Retrieve Aggregated Data of a Specific Segment via REST API
In some cases, users might wish to investigate their data and get all the relevant aggregated data of a specific segment, for example, the distribution of a metric in this segment in a specific time range.
For this, users can now leverage Mona's /get_segment REST API endpoint to retrieve said segment.
To access Mona's API, first, an accessToken must be generated and added to the request.
Once users have their accessToken they can add it to their authorization and send a POST request with the following mandatory params:
- "contextClass"- The context class of the desired segment.
- "timestampFrom" - the start time in epoch timestamp.
- "timestampTo" - the end time in epoch timestamp.
Optional args:
- "timeSeriesResolutions" - Allows you to get a time-series with values of the metric. Expected format is an array of TimeResolutions.
- "withHistogram" - Default is False. Setting true will add the distribution histogram of the chosen metric to the response.
- "timeZone" - The desired time zone.
- "metrics" - The metric to get values of in the segment. Expected format is:
"metrics": [
{
"field": "feature_1",
"types": [
"average"
]
}
]
Possible values for "types" are: average, sum, variance, normalized_variance, std, standard_error, interquartile range, and existence ratio.
If no ״metrics״ param is stated, the call will return the first field in the config.
Optional args for segments:
- "excludedSegments" - Segments users wish to exclude from the desired segment. Expected format is SegmentsListType
- "requestedSegments" - The requested segments. If none is stated, response will be "all data". Expected format is SegmentsListType
- "baselineSegment" - The baseline of the requested segment. If none is stated, baseline will be "all data". Expected format is SegmentsType
In the following example, we are looking for the segment "occupation": "retail" in a 7 days time range with the average of "feature_1" as our metric, a time resolution of 1 day, and with a histogram.
curl --location --request POST 'https://api{YOUR-USER-ID}.monalabs.io/get_segment' \
--header 'Authorization: Bearer YOUR-ACCESS-TOKEN' \
--header 'Content-Type: application/json' \
--data-raw '{
"contextClass": "LOAN_APPLICATION_TUTORIAL",
"timestampTo": 1619654399,
"timestampFrom": 1619049600,
"requestedSegments": [
{
"occupation": [
{
"value": "retail"
}
]
}
],
"excludedSegments": [
{
"city": [
{
"value": "Arizona_Mesa"
}
]
}
],
"metrics": [
{
"field": "feature_1",
"types": [
"average"
]
}
],
"timeSeriesResolutions": [
"1d"
],
"withHistogram": true
}'
import requests
url = "https://api{YOUR-USER-ID}.monalabs.io/get_segment"
payload={
"contextClass": "LOAN_APPLICATION_TUTORIAL",
"timestampTo": 1619654399,
"timestampFrom": 1619049600,
"requestedSegments": [
{
"occupation": [
{
"value": "retail"
}
]
}
],
"negativeSegments": [
{
"city": [
{
"value": "Arizona_Mesa"
}
]
}
],
"metrics": [
{
"metricName": "feature_1",
"metricTypes": [
"average"
]
}
],
"timeSeriesResolutions": [
"1d"
],
"withHistogram": true
}
headers = {
'Authorization': 'Bearer YOUR-ACCESS-TOKEN',
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, json=payload)
print(response.text)
from mona_sdk.client import Client
api_key = <YOUR-API-KEY>
secret = <YOUR-SECRET>
my_mona_client = Client(api_key, secret)
my_mona_client.get_aggregated_data_of_a_specific_segment(
context_class="LOAN_APPLICATION_TUTORIAL",
timestamp_to=1619654399,
timestamp_from=1619049600,
requested_segments=[{"occupation": [{"value": "retail"}]}],
excluded_segments=[{"city": [{"value": "Arizona_Mesa"}]}],
metrics=[{"field": "feature_1", "types": ["average"]}],
time_series_resolutions=["1d"],
with_histogram=True,
)
The response will be aggregated info of given metrics in the baseline and the requested segments, and the distribution histogram of the metric in both.
