# Optunacy - alternate Optuna contour plotter
Plots Optuna contour plots trivially and with detailed mouseovers.
It collects data from all the hashes in Optuna, as well as the Objectives if you provide them.
It provides mouseovers with details, too.
## Example
```python
from optunacy.oplot import OPlot
def optuna_objective(trial, train_loader, test_loader):
...
trial.set_user_attr("Neurons per Hidden Layer", nphl)
...
# The objectives have a printable name and direction
return validation_accuracy, spikes_per_digit
objective_names = ["Validation Accuracy", "Spikes per Digit"]
objective_directions = ["maximize", "minimize"]
study = optuna.create_study(study_name="Minimize spikes, maximize accuracy",
directions=objective_directions)
study.optimize( lambda trial:
optuna_objective(trial, train_loader, test_loader),
n_trials=100)
see = OPlot(study, objective_names)
see.plot("Hidden Layers", "Neurons per Hidden Layer", "Validation Accuracy", z_clip=(0.8,1))
```
## Options
The plot function has these options:
``` python
def plot(self,
x_name, # string
y_name, # string
z_name=None, # string
x_range=None, # range, ex: x_range=(3,9)
y_range=None, # range
z_clip=None, # Clamps colors to max and min
interpol="linear"): # <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html">interpolation method</a>
```
## Installation
```pip install optunacy```
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"description": "\n# Optunacy - alternate Optuna contour plotter\n\nPlots Optuna contour plots trivially and with detailed mouseovers.\nIt collects data from all the hashes in Optuna, as well as the Objectives if you provide them.\n\nIt provides mouseovers with details, too.\n\n## Example\n\n```python\nfrom optunacy.oplot import OPlot\ndef optuna_objective(trial, train_loader, test_loader):\n ...\n trial.set_user_attr(\"Neurons per Hidden Layer\", nphl)\n ...\n # The objectives have a printable name and direction\n return validation_accuracy, spikes_per_digit\nobjective_names = [\"Validation Accuracy\", \"Spikes per Digit\"]\nobjective_directions = [\"maximize\", \"minimize\"]\n\nstudy = optuna.create_study(study_name=\"Minimize spikes, maximize accuracy\",\n directions=objective_directions)\n\nstudy.optimize( lambda trial:\n optuna_objective(trial, train_loader, test_loader),\n n_trials=100)\n\nsee = OPlot(study, objective_names)\nsee.plot(\"Hidden Layers\", \"Neurons per Hidden Layer\", \"Validation Accuracy\", z_clip=(0.8,1))\n```\n\n\n\n## Options\nThe plot function has these options:\n\n``` python\ndef plot(self, \n x_name, # string\n y_name, # string\n z_name=None, # string\n x_range=None, # range, ex: x_range=(3,9)\n y_range=None, # range\n z_clip=None, # Clamps colors to max and min\n interpol=\"linear\"): # <a href=\"https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html\">interpolation method</a>\n```\n\n\n## Installation\n\n```pip install optunacy```\n",
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