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Vectorizer.py
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Vectorizer.py
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# Local module imports
from dataparser import Data
# numpy import
import numpy as np
#sci kit imports
from sklearn.svm import SVC, LinearSVC
from sklearn.feature_selection import SelectKBest, chi2
from sklearn import metrics
from sklearn.multiclass import OneVsOneClassifier
"""
Vectorizer:
Class that takes in data of real tweets and troll tweets,
transforms the data into useful feature matrices,
and runs experiments:
- SVM with different parameter
- k best features with different parameters
"""
class Vectorizer():
"""
__init__(): Initializes our vectorizer with a real tweet and troll tweet file
Input: realTweetFile: specifying path of real tweets
trollTweetFile: self explanatory
realTweetSize: self explanatory
trollTweetSize: self explanatory
"""
def __init__(self, realTweetFile, trollTweetFile, realTweetSize=5000, trollTweetSize=1000):
self.data = Data(realTweetFile, trollTweetFile, realTweetSize, trollTweetSize)
"""
runSVM(): Performs SVM classification and prints results
Input: contentMatrix: Boolean of whether we want the content matrix as features
contentMatrixFeatures: if contentMatrix, specify features to be used
wordN: if contentMatrix, n for word n-grams
stylisticMatrix: As before
stylisticMatrixFeatures: As before
charN: As before
testSize: How big we want our test set compare to entire data set
r: r for svc
hyper_param: for svc
kernel_type: 0 is linear, 1 is poly
degree: degree of function
Output: performance: dictionary of performance metrics
"""
def runSVM(self, contentMatrix=True, contentMatrixFeatures=[], wordN=1,\
stylisticMatrix=True, stylisticMatrixFeatures=[], charN=3, testSize=.3,\
r=1, hyper_param=0.1, kernel_type=0, degree=1):
m = {} # Dictionary to pass information to getSplitData
# If content matrix, build appropriate data
if contentMatrix:
m['content'] = [wordN, contentMatrixFeatures]
# Same for stylistic
if stylisticMatrix:
m['stylistic'] = [charN, stylisticMatrixFeatures]
# Get split data with matrices and desired test size
X_train, Y_train, X_test, Y_test, d1, d2 = self.getSplitData(m, testSize)
performance = self.runSVC(X_train, Y_train, X_test, Y_test, r, hyper_param, kernel_type, degree)
# Print results
print("Performing SVM:")
print("---------------------------------------")
if kernel_type == 0:
print("\tLinear kernel with: ")
print("\t\thyper parameter: " + str(hyper_param))
else:
print("\tQuadratic kernel with: ")
print("\t\tr: " + str(r) + ", hyper parameter: " + str(hyper_param))
print("\tContent Matrix: " + str(contentMatrix))
if contentMatrix:
print("\t\tWord ngram n: " + str(wordN))
print("\t\tContent Matrix Features: " + str(contentMatrixFeatures))
print("\tStylistic Matrix: " + str(stylisticMatrix))
if stylisticMatrix:
print("\t\tChar ngram n: " + str(charN))
print("\tStylistic Matrix Features" + str(stylisticMatrixFeatures))
print("\ttest set size: " + str(testSize))
print("-----------------------------------------")
print("\tPerformance")
for metric in performance:
print("\t\t" + metric + " : " + str(performance[metric]))
return performance
"""
runKBestFeatures(): Performs select K best features
Input: k: for k best features
n: for word or char gram n
content: boolean denoting whether content matrix is to be used
or stylistic matrix
features: features for the matrix to be used
kernel_type, r, degree and hyper_param as before
Output: features: list of k best features
performance: dictionary of performance metrics
"""
def runKBestFeatures(self, k, contentMatrix=True, contentMatrixFeatures=[], wordN=1,\
