system: You are an expert security code reviewer. You will be shown a source code file along with a multi-select question listing several CWE (Common Weakness Enumeration) categories. Your task is to identify which of the listed CWEs are present in the code.

OUTPUT FORMAT: Respond with the letter(s) of all correct options, sorted alphabetically and comma-separated, wrapped in a single \boxed{...}. Examples: `\boxed{A}` for one correct, `\boxed{A,C}` for two, `\boxed{A,C,E}` for three. Output the boxed answer ONLY -- no explanation, no other text.

user: ```python
import sys
import os
import re
import pickle
import nltk

nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

import pandas as pd
import numpy as np

from sqlalchemy import create_engine

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer

from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multioutput import MultiOutputClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import classification_report, f1_score, make_scorer
from sklearn.metrics import precision_score, recall_score, accuracy_score

def load_data(database_filepath):
    '''
    Function to load cleaned data for the ML pipeline from the database

    Args:
        database_filepath: a string containing the path of the database

    Returns:
        X: a Dataframe containing the features
        y: a Dataframe containing the labels
        category_names: a list containing the category names

    '''

    try:
        engine = create_engine('sqlite:///{}'.format(database_filepath))
        tab_name = database_filepath.split('/')[-1].split('.')[0]
        df = pd.read_sql_table(tab_name, engine)
        X = df['message']
        y = df[df.columns.drop(['id', 'message', 'original', 'genre'])] 
        category_names = y.columns.tolist()
        return X, y, category_names
    except:
         print('Fail to load to the database.')


def tokenize(text):
    '''
    Function to tokenize the text data
    
    Args:
        text: a string
        
    Returns:
        words: a list of tokenized words
    '''

    # Replace all urls
    url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
    urls = re.findall(url_regex, text)
    for url in urls:
        text.replace(url, 'urlplaceholder')
    
    # Normalize the text
    pattern = r'[^a-zA-Z0-9]'
    text = re.sub(pattern, ' ', text.lower())
    
    # Tokenize the text
    tokens = word_tokenize(text)
    
    # Lemmatize the tokens
    lemmatizer = WordNetLemmatizer()
    lemmed = [lemmatizer.lemmatize(w).strip() for w in tokens]

    # Remove stop words    
    words = [w for w in lemmed if w not in stopwords.words('english')]
    
    return words


def report_metrics(y_true, y_pred, sort_by = None):
    '''
    Function to display the classification report for each category in multi-class classification
    
    Args:
        y_true: a DataFrame with true labels, each row represents the labels for one row in X
        y_pred: 2d array-like with predicted labels, each row represents the labels for one row in X
        sort_by: a string, denoting the column to sort by, select from ['Accuracy', 'F1_score', 'Precision', 'Recall']
        
    Returns:
        df: a Dataframe containing the scores for each category
    '''
    cols = y_true.columns.tolist()
    
    df_list = []
    
    for i, col in enumerate(cols):
        # Replace y_true.to_numpy() with np.array(y_true)
        accuracy = accuracy_score(np.array(y_true)[:, i], y_pred[:, i])
        f1 = f1_score(np.array(y_true)[:, i], y_pred[:, i], average='weighted')
        precision = precision_score(np.array(y_true)[:, i], y_pred[:, i], average='weighted')
        recall = recall_score(np.array(y_true)[:, i], y_pred[:, i], average='weighted')
        
        df_list.append([col, accuracy, f1, precision, recall])
      
    df = pd.DataFrame(df_list, columns=['Category', 'Accuracy', 'F1_score', 'Precision', 'Recall'])
    
    if sort_by is None:
        return df
    
    df.sort_values(by = sort_by, axis = 0, ascending=False, inplace=True, ignore_index=True)
    return df


def mean_f1_score(y_true, y_pred):
    '''
    Function to calculate the mean f1 score for multi-class classification
    
