def __init__(self):
db_mgr = DataManager(self.DATABASE)
self.train_tweets, self.train_labels = db_mgr.select_wikipedia_train()
self.vectorizer = get_vectorizer("tfidf", min_df=1)
self.nb = Classifier(classifier="nb")
self.train_data = self.vectorizer.fit_transform(self.train_tweets)
self.nb.fit(self.train_data, self.train_labels)
from features import *
from classifiers import Classifier
from db import DataManager
N_TIMES = 1
for i in range(0,N_TIMES):
print i+1, "times"
DATABASE = "us_twitter.db"
split = 0.8
db_mgr = DataManager(DATABASE)
train_tweets, train_labels = db_mgr.select_wikipedia_train()
test_tweets, test_labels, dummy1, dummy2 = db_mgr.select_tweets(limit=10, state_fips=True, table="us_tweets", label=state_fips)
results = get("results.json")
vectorizer = get_vectorizer("tfidf", min_df=1)
classifiers = {
"BernoulliNB": Classifier(classifier="bnb"),
"MultinomialNB": Classifier(classifier="nb"),
"KNN-1000": Classifier(classifier="knn", k=1000),
"KNN-2000": Classifier(classifier="knn", k=2000),
# "SVC": Classifier(classifier="svm", params={"C" : 1.0,"kernel" : 'linear','verbose':True})
"SVC": Classifier(load="classifier-SVC")
}
import pickle
from lib import *
from labels import *
from metrics import *
from features import *
from db import DataManager
from classifiers import Classifier
DATABASE = "us_twitter.db"
db_mgr = DataManager(DATABASE)
train_data, train_labels = db_mgr.select_wikipedia_train()
vectorizers = {
"count":get_vectorizer("tfidf", min_df=1),
"tfidf":get_vectorizer("count", min_df=1)
}
print "Vectorizing Training Data..."
count_data = vectorizers["count"].fit_transform(train_data)
tf_idf_data = vectorizers["tfidf"].fit_transform(train_data)
classifiers = {
"BernoulliNB": {
"count":Classifier(classifier="bnb"),
"tfidf":Classifier(classifier="bnb")
},
"MultinomialNB": {
"count":Classifier(classifier="nb"),
"tfidf":Classifier(classifier="nb")
