# Dark Matter

<pre class="language-python" data-overflow="wrap"><code class="lang-python">class DarkMatter(input_column_size: int,
                 task_target_size: int,
                 output_column_size: int,
                 task: str,
                 X: <a data-footnote-ref href="#user-content-fn-1">DarkMatterType</a>,
                 y: <a data-footnote-ref href="#user-content-fn-1">DarkMatterType</a>,
                 <a data-footnote-ref href="#user-content-fn-2">explainability</a>: bool = False,
                 column_names: list[str] | None = None,
                 project_name: str | None = None,
                 max_iter: int = 10_000,
                 batch_size: int | float = 1000)
</code></pre>

## Parameters

### input\_column\_size: *int*

*The total number of input features.*

### output\_column\_size: *int*

*The number of desired output "features" (embedding length). Must be a number between 1 and 500, inclusive.*

### task\_target\_size: *int*

*The total number of targets. Currently, only 1 is supported.*

### task: *str*

*One of "regression" or "classification".*

### X: [*DarkMatterType*](/ensemble-ai-api-docs/dark-matter/darkmattertype.md)

*The input data feature set.*

### y: [*DarkMatterType*](/ensemble-ai-api-docs/dark-matter/darkmattertype.md)

*The input data targets.*

### [explainability](/ensemble-ai-api-docs/references/explainability-mode.md): *bool, default=False*

*Enable/disable* [*explainability mode*](/ensemble-ai-api-docs/references/explainability-mode.md)*.*

### column\_names: *list\[str] | None, default=None*

*Optional feature names for use with explainability mode. If none are specified, either the names are inferred from the X argument if it is a Pandas DataFrame or will be generic names \["feature\_0", "feature\_1", ...].*

### project\_name: *str | None, default=None*

*Optional string for use to save algorithm weights to disk. The default is a "YY-MM-DD\_hh-mm-ss" string based on the environment clock.*

### max\_iter: *int, default=10,000*

*The total number of training iterations to use during algorithm fitting. This must be greater than the sensitivity argument.*

### batch\_size: *int | float = 1000*

*The number of training examples to use for each batch during fitting. Floating point arguments must be a number between 0 and 1 to indicate the percentage of the input data to be used as a batch.*

## Methods

### fit

```python
def fit(self, src_path: str = "./src") -> "DarkMatter"
```

Fits the algorithm to the input data.

### generate

```python
def generate(self, X: Iterable) -> DarkMatterType
```

Transforms a given dataset to embeddings using Dark Matter.

### save

```python
def save(self, *, path: str = "weights", project_name: str | None = None) -> str
```

Saves the algorithm weights to a subfolder `"{path}/{project_name}/"`.

### load

```python
def load(self, *, path: str = "weights", project_name: str | None = None) -> "DarkMatter"
```

Loads the algorithm's weights from a subfolder `"{path}/{project_name}/"`.

## Notes

* NaN values are not compatible with algorithm training, these must be filled prior to calling fit or generate.
* Dark Matter utilizes a variety of Data Science and backend Python packages for algorithm training and source code management. Learn more at [The Dark Matter Environment](/ensemble-ai-api-docs/references/the-dark-matter-environment.md).

## Product Updates Coming Soon

* GPU support.
* Training progress and insights.
* Automatic hyperparameter tuning.

[^1]: [DarkMatterType](/ensemble-ai-api-docs/dark-matter/darkmattertype.md)

[^2]: [Explainability Mode](/ensemble-ai-api-docs/references/explainability-mode.md)


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