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""" |
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Initialization of CrowdTruth metrics |
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""" |
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import logging |
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import math |
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from collections import Counter |
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import numpy as np |
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import pandas as pd |
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SMALL_NUMBER_CONST = 0.00000001 |
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class Metrics(): |
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""" |
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Computes and applies the CrowdTruth metrics for evaluating units, workers and annotations. |
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""" |
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# Unit Quality Score |
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@staticmethod |
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def unit_quality_score(unit_id, unit_work_ann_dict, wqs, aqs): |
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""" |
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Computes the unit quality score. |
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The unit quality score (UQS) is computed as the average cosine similarity between |
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all worker vectors for a given unit, weighted by the worker quality (WQS) and the |
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annotation quality (AQS). The goal is to capture the degree of agreement in annotating |
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the media unit. |
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Through the weighted average, workers and annotations with lower quality will have |
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less of an impact on the final score. |
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To weigh the metrics with the annotation quality, we compute weighted_cosine, the weighted |
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version of the cosine similarity. |
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Args: |
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unit_id: Unit id. |
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unit_work_ann_dict: A dictionary that contains all the workers judgments for the unit. |
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aqs: Dict of annotation_id (string) that contains the annotation quality score (float) |
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wqs: Dict of worker_id (string) that contains the worker quality score (float) |
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Returns: |
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The quality score (UQS) of the given unit. |
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""" |
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uqs_numerator = 0.0 |
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uqs_denominator = 0.0 |
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worker_ids = list(unit_work_ann_dict[unit_id].keys()) |
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for worker_i in range(len(worker_ids) - 1): |
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for worker_j in range(worker_i + 1, len(worker_ids)): |
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# print worker_ids[i] + " - " + worker_ids[j] + "\n" |
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numerator = 0.0 |
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denominator_i = 0.0 |
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denominator_j = 0.0 |
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worker_i_vector = unit_work_ann_dict[unit_id][worker_ids[worker_i]] |
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worker_j_vector = unit_work_ann_dict[unit_id][worker_ids[worker_j]] |
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for ann in worker_i_vector: |
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worker_i_vector_ann = worker_i_vector[ann] |
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worker_j_vector_ann = worker_j_vector[ann] |
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numerator += aqs[ann] * (worker_i_vector_ann * worker_j_vector_ann) |
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denominator_i += aqs[ann] * (worker_i_vector_ann * worker_i_vector_ann) |
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denominator_j += aqs[ann] * (worker_j_vector_ann * worker_j_vector_ann) |
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weighted_cosine = numerator / math.sqrt(denominator_i * denominator_j) |
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uqs_numerator += weighted_cosine * wqs[worker_ids[worker_i]] * \ |
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wqs[worker_ids[worker_j]] |
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uqs_denominator += wqs[worker_ids[worker_i]] * wqs[worker_ids[worker_j]] |
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if uqs_denominator < SMALL_NUMBER_CONST: |
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uqs_denominator = SMALL_NUMBER_CONST |
