{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-01-08T13:45:37.929434Z","iopub.execute_input":"2022-01-08T13:45:37.929821Z","iopub.status.idle":"2022-01-08T13:45:37.9443Z","shell.execute_reply.started":"2022-01-08T13:45:37.929773Z","shell.execute_reply":"2022-01-08T13:45:37.943325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. MÔ TẢ BÀI TOÁN\n* Quora là một nền tảng để mọi người học hỏi với nhau. Mọi người có thể đăng những câu hỏi và người khác vào chia sẻ, trả lời những thắc mắc đó. Mục đích của bài toán này là để chỉ ra câu hỏi có chân thành hay không. Những câu hỏi không chân thành thường là những câu đưa ra tuyên bố quan điểm của mình hơn là để tìm những câu trả lời có ích: không trung lập, khiêu khích hoặc chê bai, không có căn cứ thực tế và nội dung khiêu dâm.\n* Một thách thức lớn đó là loại bỏ đi những câu hỏi toxic - những câu hỏi được đặt ra dựa trên những tiền đề sai lầm hoặc có ý định đưa ra một tuyên bố hơn là tìm kiếm câu trả lời hữu ích.\n* Vậy mục tiêu, thách thức của dự án này là loại bỏ những câu hỏi \"insincere\" - những câu hỏi không mang tính chất đóng góp, thiếu chân thành, thậm chí để đả kích một cá nhân, tập thể hay tổ chức nào đó. Ngoài ra, những câu hỏi có ý định đưa ra một tuyên bố hơn là tìm kiếm những câu trả lời hữu ích cũng cần bị loại bỏ.\n* Input: Câu hỏi dưới dạng văn bản\n* Output: 0/1 (Yes/No)","metadata":{}},{"cell_type":"code","source":"from sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.svm import LinearSVC\nfrom sklearn.metrics import f1_score\nfrom sklearn.model_selection import train_test_split\nfrom wordcloud import STOPWORDS\nimport seaborn as sns\n# from nltk import WordNetLemmatizer\nimport re\nimport string\nimport os\nimport seaborn as sb\nfrom sklearn.cluster import KMeans\nfrom yellowbrick.cluster import KElbowVisualizer\nimport matplotlib.pyplot as plt\nfrom scipy.sparse import coo_matrix, hstack\nfrom sklearn import preprocessing\nfrom tqdm import tqdm","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:37.946112Z","iopub.execute_input":"2022-01-08T13:45:37.946633Z","iopub.status.idle":"2022-01-08T13:45:37.955708Z","shell.execute_reply.started":"2022-01-08T13:45:37.946592Z","shell.execute_reply":"2022-01-08T13:45:37.954947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **2. KHẢO SÁT DỮ LIỆU**\nDữ liệu được cung cấp gồm 2 file chính là train.csv và test.csv.","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/quora-insincere-questions-classification/train.csv')\ntest = pd.read_csv('/kaggle/input/quora-insincere-questions-classification/test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:37.957022Z","iopub.execute_input":"2022-01-08T13:45:37.957925Z","iopub.status.idle":"2022-01-08T13:45:42.087373Z","shell.execute_reply.started":"2022-01-08T13:45:37.957878Z","shell.execute_reply":"2022-01-08T13:45:42.086728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:42.088345Z","iopub.execute_input":"2022-01-08T13:45:42.088975Z","iopub.status.idle":"2022-01-08T13:45:42.099844Z","shell.execute_reply.started":"2022-01-08T13:45:42.088943Z","shell.execute_reply":"2022-01-08T13:45:42.099011Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Train shape :\", train.shape)\nprint(\"Test shape :\", test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:42.102139Z","iopub.execute_input":"2022-01-08T13:45:42.102387Z","iopub.status.idle":"2022-01-08T13:45:42.113052Z","shell.execute_reply.started":"2022-01-08T13:45:42.102359Z","shell.execute_reply":"2022-01-08T13:45:42.112171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.info()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:42.11692Z","iopub.execute_input":"2022-01-08T13:45:42.117189Z","iopub.status.idle":"2022-01-08T13:45:42.4426Z","shell.execute_reply.started":"2022-01-08T13:45:42.11716Z","shell.execute_reply":"2022-01-08T13:45:42.441603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Nhận xét:**\nTập dữ liệu train có 1306122 câu hỏi và tập test chứa 375806 câu hỏi để đánh giá hiệu năng mô hình. Trong đó, không có giá trị null nào và có 3 trường dữ liệu: qid, question_text, target.\n\nqid : id của câu hỏi, mỗi câu hỏi có 1 id riêng phân biệt\n\nquestion_text : nội dung câu hỏi ở dạng text\n\ntarget: gồm 2 nhãn 0 và 1. Nhãn 0 là sincere là câu hỏi chân thành, nhãn 1 là insincere là câu hỏi không chân thành (toxic)","metadata":{}},{"cell_type":"code","source":"print(\"Tổng số dữ liệu trong tập train: \",train.shape[0])\nprint(\"Số câu hỏi bình thường: \", len(train[train.target == 0]))\nprint(\"Số câu hỏi toxic: \",len(train[train.target == 1]))\nprint(\"Tỉ lệ giữa 2 lớp: \",len(train[train.target == 1])/len(train[train.target == 0]))\nprint('\\n')\n\nsb.countplot(train['target'])","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:42.443973Z","iopub.execute_input":"2022-01-08T13:45:42.444199Z","iopub.status.idle":"2022-01-08T13:45:42.965238Z","shell.execute_reply.started":"2022-01-08T13:45:42.444171Z","shell.execute_reply":"2022-01-08T13:45:42.964354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Nhận xét:**\nSố lượng câu hỏi sincerce:insincere có sự chênh lệch rất lớn. Tỉ lệ dữ liệu giữa 2 nhóm là 1:15 .Số lượng câu hỏi chân thành chiếm đến gần 94% tập dữ liệu trong khi số lượng câu hỏi không chân thành chỉ chiếm 6.2%.Sự mất cân bằng nghiêm trọng này sẽ dẫn tới 2 vấn đề lớn:\n\n* Đánh giá sai chất lượng mô hình: sự mất cân bằng có thể khiến thước đo đánh giá mô hình là độ chính xác (accuracy) có thể đạt được rất cao mà không cần tới mô hình. Minh chứng đơn giản với bộ dữ liệu trên thì nếu kết quả dự đoán đưa ra tất cả đều có target = 0 thì độ chính xác đã đạt 94%.\n* Mô hình dự đoán kém chính xác: sự mất cân bằng có thể gây hiện tượng Overfitting, khiến kết quả dự đoán ra thường nghiêng về nhóm đa số và kém trên nhóm thiểu số trong khi tầm quan trọng của việc dự báo được chính xác một mẫu thuộc nhóm thiểu số lớn hơn nhiều so với dự báo mẫu thuộc nhóm đa số. ( VD ở đây việc quan trọng nhất là xác định được nhóm câu hỏi toxic)\n\n-> Dữ liệu này mất cân bằng rất lớn\n\n-> Cần áp dụng các phương pháp cân bằng dữ liệu\n\n-> F1-score sẽ được sử dụng để đánh giá hiệu năng của mô hình. F1 score là độ cân bằng đồng đều giữa precision và recall","metadata":{}},{"cell_type":"markdown","source":"# **3. CHUẨN BỊ MÔ HÌNH**\n\nTrước khi thực hiện bất cứ quá trình tiền xử lý dữ liệu hay áp dụng các model khác nhau cho bài toán thì ta nghĩ tới việc đưa ra một giải pháp nền tảng để từ đó phát triển và có thể dễ dàng đánh giá độ hiệu quả của các thay đổi sau này. Vì thế, trước tiên, ta sẽ xây dựng một mô hình đơn giản để dự đoán bài toàn trên.\n\n**3.1. TF-IDF** \n\nViệc đầu tiên cần làm là xử lý dữ liệu đầu vào. Với dữ liệu dạng text, chúng ta cần tiến hành encoding để đưa dữ liệu về dạng không gian vector. Cách đơn giản nhất là sử dụng one-hot encoding. Tuy nhiên, one-hot encoding mang lại ít giá trị thông tin và có độ khái quát yếu. Ta đưa ra một giải pháp tốt hơn 1 chút đó là sử dụng TF-IDF.\n\nnhững từ có giá trị TF-IDF cao là những từ xuất hiện nhiều trong văn bản này, và xuất hiện ít trong các văn bản khác. Việc này giúp lọc ra những từ phổ biến và giữ lại những từ có giá trị cao (từ khoá của văn bản đó).","metadata":{}},{"cell_type":"code","source":"#sử dụng tf-idf từ thư viện sklearn\ntfidf = TfidfVectorizer(ngram_range=(1, 3))","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:42.966446Z","iopub.execute_input":"2022-01-08T13:45:42.966685Z","iopub.status.idle":"2022-01-08T13:45:46.419168Z","shell.execute_reply.started":"2022-01-08T13:45:42.96665Z","shell.execute_reply":"2022-01-08T13:45:46.418213Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ở đây ta chỉnh tham số ngram_range=(1,3) để tạo được các cụm từ gồm 1-3 từ trong bộ dữ liệu. Ví dụ với câu \"Python is cool\" sẽ sinh ra được các từ và cụm từ \"Python\", \"is\", \"cool\", \"Python is\", \"Python is cool\" , \"is cool\". Điều này sẽ giúp ta tạo được mối quan hệ giữa các cụm từ xuất hiện trong tập dữ liệu.\n\n\n**3.2. LinearSVC**\n\nThử nghiệm với nhiều tập mẫu cho thấy kết quả thu được từ LinearSVC là cao và ổn định nhất. Vì vậy, ta chọn LinearSVC làm mô hình cơ sở.\n![download.png](attachment:9830a083-7cd1-4bf0-978b-cd48b5f7e20e.png)\n\nLinearSVC giúp ta tìm được đường phân chia giữa 2 lớp sao cho margin là lớn nhất. Việc margin rộng hơn sẽ mang lại hiệu ứng phân lớp tốt hơn vì sự phân chia giữa hai classes là rạch ròi hơn.\n![download.png](attachment:762a4da7-b03c-468b-8669-50211ecfa8ab.png)\n\n**3.3. Split data to train and validation**\n\nTa cần chia tập train thành 2 phần để huấn luyện và đánh giá mô hình.\n\nCách đơn giản nhất là ta có thể lấy ngẫu nhiên một tập dữ liệu con từ tập train để làm tập validation. Tuy nhiên ta để ý tới trường hợp việc lấy ngẫu nhiên từ toàn bộ tập train có thể xảy ra trường hợp ta lấy ra tập validation chứa toàn dữ liệu thuộc cùng một lớp. Vấn đề này đặc biệt dễ xảy ra với tập dữ liệu hiện tại do độ lệch lớn giữa lượng dữ liệu của 2 lớp (tỉ lệ 15:1).\n\nGiải pháp: lấy ngẫu nhiên dữ liệu từ từng lớp. Ví dụ với bộ dữ liệu hiện tại, ta muốn tạo ra tập validation có tỉ lệ 1:10 từ tập train thì ta lấy ta 1/10 dữ liệu từ lớp có target = 0 và 1/10 dữ liệu từ tập target = 1.\n\nTa sử dụng hàm train_test_split() của sklearn để chia tập theo phương pháp trên.\n","metadata":{},"attachments":{"9830a083-7cd1-4bf0-978b-cd48b5f7e20e.png":{"image/png":"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"},"762a4da7-b03c-468b-8669-50211ecfa8ab.png":{"image/png":"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"}}},{"cell_type":"code","source":"def test_funtion_split():\n    X = train.question_text\n    y = train.target\n    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n    print(\"Tỉ lệ giữa 2 lớp trong tập train\",len(y_train[y_train == 0])/len(y_train[y_train == 1]))\n    print(\"Tỉ lệ giữa 2 lớp trong tập test\",len(y_val[y_val == 0])/len(y_val[y_val == 1]))\n\ntest_funtion_split()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:46.420374Z","iopub.execute_input":"2022-01-08T13:45:46.42069Z","iopub.status.idle":"2022-01-08T13:45:46.980737Z","shell.execute_reply.started":"2022-01-08T13:45:46.42065Z","shell.execute_reply":"2022-01-08T13:45:46.979696Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Thử chia tập validation với hàm train_test_split ta có thể thấy rõ tỉ lệ giữa 2 lớp ở tập train và validation là khá tương đồng với tỉ lệ của tập train lúc ban đầu.