stylisticMatrix=True, stylisticMatrixFeatures=[],charN=3, testSize=.3):
m = {} # Dictionary to pass information to getSplitData
# If content matrix, build appropriate data
if contentMatrix:
m['content'] = [wordN, contentMatrixFeatures]
# Same for stylistic
if stylisticMatrix:
m['stylistic'] = [charN, stylisticMatrixFeatures]
# Get split data from m
X_train, Y_train, X_test, Y_test, cmFS, smFS = self.getSplitData(m, testSize)
# Find best k features
X_new = SelectKBest(chi2, k=k).fit(X_train, Y_train) # Get the best scores
# Create dictionary to search through
scores = {} # Add best scores to s
for i in range(len(X_new.scores_)):
if not np.isnan(X_new.scores_[i]):
scores[X_new.scores_[i]] = i
# Create list for best k features
features = []
ids = None
smStart = 0
if contentMatrix:
ids = self.generateNgramID(wordN)
# Get top k features from s
for s in sorted(scores.items(), reverse=True)[:k]:
# boolean to see if it is an ngram
found = False
for seq in ids:
if s[1] == ids[seq]:
features.append(seq)
found = True
# If not found among ngrams
if found == False:
cmf = len(contentMatrixFeatures)
if 3 in contentMatrixFeatures:
cmf +=2
if cmFS < s[1] < cmFS + cmf :
features.append("content: " + str(s[1] - cmFS))
smStart = len(ids) + cmFS
if stylisticMatrix:
ids = self.generateNgramID(charN, True)
# Get top k features from s
for s in sorted(scores.items(), reverse=True)[:k]:
# boolean to see if it is an ngram
found = False
for seq in ids:
if s[1] == ids[seq]:
features.append(seq)
found = True
# If not found among ngrams
if found == False:
if smStart + smFS < s[1]:
smf = len(stylisticMatrixFeatures)
if 3 in stylistixMatrixFeatures:
smf += 2
if 7 in stylisticMatrixFeatures:
smf += 2
features.append("stylistic: " + str((s[1] + smStart + smFS )))
# Fit with traning data
kBest = SelectKBest(chi2, k=100).fit(X_train, Y_train)
# transform training and test X
X_train_new = kBest.transform(X_train)
X_test_new = kBest.transform(X_test)
performance = self.runSVC(X_train_new, Y_train, X_test_new, Y_test)
# Print results
print("Performing K best features")
print("---------------------------------------")
print("\tLinear kernel with: ")
print("\t\thyper parameter: " + str(0.1))
print("\tWith: ")
if contentMatrix:
print("\t\tContent Matrix: " + str(contentMatrix))
print("\t\t\tWord ngram n: " + str(wordN))
print("\t\t\tContent Matrix Features: " + str(contentMatrixFeatures))
if stylisticMatrix:
print("\t\tStylistic Matrix: " + str(stylisticMatrix))
print("\t\t\tChar ngram n: " + str(charN))
print("\t\t\tStylistic Matrix Features" + str(stylisticMatrixFeatures))
print("\ttest set size: " + str(.3))
print("-----------------------------------------")
print("\tPerformance")
for metric in performance:
print("\t\t" + metric + " : " + str(performance[metric]))
print("-----------------------------------------")
return features, performance
"""
runSVC(): Returns performance results from SVC classifier
for matrices
Input: X_train, Y_train, X_test, Y_test: sets of vectors of features and labels
r, hyper_param, kernel_type, degree: attributes to run svc classifier
Output: X_train, Y_train, X_test, Y_test
"""
def runSVC(self, X_train, Y_train, X_test, Y_test,\
r=1, hyper_param=0.1, kernel_type=0, degree=1):
# Declare SVC
svc = SVC(C = hyper_param, kernel = 'linear', degree = degree, class_weight = 'balanced')\
if kernel_type == 0\
else SVC(C = hyper_param, kernel = 'poly', degree = degree, class_weight = 'balanced', coef0 = r)
# Fit data
svc.fit(X_train, Y_train)
Y_predicted = svc.predict(X_test)
Y_predicted_auroc = svc.decision_function(X_test)
return self.getPerformance(Y_test, Y_predicted, Y_predicted_auroc)
