> For the complete documentation index, see [llms.txt](https://seattle-university.gitbook.io/sdgne/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://seattle-university.gitbook.io/sdgne/smote-variants/anvo-smote.md).

# ANVO SMOTE

The ANVO algorithm, a variant of SMOTE, targets class imbalances by enriching the minority class with synthetic data. It assesses the distribution and relationships within the minority class to create new, meaningful samples. By adapting the neighbor analysis based on class density, ANVO ensures the generated instances enhance the diversity and coverage of the minority class in underrepresented areas of the feature space. This approach aims to balance the dataset, improving the performance of machine learning models by providing a more representative sample distribution.

```python
from sdgne.datagenerator.smote import ANVO

minority_column_label = 'class'
minority_class_label = 0

synthesizer = ANVO(dataset,minority_column_label,minority_class_label)

synthesize_data = synthesizer.data_generator()
```

## Importing SMOTE

```python
from sdgne.datagenerator.smote import ANVO
```

***

## Creating a synthesizer

```python
synthesizer = ANVO(dataset, minority_column_label, minority_class_label)
```

### Parameters

<table data-header-hidden data-full-width="false"><thead><tr><th width="216.66666666666669">variable</th><th width="140">type</th><th>datatype</th><th>info</th></tr></thead><tbody><tr><td>dataset</td><td>required</td><td>pd.Dataframe</td><td>Represents a pandas data frame containing both, the original minority and the original majority data.</td></tr><tr><td>minority_column_label</td><td>required</td><td>string</td><td>Represents the column label. Eg. 'class', 'output'</td></tr><tr><td>minority_class_label</td><td>required</td><td>string</td><td>Represents the minority class label. Eg. '1', '0'</td></tr></tbody></table>

### Returns

An instance of class SDD\_SMOTE.

## Synthetic Data Generation

### data\_generator()

The data generator function generates synthetic data using the synthesizer. It has an option parameter  `num_to_synthesize.`

* If `num_to_synthesize` is not defined, data\_generator by default generates the \`n\` number of synthetic data such that the majority data and minority datasets get balanced.
* If `num_to_synthesize` is defined and the dataset is already balanced, data\_generator generates 2 \* Number of original minority data.
* If `num_to_synthesize` is defined and the dataset is not balanced, it generates synthetic data equal to the value passed.

```python
synthesized_data = synthesizer.data_generator(no_of_syntetic_data)
```

### Parameters

<table data-header-hidden><thead><tr><th width="222">variable</th><th width="154">type</th><th width="107">datatype</th><th>info</th></tr></thead><tbody><tr><td><code>num_to_synthesize</code></td><td>optional (default: None)</td><td>integer</td><td>Represents the number of synthetic data to be generated.</td></tr></tbody></table>

### Returns

A pandas data frame that combines original data and synthetic data.

### Usage

`synthesizer.data_generator()`

`synthesizer.data_generator(num_to_synthesize=100)`