from myWordcloud import my_wordcloud
from classifiers.__init__ import run_all_classifiers
from supportFuncs import stopWords
# Run everything:
if __name__ == '__main__':
stop_words = stopWords.get_stop_words()
usePipeline = False # Pipeline currently not running crossValidation. Use the regular way.
my_wordcloud(stop_words)
run_all_classifiers(stop_words, usePipeline)
clf.fit(vectorTrain, train_data['Category'])
y_pred = clf.predict(vectorTest)
# print "Train Accuracy :: ", accuracy_score(train_data['Category'], clf.predict(vectorTrain))
# print "Test Accuracy :: ", accuracy_score(train_data['Category'], y_pred)
#y_pred = cross_val_predict(clf, X=vectorTrain, y=vectorTest, cv=10, n_jobs=multiprocessing.cpu_count())
writePredictionsToCsv.write_predictions_to_csv(y_pred, test_data,
dynamic_datasets_path)
# Best GridSearch params
# print clf.best_params_
print("Elapsed time of successional-run: ",
time.time() - start_time_successional)
print('MyMethodClassifier finished!\n')
return scores
# Run myMethodClassifier directly:
if __name__ == '__main__':
dynamic_datasets_path = '..'
data = readDatasets.read_dataset(dynamic_datasets_path)
trainData = data[0]
testData = data[1]
my_method_classifier(stopWords.get_stop_words(), trainData, testData,
dynamic_datasets_path)
exit()
# GridSearch (find the best parameters)
# parameters = {'n_neighbors': [5, 10], 'weights': ['uniform', 'distance'], 'algorithm' : ['ball_tree', 'kd_tree', 'brute'], 'p': [1, 2]}
# svr = KNeighborsClassifier()
# clf = GridSearchCV(svr, parameters)
clf.fit(vectorTrain, train_y)
y_pred = clf.predict(vectorTest)
#
print("Train Accuracy :: ", accuracy_score(train_y, clf.predict(vectorTrain)))
print("Test Accuracy :: ", accuracy_score(test_y, y_pred))
# Best GridSearch params
# print clf.best_params_
print("Elapsed time of successional-run: ", time.time() - start_time_successional)
print('knnClassifier finished!\n')
return scores
# Run svmClassifier directly:
if __name__ == '__main__':
dynamic_datasets_path = '..'
data = readDatasets.read_dataset(dynamic_datasets_path)
trainData = data[0]
testData = data[1]
usePipeline = False
knn(stopWords.get_stop_words(), trainData, testData, usePipeline)
exit()
# GridSearch
# parameters = {'n_estimators': [130, 110, 100, 80, 50, 30, 20, 10]}
# svr = RandomForestClassifier()
# clf = GridSearchCV(svr, parameters)
# clf.fit(vectorTrain, train_y)
# y_pred = clf.predict(vectorTest)
#
# print "Train Accuracy :: ", accuracy_score(train_y, clf.predict(vectorTrain))
# print "Test Accuracy :: ", accuracy_score(test_y, y_pred)
# Best GridSearch params
# print clf.best_params_
print("Elapsed time of successional-run: ",
time.time() - start_time_successional)
print('rfClassifier finished!\n')
return scores
# Run rfClassifier directly:
if __name__ == '__main__':
data = readDatasets.read_dataset()
trainData = data[0]
testData = data[1]
usePipeline = False
rf_classifier(stopWords.get_stop_words(), trainData, testData, usePipeline)
# print(neighbors)
result = getResponse(neighbors)
# print result
predictions.append(result)
# print('> predicted=' + repr(result) + ', actual=' + repr(train_data['Category'][8586+x]))
if result == train_data['Category'][8586 + x]:
count += 1
# print 'Test', test_x[1:2], 'Pred', predictions[0]
# accuracies.append(getAccuracy(test_data, predictions))
# print('Accuracy: ' + repr(accuracy) + '%')
print("Got right", count, "out of", 100)
# Final accuracy after crossValidation
# print accuracies
# print np.mean(accuracies)
print("Elapsed time of successional-run: ",
time.time() - start_time_successional)
# Run knnClassifier directly:
if __name__ == '__main__':
dynamic_datasets_path = '..'