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return uqs_numerator / uqs_denominator |
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# Worker - Unit Agreement |
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@staticmethod |
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def worker_unit_agreement(worker_id, unit_ann_dict, work_unit_ann_dict, uqs, aqs, wqs): |
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""" |
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Computes the worker agreement on a unit. |
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The worker unit agreement (WUA) is the average cosine distance between the annotations |
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of a worker i and all the other annotations for the units they have worked on, |
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weighted by the unit and annotation quality. It calculates how much a worker disagrees |
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with the crowd on a unit basis. |
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Through the weighted average, units and anntation with lower quality will have less |
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of an impact on the final score. |
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Args: |
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worker_id: Worker id. |
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unit_ann_dict: Dictionary of units and their aggregated annotations. |
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work_unit_ann_dict: Dictionary of units (and its annotation) annotated by the worker. |
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uqs: Dict unit_id that contains the unit quality scores (float). |
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aqs: Dict of annotation_id (string) that contains the annotation quality scores (float). |
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wqs: Dict of worker_id (string) that contains the worker quality scores (float). |
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Returns: |
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The worker unit agreement score for the given worker. |
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""" |
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wsa_numerator = 0.0 |
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wsa_denominator = 0.0 |
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work_unit_ann_dict_worker_id = work_unit_ann_dict[worker_id] |
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for unit_id in work_unit_ann_dict_worker_id: |
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numerator = 0.0 |
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denominator_w = 0.0 |
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denominator_s = 0.0 |
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worker_vector = work_unit_ann_dict[worker_id][unit_id] |
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unit_vector = unit_ann_dict[unit_id] |
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for ann in worker_vector: |
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worker_vector_ann = worker_vector[ann] * wqs |
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unit_vector_ann = unit_vector[ann] |
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numerator += aqs[ann] * worker_vector_ann * \ |
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(unit_vector_ann - worker_vector_ann) |
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denominator_w += aqs[ann] * \ |
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(worker_vector_ann * worker_vector_ann) |
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denominator_s += aqs[ann] * ( \ |
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(unit_vector_ann - worker_vector_ann) * \ |
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(unit_vector_ann - worker_vector_ann)) |
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weighted_cosine = None |
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if math.sqrt(denominator_w * denominator_s) < SMALL_NUMBER_CONST: |
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weighted_cosine = SMALL_NUMBER_CONST |
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else: |
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weighted_cosine = numerator / math.sqrt(denominator_w * denominator_s) |
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wsa_numerator += weighted_cosine * uqs[unit_id] |
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wsa_denominator += uqs[unit_id] |
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1 |
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if wsa_denominator < SMALL_NUMBER_CONST: |
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wsa_denominator = SMALL_NUMBER_CONST |
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return wsa_numerator / wsa_denominator |
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# Worker - Worker Agreement |
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1 |
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@staticmethod |
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140
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def worker_worker_agreement(worker_id, work_unit_ann_dict, unit_work_ann_dict, wqs, uqs, aqs): |
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""" |
