def get_data(self, backend=True):
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
self.datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
dataset = None
#if not backend:
xlsxTrainFileName = os.path.join('DatasetBuilder', 'Input',
'sentiment')
dataset = self.datasetBuilder.GetSentimentDatasetFromXLSXFile(
xlsxTrainFileName)
if backend:
dataset2 = self.datasetBuilder.GetSentimentDatasetFromBackend()
for item in dataset2:
dataset[item] = dataset2[item]
dataset = list(dataset.values())
if len(dataset) < MIN_DATA:
return
print("Data length: ", len(dataset))
self.languageModel.dataset = dataset
self.languageModel.totalNumberOfDocs = len(dataset)
self.languageModel.BuildLanguageModel()
self.languageModel.dataset = []
return dataset
def evaluate(cls, path, use_backend=True, pre_stocks=None):
validation_accuracy = {}
global stocks
if pre_stocks:
stocks = pre_stocks
for stockName in stocks:
model = cls.load(path, stockName)
if not model:
continue
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
# The serialization file to save the dataset
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
# The XLSX file name for train set
xlsxTrainFileName = os.path.join('DatasetBuilder', 'Input',
'train')
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
if use_backend:
testSet = datasetBuilder.GetDatasetFromBackend(stockName)
else:
testSet = datasetBuilder.GetDatasetFromXLSXFile(
xlsxTrainFileName, stockName)
if len(testSet) < NMIN_SET:
continue
testSet = testSet[:NVALID]
print('Using model for %s' % stockName)
configFileFeaturesExtractor = os.path.join(
'FeaturesExtractor', 'Configurations',
'Configurations-Tasi.xml')
testFeaturesExtractor = FeaturesExtractor(
configFileFeaturesExtractor, None, None,
model['languageModel_lexicon'], testSet)
#testFeaturesExtractor.ExtractLexiconFeatures()
testFeaturesExtractor.ExtractNumTfFeatures(sparse=True)
model[
'classifier_Lexicon'].testFeatures = testFeaturesExtractor.sparse_features
model[
'classifier_Lexicon'].testTargets = testFeaturesExtractor.labels
label, acc, val = model['classifier_Lexicon'].Test()
print(acc, val)
validation_accuracy[stockName] = {
'accuracy': acc,
'training_samples': model['training_samples']
}
return validation_accuracy
def get_data(self):
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
self.datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
dataset = self.datasetBuilder.getQuestionsDataset(self.dataset_path)
self.languageModel.dataset = dataset
self.languageModel.totalNumberOfDocs = len(dataset)
self.languageModel.BuildLanguageModel()
self.languageModel.dataset = []
return dataset
def init_dicts():
configFileDatasetBuilder = os.path.join('DatasetBuilder','Configurations','Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder','Output', 'dataset.bin')
xlsxTrainFileName = os.path.join('DatasetBuilder','Input','sentiment')
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [], datasetSerializationFile)
datasetBuilder.trainSet = datasetBuilder.GetSentimentDatasetFromXLSXFile(xlsxTrainFileName).values()
words_dict = {'negative':[], 'positive': [], 'neutral': []}
for item in datasetBuilder.trainSet:
words_dict[item['label']] += item['words']
for k in words_dict:
words_dict[k] = list(set(words_dict[k]))
return words_dict
def __init__(self, basePath, stockName, Retrain):
'''
Constructor
:type self:
'''
if (basePath == None):
self.basePath = self.basePath
else:
self.basePath = basePath
self.stockName = stockName
serializationFile = open(
os.path.join(self.basePath, 'StockToClassifier.bin'), 'rb')
self.StockToClassifier = pickle.load(serializationFile)
#import pdb; pdb.set_trace()
self.usedClassifier = self.StockToClassifier[self.stockName]
# Start the DatasetBuilder
#-------------------------
# Configurations file xml of the dataset builder
configFileDatasetBuilder = os.path.join(self.basePath,
"DatasetBuilder",
"Configurations",
"Configurations.xml")
# The serialization file to save the dataset
datasetSerializationFile = os.path.join(self.basePath,
"DatasetBuilder", "Output",
"dataset.bin")
if Retrain == False:
