metric.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
"""
All layers just related to metric.
"""
import numpy as np

import paddle
from paddle import _C_ops, _legacy_C_ops
from paddle.base.data_feeder import check_variable_and_dtype
from paddle.base.framework import (
    Variable,
    _create_tensor,
    in_dygraph_mode,
    in_pir_mode,
)
from paddle.base.layer_helper import LayerHelper
from paddle.nn.initializer import ConstantInitializer

__all__ = []


def accuracy(input, label, k=1, correct=None, total=None):
    """

    accuracy layer.
    Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
    This function computes the accuracy using the input and label.
    If the correct label occurs in top k predictions, then correct will increment by one.

    Note:
        the dtype of accuracy is determined by input. the input and label dtype can be different.

    Args:
        input(Tensor): The input of accuracy layer, which is the predictions of network. A Tensor with type float32,float64.
            The shape is ``[sample_number, class_dim]`` .
        label(Tensor): The label of dataset.  Tensor with type int32,int64. The shape is ``[sample_number, 1]`` .
        k(int, optional): The top k predictions for each class will be checked. Data type is int64 or int32. Default is 1.
        correct(Tensor, optional): The correct predictions count. A Tensor with type int64 or int32. Default is None.
        total(Tensor, optional): The total entries count. A tensor with type int64 or int32. Default is None.

    Returns:
        Tensor, The correct rate. A Tensor with type float32.

    Examples:
        .. code-block:: python

            >>> import numpy as np
            >>> import paddle
            >>> import paddle.static as static
            >>> import paddle.nn.functional as F
            >>> paddle.seed(2023)
            >>> paddle.enable_static()
            >>> data = static.data(name="input", shape=[-1, 32, 32], dtype="float32")
            >>> label = static.data(name="label", shape=[-1,1], dtype="int64")
            >>> fc_out = static.nn.fc(x=data, size=10)
            >>> predict = F.softmax(x=fc_out)
            >>> result = static.accuracy(input=predict, label=label, k=5)
            >>> place = paddle.CPUPlace()
            >>> exe = static.Executor(place)
            >>> exe.run(static.default_startup_program())
            >>> np.random.seed(1107)
            >>> x = np.random.rand(3, 32, 32).astype("float32")
            >>> y = np.array([[1],[0],[1]])
            >>> output = exe.run(feed={"input": x,"label": y},
            ...                  fetch_list=[result])
            >>> print(output)
            [array(0.33333334, dtype=float32)]

    """
    if in_dygraph_mode():
        if correct is None:
            correct = _create_tensor(dtype="int32")
        if total is None:
            total = _create_tensor(dtype="int32")

        _k = np.array(k).item(0) if isinstance(k, Variable) else k
        topk_out, topk_indices = _legacy_C_ops.top_k_v2(
            input, 'k', _k, 'sorted', False
        )
        _acc, _, _ = _legacy_C_ops.accuracy(
            topk_out, topk_indices, label, correct, total
        )
        return _acc
    elif in_pir_mode():
        topk_out, topk_indices = paddle.topk(input, k=k, sorted=False)
        _acc, _, _ = _C_ops.accuracy(topk_out, topk_indices, label)
        return _acc

    helper = LayerHelper("accuracy", **locals())
    check_variable_and_dtype(
        input, 'input', ['float16', 'uint16', 'float32', 'float64'], 'accuracy'
    )
    topk_out = helper.create_variable_for_type_inference(dtype=input.dtype)
    topk_indices = helper.create_variable_for_type_inference(dtype="int64")
    inputs = {"X": [input]}
    if isinstance(k, Variable):
        inputs['K'] = [k]
    else:
        attrs = {'k': k}
    attrs['sorted'] = False
    helper.append_op(
        type="top_k_v2",
        inputs=inputs,
        attrs=attrs,
        outputs={"Out": [topk_out], "Indices": [topk_indices]},
    )
    acc_out = helper.create_variable_for_type_inference(dtype="float32")
    if correct is None:
        correct = helper.create_variable_for_type_inference(dtype="int32")
    if total is None:
        total = helper.create_variable_for_type_inference(dtype="int32")
    helper.append_op(
        type="accuracy",
        inputs={"Out": [topk_out], "Indices": [topk_indices], "Label": [label]},
        outputs={
            "Accuracy": [acc_out],
            "Correct": [correct],
            "Total": [total],
        },
    )
    return acc_out


def auc(
    input,
    label,
    curve='ROC',
    num_thresholds=2**12 - 1,
    topk=1,
    slide_steps=1,
    ins_tag_weight=None,
):
    """
    **Area Under the Curve (AUC) Layer**

    This implementation computes the AUC according to forward output and label.
    It is used very widely in binary classification evaluation.