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142
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Computes the agreement between every two workers. |
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144
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The worker-worker agreement (WWA) is the average cosine distance between the annotations of |
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a worker i and all other workers that have worked on the same media units as worker i, |
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weighted by the worker and annotation qualities. |
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148
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The metric gives an indication as to whether there are consisently like-minded workers. |
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This is useful for identifying communities of thought. |
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151
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Through the weighted average, workers and annotations with lower quality will have less |
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152
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of an impact on the final score of the given worker. |
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154
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Args: |
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155
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worker_id: Worker id. |
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156
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work_unit_ann_dict: Dictionary of worker annotation vectors on annotated units. |
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157
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unit_work_ann_dict: Dictionary of unit annotation vectors. |
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158
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uqs: Dict unit_id that contains the unit quality scores (float). |
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159
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aqs: Dict of annotation_id (string) that contains the annotation quality scores (float). |
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wqs: Dict of worker_id (string) that contains the worker quality scores (float). |
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161
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162
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Returns: |
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163
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The worker worker agreement score for the given worker. |
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164
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""" |
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165
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1 |
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wwa_numerator = 0.0 |
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167
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1 |
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wwa_denominator = 0.0 |
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168
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169
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1 |
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worker_vector = work_unit_ann_dict[worker_id] |
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170
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1 |
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unit_ids = list(work_unit_ann_dict[worker_id].keys()) |
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171
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172
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1 |
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for unit_id in unit_ids: |
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1 |
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wv_unit_id = worker_vector[unit_id] |
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174
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1 |
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unit_work_ann_dict_unit_id = unit_work_ann_dict[unit_id] |
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175
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1 |
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for other_workid in unit_work_ann_dict_unit_id: |
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176
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1 |
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if worker_id != other_workid: |
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177
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1 |
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numerator = 0.0 |
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1 |
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denominator_w = 0.0 |
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denominator_ow = 0.0 |
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180
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181
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1 |
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unit_work_ann_dict_uid_oworkid = unit_work_ann_dict_unit_id[other_workid] |
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182
