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import logging |
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import math |
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# import datetime |
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# import itertools |
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# import pdb |
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from collections import Counter |
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# from pprint import pprint |
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# from datetime import datetime |
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# from collections import defaultdict |
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import numpy as np |
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import pandas as pd |
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# import crowdtruth.models.unit |
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SMALL_NUMBER_CONST = 0.00000001 |
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class Metrics(): |
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# Sentence Quality Score |
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@staticmethod |
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def sentence_quality_score(sentence_id, sent_work_rel_dict, wqs, rqs): |
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''' |
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sentence_id |
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work_sent_rel_dict |
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rqs: dict of relation_id (string) -> relation quality (float) |
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wqs: dict of worker_id (string) -> worker quality score |
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''' |
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sqs_numerator = 0.0 |
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sqs_denominator = 0.0 |
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worker_ids = list(sent_work_rel_dict[sentence_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 = sent_work_rel_dict[sentence_id][worker_ids[worker_i]] |
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worker_j_vector = sent_work_rel_dict[sentence_id][worker_ids[worker_j]] |
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for relation in worker_i_vector: |
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worker_i_vector_rel = worker_i_vector[relation] |
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worker_j_vector_rel = worker_j_vector[relation] |
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numerator += rqs[relation] * (worker_i_vector_rel * worker_j_vector_rel) |
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denominator_i += rqs[relation] * (worker_i_vector_rel * worker_i_vector_rel) |
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denominator_j += rqs[relation] * (worker_j_vector_rel * worker_j_vector_rel) |
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weighted_cosine = numerator / math.sqrt(denominator_i * denominator_j) |
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sqs_numerator += weighted_cosine * wqs[worker_ids[worker_i]] * wqs[worker_ids[worker_j]] |
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sqs_denominator += wqs[worker_ids[worker_i]] * wqs[worker_ids[worker_j]] |
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if sqs_denominator < SMALL_NUMBER_CONST: |
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sqs_denominator = SMALL_NUMBER_CONST |
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return sqs_numerator / sqs_denominator |
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# Worker - Sentence Agreement |
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@staticmethod |
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def worker_sentence_agreement(worker_id, sent_rel_dict, work_sent_rel_dict, sqs, rqs, wqs): |
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''' |
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worker_id |
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sent_rel_dict |
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work_sent_rel_dict |
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sentence_vectors: data frame of sentence vectors |
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sqs (sentence quality score): dict sentence_id -> sentence quality (float) |
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rqs: dict of relation_id (string) -> relation quality (float) |
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wqs: quality score of 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_sent_rel_dict_worker_id = work_sent_rel_dict[worker_id] |
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for sentence_id in work_sent_rel_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_sent_rel_dict[worker_id][sentence_id] |
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sentence_vector = sent_rel_dict[sentence_id] |
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for relation in worker_vector: |
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worker_vector_relation = worker_vector[relation] * wqs |
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sentence_vector_relation = sentence_vector[relation] |
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numerator += rqs[relation] * worker_vector_relation * \ |
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(sentence_vector_relation - worker_vector_relation) |
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denominator_w += rqs[relation] * \ |
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(worker_vector_relation * worker_vector_relation) |
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denominator_s += rqs[relation] * ( \ |
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(sentence_vector_relation - worker_vector_relation) * \ |
