> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/seldon-core-2/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/seldon-core-2/user-guide/examples/income.md).

# Production income classifier with drift, outlier and explanations

To run this notebook you need the inference data. This can be acquired in two ways:

* Run `make train` or,
* `gsutil cp -R gs://seldon-models/scv2/examples/income/infer-data .`

```python
import numpy as np
import json
import requests
```

```python
with open('./infer-data/test.npy', 'rb') as f:
    x_ref = np.load(f)
    x_h1 = np.load(f)
    y_ref = np.load(f)
    x_outlier = np.load(f)
```

```python
reqJson = json.loads('{"inputs":[{"name":"input_1","data":[],"datatype":"FP32","shape":[]}]}')
url = "http://0.0.0.0:9000/v2/models/model/infer"
```

```python
def infer(resourceName: str, batchSz: int, requestType: str):
    if requestType == "outlier":
        rows = x_outlier[0:0+batchSz]
    elif requestType == "drift":
        rows = x_h1[0:0+batchSz]
    else:
        rows = x_ref[0:0+batchSz]
    reqJson["inputs"][0]["data"] = rows.flatten().tolist()
    reqJson["inputs"][0]["shape"] = [batchSz, rows.shape[1]]
    headers = {"Content-Type": "application/json", "seldon-model":resourceName}
    response_raw = requests.post(url, json=reqJson, headers=headers)
    print(response_raw)
    print(response_raw.json())
```

### Pipeline with model, drift detector and outlier detector

```bash
cat ../../models/income-preprocess.yaml
echo "---"
cat ../../models/income.yaml
echo "---"
cat ../../models/income-drift.yaml
echo "---"
cat ../../models/income-outlier.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: income-preprocess
spec:
  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/preprocessor"
  requirements:
  - sklearn
---
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
---
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: income-drift
spec:
  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/drift-detector"
  requirements:
    - mlserver
    - alibi-detect
---
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: income-outlier
spec:
  storageUri: "gs://seldon-models/scv2/examples/mlserver_1.3.5/income/outlier-detector"
  requirements:
    - mlserver
    - alibi-detect

```

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

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

```
model.mlops.seldon.io/income-preprocess created
model.mlops.seldon.io/income created
model.mlops.seldon.io/income-drift created
model.mlops.seldon.io/income-outlier created
```

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

```
model.mlops.seldon.io/income-preprocess condition met
model.mlops.seldon.io/income condition met
model.mlops.seldon.io/income-drift condition met
model.mlops.seldon.io/income-outlier condition met
```

{% endtab %}

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

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

```json
{}
{}
{}
{}
```

```bash
seldon model status income-preprocess -w ModelAvailable | jq .
seldon model status income -w ModelAvailable | jq .
seldon model status income-drift -w ModelAvailable | jq .
seldon model status income-outlier -w ModelAvailable | jq .
```

```json
{}
{}
{}
{}
```

{% endtab %}
{% endtabs %}

```bash
cat ../../pipelines/income.yaml
```

```yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Pipeline
metadata:
  name: income-production
spec:
  steps:
    - name: income
    - name: income-preprocess
    - name: income-outlier
      inputs:
      - income-preprocess
    - name: income-drift
      batch:
        size: 20
  output:
    steps:
    - income
    - income-outlier.outputs.is_outlier

```

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

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

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

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

```
pipeline.mlops.seldon.io/choice condition met
```

{% endtab %}

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

```bash
seldon pipeline load -f ../../pipelines/income.yaml
```

```bash
seldon pipeline status income-production -w PipelineReady | jq -M .
```

```json
{
  "pipelineName": "income-production",
  "versions": [
    {
      "pipeline": {
        "name": "income-production",
        "uid": "cifej8iufmbc73e5int0",
        "version": 1,
        "steps": [
          {
            "name": "income"
          },
          {
            "name": "income-drift",
            "batch": {
              "size": 20
            }
          },
          {
            "name": "income-outlier",
            "inputs": [
              "income-preprocess.outputs"
            ]
          },
          {
            "name": "income-preprocess"
          }
        ],
        "output": {
          "steps": [
            "income.outputs",
            "income-outlier.outputs.is_outlier"
          ]
        },
        "kubernetesMeta": {}
      },
      "state": {
        "pipelineVersion": 1,
        "status": "PipelineReady",
        "reason": "created pipeline",
        "lastChangeTimestamp": "2023-06-30T14:41:38.343754921Z",
        "modelsReady": true
      }
    }
  ]
}

