/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * Built-in metrics.
 */
import * as tfc from '@tensorflow/tfjs-core';
import { tidy } from '@tensorflow/tfjs-core';
import * as K from './backend/tfjs_backend';
import { NotImplementedError, ValueError } from './errors';
import { categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss } from './losses';
import { binaryCrossentropy as lossBinaryCrossentropy } from './losses';
import { lossesMap } from './losses';
import * as util from './utils/generic_utils';
export function binaryAccuracy(yTrue, yPred) {
    return tidy(() => {
        const threshold = tfc.mul(.5, tfc.onesLike(yPred));
        const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);
        return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);
    });
}
export function categoricalAccuracy(yTrue, yPred) {
    return tidy(() => K.cast(tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));
}
function truePositives(yTrue, yPred) {
    return tidy(() => {
        return tfc.cast(tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 1))), 'float32');
    });
}
function falseNegatives(yTrue, yPred) {
    return tidy(() => {
        return tfc.cast(tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 0))), 'float32');
    });
}
function falsePositives(yTrue, yPred) {
    return tidy(() => {
        return tfc.cast(tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 0), tfc.equal(yPred, 1))), 'float32');
    });
}
export function precision(yTrue, yPred) {
    return tidy(() => {
        const tp = truePositives(yTrue, yPred);
        const fp = falsePositives(yTrue, yPred);
        const denominator = tfc.add(tp, fp);
        return tfc.cast(tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0), 'float32');
    });
}
export function recall(yTrue, yPred) {
    return tidy(() => {
        const tp = truePositives(yTrue, yPred);
        const fn = falseNegatives(yTrue, yPred);
        const denominator = tfc.add(tp, fn);
        return tfc.cast(tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0), 'float32');
    });
}
export function binaryCrossentropy(yTrue, yPred) {
    return lossBinaryCrossentropy(yTrue, yPred);
}
export function sparseCategoricalAccuracy(yTrue, yPred) {
    if (yTrue.rank === yPred.rank) {
        yTrue = tfc.squeeze(yTrue, [yTrue.rank - 1]);
    }
    yPred = tfc.argMax(yPred, -1);
    if (yPred.dtype !== yTrue.dtype) {
        yPred = tfc.cast(yPred, yTrue.dtype);
    }
    return tfc.cast(tfc.equal(yTrue, yPred), 'float32');
}
export function topKCategoricalAccuracy(yTrue, yPred) {
    throw new NotImplementedError();
}
export function sparseTopKCategoricalAccuracy(yTrue, yPred) {
    throw new NotImplementedError();
}
// Aliases.
export const mse = meanSquaredError;
export const MSE = meanSquaredError;
export const mae = meanAbsoluteError;
export const MAE = meanAbsoluteError;
export const mape = meanAbsolutePercentageError;
export const MAPE = meanAbsolutePercentageError;
export const categoricalCrossentropy = categoricalCrossentropyLoss;
export const cosine = cosineProximity;
export const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;
// TODO(cais, nielsene): Add serialize().
export const metricsMap = {
    binaryAccuracy,
    categoricalAccuracy,
    precision,
    categoricalCrossentropy,
    sparseCategoricalCrossentropy,
    mse,
    MSE,
    mae,
    MAE,
    mape,
    MAPE,
    cosine
};
export function get(identifier) {
    if (typeof identifier === 'string' && identifier in metricsMap) {
        return metricsMap[identifier];
    }
    else if (typeof identifier !== 'string' && identifier != null) {
        return identifier;
    }
    else {
        throw new ValueError(`Unknown metric ${identifier}`);
    }
}
/**
 * Get the shortcut function name.
 *
 * If the fn name is a string,
 *   directly return the string name.
 * If the function is included in metricsMap or lossesMap,
 *   return key of the map.
 *   - If the function relative to multiple keys,
 *     return the first found key as the function name.
 *   - If the function exists in both lossesMap and metricsMap,
 *     search lossesMap first.
 * If the function is not included in metricsMap or lossesMap,
 *   return the function name.
 *
 * @param fn loss function, metric function, or short cut name.
 * @returns Loss or Metric name in string.