# The XLSX file name for train set
xlsxTrainFileName = os.path.join(self.basePath, "DatasetBuilder",
"Input", "train")
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(
xlsxTrainFileName)
self.RunLanguageModel(self.usedClassifier, datasetBuilder.trainSet)
trainFeaturesExtractor = self.RunFeatureExtractor(
self.usedClassifier, datasetBuilder.trainSet)
self.Train(self.usedClassifier, trainFeaturesExtractor, True)
else:
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
configFileDatasetBuilder = ".\\DatasetBuilder\\Configurations\\Configurations.xml"
# The CSV file name for tweets to be manually labeled
csvManualLabelsFileName = ".\\DatasetBuilder\\Output\\ManualLabels"
xlsxManualLabelsFileName = ".\\DatasetBuilder\\Output\\ManualLabels"
# The serialization file to save the dataset
datasetSerializationFile = ".\\DatasetBuilder\\Output\\dataset.bin"
# Train/Test serialization file
trainTestSerializationFile = ".\\DatasetBuilder\\Output\\train_test_dataset.bin"
# Check if the current stage is to initialize random labels
if LOAD_DATASET_FROM_SERIALIZATION_FILE:
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
# Load the dataset
datasetBuilder.LoadDataset()
# Form or load the train/test sets
if SPLIT_DATASET_TRAIN_TEST:
datasetBuilder.SplitTrainTest()
datasetBuilder.SaveTrainTestDataset(trainTestSerializationFile)
elif LOAD_TRAIN_TEST:
datasetBuilder.LoadTrainTestDataset(trainTestSerializationFile)
elif UPDATE_LABELS_FROM_CSV:
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
# The XLSX file name for tweets to be manually labeled
xlsxManualLabelsFileName = ".\\DatasetBuilder\\Output\\Completed\\ManualLabels"
# The serialization file to save the dataset
datasetSerializationFile = ".\\DatasetBuilder\\Output\\dataset.bin"
# Train/Test serialization file
trainTestSerializationFile = ".\\DatasetBuilder\\Output\\train_test_dataset.bin"
# The XLSX file name for train set
xlsxTrainFileName = ".\\DatasetBuilder\\Input\\train"
xlsxTestFileName = ".\\DatasetBuilder\\Input\\test"
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
# Load the dataset
#datasetBuilder.LoadDataset()
# Update the labels
'''
numFiles = 50
for i in range(numFiles):
print('Updating labels from file ' + xlsxManualLabelsFileName + "_" + str(i + 1) + '...')
datasetBuilder.UpdateManualLabelsFromXLSXFile(xlsxManualLabelsFileName + "_" + str(i + 1), (i + 1)) # This should be done separately when dataset is manually labeled
# Form or load the train/test sets
datasetBuilder.SplitTrainTest()
'''
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(
f_in = open('.\\TwitterCrawler\\stocks.txt', 'r', encoding='utf-8')
lines = f_in.readlines()
queryArray = []
stock_under_test = 4
i = 1
for line in lines:
if (i == stock_under_test):
queryArray.append(line.strip())
print(line.strip() + "\n")
break
i += 1
# Check if the current stage is to initialize random labels
if LOAD_DATASET_FROM_SERIALIZATION_FILE:
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
# Load the dataset
datasetBuilder.LoadDataset()
# Form or load the train/test sets
if SPLIT_DATASET_TRAIN_TEST:
datasetBuilder.SplitTrainTest()
datasetBuilder.SaveTrainTestDataset(trainTestSerializationFile)
elif LOAD_TRAIN_TEST:
datasetBuilder.LoadTrainTestDataset(trainTestSerializationFile)
elif UPDATE_LABELS_FROM_CSV:
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
dirName = '.\\crawler\\news'
for file in os.listdir(dirName):
if file.endswith(".csv"):
full_file_name = dirName + '\\' + file
d.csvNewsFileName = full_file_name
news_headlines.extend(d.get_news_headlines())
def CollectPrices():
dirName = '.\\crawler\\prices'
for file in os.listdir('.\\crawler\\prices'):
if file.endswith(".csv"):
full_file_name = dirName + '\\' + file
print(full_file_name)
d.csvPricesFileName = full_file_name
prices.extend(d.get_prices())
d = DatasetBuilder()
CollectNews()
CollectPrices()
d.csvNewsFileName = 'news_all.csv'
d.DumpNewsCSV(news_headlines)
d.csvPricesFileName = 'prices_all.csv'
d.DumpPricesCSV(prices)
def __init__(self):
'''
Constructor
:type self:
'''
# Start the DatasetBuilder
#-------------------------
