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181 lines (164 loc) · 6 KB
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/**
* @license
* Copyright 2021, JsData. All rights reserved.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* ==========================================================================
*/
import { convertScikit2DToArray } from '../utils'
import { Scikit1D, Scikit2D, Tensor2D } from '../types'
import { TransformerMixin } from '../mixins'
import { getBackend } from '../tf-singleton'
import { isDataFrameInterface } from '../typesUtils'
/*
Next steps:
0. Support inverseTransform
1. Maybe support dtype constructor arg
2. Shouldn't OrdinalEncoder support partialFit, seems like that might be useful
3. Pass the next 5 tests
*/
export interface OrdinalEncoderParams {
/**
* Categories (unique values) per feature:
* ‘auto’ : Determine categories automatically from the training data.
* list : categories[i] holds the categories expected in the ith column.
* The passed categories should not mix strings and numeric values, and should be sorted in case of numeric values.
* **default = "auto"**
*/
categories?: 'auto' | (number | string | boolean)[][]
/** When set to ‘error’ an error will be raised in case an unknown categorical
* feature is present during transform. When set to ‘use_encoded_value’,
* the encoded value of unknown categories will be set to the value
* given for the parameter unknown_value.
* In inverse_transform, an unknown category will be denoted as null.
* **default = "error"**
*/
handleUnknown?: 'error' | 'useEncodedValue'
/**When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter
* is required and will set the encoded value of unknown categories.
* It has to be distinct from the values used to encode any of the categories in fit.
* Great choices for this number are NaN or -1. **default = NaN** */
unknownValue?: number
}
/**
* Encode categorical features as an integer array.
* The input to this transformer should be an array-like of integers or strings,
* which represent categorical (discrete) features. The features are then converted to ordinal integers.
* @example
* ```js
* const X = [
['Male', 1],
['Female', 2],
['Male', 4]
]
const encode = new OrdinalEncoder()
encode.fitTransform(X) // returns the expected object below
const expected = [
[0, 0],
[1, 1],
[0, 2]
]
* ```
*/
export class OrdinalEncoder extends TransformerMixin {
categories: (number | string | boolean)[][]
handleUnknown?: 'error' | 'useEncodedValue'
unknownValue?: number
/** This holds the categories parameter that is passed in the constructor. `this.categories`
* holds the actual learned categories or the ones passed in from the constructor */
categoriesParam: 'auto' | (number | string | boolean)[][]
/** The number of features seen during fit */
nFeaturesIn: number
/** Names of features seen during fit. Only stores feature names if input is a DataFrame */
featureNamesIn: Array<string>
/** Useful for pipelines and column transformers to have a default name for transforms */
name = 'OrdinalEncoder'
constructor({
categories = 'auto',
handleUnknown = 'error',
unknownValue = NaN
}: OrdinalEncoderParams = {}) {
super()
this.tf = getBackend()
this.categoriesParam = categories
this.categories = []
this.handleUnknown = handleUnknown
this.unknownValue = unknownValue
this.nFeaturesIn = 0
this.featureNamesIn = []
}
classesToMapping(
classes: Array<string | number | boolean>
): Map<string | number | boolean, number> {
const labels = new Map<string | number | boolean, number>()
classes.forEach((value, index) => {
labels.set(value, index)
})
return labels
}
loopOver2DArrayToSetLabels(array2D: any) {
for (let j = 0; j < array2D[0].length; j++) {
let curSet = new Set()
for (let i = 0; i < array2D.length; i++) {
curSet.add(array2D[i][j])
}
let results = Array.from(curSet)
this.categories.push(results as number[])
}
}
/**
* Fits a OrdinalEncoder to the data.
*/
// eslint-disable-next-line @typescript-eslint/no-unused-vars
public fit(X: Scikit2D, y?: Scikit1D): OrdinalEncoder {
const array2D = convertScikit2DToArray(X)
if (this.categoriesParam === 'auto') {
this.loopOver2DArrayToSetLabels(array2D)
return this
}
this.categories = this.categoriesParam
this.nFeaturesIn = array2D.length === 0 ? 0 : array2D[0].length || 0
if (isDataFrameInterface(X)) {
this.featureNamesIn = [...X.columns]
}
return this
}
loopOver2DArrayToUseLabels(array2D: any) {
let labels = this.categories.map((el) => this.classesToMapping(el))
let finalArray = []
for (let i = 0; i < array2D.length; i++) {
let curArray = []
for (let j = 0; j < array2D[0].length; j++) {
let curElem = array2D[i][j]
let val = labels[j].get(curElem)
if (val === undefined) {
if (this.handleUnknown === 'error') {
throw new Error(
`Unknown value ${curElem} encountered while transforming. Not encountered in training data`
)
} else {
val = this.unknownValue
}
}
curArray.push(val)
}
finalArray.push(curArray)
}
return finalArray
}
/**
* Encodes the data using the fitted OrdinalEncoder.
*/
// eslint-disable-next-line @typescript-eslint/no-unused-vars
public transform(X: Scikit2D, y?: Scikit1D): Tensor2D {
const array2D = convertScikit2DToArray(X)
const result2D = this.loopOver2DArrayToUseLabels(array2D)
return this.tf.tensor2d(result2D as number[][], undefined, 'int32')
}
}