```

{% endtab %}
{% endtabs %}

Show predictions from reference set. Should not be drift or outliers.

```python
batchSz=20
print(y_ref[0:batchSz])
infer("income-production.pipeline",batchSz,"normal")
```

```
[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
<Response [200]>
{'model_name': '', 'outputs': [{'data': [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}

```

```bash
seldon pipeline inspect income-production.income-drift.outputs.is_drift
```

```
seldon.default.model.income-drift.outputs	cifej9gfh5ss738i5br0	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["0"]}}

```

Show predictions from drift data. Should be drift and probably not outliers.

```python
batchSz=20
print(y_ref[0:batchSz])
infer("income-production.pipeline",batchSz,"drift")
```

```
[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
<Response [200]>
{'model_name': '', 'outputs': [{'data': [0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}

```

```bash
seldon pipeline inspect income-production.income-drift.outputs.is_drift
```

```
seldon.default.model.income-drift.outputs	cifejaofh5ss738i5brg	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["1"]}}

```

Show predictions from outlier data. Should be outliers and probably not drift.

```python
batchSz=20
print(y_ref[0:batchSz])
infer("income-production.pipeline",batchSz,"outlier")
```

```
[0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1]
<Response [200]>
{'model_name': '', 'outputs': [{'data': [0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1], 'name': 'predict', 'shape': [20, 1], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}, {'data': [1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1], 'name': 'is_outlier', 'shape': [1, 20], 'datatype': 'INT64', 'parameters': {'content_type': 'np'}}]}

```

```bash
seldon pipeline inspect income-production.income-drift.outputs.is_drift
```

```
seldon.default.model.income-drift.outputs	cifejb8fh5ss738i5bs0	{"name":"is_drift", "datatype":"INT64", "shape":["1", "1"], "parameters":{"content_type":{"stringParam":"np"}}, "contents":{"int64Contents":["0"]}}

```

### Explanations

```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}
```

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

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

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

{% endtab %}

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

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

```json
{}
```

```bash
seldon model status income-explainer -w ModelAvailable | jq .
```

```json
{}
```