# Configurations file xml of the dataset builder
configFileDatasetBuilder = os.path.join('DatasetBuilder','Configurations','Configurations.xml')
# The serialization file to save the dataset
datasetSerializationFile = os.path.join('DatasetBuilder','Output', 'dataset.bin')
# The XLSX file name for train set
xlsxTrainFileName = os.path.join('DatasetBuilder','Input','train')
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [], datasetSerializationFile)
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(xlsxTrainFileName)
# Configurations file xml of the language model
configFileLanguageModel_lexicon = os.path.join('LanguageModel', 'Configurations', 'Configurations-lexicon.xml')
configFileLanguageModel_Tasi = os.path.join('LanguageModel', 'Configurations', 'Configurations-Tasi.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input', 'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output', 'links_database.txt')
# The serialization file to save the model
languageModelSerializationFile = os.path.join('LanguageModel', 'Output', 'language_model.bin')
# Start the LanguageModel:
# Initialize the LanguageModel_Lexicon
self.languageModel_lexicon = LanguageModel(configFileLanguageModel_lexicon, stopWordsFileName, languageModelSerializationFile, linksDBFile, datasetBuilder.trainSet)
self.languageModel_lexicon.BuildLanguageModel()
# Initialize the LanguageModel_Tasi
self.languageModel_Tasi = LanguageModel(configFileLanguageModel_Tasi, stopWordsFileName, languageModelSerializationFile, linksDBFile, datasetBuilder.trainSet)
self.languageModel_Tasi.BuildLanguageModel()
# Configurations file xml of the features extractor
configFileFeaturesExtractor_Lexicon = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-lexicon.xml')
configFileFeaturesExtractor_Tasi = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations-Tasi.xml')
# The serialization file to save the features
trainFeaturesSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_features.bin')
trainLabelsSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_labels.bin')
# Start the FeaturesExtractor:
#-----------------------------
# Initialize the FeaturesExtractor _ Lexicon
trainFeaturesExtractor_Lexicon = FeaturesExtractor(configFileFeaturesExtractor_Lexicon, trainFeaturesSerializationFile, trainLabelsSerializationFile, self.languageModel_lexicon, datasetBuilder.trainSet)
trainFeaturesExtractor_Lexicon.ExtractNumTfFeatures()
# Initialize the FeaturesExtractor _ Tasi
trainFeaturesExtractor_Tasi = FeaturesExtractor(configFileFeaturesExtractor_Tasi, trainFeaturesSerializationFile, trainLabelsSerializationFile, self.languageModel_Tasi, datasetBuilder.trainSet)
trainFeaturesExtractor_Tasi.ExtractNumTfFeatures()
# The serialization file to save the features
configFileClassifier_Lexicon = os.path.join('Classifier', 'Configurations', 'Configurations-lexicon.xml')
configFileClassifier_Tasi = os.path.join('Classifier', 'Configurations', 'Configurations-Tasi.xml')
modelSerializationFile = os.path.join('Classifier', 'Output', 'classifier_model.bin')
# Start the Classifier:
#---------------------
print(trainFeaturesExtractor_Tasi.labels[:4])
print([i['label'] for i in trainFeaturesExtractor_Lexicon.dataSet[:4]])
self.classifier_Lexicon = Classifier(configFileClassifier_Lexicon, modelSerializationFile, trainFeaturesExtractor_Lexicon.features, trainFeaturesExtractor_Lexicon.labels, [], [])
self.classifier_Tasi = Classifier(configFileClassifier_Tasi, modelSerializationFile, trainFeaturesExtractor_Tasi.features,
trainFeaturesExtractor_Tasi.labels, [],[])
# Train
self.classifier_Lexicon.Train()
self.classifier_Tasi.Train()
'''
Created on Mar 23, 2015
@author: aelsalla
'''
from DatasetBuilder.DatasetBuilder import DatasetBuilder
from FeaturesExtractor.FeaturesExtractor import FeaturesExtractor
from Classifier.Classifier import Classifier
import matplotlib.pyplot as plt
import numpy as np
# Initialize the DatasetBuilder
###############################
dataSetBuilder = DatasetBuilder()
testSetShare = 0.1
dataSetBuilder.csvPricesFileName = '.\\crawler\\prices\\prices_16_4_2015_15_30_55.csv'
