Optimization Dashboard
Visualize multidimensional data from studies performed by Hyperactive and Gradient-Free-Optimizers.
Examples
import pandas as pd
from dashboard import Dashboard
# create so test dataset for this example
df_array = [[0.5, 6, 50, 0.6], [0.9, 7, 40, 0.7], [0.2, 9, 70, 0.8]]
columns = ["x1", "x2", "x3", "score"]
search_data = pd.DataFrame(df_array, columns=columns)
# save the dataframe to file
search_data.to_csv("./search_data")
board = Dashboard()
# the dashboard must read the dataframe from a file
board.open_search_data_file("./search_data")
The search data that is loaded from file must always follow the pattern below:
| score | x1 | x2 | ... |
| 0.756 | 0.1 | 0.2 | ... |
| 0.823 | 0.3 | 0.1 | ... |
| ... | ... | ... | ... |
| ... | ... | ... | ... |
Hereby "x1", "x2", ... can be given any name. But "score" is always necessary!
API reference
- Dashboard(plots=[ "score_statistics", "1d_scatter", "2d_scatter", "3d_scatter", "parallel_coordinates", ] )
- .open_search_data_file(path)
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Metadata
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Release files for optimization-dashboard 0.0.1
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Release files / optimization_dashboard-0.0.1-py3-none-any.whl
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| Size | 5.0 kB |
| Tags | Python 3 |
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