{% endtab %}
{% endtabs %}

```python
batchSz=1
print(y_ref[0:batchSz])
infer("income-explainer",batchSz,"normal")
```

```
[0]
<Response [200]>
{'model_name': 'income-explainer_1', 'model_version': '1', 'id': 'cdd68ba5-c569-4930-886f-fbdc26e24866', '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": "0.9.1"}, "data": {"anchor": ["Marital Status = Never-Married", "Relationship = Own-child", "Capital Gain <= 0.00"], "precision": 0.9942028985507246, "coverage": 0.0657, "raw": {"feature": [3, 5, 8], "mean": [0.7914951989026063, 0.9400749063670412, 0.9942028985507246], "precision": [0.7914951989026063, 0.9400749063670412, 0.9942028985507246], "coverage": [0.3043, 0.069, 0.0657], "examples": [{"covered_true": [[30, 0, 1, 1, 0, 1, 1, 0, 0, 0, 50, 2], [49, 4, 2, 1, 6, 0, 4, 1, 0, 0, 60, 9], [39, 2, 5, 1, 5, 0, 4, 1, 0, 0, 40, 9], [33, 4, 2, 1, 5, 0, 4, 1, 0, 0, 40, 9], [63, 4, 1, 1, 8, 1, 4, 0, 0, 0, 40, 9], [23, 4, 1, 1, 7, 1, 4, 1, 0, 0, 66, 8], [45, 4, 1, 1, 8, 0, 1, 1, 0, 0, 40, 1], [54, 4, 1, 1, 8, 4, 4, 1, 0, 0, 45, 9], [32, 6, 1, 1, 8, 4, 2, 0, 0, 0, 30, 9], [40, 5, 1, 1, 2, 0, 4, 1, 0, 0, 40, 9]], "covered_false": [[57, 4, 5, 1, 5, 0, 4, 1, 0, 1977, 45, 9], [53, 0, 5, 1, 0, 1, 4, 0, 8614, 0, 35, 9], [37, 4, 1, 1, 5, 0, 4, 1, 0, 0, 45, 9], [53, 4, 5, 1, 8, 0, 4, 1, 0, 1977, 55, 9], [35, 4, 1, 1, 8, 0, 4, 1, 7688, 0, 50, 9], [32, 4, 1, 1, 5, 1, 4, 1, 0, 0, 40, 9], [42, 4, 1, 1, 5, 0, 4, 1, 99999, 0, 40, 9], [32, 4, 1, 1, 8, 0, 4, 1, 15024, 0, 50, 9], [53, 7, 5, 1, 8, 0, 4, 1, 0, 0, 42, 9], [52, 1, 1, 1, 8, 0, 4, 1, 0, 0, 45, 9]], "uncovered_true": [], "uncovered_false": []}, {"covered_true": [[52, 7, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [27, 4, 1, 1, 8, 3, 4, 1, 0, 0, 40, 9], [28, 4, 1, 1, 6, 3, 4, 1, 0, 0, 60, 9], [46, 6, 5, 1, 2, 3, 4, 1, 0, 0, 50, 9], [53, 2, 5, 1, 5, 3, 2, 0, 0, 1669, 35, 9], [27, 4, 5, 1, 8, 3, 4, 0, 0, 0, 40, 9], [25, 4, 1, 1, 8, 3, 4, 0, 0, 0, 40, 9], [29, 6, 5, 1, 2, 3, 4, 1, 0, 0, 30, 9], [64, 0, 1, 1, 0, 3, 4, 1, 0, 0, 50, 9], [63, 0, 5, 1, 0, 3, 4, 1, 0, 0, 30, 9]], "covered_false": [[50, 5, 1, 1, 8, 3, 4, 1, 15024, 0, 60, 9], [45, 6, 1, 1, 6, 3, 4, 1, 14084, 0, 45, 9], [37, 4, 1, 1, 8, 3, 4, 1, 15024, 0, 40, 9], [33, 4, 1, 1, 8, 3, 4, 1, 15024, 0, 60, 9], [41, 6, 5, 1, 8, 3, 4, 1, 7298, 0, 70, 9], [42, 6, 1, 1, 2, 3, 4, 1, 15024, 0, 60, 9]], "uncovered_true": [], "uncovered_false": []}, {"covered_true": [[41, 4, 1, 1, 1, 3, 4, 1, 0, 0, 40, 9], [55, 2, 5, 1, 8, 3, 4, 1, 0, 0, 50, 9], [35, 4, 5, 1, 5, 3, 4, 0, 0, 0, 32, 9], [31, 4, 1, 1, 2, 3, 4, 1, 0, 0, 40, 9], [47, 4, 1, 1, 1, 3, 4, 1, 0, 0, 40, 9], [33, 4, 5, 1, 5, 3, 4, 1, 0, 0, 40, 9], [58, 0, 1, 1, 0, 3, 4, 0, 0, 0, 50, 9], [44, 6, 1, 1, 2, 3, 4, 1, 0, 0, 90, 9], [30, 4, 1, 1, 6, 3, 4, 1, 0, 0, 40, 9], [25, 4, 1, 1, 5, 3, 4, 1, 0, 0, 40, 9]], "covered_false": [], "uncovered_true": [], "uncovered_false": []}], "all_precision": 0, "num_preds": 1000000, "success": true, "names": ["Marital Status = Never-Married", "Relationship = Own-child", "Capital Gain <= 0.00"], "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]]}}}']}]}

```

### Cleanup

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

```bash
kubectl delete -f ../../piplines/income-production.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/income-preprocess.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/income.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/income-drift.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/income-outlier.yaml -n ${NAMESPACE}
kubectl delete -f ../../models/income-explainer.yaml -n ${NAMESPACE}
```

{% endtab %}

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

```bash
seldon pipeline unload income-production
seldon model unload income-preprocess
seldon model unload income
seldon model unload income-drift
seldon model unload income-outlier
seldon model unload income-explainer
```

{% endtab %}
{% endtabs %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://docs.seldon.ai/seldon-core-2/user-guide/examples/income.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.