\n\n**3.4. Xây dựng mô hình**","metadata":{}},{"cell_type":"code","source":"#Hàm dự đoán sử dụng linear\ndef predict_linearSVC(X_train,y_train,X_test):\n    tfidf.fit(X_train)\n    X_train = tfidf.transform(X_train)\n    X_test = tfidf.transform(X_test)\n    svm = LinearSVC()\n    svm.fit(X_train,y_train)\n    return svm.predict(X_test)\n \n#Dự đoán trên tập validation\ndef validate_base_model():\n    X = train.question_text\n    y = train.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    return f1_score(predict,y_test)","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:46.982127Z","iopub.execute_input":"2022-01-08T13:45:46.982376Z","iopub.status.idle":"2022-01-08T13:45:46.99023Z","shell.execute_reply.started":"2022-01-08T13:45:46.982337Z","shell.execute_reply":"2022-01-08T13:45:46.988876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('F1-Score của base-model trên tập validation: ',validate_base_model())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:45:46.992305Z","iopub.execute_input":"2022-01-08T13:45:46.992789Z","iopub.status.idle":"2022-01-08T13:50:31.635888Z","shell.execute_reply.started":"2022-01-08T13:45:46.992745Z","shell.execute_reply":"2022-01-08T13:50:31.634844Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_file_submission(predict):\n    submission = pd.DataFrame(test['qid'])\n    submission['prediction'] = predict\n    submission.to_csv('submission.csv', index=False)\n    \ndef submit_base_model():\n    X_train = train['question_text']\n    y_train = train.target\n    X_test = test['question_text']\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    create_file_submission(predict)","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:31.637597Z","iopub.execute_input":"2022-01-08T13:50:31.638522Z","iopub.status.idle":"2022-01-08T13:50:31.646415Z","shell.execute_reply.started":"2022-01-08T13:50:31.638465Z","shell.execute_reply":"2022-01-08T13:50:31.645171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ta submit thử với base model để kiểm tra điểm số đạt được trên tập test so với tập validation","metadata":{}},{"cell_type":"code","source":"# submit_base_model()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:31.648132Z","iopub.execute_input":"2022-01-08T13:50:31.649284Z","iopub.status.idle":"2022-01-08T13:50:31.66161Z","shell.execute_reply.started":"2022-01-08T13:50:31.64923Z","shell.execute_reply":"2022-01-08T13:50:31.660716Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download.png](attachment:24719a62-0017-41cd-a1a1-daa8f62efd7b.png)\n\n**Nhận xét:**\nĐiểm số đạt được trên tập test khá tương đồng với điểm số trên tập validation cho thấy tập validation đã đánh giá chính xác hiệu năng của model đồng thời cho thấy mô hình không xảy ra các hiện tượng underfiting, overfiting.\n\n\n# **4.XỬ LÝ DỮ LIỆU**\n","metadata":{},"attachments":{"24719a62-0017-41cd-a1a1-daa8f62efd7b.png":{"image/png":"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"}}},{"cell_type":"code","source":"train.question_text.iloc[0]","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:31.665646Z","iopub.execute_input":"2022-01-08T13:50:31.665963Z","iopub.status.idle":"2022-01-08T13:50:31.676307Z","shell.execute_reply.started":"2022-01-08T13:50:31.665931Z","shell.execute_reply":"2022-01-08T13:50:31.675442Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Đối với dữ liệu dạng text, chi tiết hơn là dạng câu hỏi trong bài toán này, ta thấy rằng có thể có những phần dữ liệu dư thừa - những phần dữ liệu không mang lại giá trị thể hiện nội dung hay phân loại câu như dấu, chữ cái in hoa, chữ số, các kí tự đặc biệt, stopword (những chữ không mang lại giá trị về nội dung như is, are, am,the,..) .\n\nSau khi đánh giá ta thấy dữ liệu còn khá phức tạp và nhiều nhiễu. Để đơn giản hoá dữ liệu ta có thể thực hiện một số bước sau:\n\n# * **Clean tag**\n\nĐưa các biểu thức toán học về \"MATH EQUATION\" và đưa đường dẫn web về \"URL\".\n\n","metadata":{}},{"cell_type":"code","source":"print(\"Math:\")\na = 0\nfor i in range(0,len(train)):\n    if '[math]' in train.question_text.iloc[i]:\n        print(train.question_text.iloc[i])\n        a = a + 1\n        if a == 5:\n            break\n            \nprint()\nprint(\"URL:\")\na = 0\nfor i in range(0,len(train)):\n    if 'https' in train.question_text.iloc[i]:\n        print(train.question_text.iloc[i])\n        a = a + 1\n        if a == 5:\n            break","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:31.677438Z","iopub.execute_input":"2022-01-08T13:50:31.677823Z","iopub.status.idle":"2022-01-08T13:50:32.334313Z","shell.execute_reply.started":"2022-01-08T13:50:31.677792Z","shell.execute_reply":"2022-01-08T13:50:32.332633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ta thấy rằng nhiều câu hỏi có chứa các biểu thức toán học và đường dẫn URL. Các biểu thức và đường dẫn gần như không có ý nghĩa trong việc phân loại câu hỏi và việc để nguyên các đường dẫn và biểu thức toán này trong quá trình encoding với Tfidf sẽ biến thành các trường khác nhau dễ gây ra nhiễu ảnh hưởng tới quá trình huấn luyện và dự đoán.\n\nTa tiến hành chuyển các biểu thức toán về \"MATH EQUATION\" và đường dẫn về \"URL\". Điều này sẽ khiến chúng được mã hóa giống nhau và thể hiện cùng một phân bố trong quá trình huấn luyện mô hình (mô hình sẽ hiểu các từ đó mang cùng ý nghĩa, tính chất)","metadata":{}},{"cell_type":"code","source":"# Xứ lý kí tự toán học và link URL\n\ndef clean_tag(x):\n  if '[math]' in x:\n    x = re.sub('\\[math\\].*?math\\]', 'MATH EQUATION', x) #replacing with [MATH EQUATION]    \n  if 'http' in x or 'www' in x:\n    x = re.sub('(?:(?:https?|ftp):\\/\\/)?[\\w/\\-?=%.]+\\.[\\w/\\-?=%.]+', 'URL', x) #replacing with [url]\n  return x","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:32.336166Z","iopub.execute_input":"2022-01-08T13:50:32.336802Z","iopub.status.idle":"2022-01-08T13:50:32.34353Z","shell.execute_reply.started":"2022-01-08T13:50:32.336757Z","shell.execute_reply":"2022-01-08T13:50:32.34264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# * Clean punct\nLoại bỏ dấu và các kí tự đặc biệt.\n\nCác dấu và kí tự đặc biệt thường có tác dụng phân loại vì thế ta đơn giản xóa bỏ chúng khỏi câu.","metadata":{}},{"cell_type":"code","source":"# Loại bỏ kí tự đặc biệt\n# Nguồn: https://www.kaggle.com/canming/ensemble-mean-iii-64-36\n\npuncts = [',', '.', '\"', ':', ')', '(', '-', '!', '?', '|', ';', \"'\", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\\\', \n        '•', '~', '@', '£', '·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', \n        '█', '…', '“', '★', '”', '–', '●', '►', '−', '¢', '¬', '░', '¡', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', \n        '—', '‹', '─', '▒', '：', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', '¯', '♦', '¤', '▲', '¸', '⋅', '‘', '∞', \n        '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '・', '╦', '╣', '╔', '╗', '▬', '❤', '≤', '‡', '√', '◄', '━', \n        '⇒', '▶', '≥', '╝', '♡', '◊', '。', '✈', '≡', '☺', '✔', '↵', '≈', '✓', '♣', '☎', '℃', '◦', '└', '‟', '～', '！', '○', \n        '◆', '№', '♠', '▌', '✿', '▸', '⁄', '□', '❖', '✦', '．', '÷', '｜', '┃', '／', '￥', '╠', '↩', '✭', '▐', '☼', '☻', '┐', \n        '├', '«', '∼', '┌', '℉', '☮', '฿', '≦', '♬', '✧', '〉', '－', '⌂', '✖', '･', '◕', '※', '‖', '◀', '‰', '\\x97', '↺', \n        '∆', '┘', '┬', '╬', '،', '⌘', '⊂', '＞', '〈', '⎙', '？', '☠', '⇐', '▫', '∗', '∈', '≠', '♀', '♔', '˚', '℗', '┗', '＊', \n        '┼', '❀', '＆', '∩', '♂', '‿', '∑', '‣', '➜', '┛', '⇓', '☯', '⊖', '☀', '┳', '；', '∇', '⇑', '✰', '◇', '♯', '☞', '´', \n        '↔', '┏', '｡', '◘', '∂', '✌', '♭', '┣', '┴', '┓', '✨', '\\xa0', '˜', '❥', '┫', '℠', '✒', '［', '∫', '\\x93', '≧', '］', \n        '\\x94', '∀', '♛', '\\x96', '∨', '◎', '↻', '⇩', '＜', '≫', '✩', '✪', '♕', '؟', '₤', '☛', '╮', '␊', '＋', '┈', '％', \n        '╋', '▽', '⇨', '┻', '⊗', '￡', '।', '▂', '✯', '▇', '＿', '➤', '✞', '＝', '▷', '△', '◙', '▅', '✝', '∧', '␉', '☭', \n        '┊', '╯', '☾', '➔', '∴', '\\x92', '▃', '↳', '＾', '׳', '➢', '╭', '➡', '＠', '⊙', '☢', '˝', '∏', '„', '∥', '❝', '☐', \n        '▆', '╱', '⋙', '๏', '☁', '⇔', '▔', '\\x91', '➚', '◡', '╰', '\\x85', '♢', '˙', '۞', '✘', '✮', '☑', '⋆', 'ⓘ', '❒', \n        '☣', '✉', '⌊', '➠', '∣', '❑', '◢', 'ⓒ', '\\x80', '〒', '∕', '▮', '⦿', '✫', '✚', '⋯', '♩', '☂', '❞', '‗', '܂', '☜', \n        '‾', '✜', '╲', '∘', '⟩', '＼', '⟨', '·', '✗', '♚', '∅', 'ⓔ', '◣', '͡', '‛', '❦', '◠', '✄', '❄', '∃', '␣', '≪', '｢', \n        '≅', '◯', '☽', '∎', '｣', '❧', '̅', 'ⓐ', '↘', '⚓', '▣', '˘', '∪', '⇢', '✍', '⊥', '＃', '⎯', '↠', '۩', '☰', '◥', \n        '⊆', '✽', '⚡', '↪', '❁', '☹', '◼', '☃', '◤', '❏', 'ⓢ', '⊱', '➝', '̣', '✡', '∠', '｀', '▴', '┤', '∝', '♏', 'ⓐ', \n        '✎', ';', '␤', '＇', '❣', '✂', '✤', 'ⓞ', '☪', '✴', '⌒', '˛', '♒', '＄', '✶', '▻', 'ⓔ', '◌', '◈', '❚', '❂', '￦', \n        '◉', '╜', '̃', '✱', '╖', '❉', 'ⓡ', '↗', 'ⓣ', '♻', '➽', '׀', '✲', '✬', '☉', '▉', '≒', '☥', '⌐', '♨', '✕', 'ⓝ', \n        '⊰', '❘', '＂', '⇧', '̵', '➪', '▁', '▏', '⊃', 'ⓛ', '‚', '♰', '́', '✏', '⏑', '̶', 'ⓢ', '⩾', '￠', '❍', '≃', '⋰', '♋', \n        '､', '̂', '❋', '✳', 'ⓤ', '╤', '▕', '⌣', '✸', '℮', '⁺', '▨', '╨', 'ⓥ', '♈', '❃', '☝', '✻', '⊇', '≻', '♘', '♞', \n        '◂', '✟', '⌠', '✠', '☚', '✥', '❊', 'ⓒ', '⌈', '❅', 'ⓡ', '♧', 'ⓞ', '▭', '❱', 'ⓣ', '∟', '☕', '♺', '∵', '⍝', 'ⓑ', \n        '✵', '✣', '٭', '♆', 'ⓘ', '∶', '⚜', '◞', '்', '✹', '➥', '↕', '̳', '∷', '✋', '➧', '∋', '̿', 'ͧ', '┅', '⥤', '⬆', '⋱', \n        '☄', '↖', '⋮', '۔', '♌', 'ⓛ', '╕', '♓', '❯', '♍', '▋', '✺', '⭐', '✾', '♊', '➣', '▿', 'ⓑ', '♉', '⏠', '◾', '▹', \n        '⩽', '↦', '╥', '⍵', '⌋', '։', '➨', '∮', '⇥', 'ⓗ', 'ⓓ', '⁻', '⎝', '⌥', '⌉', '◔', '◑', '✼', '♎', '♐', '╪', '⊚', \n        '☒', '⇤', 'ⓜ', '⎠', '◐', '⚠', '╞', '◗', '⎕', 'ⓨ', '☟', 'ⓟ', '♟', '❈', '↬', 'ⓓ', '◻', '♮', '❙', '♤', '∉', '؛', \n        '⁂', 'ⓝ', '־', '♑', '╫', '╓', '╳', '⬅', '☔', '☸', '┄', '╧', '׃', '⎢', '❆', '⋄', '⚫', '̏', '☏', '➞', '͂', '␙', \n        'ⓤ', '◟', '̊', '⚐', '✙', '↙', '̾', '℘', '✷', '⍺', '❌', '⊢', '▵', '✅', 'ⓖ', '☨', '▰', '╡', 'ⓜ', '☤', '∽', '╘', \n        '˹', '↨', '♙', '⬇', '♱', '⌡', '⠀', '╛', '❕', '┉', 'ⓟ', '̀', '♖', 'ⓚ', '┆', '⎜', '◜', '⚾', '⤴', '✇', '╟', '⎛', \n        '☩', '➲', '➟', 'ⓥ', 'ⓗ', '⏝', '◃', '╢', '↯', '✆', '˃', '⍴', '❇', '⚽', '╒', '̸', '♜', '☓', '➳', '⇄', '☬', '⚑', \n        '✐', '⌃', '◅', '▢', '❐', '∊', '☈', '॥', '⎮', '▩', 'ு', '⊹', '‵', '␔', '☊', '➸', '̌', '☿', '⇉', '⊳', '╙', 'ⓦ', \n        '⇣', '｛', '̄', '↝', '⎟', '▍', '❗', '״', '΄', '▞', '◁', '⛄', '⇝', '⎪', '♁', '⇠', '☇', '✊', 'ி', '｝', '⭕', '➘', \n        '⁀', '☙', '❛', '❓', '⟲', '⇀', '≲', 'ⓕ', '⎥', '\\u06dd', 'ͤ', '₋', '̱', '̎', '♝', '≳', '▙', '➭', '܀', 'ⓖ', '⇛', '▊', \n        '⇗', '̷', '⇱', '℅', 'ⓧ', '⚛', '̐', '̕', '⇌', '␀', '≌', 'ⓦ', '⊤', '̓', '☦', 'ⓕ', '▜', '➙', 'ⓨ', '⌨', '◮', '☷', \n        '◍', 'ⓚ', '≔', '⏩', '⍳', '℞', '┋', '˻', '▚', '≺', 