| Name | lossrider JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | A plotting library that create Line Rider maps |
| upload_time | 2024-08-19 17:40:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.6 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Loss Rider
Finally, a Python plotting library that can (only) output __Line Rider__ maps!
<p align="center" width="100%">
<br>
<img src="https://github.com/user-attachments/assets/219ac1fa-57a5-4c7f-bab3-7bbb5a78a70b">
<br>
</p>
ML practitioners can experience gradient descent like never before!
<p align="center" width="100%">
<br>
<img src="https://github.com/user-attachments/assets/025fb50e-7b03-452e-8b45-a15e258012db">
<br>
</p>
With support for all important features of a line graph.
<p align="center" width="100%">
<br>
<img src="https://github.com/user-attachments/assets/da32dd51-ba91-4d3d-9bff-30c5f6c051d8">
<br>
</p>
And don't forget interactive plotting in Jupyter Notebooks!
<p align="center" width="100%">
<br>
<img src="https://github.com/user-attachments/assets/12bed788-a3a3-441c-a991-a6565b526e00">
<br>
</p>
# Installation
```bash
pip install lossrider
```
# Usage
```python
import pandas as pd
from lossrider import lossrider
# Load a csv that contains columns named "Validation Loss", "Run Count" and "model_type"
data = pd.read_csv("./_data/sweep_df.csv")
# Plot it!
lossrider(
data,
x="Run Count",
y="Validation Loss",
hue="model_type",
xlim=(0.6, 340),
ylim=(3.2, 3.8),
xticks=(1, 10, 100),
yticks=[x/10 for x in range(32, 39)],
width=1000, height=500, fontsize=30,
logx=True, grid=False,
legend=True, legend_loc=(.65, 1),
outfile='maps/sweep_strategies',
)
```
The above produces the below plot
Raw data
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"description": "# Loss Rider\nFinally, a Python plotting library that can (only) output __Line Rider__ maps!\n\n<p align=\"center\" width=\"100%\">\n <br>\n <img src=\"https://github.com/user-attachments/assets/219ac1fa-57a5-4c7f-bab3-7bbb5a78a70b\">\n <br>\n</p>\n\nML practitioners can experience gradient descent like never before!\n<p align=\"center\" width=\"100%\">\n <br>\n <img src=\"https://github.com/user-attachments/assets/025fb50e-7b03-452e-8b45-a15e258012db\">\n <br>\n</p>\n\n\nWith support for all important features of a line graph.\n<p align=\"center\" width=\"100%\">\n <br>\n <img src=\"https://github.com/user-attachments/assets/da32dd51-ba91-4d3d-9bff-30c5f6c051d8\">\n <br>\n</p>\n\n\nAnd don't forget interactive plotting in Jupyter Notebooks!\n<p align=\"center\" width=\"100%\">\n <br>\n <img src=\"https://github.com/user-attachments/assets/12bed788-a3a3-441c-a991-a6565b526e00\">\n <br>\n</p>\n\n# Installation\n```bash\npip install lossrider\n```\n\n# Usage\n\n```python\nimport pandas as pd\nfrom lossrider import lossrider\n\n# Load a csv that contains columns named \"Validation Loss\", \"Run Count\" and \"model_type\"\ndata = pd.read_csv(\"./_data/sweep_df.csv\")\n\n# Plot it!\nlossrider(\n data,\n x=\"Run Count\", \n y=\"Validation Loss\",\n hue=\"model_type\",\n xlim=(0.6, 340),\n ylim=(3.2, 3.8),\n xticks=(1, 10, 100), \n yticks=[x/10 for x in range(32, 39)],\n width=1000, height=500, fontsize=30,\n logx=True, grid=False,\n legend=True, legend_loc=(.65, 1),\n outfile='maps/sweep_strategies',\n)\n```\nThe above produces the below plot\n\n\n\n",
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