def __init__(self, param, model):
an = Analiser(training_data='data/coba_train.csv')
# load model
self.model = model
if model == 'load_model':
filename = 'model'
an.load_model(filename)
elif model == 'train_model':
filename = 'model'
an.train(filename)
elif model == 'retrain':
filename = 'model'
an.retrain(filename)
else:
exit()
self.instance_var1 = param
from analiser import Analiser
from os import path
an = Analiser(training_data='data/coba_train.csv')
# retrain model
filename = 'model'
an.retrain(filename)
kata1 = input("Pos > ")
print(kata1)
print(an.testFromTrained([an.tfidf_data.transform(kata1)]))
kata2 = input("Neg > ")
print(kata2)
print(an.testFromTrained([an.tfidf_data.transform(kata2)]))
from analiser import Analiser # start analiser with set training data an = Analiser(training_data='data/training_all_random.csv') # train new model an.train(filename='model') test = "ahok itu pemimpin yang beres memimpin" print test print an.testFromTrained([an.tfidf_data.transform(test)]) test = "ahok itu pemimpin yang ga beres memimpin" print test print an.testFromTrained([an.tfidf_data.transform(test)])
import collections
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import re
from TwitterConfig import *
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sqlalchemy import create_engine
from analiser import Analiser
twitter = login()
an = Analiser(training_data='data/coba_train.csv')
# load model
filename = 'model'
an.load_model(filename)
def MineData(apiobj, query, pagestocollect=10):
results = apiobj.search(q=query,
include_entities='true',
tweet_mode='extended',
count='450',
result_type='recent')
data = results['statuses']
from analiser import Analiser
an = Analiser()
an.train(output_file='EIGHT_MODEL')
an.showPlot()
while True:
sentence = input()
print(an.testFromTrained([an.tfidf_data.transform(sentence)]))
from analiser import Analiser
# FIRST_MODEL pool1 0.005 2 20
# SECOND_MODEL pool2 0.005 2 20
# THIRD_MODEL pool2 0.01 16 25
# FOURTH_MODEL pool2 0.01 16 40
# FIFTH_MODEL pool2 0.01 32 45
# SIXTH_MODEL pool2 0.02 16 25
# SEVENTH_MODEL pool2 0.025 16 20
# EIGHTH_MODEL pool2 0.025 16 20 softmax
an = Analiser()
an.load_model(file_name='EIGHT_MODEL')
while True:
sentence = input()
print(an.testFromTrained([an.tfidf_data.transform(sentence)]))
import os
import discord
import numpy as np
import math
from dotenv import load_dotenv
from analiser import Analiser
load_dotenv()
token = os.getenv('DISCORD_TOKEN')
client = discord.Client()
print("Loading Model...")
an = Analiser()
model = an.load_model('EIGHT_MODEL')
@client.event
async def on_ready():
print(f'{client.user.name} has connected to Discord!')
@client.event
async def on_message(message):
if message.author == client.user:
return
# u"\u2588" u"\u2581"
y = an.model_load.predict_proba(
np.array([an.tfidf_data.transform(message.content)]))
verdict = an.getBinaryResult(y)