dataSetBuilder.csvNewsFileName = '.\\news_all.csv'
trainSet, testSet = dataSetBuilder.BuildDataSet(testSetShare)
dataSet = []
dataSet.extend(trainSet)
dataSet.extend(testSet)
'''
fullPrices = []
for price in dataSetBuilder.get_prices():
fullPrices.append(float(price['value']))
'''
fullPrices = []
labels = []
sizes = []
def init(cls, save_path, use_backend=True, pre_stocks=None):
'''
Constructor
:type self:
'''
global stocks
if pre_stocks:
stocks = pre_stocks
for stock in stocks:
print('Buildind model for %s' % stock)
stock_model = {}
# Start the DatasetBuilder
#-------------------------
# Configurations file xml of the dataset builder
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
# The serialization file to save the dataset
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
# The XLSX file name for train set
xlsxTrainFileName = os.path.join('DatasetBuilder', 'Input',
'train')
# Initialize the DatasetBuilder from serialization file
datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
if use_backend:
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromBackend(
stock)
else:
datasetBuilder.trainSet = datasetBuilder.GetDatasetFromXLSXFile(
xlsxTrainFileName, stock)
if len(datasetBuilder.trainSet) < NMIN_SET:
print("Not enough data: ", len(datasetBuilder.trainSet))
continue
datasetBuilder.trainSet = datasetBuilder.trainSet[NVALID:]
# Configurations file xml of the language model
configFileLanguageModel_lexicon = os.path.join(
'LanguageModel', 'Configurations', 'Configurations-Tasi.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input',
'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output',
'links_database.txt')
# The serialization file to save the model
languageModelSerializationFile = os.path.join(
'LanguageModel', 'Output', 'language_model.bin')
# Start the LanguageModel:
# Initialize the LanguageModel_Lexicon
stock_model['languageModel_lexicon'] = LanguageModel(
configFileLanguageModel_lexicon, stopWordsFileName,
languageModelSerializationFile, linksDBFile,
datasetBuilder.trainSet)
stock_model['languageModel_lexicon'].BuildLanguageModel()
# Configurations file xml of the features extractor
configFileFeaturesExtractor_Lexicon = os.path.join(
'FeaturesExtractor', 'Configurations',
'Configurations-Tasi.xml')
# The serialization file to save the features
trainFeaturesSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_features.bin')
trainLabelsSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_labels.bin')
# Start the FeaturesExtractor:
#-----------------------------
# Initialize the FeaturesExtractor _ Lexicon
trainFeaturesExtractor_Lexicon = FeaturesExtractor(
configFileFeaturesExtractor_Lexicon,
trainFeaturesSerializationFile, trainLabelsSerializationFile,
stock_model['languageModel_lexicon'], datasetBuilder.trainSet)
trainFeaturesExtractor_Lexicon.ExtractNumTfFeatures(sparse=True)
#print(trainFeaturesExtractor_Lexicon.features[0])
# The serialization file to save the features
configFileClassifier_Lexicon = os.path.join(
'Classifier', 'Configurations', 'Configurations-Tasi.xml')
modelSerializationFile = os.path.join('Classifier', 'Output',
'classifier_model.bin')
# Start the Classifier:
#---------------------
stock_model['classifier_Lexicon'] = Classifier(
configFileClassifier_Lexicon, modelSerializationFile,
trainFeaturesExtractor_Lexicon.sparse_features,
trainFeaturesExtractor_Lexicon.labels, [], [])
#stock_model['classifier_Lexicon'] = Classifier(configFileClassifier_Lexicon, modelSerializationFile, trainFeaturesExtractor_Lexicon.features, trainFeaturesExtractor_Lexicon.labels, [], [])
#print(trainFeaturesExtractor_Lexicon.labels[:4])
#print([i['label'] for i in trainFeaturesExtractor_Lexicon.dataSet[:4]])
# Train
stock_model['classifier_Lexicon'].Train()
stock_model['training_samples'] = len(datasetBuilder.trainSet)
cls.save(save_path, stock, stock_model)
print("----------------------------------------------------")
from DatasetBuilder.DatasetBuilder import DatasetBuilder
import datetime, time
import os