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(sentence_vector_relation - worker_vector_relation)) |
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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 * sqs[sentence_id] |
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wsa_denominator += sqs[sentence_id] |
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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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@staticmethod |
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def worker_worker_agreement(worker_id, work_sent_rel_dict, sent_work_rel_dict, wqs, sqs, rqs): |
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''' |
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worker_id |
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work_sent_rel_dict |
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sent_work_rel_dict |
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worker_vectors: data frame of worker vectors |
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sqs (sentence quality score): dict sentence_id -> sentence quality (float) |
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rqs: dict of relation_id (string) -> relation quality (float) |
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''' |
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wwa_numerator = 0.0 |
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wwa_denominator = 0.0 |
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wv = work_sent_rel_dict[worker_id] |
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sentence_ids = list(work_sent_rel_dict[worker_id].keys()) |
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for sentence_id in sentence_ids: |
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wv_sentence_id = wv[sentence_id] |
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sent_work_rel_dict_sentence_id = sent_work_rel_dict[sentence_id] |
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for other_worker_id in sent_work_rel_dict_sentence_id: |
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if worker_id != other_worker_id: |
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numerator = 0.0 |
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denominator_w = 0.0 |
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denominator_ow = 0.0 |
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sent_work_rel_dict_sentence_id_other_worker_id = sent_work_rel_dict_sentence_id[other_worker_id] |
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for relation in wv_sentence_id: |
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sent_work_rel_dict_sentence_id_other_worker_id_relation = sent_work_rel_dict_sentence_id_other_worker_id[relation] |
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wv_sentence_id_relation = wv_sentence_id[relation] |
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numerator += rqs[relation] * (wv_sentence_id_relation * sent_work_rel_dict_sentence_id_other_worker_id_relation) |
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denominator_w += rqs[relation] * (wv_sentence_id_relation * wv_sentence_id_relation) |
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denominator_ow += rqs[relation] * (sent_work_rel_dict_sentence_id_other_worker_id_relation * |
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sent_work_rel_dict_sentence_id_other_worker_id_relation) |
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weighted_cosine = numerator / math.sqrt(denominator_w * denominator_ow) |
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# pdb.set_trace() |
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wwa_numerator += weighted_cosine * wqs[other_worker_id] * sqs[sentence_id] |
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wwa_denominator += wqs[other_worker_id] * sqs[sentence_id] |
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if wwa_denominator < SMALL_NUMBER_CONST: |
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wwa_denominator = SMALL_NUMBER_CONST |
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return wwa_numerator / wwa_denominator |
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# Sentence - Relation Score |
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@staticmethod |
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def sentence_relation_score(sentence_id, relation, sent_work_rel_dict, wqs): |
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srs_numerator = 0.0 |
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srs_denominator = 0.0 |
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worker_ids = sent_work_rel_dict[sentence_id] |
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for worker_id in worker_ids: |
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srs_numerator += worker_ids[worker_id][relation] * wqs[worker_id] |
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srs_denominator += wqs[worker_id] |
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if srs_denominator < SMALL_NUMBER_CONST: |
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srs_denominator = SMALL_NUMBER_CONST |
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return srs_numerator / srs_denominator |
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# Relation Quality Score |
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@staticmethod |
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def relation_quality_score(relations, work_sent_rel_dict, sqs, wqs): |
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rqs_numerator = dict() |
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rqs_denominator = dict() |
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for relation in relations: |
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rqs_numerator[relation] = 0.0 |
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rqs_denominator[relation] = 0.0 |
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worker_ids = list(work_sent_rel_dict.keys()) |
