## # News Categorization using Multinomial Naive Bayes ## ### Filename ### "News_Categorization_MNB.ipynb" ### cell_ids ### 0 ## Author ## ## Datasets ## ### description ### "" ### links ### "" ### cell_ids ### [] ## References ## ### source ### ``` ``` https://www.linkedin.com/in/andres-soto-villaverde-36198a5/ https://www.kaggle.com/uciml/news-aggregator-dataset http://archive.ics.uci.edu/ml http://archive.ics.uci.edu/ml/datasets/News+Aggregator http://pandas.pydata.org/ http://ipython.readthedocs.io/en/stable/interactive/magics.html# https://ipython.org/ipython-doc/3/interactive/magics.html https://docs.python.org/3/library/collections.html#counter-objects http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py http://scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html http://scikit-learn.org/stable/modules/feature_extraction.html#common-vectorizer-usage http://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting http://scikit-learn.org/stable/modules/naive_bayes.html#multinomial-naive-bayes http://scikit-learn.org/stable/modules/pipeline.html http://scikit-learn.org/stable/modules/classes.html https://en.wikipedia.org/wiki/Precision_and_recall http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html http://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html ### cell_ids ### [0,1,3,4,9,10,16,18,20,25,33,33,33,33,36,38,41,47] ## Libraries Used ## ### lib ### {"pandas":["import pandas as pd"],"numpy":["import numpy as np"],"matplotlib":["import matplotlib.pyplot as plt"],"sklearn":["from sklearn.utils import shuffle","from sklearn.feature_extraction.text import CountVectorizer","from sklearn.feature_extraction.text import TfidfTransformer","from sklearn.naive_bayes import MultinomialNB","from sklearn.pipeline import Pipeline","from sklearn import metrics"],"tensorflow":[],"pytorch":[],"OTHER":["from collections import Counter","import pylab as pl","import itertools"]} ### info ### {"numpy":{"description":"Library numerical computation and N-dimensional arrays, mostly used in preprocessing.","link":"https://pandas.pydata.org/docs/"},"pandas":{"description":"Library for data analysis and manipulation, mostly used in preprocessing to create dataframes.","link":"https://numpy.org/doc/1.19/"},"matplotlib":{"description":"Library to create visualizations of data, mostly used for graphing.","link":"https://matplotlib.org/contents.html"},"sklearn":{"description":"Machine learning framework, built on NumPy, mostly used for model training and evaluation.","link":"https://scikit-learn.org/stable/user_guide.html"},"tensorflow":{"description":"Machine learning framework based on tensors, mostly used for model training and evaluation.","link":"https://www.tensorflow.org/api_docs"},"pytorch":{"description":"Machine learning frameork based on tensors, mostly used for model trainng and evaluation.","link":"https://pytorch.org/docs/stable/index.html"},"OTHER":{"description":""}} ### cell_ids ### [14,14,20,14,22,22,27,35,35,35,35,35,35,35,35,45,45,45,45,35,45,45,45,45,45,45,45,45,14,20,35] ## Hyperparameters ## ### cell_ids ### [35] ### lineNumbers ### [74] ### source ### ``` from sklearn.naive_bayes import MultinomialNB ``` ### values ### "alpha,fit_prior" ### description ### {"from sklearn.naive_bayes import multinomialnb":"undefined 'alpha': {\r\n 'type': 'number',\r\n 'distribution':'loguniform',\r\n 'minimumForOptimizer': 1e-10,\r\n 'maximumForOptimizer': 1.0,\r\n 'default': 1.0,\r\n 'description': 'Additive (Laplace/Lidstone) smoothing parameter'},\r\n 'fit_prior': {\r\n 'type': 'boolean',\r\n 'default': True,\r\n 'description': 'Whether to learn class prior probabilities or not.'