"""
getSplitData(): Returns split data given dictionary of desired features
for matrices
Input: matrices: dictionary informing about desired features
testSize: size of test set compared to entire data set
Output: X_train, Y_train, X_test, Y_test
"""
def getSplitData(self, matrices={'content':[1, []], 'stylistic':[3, []]}, testSize=0.3):
# Get split data from self.data with testSize
X_train, X_test, Y_train, Y_test = self.data.getRandomSplitData(testSize)
# Declare variable for content train and test matrices
cmTr, cmTe = None, None
if 'content' in matrices:
cmTr, cmFS = self.getContentMatrix(X_train, matrices['content'][0], matrices['content'][1])
cmTe, dummy = self.getContentMatrix(X_test, matrices['content'][0], matrices['content'][1])
# Same here
smTr, smTe = None, None
if 'stylistic' in matrices:
smTr, smFS = self.getStylisticMatrix(X_train, matrices['stylistic'][0], matrices['stylistic'][1])
smTe, dummy= self.getStylisticMatrix(X_test, matrices['stylistic'][0], matrices['stylistic'][1])
# If both we want them to be merged
x_train, x_test = None, None
if 'stylistic' in matrices and 'content' in matrices:
x_train = np.concatenate((cmTr, smTr), 1)
x_test = np.concatenate((cmTe, smTe), 1)
# Otherwise choose the one
if x_train is None and x_test is None:
x_train = cmTr if cmTr is not None else smTr
x_test = cmTe if cmTe is not None else smTe
# Get np.array of Y_train
y_train = np.array(Y_train)
y_test = np.array(Y_test)
# Return
return x_train, y_train, x_test, y_test, cmFS, smFS
"""
getPerformance: given, y_true, y_pred and y_pred auroc, return performance metrics
Input: y_true: actual y labels for the corresponding matrix
y_pred: y labels predicted from our algorithms
y_pred_auroc: special case for auroc
Output: dictionary of metric scores
"""
def getPerformance(self, y_true, y_pred, y_pred_auroc):
confusion_matrix = metrics.confusion_matrix(y_true, y_pred, labels=[0,1])
return {
"accuracy": metrics.accuracy_score(y_true, y_pred),
"f1-score": metrics.f1_score(y_true, y_pred),
"auroc": metrics.roc_auc_score(y_true, y_pred_auroc),
"precision": metrics.precision_score(y_true, y_pred),
"sensitivity": confusion_matrix[0][0]/ (confusion_matrix[0][0] + confusion_matrix[0][1]),
"specificity": confusion_matrix[1][1]/ (confusion_matrix[1][0] + confusion_matrix[1][1])}
"""
getContentMatrix(): Returns the feature matrix for content features
Input: cols : Integer, the number of columns in the feature matrix
n : Integer, Describes n for word n grams
Output: Numpy Array of dimension(number of tweets, cols)
"""
def getContentMatrix(self, tweetSet, n, features):
# Generate n gram ids
ids = self.generateNgramID(n)
cols = len(ids) + len(features) + 1
if 3 in features:
cols += 2
# Declare np array of zeros
fm = np.zeros((len(tweetSet), cols))
# feature column start
# The index where the features other than word n grams
fCS = len(ids)
# for each tweet
# build the feature row
for i in range(len(tweetSet)):
t = tweetSet[i]
tweetNgram = t.getNgram(n)
for seq in tweetNgram:
if seq in ids:
fm[i][ids[seq]] = float(tweetNgram[seq])
# For each feature in features,
# add the feature to the matrix if desired
if 0 in features:
fm[i][fCS] = t.getAvgEmojis()
if 1 in features:
fm[i][fCS] = t.getNumURL()
if 2 in features:
fm[i][fCS], fm[i][fCS + 3] = t.getNumTags()
if 3 in features:
fm[i][fCS+ 4], fm[i][fCS + 5], fm[i][fCS + 6], dummy, dummy2 = t.getPOSTaggedDistribution()
if 4 in features:
fm[i][fCS + 7] = t.getNumTokens()
return fm, fCS
"""
getStylisticMatrix(): Returns the feature matrix for stylistic features
Input: cols : Integer, the number of columns in the feature matrix
n : Integer, Describes n for character n grams
