# Explainer examples

## Explainer Examples

## Anchor Tabular Explainer for SKLearn Income Model

```bash
cat ./models/income.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: income
spec:
  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/classifier"
  requirements:
  - sklearn

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f ./models/income.yaml -n ${NAMESPACE}
```

```
pipeline.mlops.seldon.io/income created
```

```bash
kubectl wait --for condition=ready --timeout=300s model income -n ${NAMESPACE}
```

```
model.mlops.seldon.io/income condition met
```

```bash
curl --location 'http://${SELDON_INFER_HOST}/v2/models/income/infer' \
	--header 'Content-Type: application/json'  \
    --data '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
```

```json
{
	"model_name": "income_1",
	"model_version": "1",
	"id": "c65b8302-85af-4bac-aac5-91e3bedebee8",
	"parameters": {},
	"outputs": [
		{
			"name": "predict",
			"shape": [
				1,
				1
			],
			"datatype": "INT64",
			"data": [
				0
			]
		}
	]
}

```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model unload mnist-pytorch
```

```bash
seldon model load -f ./models/income.yaml
```

```json
{}
```

```bash
seldon model status income -w ModelAvailable
```

```json
{}
```

```bash
seldon model infer income \
  '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
```

```json
{
	"model_name": "income_1",
	"model_version": "1",
	"id": "c65b8302-85af-4bac-aac5-91e3bedebee8",
	"parameters": {},
	"outputs": [
		{
			"name": "predict",
			"shape": [
				1,
				1
			],
			"datatype": "INT64",
			"data": [
				0
			]
		}
	]
}

```

{% endtab %}
{% endtabs %}

```bash
cat ./models/income-explainer.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: income-explainer
spec:
  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/explainer"
  explainer:
    type: anchor_tabular
    modelRef: income

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f ./models/income-explainer.yaml -n ${NAMESPACE}
```

```
model.mlops.seldon.io/income-explainer created
```

```bash
kubectl wait --for condition=ready --timeout=300s model income-explainer -n ${NAMESPACE}
```

```
model.mlops.seldon.io/income-explainer condition met
```

```bash
curl --location 'http://${SELDON_INFER_HOST}/v2/models/iris/infer' \
	--header 'Content-Type: application/json'  \
    --data '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
```

```json
{
	"model_name": "income-explainer_1",
	"model_version": "1",
	"id": "a22c3785-ff3b-4504-9b3c-199aa48a62d6",
	"parameters": {},
	"outputs": [
		{
			"name": "explanation",
			"shape": [
				1,
				1
			],
			"datatype": "BYTES",
			"parameters": {
				"content_type": "str"
			},
			"data": [
				"{\"meta\": {\"name\": \"AnchorTabular\", \"type\": [\"blackbox\"], \"explanations\": [\"local\"], \"params\": {\"seed\": 1, \"disc_perc\": [25, 50, 75], \"threshold\": 0.95, \"delta\": 0.1, \"tau\": 0.15, \"batch_size\": 100, \"coverage_samples\": 10000, \"beam_size\": 1, \"stop_on_first\": false, \"max_anchor_size\": null, \"min_samples_start\": 100, \"n_covered_ex\": 10, \"binary_cache_size\": 10000, \"cache_margin\": 1000, \"verbose\": false, \"verbose_every\": 1, \"kwargs\": {}}, \"version\": \"0.9.0\"}, \"data\": {\"anchor\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"precision\": 0.9518716577540107, \"coverage\": 0.07165109034267912, \"raw\": {\"feature\": [3, 5], \"mean\": [0.7959381044487428, 0.9518716577540107], \"precision\": [0.7959381044487428, 0.9518716577540107], \"coverage\": [0.3037383177570093, 0.07165109034267912], \"examples\": [{\"covered_true\": [[52, 5, 5, 1, 8, 1, 2, 0, 0, 0, 50, 9], [49, 4, 1, 1, 4, 4, 1, 0, 0, 0, 40, 1], [23, 4, 1, 1, 6, 1, 4, 1, 0, 0, 40, 9], [55, 2, 1, 1, 5, 1, 4, 0, 0, 0, 48, 9], [22, 4, 1, 1, 2, 3, 4, 0, 0, 0, 15, 9], [51, 4, 2, 1, 5, 0, 1, 1, 0, 0, 99, 4], [40, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [40, 6, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [50, 5, 5, 1, 6, 0, 4, 1, 0, 0, 55, 9], [41, 4, 1, 1, 6, 0, 4, 1, 0, 0, 40, 9]], \"covered_false\": [[42, 4, 1, 1, 8, 0, 4, 1, 0, 2415, 60, 9], [48, 6, 2, 1, 5, 4, 4, 0, 0, 0, 60, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [57, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [63, 7, 2, 1, 8, 0, 4, 1, 0, 1902, 50, 9], [51, 4, 5, 1, 8, 0, 4, 1, 0, 1887, 47, 9], [51, 2, 2, 1, 8, 1, 4, 0, 0, 0, 45, 9], [68, 7, 5, 1, 5, 0, 4, 1, 0, 2377, 42, 0], [45, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 40, 9], [45, 4, 1, 1, 8, 0, 4, 1, 0, 1977, 60, 9]], \"uncovered_true\": [], \"uncovered_false\": []}, {\"covered_true\": [[44, 6, 5, 1, 8, 3, 4, 0, 0, 1902, 60, 9], [58, 7, 2, 1, 5, 3, 1, 1, 4064, 0, 40, 1], [50, 7, 1, 1, 1, 3, 2, 0, 0, 0, 37, 9], [34, 4, 2, 1, 5, 3, 4, 1, 0, 0, 45, 9], [45, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [33, 7, 5, 1, 5, 3, 1, 1, 0, 0, 30, 6], [61, 7, 2, 1, 5, 3, 4, 1, 0, 0, 40, 0], [35, 4, 5, 1, 1, 3, 4, 1, 0, 0, 40, 9], [71, 2, 1, 1, 5, 3, 4, 0, 0, 0, 6, 9], [44, 4, 1, 1, 8, 3, 2, 1, 0, 0, 35, 9]], \"covered_false\": [[30, 4, 5, 1, 5, 3, 4, 1, 10520, 0, 40, 9], [54, 7, 2, 1, 8, 3, 4, 1, 0, 1902, 50, 9], [66, 6, 2, 1, 6, 3, 4, 1, 0, 2377, 25, 9], [35, 4, 2, 1, 5, 3, 4, 1, 7298, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7298, 0, 48, 9], [31, 4, 1, 1, 8, 3, 4, 0, 13550, 0, 50, 9], [35, 4, 1, 1, 8, 3, 4, 1, 8614, 0, 45, 9]], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000000, \"success\": true, \"names\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"prediction\": [0], \"instance\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \"instances\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}"
			]
		}
	]
}

```

