# Copyright (c) 2020 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. import math import numpy as np import paddle.fluid as fluid from paddlerec.core.metric import Metric from paddle.fluid.layers.tensor import Variable class AUC(Metric): """ Metric For Fluid Model """ def __init__(self, **kwargs): """ """ if "input" not in kwargs or "label" not in kwargs: raise ValueError("AUC expect input and label as inputs.") predict = kwargs.get("input") label = kwargs.get("label") curve = kwargs.get("curve", 'ROC') num_thresholds = kwargs.get("num_thresholds", 2**12 - 1) topk = kwargs.get("topk", 1) slide_steps = kwargs.get("slide_steps", 1) if not isinstance(predict, Variable): raise ValueError("input must be Variable, but received %s" % type(predict)) if not isinstance(label, Variable): raise ValueError("label must be Variable, but received %s" % type(label)) auc_out, batch_auc_out, [ batch_stat_pos, batch_stat_neg, stat_pos, stat_neg ] = fluid.layers.auc(predict, label, curve=curve, num_thresholds=num_thresholds, topk=topk, slide_steps=slide_steps) prob = fluid.layers.slice(predict, axes=[1], starts=[1], ends=[2]) label_cast = fluid.layers.cast(label, dtype="float32") label_cast.stop_gradient = True sqrerr, abserr, prob, q, pos, total = \ fluid.contrib.layers.ctr_metric_bundle(prob, label_cast) self._global_metric_state_vars = dict() self._global_metric_state_vars['stat_pos'] = (stat_pos.name, "float32") self._global_metric_state_vars['stat_neg'] = (stat_neg.name, "float32") self._global_metric_state_vars['total_ins_num'] = (total.name, "float32") self._global_metric_state_vars['pos_ins_num'] = (pos.name, "float32") self._global_metric_state_vars['q'] = (q.name, "float32") self._global_metric_state_vars['prob'] = (prob.name, "float32") self._global_metric_state_vars['abserr'] = (abserr.name, "float32") self._global_metric_state_vars['sqrerr'] = (sqrerr.name, "float32") self.metrics = dict() self.metrics["AUC"] = auc_out self.metrics["BATCH_AUC"] = batch_auc_out def calculate_bucket_error(self, global_pos, global_neg): """R """ num_bucket = len(global_pos) last_ctr = -1.0 impression_sum = 0.0 ctr_sum = 0.0 click_sum = 0.0 error_sum = 0.0 error_count = 0.0 click = 0.0 show = 0.0 ctr = 0.0 adjust_ctr = 0.0 relative_error = 0.0 actual_ctr = 0.0 relative_ctr_error = 0.0 k_max_span = 0.01 k_relative_error_bound = 0.05 for i in range(num_bucket): click = global_pos[i] show = global_pos[i] + global_neg[i] ctr = float(i) / num_bucket if abs(ctr - last_ctr) > k_max_span: last_ctr = ctr impression_sum = 0.0 ctr_sum = 0.0 click_sum = 0.0 impression_sum += show ctr_sum += ctr * show click_sum += click if impression_sum == 0: continue adjust_ctr = ctr_sum / impression_sum if adjust_ctr == 0: continue relative_error = \ math.sqrt((1 - adjust_ctr) / (adjust_ctr * impression_sum)) if relative_error < k_relative_error_bound: actual_ctr = click_sum / impression_sum relative_ctr_error = abs(actual_ctr / adjust_ctr - 1) error_sum += relative_ctr_error * impression_sum error_count += impression_sum last_ctr = -1 bucket_error = error_sum / error_count if error_count > 0 else 0.0 return bucket_error def calculate_auc(self, global_pos, global_neg): """R """ num_bucket = len(global_pos) area = 0.0 pos = 0.0 neg = 0.0 new_pos = 0.0 new_neg = 0.0 total_ins_num = 0 for i in range(num_bucket): index = num_bucket - 1 - i new_pos = pos + global_pos[index] total_ins_num += global_pos[index] new_neg = neg + global_neg[index] total_ins_num += global_neg[index] area += (new_neg - neg) * (pos + new_pos) / 2 pos = new_pos neg = new_neg auc_value = None if pos * neg == 0 or total_ins_num == 0: auc_value = 0.5 else: auc_value = area / (pos * neg) return auc_value def calculate(self, global_metrics): result = dict() for key in self._global_metric_state_vars: if key not in global_metrics: raise ValueError("%s not existed" % key) result[key] = global_metrics[key][0] if result['total_ins_num'] == 0: result['auc'] = 0 result['bucket_error'] = 0 result['actual_ctr'] = 0 result['predict_ctr'] = 0 result['mae'] = 0 result['rmse'] = 0 result['copc'] = 0 result['mean_q'] = 0 else: result['auc'] = self.calculate_auc(result['stat_pos'], result['stat_neg']) result['bucket_error'] = self.calculate_auc(result['stat_pos'], result['stat_neg']) result['actual_ctr'] = result['pos_ins_num'] / result[ 'total_ins_num'] result['mae'] = result['abserr'] / result['total_ins_num'] result['rmse'] = math.sqrt(result['sqrerr'] / result['total_ins_num']) result['predict_ctr'] = result['prob'] / result['total_ins_num'] if abs(result['predict_ctr']) > 1e-6: result['copc'] = result['actual_ctr'] / result['predict_ctr'] result['mean_q'] = result['q'] / result['total_ins_num'] result_str = "AUC=%.6f BUCKET_ERROR=%.6f MAE=%.6f RMSE=%.6f " \ "Actural_CTR=%.6f Predicted_CTR=%.6f COPC=%.6f MEAN Q_VALUE=%.6f Ins number=%s" % \ (result['auc'], result['bucket_error'], result['mae'], result['rmse'], result['actual_ctr'], result['predict_ctr'], result['copc'], result['mean_q'], result['total_ins_num']) return result_str def get_result(self): return self.metrics