'ْ', '▟', '➻', '̪', '⏪', '̉', '⎞', '┇', '⍟', '⇪', '▎', '⇦', '␝', \n        '⤷', '≖', '⟶', '♗', '̴', '♄', 'ͨ', '̈', '❜', '̡', '▛', '✁', '➩', 'ா', '˂', '↥', '⏎', '⎷', '̲', '➖', '↲', '⩵', '̗', '❢', \n        '≎', '⚔', '⇇', '̑', '⊿', '̖', '☍', '➹', '⥊', '⁁', '✢']\n\ndef clean_punct(x):\n  x = str(x)\n  for punct in puncts:\n    if punct in x:\n      x = x.replace(punct, ' ')\n  return x","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:32.345208Z","iopub.execute_input":"2022-01-08T13:50:32.345429Z","iopub.status.idle":"2022-01-08T13:50:32.39946Z","shell.execute_reply.started":"2022-01-08T13:50:32.345403Z","shell.execute_reply":"2022-01-08T13:50:32.398294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# * Mispell Dict\n\nSửa từ viết sai và đưa một số từ về dạng thông dụng.","metadata":{}},{"cell_type":"code","source":"def find_unique_words():\n    unique_words = {'a'}\n    non_unique_words = {'a'}\n    for i in tqdm(range(0,len(train))):\n        for word in train.question_text.iloc[i].split():\n            if word in unique_words:\n                non_unique_words.add(word)\n            else:\n                unique_words.add(word)\n    a = pd.DataFrame(unique_words - non_unique_words)\n    return a\n\nfind_unique_words().head(30)","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:50:32.400882Z","iopub.execute_input":"2022-01-08T13:50:32.401192Z","iopub.status.idle":"2022-01-08T13:51:03.159016Z","shell.execute_reply.started":"2022-01-08T13:50:32.401144Z","shell.execute_reply":"2022-01-08T13:51:03.157947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ta tìm ra những từ chỉ xuất hiện 1 lần trên toàn tập train và thấy rằng trong đó bao gồm:\n\n* Các từ bị viết sai chính tả.\n* Tên gọi của người.\n* Tên gọi trong một số lĩnh vực khác nhau như tên con chíp, tên các loại tiền ảo, tên app,....\n* Các từ bị dính bởi dấu câu, kí tự đặc biệt (vd: shop?) - vấn đề này sẽ được loại bỏ với hàm clean punct.\n\nTa tiến hành sửa các từ viết sai chính tả. Đối với các tên gọi trong các lĩnh vực khác nhau, ta đưa chung chúng về một dạng để khi mã hóa chúng sẽ thể hiện cùng một phân bố (ví dụ như tên gpu \"1080ti\" ta chuyển về \"GPU\" hay tên một số loại tiền ảo ta chuyển chung về \"bitcoin\") trong không gian vector (mô hình sẽ hiểu các từ đó mang cùng ý nghĩa, tính chất).","metadata":{}},{"cell_type":"code","source":"# Sửa từ viết sai và đưa một số từ về dạng thông dụng.\n# Nguồn: https://www.kaggle.com/oysiyl/107-place-solution-using-public-kernel\n\nmispell_dict = {'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', \"mastrubating\": 'masturbating', 'pennis': 'penis', 'Etherium': 'bitcoin', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', \"whst\": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization', \n                'electroneum':'bitcoin','nanodegree':'degree','hotstar':'star','dream11':'dream','ftre':'fire','tensorflow':'framework','unocoin':'bitcoin',\n                'lnmiit':'limit','unacademy':'academy','altcoin':'bitcoin','altcoins':'bitcoin','litecoin':'bitcoin','coinbase':'bitcoin','cryptocurency':'cryptocurrency',\n                'simpliv':'simple','quoras':'quora','schizoids':'psychopath','remainers':'remainder','twinflame':'soulmate','quorans':'quora','brexit':'demonetized',\n                'iiest':'institute','dceu':'comics','pessat':'exam','uceed':'college','bhakts':'devotee','boruto':'anime',\n                'cryptocoin':'bitcoin','blockchains':'blockchain','fiancee':'fiance','redmi':'smartphone','oneplus':'smartphone','qoura':'quora','deepmind':'framework','ryzen':'cpu','whattsapp':'whatsapp',\n                'undertale':'adventure','zenfone':'smartphone','cryptocurencies':'cryptocurrencies','koinex':'bitcoin','zebpay':'bitcoin','binance':'bitcoin','whtsapp':'whatsapp',\n                'reactjs':'framework','bittrex':'bitcoin','bitconnect':'bitcoin','bitfinex':'bitcoin','yourquote':'your quote','whyis':'why is','jiophone':'smartphone',\n                'dogecoin':'bitcoin','onecoin':'bitcoin','poloniex':'bitcoin','7700k':'cpu','angular2':'framework','segwit2x':'bitcoin','hashflare':'bitcoin','940mx':'gpu',\n                'openai':'framework','hashflare':'bitcoin','1050ti':'gpu','nearbuy':'near buy','freebitco':'bitcoin','antminer':'bitcoin','filecoin':'bitcoin','whatapp':'whatsapp',\n                'empowr':'empower','1080ti':'gpu','crytocurrency':'cryptocurrency','8700k':'cpu','whatsaap':'whatsapp','g4560':'cpu','payymoney':'pay money',\n                'fuckboys':'fuck boys','intenship':'internship','zcash':'bitcoin','demonatisation':'demonetization','narcicist':'narcissist','mastuburation':'masturbation',\n                'trignometric':'trigonometric','cryptocurreny':'cryptocurrency','howdid':'how did','crytocurrencies':'cryptocurrencies','phycopath':'psychopath',\n                'bytecoin':'bitcoin','possesiveness':'possessiveness','scollege':'college','humanties':'humanities','altacoin':'bitcoin','demonitised':'demonetized',\n                'brasília':'brazilia','accolite':'accolyte','econimics':'economics','varrier':'warrier','quroa':'quora','statergy':'strategy','langague':'language',\n                'splatoon':'game','7600k':'cpu','gate2018':'gate 2018','in2018':'in 2018','narcassist':'narcissist','jiocoin':'bitcoin','hnlu':'hulu','7300hq':'cpu',\n                'weatern':'western','interledger':'blockchain','deplation':'deflation', 'cryptocurrencies':'cryptocurrency', 'bitcoin':'blockchain cryptocurrency',}\n\ndef correct_mispell(x):\n  words = x.split()\n  for i in range(0, len(words)):\n    if mispell_dict.get(words[i]) is not None:\n      words[i] = mispell_dict.get(words[i])\n    elif mispell_dict.get(words[i].lower()) is not None:\n      words[i] = mispell_dict.get(words[i].lower())\n        \n  words = \" \".join(words)\n  return words","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.160642Z","iopub.execute_input":"2022-01-08T13:51:03.161172Z","iopub.status.idle":"2022-01-08T13:51:03.186686Z","shell.execute_reply.started":"2022-01-08T13:51:03.161134Z","shell.execute_reply":"2022-01-08T13:51:03.185628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# * Contraction Mapping\n\nĐưa các từ viết tắt về dạng bình thường","metadata":{}},{"cell_type":"code","source":"# Bỏ viết tắt\n# Nguồn: https://www.kaggle.com/theoviel/improve-your-score-with-text-preprocessing-v2 \n\ncontraction_mapping = {\"We'd\": \"We had\", \"That'd\": \"That had\", \"AREN'T\": \"Are not\", \"HADN'T\": \"Had not\", \"Could've\": \"Could have\", \"LeT's\": \"Let us\", \"How'll\": \"How will\", \"They'll\": \"They will\", \"DOESN'T\": \"Does not\", \"HE'S\": \"He has\", \"O'Clock\": \"Of the clock\", \"Who'll\": \"Who will\", \"What'S\": \"What is\", \"Ain't\": \"Am not\", \"WEREN'T\": \"Were not\", \"Y'all\": \"You all\", \"Y'ALL\": \"You all\", \"Here's\": \"Here is\", \"It'd\": \"It had\", \"Should've\": \"Should have\", \"I'M\": \"I am\", \"ISN'T\": \"Is not\", \"Would've\": \"Would have\", \"He'll\": \"He will\", \"DON'T\": \"Do not\", \"She'd\": \"She had\", \"WOULDN'T\": \"Would not\", \"She'll\": \"She will\", \"IT's\": \"It is\", \"There'd\": \"There had\", \"It'll\": \"It will\", \"You'll\": \"You will\", \"He'd\": \"He had\", \"What'll\": \"What will\", \"Ma'am\": \"Madam\", \"CAN'T\": \"Can not\", \"THAT'S\": \"That is\", \"You've\": \"You have\", \"She's\": \"She is\", \"Weren't\": \"Were not\", \"They've\": \"They have\", \"Couldn't\": \"Could not\", \"When's\": \"When is\", \"Haven't\": \"Have not\", \"We'll\": \"We will\", \"That's\": \"That is\", \"We're\": \"We are\", \"They're\": \"They' are\", \"You'd\": \"You would\", \"How'd\": \"How did\", \"What're\": \"What are\", \"Hasn't\": \"Has not\", \"Wasn't\": \"Was not\", \"Won't\": \"Will not\", \"There's\": \"There is\", \"Didn't\": \"Did not\", \"Doesn't\": \"Does not\", \"You're\": \"You are\", \"He's\": \"He is\", \"SO's\": \"So is\", \"We've\": \"We have\", \"Who's\": \"Who is\", \"Wouldn't\": \"Would not\", \"Why's\": \"Why is\", \"WHO's\": \"Who is\", \"Let's\": \"Let us\", \"How's\": \"How is\", \"Can't\": \"Can not\", \"Where's\": \"Where is\", \"They'd\": \"They had\", \"Don't\": \"Do not\", \"Shouldn't\":\"Should not\", \"Aren't\":\"Are not\", \"ain't\": \"is not\", \"What's\": \"What is\", \"It's\": \"It is\", \"Isn't\":\"Is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\", \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\", \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",  \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\", \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\", \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\", \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\", \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\", \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\", \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\", \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\", \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\", \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\", \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\", \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\", \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",  \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\", \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\", \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\", \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\", \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\", \"you're\": \"you are\", \"you've\": \"you have\" }\n\ndef clean_contractions(text):\n    specials = [\"’\", \"‘\", \"´\", \"`\"]\n    for s in specials:\n        text = text.replace(s, \"'\")\n    \n    text = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in text.split(\" \")])\n    return text","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.188355Z","iopub.execute_input":"2022-01-08T13:51:03.188662Z","iopub.status.idle":"2022-01-08T13:51:03.217677Z","shell.execute_reply.started":"2022-01-08T13:51:03.188623Z","shell.execute_reply":"2022-01-08T13:51:03.216916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# * Lemmatizer\n\nĐưa từ được chia về dạng bình thường\n\nCùng một từ trong những câu khác nhau sẽ chia ở các dạng khác nhau (ví dụ go, goes). Ta đưa các từ đó về dạng gốc để quá trình encoding không hiểu sai chúng thành 2 từ khác nhau","metadata":{}},{"cell_type":"code","source":"# Đưa từ được chia về dạng bình thường\n\n# lemmatizer = WordNetLemmatizer()\n# def lemma_text(x):\n#   x = x.split()\n#   x = [lemmatizer.lemmatize(word) for word in x]\n#   