From 29dabecb9edb243a92ae53321483d3f6173e9ddc Mon Sep 17 00:00:00 2001 From: Anne Lorenz Date: Fri, 23 Nov 2018 12:09:48 +0100 Subject: [PATCH] some updates --- BagOfWords.py | 2 +- DecisionTree.py | 2 +- FileHandler.py | 38 +- NER.py | 46 +- obj/dict_200_most_common_words.pkl | Bin 0 -> 3388 bytes obj/dict_200_most_common_words_stemmed.pkl | Bin 0 -> 3388 bytes obj/dict_organizations.pkl | Bin 412609 -> 368344 bytes obj/num_mentions_companies.pkl | Bin 20034 -> 20034 bytes scheduler.py | 16 - .../10_most_common_words_2018-11-06.eps | 1392 ++++ .../10_most_common_words_2018-11-06.png | Bin 0 -> 19757 bytes ...0_most_common_words_2018-11-06_breiter.png | Bin 0 -> 20865 bytes visualization/NER_2018-11-06.eps | 5200 +++++++++++++++ visualization/NER_2018-11-06.png | Bin 0 -> 23539 bytes visualization/NER_2_2018-11-05.eps | 1715 +++++ visualization/NER_2_2018-11-05.png | Bin 0 -> 22437 bytes visualization/NER_2_2018-11-06.eps | 2265 +++++++ visualization/NER_2_2018-11-06.png | Bin 0 -> 24097 bytes visualization/WordCloud_2018-11-05.eps | 5821 +++++++++++++++++ visualization/WordCloud_2018-11-05.png | Bin 0 -> 184004 bytes visualization/WordCloud_2018-11-07.eps | 5821 +++++++++++++++++ visualization/WordCloud_2018-11-07.png | Bin 0 -> 177116 bytes 22 files changed, 22277 insertions(+), 41 deletions(-) create mode 100644 obj/dict_200_most_common_words.pkl create mode 100644 obj/dict_200_most_common_words_stemmed.pkl delete mode 100644 scheduler.py create mode 100644 visualization/10_most_common_words_2018-11-06.eps create mode 100644 visualization/10_most_common_words_2018-11-06.png create mode 100644 visualization/10_most_common_words_2018-11-06_breiter.png create mode 100644 visualization/NER_2018-11-06.eps create mode 100644 visualization/NER_2018-11-06.png create mode 100644 visualization/NER_2_2018-11-05.eps create mode 100644 visualization/NER_2_2018-11-05.png create mode 100644 visualization/NER_2_2018-11-06.eps create mode 100644 visualization/NER_2_2018-11-06.png create mode 100644 visualization/WordCloud_2018-11-05.eps create mode 100644 visualization/WordCloud_2018-11-05.png create mode 100644 visualization/WordCloud_2018-11-07.eps create mode 100644 visualization/WordCloud_2018-11-07.png diff --git a/BagOfWords.py b/BagOfWords.py index 6273690..53e88c1 100644 --- a/BagOfWords.py +++ b/BagOfWords.py @@ -13,12 +13,12 @@ also work => considered by 'rel_freq'(relative word frequencies) as parameter. ''' from collections import OrderedDict import csv +import pickle import re import numpy as np import pandas as pd from nltk.stem.porter import PorterStemmer -import pickle class BagOfWords: diff --git a/DecisionTree.py b/DecisionTree.py index 5343679..66f7a4b 100644 --- a/DecisionTree.py +++ b/DecisionTree.py @@ -19,7 +19,7 @@ import graphviz import numpy as np import pandas as pd from sklearn import tree -# from sklearn.feature_extraction.text import CountVectorizer +from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_selection import SelectPercentile from sklearn.metrics import f1_score from sklearn.model_selection import StratifiedKFold diff --git a/FileHandler.py b/FileHandler.py index aa5b1e7..bfc1864 100644 --- a/FileHandler.py +++ b/FileHandler.py @@ -110,6 +110,42 @@ class FileHandler: quoting=csv.QUOTE_NONNUMERIC, quotechar='\'') + def remove_duplicates(): + '''remove articles with exactly same headline + ''' + # read data set + file = 'data\\cleaned_data_set_without_header.csv' + df = pd.read_csv(file, + delimiter='|', + header=None, + index_col=None, + engine='python', + #usecols=[1,2], + #nrows=100, + quoting=csv.QUOTE_NONNUMERIC, + quotechar='\'') + print(type(df)) + + # for each article(row) + i = 1 + while i < len(df): + # check if already in column + if df.iloc[i][1] in df.iloc[0:(i-1)][1]: + # remove duplicate + del df.iloc[i] + else: + i += 1 + + # save cleaned dataframe + df.to_csv('data\\cleaned_data_set_without_header.csv', + header=False, + index=False, + sep='|', + mode='a', + encoding='utf-8', + quoting=csv.QUOTE_NONNUMERIC, + quotechar='\'') + def write_articles_to_csv_files(): '''read JSON files, select articles and write them to csv. ''' @@ -197,4 +233,4 @@ class FileHandler: if __name__ == '__main__': # FileHandler.write_articles_to_csv_files() # FileHandler.create_labeling_dataset() - FileHandler.clean_articles() \ No newline at end of file + FileHandler.remove_duplicates() \ No newline at end of file diff --git a/NER.py b/NER.py index 0658be3..4fd5e3c 100644 --- a/NER.py +++ b/NER.py @@ -8,23 +8,24 @@ like persons, organizations and countries, e.g. from collections import OrderedDict import csv import os +import pickle +import re import numpy as np import pandas as pd from nltk.tag import StanfordNERTagger from nltk.tokenize import word_tokenize -import pickle -import re class NER: # common company abbreviations to be stripped company_abbrevs = ['Inc.', 'Inc', 'Corp', '& Co', 'Co', 'Ltd.', 'Ltd', - 'AG', 'LP', 'Limited', 'Tbk', 'Group', 'Co.', 'Groups' + 'AG', 'LP', 'Limited', 'Tbk', 'Group', 'Co.', 'Groups', 'LLC', 'LBO', 'IPO', 'HQ', 'CIO', 'NGO', 'AB', 'Plc', 's.r.l.', 'Holding', 'Holdings', 'GmbH', 'plc', 'Incs', - 'Plcs', 'PLC', 'Ltds', 'SA', 'Incs', 'S.A.R.L', 'LLC' - 'Company', '& Co.', 'Corporation', 'Pte', 'Pty', 'LLP'] + 'Plcs', 'PLC', 'Ltds', 'SA', 'Incs', 'S.A.R.L', 'LLC', + 'Company', '& Co.', 'Corporation', 'Pte', 'Pty', 'LLP', + 'Corp.'] # organizations that are no companies regex = r'.*Reuters.*|.*Ministry.*|.*Trump.*|.*Commission.*|.*BRIEF.*|\ @@ -153,20 +154,21 @@ class NER: print(n_dict) if __name__ == '__main__': - print('# starting NER...') - print() - # read data set - file = 'data\\cleaned_data_set_without_header.csv' - df = pd.read_csv(file, - delimiter='|', - header=None, - index_col=None, - engine='python', - # usecols=[1,2], - # nrows=100, - quoting=csv.QUOTE_NONNUMERIC, - quotechar='\'') - #print(df) - texts = df[1] + '. ' + df[2] - NER.count_companies(texts) - # NER.show_most_common_companies() \ No newline at end of file + # print('# starting NER...') + # print() + # # read data set + # file = 'data\\cleaned_data_set_without_header.csv' + # df = pd.read_csv(file, + # delimiter='|', + # header=None, + # index_col=None, + # engine='python', + # # usecols=[1,2], + # # nrows=100, + # quoting=csv.QUOTE_NONNUMERIC, + # quotechar='\'') + # #print(df) + # texts = df[1] + '. 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visualization\10_most_common_words_2018-11-06.eps +%%Creator: matplotlib version 2.1.2, http://matplotlib.org/ +%%CreationDate: Tue Nov 6 09:49:02 2018 +%%Orientation: portrait +%%BoundingBox: 75 223 536 568 +%%EndComments +%%BeginProlog +/mpldict 8 dict def +mpldict begin +/m { moveto } bind def +/l { lineto } bind def +/r { rlineto } bind def +/c { curveto } bind def +/cl { closepath } bind def +/box { +m +1 index 0 r +0 exch r +neg 0 r +cl +} bind def +/clipbox { +box +clip +newpath +} bind def +%!PS-Adobe-3.0 Resource-Font +%%Title: DejaVu Sans +%%Copyright: Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. DejaVu changes are in public domain +%%Creator: Converted from TrueType to type 3 by PPR +25 dict begin +/_d{bind def}bind def +/_m{moveto}_d +/_l{lineto}_d +/_cl{closepath eofill}_d +/_c{curveto}_d +/_sc{7 -1 roll{setcachedevice}{pop pop pop pop pop pop}ifelse}_d +/_e{exec}_d +/FontName /DejaVuSans def +/PaintType 0 def +/FontMatrix[.001 0 0 .001 0 0]def +/FontBBox[-1021 -463 1793 1232]def +/FontType 3 def +/Encoding [ /space /period /zero /one /two /four /six /eight /R /a /b /c /d /e /f /i /k /l /m /n /o /p /q /r /s /t /u /v /w /y ] def +/FontInfo 10 dict dup begin +/FamilyName (DejaVu Sans) def +/FullName (DejaVu Sans) def +/Notice (Copyright (c) 2003 by Bitstream, Inc. All Rights Reserved. Copyright (c) 2006 by Tavmjong Bah. All Rights Reserved. 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