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.