os.environ["DJANGO_SETTINGS_MODULE"] = "website.settings"
from app.models import NewsHeadline, Price
# Open the file
priceStartDate = '2015-03-01'
while True:
logFile = open('crawl_log_file.txt', 'a')
print('Crawling now ' + str(datetime.datetime.now()))
logFile.write('Crawling now ' + str(datetime.datetime.now()) +'\n')
logFile.close()
d = DatasetBuilder()
news_headlines = d.ParseNewsURL()
for headline in news_headlines:
print(headline['text'] + '\n' + headline['time_stamp'])
headline_exist = NewsHeadline.objects.filter(text=headline['text'])
if(len(headline_exist) == 0):
headline_entry = NewsHeadline()
headline_entry.text = headline['text']
headline_entry.time_stamp = headline['time_stamp']
headline_entry.save()
d.csvNewsFileName = '.\\crawler\\news\\news_' + str(datetime.datetime.now().day) +'_' + str(datetime.datetime.now().month) +'_' + str(datetime.datetime.now().year) +'_' + str(datetime.datetime.now().hour) +'_' + str(datetime.datetime.now().minute) + '_' + str(datetime.datetime.now().second) +'.csv'
class SentimentModel(object):
def __init__(self, modeln=1):
self.modeln = modeln
configFileLanguageModel = os.path.join('LanguageModel',
'Configurations',
'Configurations_sentiment.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input',
'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output',
'links_database.txt')
languageModelSerializationFile = os.path.join('LanguageModel',
'Output',
'language_model.bin')
self.languageModel = LanguageModel(configFileLanguageModel,
stopWordsFileName,
languageModelSerializationFile,
linksDBFile, [])
self.configFileFeaturesExtractor = os.path.join(
'FeaturesExtractor', 'Configurations',
'Configurations_sentiment.xml')
self.trainFeaturesSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_features.bin')
self.trainLabelsSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_labels.bin')
def get_data(self, backend=True):
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
self.datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
dataset = None
#if not backend:
xlsxTrainFileName = os.path.join('DatasetBuilder', 'Input',
'sentiment')
dataset = self.datasetBuilder.GetSentimentDatasetFromXLSXFile(
xlsxTrainFileName)
if backend:
dataset2 = self.datasetBuilder.GetSentimentDatasetFromBackend()
for item in dataset2:
dataset[item] = dataset2[item]
dataset = list(dataset.values())
if len(dataset) < MIN_DATA:
return
print("Data length: ", len(dataset))
self.languageModel.dataset = dataset
self.languageModel.totalNumberOfDocs = len(dataset)
self.languageModel.BuildLanguageModel()
self.languageModel.dataset = []
return dataset
def prepare_data(self, dataset):
trainFeaturesExtractor = FeaturesExtractor(
self.configFileFeaturesExtractor,
self.trainFeaturesSerializationFile,
self.trainLabelsSerializationFile,
self.languageModel,
dataset,
sentiment_features=True)
trainFeaturesExtractor.ExtractNumTfFeatures(init_dicts())
maxid = max([max(i.keys()) for i in trainFeaturesExtractor.features])
X = []
Y = []
for i, item in enumerate(trainFeaturesExtractor.features):
itemx = [0 for _ in range(maxid)]
l = [0, 0, 0]
l[trainFeaturesExtractor.labels[i] - 1] = 1
for j in trainFeaturesExtractor.features[i]:
v = trainFeaturesExtractor.features[i][j]
itemx[j - 1] = v
X.append(itemx)
Y.append(trainFeaturesExtractor.labels[i])
trainFeaturesExtractor.dataset = []
trainFeaturesExtractor.features = []
trainFeaturesExtractor.labels = []
return X, Y
def transform_data(self, X, Y):
X = np.array(X)
Y = np.array(Y)
ri = range(X.shape[0])
rl = range(X.shape[1])
d = pd.DataFrame(X, index=ri, columns=rl)
d['class'] = Y
return d
def split_data(self, d):
training_indices, testing_indices = train_test_split(
d.index,
stratify=d['class'].values,
train_size=0.75,
test_size=0.25)
return training_indices, testing_indices
def train(self, backend=True):
rawdata = self.get_data(backend)
dxy = self.prepare_data(rawdata)
d = self.transform_data(dxy[0], dxy[1])
self.training_indices, self.testing_indices = self.split_data(d)
X = d.loc[self.training_indices].drop('class', axis=1).values
Y = d.loc[self.training_indices, 'class'].values
