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Indicate SVM support in the README (#91)
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@izeigerman @krinart
izeigerman authored and krinart committed May 15, 2019
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Expand Up @@ -29,73 +29,43 @@ pip install m2cgen
- Go

## Supported Models
<table>
<thead>
<tr>
<th width="10%"></th>
<th width="45%">Classification</th>
<th width="45%">Regression</th>
</tr>
</thead>
<tbody>
<tr>
<th>Linear</th>
<td>LogisticRegression, LogisticRegressionCV, RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier</td>
<td>LinearRegression, HuberRegressor, ElasticNet, ElasticNetCV, TheilSenRegressor, Lars, LarsCV, Lasso, LassoCV, LassoLars, LassoLarsIC, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, Ridge, RidgeCV, BayesianRidge, ARDRegression, SGDRegressor, PassiveAggressiveRegressor</td>
</tr>
<tr>
<th>SVM</th>
<td>LinearSVC</td>
<td>LinearSVR</td>
</tr>
<tr>
<th>Tree</th>
<td>DecisionTreeClassifier, ExtraTreeClassifier</td>
<td>DecisionTreeRegressor, ExtraTreeRegressor</td>
</tr>
<tr>
<th>Random Forest</th>
<td>RandomForestClassifier, ExtraTreesClassifier</td>
<td>RandomForestRegressor, ExtraTreesRegressor</td>
</tr>
<tr>
<th>Boosting</th>
<td>XGBClassifier(gbtree/dart booster only), LGBMClassifier(gbdt/dart booster only)</td>
<td>XGBRegressor(gbtree/dart booster only), LGBMRegressor(gbdt/dart booster only)</td>
</tr>
</tbody>
</table>

| | Classification | Regression |
| --- | --- | --- |
| **Linear** | LogisticRegression, LogisticRegressionCV, RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier | LinearRegression, HuberRegressor, ElasticNet, ElasticNetCV, TheilSenRegressor, Lars, LarsCV, Lasso, LassoCV, LassoLars, LassoLarsIC, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, Ridge, RidgeCV, BayesianRidge, ARDRegression, SGDRegressor, PassiveAggressiveRegressor |
| **SVM** | SVC, NuSVC, LinearSVC | SVR, NuSVR, LinearSVR |
| **Tree** | DecisionTreeClassifier, ExtraTreeClassifier | DecisionTreeRegressor, ExtraTreeRegressor |
| **Random Forest** | RandomForestClassifier, ExtraTreesClassifier | RandomForestRegressor, ExtraTreesRegressor |
| **Boosting** | XGBClassifier(gbtree/dart booster only), LGBMClassifier(gbdt/dart booster only) | XGBRegressor(gbtree/dart booster only), LGBMRegressor(gbdt/dart booster only) |

## Classification Output
<table>
<thead>
<tr>
<th width="10%"></th>
<th width="35%">Binary</th>
<th width="35%">Multiclass</th>
<th width="20%">Comment</th>
</tr>
</thead>
<tbody>
<tr>
<th>Linear</th>
<td>Scalar value; signed distance of the sample to the hyperplane for the second class </td>
<td>Vector value; signed distance of the sample to the hyperplane per each class</td>
<td>The output is consistent with the output of <i>LinearClassifierMixin.decision_function</i></td>
</tr>
<tr>
<th>Tree/Random Forest/XGBoost/LightGBM</th>
<td>Vector value; class probabilities</td>
<td>Vector value; class probabilities</td>
<td>The output is consistent with the output of the <i>predict_proba</i> method of <i>DecisionTreeClassifier</i>/<i>ForestClassifier</i>/<i>XGBClassifier</i>/<i>LGBMClassifier</i></td>
</tr>
</tbody>
</table>
### Linear/Linear SVM
#### Binary
Scalar value; signed distance of the sample to the hyperplane for the second class.
#### Multiclass
Vector value; signed distance of the sample to the hyperplane per each class.
#### Comment
The output is consistent with the output of ```LinearClassifierMixin.decision_function```.

### SVM
#### Binary
Scalar value; signed distance of the sample to the hyperplane for the second class.
#### Multiclass
Vector value; one-vs-one score for each class, shape (n_samples, n_classes * (n_classes-1) / 2).
#### Comment
The output is consistent with the output of ```BaseSVC.decision_function``` when the `decision_function_shape` is set to `ovo`.

### Tree/Random Forest/XGBoost/LightGBM
#### Binary
Vector value; class probabilities.
#### Multiclass
Vector value; class probabilities.
#### Comment
The output is consistent with the output of the `predict_proba` method of `DecisionTreeClassifier`/`ForestClassifier`/`XGBClassifier`/`LGBMClassifier`.

## Usage

Here's a simple example of how a trained linear model can be represented in Java code:
Here's a simple example of how a linear model trained in Python environment can be represented in Java code:
```python
from sklearn.datasets import load_boston
from sklearn import linear_model

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@codeyp

codeyp Jun 7, 2019

I’m not getting the wrong code for this s
Expand All @@ -110,7 +80,7 @@ estimator.fit(X, y)
code = m2c.export_to_java(estimator)
```

The example of the generated code:
Generated Java code:
```java
public class Model {

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