/
interactions.py
174 lines (148 loc) · 8.7 KB
/
interactions.py
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import numpy as np
import pandas as pd
import matchzoo
import collections
from tqdm import tqdm
from handlers.output_handler import FileHandler
class MatchInteraction(object):
def __init__(self, data_pack: matchzoo.DataPack, **kargs):
FileHandler.myprint("Converting DataFrame to Normal Dictionary of Data")
self.unique_query_ids, \
self.dict_query_contents, \
self.dict_query_lengths, \
self.dict_query_raw_contents, \
self.dict_query_positions = self.convert_leftright(data_pack.left, text_key = "text_left",
length_text_key = "length_left",
raw_text_key = "raw_text_left")
self.data_pack = data_pack
assert len(self.unique_query_ids) == len(set(self.unique_query_ids)), "Must be unique ids"
self.unique_doc_ids, \
self.dict_doc_contents, \
self.dict_doc_lengths, \
self.dict_doc_raw_contents, \
self.dict_doc_positions = self.convert_leftright(data_pack.right, text_key = "text_right",
length_text_key = "length_right",
raw_text_key = "raw_text_right")
assert len(self.unique_doc_ids) == len(set(self.unique_doc_ids)), "Must be unique ids for doc ids"
assert len(self.unique_query_ids) != len(self.unique_doc_ids)
self.pos_queries, \
self.pos_docs, \
self.negatives, \
self.unique_queries_test = self.convert_relations(data_pack.relation)
# for queries, padded
self.np_query_contents = np.array([self.dict_query_contents[q] for q in self.pos_queries])
self.np_query_lengths = np.array([self.dict_query_lengths[q] for q in self.pos_queries])
self.query_positions = np.array([self.dict_query_positions[q] for q in self.pos_queries])
# for docs, padded
self.np_doc_contents = np.array([self.dict_doc_contents[d] for d in self.pos_docs])
self.np_doc_lengths = np.array([self.dict_doc_lengths[d] for d in self.pos_docs])
self.doc_positions = np.array([self.dict_doc_positions[d] for d in self.pos_docs])
assert self.np_query_lengths.shape == self.np_doc_lengths.shape
self.padded_doc_length = len(self.np_doc_contents[0])
self.padded_query_length = len(self.np_query_contents[0])
def convert_leftright(self, part: pd.DataFrame, text_key: str, length_text_key: str, raw_text_key: str, **kargs):
""" Converting the dataframe of interactions """
ids, contents_dict, lengths_dict, position_dict = [], {}, {}, {}
raw_content_dict = {}
FileHandler.myprint("[NOTICE] MatchZoo use queryID and docID as index in dataframe left and right, "
"therefore, iterrows will return index which is left_id or right_id")
for index, row in part.iterrows():
ids.append(index)
text_ = row[text_key] # text_ here is converted to numbers and padded
raw_content_dict[index] = row[raw_text_key]
if length_text_key not in row: length_ = len(text_)
else: length_ = row[length_text_key]
assert length_ != 0
assert index not in contents_dict
contents_dict[index] = text_
lengths_dict[index] = length_
position_dict[index] = np.pad(np.arange(length_) + 1, (0, len(text_) - length_), 'constant')
return np.array(ids), contents_dict, lengths_dict, raw_content_dict, position_dict
def convert_relations(self, relation: pd.DataFrame):
""" Convert relations.
Retrieving positive interactions and negative interactions. Particularly,
for every pair (query, doc) = 1, we get a list of negatives of the query q
It is possible that a query may have multiple positive docs. Therefore, negatives[q]
may vary the lengths but not too much.
