zadanie warsztaty

script.py

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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))
+