Evidently
DVCLive can be used to track the results of Evidently. In the following we demonstrate it through an example.
Setup
$ pip install dvc dvclive evidently pandasLoad the data
Load the data from UCI repository and save it locally. For demonstration purposes, we treat this data as the input data for a live model. To use with production models, you should make your prediction logs available.
$ wget https://archive.ics.uci.edu/static/public/275/bike+sharing+dataset.zip
$ unzip bike+sharing+dataset.zipimport pandas as pd
df = pd.read_csv("raw_data/day.csv", header=0, sep=',', parse_dates=['dteday'])
df.head()This is how it looks:
Define column mapping
You should specify the categorical and numerical features so that Evidently performs the correct statistical test for each of them. While Evidently can parse the data structure automatically, manually specifying the column type can minimize errors.
from evidently.pipeline.column_mapping import ColumnMapping
data_columns = ColumnMapping()
data_columns.numerical_features = ['weathersit', 'temp', 'atemp', 'hum', 'windspeed']
data_columns.categorical_features = ['holiday', 'workingday']Define what to log
Specify which metrics you want to calculate. In this case, you can generate the Data Drift report and log the drift score for each feature.
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
def eval_drift(reference, production, column_mapping):
data_drift_report = Report(metrics=[DataDriftPreset()])
data_drift_report.run(
reference_data=reference, current_data=production, column_mapping=column_mapping
)
report = data_drift_report.as_dict()
drifts = []
for feature in (
column_mapping.numerical_features + column_mapping.categorical_features
):
drifts.append(
(
feature,
report["metrics"][1]["result"]["drift_by_columns"][feature][
"drift_score"
],
)
)
return drifts
You can adapt what you want to calculate by selecting a different Preset or Metric from those available in Evidently.
Define the comparison windows
Specify the period that is considered reference: Evidently will use it as the base for the comparison. Then, you should choose the periods to treat as experiments. This emulates the production model runs.
#set reference dates
reference_dates = ('2011-01-01 00:00:00','2011-01-28 23:00:00')
#set experiment batches dates
experiment_batches = [
('2011-01-01 00:00:00','2011-01-29 23:00:00'),
('2011-01-29 00:00:00','2011-02-07 23:00:00'),
('2011-02-07 00:00:00','2011-02-14 23:00:00'),
('2011-02-15 00:00:00','2011-02-21 23:00:00'),
]Run and log experiments in DVC
There are two ways to track the results of Evidently with DVCLive:
- you can save the results of each item in the batch in one single experiment (each experiment corresponds to a git commit), in separate steps
- or you can save the result of each item in the batch as a separate experiment
We will demonstrate both, and show you how to inspect the results regardless of your IDE. However, if you are using VSCode, we recommend using the DVC extension for VS Code to inspect the results.
1. One single experiment
from dvclive import Live
with Live() as live:
for date in experiment_batches:
live.log_param("begin", date[0])
live.log_param("end", date[1])
metrics = eval_drift(
df.loc[df.dteday.between(reference_dates[0], reference_dates[1])],
df.loc[df.dteday.between(date[0], date[1])],
column_mapping=data_columns,
)
for feature in metrics:
live.log_metric(feature[0], round(feature[1], 3))
live.next_step()You can then inspect the results using
$ dvc plots showand inspecting the resulting dvc_plots/index.html, which should look like
this:
In a Jupyter notebook environment, you can show the plots as a cell output
simply by using Live(report="notebook").
2. Multiple experiments
from dvclive import Live
for step, date in enumerate(experiment_batches):
with Live() as live:
live.log_param("begin", date[0])
live.log_param("end", date[1])
live.log_param("step", step)
metrics = eval_drift(
df.loc[df.dteday.between(reference_dates[0], reference_dates[1])],
df.loc[df.dteday.between(date[0], date[1])],
column_mapping=data_columns,
)
for feature in metrics:
live.log_metric(feature[0], round(feature[1], 3))
You can the inspect the results using
$ dvc exp show────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Experiment Created weathersit temp atemp hum windspeed holiday workingday step begin end
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
workspace - 0.231 0 0 0.062 0.012 0.275 0.593 3 2011-02-15 00:00:00 2011-02-21 23:00:00
master 10:02 AM - - - - - - - - - -
├── a96b45c [muggy-rand] 10:02 AM 0.231 0 0 0.062 0.012 0.275 0.593 3 2011-02-15 00:00:00 2011-02-21 23:00:00
├── 78c6668 [pawky-arcs] 10:02 AM 0.155 0.399 0.537 0.684 0.611 0.588 0.699 2 2011-02-07 00:00:00 2011-02-14 23:00:00
├── c1dd720 [joint-wont] 10:02 AM 0.779 0.098 0.107 0.03 0.171 0.545 0.653 1 2011-01-29 00:00:00 2011-02-07 23:00:00
└── d0ddb8d [osmic-impi] 10:02 AM 0.985 1 1 1 1 0.98 0.851 0 2011-01-01 00:00:00 2011-01-29 23:00:00
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────In a Jupyter notebook environment, you can access the experiments results using the Python DVC api:
import dvc.api
dvc.api.exp_show()