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Copy pathutils.ts
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407 lines (383 loc) · 10.1 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 {
ArrayType1D,
ArrayType2D,
DataFrameInterface,
Initializers,
LossTypes,
OptimizerTypes,
Scikit1D,
Scikit2D,
ScikitVecOrMatrix,
SeriesInterface,
TypedArray,
DataType,
Tensor2D,
Tensor1D,
Tensor,
TensorLike
} from './types'
import {
assert,
inferShape,
isDataFrameInterface,
isScikitVecOrMatrix,
isSeriesInterface,
isTypedArray,
isTensor
} from './typesUtils'
import { getBackend } from './tf-singleton'
/**
* Generates an array of dim (row x column) with inner values set to zero
* @param row
* @param column
*/
export const zeros = (
row: number,
column: number
): ArrayType1D | ArrayType2D => {
const zeroData = []
for (let i = 0; i < row; i++) {
const colData = Array(column)
for (let j = 0; j < column; j++) {
colData[j] = 0
}
zeroData.push(colData)
}
return zeroData
}
/**
* Checks if array is 1D
* @param arr The array
*/
export const is1DArray = (arr: ArrayType1D | ArrayType2D): boolean => {
if (
typeof arr[0] == 'number' ||
typeof arr[0] == 'string' ||
typeof arr[0] == 'boolean'
) {
return true
} else {
return false
}
}
/**
*
* @param data Scikit1D One dimensional array of data
* @returns Tensor1D. If you pass in something that isn't 1D, then it will throw an error.
* This is the case with 2D Tensors as well. If you really want to reshape them then use tf.reshape
*/
export function convertToTensor1D(data: Scikit1D, dtype?: DataType): Tensor1D {
let tf = getBackend()
if (isSeriesInterface(data)) {
// Do type inference if no dtype is passed, otherwise try to parse as that dtype
return dtype
? (data.tensor.asType(dtype) as unknown as Tensor1D)
: (data.tensor as unknown as Tensor1D)
}
if (isTensor(data)) {
if (data.shape.length === 1) {
if (!dtype || data.dtype == dtype) {
return data
}
return data.asType(dtype)
} else {
throw new Error(
'ParamError: if data is a Tensor it must be a Tensor1D. If you really meant to reshape this tensor than use tf.reshape'
)
}
}
return dtype ? tf.tensor1d(data, dtype) : tf.tensor1d(data)
}
export function convertToNumericTensor1D(data: Scikit1D, dtype?: DataType) {
const newTensor = convertToTensor1D(data, dtype)
if (newTensor.dtype === 'string') {
throw new Error(
"ParamError: data has string dtype, can't convert to numeric Tensor"
)
}
return newTensor
}
export function convertToTensor2D(data: Scikit2D, dtype?: DataType): Tensor2D {
let tf = getBackend()
if (isDataFrameInterface(data)) {
return dtype
? (data.tensor.asType(dtype) as unknown as Tensor2D)
: (data.tensor as unknown as Tensor2D)
}
if (isTensor(data)) {
if (data.shape.length === 2) {
if (!dtype || data.dtype == dtype) {
return data
}
return data.asType(dtype)
} else {
throw new Error(
'ParamError: if data is a Tensor it must be a Tensor2D. If you really meant to reshape this tensor than use tf.reshape'
)
}
}
if (Array.isArray(data) && isTypedArray(data[0])) {
const shape = inferShape(data) as [number, number]
const newData = data.map((el) => Array.from(el as number[]))
return dtype
? tf.tensor2d(newData, shape, dtype)
: tf.tensor2d(newData, shape)
}
return dtype
? tf.tensor2d(data as any, undefined, dtype)
: tf.tensor2d(data as any, undefined)
}
export function convertToTensor1D_2D(
data: ScikitVecOrMatrix,
dtype?: DataType
): Tensor1D | Tensor2D {
try {
const new1DTensor = convertToTensor1D(data as Tensor1D, dtype)
return new1DTensor
} catch (e) {
try {
const new2DTensor = convertToTensor2D(data as Tensor2D, dtype)
return new2DTensor
} catch (newE) {
throw new Error('ParamError: Can"t convert data into 1D or 2D tensor')
}
}
}
export function convertToNumericTensor2D(data: Scikit2D, dtype?: DataType) {
const newTensor = convertToTensor2D(data, dtype)
if (newTensor.dtype === 'string') {
throw new Error(
"ParamError: data has string dtype, can't convert to numeric Tensor"
)
}
return newTensor
}
export function convertToNumericTensor1D_2D(
data: ScikitVecOrMatrix,
dtype?: DataType
) {
const newTensor = convertToTensor1D_2D(data, dtype)
if (newTensor.dtype === 'string') {
throw new Error(
"ParamError: data has string dtype, can't convert to numeric Tensor"
)
}
return newTensor
}
export function convertToTensor(
data: TensorLike | Tensor | DataFrameInterface | SeriesInterface,
shape?: number[],
dtype?: DataType
): Tensor {
let tf = getBackend()
if (isDataFrameInterface(data)) {
return data.tensor as unknown as Tensor2D
}
if (isSeriesInterface(data)) {
return data.tensor as unknown as Tensor2D
}
if (isTensor(data)) {
let newData = data
if (shape) {
newData = newData.reshape(shape)
}
if (dtype) {
newData = newData.asType(dtype)
}
return newData
}
return tf.tensor(data, shape, dtype)
}
/**
* Check that if two tensor are of same shape
* @param tensor1
* @param tensor2
* @returns
*/
export const shapeEqual = (tensor1: Tensor, tensor2: Tensor): boolean => {
const shape1 = tensor1.shape
const shape2 = tensor2.shape
if (shape1.length != shape2.length) {
return false
}
for (let i = 0; i < shape1.length; i++) {
if (shape1[i] !== shape2[i]) {
return false
}
}
return true
}
/**
* Check that two tensors are equal to within some additive tolerance.