"""
queries, docs, negatives = [], [], collections.defaultdict(list)
unique_queries = collections.defaultdict(list)
for index, row in relation.iterrows():
query = row["id_left"]
doc = row["id_right"]
label = row["label"]
assert label == 0 or label == 1
unique_queries[query] = unique_queries.get(query, [[], [], [], []]) # doc, label, content, length
a, b, c, d = unique_queries[query]
a.append(doc)
b.append(label)
c.append(self.dict_doc_contents[doc])
d.append(self.dict_doc_lengths[doc])
if label == 1:
queries.append(query)
docs.append(doc)
elif label == 0:
negatives[query].append(doc)
assert len(queries) == len(docs)
return np.array(queries), np.array(docs), negatives, unique_queries
class MatchInteractionVisual(MatchInteraction):
"""Adding visual information. format is same as usual but now I loaded images as visual components"""
def __init__(self, data_pack: matchzoo.DataPack):
super(MatchInteraction, self).__init__()
FileHandler.myprint("Converting DataFrame to Normal Dictionary of Data")
self.unique_query_ids, \
self.dict_query_contents, \
self.dict_query_lengths, \
self.dict_query_raw_contents, \
self.dict_query_positions, \
self.dict_query_imgages = self.convert_leftright(data_pack.left, text_key = "text_left",
length_text_key = "length_left",
raw_text_key = "raw_text_left", images_key = "images_left")
self.data_pack = data_pack
assert len(self.unique_query_ids) == len(set(self.unique_query_ids)), "Must be unique ids"
self.unique_doc_ids, \
self.dict_doc_contents, \
self.dict_doc_lengths, \
self.dict_doc_raw_contents, \
self.dict_doc_positions, \
self.dict_doc_imgages = self.convert_leftright(data_pack.right, text_key = "text_right",
length_text_key = "length_right",
raw_text_key = "raw_text_right", images_key="images_right")
assert len(self.unique_doc_ids) == len(set(self.unique_doc_ids)), "Must be unique ids for doc ids"
assert len(self.unique_query_ids) != len(self.unique_doc_ids)
self.pos_queries, \
self.pos_docs, \
self.negatives, \
self.unique_queries_test = self.convert_relations(data_pack.relation)
# for queries, padded
self.np_query_contents = np.array([self.dict_query_contents[q] for q in self.pos_queries])
self.np_query_lengths = np.array([self.dict_query_lengths[q] for q in self.pos_queries])
self.query_positions = np.array([self.dict_query_positions[q] for q in self.pos_queries])
self.query_images = np.array([self.dict_query_imgages[q] for q in self.pos_queries])
# for docs, padded
self.np_doc_contents = np.array([self.dict_doc_contents[d] for d in self.pos_docs])
self.np_doc_lengths = np.array([self.dict_doc_lengths[d] for d in self.pos_docs])
self.doc_positions = np.array([self.dict_doc_positions[d] for d in self.pos_docs])
self.doc_images = np.array([self.dict_doc_imgages[d] for d in self.pos_docs])
assert self.np_query_lengths.shape == self.np_doc_lengths.shape
self.padded_doc_length = len(self.np_doc_contents[0])
self.padded_query_length = len(self.np_query_contents[0])
self.padded_doc_images_len = len(self.doc_images[0])
def convert_leftright(self, part: pd.DataFrame, text_key: str, length_text_key: str, raw_text_key: str, **kargs):
""" Converting the dataframe of interactions """
images_key = kargs["images_key"]
ids, contents_dict, lengths_dict, position_dict, imgages_dict = [], {}, {}, {}, {}
raw_content_dict = {}
for index, row in part.iterrows():
ids.append(index)
text_ = row[text_key] # text_ here is converted to numbers and padded
raw_content_dict[index] = row[raw_text_key]
if length_text_key not in row: length_ = len(text_)
else: length_ = row[length_text_key]
assert length_ != 0
assert index not in contents_dict
contents_dict[index] = text_
lengths_dict[index] = length_
position_dict[index] = np.pad(np.arange(length_) + 1, (0, len(text_) - length_), 'constant')
imgages_dict[index] = row[images_key] # added images
return np.array(ids), contents_dict, lengths_dict, raw_content_dict, position_dict, imgages_dict