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1 |
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for ann in wv_unit_id: |
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183
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1 |
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unit_work_ann_dict_uid_oworkid_ann = unit_work_ann_dict_uid_oworkid[ann] |
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184
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1 |
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wv_unit_id_ann = wv_unit_id[ann] |
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185
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186
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1 |
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numerator += aqs[ann] * (wv_unit_id_ann * \ |
|
187
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unit_work_ann_dict_uid_oworkid_ann) |
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188
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189
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1 |
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denominator_w += aqs[ann] * (wv_unit_id_ann * wv_unit_id_ann) |
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190
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191
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1 |
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denominator_ow += aqs[ann] * \ |
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192
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(unit_work_ann_dict_uid_oworkid_ann *\ |
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193
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unit_work_ann_dict_uid_oworkid_ann) |
|
194
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|
195
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1 |
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weighted_cosine = numerator / math.sqrt(denominator_w * denominator_ow) |
|
196
|
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# pdb.set_trace() |
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197
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1 |
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wwa_numerator += weighted_cosine * wqs[other_workid] * uqs[unit_id] |
|
198
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1 |
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wwa_denominator += wqs[other_workid] * uqs[unit_id] |
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199
|
1 |
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if wwa_denominator < SMALL_NUMBER_CONST: |
|
200
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1 |
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wwa_denominator = SMALL_NUMBER_CONST |
|
201
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1 |
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return wwa_numerator / wwa_denominator |
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202
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203
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204
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|
205
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# Unit - Annotation Score (UAS) |
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206
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1 |
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@staticmethod |
|
207
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def unit_annotation_score(unit_id, annotation, unit_work_annotation_dict, wqs): |
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208
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""" |
|
209
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Computes the unit annotation score. |
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210
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|
211
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The unit - annotation score (UAS) calculates the likelihood that annotation a |
|
212
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is expressed in unit u. It is the ratio of the number of workers that picked |
|
213
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annotation a over all workers that annotated the unit, weighted by the worker quality. |
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214
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215
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Args: |
|
216
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unit_id: Unit id. |
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217
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annotation: Annotation. |
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218
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unit_work_annotation_dict: Dictionary of unit annotation vectors. |
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219
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wqs: Dict of worker_id (string) that contains the worker quality scores (float). |
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220
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221
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Returns: |
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222
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The unit annotation score for the given unit and annotation. |