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for worker_i, work_sent_rel_dict_worker_i in work_sent_rel_dict.items(): |
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#work_sent_rel_dict_worker_i = work_sent_rel_dict[worker_i] |
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work_sent_rel_dict_i_keys = list(work_sent_rel_dict_worker_i.keys()) |
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for worker_j, work_sent_rel_dict_worker_j in work_sent_rel_dict.items(): |
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#work_sent_rel_dict_worker_j = work_sent_rel_dict[worker_j] |
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work_sent_rel_dict_j_keys = list(work_sent_rel_dict_worker_j.keys()) |
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#print worker_i, worker_j,np.intersect1d(np.array(work_sent_rel_dict[worker_i].keys()),np.array(work_sent_rel_dict[worker_j].keys())) |
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if worker_i != worker_j and len(np.intersect1d(np.array(work_sent_rel_dict_i_keys),np.array(work_sent_rel_dict_j_keys))) > 0: |
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for relation in relations: |
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numerator = 0.0 |
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denominator = 0.0 |
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for sentence_id, work_sent_rel_dict_worker_i_sent in work_sent_rel_dict_worker_i.items(): |
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if sentence_id in work_sent_rel_dict_worker_j: |
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#work_sent_rel_dict_worker_i_sent = work_sent_rel_dict_worker_i[sentence_id] |
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work_sent_rel_dict_worker_j_sent = work_sent_rel_dict_worker_j[sentence_id] |
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work_sent_rel_dict_worker_j_sent_rel = work_sent_rel_dict_worker_j_sent[relation] |
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#print worker_i,worker_j,sentence_id,relation |
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numerator += sqs[sentence_id] * (work_sent_rel_dict_worker_i_sent[relation] * |
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work_sent_rel_dict_worker_j_sent_rel) |
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denominator += sqs[sentence_id] * work_sent_rel_dict_worker_j_sent_rel |
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if denominator > 0: |
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rqs_numerator[relation] += wqs[worker_i] * wqs[worker_j] * numerator / denominator |
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rqs_denominator[relation] += wqs[worker_i] * wqs[worker_j] |
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rqs = dict() |
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for relation in relations: |
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if rqs_denominator[relation] > SMALL_NUMBER_CONST: |
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rqs[relation] = rqs_numerator[relation] / rqs_denominator[relation] |
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# prevent division by zero by storing very small value instead |
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if rqs[relation] < SMALL_NUMBER_CONST: |
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rqs[relation] = SMALL_NUMBER_CONST |
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else: |
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rqs[relation] = SMALL_NUMBER_CONST |
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return rqs |
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@staticmethod |
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def run(results, config, max_delta = 0.001): |
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judgments = results['judgments'].copy() |
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units = results['units'].copy() |
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#sent_work_rel_dict, work_sent_rel_dict, sent_rel_dict |
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# TODO: change to use all vectors in one unit |
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col = list(config.output.values())[0] |
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sent_rel_dict = dict(units.copy()[col]) |
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def expandedVector(worker, unit): |
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#print worker, unit |
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vector = Counter() |
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for rel in unit: |
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if rel in worker: |
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vector[rel] = worker[rel] |
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else: |
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vector[rel] = 0 |
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return vector |
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# fill judgment vectors with unit keys |
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for index,row in judgments.iterrows(): |
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# judgments.set_value(index, col, expandedVector(row[col], units.at[row['unit'], col])) |
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judgments.at[index, col] = expandedVector(row[col], units.at[row['unit'], col]) |
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256
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#print judgments.head() |
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sent_work_rel_dict = judgments[['unit','worker',col]].copy().groupby('unit') |
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sent_work_rel_dict = {name : group.set_index('worker')[col].to_dict() for name, group in sent_work_rel_dict} |
