import vetiver
import pandas as pd
# Example data
df = pd.DataFrame(
{'index': {0: pd.Timestamp('2021-01-01 00:00:00'),
1: pd.Timestamp('2021-01-01 00:00:00'),
2: pd.Timestamp('2021-01-02 00:00:00'),
3: pd.Timestamp('2021-01-02 00:00:00')},
'n': {0: 1, 1: 1, 2: 1, 3: 1},
'metric': {0: 'mean_squared_error',
1: 'mean_absolute_error',
2: 'mean_squared_error',
3: 'mean_absolute_error'},
'estimate': {0: 4.0, 1: 2.0, 2: 1.0, 3: 1.0}}
)
plot = vetiver.plot_metrics(
df_metrics = df,
date = "index",
estimate = "estimate",
metric = "metric",
n = "n")
plot.show()plot_metrics
plot_metrics(df_metrics, date, estimate, metric, n, **kw)Plot metrics over a given time period
Parameters
df_metrics : DataFrame-
Pandas dataframe of metrics over time, such as created by
compute_metrics() date : = 'index'-
Name of column in
df_metricscontaining dates estimate : = 'estimate'-
Name of column in
df_metricscontaining metric output metric : = 'metric'-
Name of column in
df_metricscontaining metric name n : = 'n'-
Name of column in
df_metricscontaining number of observations
Returns
: plotly.express.line-
A plotly line plot is returned with metrics over time
Examples
First, we will set up some example data.