Evidently integration (#4918)

* minor stylistic adjustments

* small corrections after dberenbaum review

* beautify link to colab, add jupyter viz instructions

* Update content/docs/user-guide/integrations/evidently.md

Co-authored-by: Dave Berenbaum <[email protected]>

---------

Co-authored-by: Francesco Calcavecchia <[email protected]>
Co-authored-by: Dave Berenbaum <[email protected]>

content/docs/sidebar.json

@@ -217,6 +217,10 @@
           {
             "label": "Databricks",
             "slug": "databricks"
+          },
+          {
+            "label": "Evidently",
+            "slug": "evidently"
           }
         ]
       },

content/docs/user-guide/integrations/evidently.md

@@ -0,0 +1,216 @@
+# Evidently
+
+[DVCLive](/doc/dvclive) can be used to track the results of
+[Evidently](https://www.evidentlyai.com/). In the following we demonstrate it
+through an example.
+
+<p align='center'>
+  <a href="https://colab.research.google.com/github/iterative/dvclive/blob/main/examples/DVCLive-Evidently.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" />
+  </a>
+</p>
+
+## Setup
+
+```cli
+$ pip install dvc dvclive evidently pandas
+```
+
+## Load the data
+
+Load the
+[data from UCI repository](https://archive.ics.uci.edu/static/public/275/bike+sharing+dataset.zip)
+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.
+
+```cli
+$ wget https://archive.ics.uci.edu/static/public/275/bike+sharing+dataset.zip
+$ unzip bike+sharing+dataset.zip
+```
+
+```python
+import pandas as pd
+
+df = pd.read_csv("raw_data/day.csv", header=0, sep=',', parse_dates=['dteday'])
+df.head()
+```
+
+This is how it looks: ![Sample data](/img/evidently-sample-data-head.png)
+
+## 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.
+
+```python
+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.
+
+```python
+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.
+
+```python
+#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:
+
+1. 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
+2. 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](https://marketplace.visualstudio.com/items?itemName=Iterative.dvc)
+to inspect the results.
+
+### 1. One single experiment
+
+```python
+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
+
+```cli
+$ dvc plots show
+```
+
+and inspecting the resulting `dvc_plots/index.html`, which should look like
+this: ![DVC plot show](/img/evidently-dvc-plot-show.png)
+
+<admon icon="book">
+
+In a Jupyter notebook environment, you can show the plots as a cell output
+simply by using `Live(report="notebook")`.
+
+</admon>
+
+### 2. Multiple experiments
+
+```python
+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
+
+```cli
+$ 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
+ ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+```
+
+<admon icon="book">
+
+In a Jupyter notebook environment, you can access the experiments results using
+the [Python DVC api](/doc/api-reference):
+
+```python
+import dvc.api
+
+dvc.api.exp_show()
+```
+
+</admon>