x = ' '.join(x)\n#   return x","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.21868Z","iopub.execute_input":"2022-01-08T13:51:03.219413Z","iopub.status.idle":"2022-01-08T13:51:03.235522Z","shell.execute_reply.started":"2022-01-08T13:51:03.219377Z","shell.execute_reply":"2022-01-08T13:51:03.23488Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# * Stopword\n\nXóa stopword\n\nTrong các câu chứa rất nhiều những từ xuất hiện nhiều nhưng không có giá trị trong phân loại câu, chúng được gọi là stopword. Xóa bỏ stopword giúp cải thiện hiệu năng, giữ lại những từ quan trọng giúp cải thiện hiệu quả phân lớp của mô hình","metadata":{}},{"cell_type":"code","source":"# Xóa stopword\nstopwords = STOPWORDS  - {'ought', 'whom',\"wouldn't\", \"you'll\", \"you've\"}# bỏ đi một số từ khiến hiệu quả dự đoán giảm\n\n\ndef remove_stopwords(x):\n  x = [word for word in x.split() if word not in stopwords]\n  x = ' '.join(x)\n  return x","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.237841Z","iopub.execute_input":"2022-01-08T13:51:03.238693Z","iopub.status.idle":"2022-01-08T13:51:03.249927Z","shell.execute_reply.started":"2022-01-08T13:51:03.238648Z","shell.execute_reply":"2022-01-08T13:51:03.248944Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Khi tiền hành validate trên bộ dữ liệu được loại bỏ stopword, xảy ra hiện tượng có sự giảm nhẹ của F1-score.","metadata":{}},{"cell_type":"code","source":"# Lấy từ stopword đã bỏ trong câu\ndef UncommonWords(A, B):\n  \n    count = {'a'}\n      \n    # insert words of string A to hash\n    for word in A.split():\n        if word not in B:\n            count.add(word)\n    # return required list of words\n    \n    return count","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.25117Z","iopub.execute_input":"2022-01-08T13:51:03.251396Z","iopub.status.idle":"2022-01-08T13:51:03.264512Z","shell.execute_reply.started":"2022-01-08T13:51:03.251368Z","shell.execute_reply":"2022-01-08T13:51:03.263533Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Tạo bảng phân tích đánh giá ảnh hưởng stopword\ndef create_check_stopword_affection():\n    train['question_text_cleaned'] = train['question_text'].apply(lambda x: remove_stopwords(x))\n    test['question_text_cleaned'] = test['question_text'].apply(lambda x: remove_stopwords(x))\n    \n    X = train\n    y = train.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n    result_predict_to_check_stopword = pd.DataFrame()\n    result_predict_to_check_stopword ['predict_with_stopword'] = predict_linearSVC(X_train.question_text,y_train,X_test.question_text)\n    result_predict_to_check_stopword ['predict_without_stopword'] = predict_linearSVC(X_train.question_text_cleaned,y_train,X_test.question_text_cleaned)\n    \n    a = pd.DataFrame(y_test).reset_index(drop=True, inplace=False)\n    result_predict_to_check_stopword = pd.concat([result_predict_to_check_stopword,a],axis = 1)\n    a = pd.DataFrame(X_test.question_text).reset_index(drop=True, inplace=False)\n    result_predict_to_check_stopword = pd.concat([result_predict_to_check_stopword,a],axis = 1)\n    a = pd.DataFrame(X_test.question_text_cleaned).reset_index(drop=True, inplace=False)\n    result_predict_to_check_stopword = pd.concat([result_predict_to_check_stopword,a],axis = 1)\n    \n    result_predict_to_check_stopword['check_with_stopword'] = result_predict_to_check_stopword.predict_with_stopword == result_predict_to_check_stopword.target\n    result_predict_to_check_stopword['check_without_stopword'] = result_predict_to_check_stopword.predict_without_stopword == result_predict_to_check_stopword.target\n    \n    result_predict_to_check_stopword.to_csv(\"check_stopword_affection.csv\",index = False)\n    \n    \ncreate_check_stopword_affection()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:51:03.265782Z","iopub.execute_input":"2022-01-08T13:51:03.266366Z","iopub.status.idle":"2022-01-08T13:59:30.438649Z","shell.execute_reply.started":"2022-01-08T13:51:03.266322Z","shell.execute_reply":"2022-01-08T13:59:30.437529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# file check_stopword_affection lưu trong out/kaggle/working sử dụng để đánh giá sự ảnh hưởng của stopword\ncheck_stopword_affection = pd.read_csv(\"./check_stopword_affection.csv\")\n\n\n# Tập hợp câu lúc đầu đoán sai, sau khi bỏ stopword đoán đúng\neff = check_stopword_affection[(check_stopword_affection.check_with_stopword == False) & (check_stopword_affection.check_without_stopword == True)]\n# Tập hợp câu lúc đầu đoán đúng, sau khi bỏ stopword đoán sai\nineff = check_stopword_affection[(check_stopword_affection.check_with_stopword == True) & (check_stopword_affection.check_without_stopword == False)]\n\n# Tập stopword đã bỏ trong những câu đoán sai thành đúng (hiệu quả)\nword_effective = {'a'}\nfor i in range(0,len(eff)):\n    word_effective.update(UncommonWords(eff.question_text.iloc[i],eff.question_text_cleaned.iloc[i]))\n\n# Tập stopword đã bỏ trong những câu đoán đúng thành sai (không hiệu quả)    \nword_not_effective = {'a'}\nfor i in range(0,len(ineff)):\n    word_not_effective.update(UncommonWords(ineff.question_text.iloc[i],ineff.question_text_cleaned.iloc[i]))\n    \n\nprint(\"Sau khi bỏ stopword, số lượng câu hỏi dự đoán đúng giảm:\",len(ineff) - len(eff))\nprint(\"Số câu hỏi bị ảnh hưởng (thay đổi kết quả) bới stopword: \",len(ineff) + len(eff))\nprint(\"Tổng số câu hỏi: \",len(check_stopword_affection))\nprint(\"Tổng số câu hỏi lớp 0: \",len(check_stopword_affection[check_stopword_affection.target == 0]))\nprint(\"Tổng số câu hỏi lớp 1: \",len(check_stopword_affection[check_stopword_affection.target == 1]))\nprint(\"Trong đó:\")\nprint(\"    - Với câu hỏi có target = 0 : Đúng thêm \",len(eff[eff.target == 0]),\", làm sai mất: \",len(ineff[ineff.target == 0]))\nprint(\"    - Với câu hỏi có target = 1 : Đúng thêm \",len(eff[eff.target == 1]),\", làm sai mất: \",len(ineff[ineff.target == 1]))\nprint()\nprint(\"Những từ stopword không hiệu quả (khi bỏ đi biến câu đúng thành câu sai): \",word_not_effective - word_effective)","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:59:30.440249Z","iopub.execute_input":"2022-01-08T13:59:30.440521Z","iopub.status.idle":"2022-01-08T13:59:31.647449Z","shell.execute_reply.started":"2022-01-08T13:59:30.440492Z","shell.execute_reply":"2022-01-08T13:59:31.646574Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Loại bỏ stopword không gây quá nhiều ảnh hưởng tới dự đoán của mô hình (khoảng trên 2%). Sau khi bỏ stopword, số câu đoán đúng giảm 750 câu, tuy nhiên số liệu ở trên giúp ta phân tích kĩ hơn với từng lớp:\n\n* Với câu hỏi target = 1 (lớp thiểu số), số câu hỏi dự đoán đúng tăng khá đáng kể.\n* Số câu hỏi được đoán thuộc lớp 1 có xu hương tăng ( hơn 1800 câu so với trước khi bỏ stop word).\n* Với lớp 0, lượng đoán đúng tăng 0.48% và lượng đoán sai tăng 1%.\n* Với lớp 1, lượng đoán đúng tăng 8.16% và lượng đoán sai tăng 4,7%.\nVới dữ liệu độ lệch lớn như hiện tại, dự đoán sẽ có xu hướng lệch về lớp đa số (lớp 0). Số liệu trên cho thấy, lớp 1 có xu hướng và tỉ lệ dự đoán đúng tăng , điều đó chứng tỏ hiệu quả dự đoán lớp thiểu số đang được tăng lên.\n\nSố lượng câu đoán đúng giảm nhưng đó là trên bộ dữ liệu thiếu cân bằng. Với số liệu ở dạng % ta có thể thấy nếu bộ dữ liệu có tỉ lệ ngang nhau giữa 2 lớp, loại bỏ stopword có thể giúp tăng gần 3% số câu trả lời đúng. Đây là con số khả quan chứng tỏ việc loại bỏ stopword là có hiệu quả.\n\nMột số stopword không hiệu quả được lọc ra phía trên để loại bỏ khỏi bộ stopword.\n\n# * Dữ liệu được xử lý","metadata":{}},{"cell_type":"code","source":"# Gọi tất cả các hàm tiền xử lý dữ liệu trên\ndef data_cleaning(x):\n  x = clean_tag(x)\n  x = clean_punct(x)\n  x = correct_mispell(x)\n  x = clean_contractions(x)\n  x = remove_stopwords(x)\n#   x = lemma_text(x)\n  return x","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:59:31.648916Z","iopub.execute_input":"2022-01-08T13:59:31.649404Z","iopub.status.idle":"2022-01-08T13:59:31.65523Z","shell.execute_reply.started":"2022-01-08T13:59:31.649359Z","shell.execute_reply":"2022-01-08T13:59:31.654447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Tạo ra 1 tập dữ liệu đã được cleaning\ntrain['question_text_cleaned'] = train['question_text'].apply(lambda x: data_cleaning(x))\ntest['question_text_cleaned'] = test['question_text'].apply(lambda x: data_cleaning(x))\ndisplay(train.head(),test.head())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T13:59:31.656463Z","iopub.execute_input":"2022-01-08T13:59:31.656696Z","iopub.status.idle":"2022-01-08T14:00:59.918493Z","shell.execute_reply.started":"2022-01-08T13:59:31.656666Z","shell.execute_reply":"2022-01-08T14:00:59.917481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def validate_baseModel_dataCleaded():\n    X = train.question_text_cleaned\n    y = train.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    return f1_score(predict,y_test)\n\nprint(\"F1-Score trên tập dữ liệu đã xử lý: \",validate_baseModel_dataCleaded())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:00:59.920448Z","iopub.execute_input":"2022-01-08T14:00:59.9208Z","iopub.status.idle":"2022-01-08T14:04:24.424229Z","shell.execute_reply.started":"2022-01-08T14:00:59.920756Z","shell.execute_reply":"2022-01-08T14:04:24.423323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def submit():\n    X_train = train['question_text_cleaned']\n    y_train = train.target\n    X_test = test['question_text_cleaned']\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    create_file_submission(predict)\n    \n# submit()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:04:24.42549Z","iopub.execute_input":"2022-01-08T14:04:24.425765Z","iopub.status.idle":"2022-01-08T14:04:24.431279Z","shell.execute_reply.started":"2022-01-08T14:04:24.425717Z","shell.execute_reply":"2022-01-08T14:04:24.430425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download.png](attachment:97c08c05-645a-462a-952b-9e335e86080a.png)\n\n**Nhận xét:**\nĐiểm số sau khi xử lý dữ liệu đã tăng thêm khoảng 0.4%. Ngoài ra, việc loại bỏ đi stopword và các kí tự thừa cũng khiến thời gian mã hóa dữ liệu từ text sang vector và thời gian huấn luyện mô hình giảm đi đáng kể.\n\n\n# 5. THUẬT TOÁN\n\nUnder sampling là việc ta giảm số lượng các quan sát của nhóm đa số để nó trở nên cân bằng với số quan sát của nhóm thiểu số. Ưu điểm của under sampling là làm cân bằng mẫu một cách nhanh chóng, dễ dàng tiến hành thực hiện mà không cần đến thuật toán giả lập mẫu.