 */
export function getLossOrMetricName(fn) {
    util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);
    if (typeof fn === 'string') {
        return fn;
    }
    else {
        let fnName;
        for (const key of Object.keys(lossesMap)) {
            if (lossesMap[key] === fn) {
                fnName = key;
                break;
            }
        }
        if (fnName !== undefined) {
            return fnName;
        }
        for (const key of Object.keys(metricsMap)) {
            if (metricsMap[key] === fn) {
                fnName = key;
                break;
            }
        }
        if (fnName !== undefined) {
            return fnName;
        }
        return fn.name;
    }
}
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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/**\n * Built-in metrics.\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {Tensor, tidy} from '@tensorflow/tfjs-core';\n\nimport * as K from './backend/tfjs_backend';\nimport {NotImplementedError, ValueError} from './errors';\nimport {categoricalCrossentropy as categoricalCrossentropyLoss, cosineProximity, meanAbsoluteError, meanAbsolutePercentageError, meanSquaredError, sparseCategoricalCrossentropy as sparseCategoricalCrossentropyLoss} from './losses';\nimport {binaryCrossentropy as lossBinaryCrossentropy} from './losses';\nimport {lossesMap} from './losses';\nimport {LossOrMetricFn} from './types';\nimport * as util from './utils/generic_utils';\n\nexport function binaryAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const threshold = tfc.mul(.5, tfc.onesLike(yPred));\n    const yPredThresholded = K.cast(tfc.greater(yPred, threshold), yTrue.dtype);\n    return tfc.mean(tfc.equal(yTrue, yPredThresholded), -1);\n  });\n}\n\nexport function categoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(\n      () => K.cast(\n          tfc.equal(tfc.argMax(yTrue, -1), tfc.argMax(yPred, -1)), 'float32'));\n}\n\nfunction truePositives(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    return tfc.cast(\n        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 1))),\n        'float32');\n  });\n}\n\nfunction falseNegatives(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    return tfc.cast(\n        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 1), tfc.equal(yPred, 0))),\n        'float32');\n  });\n}\n\nfunction falsePositives(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    return tfc.cast(\n        tfc.sum(tfc.logicalAnd(tfc.equal(yTrue, 0), tfc.equal(yPred, 1))),\n        'float32');\n  });\n}\n\nexport function precision(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fp = falsePositives(yTrue, yPred);\n\n    const denominator = tfc.add(tp, fp);\n\n    return tfc.cast(\n        tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0),\n        'float32');\n  });\n}\n\nexport function recall(yTrue: Tensor, yPred: Tensor): Tensor {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fn = falseNegatives(yTrue, yPred);\n\n    const denominator = tfc.add(tp, fn);\n\n    return tfc.cast(\n        tfc.where(tfc.greater(denominator, 0), tfc.div(tp, denominator), 0),\n        'float32');\n  });\n}\n\nexport function binaryCrossentropy(yTrue: Tensor, yPred: Tensor): Tensor {\n  return lossBinaryCrossentropy(yTrue, yPred);\n}\n\nexport function sparseCategoricalAccuracy(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  if (yTrue.rank === yPred.rank) {\n    yTrue = tfc.squeeze(yTrue, [yTrue.rank - 1]);\n  }\n  yPred = tfc.argMax(yPred, -1);\n  if (yPred.dtype !== yTrue.dtype) {\n    yPred = tfc.cast(yPred, yTrue.dtype);\n  }\n  return tfc.cast(tfc.equal(yTrue, yPred), 'float32');\n}\n\nexport function topKCategoricalAccuracy(yTrue: Tensor, yPred: Tensor): Tensor {\n  throw new NotImplementedError();\n}\n\nexport function sparseTopKCategoricalAccuracy(\n    yTrue: Tensor, yPred: Tensor): Tensor {\n  throw new NotImplementedError();\n}\n\n// Aliases.\nexport const mse = meanSquaredError;\nexport const MSE = meanSquaredError;\nexport const mae = meanAbsoluteError;\nexport const MAE = meanAbsoluteError;\nexport const mape = meanAbsolutePercentageError;\nexport const MAPE = meanAbsolutePercentageError;\nexport const categoricalCrossentropy = categoricalCrossentropyLoss;\nexport const cosine = cosineProximity;\nexport const sparseCategoricalCrossentropy = sparseCategoricalCrossentropyLoss;\n\n// TODO(cais, nielsene): Add serialize().\n\nexport const metricsMap: {[functionName: string]: LossOrMetricFn} = {\n  binaryAccuracy,\n  categoricalAccuracy,\n  precision,\n  categoricalCrossentropy,\n  sparseCategoricalCrossentropy,\n  mse,\n  MSE,\n  mae,\n  MAE,\n  mape,\n  MAPE,\n  cosine\n};\n\nexport function get(identifier: string|LossOrMetricFn): LossOrMetricFn {\n  if (typeof identifier === 'string' && identifier in metricsMap) {\n    return metricsMap[identifier];\n  } else if (typeof identifier !== 'string' && identifier != null) {\n    return identifier;\n  } else {\n    throw new ValueError(`Unknown metric ${identifier}`);\n  }\n}\n\n/**\n * Get the shortcut function name.\n *\n * If the fn name is a string,\n *   directly return the string name.\n * If the function is included in metricsMap or lossesMap,\n *   return key of the map.\n *   - If the function relative to multiple keys,\n *     return the first found key as the function name.\n *   - If the function exists in both lossesMap and metricsMap,\n *     search lossesMap first.\n * If the function is not included in metricsMap or lossesMap,\n *   return the function name.\n *\n * @param fn loss function, metric function, or short cut name.\n * @returns Loss or Metric name in string.\n */\nexport function getLossOrMetricName(fn: string|LossOrMetricFn): string {\n  util.assert(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n  if (typeof fn === 'string') {\n    return fn;\n  } else {\n    let fnName;\n    for (const key of Object.keys(lossesMap)) {\n      if (lossesMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== undefined) {\n      return fnName;\n    }\n    for (const key of Object.keys(metricsMap)) {\n      if (metricsMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== undefined) {\n      return fnName;\n    }\n    return (fn as Function).name;\n  }\n}\n"]}