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# Query Quickstart
To access information about a model's performance, drift, bias, or other enabled enrichments, write a `query` object
and submit it with the Arthur SDK using `arthur_model.query(query)`
For a general overview of this endpoint, including a more thorough descriptions of its rules, power, and customizability,
see the {doc}`endpoint_overview` page.
In each of the following examples, let our model be a binary classifier, and let `GT1` and `PRED1` be the names of our model's grouth truth attribute and predicted value, respectively.
## Accuracy
This is usually the simplest way to check for classifier performance. We can fetch a model's accuracy rate by querying a `select` on the function `accuracyRate` using the typical threshold `0.5`.
Given the following query:
```python
GT1 = 'gt_isFraud'
PRED1 = 'pred_isFraud'
query = {
"select": [
{
"function": "accuracyRate",
"parameters":
{
"threshold" : 0.5,
"ground_truth_property" : GT1,
"predicted_property" : PRED1
}
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'accuracyRate': 0.999026947368421}]
```
## Accuracy by batch
To expand the accuracy query by batch, add the `batch_id` property to the query's `select`, and add a `group_by` to the query using `batch_id`.
Given the following query:
```python
GT1 = 'gt_isFraud'
PRED1 = 'pred_isFraud'
query = {
"select": [
{
"function": "accuracyRate",
"parameters":
{
"threshold" : 0.5,
"ground_truth_property" : GT1,
"predicted_property" : PRED1
}
},
{
"property": "batch_id"
}
],
"group_by": [
{
"property": "batch_id"
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'accuracyRate': 0.999704, 'batch_id': 'newbatch3'},
{'accuracyRate': 0.999744, 'batch_id': 'newbatch0'},
{'accuracyRate': 0.992952, 'batch_id': 'newbatch19'},
{'accuracyRate': 0.999616, 'batch_id': 'newbatch5'},
{'accuracyRate': 0.999144, 'batch_id': 'newbatch6'},
...]
```
## Batch IDs
Querying accuracy by batch includes the `batch_id` values in the query result. But to query the `batch_id`s on their own, only `select` and `group_by` the `batch_id`.
Given the following query:
```python
query = {
"select": [
{
"property": "batch_id"
}
],
"group_by": [
{
"property": "batch_id"
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'batch_id': 'newbatch19'},
{'batch_id': 'newbatch18'},
{'batch_id': 'newbatch13'},
{'batch_id': 'newbatch12'},
{'batch_id': 'newbatch16'},
...]
```
## Accuracy (single batch)
To query the accuracy for only one batch, add a `filter` to the query according to the rule `batch_id == BATCHNAME`
Given the following query (for a specified batch name):
```python
GT1 = 'gt_isFraud'
PRED1 = 'pred_isFraud'
BATCHNAME = "newbatch19"
query = {
"select": [
{
"function": "accuracyRate",
"parameters":
{
"threshold" : 0.5,
"ground_truth_property" : GT1,
"predicted_property" : PRED1
}
},
{
"property": "batch_id"
}
],
"group_by": [
{
"property": "batch_id"
}
],
"filter": [
{
"property": "batch_id",
"comparator": "eq",
"value": BATCHNAME
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'accuracyRate': 0.992952, 'batch_id': 'newbatch19'}]
```
## Confusion Matrix
A confusion matrix counts the number of true positive, true negative, false positive, and false negative classifications; knowing these values is usually more useful than just accuracy when it is time to improve your model.
To query a confusion matrix, we use the `confusionMatrix` function in our query's `select`.
```{note} For the confusionMatrix function, the ground_truth_property and predicted_property parameters are optional.