\n\n**5.1. Simple Under Sampling**\n\nÁp dụng với bộ dữ liệu hiện tại, ta giảm số lượng mẫu của lớp 0 (target = 0) sao cho tỉ lệ giữa 2 lớp là 4:1 bằng cách lấy ngẫu nhiên một số lượng phần tử từ lớp này.\n","metadata":{},"attachments":{"97c08c05-645a-462a-952b-9e335e86080a.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA8sAAADdCAYAAACMluC0AAAgAElEQVR4Ae29j29U14H3nT8hUvU8L9XT3SjdbttslbYprVbRW7Vq+1SqIqurjapK/WFFLY2qCC3SCiqtnKiNqi5iA6mCknQ0CmQdTEjhqQnEpvAYbNmAcWBqwxAHWzg2MbxThjixE9cGT2z0fXXOvXfm3Du/bDweZuwPEvLM3HvPOfdzvufc8z2/7j3iHwQgAAEIQAACEIAABCAAAQhAAAIhAveEvvEFAhCAAAQgAAEIQAACEIAABCAAAWGWEQEEIAABCEAAAhCAAAQgAAEIQCBCALMcAcJXCEAAAhCAAAQgAAEIQAACEIAAZhkNQAACEIAABCAAAQhAAAIQgAAEIgRKmuW5TEbpG+/pyrvjGrvyLv9hgAbQABpAA2gADaABNIAG0AAaQAM1qwHjXY2HNV52uf+KmmUTuIloaupDffzxx5qfn+c/DNAAGkADaAANoAE0gAbQABpAA2igZjVgvKvxsMbLLtcwFzXLxo2bSDDJdBKgATSABtAAGkADaAANoAE0gAbQQD1pwHhZ42mX86+oWTZOnBFlCkQ9FQjSil7RABpAA2gADaABNIAG0AAaMBowXtZ42uX8K2qWzRplhIbQ0AAaQANoAA2gATSABtAAGkADaKAeNWA87XL+YZZZb0CnCBpAA2gADaABNIAG0AAaQANoYNVpALOMqFedqOux14o009uKBtAAGkADaAANoAE0gAZqSwOYZcwyZhkNoAE0gAbQABpAA2gADaABNIAGIhrALEeA0JtTW7055Af5gQbQABpAA2gADaABNIAG0MDd0ABmGbNMDxIaQANoAA2gATSABtAAGkADaAANRDSAWY4AuRs9FsRJTxkaQANoAA2gATSABtAAGkADaKC2NIBZxizTg4QG0AAaQANoAA2gATSABtAAGkADEQ1gliNA6M2prd4c8oP8QANoAA2gATSABtAAGkADaOBuaACzjFmmBwkNoAE0gAbQABpAA2gADaABNIAGIhrALEeA3I0eC+KkpwwNoAE0gAbQABpAA2gADaABNFBbGsAsY5bpQUIDaAANoAE0gAbQABpAA2gADaCBiAYwyxEg9ObUVm8O+UF+oAE0gAbQABpAA2gADaABNHA3NIBZxizTg4QG0AAaQANoAA2gATSABtAAGkADEQ3Ut1m+OavZ2VnNRW7K9jrM+ccy9MLcjV6Yux3n3OysZm9WNu9nrw0qOTJRWG+FNFjkt7mJESXfTmm2yPG7zY74K6sbeMITDaABNIAG0AAaQAP1qYH6NsvpAbXGmtVxeTrSCzKpwbaYYseGNb1qDcmsJsbHlf5wLnLvdSDEuWmlx8c18beVSuu0ho/FFDuXqiCbaY0cjynWnFB6mZpKJ5oVi3VoZHql7p9weSChATSABtAAGkADaAANoIHlaqC+zfL8nFLnDijWfFrjziji7GiPmmPtGvxgNQskpUQspqPD0Y6COrjnD4d1NBZTIrVSaV0Jszyv+cycZucqkea5io96L7ci4PpK5CthoCM0gAbQABpAA2gADawmDdS5WZ7X/M1xnW6OqfV82htFDL4n0rnpsrMTGrlwWl1vHFVP/7BS7ojebEqD/QMac431B2Ma6B9UataIfVJj/QMaTE1qYiShrjdOa8y93h9lnBwd0MDbKU1OjGig+6i6egeVMiOnNyc00t+jo8d6NBCdwpuZ1vjbfeo5dtieP/bBbHgkNDOt1HBSpzsP62j3gIav+8dtmnvUHovpwPE+DfSPabLAaOdsatAem5geV7L7qA5f8Bll4z2qnjcHNTaZPzo9PT6oPnNN52kNjk3mTRkOjh/t7rPHc1PhXV5e2rMsTBoN2zMdOhCLqb3bY1ZsOvJ0aljJ3i4dNuyG07k0lM2znFmevT5s+XX1JjXynsM3CGNiVqm3T6vLyR8z3dowD6V7fl5BHgfpnf1gTINv9ujoG106/faYJp0Om/lieTc/ryBf3DybfW/EudeUpp3lA8H5E39LefE5aV1NlRH3wsMVDaABNIAG0AAaQANooJY0UP9meX5e05c71Bw7quHJeaXPt4ZHmieH1dEc095jpzXQP6C+TnP8sJJpX4iFRjlTCcVMeB+ac7wR3AP7W/VahwkjqfECZjl1LqbY/lYdtvGc1tFXY4q1dqnrz+06beLtOqzmWLN6Rn3DdjOlRGtMzYe61Nc/oIFT7WqO7VXPWGDozLTfZns8OTKukQtddsq5vX6RZnl6+KhisQM6cKhdPW8OqO/yhOadeAeGhzXYe1R7Y61KpALDbEbrPUZdbw5o4M0uHTadEedSfueDc7x/WMNve/eaO+7zaj2s9i5j5H0W+xNKmVHZRZplm6fNh9V1YUTjI0l1GVbdY55hLptnvllubVW7n++nj+1VzL1PP4zW1qMy9xnkT0dnhw53OuluHchOu7Z57E/tn7ueUGtsr46eG9bY2LD6jjQrlj23RN4Zvdp8SSgVdLQMG/3u1dHeAQ3093n3eiiZjdc7v1WtbUcdLdXprIICnTq1VCGSFh7QaAANoAE0gAbQABpAA4EGVoVZNqO/Zo1q85F2tbuGdH5O42eaFWsbdEZeZzXSGVOsc2SRxsszf82947mR6gINfs8s54zVfHrAjp6efjcwoZMa/nNuHe3k2+1hUz8/r3T/AcX2+2H8bURdsZj6xnNinX1vXOmsUS8/DdszWeHp6DbeIA7/Pmy8wfruDwYtw1y65zU3ntBhM6JuRsrt8QMaCDobTBj2XsOdCyFeE0kddqddFzK7IaZ+Hp0Zz420/21C42l/ynmh60MdHL5ZDt2nH2Zwn34YXSNB54R//A1HK9f6nE6TeblmOd2/V7Ejzrk3J5VK+SPwZfIuZJbnxtXXHFP7W5POvXp5H6QtPx/nNHZyta/Jz+k+qKz4CxM0gAbQABpAA2gADaCBampglZhlz7C1xjwDkZvemlaiOabDrhEx02BHexSLnda4Geksa7zKm1KTYa6RshmYF25uavD8vL9ZVNSAW9N52F9r7Y1Oxl49qtMXhjWWntScMzU3GPEutWY5ZMqsGfXj7UpqfHw89/9Cl2Ixb6TTjOjGYn3ZUc+oGL3jXUq6148nrbH31iAX4BVlEf0eMspeBeDNFjCjrUkNj6XDU5wLXV/ILEc2+ArdW4Ew8vIwFGY4j72R5WYd7urT4EhKE38LOkVM+kvnXShfrifUHAvyPKj8ZjXWHVPM10fofJ9VXloLMIzmHd8DvvxFC2gADaABNIAG0AAaQAPlNbB6zLI/XTpsHgsYN2Mq7IihvxtxAdM0HzJJRcKImJM885IXbtgsF9ytedJsfNWsxHU/4+y61wG7pvm1ZrMT82FnunT5dOWbLD8NbT12SrqZlp777617zr8mLCLveLt6Qtd64XjrvgukK8oi+j3CMii4Zs2yWf99+I9m9+hmHU74U8ELXR/KM+8+DwTr2P3w5949rVisR2NmLXqBMPLyMBRm2CybNNo1y2ZNdauZ4h3T3s6R3AyGEnkXYhyJI7j31JsxxY6P2N3cQ+f795KX1iIMg/D4G9YxPOCBBtAAGkADaAANoAE0UE4Dq9wse6ap+Ux4CnVourNvmvqu5cQyN2ZGnsPTisMmPHduADjPvOSZMdcsG+MVnR4+r9kRM8LrmzljfjJzztRvb6p57nVYBUxpxDAVNlnNuSnowfnuiHXKjHSGp27PmynQ42lNm5F4e7xLI0Vf+1QgXVEW0e9BOiJ/55ydpycvmfXXfp6UzTOfdTDV3g+3UL67O3Ln5WHEyEaPu+mbu9Znp91nwyuRd6F8sffSrL5xd2Q6rYH9MQVmP3S+fy/RtAQ65G9+2YQJTNAAGkADaAANoAE0gAbuRAOr3Cx7U66bY63qG5vU3M05TacH7YZfuTWinjGJtfZpZGJak6mkuuxI5sqaZbPO174j+q20Zm/OaXZyTH1mE6tgara/drjj0qRnmP+W0kBbTM0n/U2u5n1D1TumyVnXaOUKQiGT5cXbqh6zu/TcvOb+ltbgcddApzVg0nF8UOnpWc1O+8ezm1d5x1tPDittph7PzSr9ltmgKjDQizDLs2PqMa+9upjWdMG0e+/Jbj4+rEljmDOzSp03a7yDjoRyeeZ3kjQfVs/IhGZnZzU5nlC7u2t6AcOeZ0CLmmV/LXxrQuO202BO05fN68palXwvWNfdrGJ5F84XM+XabA7WZ3cln7s57fPMdViEz/fyNy+tkY6GO6kMuCZXdmABCzSABtAAGkADaAANoIFVb5bn5+c08XaX7DRms6bZ7Dp8wXkN0fy85tJJb/dqM5W2bUCpsfzdsCs9smwK3+y7CbWbXbNtupp1+NSIJp1R3snR03Ynau+42dE7qbTzeqLpseB4YCLDgi5ksvLj9cO1r8nyr//buBJt3tRiE3fzodMam3TCjhw366qTwWutCk2HzzOmc0pfNLtwmx3EnU3RXMM3OabTh8z0a5+PiSOd6xQonWeeWT56KaWRzuA+DF/nFVt5acqfZh2ejh85fjOtpN1h209f82GdHs1t0lUq7/Ly5eaEBrteU7N7r1me+btnmzzELDt6dHXD59xGcbCABRpAA2gADaABNIAGlqWBVWSWyzWe5+wIY+59wOXOr97xudnZyOZd4bjNcTMKXOnerbLh3pzVbMGRXz8t5Y5XonBWIg4Txgrws/kxV5pRWcYuozJhVTr/Ca/yZQqmMEUDaAANoAE0gAbQwOrRwBoyy6sn0yiA5CUaQANoAA2gATSABtAAGkADaGBlNYBZdkf2+Fzx0WsK8MoWYPjCFw2gATSABtAAGkADaAANrIwGMMsYZAwyGkADaAANoAE0gAbQABpAA2gADUQ0gFmOAKFXZmV6ZeAKVzSABtAAGkADaAANoAE0gAbqSQOYZcwyPUhoAA2gATSABtAAGkADaAANoAE0ENEAZjkCpJ56OkgrPXNoAA2gATSABtAAGkADaAANoIGV0QBmGbNMDxIaQANoAA2gATSABtAAGkADaAANRDSAWY4AoVdmZXpl4ApXNIAG0AAaQANoAA2gATSABupJA5hlzDI9SGgADaABNIAG0AAaQANoAA2gATQQ0QBmOQKknno6SCs9c2gADaABNIAG0AAaQANoAA2ggZXRAGYZs0wPEhpAA2gADaABNIAG0AAaQANoAA1ENIBZjgChV2ZlemXgClc0gAbQABpAA2gADaABNIAG6kkDmGXMMj1IaAANoAE0gAbQABpAA2gADaABNBDRAGY5AqSeejpIKz1zaAANoAE0gAbQABpAA2gADaCBldEAZhmzTA8SGkADaAANoAE0gAbQABpAA2gADUQ0gFmOAKFXZmV6ZeAKVzSABtAAGkADaAANoAE0gAbqSQOYZcwyPUhoAA2gATSABtAAGkADaAANoAE0ENHAipnl/y/1V73/wQf8hwEaQANoAA2gATSABtAAGkADaAAN1J0GjKddzr97il28XBdeLFx+hwAEIAABCEAAAhCAAAQgAAEIrDSB5XpazPJK5xDhQwACEIAABCAAAQhAAAIQgEDVCWCWq46cCCEAAQhAAAIQgAAEIAABCECg1glglms9h0gfBCAAAQhAAAIQgAAEIAABCFSdAGa56siJEAIQgAAEIAABCEAAAhCAAARqnQBmudZziPRBAAIQgAAEIAABCEAAAhCAQNUJYJarjpwIIQABCEAAAhCAAAQgAAEIQKDWCWCWaz2HSB8EIAABCEAAAhCAAAQgAAEIVJ0AZrnqyIkQAhCAAAQgAAEIQAACEIAABGqdAGa51nOI9EEAAhCAAAQgAAEIQAACEIBA1QlglquOnAghAAEIQAACEIAABCAAAQhAoNYJYJZrPYdIHwQgAAEIQAACEIAABCAAAQhUnQBmuerIiRACEIAABCAAAQhAAAIQgAAEap0AZrnWc4j0QQACEIAABCAAAQhAAAIQgEDVCWCWq46cCCEAAQhAAAIQgAAEIAABCECg1glglms9h0gfBCAAAQhAAAIQgAAEIAABCFSdAGa56siJEAIQgAAEIAABCEAAAhCAAARqnQBmudZziPRBAAIQgAAEIAABCEAAAhCAQNUJYJarjpwIIQABCEAAAhCAAAQgAAEIQKDWCWCWaz2HSB8EIAABCEAAAhCAAAQgAAEIVJ0AZrnqyIkQAhCAAAQgAAEIQAACEIAABGqdAGa51nOI9EEAAhCAAAQgAAEIQAACEIBA1QlglquOnAghAAEIQAACEIAABCAAAQhAoNYJYJZrPYdIHwQgAAEIQAACEIAABCAAAQhUnQBmuerIiRACEIAABCAAAQhAAAIQgAAEap0AZrnWc4j0QQACEIAABCAAAQhAAAIQgEDVCWCWq46cCCEAAQhAAAIQgAAEIAABCECg1glglms9h0gfBCAAAQhAAAIQgAAEIAABCFSdQN2b5YVMRpmPC3FbUCaT0cLtQsf4DQIQgMASCNxe0OxUWqn3p4vUN6XDsvXUQulzlnY0o/TwoMamKhpo0SQUr2eLXsIBCNwRAau1TFTX3vM87+fb/nO+XEwfT2nsrSGlb5U7sfTxzPUhDV6ZUjR1pa/i6LIJ+Pls2nTh/7WcE9WtowPGaDQgwd8VJbCQ0fT7KaWnZpVXL69oxHcn8Do3y7MaOR5X/PUBpaOGeWZEHfG4Bm7cHbDECgEIrA4C0+/2qa0lrng8+L9PHW9NLKHBnNZAPK6OkdnKAZkdU2c8rpbzE36YGU2lUpqYCTceM1Mppd6bXUJaCyXRr2f704UO8hsEKkhgQak344ofGdK0G+r7SbUavUc0OD3Upni8T6mw7N0rvc/vDaglHlfnqF8GF2Y1kUppqoR5XpiZUCo1pUw2tFmNdcUVbxlQUOqyh/iwsgRuDDj1b1APm78DqpVaKa+uzaujK48IjVaeKSGWI7Cgibc6tC/bHjJ1Ypv63g3V2OUCqbvjq8Msx+NqTaTDDULMct2JkQRDoOYIvJ/UoXiLOoeCRvOCZq+es7+dvpprRpdO9wqYZRPhxxmnziscR7o/rvjxES3PpmOWS+cvRytJIPPuacXjHRqZyYXqmeKoifbN66lxx9Dmrol+WnCHPxbRPpgd6cg3Y2aEs5wxj0bM9+UTsGa5Q0Mf1O7IcsG6NlRHLx9DNAQ0GiXC95UmYOrnlvghnbvqtypuZzQx1Gl/G/xgpWO/e+GvDrP8+iHbeO275jzFCj0MZ9MaeatPPUd61HdhSCmnBZm5MaTkhXFN3Upr6M0edZxK+lMczVQa/5rhdN5DeTY1pHOnOtRx6pyGrk47jde7l6nEDAEIVIZAwcaIMkoPJZX0e1KDusPtV51+N6lktr4IjOy0pq8mdfp4h04nllv/TGv8QlJDNzJSJq2hC6d11HQadp2z9di0SeNwUqf/HFf8T506dyGp8Y9yTMrVW+Z4X3ebet40U1envRk8kVG9XGh8gkAFCfjP7tzz3OusaT3eoTbXRC+k1OfO2Lg9q/TIoK/bpIZCD3hTRvwy8NG4kmc77Uj10VNuOc3dgym/57paFY8f1emgnEkKlWtb7pIan/LbCMdPK+lP0TZTYXPlJxeu+ZT5YEyDb/aorbtPgyMTeW2K8Nl8swR8s+x2oLhkFqbHNWjydyr362xqUMmLY8quVMmYqfh+W+6tEU1EZxXcnlVq+JxOH/fqvfGPcp2hpev4YnWtU0cHySoRhxScP62pK4GOiy8dQKMBVP5Wk0DBTiFN5dojQWKy5a1Dpy+MKO34LXtKqeNB3frBrFJvnVbHkcHsbJ5ybZcg+kr/XR1muT+t6aGjirec1nhQAUbNsj9C1NaT1MjVMQ12H1K8pVNjfu+11yg+pEPHe3Tuwjn1tLXY4z1dbepJJJV805t2cOhiMAFrQel+E0abei6MaGS4Tx2vxXWoPzLCXekcIzwIQKBqBBZS59RiR5YnlCmy/0EhQx1+oHhmufX1Nh3tMWY2V7+M+A576fVPYMBnl2iWy9db08MdaonvU8ebSSUv9Knjz53qPBJXHLNcNd2t7YimNXTEmXKdGdfpeIsGbkwo+ae4Tr/rmxg7tbpNQ7YTaELJ1+NqaetRcmRcY2/1eDNCgmnXbntgGWY5VK79MA+93mHbCOd62ry6ortTbd2mnHttgvjryWxDb3rEjMCYsjWkkZGkbWe0dI2Ep5yv7cwvfPdlzLKU0fipltwsmlvjOt3Skpt2Pz2izpa49h3v09DIiJImr1o6FdS/+jitAV8/pmPRy8tDGrjhDcCUruOLmWWnjjZ3FYkjeeao1UJuhlL0OeHr508DSjvjQAGgYmYZjQaE+LsSBKaHjyrujywXkKUXpVPekheS6ju+z/qpbHmLHD9n/VibBt/3U+zXra1/atPRU+eUTIzJ7BRxNz3XqjHLplfOrF9u6Rrzphy6D0dJ+Ws70jrnrGHyKsMejQVm+9aYeuJxtQ3lxovSCWdK40dDOhpvVTLIXJPHdl1VePrYSoiVMCEAgWoRWNDUcI+/PmefDpnRo5G0Zh3jXLohZdLpNYLi3WPOKJLX8A/WYC65/vHDzK2DjjTMfDyhhpP5rVy95Y/WHXXqPU17+z9glqulOeKZuNiq+J98k3nDrDf21iWn+1sUfzNlZ3DZqdnBOQXWINvndZH2gCLtg0LEy5ZrP4yeK8EIZEZj3ZGp4tfP5aaUL6R0riWu3PmSbDvDdAQUSgG/ZQlYs9yi/W1tajvi/H8rGLyQNDOmzpYW9V3LKN3fao2z13pbUOpsi8L1r5dXQf1rDUBLn1LZvW8WlOpvU9ub3hT/slowtXzekpdwnZwfhzRxwdG5X6e3+Pq29z41qLYSe++UTRcazUqID5UikFEqYToGzVrl/XaGjJlVG9SCkl/e/uzuOzGtke42HX3blNdCx/26M2gj+boNtUPKtV0qdXtFwllFZtnMkTKNuhZ1mu6LQg/DhYymbqSUujKUHSkOGpv5lU64ojP83MpwdrRT8XiPBlMppbL/B63B5sFXRG38DIF6JWDqjtSIBt/s1H6z2ddrPRoLjQqHN5px64rALAd1TYBg4nyL4v/XG1Vaav2TH2Z+fWXiCadDKltvhUbrgpROa+T/MrIc0OBvFQhYg+x1PFvj7K9LXrjW55tob71yYHa8FC0oY3asT41pKBjVDdbrR9sD0e8Fbim/TEbKU4EwouVN7oioX7bOjbpthhGdO1zhzf8K3Evd/2Q5tqrzrJnt4vyPbCo0fblD8df2aV/8qD/jwNz5hAZa4mpLjDlttZRGzh7yR6L9te+J4luFldVCgbo2XEf7cbhG2CTNDrAEsyMK1OEFNObmZdl0FbgejboE+XzHBD6e1sQVswz1qB1MaGkLNlr2ylvH5dxAYzgOvzy6HfJmeYrdq8LfrLGAbsu2XcKRVPzb6jLL1i93etNrrod3w164MWB3tN33ulmvnNTQFc/YBg3Y/Eonv+JyKxnvfG89U6jyDtZFVTyrCBACEKgJAh+nde5PcbWc9Ua48uuOSKPaHzHITh/1b8IdGcsPo3T9E26ImQDzz7e/RkY7ytZbbuM+C5sNvrIo+FAdAv4Mh9Pvpu2U7OA5LTslu01D73vrlbPrms0UV7N06rVD6jHLB4bHNNjjzASLNr6i3wvcVX6ZjJTrAmG4bQQbpFueihk+Z010gWTwkyHgcixBxLTzzK7p8dDUZa9u9PZzcIy2Md12X4ny9VtZLSzCLNs3t0SXsvgDPAPvmZsqUIcX0Jh7+2XTVeB6NOoS5HNFCPizz44OG4NcQMehSIoct7NwOjVm1jYX0G3Ztksojsp/WXVm2U7H7mpRy2v77DQBb5TXrwxDPYfeNOzgIZxf6eRnaKiSsT3fzrTtyucNIUIAAneVwIKdbtR2yl/akU2LP2XIn+Lp1R3nnFeYLGi812mo+w+P1gvOlEH5YfgjZkuuf/IeSPn1lUluqM4yP5Srt/yHVNaEmGv86aNMw84KgA8rTsCsQY2r5dRp9cSDkTcTqfcs7+gya39Pa9yf+5dfBqXQsqlo4yv6vcD95JfJSHkqEEah8pbd2dueH1m6ZeJ1lnQUSAY/GQKLMsveuvVD/YN2/fGh88Hr/TzNhOvfMHc7vT80bVRyXwWVr69oHR/Rhs21cJ1cMI4rPYpndRw+3wZRQGOuINCoS4PPK09gSkOdbWrL7t0UxOgvK7OvsvTKWzCY4J2xoNn3UkpNmQq70PHIkoRCui/XdgmSskJ/V6FZDtaueO/i88yy9+CNHx/StFmRnt3qPDf9Kb/Sya+4wg9Cv2LuNbsqLkgLwfbpGOgV0irBQqDqBLzXJLTo6PlxTc2a15ZMK/2WWcPckttoyH8H7KGzY5qa9Y7bqdrBFFDf2La81qHkjVllMrOaGDntvX7K36xo6fVPtH7yHlatb45r2nm3jbf2s