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223
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""" |
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224
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|
225
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1 |
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uas_numerator = 0.0 |
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226
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1 |
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uas_denominator = 0.0 |
|
227
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228
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1 |
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worker_ids = unit_work_annotation_dict[unit_id] |
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229
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1 |
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for worker_id in worker_ids: |
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230
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1 |
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uas_numerator += worker_ids[worker_id][annotation] * wqs[worker_id] |
|
231
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1 |
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uas_denominator += wqs[worker_id] |
|
232
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1 |
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if uas_denominator < SMALL_NUMBER_CONST: |
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233
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1 |
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uas_denominator = SMALL_NUMBER_CONST |
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234
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1 |
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return uas_numerator / uas_denominator |
|
235
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|
236
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|
237
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# Annotation Quality Score (AQS) |
|
238
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1 |
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@staticmethod |
|
239
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|
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def annotation_quality_score(annotations, work_unit_ann_dict, uqs, wqs): |
|
240
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|
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""" |
|
241
|
|
|
Computes the annotation quality score. |
|
242
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|
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|
|
243
|
|
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The annotation quality score AQS calculates the agreement of selecting an annotation a, |
|
244
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over all the units it appears in. Therefore, it is only applicable to closed tasks, where |
|
245
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the same annotation set is used for all units. It is based on the probability that if a |
|
246
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worker j annotates annotation a in a unit, worker i will also annotate it. |
|
247
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|
248
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The annotation quality score is the weighted average of these probabilities for all possible |
|
249
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|
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pairs of workers. Through the weighted average, units and workers with lower quality will |
|
250
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|
have less of an impact on the final score of the annotation. |
|
251
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|
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|
|
252
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|
|
Args: |
|
253
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|
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annotations: Possible annotations. |
|
254
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|
|
work_unit_annotation_dict: Dictionary of worker annotation vectors on annotated units. |
|
255
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uqs: Dict unit_id that contains the unit quality scores (float). |
|
256
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|
|
wqs: Dict of worker_id (string) that contains the worker quality scores (float). |
|
257
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|
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|
|
258
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Returns: |
|
259
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The worker worker agreement score for the given worker. |
|
260
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|
""" |
|
261
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|
262
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1 |
|
aqs_numerator = dict() |
|
263
|
1 |
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aqs_denominator = dict() |
|
264
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|
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|
|
265
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1 |
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for ann in annotations: |