    Note: If input label contains values other than 0 and 1, it will be cast
    to `bool`. Find the relevant definitions `here <https://en.wikipedia.org\
    /wiki/Receiver_operating_characteristic#Area_under_the_curve>`_.

    There are two types of possible curves:

        1. ROC: Receiver operating characteristic;
        2. PR: Precision Recall

    Args:
        input(Tensor): A floating-point 2D Tensor, values are in the range
                         [0, 1]. Each row is sorted in descending order. This
                         input should be the output of topk. Typically, this
                         Tensor indicates the probability of each label.
                         A Tensor with type float32,float64.
        label(Tensor): A 2D int Tensor indicating the label of the training
                         data. The height is batch size and width is always 1.
                         A Tensor with type int32,int64.
        curve(str, optional): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
        num_thresholds(int, optional): The number of thresholds to use when discretizing
                             the roc curve. Default 4095.
        topk(int, optional): only topk number of prediction output will be used for auc.
        slide_steps(int, optional): when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
        ins_tag_weight(Tensor, optional): A 2D int Tensor indicating the data's tag weight, 1 means real data, 0 means fake data. Default None, and it will be assigned to a tensor of value 1.
                         A Tensor with type float32,float64.

    Returns:
        Tensor: A tuple representing the current AUC. Data type is Tensor, supporting float32, float64.
        The return tuple is auc_out, batch_auc_out, [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg ]

            auc_out: the result of the accuracy rate
            batch_auc_out: the result of the batch accuracy
            batch_stat_pos: the statistic value for label=1 at the time of batch calculation
            batch_stat_neg: the statistic value for label=0 at the time of batch calculation
            stat_pos: the statistic for label=1 at the time of calculation
            stat_neg: the statistic for label=0 at the time of calculation


    Examples:
        .. code-block:: python
            :name: example-1

            >>> # doctest: +SKIP("This has diff in xdoctest env")
            >>> import paddle
            >>> import numpy as np
            >>> paddle.enable_static()

            >>> paddle.seed(2023)
            >>> data = paddle.static.data(name="input", shape=[-1, 32,32], dtype="float32")
            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int64")
            >>> fc_out = paddle.static.nn.fc(x=data, size=2)
            >>> predict = paddle.nn.functional.softmax(x=fc_out)
            >>> result=paddle.static.auc(input=predict, label=label)

            >>> place = paddle.CPUPlace()
            >>> exe = paddle.static.Executor(place)

            >>> exe.run(paddle.static.default_startup_program())
            >>> np.random.seed(1107)
            >>> x = np.random.rand(3,32,32).astype("float32")
            >>> y = np.array([1,0,1])
            >>> output= exe.run(feed={"input": x,"label": y},
            ...                 fetch_list=[result[0]])
            >>> print(output)
            [array(1.)]


        .. code-block:: python
            :name: example-2

            # you can learn the usage of ins_tag_weight by the following code.

            >>> # doctest: +SKIP("This has diff in xdoctest env")
            >>> import paddle
            >>> import numpy as np
            >>> paddle.enable_static()

            >>> paddle.seed(2023)
            >>> data = paddle.static.data(name="input", shape=[-1, 32,32], dtype="float32")
            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int64")
            >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1,16], dtype='float64')
            >>> fc_out = paddle.static.nn.fc(x=data, size=2)
            >>> predict = paddle.nn.functional.softmax(x=fc_out)
            >>> result=paddle.static.auc(input=predict, label=label, ins_tag_weight=ins_tag_weight)

            >>> place = paddle.CPUPlace()
            >>> exe = paddle.static.Executor(place)

            >>> exe.run(paddle.static.default_startup_program())
            >>> np.random.seed(1107)
            >>> x = np.random.rand(3,32,32).astype("float32")
            >>> y = np.array([1,0,1])
            >>> z = np.array([1,0,1]).astype("float64")
            >>> output= exe.run(feed={"input": x,"label": y, "ins_tag_weight":z},
            ...                 fetch_list=[result[0]])
            >>> print(output)
            [array(1.)]

    """
    if in_pir_mode():
        if ins_tag_weight is None:
            ins_tag_weight = paddle.full(
                shape=[1, 1], dtype="float32", fill_value=1.0
            )
        check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'auc')
        check_variable_and_dtype(label, 'label', ['int32', 'int64'], 'auc')
        check_variable_and_dtype(
            ins_tag_weight, 'ins_tag_weight', ['float32', 'float64'], 'auc'
        )
        stat_pos = paddle.zeros(shape=[1, num_thresholds + 1], dtype="int64")
        stat_neg = paddle.zeros(shape=[1, num_thresholds + 1], dtype="int64")
        auc_out, batch_stat_pos, batch_stat_neg = _C_ops.auc(
            input,
            label,
            stat_pos,
            stat_neg,
            ins_tag_weight,
            curve,
            num_thresholds,
            0,
        )
        return (
            auc_out,
            batch_stat_pos,
            batch_stat_neg,
        )
    helper = LayerHelper("auc", **locals())