* @param tensor1
* @param tensor2
* @param
*/
export const tensorEqual = (
tensor1: Tensor,
tensor2: Tensor,
tol = 0
): boolean => {
if (!shapeEqual(tensor1, tensor2)) {
throw new Error('tensor1 and tensor2 not of same shape')
}
let tf = getBackend()
return Boolean(
tf.lessEqual(tf.max(tf.abs(tf.sub(tensor1, tensor2))), tol).dataSync()[0]
)
}
export const arrayEqual = (
array: Array<any> | any,
array2: Array<any> | any,
tol = 0
): boolean => {
if (!Array.isArray(array) && !Array.isArray(array2)) {
return Math.abs(array - array2) <= tol
}
if (array.length !== array2.length) {
return false
}
for (let i = 0; i < array.length; i++) {
if (!arrayEqual(array[i], array2[i], tol)) {
return false
}
}
return true
}
export function convertScikit2DToArray(
data: Scikit2D
): any[][] | TypedArray[] {
if (isDataFrameInterface(data)) {
return data.values as any[][]
}
if (isTensor(data)) {
return data.arraySync()
}
return data
}
export function convertScikit1DToArray(data: Scikit1D): any[] | TypedArray {
if (isSeriesInterface(data)) {
return data.values
}
if (isTensor(data)) {
return data.arraySync()
}
return data
}
export function arrayTo2DColumn(array: any[] | TypedArray) {
let newArray = []
for (let i = 0; i < array.length; i++) {
newArray.push([array[i]])
}
return newArray
}
export function getLength(X: Scikit2D | Scikit1D): number {
assert(isScikitVecOrMatrix(X), "X isn't a Scikit2D or Scikit1D object")
if (isTensor(X)) {
return X.shape[0]
}
if (isDataFrameInterface(X) || isSeriesInterface(X)) {
return X.size
}
return X.length
}
/**
* Modified Fisher-Yates algorithm which takes
* a seed and selects n random numbers from a
* set of integers going from 0 to size-1
*/
export function sampleWithoutReplacement(
size: number,
n: number,
seed?: number
) {
let tf = getBackend()
let curMap = new Map<number, number>()
let finalNumbs = []
let randoms = tf.randomUniform([n], 0, size, 'float32', seed).dataSync()
for (let i = 0; i < randoms.length; i++) {
randoms[i] = (randoms[i] * (size - i)) / size
let randInt = Math.floor(randoms[i])
let lastIndex = size - i - 1
if (curMap.get(randInt) === undefined) {
curMap.set(randInt, randInt)
}
if (curMap.get(lastIndex) === undefined) {
curMap.set(lastIndex, lastIndex)
}
let holder = curMap.get(lastIndex) as number
curMap.set(lastIndex, curMap.get(randInt) as number)
curMap.set(randInt, holder)
finalNumbs.push(curMap.get(lastIndex) as number)
}
return finalNumbs
}
export function optimizer(opt: OptimizerTypes) {
let tf = getBackend()
switch (opt) {
case 'sgd':
return tf.train.sgd(0.1)
case 'momentum':
return tf.train.momentum(0.1, 0.9)
case 'adadelta':
return tf.train.adadelta()
case 'adagrad':
return tf.train.adagrad(0.1)
case 'rmsprop':
return tf.train.rmsprop(0.1)
case 'adamax':
return tf.train.adamax()
case 'adam':
return tf.train.adam()
}
}
export function getLoss(loss: LossTypes) {
let tf = getBackend()
switch (loss) {
case 'meanSquaredError':
return tf.losses.meanSquaredError
case 'sigmoidCrossEntropy':
return tf.losses.sigmoidCrossEntropy
case 'softmaxCrossEntropy':
return tf.losses.softmaxCrossEntropy
case 'logLoss':
return tf.losses.logLoss
case 'huberLoss':
return tf.losses.huberLoss
case 'hingeLoss':
return tf.losses.hingeLoss
case 'cosineDistance':
return tf.losses.cosineDistance
case 'computeWeightedLoss':
return tf.losses.computeWeightedLoss
case 'absoluteDifference':
return tf.losses.absoluteDifference
default:
throw new Error(`${loss} loss not supported`)
}
}
export function initializer(init: Initializers) {
let tf = getBackend()
switch (init) {
case 'Zeros':
return tf.initializers.zeros()
case 'Ones':
return tf.initializers.ones()
default:
throw new Error(`${init} initializer not supported`)
break
}
}
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