data = readDatasets.read_dataset(dynamic_datasets_path)
trainData = data[0]
testData = data[1]
knn_classifier(stopWords.get_stop_words(), trainData, testData)
exit()
y = np.array(accuraccies)
x_new = np.linspace(x.min(), x.max(), 500)
f = interp1d(x, y, kind='quadratic')
y_smooth = f(x_new)
plt.xlabel('n_components')
plt.ylabel('Accuracy')
plt.title('RandomForestClassifier Accuracy Graph')
plt.plot(x_new, y_smooth, color='r')
plt.scatter(x, y)
plt.savefig(
os.path.join(dynamic_datasets_path, 'Resources', 'images',
'RandomForestClassifier Accuracy Graph.png'))
plt.show()
print('rfClassifier finished!\n')
return scores
# Run rfClassifier directly:
if __name__ == '__main__':
dynamic_datasets_path = '..'
data = readDatasets.read_dataset(dynamic_datasets_path)
trainData = data[0]
testData = data[1]
usePipeline = False
rf_classifier_with_graph(stopWords.get_stop_words(), trainData, testData,
usePipeline)
exit()
with open('Resources/csv/train_set.csv', mode='r',
encoding="utf8") as csvfile:
csvReader = csv.DictReader(csvfile, delimiter='\t', quotechar='|')
for row in csvReader:
category = row["Category"]
if category == 'Business':
businessStr += row["Content"]
elif category == 'Politics':
politicsStr += row["Content"]
elif category == 'Football':
footballStr += row["Content"]
elif category == 'Film':
filmStr += row["Content"]
elif category == 'Technology':
technologyStr += row["Content"]
show_wordcloud(stop_words, businessStr, 'Business')
show_wordcloud(stop_words, politicsStr, 'Politics')
show_wordcloud(stop_words, footballStr, 'Football')
show_wordcloud(stop_words, filmStr, 'Film')
show_wordcloud(stop_words, technologyStr, 'Technology')
print('myWordcloud finished!\n')
# Run myWordcloud directly:
if __name__ == '__main__':
my_wordcloud(stopWords.get_stop_words())
# Write the scores.
csvWriter.writerow(['Accuracy'] + ['{:.3}'.format(nbScores[0])] +
['{:.3}'.format(rfScores[0])] +
['{:.3}'.format(svmScores[0])] + ['knn'] +
['{:.3}'.format(mymethodScores[0])])
# + [knnScores[0]] + [mymethodScores[0]])
csvWriter.writerow(['Precision'] + ['{:.3}'.format(nbScores[1])] +
['{:.3}'.format(rfScores[1])] +
['{:.3}'.format(svmScores[1])] + ['knn'] +
['{:.3}'.format(mymethodScores[1])])
# + [knnScores[1]]] + [mymethodScores[1]])
csvWriter.writerow(['Recall'] + ['{:.3}'.format(nbScores[2])] +
['{:.3}'.format(rfScores[2])] +
['{:.3}'.format(svmScores[2])] + ['knn'] +
['{:.3}'.format(mymethodScores[2])])
# + [knnScores[2]] + [mymethodScores[2]])
csvWriter.writerow(['F-Measure'] + ['{:.3}'.format(nbScores[3])] +
['{:.3}'.format(rfScores[3])] +
['{:.3}'.format(svmScores[3])] + ['knn'] +
['{:.3}'.format(mymethodScores[3])])
# + [knnScores[3]]] + [mymethodScores[3]])
print('Finished writing to the outputCsvFile!')
# Run all classifiers:
if __name__ == '__main__':
usePipeline = False
run_all_classifiers(stopWords.get_stop_words(), usePipeline)
csvReader = csv.DictReader(csvfile, delimiter='\t', quotechar='|')
for row in csvReader:
category = row["Category"]
if category == 'Business':
businessStr += row["Content"]
elif category == 'Politics':
politicsStr += row["Content"]
elif category == 'Football':
footballStr += row["Content"]
elif category == 'Film':
filmStr += row["Content"]
elif category == 'Technology':
technologyStr += row["Content"]
show_wordcloud(stop_words, businessStr, 'Business', dynamic_datasets_path)
show_wordcloud(stop_words, politicsStr, 'Politics', dynamic_datasets_path)
show_wordcloud(stop_words, footballStr, 'Football', dynamic_datasets_path)
show_wordcloud(stop_words, filmStr, 'Film', dynamic_datasets_path)
show_wordcloud(stop_words, technologyStr, 'Technology',
dynamic_datasets_path)
print('myWordcloud finished!\n')
# Run myWordcloud directly:
if __name__ == '__main__':
dynamic_datasets_path = ''
my_wordcloud(stopWords.get_stop_words(), dynamic_datasets_path)
exit()