    if ins_tag_weight is None:
        ins_tag_weight = paddle.tensor.fill_constant(
            shape=[1, 1], dtype="float32", value=1.0
        )
    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'auc')
    check_variable_and_dtype(label, 'label', ['int32', 'int64'], 'auc')
    check_variable_and_dtype(
        ins_tag_weight, 'ins_tag_weight', ['float32', 'float64'], 'auc'
    )
    auc_out = helper.create_variable_for_type_inference(dtype="float64")
    batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
    # make tp, tn, fp, fn persistable, so that can accumulate all batches.

    # for batch auc
    # we create slide_step+1 buckets, the first slide_steps buckets store
    # historical batch-level values, and the last bucket stores the sum values of
    # previous slide_step buckets.
    # The index of bucket that the newest batch will use is determined by batch_id mod slide_steps,
    # and batch_id is store in the last position of following variable
    batch_stat_pos = helper.create_global_variable(
        persistable=True,
        dtype='int64',
        shape=[(1 + slide_steps) * (num_thresholds + 1) + 1],
    )
    batch_stat_neg = helper.create_global_variable(
        persistable=True,
        dtype='int64',
        shape=[(1 + slide_steps) * (num_thresholds + 1) + 1],
    )

    # for global auc
    # Needn't maintain the batch id
    stat_pos = helper.create_global_variable(
        persistable=True, dtype='int64', shape=[1, num_thresholds + 1]
    )
    stat_neg = helper.create_global_variable(
        persistable=True, dtype='int64', shape=[1, num_thresholds + 1]
    )

    for var in [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg]:
        helper.set_variable_initializer(
            var,
            ConstantInitializer(value=0.0, force_cpu=False),
        )

    # "InsTagWeight": [ins_tag_weight]
    # Batch AUC
    helper.append_op(
        type="auc",
        inputs={
            "Predict": [input],
            "Label": [label],
            "StatPos": [batch_stat_pos],
            "StatNeg": [batch_stat_neg],
        },
        attrs={
            "curve": curve,
            "num_thresholds": num_thresholds,
            "slide_steps": slide_steps,
        },
        outputs={
            "AUC": [batch_auc_out],
            "StatPosOut": [batch_stat_pos],
            "StatNegOut": [batch_stat_neg],
        },
    )
    # Global AUC
    helper.append_op(
        type="auc",
        inputs={
            "Predict": [input],
            "Label": [label],
            "StatPos": [stat_pos],
            "StatNeg": [stat_neg],
        },
        attrs={
            "curve": curve,
            "num_thresholds": num_thresholds,
            "slide_steps": 0,
        },
        outputs={
            "AUC": [auc_out],
            "StatPosOut": [stat_pos],
            "StatNegOut": [stat_neg],
        },
    )
    return (
        auc_out,
        batch_auc_out,
        [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg],
    )


def ctr_metric_bundle(input, label, ins_tag_weight=None):
    """
    ctr related metric layer

    This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
    To compute the final values of these metrics, we should do following computations using
    total instance number:
    MAE = local_abserr / instance number
    RMSE = sqrt(local_sqrerr / instance number)
    predicted_ctr = local_prob / instance number
    q = local_q / instance number
    Note that if you are doing distribute job, you should all reduce these metrics and instance
    number first

    Args:
        input(Tensor): A floating-point 2D Tensor, values are in the range
                         [0, 1]. Each row is sorted in descending order. This
                         input should be the output of topk. Typically, this
                         Tensor indicates the probability of each label.
        label(Tensor): A 2D int Tensor indicating the label of the training
                         data. The height is batch size and width is always 1.
        ins_tag_weight(Tensor): A 2D int Tensor indicating the ins_tag_weight of the training
                         data. 1 means real data, 0 means fake data.
                         A DenseTensor or Tensor with type float32,float64.

    Returns:
        local_sqrerr(Tensor): Local sum of squared error
        local_abserr(Tensor): Local sum of abs error
        local_prob(Tensor): Local sum of predicted ctr
        local_q(Tensor): Local sum of q value
        local_pos_num (Tensor): Local number of positive examples
        local_ins_num (Tensor): Local number of instances

    Examples:
        .. code-block:: python
            :name: example-1

            >>> # doctest: +SKIP("This has diff in xdoctest env")
            >>> import paddle
            >>> paddle.enable_static()
            >>> data = paddle.static.data(name="data", shape=[-1, 32], dtype="float32")
            >>> label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
            >>> predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(x=data, size=1))
            >>> auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label)

