,12
27 ,142.9,29.3 ,12.6 ,15
28 ,240.1,16.7 ,22.9 ,15.9
29 ,248.8,27.1 ,22.9 ,18.9
30 ,70.6 ,16 ,40.8 ,10.5
31 ,292.9,28.3 ,43.2 ,21.4
32 ,112.9,17.4 ,38.6 ,11.9
33 ,97.2 ,1.5 ,30 ,9.6
34 ,265.6,20 ,0.3 ,17.4
35 ,95.7 ,1.4 ,7.4 ,9.5
36 ,290.7,4.1 ,8.5 ,12.8
37 ,266.9,43.8 ,5 ,25.4
38 ,74.7 ,49.4 ,45.7 ,14.7
39 ,43.1 ,26.7 ,35.1 ,10.1
40 ,228 ,37.7 ,32 ,21.5
41 ,202.5,22.3 ,31.6 ,16.6
42 ,177 ,33.4 ,38.7 ,17.1
43 ,293.6,27.7 ,1.8 ,20.7
44 ,206.9,8.4 ,26.4 ,12.9
45 ,25.1 ,25.7 ,43.3 ,8.5
46 ,175.1,22.5 ,31.5 ,14.9
47 ,89.7 ,9.9 ,35.7 ,10.6
48 ,239.9,41.5 ,18.5 ,23.2
49 ,227.2,15.8 ,49.9 ,14.8
50 ,66.9 ,11.7 ,36.8 ,9.7
51 ,199.8,3.1 ,34.6 ,11.4
52 ,100.4,9.6 ,3.6 ,10.7
53 ,216.4,41.7 ,39.6 ,22.6
54 ,182.6,46.2 ,58.7 ,21.2
55 ,262.7,28.8 ,15.9 ,20.2
56 ,198.9,49.4 ,60 ,23.7
57 ,7.3 ,28.1 ,41.4 ,5.5
58 ,136.2,19.2 ,16.6 ,13.2
59 ,210.8,49.6 ,37.7 ,23.8
60 ,210.7,29.5 ,9.3 ,18.4
61 ,53.5 ,2 ,21.4 ,8.1
62 ,261.3,42.7 ,54.7 ,24.2
63 ,239.3,15.5 ,27.3 ,15.7
64 ,102.7,29.6 ,8.4 ,14
65 ,131.1,42.8 ,28.9 ,18
66 ,69 ,9.3 ,0.9 ,9.3
67 ,31.5 ,24.6 ,2.2 ,9.5
68 ,139.3,14.5 ,10.2 ,13.4
69 ,237.4,27.5 ,11 ,18.9
70 ,216.8,43.9 ,27.2 ,22.3
71 ,199.1,30.6 ,38.7 ,18.3
72 ,109.8,14.3 ,31.7 ,12.4
73 ,26.8 ,33 ,19.3 ,8.8
74 ,129.4,5.7 ,31.3 ,11
75 ,213.4,24.6 ,13.1 ,17
76 ,16.9 ,43.7 ,89.4 ,8.7
77 ,27.5 ,1.6 ,20.7 ,6.9
78 ,120.5,28.5 ,14.2 ,14.2
79 ,5.4 ,29.9 ,9.4 ,5.3
80 ,116 ,7.7 ,23.1 ,11
81 ,76.4 ,26.7 ,22.3 ,11.8
82 ,239.8,4.1 ,36.9 ,12.3
83 ,75.3 ,20.3 ,32.5 ,11.3
84 ,68.4 ,44.5 ,35.6 ,13.6
85 ,213.5,43 ,33.8 ,21.7
86 ,193.2,18.4 ,65.7 ,15.2
87 ,76.3 ,27.5 ,16 ,12
88 ,110.7,40.6 ,63.2 ,16
89 ,88.3 ,25.5 ,73.4 ,12.9
90 ,109.8,47.8 ,51.4 ,16.7
91 ,134.3,4.9 ,9.3 ,11.2
92 ,28.6 ,1.5 ,33 ,7.3
93 ,217.7,33.5 ,59 ,19.4
94 ,250.9,36.5 ,72.3 ,22.2
95 ,107.4,14 ,10.9 ,11.5
96 ,163.3,31.6 ,52.9 ,16.9
97 ,197.6,3.5 ,5.9 ,11.7
98 ,184.9,21 ,22 ,15.5
99 ,289.7,42.3 ,51.2 ,25.4
100,135.2,41.7 ,45.9 ,17.2
101,222.4,4.3 ,49.8 ,11.7
102,296.4,36.3 ,100.9,23.8
103,280.2,10.1 ,21.4 ,14.8
104,187.9,17.2 ,17.9 ,14.7
105,238.2,34.3 ,5.3 ,20.7
106,137.9,46.4 ,59 ,19.2
107,25 ,11 ,29.7 ,7.2
108,90.4 ,0.3 ,23.2 ,8.7
109,13.1 ,0.4 ,25.6 ,5.3
110,255.4,26.9 ,5.5 ,19.8
111,225.8,8.2 ,56.5 ,13.4
112,241.7,38 ,23.2 ,21.8
113,175.7,15.4 ,2.4 ,14.1
114,209.6,20.6 ,10.7 ,15.9
