Prosphera

https://github.com/woldemarg/proshpera
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Prosphera generates engaging, instinctive spherical visualizations of multi-dimensional data using...
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License:GPL-3.0
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### Install Prosphera Library

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Installs the Prosphera package via pip.

```bash
!pip install prosphera
```

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### Initialize the Projector

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Instantiate the Projector class to begin the visualization process.

```python
import pandas as pd
from sklearn import datasets
from sklearn.datasets import make_blobs
from prosphera.projector import Projector

# Instantiate the class
visualizer = Projector()
```

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### Initialize Projector

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Instantiates the Projector class with a specified renderer.

```python
# Instantiate the class
visualizer = Projector(renderer='colab')
```

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### Execute Visualization

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Generates and displays the visualization using the project method.

```python
# Call the visualize method to generate and display the visualization
visualizer.project(data=data, labels=labels)
```

--------------------------------

### Visualize Housing Dataset

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Project the California housing dataset using binned age values as labels.

```python
housing = datasets.fetch_california_housing()

visualizer.project(
    data=housing['data'],
    labels=pd.qcut(housing['data'][:, 1], 5).astype(str))
```

--------------------------------

### Import Prosphera and Scikit-Learn

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Imports the necessary modules for data generation and visualization.

```python
from sklearn.datasets import make_blobs
from prosphera.projector import Projector
```

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### Visualize Standard Datasets

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Project standard scikit-learn datasets like Wine and Breast Cancer.

```python
wine = datasets.load_wine()

visualizer.project(
    data=wine['data'],
    labels=wine['target'])
```

```python
cancer = datasets.load_breast_cancer()

visualizer.project(
    data=cancer['data'],
    labels=cancer['target'])
```

--------------------------------

### Visualize Generated Blobs

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Generate synthetic blob data and display it using the project method.

```python
# Generate data
data, labels = make_blobs(
    n_samples=5000,
    centers=50,
    n_features=25,
    random_state=1234)

# Call the visualize method to generate and display the visualization
visualizer.project(
    data=data,
    labels=labels)
```

--------------------------------

### Configure Renderer

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Set the renderer to 'iframe' to save the plot locally as an HTML file.

```python
visualizer = Projector(renderer='iframe')
```

--------------------------------

### Configure Matplotlib Inline Backend

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Enables inline plotting for Matplotlib within Jupyter environments.

```python
%matplotlib inline
```

--------------------------------

### Visualize Digits Dataset

Source: https://github.com/woldemarg/proshpera/blob/main/README.md

Project the digits dataset with or without explicit labels.

```python
digits = datasets.load_digits(n_class=5)

visualizer.project(
    data=digits['data'],
    meta=digits['target'])
```

```python
visualizer.project(
    data=digits['data'],
    labels=digits['target'])
```

--------------------------------

### Generate Synthetic Data

Source: https://github.com/woldemarg/proshpera/blob/main/demo/prosphera_demo_notebook.ipynb

Creates a synthetic dataset using make_blobs for testing purposes.

```python
# Generate data
data, labels = make_blobs(
    n_samples=5000,
    centers=50,
    n_features=25,
    random_state=1234)
```
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