},\r\n"} ## Miscellaneous ## ### cell_ids ### [12,14,16,18] ### cells ### "[object Object][object Object][object Object][object Object]" ### lineNumbers ### [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19] ### source ### ``` #%matplotlib inline import pandas as pd titles = [] # list of news titles categories = [] # list of news categories labels = [] # list of different categories (without repetitions) nlabels = 4 # number of different categories lnews = [] # list of dictionaries with two fields: one for the news and # the other for its categorydef import_data(): global titles, labels, categories # importing news aggregator data via Pandas (Python Data Analysis Library) news = pd.read_csv("uci-news-aggregator.csv") # function 'head' shows the first 5 items in a column (or # the first 5 rows in the DataFrame) print(news.head()) categories = news['CATEGORY'] titles = news['TITLE'] labels = sorted(list(set(categories))) #%time import_data() ``` ### functions ### [] ### figures ### ### description ### "" ### outputs ### ID TITLE \ ,0 1 Fed official says weak data caused by weather,... ,1 2 Fed's Charles Plosser sees high bar for change... ,2 3 US open: Stocks fall after Fed official hints ... ,3 4 Fed risks falling 'behind the curve', Charles ... ,4 5 Fed's Plosser: Nasty Weather Has Curbed Job Gr... , , URL PUBLISHER \ ,0 http://www.latimes.com/business/money/la-fi-mo... Los Angeles Times ,1 http://www.livemint.com/Politics/H2EvwJSK2VE6O... Livemint ,2 http://www.ifamagazine.com/news/us-open-stocks... IFA Magazine ,3 http://www.ifamagazine.com/news/fed-risks-fall... IFA Magazine ,4 http://www.moneynews.com/Economy/federal-reser... Moneynews , , CATEGORY STORY HOSTNAME TIMESTAMP ,0 b ddUyU0VZz0BRneMioxUPQVP6sIxvM www.latimes.com 1394470370698 ,1 b ddUyU0VZz0BRneMioxUPQVP6sIxvM www.livemint.com 1394470371207 ,2 b ddUyU0VZz0BRneMioxUPQVP6sIxvM www.ifamagazine.com 1394470371550 ,3 b ddUyU0VZz0BRneMioxUPQVP6sIxvM www.ifamagazine.com 1394470371793 ,4 b ddUyU0VZz0BRneMioxUPQVP6sIxvM www.moneynews.com 1394470372027 ,Wall time: 7.72 s ## Plotting ## ### cell_ids ### [22,27,28,30,32,45] ### cells ### "[object Object][object Object][object Object][object Object][object Object][object Object]" ### lineNumbers ### [39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128] ### source ### ``` import pylab as pl # useful for drawing graphics def categories_pie_plot(cont,tit): global labels sizes = [cont[l] for l in labels] pl.pie(sizes, explode=(0, 0, 0, 0), labels=labels, autopct='%1.1f%%', shadow=True, startangle=90) pl.title(tit) pl.show() categories_pie_plot(cont,"Plotting categories")from sklearn.utils import shuffle # Shuffle arrays in a consistent way X_train = [] y_train = [] X_test = [] y_test = [] def split_data(): global titles, categories global X_train, y_train, X_test, y_test,labels N = len(titles) Ntrain = int(N * 0.7) # Let's shuffle the data titles, categories = shuffle(titles, categories, random_state=0) X_train = titles[:Ntrain] y_train = categories[:Ntrain] X_test = titles[Ntrain:] y_test = categories[Ntrain:]#%time split_data()cont2 = count_data(labels,y_train)categories_pie_plot(cont2,"Categories % in training set")import itertools import matplotlib.pyplot as plt def plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues): """ This function prints and plots the confusion matrix. """ plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, '{:5.2f}'.format(cm[i, j]), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.colorbar() plt.show() ``` ### functions ### ["def count_data(labels, categories): c = Counter(categories)\n cont = dict(c)\n tot = sum(list(cont.values()))\n d = {[object Object]:[object Object],[object Object]:[object Object],[object Object]:[object Object]}\nprint(pd.DataFrame(d))\nprint(\"total \\t\",tot)\n return cont","def categories_pie_plot(cont, tit): global labels\n sizes = [cont[l], continue]\npl.pie(sizes,explode=(0, 0, 0, 0),labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)\npl.title(tit)\npl.show()","def count_data(labels, categories): c = Counter(categories)\n cont = dict(c)\n tot = sum(list(cont.values()))\n d = {[object Object]:[object Object],[object Object]:[object Object],[object Object]:[object Object]}\nprint(pd.DataFrame(d))\nprint(\"total \\t\",tot)\n return cont","def categories_pie_plot(cont, tit): global labels\n