{
"response_data": {
"{}": {
"amount": 2579,
"ratio": 1,
"fields": {
"feature_1": {
"field_config": {
"visible_name": "feature_1",
"field_name": "feature_1",
"field_type": "numeric",
"min_threshold": 0.0,
"max_threshold": 819.943,
"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 1.9827396218959616,
"sum": 5113,
"variance": 120.94344574365675,
"normalized_variance": 60.99814842455542,
"std": 10.997429051540035,
"standard_error": 0.2165536448431773,
"min": 0.0,
"max": 511.9643249511719,
"time_series_1d": {
"1619049600000": {
"amount": 372,
"ratio": 1,
"field_config": {
"visible_name": "feature_1",
"field_name": "feature_1",
"field_type": "numeric",
"min_threshold": 0.0,
"max_threshold": 819.943,
"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 1.8349670539697713,
"sum": 682,
"variance": 21.77625345390034,
"normalized_variance": 11.867381164577068,
"std": 4.666503343393246,
"standard_error": 0.2419469275198411,
"min": 0.005984037648886442,
"max": 62.26668930053711
},
"1619136000000": {
"amount": 387,
"ratio": 1,
"field_config": {
"visible_name": "feature_1",
"field_name": "feature_1",
"field_type": "numeric",
"min_threshold": 0.0,
"max_threshold": 819.943,
"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 2.119406214161976,
"sum": 820,
"variance": 28.09755397685753,
"normalized_variance": 13.257276396147327,
"std": 5.300712591421792,
"standard_error": 0.26945036397861977,
"min": 0.016407251358032227,
"max": 52.86306381225586
},
"1619222400000": {
"amount": 524,
"ratio": 1,
"field_config": {
"visible_name": "feature_1",
"field_name": "feature_1",
"field_type": "numeric",
"min_threshold": 0.0,
"max_threshold": 819.943,
"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 1.4575302535561376,
"sum": 763,
"variance": 10.734341173083779,
"normalized_variance": 7.364746732970877,
"std": 3.276330443206817,
"standard_error": 0.14312715995959263,
"min": 0.0,
"max": 52.82486343383789
},
"1619308800000": {
"amount": 299,
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"field_config": {
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"min_threshold": 0.0,
"max_threshold": 819.943,
"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 1.3884139638830189,
"sum": 415,
"variance": 9.272391468157307,
"normalized_variance": 6.678405511152404,
"std": 3.0450601748007062,
"standard_error": 0.1761003767822978,
"min": 0.0,
"max": 48.74291229248047
},
"1619395200000": {
"amount": 238,
"ratio": 1,
"field_config": {
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"field_name": "feature_1",
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"min_threshold": 0.0,
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"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 2.2817749975546566,
"sum": 543,
"variance": 55.70004296545661,
"normalized_variance": 24.410839379496004,
"std": 7.463246141288428,
"standard_error": 0.4837703938569482,
"min": 0.0,
"max": 79.43721771240234
},
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"interval": 16.39886,
"metrics": [
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],
"percents": []
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"average": 1.5853041838954964,
"sum": 588,
"variance": 6.159730160695364,
"normalized_variance": 3.885519399538414,
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"ratio": 1,
"field_config": {
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"min_threshold": 0.0,
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"interval": 16.39886,
"metrics": [
"average"
],
"percents": []
},
"average": 3.351998985063323,
"sum": 1300,
"variance": 690.5326681304468,
"normalized_variance": 206.00622828571707,
"std": 26.277988281648327,
"standard_error": 1.3340627314598905,
"min": 0.06280815601348877,
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}
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},
"sampling_factor": 1,
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},
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"amount": 418,
"ratio": 0.16207832493214425,
"fields": {
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"field_config": {
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"min_threshold": 0.0,
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"interval": 16.39886,
"metrics": [
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],
"percents": []
},
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"standard_error": 0.1627755829800218,
"min": 0.04216957092285156,
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"time_series_1d": {
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"field_config": {
"visible_name": "feature_1",
"field_name": "feature_1",
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],
"percents": []
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},
"partial_data": false
}
Updated over 1 year ago