Xtest = d.loc[self.testing_indices].drop('class', axis=1).values
Ytest = d.loc[self.testing_indices, 'class'].values
if self.modeln == 1:
print(self.fit_model1(X, Y))
print(self.evaluate_model1(Xtest, Ytest))
if self.modeln == 2:
print(self.fit_model2(X, Y))
print(self.evaluate_model2(Xtest, Ytest))
if self.modeln == 3:
print(self.fit_model3(X, Y))
print(self.evaluate_model3(Xtest, Ytest))
if self.modeln == 4:
print(self.fit_model4(X, Y))
print(self.evaluate_model4(Xtest, Ytest))
def fit_model1(self, X, Y):
self.model1 = LinearSVC(C=0.01,
penalty="l1",
dual=False,
random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model1(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model2(self, X, Y):
self.model1 = LinearSVC(C=0.18,
penalty="l1",
dual=False,
random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model2(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model3(self, X, Y):
pre_recall = 0.0
for g in [0.01, 0.05, 0.1, 0.3, 0.5]:
model = SVC(C=0.18, gamma=g, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
print(recall)
if recall > pre_recall:
pre_recall = recall
self.model1 = model
return recall
def evaluate_model3(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model4(self, X, Y):
model = SVC(C=0.18, gamma=0.1, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
self.model1 = model
return recall
def evaluate_model4(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def classify(self, tweets):
dataset = []
for tw in tweets:
dataset.append({'text': tw, 'label': 'neutral'})
X, Y = self.prepare_data(dataset)
return self.model1.predict(X)
@classmethod
def load(cls, path):
return pickle.load(open(path, 'rb'))
def save(self, path):
return pickle.dump(self, open(path, 'wb'))
class SentimentModel(object):
def __init__(self, modeln = 1):
self.modeln = modeln
configFileLanguageModel = os.path.join('LanguageModel', 'Configurations', 'Configurations_sentiment.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input', 'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output', 'links_database.txt')
languageModelSerializationFile = os.path.join('LanguageModel', 'Output', 'language_model.bin')
self.languageModel = LanguageModel(configFileLanguageModel, stopWordsFileName, languageModelSerializationFile, linksDBFile, [])
self.configFileFeaturesExtractor = os.path.join('FeaturesExtractor', 'Configurations', 'Configurations_sentiment.xml')
self.trainFeaturesSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_features.bin')
self.trainLabelsSerializationFile = os.path.join('FeaturesExtractor', 'Output', 'train_labels.bin')
def get_data(self, backend=True):
configFileDatasetBuilder = os.path.join('DatasetBuilder','Configurations','Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder','Output', 'dataset.bin')
self.datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [], datasetSerializationFile)
dataset = None
#if not backend:
xlsxTrainFileName = os.path.join('DatasetBuilder','Input','sentiment')
dataset = self.datasetBuilder.GetSentimentDatasetFromXLSXFile(xlsxTrainFileName)
if backend:
dataset2 = self.datasetBuilder.GetSentimentDatasetFromBackend()
for item in dataset2:
dataset[item] = dataset2[item]
dataset = list(dataset.values())
if len(dataset) < MIN_DATA:
return
print("Data length: ", len(dataset))
self.languageModel.dataset = dataset
self.languageModel.totalNumberOfDocs = len(dataset)
self.languageModel.BuildLanguageModel()
self.languageModel.dataset = []
return dataset
def prepare_data(self, dataset):
trainFeaturesExtractor = FeaturesExtractor(self.configFileFeaturesExtractor, self.trainFeaturesSerializationFile,
self.trainLabelsSerializationFile, self.languageModel, dataset,
sentiment_features=True)
trainFeaturesExtractor.ExtractNumTfFeatures(sentiment_dict=init_dicts(), sparse=True)
X= trainFeaturesExtractor.sparse_features
Y = np.array(trainFeaturesExtractor.labels)
trainFeaturesExtractor.dataset = []
trainFeaturesExtractor.features = []
trainFeaturesExtractor.labels = []
return X, Y
def transform_data(self, X, Y):
X = np.array(X)
Y = np.array(Y)
ri = range(X.shape[0])