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266
|
1 |
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aqs_numerator[ann] = 0.0 |
|
267
|
1 |
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aqs_denominator[ann] = 0.0 |
|
268
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|
269
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1 |
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for worker_i, work_unit_ann_dict_worker_i in work_unit_ann_dict.items(): |
|
270
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|
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#work_unit_ann_dict_worker_i = work_unit_ann_dict[worker_i] |
|
271
|
1 |
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work_unit_ann_dict_i_keys = list(work_unit_ann_dict_worker_i.keys()) |
|
272
|
1 |
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for worker_j, work_unit_ann_dict_worker_j in work_unit_ann_dict.items(): |
|
273
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#work_unit_ann_dict_worker_j = work_unit_ann_dict[worker_j] |
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274
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1 |
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work_unit_ann_dict_j_keys = list(work_unit_ann_dict_worker_j.keys()) |
|
275
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|
|
276
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1 |
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length_keys = len(np.intersect1d(np.array(work_unit_ann_dict_i_keys), \ |
|
277
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|
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np.array(work_unit_ann_dict_j_keys))) |
|
278
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|
279
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1 |
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if worker_i != worker_j and length_keys > 0: |
|
280
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1 |
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for ann in annotations: |
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281
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1 |
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numerator = 0.0 |
|
282
|
1 |
|
denominator = 0.0 |
|
283
|
|
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|
284
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1 |
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for unit_id, work_unit_ann_dict_worker_i_unit in work_unit_ann_dict_worker_i.items(): |
|
285
|
1 |
|
if unit_id in work_unit_ann_dict_worker_j: |
|
286
|
1 |
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work_unit_ann_dict_worker_j_unit = work_unit_ann_dict_worker_j[unit_id] |
|
287
|
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288
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1 |
|
work_unit_ann_dict_worker_j_unit_ann = work_unit_ann_dict_worker_j_unit[ann] |
|
289
|
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|
|
290
|
1 |
|
def compute_numerator_aqs(unit_id_ann_value, worker_i_ann_value, \ |
|
291
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|
|
worker_j_ann_value): |
|
292
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|
""" compute numerator """ |
|
293
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1 |
|
numerator = unit_id_ann_value * worker_i_ann_value * \ |
|
294
|
|
|
worker_j_ann_value |
|
295
|
1 |
|
return numerator |
|
296
|
|
|
|
|
297
|
1 |
|
def compute_denominator_aqs(unit_id_ann_value, worker_j_ann_value): |
|
298
|
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|
""" compute denominator """ |
|
299
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1 |
|
denominator = unit_id_ann_value * worker_j_ann_value |
|
300
|
1 |
|
return denominator |
|
301
|
|
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|
|
302
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1 |
|
numerator += compute_numerator_aqs(uqs[unit_id], \ |
|
303
|
|
|
work_unit_ann_dict_worker_i_unit[ann], \ |
|
304
|
|
|
work_unit_ann_dict_worker_j_unit_ann) |
|
305
|
1 |
|
denominator += compute_denominator_aqs(uqs[unit_id], \ |
|
306
|
|
|
work_unit_ann_dict_worker_j_unit_ann) |
|
307
|
|
|
|
|
308
|
1 |
|
if denominator > 0: |
|
309
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1 |
|
aqs_numerator[ann] += wqs[worker_i] * wqs[worker_j] * \ |
|
310
|
|
|
numerator / denominator |
|
311
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1 |
|
aqs_denominator[ann] += wqs[worker_i] * wqs[worker_j] |
|
312
|
|
|
|
|
313
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1 |
|
def aqs_dict(annotations, aqs_numerator, aqs_denominator): |
|
314
|
|
|
""" create the dictionary of aqs values """ |
|
315
|
1 |
|
aqs = dict() |
|
316
|
1 |
|
for ann in annotations: |
|
317
|
1 |
|
if aqs_denominator[ann] > SMALL_NUMBER_CONST: |
|
318
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1 |
|
aqs[ann] = aqs_numerator[ann] / aqs_denominator[ann] |
|
319
|
|
|
|
|
320
|
|
|
# prevent division by zero by storing very small value instead |
|
321
|
1 |
|
if aqs[ann] < SMALL_NUMBER_CONST: |
|
322
|
1 |
|
aqs[ann] = SMALL_NUMBER_CONST |
|
323
|
|
|
else: |
|
324
|
1 |
|
aqs[ann] = SMALL_NUMBER_CONST |
|
325
|
1 |
|
return aqs |
|
326
|
|
|
|
|
327
|
1 |
|
return aqs_dict(annotations, aqs_numerator, aqs_denominator) |
|
328
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|
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|