```bash
kubectl delete -f ./models/income-explainer.yaml -n ${NAMESPACE}
kubectl delete -f ./models/income.yaml -n ${NAMESPACE}

```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model load -f ./models/income-explainer.yaml
```

```json
{}

```

```bash
seldon model status income-explainer -w ModelAvailable
```

```json
{}

```

```bash
seldon model infer income-explainer \
  '{"inputs": [{"name": "predict", "shape": [1, 12], "datatype": "FP32", "data": [[47,4,1,1,1,3,4,1,0,0,40,9]]}]}'
```

```json
{
	"model_name": "income-explainer_1",
	"model_version": "1",
	"id": "a22c3785-ff3b-4504-9b3c-199aa48a62d6",
	"parameters": {},
	"outputs": [
		{
			"name": "explanation",
			"shape": [
				1,
				1
			],
			"datatype": "BYTES",
			"parameters": {
				"content_type": "str"
			},
			"data": [
				"{\"meta\": {\"name\": \"AnchorTabular\", \"type\": [\"blackbox\"], \"explanations\": [\"local\"], \"params\": {\"seed\": 1, \"disc_perc\": [25, 50, 75], \"threshold\": 0.95, \"delta\": 0.1, \"tau\": 0.15, \"batch_size\": 100, \"coverage_samples\": 10000, \"beam_size\": 1, \"stop_on_first\": false, \"max_anchor_size\": null, \"min_samples_start\": 100, \"n_covered_ex\": 10, \"binary_cache_size\": 10000, \"cache_margin\": 1000, \"verbose\": false, \"verbose_every\": 1, \"kwargs\": {}}, \"version\": \"0.9.0\"}, \"data\": {\"anchor\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"precision\": 0.9518716577540107, \"coverage\": 0.07165109034267912, \"raw\": {\"feature\": [3, 5], \"mean\": [0.7959381044487428, 0.9518716577540107], \"precision\": [0.7959381044487428, 0.9518716577540107], \"coverage\": [0.3037383177570093, 0.07165109034267912], \"examples\": [{\"covered_true\": [[52, 5, 5, 1, 8, 1, 2, 0, 0, 0, 50, 9], [49, 4, 1, 1, 4, 4, 1, 0, 0, 0, 40, 1], [23, 4, 1, 1, 6, 1, 4, 1, 0, 0, 40, 9], [55, 2, 1, 1, 5, 1, 4, 0, 0, 0, 48, 9], [22, 4, 1, 1, 2, 3, 4, 0, 0, 0, 15, 9], [51, 4, 2, 1, 5, 0, 1, 1, 0, 0, 99, 4], [40, 4, 1, 1, 5, 1, 4, 0, 0, 0, 40, 9], [40, 6, 1, 1, 2, 0, 4, 1, 0, 0, 50, 9], [50, 5, 5, 1, 6, 0, 4, 1, 0, 0, 55, 9], [41, 4, 1, 1, 6, 0, 4, 1, 0, 0, 40, 9]], \"covered_false\": [[42, 4, 1, 1, 8, 0, 4, 1, 0, 2415, 60, 9], [48, 6, 2, 1, 5, 4, 4, 0, 0, 0, 60, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [57, 4, 5, 1, 8, 0, 4, 1, 0, 0, 50, 9], [63, 7, 2, 1, 8, 0, 4, 1, 0, 1902, 50, 9], [51, 4, 5, 1, 8, 0, 4, 1, 0, 1887, 47, 9], [51, 2, 2, 1, 8, 1, 4, 0, 0, 0, 45, 9], [68, 7, 5, 1, 5, 0, 4, 1, 0, 2377, 42, 0], [45, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 