115,78.2 ,46.8 ,34.5 ,14.6
116,75.1 ,35 ,52.7 ,12.6
117,139.2,14.3 ,25.6 ,12.2
118,76.4 ,0.8 ,14.8 ,9.4
119,125.7,36.9 ,79.2 ,15.9
120,19.4 ,16 ,22.3 ,6.6
121,141.3,26.8 ,46.2 ,15.5
122,18.8 ,21.7 ,50.4 ,7
123,224 ,2.4 ,15.6 ,11.6
124,123.1,34.6 ,12.4 ,15.2
125,229.5,32.3 ,74.2 ,19.7
126,87.2 ,11.8 ,25.9 ,10.6
127,7.8 ,38.9 ,50.6 ,6.6
128,80.2 ,0 ,9.2 ,8.8
129,220.3,49 ,3.2 ,24.7
130,59.6 ,12 ,43.1 ,9.7
131,0.7 ,39.6 ,8.7 ,1.6
132,265.2,2.9 ,43 ,12.7
133,8.4 ,27.2 ,2.1 ,5.7
134,219.8,33.5 ,45.1 ,19.6
135,36.9 ,38.6 ,65.6 ,10.8
136,48.3 ,47 ,8.5 ,11.6
137,25.6 ,39 ,9.3 ,9.5
138,273.7,28.9 ,59.7 ,20.8
139,43 ,25.9 ,20.5 ,9.6
140,184.9,43.9 ,1.7 ,20.7
141,73.4 ,17 ,12.9 ,10.9
142,193.7,35.4 ,75.6 ,19.2
143,220.5,33.2 ,37.9 ,20.1
144,104.6,5.7 ,34.4 ,10.4
145,96.2 ,14.8 ,38.9 ,11.4
146,140.3,1.9 ,9 ,10.3
147,240.1,7.3 ,8.7 ,13.2
148,243.2,49 ,44.3 ,25.4
149,38 ,40.3 ,11.9 ,10.9
150,44.7 ,25.8 ,20.6 ,10.1
151,280.7,13.9 ,37 ,16.1
152,121 ,8.4 ,48.7 ,11.6
153,197.6,23.3 ,14.2 ,16.6
154,171.3,39.7 ,37.7 ,19
155,187.8,21.1 ,9.5 ,15.6
156,4.1 ,11.6 ,5.7 ,3.2
157,93.9 ,43.5 ,50.5 ,15.3
158,149.8,1.3 ,24.3 ,10.1
159,11.7 ,36.9 ,45.2 ,7.3
160,131.7,18.4 ,34.6 ,12.9
161,172.5,18.1 ,30.7 ,14.4
162,85.7 ,35.8 ,49.3 ,13.3
163,188.4,18.1 ,25.6 ,14.9
164,163.5,36.8 ,7.4 ,18
165,117.2,14.7 ,5.4 ,11.9
166,234.5,3.4 ,84.8 ,11.9
167,17.9 ,37.6 ,21.6 ,8
168,206.8,5.2 ,19.4 ,12.2
169,215.4,23.6 ,57.6 ,17.1
170,284.3,10.6 ,6.4 ,15
171,50 ,11.6 ,18.4 ,8.4
172,164.5,20.9 ,47.4 ,14.5
173,19.6 ,20.1 ,17 ,7.6
174,168.4,7.1 ,12.8 ,11.7
175,222.4,3.4 ,13.1 ,11.5
176,276.9,48.9 ,41.8 ,27
177,248.4,30.2 ,20.3 ,20.2
178,170.2,7.8 ,35.2 ,11.7
179,276.7,2.3 ,23.7 ,11.8
180,165.6,10 ,17.6 ,12.6
181,156.6,2.6 ,8.3 ,10.5
182,218.5,5.4 ,27.4 ,12.2
183,56.2 ,5.7 ,29.7 ,8.7
184,287.6,43 ,71.8 ,26.2
185,253.8,21.3 ,30 ,17.6
186,205 ,45.1 ,19.6 ,22.6
187,139.5,2.1 ,26.6 ,10.3
188,191.1,28.7 ,18.2 ,17.3
189,286 ,13.9 ,3.7 ,15.9
190,18.7 ,12.1 ,23.4 ,6.7
191,39.5 ,41.1 ,5.8 ,10.8
192,75.5 ,10.8 ,6 ,9.9
193,17.2 ,4.1 ,31.6 ,5.9
194,166.8,42 ,3.6 ,19.6
195,149.7,35.6 ,6 ,17.3
196,38.2 ,3.7 ,13.8 ,7.6
197,94.2 ,4.9 ,8.1 ,9.7
198,177 ,9.3 ,6.4 ,12.8
199,283.6,42 ,66.2 ,25.5
200,232.1,8.6 ,8.7 ,13.4

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