sizes = [cont[l], continue]\npl.pie(sizes,explode=(0, 0, 0, 0),labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)\npl.title(tit)\npl.show()","def categories_pie_plot(cont, tit): global labels\n sizes = [cont[l], continue]\npl.pie(sizes,explode=(0, 0, 0, 0),labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)\npl.title(tit)\npl.show()","def count_data(labels, categories): c = Counter(categories)\n cont = dict(c)\n tot = sum(list(cont.values()))\n d = {[object Object]:[object Object],[object Object]:[object Object],[object Object]:[object Object]}\nprint(pd.DataFrame(d))\nprint(\"total \\t\",tot)\n return cont"] ### figures ### ### description ### "" ### outputs ### Wall time: 1.06 s , category news percent ,0 b 81238 0.274738 ,1 e 106844 0.361334 ,2 m 31930 0.107984 ,3 t 75681 0.255945 ,total 295693 ## Data Cleaning ## ### cell_ids ### [20,43,49] ### cells ### "[object Object][object Object][object Object]" ### lineNumbers ### [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,99,100,101,130,131,132,133,134,135,136,137,138,139,140,141,142,143] ### source ### ``` from collections import Counter def count_data(labels,categories): c = Counter(categories) cont = dict(c) # total number of news tot = sum(list(cont.values())) d = { "category" : labels, "news" : [cont[l] for l in labels], "percent" : [cont[l]/tot for l in labels] } print(pd.DataFrame(d)) print("total \t",tot) return cont cont = count_data(labels,categories)mat = metrics.confusion_matrix(y_test, predicted,labels=labels) cm = mat.astype('float') / mat.sum(axis=1)[:, np.newaxis] cmdef resume_data(labels,y_train,f1s): c = Counter(y_train) cont = dict(c) tot = sum(list(cont.values())) nlabels = len(labels) d = { "category" : [labels[i] for i in range(nlabels)], "percent" : [cont[labels[i]]/tot for i in range(nlabels)], "f1-score" : [f1s[i] for i in range(nlabels)] } print(pd.DataFrame(d)) print("total \t",tot) return cont ``` ### functions ### [] ### figures ### ### description ### "" ### outputs ### category news percent ,0 b 115967 0.274531 ,1 e 152469 0.360943 ,2 m 45639 0.108042 ,3 t 108344 0.256485 ,total 422419 ## Preprocessing ## ### cell_ids ### [] ### cells ### [] ### lineNumbers ### [] ### source ### ``` ``` ### functions ### [] ### figures ### ### description ### "" ### outputs ### ## Model Training ## ### cell_ids ### [] ### cells ### [] ### lineNumbers ### [] ### source ### ``` ``` ### functions ### [] ### figures ### ### description ### "" ### outputs ### ## Evaluation ## ### cell_ids ### [35,36,38,40,46,50] ### cells ### "[object Object][object Object][object Object][object Object][object Object][object Object]" ### lineNumbers ### [71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,129,144,145] ### source ### ``` from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn import metrics import numpy as np import pprint # lmats = [] # list of confussion matrix nrows = nlabels ncols = nlabels # conf_mat_sum = np.zeros((nrows, ncols)) # f1_acum = [] # list of f1-score def train_test(): global X_train, y_train, X_test, y_test, labels #lmats, \ # conf_mat_sum, f1_acum, ncategories text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB()), ]) text_clf = text_clf.fit(X_train, y_train) predicted = text_clf.predict(X_test) return predicted#%time predicted = train_test()metrics.accuracy_score(y_test, predicted)print(metrics.classification_report(y_test, predicted, target_names=labels))plot_confusion_matrix(cm, labels, title='Confusion matrix')f1s = metrics.f1_score(y_test, predicted, labels=labels, average=None) cont3 = resume_data(labels,y_train,f1s) ``` ### functions ### [] ### figures ### ### description ### "" ### outputs ### Wall time: 27.1 s , precision recall f1-score support , , b 0.90 0.91 0.90 34729 , e 0.95 0.97 0.96 45625 , m 0.97 0.85 0.90 13709 , t 0.90 0.90 0.90 32663 , ,avg / total 0.92 0.92 0.92 126726 , , category f1-score percent ,0 b 0.903839 0.274738 ,1 e 0.959225 0.361334 ,2 m 0.902814 0.107984 ,3 t 0.903314 0.255945 ,total 295693