    Args:
        y_true: a DataFrame with true labels, each row represents the labels for one row in X
        y_pred: 2d array-like with predicted labels, each row represents the labels for one row in X
        
    Returns:
        f1_mean: a float for the mean f1 score
    '''
    # f1_list = [f1_score(np.array(y_true)[:, i], y_pred[:, i], average='weighted', 
    #     labels = np.unique(y_pred), zero_division = 0) for i in range(y_true.shape[1])]

    f1_list = [f1_score(np.array(y_true)[:, i], y_pred[:, i], 
        average='weighted') for i in range(y_true.shape[1])]

    f1_mean = sum(f1_list) / len(f1_list)
    return f1_mean


def build_model():
    '''
    Function to build the model for grid search

    Args:
        None

    Retuns:
        cv: a GridSearchCV object containing the ML pipeline
    '''

    pipeline = Pipeline([
        ('tfidfVect', TfidfVectorizer(tokenizer=tokenize)),
        ('RFclf', MultiOutputClassifier(RandomForestClassifier()))
        ])

    parameters = {
    'tfidfVect__min_df': [1, 5, 10],
    # 'tfidfVect__smooth_idf': [True, False],
    # 'tfidfVect__use_idf': [True, False],
    'RFclf__estimator__bootstrap': [True, False],
    # 'RFclf__estimator__criterion': ['gini', 'entropy'],
    'RFclf__estimator__min_samples_leaf': [1, 2, 5]    
    }

    score_method = make_scorer(mean_f1_score, greater_is_better=True)

    cv = GridSearchCV(pipeline, param_grid=parameters, 
        scoring=score_method, cv=3, verbose=10)
    
    return cv


def evaluate_model(model, X_test, y_test, category_names):
    '''
    Function to print the metrics for multi-class classifications

    Args:
        model: an estimator object
        X_test: a Dataframe containing the features in the test set
        y_test: a Dataframe containing the labels in the test set
        category_names: a list containing the category names

    Returns:
        None
    '''

    y_pred = model.predict(X_test)
    results = report_metrics(y_test, y_pred)
    for cat in category_names:
        metrics = results[results['Category'] == cat].values.tolist()[0]
        print(cat, ': \n Accuracy: {}, F1_score: {}, Precision {}, Recall: {}'
            .format(metrics[0], metrics[1], metrics[2], metrics[3]))


def save_model(model, model_filepath):
    '''
    Function to save the fitted model

    Args:
        model: an estimator object
        model_filepath: a string containing the path to save the model

    Reuturns:
        None
    '''

    try:
        pickle.dump(model, open(model_filepath, 'wb'))
    except:
        print('Fail to save the model.')


def main():
    if len(sys.argv) == 3:
        database_filepath, model_filepath = sys.argv[1:]
        print('Loading data...\n    DATABASE: {}'.format(database_filepath))
        X, y, category_names = load_data(database_filepath)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
        
        print('Building model...')
        model = build_model()
        
        print('Training model...')
        model.fit(X_train, y_train)
        
        print('Evaluating model...')
        evaluate_model(model, X_test, y_test, category_names)

        print('Saving model...\n    MODEL: {}'.format(model_filepath))
        save_model(model, model_filepath)

        print('Trained model saved!')

    else:
        print('Please provide the filepath of the disaster messages database '\
              'as the first argument and the filepath of the pickle file to '\
              'save the model to as the second argument. \n\nExample: python '\
              'train_classifier.py ../data/DisasterResponse.db classifier.pkl')


if __name__ == '__main__':
    main()
```

Which of the following CWE vulnerability categories are present in this code? Multiple options may be correct.

  A) CWE-20: Improper Input Validation
  B) CWE-295: Improper Certificate Validation
  C) CWE-918: Server-Side Request Forgery (SSRF)
  D) CWE-215: Insertion of Sensitive Information Into Debugging Code
  E) CWE-117: Improper Output Neutralization for Logs
  F) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
Answer: A