40, 9], [45, 4, 1, 1, 8, 0, 4, 1, 0, 1977, 60, 9]], \"uncovered_true\": [], \"uncovered_false\": []}, {\"covered_true\": [[44, 6, 5, 1, 8, 3, 4, 0, 0, 1902, 60, 9], [58, 7, 2, 1, 5, 3, 1, 1, 4064, 0, 40, 1], [50, 7, 1, 1, 1, 3, 2, 0, 0, 0, 37, 9], [34, 4, 2, 1, 5, 3, 4, 1, 0, 0, 45, 9], [45, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9], [33, 7, 5, 1, 5, 3, 1, 1, 0, 0, 30, 6], [61, 7, 2, 1, 5, 3, 4, 1, 0, 0, 40, 0], [35, 4, 5, 1, 1, 3, 4, 1, 0, 0, 40, 9], [71, 2, 1, 1, 5, 3, 4, 0, 0, 0, 6, 9], [44, 4, 1, 1, 8, 3, 2, 1, 0, 0, 35, 9]], \"covered_false\": [[30, 4, 5, 1, 5, 3, 4, 1, 10520, 0, 40, 9], [54, 7, 2, 1, 8, 3, 4, 1, 0, 1902, 50, 9], [66, 6, 2, 1, 6, 3, 4, 1, 0, 2377, 25, 9], [35, 4, 2, 1, 5, 3, 4, 1, 7298, 0, 40, 9], [44, 4, 1, 1, 8, 3, 4, 1, 7298, 0, 48, 9], [31, 4, 1, 1, 8, 3, 4, 0, 13550, 0, 50, 9], [35, 4, 1, 1, 8, 3, 4, 1, 8614, 0, 45, 9]], \"uncovered_true\": [], \"uncovered_false\": []}], \"all_precision\": 0, \"num_preds\": 1000000, \"success\": true, \"names\": [\"Marital Status = Never-Married\", \"Relationship = Own-child\"], \"prediction\": [0], \"instance\": [47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0], \"instances\": [[47.0, 4.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 0.0, 0.0, 40.0, 9.0]]}}}"
			]
		}
	]
}

```

```bash
seldon model unload income-explainer
```

```json
{}

```

```bash
seldon model unload income
```

```json
{}

```

{% endtab %}
{% endtabs %}

### Anchor Text Explainer for SKLearn Movies Sentiment Model

```bash
cat ./models/moviesentiment.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: sentiment
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn"
  requirements:
  - sklearn

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f ./models/moviesentiment.yaml -n ${NAMESPACE}
```

```
model.mlops.seldon.io/sentiment created
```

```bash
kubectl wait --for condition=ready --timeout=300s model sentiment -n ${NAMESPACE}
```

```
model.mlops.seldon.io/sentiment condition met
```

```bash
curl --location 'http://${SELDON_INFER_HOST}/v2/models/iris/infer' \
	--header 'Content-Type: application/json'  \
    --data '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
```

```json
{
	"model_name": "sentiment_2",
	"model_version": "1",
	"id": "f5c07363-7e9d-4f09-aa30-228c81fdf4a4",
	"parameters": {},
	"outputs": [
		{
			"name": "predict",
			"shape": [
				1,
				1
			],
			"datatype": "INT64",
			"parameters": {
				"content_type": "np"
			},
			"data": [
				0
			]
		}
	]
}