```
Given the following query:
```python
query = {
"select": [
{
"function": "confusionMatrix",
"parameters":
{
"threshold" : 0.5
}
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'confusionMatrix':
{'false_negative': 4622,
'false_positive': 0,
'true_negative': 4745195,
'true_positive': 183}}]
```
## Confusion Matrix (single batch)
As we did with accuracy, to get a confusion matrix for a single batch we add the property `batch_id` to the query's `select`, add a `group_by` using `batch_id`, and then add a `filter` according to the rule `batch_id == BATCHNAME`
Given the following query (for a specified batch name):
```python
BATCHNAME = 'newbatch19'
query = {
"select": [
{
"function": "confusionMatrix",
"parameters":
{
"threshold" : 0.5
}
},
{
"property": "batch_id"
}
],
"group_by": [
{
"property": "batch_id"
}
],
"filter": [
{
"property": "batch_id",
"comparator": "eq",
"value": BATCHNAME
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'batch_id': 'newbatch19',
'confusionMatrix':
{'false_negative': 1762,
'false_positive': 0,
'true_negative': 248238,
'true_positive': 0}}]
```
## Confusion Matrix (by group)
Instead of querying for metrics and grouping by batch, we can group by other groupings as well. Here, we use the model's non-input attribute `race` so that we can compare model performance across different demographics. To do this, we add the group name `race` to our query's `select` and to its `group_by`
Given the following query:
```python
GROUP = 'race'
query = {
"select": [
{
"function": "confusionMatrix",
"parameters": {
"threshold" : 0.5
}
},
{
"property": GROUP
}
],
"group_by": [
{
"property": GROUP
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'confusionMatrix': {'false_negative': 1162,
'false_positive': 0,
'true_negative': 1184707,
'true_positive': 44},
'race': 'hispanic'},
{'confusionMatrix': {'false_negative': 1145,
'false_positive': 0,
'true_negative': 1186659,
'true_positive': 49},
'race': 'asian'},
{'confusionMatrix': {'false_negative': 1137,
'false_positive': 0,
'true_negative': 1187500,
'true_positive': 38},
'race': 'black'},
{'confusionMatrix': {'false_negative': 1178,
'false_positive': 0,
'true_negative': 1186329,
'true_positive': 52},
'race': 'white'}]
```
## Predictions
Here we aren't querying any metrics - we are just accessing all the predictions that have output by the model.
Given the following query:
```python
PRED1 = 'pred_isFraud'
query = {
"select": [
{
"property": PRED1
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'pred_isFraud_1': 0.005990342859493804},
{'pred_isFraud_1': 0.02271116879043313},
{'pred_isFraud_1': 0.15305224676085477},
{'pred_isFraud_1': 0},
{'pred_isFraud_1': 0.03280797449330532},
...]
```
## Predictions (average)
To _only_ query the average value across all these predictions (since querying all predictions and then averaging locally can be slow for production-sized query results), we only need to add the `avg` function to our query's `select`, with our predicted value `PRED1` now being a parameter of `avg` instead of a property we directly select.
Given the following query:
```python
PRED1 = 'pred_isFraud'
query = {
"select": [
{
"function": "avg",
"parameters": {
"property": PRED1
}
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'avg': 0.016030786000398464}]
```
## Predictions (average over time)
To get the average predictions on each day, we add the function `roundTimestamp` to our select using a `time_interval` of `day` - this groups the timestamp information according to `day` instead of options like `hour` or `week`. Then, we add a `group_by` to the query using the `alias` (`DAY`) specified in the `roundTimestamp` function.
Given the following query:
```python
PRED1 = 'pred_isFraud'
query = {
"select": [
{
"function": "avg",
"parameters": {
"property": PRED1
}
},
{
"function": "roundTimestamp",
"alias": "DAY",
"parameters": {
"property": "inference_timestamp",
"time_interval": "day"
}
}
],
"group_by": [
{
"alias": "DAY"
}
]
}
query_result = arthur_model.query(query)
```
The `query_result` will be:
```python
[{'avg': 0.016030786000359423, 'DAY': '2022-07-11T00:00:00Z'},
{'avg': 0.018723459201003300, 'DAY': '2022-07-12T00:00:00Z'},
{'avg': 0.014009919280009284, 'DAY': '2022-07-13T00:00:00Z'},
{'avg': 0.016663649020394829, 'DAY': '2022-07-14T00:00:00Z'},
{'avg': 0.017791902929210039, 'DAY': '2022-07-15T00:00:00Z'},
...]
``` |