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import { InferenceOutputError } from "../../lib/InferenceOutputError"; | |
import type { BaseArgs, Options } from "../../types"; | |
import { request } from "../custom/request"; | |
export type TabularRegressionArgs = BaseArgs & { | |
inputs: { | |
/** | |
* A table of data represented as a dict of list where entries are headers and the lists are all the values, all lists must have the same size. | |
*/ | |
data: Record<string, string[]>; | |
}; | |
}; | |
/** | |
* a list of predicted values for each row | |
*/ | |
export type TabularRegressionOutput = number[]; | |
/** | |
* Predicts target value for a given set of features in tabular form. | |
* Typically, you will want to train a regression model on your training data and use it with your new data of the same format. | |
* Example model: scikit-learn/Fish-Weight | |
*/ | |
export async function tabularRegression( | |
args: TabularRegressionArgs, | |
options?: Options | |
): Promise<TabularRegressionOutput> { | |
const res = await request<TabularRegressionOutput>(args, { | |
...options, | |
taskHint: "tabular-regression", | |
}); | |
const isValidOutput = Array.isArray(res) && res.every((x) => typeof x === "number"); | |
if (!isValidOutput) { | |
throw new InferenceOutputError("Expected number[]"); | |
} | |
return res; | |
} | |