```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model load -f ./models/moviesentiment.yaml
```

```json
{}

```

```bash
seldon model status sentiment -w ModelAvailable
```

```json
{}

```

```bash
seldon model infer sentiment \
  '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
```

```json
{
	"model_name": "sentiment_2",
	"model_version": "1",
	"id": "f5c07363-7e9d-4f09-aa30-228c81fdf4a4",
	"parameters": {},
	"outputs": [
		{
			"name": "predict",
			"shape": [
				1,
				1
			],
			"datatype": "INT64",
			"parameters": {
				"content_type": "np"
			},
			"data": [
				0
			]
		}
	]
}

```

{% endtab %}
{% endtabs %}

```bash
cat ./models/moviesentiment-explainer.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: sentiment-explainer
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/moviesentiment-sklearn-explainer"
  explainer:
    type: anchor_text
    modelRef: sentiment

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl apply -f ./models/moviesentiment-explainer.yaml -n ${NAMESPACE}
```

```
model.mlops.seldon.io/sentiment-explainer created
```

```bash
kubectl wait --for condition=ready --timeout=300s model sentiment-explainer -n ${NAMESPACE}
```

```
model.mlops.seldon.io/sentiment-explainer condition met
```

```bash
curl --location 'http://${SELDON_INFER_HOST}/v2/models/iris/infer' \
	--header 'Content-Type: application/json'  \
    --data '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model load -f ./models/moviesentiment-explainer.yaml
```

```json
{}

```

```bash
seldon model status sentiment-explainer -w ModelAvailable
```

```json
{}

```

```bash
seldon model infer sentiment-explainer \
  '{"parameters": {"content_type": "str"}, "inputs": [{"name": "foo", "data": ["I am good"], "datatype": "BYTES","shape": [1]}]}'
```

{% endtab %}
{% endtabs %}

```
Error: V2 server error: 500 Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/errors.py", line 162, in __call__
    await self.app(scope, receive, _send)
  File "/opt/conda/lib/python3.8/site-packages/starlette_exporter/middleware.py", line 307, in __call__
    await self.app(scope, receive, wrapped_send)
  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py", line 24, in __call__
    await responder(scope, receive, send)
  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/gzip.py", line 44, in __call__
    await self.app(scope, receive, self.send_with_gzip)
  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
    raise exc
  File "/opt/conda/lib/python3.8/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
    await self.app(scope, receive, sender)
  File "/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 21, in __call__
    raise e
  File "/opt/conda/lib/python3.8/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
    await self.app(scope, receive, send)
  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 706, in __call__
    await route.handle(scope, receive, send)
  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 276, in handle
    await self.app(scope, receive, send)
  File "/opt/conda/lib/python3.8/site-packages/starlette/routing.py", line 66, in app
    response = await func(request)
  File "/opt/conda/lib/python3.8/site-packages/mlserver/rest/app.py", line 42, in custom_route_handler
    return await original_route_handler(request)
  File "/opt/conda/lib/python3.8/site-packages/fastapi/routing.py", line 237, in app
    raw_response = await run_endpoint_function(
  File "/opt/conda/lib/python3.8/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
    return await dependant.call(**values)
  File "/opt/conda/lib/python3.8/site-packages/mlserver/rest/endpoints.py", line 99, in infer
    inference_response = await self._data_plane.infer(
  File "/opt/conda/lib/python3.8/site-packages/mlserver/handlers/dataplane.py", line 103, in infer
    prediction = await model.predict(payload)
  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py", line 86, in predict
    output_data = await self._async_explain_impl(input_data, payload.parameters)
  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/runtime.py", line 119, in _async_explain_impl
    explanation = await loop.run_in_executor(self._executor, explain_call)
  File "/opt/conda/lib/python3.8/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/opt/conda/lib/python3.8/site-packages/mlserver_alibi_explain/explainers/black_box_runtime.py", line 62, in _explain_impl
    input_data = input_data[0]
KeyError: 0

```

{% tabs %}
{% tab title="kubectl" %}

```bash
kubectl delete -f ./models/moviesentiment-explainer.yaml -n ${NAMESPACE}
```

```bash
kubectl delete -f ./models/moviesentiment.yaml -n ${NAMESPACE}
```

{% endtab %}

{% tab title="seldon-cli" %}

```bash
seldon model unload sentiment-explainer
```

```json
{}
```

```bash
seldon model unload sentiment
```

```json
{}
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.seldon.ai/seldon-core-2/v2.10/user-guide/examples/explainer-examples.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.