|
329
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|
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|
|
330
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1 |
|
@staticmethod |
|
331
|
1 |
|
def run(results, config, max_delta=0.001): |
|
332
|
|
|
''' |
|
333
|
|
|
iteratively run the CrowdTruth metrics |
|
334
|
|
|
''' |
|
335
|
|
|
|
|
336
|
1 |
|
judgments = results['judgments'].copy() |
|
337
|
1 |
|
units = results['units'].copy() |
|
338
|
|
|
|
|
339
|
|
|
# unit_work_ann_dict, work_unit_ann_dict, unit_ann_dict |
|
340
|
|
|
# to be done: change to use all vectors in one unit |
|
341
|
1 |
|
col = list(config.output.values())[0] |
|
342
|
1 |
|
unit_ann_dict = dict(units.copy()[col]) |
|
343
|
|
|
|
|
344
|
1 |
|
def expanded_vector(worker, unit): |
|
345
|
|
|
''' |
|
346
|
|
|
expand the vector of a worker on a given unit |
|
347
|
|
|
''' |
|
348
|
1 |
|
vector = Counter() |
|
349
|
1 |
|
for ann in unit: |
|
350
|
1 |
|
if ann in worker: |
|
351
|
1 |
|
vector[ann] = worker[ann] |
|
352
|
|
|
else: |
|
353
|
1 |
|
vector[ann] = 0 |
|
354
|
1 |
|
return vector |
|
355
|
|
|
|
|
356
|
|
|
# fill judgment vectors with unit keys |
|
357
|
1 |
|
for index, row in judgments.iterrows(): |
|
358
|
1 |
|
judgments.at[index, col] = expanded_vector(row[col], units.at[row['unit'], col]) |
|
359
|
|
|
|
|
360
|
1 |
|
unit_work_ann_dict = judgments[['unit', 'worker', col]].copy().groupby('unit') |
|
361
|
1 |
|
unit_work_ann_dict = {name : group.set_index('worker')[col].to_dict() \ |
|
362
|
|
|
for name, group in unit_work_ann_dict} |
|
363
|
|
|
|
|
364
|
1 |
|
work_unit_ann_dict = judgments[['worker', 'unit', col]].copy().groupby('worker') |
|
365
|
1 |
|
work_unit_ann_dict = {name : group.set_index('unit')[col].to_dict() \ |
|
366
|
|
|
for name, group in work_unit_ann_dict} |
|
367
|
|
|
|
|
368
|
|
|
#initialize data structures |
|
369
|
1 |
|
uqs_list = list() |
|
370
|
1 |
|
wqs_list = list() |
|
371
|
1 |
|
wwa_list = list() |
|
372
|
1 |
|
wsa_list = list() |
|
373
|
1 |
|
aqs_list = list() |
|
374
|
|
|
|
|
375
|
1 |
|
uqs = dict((unit_id, 1.0) for unit_id in unit_work_ann_dict) |
|
376
|
1 |
|
wqs = dict((worker_id, 1.0) for worker_id in work_unit_ann_dict) |
|
377
|
1 |
|
wwa = dict((worker_id, 1.0) for worker_id in work_unit_ann_dict) |
|
378
|
1 |
|
wsa = dict((worker_id, 1.0) for worker_id in work_unit_ann_dict) |
|
379
|
|
|
|
|
380
|
1 |
|
uqs_list.append(uqs.copy()) |
|
381
|
1 |
|
wqs_list.append(wqs.copy()) |
|
382
|
1 |
|
wwa_list.append(wwa.copy()) |
|
383
|
1 |
|
wsa_list.append(wsa.copy()) |
|
384
|
|
|
|
|
385
|
1 |
|
def init_aqs(config, unit_ann_dict): |
|
386
|
|
|
""" initialize aqs depending on whether or not it is an open ended task """ |
|
387
|
1 |
|
aqs = dict() |
|
388
|
1 |
|
if not config.open_ended_task: |
|
389
|
1 |
|
aqs_keys = list(unit_ann_dict[list(unit_ann_dict.keys())[0]].keys()) |
|
390
|
1 |
|
for ann in aqs_keys: |
|
391
|
1 |
|
aqs[ann] = 1.0 |
|
392
|
|
|
else: |
|
393
|
1 |
|
for unit_id in unit_ann_dict: |
|
394
|
1 |
|
for ann in unit_ann_dict[unit_id]: |
|
395
|
1 |
|
aqs[ann] = 1.0 |
|
396
|
1 |
|
return aqs |
|
397
|
|
|
|
|
398
|
1 |
|
aqs = init_aqs(config, unit_ann_dict) |
|
399
|
1 |
|
aqs_list.append(aqs.copy()) |
|
400
|
|
|
|
|
401
|
1 |
|
uqs_len = len(list(uqs.keys())) * 1.0 |
|
402
|
1 |
|
wqs_len = len(list(wqs.keys())) * 1.0 |
|
403
|
1 |
|
aqs_len = len(list(aqs.keys())) * 1.0 |
|
404
|
|
|
|
|
405
|
|
|
# compute metrics until stable values |
|
406
|
1 |
|
iterations = 0 |
|
407
|
1 |
|
while max_delta >= 0.001: |
|
408
|
1 |
|
uqs_new = dict() |
|
409
|
1 |
|
wqs_new = dict() |
|
410
|
1 |
|
wwa_new = dict() |
|
411
|
1 |
|
wsa_new = dict() |
|
412
|
|
|
|
|
413
|
1 |
|
avg_uqs_delta = 0.0 |
|
414
|
1 |
|
avg_wqs_delta = 0.0 |
|
415
|
1 |
|
avg_aqs_delta = 0.0 |
|
416
|
1 |
|
max_delta = 0.0 |
|
417
|
|
|
|
|
418
|
|
|
# pdb.set_trace() |
|
419
|
|
|
|
|
420
|
1 |
|
def compute_wqs(wwa_new, wsa_new, wqs_new, work_unit_ann_dict, unit_ann_dict, \ |
|
421
|
|
|
unit_work_ann_dict, wqs_list, uqs_list, aqs_list, wqs_len, \ |
|
422
|
|
|
max_delta, avg_wqs_delta): |
|
423
|
|
|
""" compute worker quality score (WQS) """ |
|
424
|
1 |
|
for worker_id, _ in work_unit_ann_dict.items(): |
|
425
|
1 |
|
wwa_new[worker_id] = Metrics.worker_worker_agreement( \ |
|
426
|
|
|
worker_id, work_unit_ann_dict, \ |
|
427
|
|
|
unit_work_ann_dict, \ |
|
428
|
|
|
wqs_list[len(wqs_list) - 1], \ |
|
429
|
|
|
uqs_list[len(uqs_list) - 1], \ |
|
430
|
|
|
aqs_list[len(aqs_list) - 1]) |
|
431
|
1 |
|
wsa_new[worker_id] = Metrics.worker_unit_agreement( \ |
|
432
|
|
|
worker_id, \ |
|
433
|
|
|
unit_ann_dict, \ |
|
434
|
|
|
work_unit_ann_dict, \ |
|
435
|
|
|
uqs_list[len(uqs_list) - 1], \ |
|
436
|
|
|
aqs_list[len(aqs_list) - 1], \ |
|
437
|
|
|
wqs_list[len(aqs_list) - 1][worker_id]) |
|
438
|
1 |
|
wqs_new[worker_id] = wwa_new[worker_id] * wsa_new[worker_id] |
|
439
|
1 |
|
max_delta = max(max_delta, \ |
|
440
|
|
|
abs(wqs_new[worker_id] - wqs_list[len(wqs_list) - 1][worker_id])) |
|
441
|
1 |
|
avg_wqs_delta += abs(wqs_new[worker_id] - \ |
|
442
|
|
|
wqs_list[len(wqs_list) - 1][worker_id]) |
|
443
|
1 |
|
avg_wqs_delta /= wqs_len |
|
444
|
|
|
|
|
445
|
1 |
|
return wwa_new, wsa_new, wqs_new, max_delta, avg_wqs_delta |
|
446
|
|
|
|
|
447
|
1 |
|
def reconstruct_unit_ann_dict(unit_ann_dict, work_unit_ann_dict, wqs_new): |
|
448
|
|
|
""" reconstruct unit_ann_dict with worker scores """ |
|
449
|
1 |
|
new_unit_ann_dict = dict() |