Output: Numpy Array of dimension(number of tweets, cols)
"""
def getStylisticMatrix(self, tweetSet, n, features=[]):
ids = self.generateNgramID(n, True) # Generate ngram ids
cols = len(ids) + len(features)
if 3 in features:
cols += 1
if 7 in features:
cols += 26
fm = np.zeros((len(tweetSet), cols)) # make np array
fCS = len(ids) # Keep track of temp_col
# For each tweet
# Fill in the matrix accordingly
for i in range(len(tweetSet)):
t = tweetSet[i]
tweetCharGram = t.getCharNgram(n)
for seq in tweetCharGram:
if seq in ids:
fm[i][ids[seq]] = tweetCharGram[seq]
# For each feature in features
# add the feature to the matrix if desired
if 0 in features:
fm[i][fCS] = t.getAvgNumPunct()
if 1 in features:
fm[i][fCS] = t.getAvgWordSize()
if 2 in features:
fm[i][fCS] = t.getVocabSize()
if 3 in features:
dummy, dummy1, dummy2, fm[i][fCS + 3], fm[i][fCS + 4] = t.getPOSTaggedDistribution()
if 4 in features:
fm[i][fCS+ 5] = t.getDigitFrequency()
if 5 in features:
fm[i][fCS + 6] = t.getAvgHashTagLength()
if 6 in features :
fm[i][fCS+ 7] = t.getAvgCapitalizations()
# add letter frequency in this case
if 7 in features:
letters = t.getLetterFrequency()
idx = 0
for j in range(fCS+ 8, fCS+34):
fm[i][j] = letters[idx]
idx += 1
return fm, fCS
"""
genGram(): Generate dictionary of ngrams or charngrams depending on
char's boolean value
Input: n : Integer describing n for word/character n grams
char : boolean value determining whether this is a word ngram
or char n gram
Output: dict final_g: dictionary of ngrams or char ngrams and their occurences,
omitting those that appeared less than 5 times
"""
def genGram(self, n, char=False):
g = {} # Declare dictionary
for tweet in self.data.getAllTweets():
gram = tweet.getNgram(n) if char==False else tweet.getCharNgram(n)
for ngram in gram:
if ngram in g:
g[ngram] += gram[ngram]
else:
g[ngram] = gram[ngram]
final_g = {}
for gram in g:
if g[gram] >= 5:
final_g[gram] = g[gram]
return final_g
"""
genNgram(): Returns dictionary of ids for each ngram and a dictionary of ngrams
Input: n : Integer, Describes n for word/character n grams
type : Integer, 0 for word Ngram, anything other integer for character Ngrams
Output: word_dict: Returns dictionary of ids for each ngram
ngrams: dictionary of word/character ngrams depending on the type
"""
def genNgram(self, n):
return self.genGram(n)
"""
generateCharNgram(): Returns dictionary of ids for each ngram and a dictionary of ngrams
Input: n : Integer, Describes n for word/character n grams
type : Integer, 0 for word Ngram, anything other integer for character Ngrams
Output: word_dict: Returns dictionary of ids for each ngram
ngrams: dictionary of word/character ngrams depending on the type
"""
def genCharNgram(self, n):
return self.genGram(n, True)
"""
generateNgramID(): Returns dictionary of ids for each ngram and a dictionary of ngrams
Input: n : Integer, Describes n for word/character n grams
type : Integer, 0 for word Ngram, anything other integer for character Ngrams
Output: ngramIds: Dictionary of ngrams with associated id
"""
def generateNgramID(self, n, charGram=False):
ind = 0
ngram_dict = {}
grams = self.genNgram(n) if charGram == False else self.genCharNgram(n)
for seq in grams:
if seq not in ngram_dict:
ngram_dict[seq] = ind
ind += 1
return ngram_dict
# For testing purposes
# And running experiments
if __name__ == "__main__":
print("Running Vectorizer.py main")
electionTweets = "./data/2016_US_election_tweets_100k.csv"
electionTrolls = "./data/IRAhandle_tweets_1.csv"
# Initialize vectorizer
f = Vectorizer(electionTweets, electionTrolls)
for feature in f.runKBestFeatures(100, 3):
print(feature)
# Run the experiment
f.runSVM()