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#print sent_work_rel_dict |
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work_sent_rel_dict = judgments[['worker','unit',col]].copy().groupby('worker') |
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work_sent_rel_dict = {name : group.set_index('unit')[col].to_dict() for name, group in work_sent_rel_dict} |
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# print [i for i in list(sent_work_rel_dict)] |
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# sent_work_rel_dict = {k : dict(sent_work_rel_dict[k]) for k in sent_work_rel_dict} |
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268
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#pprint(work_sent_rel_dict) |
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#initialize data structures |
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sqs_list = list() |
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wqs_list = list() |
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wwa_list = list() |
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wsa_list = list() |
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rqs_list = list() |
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1 |
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sqs = dict((sentence_id, 1.0) for sentence_id in sent_work_rel_dict) |
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278
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1 |
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wqs = dict((worker_id, 1.0) for worker_id in work_sent_rel_dict) |
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279
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1 |
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wwa = dict((worker_id, 1.0) for worker_id in work_sent_rel_dict) |
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280
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1 |
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wsa = dict((worker_id, 1.0) for worker_id in work_sent_rel_dict) |
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281
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282
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1 |
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sqs_list.append(sqs.copy()) |
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283
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1 |
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wqs_list.append(wqs.copy()) |
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284
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1 |
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wwa_list.append(wwa.copy()) |
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285
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1 |
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wsa_list.append(wsa.copy()) |
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286
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287
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# initialize RQS depending on whether or not it is an open ended task |
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288
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1 |
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rqs = dict() |
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289
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1 |
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if not config.open_ended_task: |
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290
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1 |
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rqs_keys = list(sent_rel_dict[list(sent_rel_dict.keys())[0]].keys()) |
|
291
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1 |
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for relation in rqs_keys: |
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292
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1 |
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rqs[relation] = 1.0 |
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293
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else: |
|
294
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1 |
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for sentence_id in sent_rel_dict: |
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295
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1 |
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for relation in sent_rel_dict[sentence_id]: |
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296
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1 |
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rqs[relation] = 1.0 |
|
297
|
1 |
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rqs_list.append(rqs.copy()) |
|
298
|
|
|
|
|
299
|
1 |
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sqs_len = len(list(sqs.keys())) * 1.0 |
|
300
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1 |
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wqs_len = len(list(wqs.keys())) * 1.0 |
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301
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1 |
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rqs_len = len(list(rqs.keys())) * 1.0 |
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302
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303
|
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# compute metrics until stable values |
|
304
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1 |
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iterations = 0 |
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305
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1 |
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while max_delta >= 0.001: |
|
306
|
1 |
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sqs_new = dict() |
|
307
|
1 |
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wqs_new = dict() |