0/j0xkt2EZ5uXrLe5DFXz9nrzH3O37WbIbDNOyqi2+NR+iVicgaYDN7zL5b2e2MCqbwdWjoI2/Lmcz7Q3ZDp+xr06KNLztCHVfH2xOadcqLizx4hdXg+7PZ10WF2gDRMIt0TmXNsr8JVcuxAaVMQ+T2gqbta+gKGGg3IXz2zXKbwlPYzXT24JV+Rhdmk1dv3fHCewM6FD+U3VPG1uMtRzWQmrb1oNk9+9zrcWUNdPYVgUYPGc36+slu6Fq2jpfy69pIHe3Ekfl4QRk/Dbk1ypHzTb4X0JgrBzTq0uBzNQhMXDxkN/g6PZzWdCajzOyUxuwa5kh5s5uATSuTyfj1XK7N5LWrzOunprXw8UK2vGXXKBfUfbm2y8re/eo0y/78d7MAPbt+eHpMPf+nxX+x/T71XB4KvXZi6Y1VszlHSgN/3ueHadYxdmjwvdwy95XNOkKHAARWnsCCpkb6dPQ1r/Mtbje1aFPfiNmbMfi3oIm3vN3y4/F9OnohrfHQ9GevETRw1Z8qasIwO+K+FYx8SEuvf/IbVrNX++xSk9xIhWlsjavPTE+NO7sIl6u3Qsf3qWc0zaujgoXDNOEAACAASURBVKzmb/UIfDRkNzdqjY5i+MbF3XzTzCgb69nvbToTj2tf94iG3nQMdV7ja0ETb/tlNtgkLHpnCxMaNLu4mley+WlYnlk2OyJPachJp3mbRt+VYmv7oglaw9/tyLJTB9s61Hz394rwpzPnlrosKPVmi+LZ0WJvo0bbiWmvbVHbmTFNO6P6mWsDTj1vjo87u5SXq+ML1bX5dXR+HG4a8s8vZ5aFRtdwobhbt55R+oK/d0tQDs1AwHXX+0TLW7i9Yzb5Mhun5sqjdzwbQl597d9rqG1SXc9V52Z56WJZMD0huVbu0gModMXHZsSp0oEWiojfIACBu0XA1h0VKOcmHG+U927diR9vmXprRerKu3zLRL/KCZTRdM3c/QJthruTFwt2pKtU/VuNeq9mngGlMgGNlqLDMXllqbT3KVfe/ONLpXkX6vk1Z5aXmiecDwEIQAACEIAABCAAAQhAAAJrjwBmee3lOXcMAQhAAAIQgAAEIAABCEAAAmUIYJbLAOIwBCAAAQhAAAIQgAAEIAABCKw9ApjltZfn3DEEIAABCEAAAhCAAAQgAAEIlCGAWS4DiMMQgAAEIAABCEAAAhCAAAQgsPYIYJbXXp5zxxCAAAQgAAEIQAACEIAABCBQhgBmuQwgDkMAAhCAAAQgAAEIQAACEIDA2iOAWV57ec4dQwACEIAABCAAAQhAAAIQgEAZAmvbLC9kNDOTKYNoaYcz15JK3lh+mJUKZ2mp52wIQCBE4KNRJS7PhH7iCwQgUB0CmZkZVfgRXZ2El4rl1iq8p1L3W8ljmRmlR4d0dXL5baxKJqtSYRm9ZxYqFRrhQKA4gcxMWqPDVzV5q/g5oSO3JnV1eFTpEhVyZvKqhhYTpvVeYa3but7U99H/0fTZdFS/DljbZnl4vxq2dWsypIjlfJlRIt6oxnhCy2teVyqc5dwL10IAAlff2KKGze26WhEUM7qaHNUqbedVhBCBQMASuHVV3S81qbGhQVt7KveErgW6Q6+tvnuqBtf0qZ3a0PiEmp5qUtPGRm3Y1q7RaEO6GglZsTgm1b2tQfuHVywCAoaApLQS8S1qeHyLLUtPNG7QzlPpEmQyuno8V/a2PN6gLfGEQlfcGlX7tg1q3NiUDXPrG6Mq3KWV0eiBLWpocLXuad/8lvf/tSE/bRmNvrE1XAc816GrVaoDMMsVNcsl9MYhCEBgjRMY0v6GreqeWOMYuH0IlCLw125tfXyDtr7Rq/b/XH3GErNcKvOLHPtrh55u3K7uG8HxjIb2btKmQ6PBD6vgL2Z5FWRizd/CZM9WNWzrUDqYwXCjW9sbn1bHXwsnPTO8X5s279dQYEoX0urY1qCdZ4IhwYySrzRqy2tDOXM8k9DuYmGOHtSWzdu1/SnXLBeK25SHRsXO+vGMHtSmTXuUDKLVjBIvN+rp4yHbXiigivxW/2Z5YVJDPQcV29aknS+3q/dari8jffagDp51Qc5o6PhBJQJR+CPL6Y+G1PHyTjU9t1vtZ66GRoVNGB3Dk5pMtiu2batirb22JyNzI2Gv2fqHg+p9N5t7yotzJq3E8d3a+dRWxfZ2aOgjJ9+WkvaFSSWP7/HSsLdDyVBne1qJVhO2uT8T107tPp7UZFAYnCjdjzPv9qq9yH2rWLozV9Vr43JDkq6ePaj2cKLCJ/ANAnVIYGa4QwePD3l1wsyQOoz2c8Vd+msid9zcX7Eyba+N6emGTdr+SrReyoHx6psZzbzdod3PmTotWta9OArXBZNKHmlX0jHjNv1vJJ3ZM5NKvnFQiWyjMxc3nyBQEwTeTajX6tMzD+VGlieHu3XwD1uzz+9cC6BUWcnd6WSyQ3v+sFVb/7BHHdFnWKnnri3TCaVvmWdiTFu3xXQw0n4wsQTps22FaxnlmeViz9pcEvkUrWcNkXIzA4vVxQHNmavqfcNvL73Rq6tuvW7OKZX35niJ6229ezatzLVeq81oO9FLwozSf8nV80MfpfNGlmf+6rUzm7bFtCd4DgXp5y8ElkwgrY6nGrUnGaolNdparOMpo+TLDdp9Pnx+ZmJUydFJ3xzPKP12Ulddb6NiHT9pdfxmi/Zfztd69FY8k35QQXfYzNlY3kxga/yzI8/RECr7vc7N8owSf2jUlniHktfSuvqX/Xr68Vzvo3koNYRARjLQVLZPbdf2bTF1/CWpZLJX+38b7iExYTRt26ndbySUTCbU/myjGp/dqd0vdSiRTCpxPKYtzmhROM5RHdzcqKcP9Gr0RlpD5txsD80S0p4Z1cGmDTacZDKp5Jn9errRCC4QsBmxatL253Zq/5ncfTT+ofh08MkzO7Xh8a06aM9PqP25DdrwStIXf7l0u71KkjJJ7SnWi1RZvRIaBKpKwOuF9ZdqTHRrq1PWbUJCDbYSZTozqdFku3Y2NGnPqaSS16ItM++2bH3z7E7tPNArU9Z7DzytxsaYEsGDqExdMPRaozNt1XvQNbhptvewW5FnZVWZEhkEFkegvFk2DajGzTF1JK8qfS2h/b/doO09fgd5pKyYTuumx7c6IygZjbY2aUPTbv/53609TRu0NRipiFyf99z164PtL/nX/6VDsc0N2uKMdqZ7tnvpC9oX23Zq+zZ3tLzUs3ZxlNbqWcUb+IZIibrYHJ7o1c7Htyh23LTrkupt3aoNj+9RMhg98/O+yW/nJXv2hLUTud62Axu3q9fvqPSeG9sj7cQtOhi0/GU6Tbao8bf71WvadEY72yKjbWYErvFp7T8zqvSNIXXEt4RH79ZqxnPfd05gJqFYw3b1hgbbJL29J8+IepGMar/Vtdexc7D1oA4eTyhduPmSS5cZrX7cabf4R9JdW7VprxmBjnix3JX+J3PcGVU2v5r6tnG3Etm40+p+dkNu5DkvjMr+UOdm2Z/W6IySzEymsxskhI2rARfJINPQbYhMP7iVDE0fsGG8HJhISTdMgznmZNikep/N9byE4rQP0z0KZtxLGc1MzPiGdPFptxXvc72hEe/M+d1q/E2Hv27AhNWg2F8C8+wLK5ROVzijOrhpk1NxG8M7qu7Wbm8NUMl0G2+8R43O9HX7PZsWNx4+Q6C+CSzNLJcu05J/3Bn5jdKx9Uc8kZvO5NdZwVSkcnVBuGwOaf/je7R/b+6hY3tn3fosmgC+Q6BmCJQ3y6a8bO1yZo99NKm0b3hmzuxUY0Tr9regU/ijXu1s2KneoCPK3PdEUu2HvPV45cqabbw1NKn9mgNseL8aN/mjIYU6kW37wjHLZZ61Tsh8dAiYUactbieic8z7WLouNkb76WOObiQ7uhZM6TQ6aYi0uczswoN/8a4xnZKbWrPO10aZPva0Gn1tWe08Fd7rwl4TdKTYaeW7c+bchGB+c9Zx2jD25lqPysxo8iOnjZd3z/wAgTIEbH2z3/Ek/vl24DDwE04Y1lzv1J5XmtT0fLt6k71qf75JjdlBP+dcU4beMGuWt2jD5pi6341o1XinxkDzES8WDsbzGJtzo8rB4ZnkQT29ydu3YMvjT2j7kWLrooMrKve3zs2y13vY2LRT7Wfyd0cLGVfLLJJBRiDBgy3L1ITZkO2tyAujgNjsA9vfhCR8vuk1btCGbfvV/ZfoLnKLTbs3OrT9VKQryIo4MO2eWQ5vDFGiYf7XDjXl3XcWgJF8iXQHI8nB2ktvvUJu/YIbDp8hUN8EbIMl6BiyZT/QvX9fBUaWi9VHizbLodkwcqZtLqIuyCS1O+gkM2t8Xk5qxnRuWdPgXR8Y7/rOGVK/+gmUN8t2ZLmxSTvf6NXQtclsR7npmDbTB7e2eiOHdkaWGcE7slMNT3mNQtPh3FB09tUiylqh+sBtHxR8zvrpym5aVuZZu/ozeel3aNdYbtH+4UhjPBRSqfaVmYr6hF2qltWFGV3e26SGRdWT3lTW/ZdDEUp2TaXXwA89N/zT7G9+3V5YeyZcZx2nGVlu2KCtr3UrMZpefbvCR/DxtQoE3PrJjc60Y/6zO7xplzluzw/PljF1a7H9AmbM24CCmRqbY85yLzOo6I4CR7yYmxaZqdq5Dv7soRvBbJCkrk5O6mrSzOTZoJ1nIt4oe0FlP9S5WTYwZpROdntrbzc2qrEptxA9bFzNuZEMKiKQob25nt+8MAqIrbhZ9ta9jJ7x1kSZXeQ2PNfrCHIxafdGrve8Hc34Ie3JTq9colm+vF+NhQqGG8XCpIqn2zTg/YX1tvc80jvvhsNnCNQxgVCjx5b9UmbZ3GjxMr18s7yYusDr7DNrjEYPbfI6/YyBtp1jQ9rfSFmtYzmusaSXN8u2xP01qW6z9rTpCTU2NvkmyisrT//hoOzUQTN9MPvfHzk+tV0N7shdiO4iylqh+sBtH5jnrG/M3aDd9oX9vcyz1r12zX+eGdL+zVu053x2LmYJJMXqYjO11N87IqsJXx92XXCxvA+iMtdvdTYb83+3sw69UbvQc8M/7JrlSaO9yKwHs0tx9386ZtlcNzmqXrtXzRZtaCi3a3GQPv5CoAgB25keacOY8S/Tcfhsr7O3SXC98RZNztIV//ei07aD67yZGo1+55Dt1HymQ1ezr4W6qo7/bNCev8xoJlj64F9qZ8fljSp7g3JNRyLvJbnWrqbG/dl1zbnYK/9pFZhlF4p55VJDdofE9LGmyJrlSGVkzHJDdP1euNdw2WbZTd7CVbU/lRu1dg/ZNTZO2t14TaM3b9dH02udFckSzbIdlY7et2Tf8RdOlPetULpNL+pvOnS1ZO98ocD4DQL1QyDU6Cn0oCn50AjXR8s3y7IGuHRdINkH02sdan8qeCgaA92kjp6D2hSMktdPFpDSNUtgcWbZxWOXGfizpkYPNKopMtXWPdeOBOaZ2Yxm/PeIln3uljPLk73aHszyyEbsjR4W3bSs0LM2e+0a/3DLGOXG3Jr0JeFw62LPDEc3LXKDM3mfp51M0LD3OiSjM3RcwxF6bvgBu2ZZpiMlOrvPPl8iZtlN1LvtasrTk3sCnyFQjoBXp4ZngnpGNK9dYYPyzo+WFavloLPnllnCGdn41PTzmF23/Zk71s8Uei2U+S00k67IqLI/0BlNhzfyHcywLXfvyzte32bZPKzc1wn4W5oHDyLbQ7FptxJmlH5hRqPHzLvCnMrImOXGRjW95u92a95A1mU25MjNlXdNq0Xt9hz77M05QZyh84f3a4M7t38mqT2bGr336JVJeyic6GsTFszC9kZnI5HyZjkz2qE9rUl/VNtMBfM2Mgv6Z81UzU1B5V0q3Vm9mXXPW7Rlc/7OetlT+ACBOicQbvSYirxBW494O+ZnJhLa09SY2xijTJn2zHKT2t8tDiVU7v3T3PrFrmuL1HnhuiDYCKMxNKpl7qOxsXqvWSh+hxyBwGIJlDPL5njYPKWPm9ei+BvyRZ+bmrGbKj1xIFgH6jXMtjtrnu31wVrV6PXR565tCwQdUv49hdoH3lRg80qV4Dmb7vLaIEF7wezoXLSNsFhMa+E8n31287Vy91ymLjbG1q67DDLGhm82h/OndBbIe/d1OXnX2xHv3NTR8HPDS6z9LWsMvI3dctoz2mxSo9M+HXptQ2hDL9tGayyw3rQcC45DwCHgaXeP54vMXLi3zfr/3OZ0upHUwb0d2XeY2873zbnzM381r5py2/1eu2jLK4ncG3gmE9qzOVcenOj9j17dHl466u+H5Pgv97r08afV8JuDuTcKLUwq8coWldrI2L1+uZ/r2ywbc3t2t5oaG/VEk5mm0qiml9yXZaeVeKlJjbZHw0xhGQpvzW/M8rZuDfkvu3/i8QY1Nu125tl76wVDPR+hh6GH323Mhhu7/ku0GzZoi5kiZtafOC/qLpX2cDhG0Ae11aTv8Q02nKdfS2YfwF4j3OkEsMkyBjr3ILdCMxtiBA+HW1fV8byXpg0m3I071ZF97VbpdAei88Lcw866ARD+rjoC0UZPZrTdlsOGBlNX7NfQX7w6JFg1U6pMGzhpU9eY+qjIWslouTfXuPWL+V66LjBneA+iUE+xmYlSaDqVOZ1/EKhJAuXMstlwM6HdTY1q3Ngks8wp+vzOlpWNT9j2wRPPtGcbgfaWP0pq/282qKFxg+1I3/Cb/Uo6G35lry/03C1rliXdGlX7NhP+E3ri8UY1HRhSwulcN+v/Rt/YaqfYFmoj1GS23IVE2Xq44MhUcfNYui7O6GqPaTs2aMNGrx309Mtu21GaOW/erlKszeVc/7iXvzuPX81uzBh9bhhkYbOc027D40/oicYC7dNAO49vUdPGRjU8vlXto6XWad+FjCHKOiRg6pzteqKxQaYdE9WVHWRs2OJ06ue0bs9vbNLunpzWLYCZUbU/Y8qRH2ahc0KkCpnlYqPKwYUz4XQ3NCqvPg9OXYG/dW+WPSZm6tSMs7nHHZBayE2/uoOrS19iww52wY6eurS026nSZd6fHI2h5HcztcifdpZ3Xsl0553NDxBYdQRsA6eIsS1+s0sr08XDKX2k4nVB6eg4CoGaJWDKQnTtm5tYe7yUz7hV/vrMcp67ZcIXz1o3uyr4uVxdXO64vzytaN771y8jxYvSZrE22jLi5dK1TsBot1SlmM8nU+78lfRRTnLuRttnlZhlhyIfIQABCFSCwK20Ei8X2C+gEmETBgQgAAEIQAACEIBAzRPALNd8FpFACEDgbhAw7wzc+nK3rkZ2a7wbaSFOCEAAAhCAAAQgAIHqE8AsV585MUIAAhCAAAQgAAEIQAACEIBAjRPALNd4BpE8CEAAAhCAAAQgAAEIQAACEKg+Acxy9ZkTIwQgAAEIQAACEIAABCAAAQjUOAHMco1nEMmDAAQgAAEIQAACEIAABCAAgeoTwCxXnzkxQgACEIAABCAAAQhAAAIQgECNE8As13gGkTwIQAACEIAABCAAAQhAAAIQqD4BzHL1mRMjBCAAAQhAAAIQgAAEIAABCNQ4AcxyjWcQyYMABCAAAQhAAAIQgAAEIACB6hPALFefOTFCAAIQgAAEIAABCEAAAhCAQI0TwCzXeAaRPAhAAAIQgAAEIAABCEAAAhCoPgHMcvWZEyMEIAABCEAAAhCAAAQgAAEI1DgBzHKNZxDJgwAEIAABCEAAAhCAAAQgAIHqE6hrs/zEn74m/sMADaABNIAG0AAaQANoAA2gATSw9jSw0vYZs4zhpsMBDaABNIAG0AAaQANoAA2gATRQdxrALJcgQO/R2us9Is/JczSABtAAGkADaAANoAE0gAaMBlb6HyPL9CDVXQ8SlSOVIxpAA2gADaABNIAG0AAaQAOY5RIEKCAUEDSABtAAGkADaAANoAE0gAbQwNrUQAmrWJFDjCwzsszIMhpAA2gADaABNIAG0AAaQANooO40UBFHXCIQzDKFou4KBT2Ha7PnkHwn39EAGkADaAANoAE0gAZcDZTwuRU5hFnGLGOW0QAaQANoAA2gATSABtAAGkADdaeBijjiEoFglikUdVco3N4kPtO7iAbQABpAA2gADaABNIAG1qYGSvjcihzCLGOWMctoAA2gATSABtAAGkADaAANoIG600BFHHGJQNaoWf6qfvHKl/SjZ7+ox/Z+tUZE8VX9Yu96/fLA2uwVojeQfEcDaAANoAE0gAbQABpAA2hgKRoo4XMrcmjNmeVfvviP+uf/fa/uW5/7/8BPP6MftdxlYf735/XP6+/Vd3cuNh3r1fjsF/ST/64Vs7/YdHPeUioAzkUvaAANoAE0gAbQABpAA2igsAYq4ohLBLK2zPJLn9PX1t+rL//y8/rJK8ZkflW/iD+g7zz6Cd33/X/QT/5YOBOqIs4lm+UH9d319+qff/tQjYyM30V2TJlBA2gADaABNIAG0AAaQANoYM1poITPrcihNWSW1+tff3av7vvh5/RYtCD5Jvob29bnBLb3S/rX/7hfX//Bp/SNX31eP3rFMYN7H1TDrz6jH8TX60e/vl9f/+l9+t7WL+mXf/qafvHc5/Stn31SX9/4OWe0+sv6wa8+o4adD+nnz+aO/9ANs5BZzqbh7/Wt//i8fhKMftv477PG/8HHPq3v/eqfcvd04CH98Nef1jd++il9Y9Pn9IPdjDxXpbMjqim+58oSLGCBBtAAGkADaAANoAE0sAIaqIgjLhHI2jHLf/yCvlViJPaXe9frF8HI8u7P6+Fv3qsHfnq/vverz+g7P1un+775ST3yom+YfWP75e9/Sl/f9Bl9Z8Mn9dn1n9DDP/s7feVnxrze7031/v5n9BMrCm8U+Cs//ZS+4of5rZ/+D933zb/Tv+wOh5mdhu2koeG3n9f3TBzB+cXM8r4H9d3v36vPPvr3+t5vP6+GjZ/SA+vX6bs7McwYZqezZwUqKvjCFw2gATSABtAAGkADaKDaGijhcytyaO2Y5UIjtwVNw1f1w3/7RGQE2h+V/tkD+oW5xg/r61uDkWj/+A+cUevnPq371n9K//LfptB4Zvm+n35eP8/G+WU1/PBeffbfvmBHpIMwPbPspyGIz17jxfHZf3/Q75XKn4b92K//l+57JDDoXmH9ya/+H4XjpRBXuxATH5pDA2gADaABNIAG0AAaQAOV10BFHHGJQDDLWfMaZN4X9d1v3quvPPXl0FSJX2y7T/etv18/NKPPBYz3j/793rAp3fkPeWY5ur74sac+mTO3oTD9NGx6QD989gvZ///yb+ucOKJm+SH962P36r4N/5g93177H3+v+9b/g36Ud5/B/fKXigsNoAE0gAbQABpAA2gADaCB+tNACZ9bkUNrxyz/6UF9Z32+CQ4KRW4adtSE+qKxI8V/p38164xDxtY7fkdm+elP5YxsKEwvDd565M/YqeBmOrj9/+sHvdFtf7Q6Z8Af0r/81KzJvi98vr3OWdOMaQ51ggT5z9/6qxzJM/IMDaABNIAG0AAaQANrWwMVccQlAllDZtmf2vzIp/WjfRFRvfgZfXn9J/SdZ83aXs90ZqdH++bSTmcOpjiHjK0X1mLMcm4Ktbnmq/rhxnt132MPeFOzQ2F6aXjwV18MG7vQO5jzTf2P/v0Tui80dftreiJ0TeS+Mc5hvvCABxpAA2gADaABNIAG0AAaqBsNlPC5FTm0hsyyGRF+wG7c9dlH71fDs1/Sz1/5kn7420/ra9+8V5997PPZHaXNlOvPrl+n7/zXl/XLfV/Vz1/8nL3ua8HU7JCxXYJZ/uYnbZi/2Ltej/3Xp61Bz+7AHQnTpuGb/0vfffbL+uWBr+mXu/9J3/n+vcoZ6C/qe4/cqwc3/pMe2+tv4GVN/zp947dftJuV/bLli2p4rICBpgKomwqA3lI6eNAAGkADaAANoAE0gAbQQGENVMQRlwhkbZllYxJbHtT3Gv+nPrv+Xt1n/n/zf+rhX31BPw+NwH5Vjb/+e33hm/456/+H/vk/vuhPf17GNOyn/kmPmF2wbdxemOZ1U1b8EbNsRp7DafiEvvLEA3rMSefP/+t+fcWm8T79YK8Xzi+2/4O+9r+DdJsdvf9RP/GPUcgKFzK4wAUNoAE0gAbQABpAA2gADdSfBkr43IocWntmOTCnB76qXwQjssFveX/NOeu93arzji1FTOEp02Z9tBktXlyB9NOw6PO9cHNrsBcbD+ctLj/gBCc0gAbQABpAA2gADaABNFALGqiIIy4RyNo1y8syv0stHGGzXAvCIg1LzUPORzNoAA2gATSABtAAGkADaKCWNFDC51bkEGa5Kqb5i3rkB5/Ut7Y9tMjRZAphLRVC0oIe0QAaQANoAA2gATSABtBA7WmgIo64RCCY5aqY5doTFoWdPEEDaAANoAE0gAbQABpAA2ignjVQwudW5BBmGbPMaDcaQANoAA2gATSABtAAGkADaKDuNFARR1wiEMwyhaLuCkU9936Rdnpv0QAaQANoAA2gATSABtBAZTRQwudW5BBmGbOMWUYDaAANoAE0gAbQABpAA2gADdSdBiriiEsEglmmUNRdoaAnrjI9cXCEIxpAA2gADaABNIAG0EA9a6CEz63IIcwyZhmzjAbQABpAA2gADaABNIAG0AAaqDsNVMQRlwgEs0yhqLtCUc+9X6Sd3ls0gAbQABpAA2gADaABNFAZDZTwuRU5hFnGLGOW0QAaQANoAA2gATSABtAAGkADdaeBijjiEoHUtVkucV8cggAEIAABCEAAAhCAAAQgAAEI3DEBzPIdo+NCCEAAAhCAAAQgAAEIQAACEFitBDDLqzVnuS8IQAACEIAABCAAAQhAAAIQuGMCmOU7RseFEIAABCAAAQhAAAIQgAAEILBaCWCWV2vOcl8QgAAEIAABCEAAAhCAAAQgcMcEMMt3jI4LIQABCEAAAhCAAAQgAAEIQGC1EsAsr9ac5b4gAAEIQAACEIAABCAAAQhA4I4JYJbvGB0XQgACEIAABCAAAQhAAAIQgMBqJYBZXq05y31BAAIQgAAEIAABCEAAAhCAwB0TwCzfMTouhAAEIAABCEAAAhCAAAQgAIHVSgCzvFpzlvuCAAQgAAEIQAACEIAABCAAgTsmgFm+Y3RcCAEIQAACEIAABCAAAQhAAAKrlcAqM8u3dWt6WrcyqzW7VvN93dSl/nc0NlPiHifGdWTwwxIn1MOhed36sMIanZnW1Mz8Ct58JcqVue8Z3bq9gslcdNCVuJ9FR8aJEIAABCAAAQhAAAJ1SmCVmeVJvRo/od8layk3pnWpf1zX52opTTWYlg8va+OOE9rYN+0nLp/bpbYu3bPzvC7VYPKLJelWalydY8E9mbOu6HfPVFajZw6c0D0HrhRLQgV+r0S5Mvd9Uq9er0Bylh1EJe5n2YkgAAhAAAIQgAAEIACBGieAWV7xDKolk7DiN1vBCFYHt+snTkaMLGa5giK5w6Awy3cIjssgAAEIQAACEIDAmiKwqs3y1MWL2nFsXFNOlo6d6deui/5I34fj2tU6rEsfptR6oFff3nVWu85/KN3+UAMn+rUx1qvNx97R9ey07hs60npRZz74UGeOndWG8xIoLgAAD0dJREFU53u1ue2SLn1YZG6pDb9PjzzTpR//sV87ztzIpSQzqc5jZ7UxdlIbD1xU543i02iv95/XjlORIbkPrmhX6yVdCtJm7qGtcJoWzWHiinbt7dW3267l0hl88lmNzaTU2tqnR2N9ejLExj+xRDrsGR/e0BHLzrvvMxMBO8O2X0fGJRXhln8f87ref1FPNp/Uo81ntat/MkitpAJ5VSi9zhVe+spwPHNDt965pB02zn61Xi4+b9xo7cmXunTPi33aYXRjZ5D7Zvl8oKGT2mjyMRpMOY5OuvNHlktxkWS116/Nu7q1Ye95vRqd2n57RmN9F/3jF3Vm4v38GRsl9Gvz6cx1TZ33wnjBTgXwOz9SwX0XKTslwrW3XCrtQXmO6ngx9+Pw5CMEIAABCEAAAhCAAAQMgVVtlu2oXnxQrs00xuLRE76puj6oR585qR/vOatX+99RZ9dZPfxMtza83KvfnXxHnf3D+l3shB46cEW3rF5Mg79bP37ppDYeu6zO/nfU2tqtdTsTOhM1O+b8ufc10H9eG57p1pMmvGA67ty4djx/Qo+0DtswOk8m9MiOLv1uMHC+EXEO9+uh5/o1EPhKk3HHurVu3zteuq5f0oYdXdk0HTnWp4d3nFSrf+OL49CtR3b16gU3nW4yAla7TvpsLmvXni6te2lQY0G6yqRDt69px07vvgeu3dAZk84sO2fEtQi38H1kNNDarftjCbWavPPz6uHW8XBe7Qrn5bqXh0OdJ+4tqkz6vfhPavOeizrS/44s52e6tGM4ABAKTVNj76j1QLfuefm8OrNT8b37fPj5IF0ex3tevKix4PJIOlpbT+r+HWfVWUhjksJmOcLFmN7nT+jRY74Y/Dx42NzD8DUN9J3Xj0Pau6kzB7q0LuDad1Eb4yf14+edqeMR/RoO395xUruueBwspxe7tWHXeZs3l2xxW0TZiYSbVy7Kpd1qNKrjRdxPwF3SQOsJ3fNM8f876mkNgHNffIQABCAAAQhAAAIQWDoBzPIz3dqVXe55W2f+eEIPHXZGVgcTWvd8YGQ8o/PIUdd+31Tn3hN65JgzahzKB3NNeK2mNRO7LoVM262zfVrnGqZQGNe1y5qVwJTd0K4XT+jJhDHXt61Zeuh1J826rbGjOTMdNplewPmdBt164Z0g/FDk3hdrRE5o89mbuYO3ry8pHbJhnNWZbDQZTaU+9Dd98tjm1psX4RZ0ftiwetX6QS45mrmszTu6tcuMTvtrgzf2Oen1rzlScI+wRXJ8/rwuZdPvX+PqxUmO+WjZh9YTe/cZStfcZW3OauS2Bl7vUlhj/m9FNBYyyx+YjpO+sLG2v51Vp1k3fzujqbEbmsregzyttFpo0vhFPRK9/spFPeKss5462at1ey/7nRLeDdvf/M4be88hTrn8CN9XuOyULRfl0m7zN6LjRdxPKMsyN/TqS4XN8o87ipXxUAh8gQAEIAABCEAAAhBYJQQwy1mT4uVoyESan2wDPKEz9rBv6AbDuW+M7j1FRyyjpi9jzfWPu90pw2bq8bA2PtOrwkbOH0kOTJcxANmRZt9IR9KkYDQ6MGyByfSTHrpPe49hQx++w4DDSbVOhI8YU+51LpRPRzCyfH88oVf7rmls2h1J99lmN2eLcvONp38flvlLl0KzBqRpHXn5hDaeMtPso+GZdOeHmbub8ukv1OmQb4ZzIZpP+cfLpcuko0ubO8xoee6/HaGOGNQgJtcsWy6xfjvynbvem92Q6xSa160b13Wm/7JebevX5lhug7DCWvbZ2Lzx9PtoqzezIhvHkV7d43cqFeLksTej8EGqvb9Tp3r9srPYclE87V5ZDeu4/P2E02O/FTDMGOUCnPgJAhCAAAQgAAEIrHICmOUlm2V3JNpXRzKheyJmNKebqEGbVOtLJ/TkeWdoz5x8+4qejKQlF4ZvVs1U3DnPOOdGksf1ux0n9WoqdLaUMlPMPZNfyLzcmVnu80YnnahyZrB8OuxlmUkNnPTWGT+844Tu33XJn8YdNZFRbmGzfL37pO75Y3ZKgJ8ib2aAN80+Gp45JT/M3K2UT38hjrn7z4Xkfso/Xi5dJh3+GvfWfu1w/0fW3wfxuGbZcrFrpCPXBuvBZ67od8+f0P0v9unJ1vN6tW9cnW25Tcjs9Xmm/H29GgumYXv6faQ5P/wdrcN2KnkhTln2UZ2eP+uXnUWUizJpL2SWy99PQDHy1zHMGOUIG75CAAIQgAAEIACBNUKgvs3yjWvqHHzfmQ7qjoBJdlQpZGJdM5UbLXVfZxMykUYEBUaWN591R0Qj01jzhJNv0AYOd4WneptrrlzUt3ckNJB3ffDDtFp3manX17Tredewu6Opwbn+vfsjr3fCIReS/8mf4pobnTS/u6OB5dNhQ7rtdBJkUnrh+WIjwfncQibMGTnPpdUdHS5nSnNXeZ/Kpz8Uv395vhkOh5t/vFy6PNMY1Vg41PA31ywrtGwgfJ75ZkdyI0sAzOyA7KunzPVm1oJ7qZ0mHphlb13vt0NLEdyTw50auSPefUfvyy4X8KeAlysXZdNeaIbEIu4nl8bIp8wNnbkYmQESOYWvEIAABCAAAQhAAAKrl0B9m2VjmMyo6rjZSfq2ps4n9LA7ldlMV37mpF4wOxbfzuh631l9e0f+Bl9LNcv3mE2pgh2wxwftBknFNnnyRtS69cJlZ7drswY0m27jOb11kg+XWPtqJGjMwrqdXXoosrbZmGG7yViQpg+v6Hc7AxMqbx3qEjnkSd4akRPehl62r8DnvaNXR/x1w2XTkUzo/uyGXmbq+Tt68jljwoyBjppI8z3MLWRW/fXSuVG/eY11nNS6ncFGaNHwzB2Z38LTdN37LJf+UPz+hflm2A3RN457hp01wuXTlZcOXx8/7no/HLj/LWSW87j4+fT7hM6YfEsmtO7F87ld1Ccu68mduWnYwVT5LNfbMzpzoFvrduTMsunYCenXntOlB/6Pt+65EKcgf9c9X6LsRMONlotyaS9klv1NwUreT0Gq/AgBCEAAAhCAAAQgsNYJ1LdZ1k1dOtarB3b4G/Ls6NaTZ93dm25qoO2k7vd3t/126xUdKbAb9lLN8pMdZhOlE3rg9yd0z45ubT5V2MR44rqtse5eLw3Zdc3GwPTr0R0nrAlZ98wJPXLgsmOoisjSH+HL7uadPe2mLp0wOxKf0P2GxY4ubTiWckbcl84hG3TwwTciL5xI2Hi8ez+pFy45G2iZ/FhkOh7e2SVz34+2XfPTGTWR+dzyTNjEFe2Im/v17vv+FxPqzL6KKhqeuZHSZlll0p8Xf8E1yQEw/68/dfgeu2t2kAbHeNrToulyOP7e4/TIvuHcruORKEJm2RzLcumyZWPd7518uv2+juzptuwffu6EjHl91ZmGbYNODdsdtI2OTNna0H0l8uooR7+/77LafuCl8xrwd+suxCnL/nypsuOEa8pGtFyUS3shs2xuqOz9RIDyFQIQgAAEIAABCEAAAqvm1VFml9wPXdO2UnnrGrB53fpwxt/J+U7jM2FM61Z4VnfxwObe0ZM7IjtAu2dbDstNkxug89k1IuV4l0tH5qamls3OSdvMtKZmnJF759AdfSyX/jsK9A4usulYgj6iURguoU3UnBPmZoofs6fd1q3paU3NOdPmncu9j55+S5+Td5GkcmWnTLkom/ZCcS7mfgpdx28QgAAEIAABCEAAAmuVQF2PLJd6H+pKHAumkgavN1qJOKJhesK8qakbk3aUPHi3cvS8lfxu0+CY5ZWMi7ALv7YILivD5f99NRF6r3LbO5Ht3tfqk4H7hgAEIAABCEAAAhBQXZvl6uffNb3wfLdeuFTlmKff0ZPPd+vRfYO65E91rXIKpAkzNbdPR/ASVUdPhBCAAAQgAAEIQAACEIBA9QlglqvPnBghAAEIQAACEIAABCAAAQhAoMYJYJZrPINIHgQgAAEIQAACEIAABCAAAQhUnwBmufrMiRECEIAABCAAAQhAAAIQgAAEapwAZrnGM4jkQQACEIAABCAAAQhAAAIQgED1CWCWq8+cGCEAAQhAAAIQgAAEIAABCECgxglglms8g0geBCAAAQhAAAIQgAAEIAABCFSfAGa5+syJEQIQgAAEIAABCEAAAhCAAARqnABmucYziORBAAIQgAAEIAABCEAAAhCAQPUJYJarz5wYIQABCEAAAhCAAAQgAAEIQKDGCWCWazyDSB4EIAABCEAAAhCAAAQgAAEIVJ8AZrn6zIkRAhCAAAQgAAEIQAACEIAABGqcAGa5xjOI5EEAAhCAAAQgAAEIQAACEIBA9QlglqvPnBghAAEIQAACEIAABCAAAQhAoMYJYJZrPINIHgQgAAEIQAACEIAABCAAAQhUnwBmufrMiRECEIAABCAAAQhAAAIQgAAEapwAZrnGM4jkQQACEIAABCAAAQhAAAIQgED1CWCWq8+cGCEAAQhAAAIQgAAEIAABCECgxglglms8g0geBCAAAQhAAAIQgAAEIAABCFSfAGa5+syJEQIQgAAEIAABCEAAAhCAAARqnABmucYziORBAAIQgAAEIAABCEAAAhCAQPUJYJarz5wYIQABCEAAAhCAAAQgAAEIQKDGCWCWazyDSB4EIAABCEAAAhCAAAQgAAEIVJ8AZrn6zIkRAhCAAAQgAAEIQAACEIAABGqcwIqZ5Svvjuv27ds1fvskDwIQgAAEIAABCEAAAhCAAAQgECZgvKzxtMv5d0+xi9M33tNH09PFDvM7BCAAAQhAAAIQgAAEIAABCECgJgkYL2s87XL+FTXLc5mMzLC1iYQR5uUg5loIQAACEIAABCAAAQhAAAIQqAYB412NhzWjysbTLudfUbNsAjWBGzduIjLGmf8wQANoAA2gATSABtAAGkADaAANoIFa1YDxrsbDLtcoGz9c0iwvx4VzLQQgAAEIQAACEIAABCAAAQhAoF4JYJbrNedINwQgAAEIQAACEIAABCAAAQisGAHM8oqhJWAIQAACEIAABCAAAQhAAAIQqFcCmOV6zTnSDQEIQAACEIAABCAAAQhAAAIrRgCzvGJoCRgCEIAABCAAAQhAAAIQgAAE6pUAZrlec450QwACEIAABCAAAQhAAAIQgMCKEcAsrxhaAoYABCAAAQhAAAIQgAAEIACBeiWAWa7XnCPdEIAABCAAAQhAAAIQgAAEILBiBDDLK4aWgCEAAQhAAAIQgAAEIAABCECgXgn8/7qpgHNMWoYyAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"def validate_undersampling():\n    X_target0 = train[train.target == 0].sample(frac = 0.26) #lấy random tập data con từ lớp 0 có kích thước bằng 0.26 lớp 0 ban đầu\n    data = X_target0.append(train[train.target == 1])  # ghép với tập data lớp 1\n    X = data.question_text_cleaned\n    y = data.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    return f1_score(predict,y_test)\n    \nprint(\"F1-Score với Under Sampling: \",validate_undersampling())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:04:24.432267Z","iopub.execute_input":"2022-01-08T14:04:24.432937Z","iopub.status.idle":"2022-01-08T14:05:30.864134Z","shell.execute_reply.started":"2022-01-08T14:04:24.432901Z","shell.execute_reply":"2022-01-08T14:05:30.863129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def submit_undersampling():\n    X_target0 = train[train.target == 0].sample(frac = 0.26) #lấy random tập data con từ lớp 0\n    data = X_target0.append(train[train.target == 1])  # ghép với tập data lớp 1\n    X_train = data.question_text_cleaned\n    y_train = data.target\n    X_test = test.question_text_cleaned\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    create_file_submission(predict)\n\n# submit_undersampling()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:05:30.865322Z","iopub.execute_input":"2022-01-08T14:05:30.865551Z","iopub.status.idle":"2022-01-08T14:05:30.872375Z","shell.execute_reply.started":"2022-01-08T14:05:30.865524Z","shell.execute_reply":"2022-01-08T14:05:30.871139Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download.png](attachment:40fada2c-1002-4c67-8c52-6a5ff7d261e6.png)\n\nTa có thể thấy áp dụng under sampling cải thiện tốt khả năng dự đoán trên tập validation nhưng lại làm thụt giảm nghiêm trọng khả năng dự đoán trên tập test.\n\nĐiểm số F1_score trên tập validation tăng chủ yếu do so lượng mẫu dùng để đánh giá mô hình trên tập này đã giảm rất nhiều so với ban đầu.\n\nSố lượng mẫu ở tập training bị giảm mạnh (chỉ còn khoảng 1/3 so với lúc đầu) dẫn tới tập dữ liệu trainning không đại diện được cho phân phối của toàn bộ tập dữ liệu dẫn tới hiện tượng overfitting\n\n\n**5.2. Under Sampling combined K-means**\n\nK-means thuật toán đơn giản nhất của dạng bài toán học có giám sát. K-means dựa vào khoảng cách giữa các vector trong không gian, chia dữ liệu thành các cụm và tìm ra tâm cụm thích hợp.\n![download.png](attachment:fec42f6e-1a43-4f5e-8160-ab00a2f28daf.png)\n\nProblem: Vấn đề ta gặp phải khi sử dụng Under Sampling chính là việc cắt giảm số lượng lớn mẫu từ tập 0 khiến tập dữ liệu mới có khả năng không thể hiện được hết phân bố tập dữ liệu ban đầu. Ta biểu diễn đơn giản vấn đề trên trong không gian 2 chiều\n\n\n![download.png](attachment:fcd1af33-ce07-45c5-87a6-544ce1cb4024.png)\n\nTa thấy rằng trong trường hợp ta loại bỏ những điểm dữ liệu như trên hình, phân bố dữ liệu tại khu vực đó hoàn toàn bị thay đổi khiến phương trình đường phân lớp tiến sâu vào vùng dữ liệu target 0 và khiến dự đoán bị sai.\n\nSolution: ta sẽ sự dụng K-mean nhằm chia tập câu hỏi đầu vào thành các cụm chứa các câu hỏi có đặc trưng tương đồng (phân bố gần nhau trên tập không gian vector). Sau đó ta tiến hánh loại bớt mẫu ở mỗi cụm. Việc này giúp ta có được tập câu hỏi mới có độ bao quát cao, có thể đại diện cho phân phối toàn tập dữ liệu bạn đầu\n\n![download.png](attachment:abca6ca6-8577-4097-a927-97d93a9c5a0d.png)\n\nTa có thể thấy khi phân cụm, những điểm bị xóa đi ở từng cụm sẽ ít gây ra sự thay đổi phân bố trên toàn tập dữ liệu.\nThông thường, khi sử dụng K-mean, ta sẽ sử dụng phương pháp Elbow chọn ra số cluster thích hợp nhất cho tập dữ liệu. Tuy nhiên với bài toán hiện tại, việc tìm ra số cụm thích hợp là không cần thiết. Ta dễ thấy rằng nếu chia ra càng nhiều cụm (tức là chia dữ liệu thành nhiều tập chứa các điểm gần nhau) thì việc xóa bỏ phần tử của mỗi cụm sẽ càng gây ít ảnh hưởng tới phân bố dữ liệu. Vì vậy mục tiêu của ta sẽ là chia ra với số cụm tối đa.\n\nVí dụ ở bài toán này, tỉ lệ dữ liệu 2 lớp đang là 1:15. Ta muốn giảm dữ liệu lớp 0 xuống sao cho tỉ lệ 2 lớp còn khoảng 1:5, vậy ta sẽ giảm đi 2/3 lượng dữ liệu ở lớp 0. Lớp 0 có 1200000 dữ liệu, ta chia lớp 0 thành 400000 lớp, mỗi lớp sẽ có 3 phần tử và ở mỗi lớp ta sẽ tiến hành xóa 2 phần tử trong đó.