        .. code-block:: python
            :name: example-2

            >>> # doctest: +SKIP("This has diff in xdoctest env")
            >>> import paddle
            >>> paddle.enable_static()
            >>> data = paddle.static.data(name="data", shape=[-1, 32], dtype="float32")
            >>> label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32")
            >>> predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(x=data, size=1))
            >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1, 1], dtype='int64')
            >>> auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight)
    """
    if ins_tag_weight is None:
        ins_tag_weight = paddle.tensor.fill_constant(
            shape=[1, 1], dtype="float32", value=1.0
        )

    assert input.shape == label.shape
    helper = LayerHelper("ctr_metric_bundle", **locals())

    local_abserr = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )
    local_sqrerr = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )
    local_prob = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )
    local_q = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )
    local_pos_num = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )
    local_ins_num = helper.create_global_variable(
        persistable=True, dtype='float32', shape=[1]
    )

    tmp_res_elesub = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[-1]
    )
    tmp_res_sigmoid = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[-1]
    )
    tmp_ones = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[-1]
    )

    batch_prob = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    batch_abserr = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    batch_sqrerr = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    batch_q = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    batch_pos_num = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    batch_ins_num = helper.create_global_variable(
        persistable=False, dtype='float32', shape=[1]
    )
    for var in [
        local_abserr,
        batch_abserr,
        local_sqrerr,
        batch_sqrerr,
        local_prob,
        batch_prob,
        local_q,
        batch_q,
        batch_pos_num,
        batch_ins_num,
        local_pos_num,
        local_ins_num,
    ]:
        helper.set_variable_initializer(
            var,
            paddle.nn.initializer.ConstantInitializer(
                value=0.0, force_cpu=True
            ),
        )

    helper.append_op(
        type="elementwise_sub",
        inputs={"X": [input], "Y": [label]},
        outputs={"Out": [tmp_res_elesub]},
    )

    helper.append_op(
        type="squared_l2_norm",
        inputs={"X": [tmp_res_elesub]},
        outputs={"Out": [batch_sqrerr]},
    )
    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_sqrerr], "Y": [local_sqrerr]},
        outputs={"Out": [local_sqrerr]},
    )

    helper.append_op(
        type="l1_norm",
        inputs={"X": [tmp_res_elesub]},
        outputs={"Out": [batch_abserr]},
    )
    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_abserr], "Y": [local_abserr]},
        outputs={"Out": [local_abserr]},
    )

    helper.append_op(
        type="reduce_sum", inputs={"X": [input]}, outputs={"Out": [batch_prob]}
    )
    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_prob], "Y": [local_prob]},
        outputs={"Out": [local_prob]},
    )
    helper.append_op(
        type="sigmoid",
        inputs={"X": [input]},
        outputs={"Out": [tmp_res_sigmoid]},
    )
    helper.append_op(
        type="reduce_sum",
        inputs={"X": [tmp_res_sigmoid]},
        outputs={"Out": [batch_q]},
    )

    helper.append_op(
        type="reduce_sum",
        inputs={"X": [label]},
        outputs={"Out": [batch_pos_num]},
    )
    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_pos_num], "Y": [local_pos_num]},
        outputs={"Out": [local_pos_num]},
    )

    helper.append_op(
        type='fill_constant_batch_size_like',
        inputs={"Input": label},
        outputs={'Out': [tmp_ones]},
        attrs={
            'shape': [-1, 1],
            'dtype': tmp_ones.dtype,
            'value': 1.0,
        },
    )
    helper.append_op(
        type="reduce_sum",
        inputs={"X": [tmp_ones]},
        outputs={"Out": [batch_ins_num]},
    )

    # if data is fake, return 0
    inputs_slice = {'Input': ins_tag_weight}
    attrs = {'axes': [0]}
    attrs['starts'] = [0]
    attrs['ends'] = [1]
    helper.append_op(
        type="slice",
        inputs=inputs_slice,
        attrs=attrs,
        outputs={"Out": ins_tag_weight},
    )

    axis = helper.kwargs.get('axis', 0)
    helper.append_op(
        type="elementwise_mul",
        inputs={"X": [batch_ins_num], "Y": [ins_tag_weight]},
        outputs={"Out": [batch_ins_num]},
        attrs={'axis': axis},
    )

    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_ins_num], "Y": [local_ins_num]},
        outputs={"Out": [local_ins_num]},
    )

    helper.append_op(
        type="elementwise_mul",
        inputs={"X": [batch_q], "Y": [ins_tag_weight]},
        outputs={"Out": [batch_q]},
        attrs={'axis': axis},
    )
    helper.append_op(
        type="elementwise_add",
        inputs={"X": [batch_q], "Y": [local_q]},
        outputs={"Out": [local_q]},
    )

    return (
        local_sqrerr,
        local_abserr,
        local_prob,
        local_q,
        local_pos_num,
        local_ins_num,
    )