|
308
|
1 |
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wwa_new = dict() |
|
309
|
1 |
|
wsa_new = dict() |
|
310
|
|
|
|
|
311
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1 |
|
avg_sqs_delta = 0.0 |
|
312
|
1 |
|
avg_wqs_delta = 0.0 |
|
313
|
1 |
|
avg_rqs_delta = 0.0 |
|
314
|
1 |
|
max_delta = 0.0 |
|
315
|
|
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|
|
316
|
|
|
# pdb.set_trace() |
|
317
|
|
|
|
|
318
|
1 |
|
if not config.open_ended_task: |
|
319
|
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# compute relation quality score (RQS) |
|
320
|
1 |
|
rqs_new = Metrics.relation_quality_score(list(rqs.keys()), work_sent_rel_dict, |
|
321
|
|
|
sqs_list[len(sqs_list) - 1], |
|
322
|
|
|
wqs_list[len(wqs_list) - 1]) |
|
323
|
1 |
|
for relation, _ in rqs_new.items(): |
|
324
|
1 |
|
max_delta = max(max_delta, abs(rqs_new[relation] - rqs_list[len(rqs_list) - 1][relation])) |
|
325
|
1 |
|
avg_rqs_delta += abs(rqs_new[relation] - rqs_list[len(rqs_list) - 1][relation]) |
|
326
|
1 |
|
avg_rqs_delta /= rqs_len |
|
327
|
|
|
|
|
328
|
|
|
# compute sentence quality score (SQS) |
|
329
|
1 |
|
for sentence_id, _ in sent_work_rel_dict.items(): |
|
330
|
1 |
|
sqs_new[sentence_id] = Metrics.sentence_quality_score(sentence_id, sent_work_rel_dict, |
|
331
|
|
|
wqs_list[len(wqs_list) - 1], |
|
332
|
|
|
rqs_list[len(rqs_list) - 1]) |
|
333
|
1 |
|
max_delta = max(max_delta, abs(sqs_new[sentence_id] - sqs_list[len(sqs_list) - 1][sentence_id])) |
|
334
|
1 |
|
avg_sqs_delta += abs(sqs_new[sentence_id] - sqs_list[len(sqs_list) - 1][sentence_id]) |
|
335
|
1 |
|
avg_sqs_delta /= sqs_len |
|
336
|
|
|
|
|
337
|
|
|
# compute worker quality score (WQS) |
|
338
|
1 |
|
for worker_id, _ in work_sent_rel_dict.items(): |
|
339
|
1 |
|
wwa_new[worker_id] = Metrics.worker_worker_agreement( |
|
340
|
|
|
worker_id, work_sent_rel_dict, |
|
341
|
|
|
sent_work_rel_dict, |
|
342
|
|
|
wqs_list[len(wqs_list) - 1], |
|
343
|
|
|
sqs_list[len(sqs_list) - 1], |
|
344
|
|
|
rqs_list[len(rqs_list) - 1]) |
|
345
|
1 |
|
wsa_new[worker_id] = Metrics.worker_sentence_agreement( |
|
346
|
|
|
worker_id, |
|
347
|
|
|
sent_rel_dict, |
|
348
|
|
|
work_sent_rel_dict, |
|
349
|
|
|
sqs_list[len(sqs_list) - 1], |
|
350
|
|
|
rqs_list[len(rqs_list) - 1], |
|
351
|
|
|
wqs_list[len(rqs_list) - 1][worker_id]) |
|
352
|
1 |
|
wqs_new[worker_id] = wwa_new[worker_id] * wsa_new[worker_id] |
|
353
|
1 |
|
max_delta = max( |
|
354
|
|
|
max_delta, |
|
355
|
|
|
abs(wqs_new[worker_id] - wqs_list[len(wqs_list) - 1][worker_id])) |
|
356
|
1 |
|
avg_wqs_delta += abs(wqs_new[worker_id] - wqs_list[len(wqs_list) - 1][worker_id]) |
|
357
|
1 |
|
avg_wqs_delta /= wqs_len |
|
358
|
|
|
|
|
359
|
|
|
# save results for current iteration |
|
360
|
1 |
|
sqs_list.append(sqs_new.copy()) |
|
361
|
1 |
|
wqs_list.append(wqs_new.copy()) |
|
362
|
1 |
|
wwa_list.append(wwa_new.copy()) |
|
363
|
1 |
|
wsa_list.append(wsa_new.copy()) |
|
364
|
1 |
|
if not config.open_ended_task: |
|
365
|
1 |
|
rqs_list.append(rqs_new.copy()) |
|
|
|
|
|
|
366
|
1 |
|
iterations += 1 |
|
367
|
|
|
|
|
368
|
|
|
# reconstruct sent_rel_dict with worker scores |
|
369
|
1 |
|
new_sent_rel_dict = dict() |
|
370
|
1 |
|
for sent_id, rel_dict in sent_rel_dict.items(): |
|
371
|
1 |
|
new_sent_rel_dict[sent_id] = dict() |
|
372
|
1 |
|
for relation, _ in rel_dict.items(): |
|
373
|
1 |
|
new_sent_rel_dict[sent_id][relation] = 0.0 |
|
374
|
1 |
|
for work_id, srd in work_sent_rel_dict.items(): |
|
375
|
1 |
|
wqs_work_id = wqs_new[work_id] |
|
376
|
1 |
|
for sent_id, rel_dict in srd.items(): |
|
377
|
1 |
|
for relation, score in rel_dict.items(): |
|
378
|
1 |
|
new_sent_rel_dict[sent_id][relation] += score * wqs_work_id |
|
379
|
|
|
# pdb.set_trace() |
|
380
|
1 |
|
sent_rel_dict = new_sent_rel_dict |
|
381
|
|
|
|
|
382
|
1 |
|
logging.info(str(iterations) + " iterations; max d= " + str(max_delta) + " ; wqs d= " + str(avg_wqs_delta) + "; sqs d= " + str(avg_sqs_delta) + "; rqs d= " + str(avg_rqs_delta)) |
|
383
|
|
|
|
|
384
|
|
|
#if iterations == 1: |
|
385
|
|
|
# break |
|
386
|
|
|
#pprint(sqs_list) |
|
387
|
|
|
#pprint(wqs_list) |
|
388
|
|
|
#pprint(rqs_list) |
|
389
|
|
|
|
|
390
|
1 |
|
srs = Counter() |
|
391
|
1 |
|
for sentence_id in sent_rel_dict: |
|
392
|
1 |
|
srs[sentence_id] = Counter() |
|
393
|
1 |
|
for relation in sent_rel_dict[sentence_id]: |
|
394
|
1 |
|
srs[sentence_id][relation] = Metrics.sentence_relation_score(sentence_id, relation, sent_work_rel_dict, wqs_list[len(wqs_list) - 1]) |
|
395
|
|
|
|
|
396
|
1 |
|
srs_initial = Counter() |
|
397
|
1 |
|
for sentence_id in sent_rel_dict: |
|
398
|
1 |
|
srs_initial[sentence_id] = Counter() |
|
399
|
1 |
|
for relation in sent_rel_dict[sentence_id]: |
|
400
|
1 |
|
srs_initial[sentence_id][relation] = Metrics.sentence_relation_score(sentence_id, relation, sent_work_rel_dict, wqs_list[0]) |
|
401
|
|
|
|
|
402
|
1 |
|
results['units']['uqs'] = pd.Series(sqs_list[-1]) |
|
403
|
1 |
|
results['units']['unit_annotation_score'] = pd.Series(srs) |
|
404
|
1 |
|
results['workers']['wqs'] = pd.Series(wqs_list[-1]) |
|
405
|
1 |
|
results['workers']['wwa'] = pd.Series(wwa_list[-1]) |
|
406
|
1 |
|
results['workers']['wsa'] = pd.Series(wsa_list[-1]) |
|
407
|
1 |
|
if not config.open_ended_task: |
|
408
|
1 |
|
results['annotations']['aqs'] = pd.Series(rqs_list[-1]) |
|
409
|
|
|
|
|
410
|
1 |
|
results['units']['uqs_initial'] = pd.Series(sqs_list[1]) |
|
411
|
1 |
|
results['units']['unit_annotation_score_initial'] = pd.Series(srs_initial) |
|
412
|
1 |
|
results['workers']['wqs_initial'] = pd.Series(wqs_list[1]) |
|
413
|
1 |
|
results['workers']['wwa_initial'] = pd.Series(wwa_list[1]) |
|
414
|
1 |
|
results['workers']['wsa_initial'] = pd.Series(wsa_list[1]) |
|
415
|
1 |
|
if not config.open_ended_task: |
|
416
|
1 |
|
results['annotations']['aqs_initial'] = pd.Series(rqs_list[1]) |
|
417
|
|
|
return results |
|
418
|
|
|
|
CrowdTruth /
CrowdTruth-core
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