# CLI

This section applies both to Dashboards and Profiles.

### Command line interface

If you prefer a video version, here is 7-min Quick Start on how to use Evidently using CLI.

{% embed url="<https://www.youtube.com/watch?v=3j3NwIkhmTs>" %}

To start, prepare your data as `csv` files.

To generate the HTML report, run the following command in bash:

```bash
$ python -m evidently calculate dashboard --config config.json 
--reference reference.csv --current current.csv --output output_folder --report_name output_file_name
```

To generate a JSON profile, run the following command in bash:

```bash
$ python -m evidently calculate profile --config config.json 
--reference reference.csv --current current.csv --output output_folder --report_name output_file_name
```

Here:

* `reference` is the path to the reference data,
* `current` is the path to the current data,
* `output` is the path to the output folder,
* `config` is the path to the configuration file,
* `pretty_print` to print the JSON profile with indents (for profile only).

You can choose the following **Tabs**:

* `data_drift` to estimate the **data drift**,
* `num_target_drift` to estimate **target drift** for the numerical target
* `cat_target_drift` to estimate target drift for the categorical target
* `regression_performance` to explore the **performance** of a regression model
* `classification_performance` to explore the **performance** of a classification model
* `prob_classification_performance` to explore the **performance** of a probabilistic classification model

To configure the report you need to create the `config.json` file or a `config.yaml` file. This file configures the way of reading your input data and the type of the report.

#### Configuration examples

Here is an example of a simple configuration, where we have comma-separated `csv` files with headers and there is no `date` column in the data.

**Dashboard**:

```yaml
{
  "data_format":{
    "separator":",",
    "header":true,
    "date_column":null
  },
  "column_mapping":{},
  "dashboard_tabs":["cat_target_drift"]
}
```

**Profile**:

```yaml
{
  "data_format":{
    "separator":",",
    "header":true,
    "date_column":null
  },
  "column_mapping":{},
  "profile_sections":["data_drift"],
  "pretty_print":true
}
```

Here is an example for a more complicated configuration, where we have comma-separated `csv` files with headers and `datetime` column. We also specified the `column_mapping` dictionary, where we added information about the `datetime`, `target` and `numerical_features`.

**Dashboard**:

```yaml
{
  "data_format":{
    "separator":",",
    "header":true,
    "date_column":"datetime"
  },
  "column_mapping":{
    "datetime":"datetime",
    "target":"target",
    "numerical_features":["mean radius", "mean texture", "mean perimeter", 
      "mean area", "mean smoothness", "mean compactness", "mean concavity", 
      "mean concave points", "mean symmetry"]},
  "dashboard_tabs":["cat_target_drift"],
  "sampling": {
      "reference": {
      "type": "none"
    },
      "current": {
      "type": "nth",
      "n": 2
    }
  }
}
```

**Profile**:

```yaml
{
  "data_format":{
    "separator":",",
    "header":true,
    "date_column":null
  },
  "column_mapping":{
    "target":"target",
    "numerical_features":["mean radius", "mean texture", "mean perimeter", 
      "mean area", "mean smoothness", "mean compactness", "mean concavity", 
      "mean concave points", "mean symmetry"]},
  "profile_sections":["data_drift", "cat_target_drift"],
  "pretty_print":true,
  "sampling": {
    "reference": {
      "type": "none"
    },
    "current": {
      "type": "random",
      "ratio": 0.8
    }
  }
}
```

#### Telemetry

Telemetry is collected in Evidently starting from version 0.1.21.dev0.

When you use Evidently in the command-line interface, we collect some basic telemetry. It includes data on the environment (e.g. Python version) and usage (type of report or profile generated). You can read more about what we collect [here](/v0.1.57/support/telemetry.md).

You can opt-out from telemetry collection by setting the environment variable:

```yaml
 EVIDENTLY_DISABLE_TELEMETRY=1
```

#### Sampling for large datasets

As shown in the configuration example above, you can specify **sampling** parameters for large files. You can use different sampling strategies for the reference and current data, or apply sampling only to one of the files.

Currently, you can choose from the following options:

* `none`- **no sampling** will be applied
* `nth` - each **Nth row** of the file will be taken. This option works together with the `n` parameter (see the example with the Dashboard above)
* `random` - **random sampling** will be applied. This option works together with `ratio` parameter (see the example with the Profile above)

If you do not specify the sampling parameters in the configuration, it will be treated as none and no sampling will be applied.


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