rl = range(X.shape[1])
d = pd.DataFrame(X, index=ri, columns=rl)
d['class'] = Y
return d
def split_data(self, X, Y):
training_indices, testing_indices = train_test_split(range(X.shape[0]), stratify = Y, train_size=0.75, test_size=0.25)
self.ntraining_samples = len(training_indices)
return training_indices, testing_indices
def train(self, backend=True):
rawdata = self.get_data(backend)
Xall, Yall = self.prepare_data(rawdata)
self.training_indices, self.testing_indices = self.split_data(Xall, Yall)
X = Xall[self.training_indices]
Y = Yall[self.training_indices]
Xtest = Xall[self.testing_indices]
Ytest = Yall[self.testing_indices]
acc = 0.0
if self.modeln == 1:
print(self.fit_model1(X, Y))
acc = self.evaluate_model1(Xtest, Ytest)
if self.modeln == 2:
print(self.fit_model2(X, Y))
acc = self.evaluate_model2(Xtest, Ytest)
if self.modeln == 3:
print(self.fit_model3(X, Y))
acc = self.evaluate_model3(Xtest, Ytest)
if self.modeln == 4:
print(self.fit_model4(X, Y))
acc = self.evaluate_model4(Xtest, Ytest)
if self.modeln == 5:
print(self.fit_model5(X, Y))
acc = self.evaluate_model5(Xtest, Ytest)
result = {'accuracy': acc, 'training_samples': self.ntraining_samples}
return result
def fit_model1(self, X, Y):
self.model1 = LinearSVC(C=0.018, dual=False, random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model1(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model2(self, X, Y):
self.model1 = LinearSVC(C=0.18, penalty="l1", dual=False, random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model2(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model3(self, X, Y):
pre_recall = 0.0
for g in [0.01, 0.05, 0.1, 0.3, 0.5]:
model = SVC(C=0.18, gamma=g, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
print(recall)
if recall > pre_recall:
pre_recall = recall
self.model1 = model
return recall
def evaluate_model3(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model4(self, X, Y):
model = SVC(C=0.18, gamma=0.1, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
self.model1 = model
return recall
def evaluate_model4(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model5(self, X, Y):
model = LogisticRegression(C=0.18, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
self.model1 = model
return recall
def evaluate_model5(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def classify(self, tweets):
dataset = []
for tw in tweets:
dataset.append({'text': tw, 'label':'neutral'})
X, Y = self.prepare_data(dataset)
return self.model1.predict(X)
@classmethod
def load(cls, path):
return pickle.load(open(path, 'rb'))
def save(self, path):
return pickle.dump(self, open(path, 'wb'))
class QuestionsModel(object):
def __init__(self, words_dict_path=None, dataset_path=None, modeln=1):
if not words_dict_path:
words_dict_path = os.path.join('data', 'questions_dict.bin')
if not dataset_path:
dataset_path = os.path.join('data', 'questions_dataset.bin')
self.modeln = modeln
self.words_dict_path = words_dict_path
self.dataset_path = dataset_path
configFileLanguageModel = os.path.join('LanguageModel',
'Configurations',
'Configurations_questions.xml')
stopWordsFileName = os.path.join('LanguageModel', 'Input',
'stop_words.txt')
linksDBFile = os.path.join('LanguageModel', 'Output',
'links_database.txt')
languageModelSerializationFile = os.path.join('LanguageModel',
'Output',
'language_model.bin')
self.languageModel = LanguageModel(configFileLanguageModel,
stopWordsFileName,
languageModelSerializationFile,
linksDBFile, [])
self.configFileFeaturesExtractor = os.path.join(
'FeaturesExtractor', 'Configurations',
'Configurations_questions.xml')
self.trainFeaturesSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_features.bin')
self.trainLabelsSerializationFile = os.path.join(
'FeaturesExtractor', 'Output', 'train_labels.bin')
def get_data(self):
configFileDatasetBuilder = os.path.join('DatasetBuilder',
'Configurations',
'Configurations.xml')
datasetSerializationFile = os.path.join('DatasetBuilder', 'Output',
'dataset.bin')
self.datasetBuilder = DatasetBuilder(configFileDatasetBuilder, [],
datasetSerializationFile)