\n\nProblem: Vấn đề xảy ra khi sử dụng Kmeans từ thư viện sklearn. Dù đã tối ưu 2 thông số max_iter(số lần lặp tối đa với mỗi lần chạy) và n_init(số lần chạy với mỗi tâm) thì khi tăng số lượng cluster lên đến quá 15 cluster thì xuất hiện tràn ram trên kaggle. Vì vậy ở đây, ta chỉ có thể thử nghiệm với số cluster lớn nhất là 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"},"f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"}}},{"cell_type":"code","source":"def remove_data():\n    \n    train_0 = train[train.target == 0] #lấy ra tập data target 0\n    tfidf.fit(train_0.question_text_cleaned) #chuyển text sang vector với tfidf\n    encode = tfidf.transform(train_0.question_text_cleaned)\n    kmeans = KMeans(n_clusters=14,verbose=1,max_iter=5,n_init=3) # số cluster = 14,khởi tạo lấy tâm ngẫu nhiên 3 lần, mỗi lần tìm tâm tối ưu 5 lần\n    kmeans.fit(encode)\n    train_0['cluster'] = kmeans.labels_ #tạo cột đánh nhãn tên cụm\n\n    #mỗi cụm lấy ra 1/4 data để\n    a = train_0[train_0.cluster==0] \n    data = a.sample(frac = 0.25)\n    for i in range(1,14):\n        a = train_0[train_0.cluster==i]\n        b = a.sample(frac = 0.25)\n        data = data.append(b)\n\n    data.drop('cluster',axis='columns',inplace=True)\n\n    data = data.append(train[train.target == 1])#gộp tập lớp 0 sau khi giảm bớt dữ liệu với tập dữ liệu lớp 1\n    return data\n\ndata_removed =  remove_data()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:05:30.873471Z","iopub.execute_input":"2022-01-08T14:05:30.873669Z","iopub.status.idle":"2022-01-08T14:14:05.860147Z","shell.execute_reply.started":"2022-01-08T14:05:30.873644Z","shell.execute_reply":"2022-01-08T14:14:05.859131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def validation_undersampling_kmean():\n    X = data_removed.question_text_cleaned\n    y = data_removed.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    return f1_score(predict,y_test)\n\nprint(\"F1-Score với Under Sampling và K-mean: \",validation_undersampling_kmean())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:14:05.862187Z","iopub.execute_input":"2022-01-08T14:14:05.862542Z","iopub.status.idle":"2022-01-08T14:15:09.33784Z","shell.execute_reply.started":"2022-01-08T14:14:05.8625Z","shell.execute_reply":"2022-01-08T14:15:09.33698Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def submit_undersampling_kmean():\n    X_train = data_removed.question_text_cleaned\n    y_train = data_removed.target\n    X_test = test.question_text_cleaned\n    predict = predict_linearSVC(X_train,y_train,X_test)\n    create_file_submission(predict)\n\n# submit_undersampling_kmean()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:15:09.339118Z","iopub.execute_input":"2022-01-08T14:15:09.339443Z","iopub.status.idle":"2022-01-08T14:15:09.345671Z","shell.execute_reply.started":"2022-01-08T14:15:09.339408Z","shell.execute_reply":"2022-01-08T14:15:09.344791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download.png](attachment:3ef3fbe9-6006-4967-a6b0-8e5298cfafa2.png)\n\n**Nhận xét:**\nCó thể thấy rằng mô hình vẫn cho ra kết quả rất cao trên tập validation nhưng lại thấp trên tập test. Tuy nhiên, việc sử dụng K-mean đã tăng F1-Score lên hơn 3% so với lúc trước chứng tỏ việc xóa phần tử ở từng cụm là có hiệu quả và đã cải thiện đáng kể phương pháp Under Sampling. Nếu có thể tăng số cụm lên thì có khả năng hiệu quả của mô hình cũng sẽ được cải thiện.\n\n\n\n# 6. CẢI THIỆN\n\nSinh thêm các trường dữ liệu có thể tăng thông tin từ đó ảnh hưởng tới khả năng dự đoán mô hình. Đối với bài toán hiện tại, một vài đặc điểm về số lượng kí tự đặc biệt, chữ số, số lượng stopword,... trong câu có thể là những thông tin quan trọng giúp phân loại câu hỏi.\n\nTa thêm 1 số trường thông tin cho câu hỏi:\n\n* freq_id : tần suất xuất hiện của id\n* q_len : độ dài của các câu hỏi\n* n_words : số từ trong câu hỏi\n* numeric_words : số lượng các chữ số trong câu\n* sp_char_words : số lượng các kí tự đặc biệt trong câu\n* char_words : số lượng các kí tự trong câu\n* unique_words : số lượng các từ duy nhất trong câu","metadata":{},"attachments":{"3ef3fbe9-6006-4967-a6b0-8e5298cfafa2.png":{"image/png":"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"}}},{"cell_type":"code","source":"def generate_feature():\n    train['qlen'] = train['question_text'].str.len() \n    train['n_words'] = train['question_text'].apply(lambda row: len(row.split(\" \")))\n    train['numeric_words'] = train['question_text'].apply(lambda row: sum(c.isdigit() for c in row))\n    train['sp_char_words'] = train['question_text'].str.findall(r'[^a-zA-Z0-9 ]').str.len()\n    train['char_words'] = train['question_text'].apply(lambda row: len(str(row)))\n    train['unique_words'] = train['question_text'].apply(lambda row: len(set(str(row).split())))\n    train['stopwords'] = train['question_text'].apply(lambda x: len([c for c in str(x).lower().split() if c in STOPWORDS]))\n    test['qlen'] = test['question_text'].str.len() \n    test['n_words'] = test['question_text'].apply(lambda row: len(row.split(\" \")))\n    test['numeric_words'] = test['question_text'].apply(lambda row: sum(c.isdigit() for c in row))\n    test['sp_char_words'] = test['question_text'].str.findall(r'[^a-zA-Z0-9 ]').str.len()\n    test['char_words'] = test['question_text'].apply(lambda row: len(str(row)))\n    test['unique_words'] = test['question_text'].apply(lambda row: len(set(str(row).split())))\n    test['stopwords'] = test['question_text'].apply(lambda x: len([c for c in str(x).lower().split() if c in STOPWORDS])-1)\n    \ngenerate_feature()\ndisplay(train.head(),test.head())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:15:09.346949Z","iopub.execute_input":"2022-01-08T14:15:09.347203Z","iopub.status.idle":"2022-01-08T14:15:44.291373Z","shell.execute_reply.started":"2022-01-08T14:15:09.347174Z","shell.execute_reply":"2022-01-08T14:15:44.290829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f, ax = plt.subplots(figsize=(11, 9))\n \ncorr = train.drop(['qid','question_text','question_text_cleaned'],axis='columns').corr() # tính độ tương quan giữa 2 các cặp dữ liệu, càng gần 0 độ tương quan càng thấp\n\n# Draw the heatmap\nsns.heatmap(corr, ax=ax)\n\nplt.title(\"Correlation matrix\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:15:44.292467Z","iopub.execute_input":"2022-01-08T14:15:44.292836Z","iopub.status.idle":"2022-01-08T14:15:45.077686Z","shell.execute_reply.started":"2022-01-08T14:15:44.292801Z","shell.execute_reply":"2022-01-08T14:15:45.076866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Từ biểu đồ mức độ tương quan giữa các trường cho ta thấy rằng nếu xét độc lập thì các trường có độ tương quan khá thấp với target (khoảng 0.1). Tuy nhiên cũng có thể target bị ảnh hưởng bởi nhiều trường. Ta tiến hành thử nghiệm với các trường.","metadata":{}},{"cell_type":"code","source":"def validate_with_new_feature():\n    X = train[['question_text_cleaned','qlen','n_words','numeric_words','sp_char_words','char_words','unique_words','stopwords']]\n    y = train.target\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n    tfidf.fit(X_train.question_text_cleaned)\n\n    A_train = tfidf.transform(X_train.question_text_cleaned)\n    A_test = tfidf.transform(X_test.question_text_cleaned)\n\n    scaler = preprocessing.MinMaxScaler()\n    print(scaler)\n    scaled_train = scaler.fit_transform(X_train[['qlen','n_words','numeric_words','sp_char_words','unique_words','stopwords']])\n    scaled_test = scaler.fit_transform(X_test[['qlen','n_words','numeric_words','sp_char_words','unique_words','stopwords']])\n\n    X_train = hstack([A_train,coo_matrix(scaled_train)])\n    X_test = hstack([A_test,coo_matrix(scaled_test)])\n    svm = LinearSVC()\n    svm.fit(X_train,y_train)\n    return f1_score(svm.predict(X_test),y_test)\n\nprint(\"F1-Score với new Feature: \",validate_with_new_feature())","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:15:45.079396Z","iopub.execute_input":"2022-01-08T14:15:45.079903Z","iopub.status.idle":"2022-01-08T14:19:14.121155Z","shell.execute_reply.started":"2022-01-08T14:15:45.079836Z","shell.execute_reply":"2022-01-08T14:19:14.120328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def submit_with_new_feature():\n\n    tfidf.fit(train.question_text_cleaned)\n\n    A_train = tfidf.transform(train.question_text_cleaned)\n    A_test = tfidf.transform(test.question_text_cleaned)\n\n    scaler = preprocessing.MinMaxScaler()\n    print(scaler)\n    scaled_train = scaler.fit_transform(train[['qlen','n_words','numeric_words','sp_char_words','unique_words','stopwords']])\n    scaled_test = scaler.fit_transform(test[['qlen','n_words','numeric_words','sp_char_words','unique_words','stopwords']])\n\n    X_train = hstack([A_train,coo_matrix(scaled_train)])\n    X_test = hstack([A_test,coo_matrix(scaled_test)])\n    svm = LinearSVC()\n    svm.fit(X_train,train.target)\n    predict = svm.predict(X_test)\n\n    create_file_submission(predict)\n    \nsubmit_with_new_feature()","metadata":{"execution":{"iopub.status.busy":"2022-01-08T14:19:14.122602Z","iopub.execute_input":"2022-01-08T14:19:14.122818Z","iopub.status.idle":"2022-01-08T14:23:48.112823Z","shell.execute_reply.started":"2022-01-08T14:19:14.122792Z","shell.execute_reply":"2022-01-08T14:23:48.111877Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![download.png](attachment:4e19873b-d169-4b0f-89d2-bc8da56805f0.png)\n\n**Nhận xét:**\nĐiểm số đã được cải thiện hơn so với trước. Tập feature mới được thêm vào đã có hiệu quả.\n\n\n\n# 7. KẾT QUẢ\n\nƯu điểm:\n* Đã cải thiện được F1-score mô hình cơ sở từ 0.6305 lên 0.6375\n* Với bài toán hiện tại, khi so sánh với những mô hình deep learning, Linear SVC cũng cho thấy khả năng phân lớp tương đối hiệu quả.\n* Mô hình được cải thiện nhẹ với tập dữ liệu sau khi được lọc bớt phần thừa và tăng thêm trường dữ liệu.\n* K-mean có thể được áp dụng để cải thiện cho phương pháp Under Sampling, tránh hiện tượng Overfitting.\n\nNhược điểm:\n* Mã hóa text bằng Tfidf là tương đối đơn giản, vector được tạo ra mang ít giá trị trong huấn luyện mô hình.\n* K-mean được sử dụng hiệu quả nhưng số lượng cluster khá thấp, không đủ để cải thiện mô hình với Under Sampling.\n\nHướng tiếp cận cải thiện mô hình:\n* Sử dụng các bộ vector biểu diễn từ được pretrain như glove thay thế Tfidf, các vector này tận dụng thông tin về mối quan hệ ngữ nghĩa giữa các từ tốt hơn, mang lại nhiều thông tin hơn trong quá trình huấn luyện mô hình.\n* Sử dụng phương pháp Over Sampling tăng thêm dữ liệu cho tập thiểu số.\n* Áp dụng một số mô hình deep learning như LSTM, RNN.","metadata":{},"attachments":{"4e19873b-d169-4b0f-89d2-bc8da56805f0.png":{"image/png":"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"}}}]}