Grid Predictions

Post a grid prediction task to the CSA Prediction Engine

The grid prediction endpoint calculates maxfit solutions across all possible combinations of attributes (independent variables). It evaluates these solutions over relevance and similarity censors, ultimately producing an adjusted-fit weighted average of all outcomes. By taking this comprehensive approach, grid prediction ensures a robust and holistic understanding of the data, making it ideal for uncovering deeper patterns and insights across multiple variable combinations without overfitting.

Whether you’re looking for high-level overviews or fine-grained control with custom settings, the grid prediction system adapts to your needs, ensuring accurate and relevant results tailored to your task. This versatile method is ideal for discovering patterns, optimizing decisions, and forecasting outcomes.

Quick Start

Getting started with the grid endpoint is straightforward. Just provide the required access ID along with the key prediction inputs: dependent variable, independent variables, and the prediction conditions. The API will then post your prediction task to the CSA Prediction Engine for processing. This approach is ideal for quickly deriving insights and making data-driven decisions, without the need for complex customizations.

post

Essential input arguments for invoking relevance-based grid predictions.

Authorizations

x-api-key

x-api-keystringRequired

API key required to access the CSA Prediction Engine API

Header parameters

Content-TypestringOptional

The content type of the request (application/json)

Example: application/json

ConnectionstringOptional

Connection header value (keep-alive)

Example: keep-alive

Body

application/json

access_idstringRequired

User access identification string for the CSA Prediction Engine

Example: CSA_ACCESS_ID

Responses

200

Response body of a successful post job (grid).

application/json

job_idintegerOptional

Prediction task job ID

Example: 1074

job_codestringOptional

Prediction task job code

Example: 66cfc21f7effa501

post

/grid

Python Example

import requests
import json

url = "https://api.csanalytics.io/v2/prediction-engine/grid"

payload = {
    "y": [[18.8], [2.3], [3.0], [6.7], [15.2], [20.9], [4.0]],
    "X": [[25.0,2.0,76.0,53.7,23.1],[29.0,1.0,78.0,39.1,5.7],[32.0,2.0,77.0,54.1,5.3],[30.0,5.0,82.0,68.7,8.7],[24.0,1.0,69.0,51.0,20.3],[25.0,2.0,79.0,50.2,20.0],[35.0,3.0,80.0,51.5,9.4]],
    "theta": [[24.0, 5.0, 81.0, 60.0, 11.0]]
}

headers = {
  'x-api-key': 'CSA_API_KEY',
  'Content-Type': 'application/json',
  'Connection': 'keep-alive'
}

response = requests.request("POST", url, headers=headers, data=json.dumps(payload))

200

Response body of a successful post job (grid).

{
  "job_id": 1074,
  "job_code": "66cfc21f7effa501"
}

Custom Settings

The grid endpoint offers extensive customization options, allowing you to fine-tune prediction options. Whether you’re adjusting relevance threshold resolution or specifying attribute combinations, these settings ensure the output is precisely aligned with your objectives. This flexibility is ideal for advanced users looking to optimize predictive performance.

post

Extended specifications for invoking relevance-based grid predictions.

Authorizations

x-api-key

x-api-keystringRequired

API key required to access the CSA Prediction Engine API

Header parameters

Content-TypestringOptional

The content type of the request (application/json)

Example: application/json

ConnectionstringOptional

Connection header value (keep-alive)

Example: keep-alive

Body

application/json

access_idstringRequired

User access identification string for the CSA Prediction Engine

Example: CSA_ACCESS_ID

verify_missing_databooleanOptional

Affects how mean and covariance are computed. When true, uses only complete rows (observations with no missing values) for mean and covariance. When false, mean and covariance use nan-aware methods (column-wise nanmean, pairwise valid counts). In both modes, rows with missing values receive zero prediction weight because relevance cannot be computed for them.

Default: falseExample: false

is_threshold_percentbooleanOptional

Specify whether the thresholds specified is in percentage (decimal) units.

Default: trueExample: true

eval_typestring · enumOptional

Specify evaluation censor type: relevance, similarity, or both.

Default: bothExample: bothPossible values:

most_evalbooleanOptional

Specify the direction of the threshold evaluation on the corresponding censor type.

• true: [eval_type] score > threshold
• false: [eval_type] score < threshold

Default: trueExample: true

thresholdnumber · float[]Optional

Array of threshold values to evaluate the censor function (relevance or similarity). For maxfit and grid, users typically specify an array of thresholds. If not specified, the default is [0, 0.20, 0.50, 0.80].

Default: [0,0.2,0.5,0.8]Example: [0,0.2,0.5,0.8]

kintegerOptional

The minimum number of predictive variables to include in each combination of variables evaluated. If not specified, the default is 1.

Default: 1Example: 1

Responses

200

Response body of a successful post job (grid).

application/json

job_idintegerOptional

Prediction task job ID

Example: 1074

job_codestringOptional

Prediction task job code

Example: 66cfc21f7effa501

post

/grid

Python Example

import requests
import json

url = "https://api.csanalytics.io/v2/prediction-engine/grid"

payload = {
    "y": [[18.8], [2.3], [3.0], [6.7], [15.2], [20.9], [4.0]],
    "X": [[25.0, 2.0, 76.0, 53.7, 23.1], [29.0, 1.0, 78.0, 39.1, 5.7], [32.0, 2.0, 77.0, 54.1, 5.3], [30.0, 5.0, 82.0, 68.7, 8.7], [24.0, 1.0, 69.0, 51.0, 20.3], [25.0, 2.0, 79.0, 50.2, 20.0], [35.0, 3.0, 80.0, 51.5, 9.4]],
    "theta": [[24.0, 5.0, 81.0, 60.0, 11.0]],
    "threshold": [0, 0.20, 0.50, 0.80],
    "is_threshold_percent": true,
    "most_eval": true,
    "eval_type": "both",
    "attribute_combi": null,
    "k": 1
}

headers = {
  'x-api-key': 'CSA_API_KEY',
  'Content-Type': 'application/json',
  'Connection': 'keep-alive'
}

response = requests.request("POST", url, headers=headers, data=json.dumps(payload))

print(response.text)

200

Response body of a successful post job (grid).

{
  "job_id": 1074,
  "job_code": "66cfc21f7effa501"
}