Metadata-Version: 2.1
Name: mlflow-extend
Version: 0.1.6
Summary: Extend MLflow's functionality
Home-page: https://github.com/harupy/mlflow-extend
Maintainer: harupy
Maintainer-email: hkawamura0130@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://mlflow-extend.readthedocs.io/en/latest/index.html
Project-URL: Source Code, https://github.com/harupy/mlflow-extend
Project-URL: Bug Tracker, https://github.com/harupy/mlflow-extend/issues
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: pyarrow
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: mlflow (>=1.7.0)
Requires-Dist: plotly

# MLflow Extend

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Extend MLflow's functionality.

## Installation

From PyPI

```bash
pip install mlflow-extend
```

From GitHub (development version)

```
pip install git+https://github.com/harupy/mlflow-extend.git
```

## Example

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from plotly import graph_objects as go

from mlflow_extend import mlflow

with mlflow.start_run():
    # mlflow native APIs
    mlflow.log_param('param', 0)
    mlflow.log_metric('metric', 1.0)

    ##### new APIs mlflow_extend provides #####

    # flatten dict
    mlflow.log_params_flatten({"a": {"b": 0}})
    mlflow.log_metrics_flatten({"a": {"b": 0.0}})

    # dict
    mlflow.log_dict({'a': 0}, 'dict.json')

    # numpy array
    mlflow.log_numpy(np.array([0]), 'array.npy')

    # pandas dataframe
    mlflow.log_df(pd.DataFrame({'a': [0]}), 'df.csv')

    # matplotlib figure
    fig, ax = plt.subplots()
    ax.plot([0, 1], [0, 1])
    mlflow.log_figure(fig, 'figure.png')

    # plotly figure
    fig = go.Figure(data=[go.Bar(x=[1, 2, 3], y=[1, 3, 2])])
    mlflow.log_figure(fig, 'figure.html')

    # confusion matrix
    mlflow.log_confusion_matrix([[1, 2], [3, 4]])

    # run "mlflow ui" and see the result.
```