|
450
|
1 |
|
for unit_id, ann_dict in unit_ann_dict.items(): |
|
451
|
1 |
|
new_unit_ann_dict[unit_id] = dict() |
|
452
|
1 |
|
for ann, _ in ann_dict.items(): |
|
453
|
1 |
|
new_unit_ann_dict[unit_id][ann] = 0.0 |
|
454
|
1 |
|
for work_id, srd in work_unit_ann_dict.items(): |
|
455
|
1 |
|
wqs_work_id = wqs_new[work_id] |
|
456
|
1 |
|
for unit_id, ann_dict in srd.items(): |
|
457
|
1 |
|
for ann, score in ann_dict.items(): |
|
458
|
1 |
|
new_unit_ann_dict[unit_id][ann] += score * wqs_work_id |
|
459
|
|
|
|
|
460
|
1 |
|
return new_unit_ann_dict |
|
461
|
|
|
|
|
462
|
1 |
|
def compute_aqs(aqs, work_unit_ann_dict, uqs_list, wqs_list, aqs_list, aqs_len, max_delta, avg_aqs_delta): |
|
463
|
|
|
""" compute annotation quality score (aqs) """ |
|
464
|
1 |
|
aqs_new = Metrics.annotation_quality_score(list(aqs.keys()), work_unit_ann_dict, \ |
|
465
|
|
|
uqs_list[len(uqs_list) - 1], \ |
|
466
|
|
|
wqs_list[len(wqs_list) - 1]) |
|
467
|
1 |
|
for ann, _ in aqs_new.items(): |
|
468
|
1 |
|
max_delta = max(max_delta, abs(aqs_new[ann] - aqs_list[len(aqs_list) - 1][ann])) |
|
469
|
1 |
|
avg_aqs_delta += abs(aqs_new[ann] - aqs_list[len(aqs_list) - 1][ann]) |
|
470
|
1 |
|
avg_aqs_delta /= aqs_len |
|
471
|
1 |
|
return aqs_new, max_delta, avg_aqs_delta |
|
472
|
|
|
|
|
473
|
1 |
|
def compute_uqs(uqs_new, unit_work_ann_dict, wqs_list, aqs_list, uqs_list, uqs_len, max_delta, avg_uqs_delta): |
|
474
|
|
|
""" compute unit quality score (uqs) """ |
|
475
|
1 |
|
for unit_id, _ in unit_work_ann_dict.items(): |
|
476
|
1 |
|
uqs_new[unit_id] = Metrics.unit_quality_score(unit_id, unit_work_ann_dict, \ |
|
477
|
|
|
wqs_list[len(wqs_list) - 1], \ |
|
478
|
|
|
aqs_list[len(aqs_list) - 1]) |
|
479
|
1 |
|
max_delta = max(max_delta, \ |
|
480
|
|
|
abs(uqs_new[unit_id] - uqs_list[len(uqs_list) - 1][unit_id])) |
|
481
|
1 |
|
avg_uqs_delta += abs(uqs_new[unit_id] - uqs_list[len(uqs_list) - 1][unit_id]) |
|
482
|
1 |
|
avg_uqs_delta /= uqs_len |
|
483
|
1 |
|
return uqs_new, max_delta, avg_uqs_delta |
|
484
|
|
|
|
|
485
|
1 |
|
if not config.open_ended_task: |
|
486
|
|
|
# compute annotation quality score (aqs) |
|
487
|
1 |
|
aqs_new, max_delta, avg_aqs_delta = compute_aqs(aqs, work_unit_ann_dict, \ |
|
488
|
|
|
uqs_list, wqs_list, aqs_list, aqs_len, max_delta, avg_aqs_delta) |
|
489
|
|
|
|
|
490
|
|
|
# compute unit quality score (uqs) |
|
491
|
1 |
|
uqs_new, max_delta, avg_uqs_delta = compute_uqs(uqs_new, unit_work_ann_dict, \ |
|
492
|
|
|
wqs_list, aqs_list, uqs_list, uqs_len, max_delta, avg_uqs_delta) |
|
493
|
|
|
|
|
494
|
|
|
# compute worker quality score (WQS) |
|
495
|
1 |
|
wwa_new, wsa_new, wqs_new, max_delta, avg_wqs_delta = compute_wqs(\ |
|
496
|
|
|
wwa_new, wsa_new, wqs_new, \ |
|
497
|
|
|
work_unit_ann_dict, unit_ann_dict, unit_work_ann_dict, wqs_list, \ |
|
498
|
|
|
uqs_list, aqs_list, wqs_len, max_delta, avg_wqs_delta) |
|
499
|
|
|
|
|
500
|
|
|
# save results for current iteration |
|
501
|
1 |
|
uqs_list.append(uqs_new.copy()) |
|
502
|
1 |
|
wqs_list.append(wqs_new.copy()) |
|
503
|
1 |
|
wwa_list.append(wwa_new.copy()) |
|
504
|
1 |
|
wsa_list.append(wsa_new.copy()) |
|
505
|
1 |
|
if not config.open_ended_task: |
|
506
|
1 |
|
aqs_list.append(aqs_new.copy()) |
|
|
|
|
|
|
507
|
1 |
|
iterations += 1 |
|
508
|
|
|
|
|
509
|
1 |
|
unit_ann_dict = reconstruct_unit_ann_dict(unit_ann_dict, work_unit_ann_dict, wqs_new) |
|
510
|
|
|
|
|
511
|
1 |
|
logging.info(str(iterations) + " iterations; max d= " + str(max_delta) + \ |
|
512
|
|
|
" ; wqs d= " + str(avg_wqs_delta) + "; uqs d= " + str(avg_uqs_delta) + \ |
|
513
|
|
|
"; aqs d= " + str(avg_aqs_delta)) |
|
514
|
|
|
|
|
515
|
1 |
|
def save_unit_ann_score(unit_ann_dict, unit_work_ann_dict, iteration_value): |
|
516
|
|
|
""" save the unit annotation score for print """ |
|
517
|
1 |
|
srs = Counter() |
|
518
|
1 |
|
for unit_id in unit_ann_dict: |
|
519
|
1 |
|
srs[unit_id] = Counter() |
|
520
|
1 |
|
for ann in unit_ann_dict[unit_id]: |
|
521
|
1 |
|
srs[unit_id][ann] = Metrics.unit_annotation_score(unit_id, \ |
|
522
|
|
|
ann, unit_work_ann_dict, \ |
|
523
|
|
|
iteration_value) |
|
524
|
1 |
|
return srs |
|
525
|
|
|
|
|
526
|
1 |
|
srs = save_unit_ann_score(unit_ann_dict, unit_work_ann_dict, wqs_list[len(wqs_list) - 1]) |
|
527
|
1 |
|
srs_initial = save_unit_ann_score(unit_ann_dict, unit_work_ann_dict, wqs_list[0]) |
|
528
|
|
|
|
|
529
|
1 |
|
results['units']['uqs'] = pd.Series(uqs_list[-1]) |
|
530
|
1 |
|
results['units']['unit_annotation_score'] = pd.Series(srs) |
|
531
|
1 |
|
results['workers']['wqs'] = pd.Series(wqs_list[-1]) |
|
532
|
1 |
|
results['workers']['wwa'] = pd.Series(wwa_list[-1]) |
|
533
|
1 |
|
results['workers']['wsa'] = pd.Series(wsa_list[-1]) |
|
534
|
1 |
|
if not config.open_ended_task: |
|
535
|
1 |
|
results['annotations']['aqs'] = pd.Series(aqs_list[-1]) |
|
536
|
|
|
|
|
537
|
1 |
|
results['units']['uqs_initial'] = pd.Series(uqs_list[1]) |
|
538
|
1 |
|
results['units']['unit_annotation_score_initial'] = pd.Series(srs_initial) |
|
539
|
1 |
|
results['workers']['wqs_initial'] = pd.Series(wqs_list[1]) |
|
540
|
1 |
|
results['workers']['wwa_initial'] = pd.Series(wwa_list[1]) |
|
541
|
1 |
|
results['workers']['wsa_initial'] = pd.Series(wsa_list[1]) |
|
542
|
1 |
|
if not config.open_ended_task: |
|
543
|
1 |
|
results['annotations']['aqs_initial'] = pd.Series(aqs_list[1]) |
|
544
|
|
|
return results |
|
545
|
|
|
|
CrowdTruth /
CrowdTruth-core
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