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from datasets import load_dataset, interleave_datasets
from modules.module_segmentedWordCloud import SegmentedWordCloud
from modules.module_customSubsetsLabel import CustomSubsetsLabel
from random import sample as random_sample
from typing import Tuple, List, Dict
import re
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
class Word2Context:
def __init__(
self,
context_ds_name: str,
vocabulary # Vocabulary class instance
) -> None:
self.context_ds_name = context_ds_name
# Vocabulary class
self.vocab = vocabulary
# Custom Label component
self.Label = CustomSubsetsLabel()
def errorChecking(
self,
word: str
) -> str:
out_msj = ""
if not word:
out_msj = "Error: Primero debe ingresar una palabra!"
else:
if word not in self.vocab:
out_msj = f"Error: La palabra '<b>{word}</b>' no se encuentra en el vocabulario!"
return out_msj
def genWebLink(
self,
text: str
) -> str:
text = text.replace("\"", "'")
text = text.replace("<u><b>", "")
text = text.replace("</b></u>", "")
url = "https://www.google.com.tr/search?q={}".format(text)
return '<a href="{}" rel="noopener noreferrer" target="_blank"><center>🌐🔍</center></a>'.format(url)
def genWordCloudPlot(
self,
word: str,
figsize: Tuple[int,int]=(9,3)
) -> plt.Figure:
freq_dic, l_group, g_group = self.vocab.getWordNeighbors(word, n_neighbors=10)
wc = SegmentedWordCloud(freq_dic, l_group, g_group)
return wc.plot(figsize)
def genDistributionPlot(
self,
word: str,
figsize: Tuple[int,int]=(6,1)
) -> plt.Figure:
x_values, y_values = self.vocab.distribution()
w_percentile = self.vocab.getPercentile(word)
w_freq = self.vocab.getFreq(word)
fig, ax = plt.subplots(figsize=figsize)
ax.plot(x_values, y_values, color='green')
ax.fill_between(x_values, y_values, color='lightgreen',)
ax.axvline(x=max(0,w_percentile-.01),
color='blue',
linewidth=7,
alpha=.1,
linestyle='-'
)
ax.axvline(x=min(100,w_percentile+.01),
color='black',
linewidth=7,
alpha=.1,
linestyle='-'
)
ax.axvline(x=w_percentile,
color='#d35400',
linewidth=2,
linestyle='--',
label=f'{w_freq}\n(frecuencia total)'
)
ax.axis('off')
plt.legend(loc='upper left', prop={'size': 7})
return fig
def findSplits(
self,
word: str,
subsets_list: List[str]
):
w_splits = self.vocab.getSplits(word)
splits_list = []
for subset in subsets_list:
current_split_list = []
for s in w_splits:
if (subset == s.split("_")[0]):
current_split_list.append(s)
if current_split_list:
splits_list.append(current_split_list)
splits_list = [random_sample(s_list, 1)[0] for s_list in splits_list]
ds_list = [
load_dataset(path=self.context_ds_name, name=split, streaming=True, split='all')
for split in splits_list
]
datasets = ds_list[0]
if len(ds_list) > 1:
datasets = interleave_datasets(ds_list, probabilities=None)
return datasets
def findContexts(
self,
sample: str,
word: str
) -> Dict[str,str]:
sample = sample['text'].strip()
context = ""
m = re.search(r'\b{}\b'.format(word), sample)
if m:
init = m.span()[0]
end = init+len(word)
context = sample[:init]+"<u><b>"+word+"</b></u>"+sample[end:]
return {'context':context}
def getSubsetsInfo(
self,
word: str
) -> Tuple:
total_freq = self.vocab.getFreq(word)
subsets_name_list = list(self.vocab.getSubsets(word).keys())
subsets_freq_list = list(self.vocab.getSubsets(word).values())
# Create subset frequency dict to subset_freq component
subsets_info = {
s_name + f" ({s_freq})": s_freq/total_freq
for s_name, s_freq in zip(subsets_name_list, subsets_freq_list)
}
subsets_origin_info = dict(sorted(subsets_info.items(), key=lambda x: x[1], reverse=True))
subsets_info = self.Label.compute(subsets_origin_info)
return subsets_info, subsets_origin_info
def getContexts(
self,
word: str,
n_context: int,
ds
) -> List[Tuple]:
ds_w_contexts = ds.map(lambda sample: self.findContexts(sample, word))
only_contexts = ds_w_contexts.filter(lambda sample: sample['context'] != "")
shuffle_contexts = only_contexts.shuffle(buffer_size=10)
list_of_dict = list(shuffle_contexts.take(n_context))
list_of_contexts = [
(i, dic['context'], dic['subset'])
for i,dic in enumerate(list_of_dict)
]
return list_of_contexts |