zadanie warsztaty

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unknown 2020-06-26 13:25:29 +02:00
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import pandas as pd # our main data management package
import matplotlib.pyplot as plt # our main display package
import string # used for preprocessing
import re # used for preprocessing
import nltk # the Natural Language Toolkit, used for preprocessing
import numpy as np # used for managing NaNs
import nltk
from prefect import task, Flow, Parameter
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords # used for preprocessing
from nltk.stem import WordNetLemmatizer # used for preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression # our model
from sklearn.model_selection import train_test_split
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
train = pd.read_csv('train.csv')
train_count=train.count()
train = train.drop(['keyword', 'location'], axis = 1)
test = pd.read_csv('test.csv')
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
pp_text_train = [] # our preprocessed text column
pp_text_test = [] # our preprocessed text column
@task(log_stdout=True)
def describe(file):
file.describe()
with Flow("Prepare") as prepare:
file = Parameter('file')
describe(file)
prepare.run(file=train)
prepare.run(file=test)
# remove urls, handles, and the hashtag from hashtags (taken from https://stackoverflow.com/questions/8376691/how-to-remove-hashtag-user-link-of-a-tweet-using-regular-expression)
def remove_urls(text):
new_text = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",text).split())
return new_text
# make all text lowercase
def text_lowercase(text):
return text.lower()
# remove numbers
def remove_numbers(text):
result = re.sub(r'\d+', '', text)
return result
# remove punctuation
def remove_punctuation(text):
translator = str.maketrans('', '', string.punctuation)
return text.translate(translator)
# tokenize
def tokenize(text):
text = word_tokenize(text)
return text
# remove stopwords
def remove_stopwords(text):
text = [i for i in text if not i in stop_words]
return text
# lemmatize
def lemmatize(text):
text = [lemmatizer.lemmatize(token) for token in text]
return text
def preprocessing(text):
text = text_lowercase(text)
text = remove_urls(text)
text = remove_numbers(text)
text = remove_punctuation(text)
text = tokenize(text)
text = remove_stopwords(text)
text = lemmatize(text)
text = ' '.join(text)
return text
@task(log_stdout=True)
def preprocess(file, pp_text):
for text_data in file['text']:
pp_text_data = preprocessing(text_data)
pp_text.append(pp_text_data)
file['pp_text'] = pp_text # add the preprocessed text as a column
with Flow("Preprocess") as preprocessData:
file = Parameter('file')
pp_text = Parameter('pp_text')
preprocess(file, pp_text)
preprocessData.run(file=train, pp_text=pp_text_train)
preprocessData.run(file=test, pp_text=pp_text_test)
train_text_data = list(train['pp_text'])
test_text_data = list(test['pp_text'])
corpus = train_text_data + test_text_data
tf=TfidfVectorizer()
fitted_vectorizer = tf.fit(corpus)
# train
train_transform = fitted_vectorizer.transform(train['pp_text'])
y = train['target']
# test
test_transform = fitted_vectorizer.transform(test['pp_text'])
X=train_transform
print(X)
X_train, X_test, y_train, y_test = train_test_split(X, y)
scikit_log_reg = LogisticRegression()
model=scikit_log_reg.fit(X_train, y_train)
predictions = model.predict(X_test)
count = 0
for guess, answer in zip(predictions, y_test):
if guess == answer:
count += 1
print(count/len(y_test))

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