dataset = self.datasetBuilder.getQuestionsDataset(self.dataset_path)
self.languageModel.dataset = dataset
self.languageModel.totalNumberOfDocs = len(dataset)
self.languageModel.BuildLanguageModel()
self.languageModel.dataset = []
return dataset
def prepare_data(self, dataset):
trainFeaturesExtractor = FeaturesExtractor(
self.configFileFeaturesExtractor,
self.trainFeaturesSerializationFile,
self.trainLabelsSerializationFile,
self.languageModel,
dataset,
questions_features=True)
print("Data length: ", len(dataset))
words_dict = self.datasetBuilder.getQuestionsDatasetDictionary(
self.words_dict_path)
trainFeaturesExtractor.ExtractNumTfFeatures(questions_dict=words_dict)
maxid = max([max(i.keys()) for i in trainFeaturesExtractor.features])
X = []
Y = []
L = len(dataset)
for i, item in enumerate(trainFeaturesExtractor.features):
itemx = [0 for _ in range(maxid)]
l = [0, 0, 0]
l[trainFeaturesExtractor.labels[i] - 1] = 1
for j in trainFeaturesExtractor.features[i]:
v = trainFeaturesExtractor.features[i][j]
itemx[j - 1] = v
X.append(itemx)
Y.append(trainFeaturesExtractor.labels[i])
return X, Y, L
def transform_data(self, X, Y):
X = np.array(X)
Y = np.array(Y)
ri = range(X.shape[0])
rl = range(X.shape[1])
d = pd.DataFrame(X, index=ri, columns=rl)
d['class'] = Y
return d
def split_data(self, d):
training_indices, testing_indices = train_test_split(
d.index,
stratify=d['class'].values,
train_size=0.75,
test_size=0.25)
return training_indices, testing_indices
def train(self):
rawdata = self.get_data()
X, Y, L = self.prepare_data(rawdata)
ret = [0, 0]
ret[0] = L
d = self.transform_data(X, Y)
self.training_indices, self.testing_indices = self.split_data(d)
X = d.loc[self.training_indices].drop('class', axis=1).values
Y = d.loc[self.training_indices, 'class'].values
Xtest = d.loc[self.testing_indices].drop('class', axis=1).values
Ytest = d.loc[self.testing_indices, 'class'].values
if self.modeln == 1:
print(self.fit_model1(X, Y))
ret[1] = self.evaluate_model1(Xtest, Ytest)
print(ret[1])
if self.modeln == 2:
print(self.fit_model2(X, Y))
ret[1] = self.evaluate_model2(Xtest, Ytest)
print(ret[1])
if self.modeln == 3:
print(self.fit_model3(X, Y))
ret[1] = self.evaluate_model2(Xtest, Ytest)
print(ret[1])
return ret
@classmethod
def load(cls, path):
return pickle.load(open(path, 'rb'))
def save(self, path):
return pickle.dump(self, open(path, 'wb'))
def fit_model1(self, X, Y):
self.model1 = LinearSVC(C=0.01,
penalty="l1",
dual=False,
random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model1(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model2(self, X, Y):
self.model1 = LinearSVC(C=0.18,
penalty="l1",
dual=False,
random_state=42)
self.model1.fit(X, Y)
recall = self.model1.score(X, Y)
return recall
def evaluate_model2(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def fit_model3(self, X, Y):
pre_recall = 0.0
for g in [0.01, 0.05, 0.1, 0.3, 0.5]:
model = SVC(C=0.18, gamma=g, random_state=42)
model.fit(X, Y)
recall = model.score(X, Y)
print(recall)
if recall > pre_recall:
pre_recall = recall
self.model1 = model
return recall
def evaluate_model3(self, X, Y):
evaluation = self.model1.score(X, Y)
return evaluation
def isQuestion(self, opinion):
dataset = [{'text': opinion.text, 'label': 'negativeq'}]
X, Y, L = self.prepare_data(dataset)
return self.model1.predict(X)[0]
def addQuestion(self, opinion):
q = models.QuestionOpinion()
q.tweet = opinion
q.since_id = opinion.twitter_id
q.save()
return q
def checkQuestion(self, twitter, q):
s = twitter.search(q='@' + q.tweet.tweeter.tweeter_name,
count='500',
result_type='mixed',
since_id=q.since_id)
replies = []
for tw in s['statuses']:
if tw['in_reply_to_status_id_str'] == str(q.tweet.twitter_id):
replies.append(tw)
diffdate = datetime.now() - q.date_created.replace(tzinfo=None)
if diffdate.days > MAX_DAYS:
q.delete()
else:
q.since_id = s['statuses'][0]['in_reply_to_status_id_str']
q.save()
return {"replies": replies, 'found': s['statuses']}
def checkQuestions(self, twitter):
for q in models.QuestionOpinion.objects.filter():
self.checkQuestion(twitter, q)
