{"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":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-01-04T14:45:58.17752Z","iopub.execute_input":"2022-01-04T14:45:58.178107Z","iopub.status.idle":"2022-01-04T14:45:59.015575Z","shell.execute_reply.started":"2022-01-04T14:45:58.178007Z","shell.execute_reply":"2022-01-04T14:45:59.014687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Introduction**\nInternet đã mở ra một kỷ nguyên mới, cho phép chúng ta có cơ hội được tiếp cận với nhiều nguồn thông tin một cách thuận tiện, nhanh chóng; từ việc đọc báo, giải trí cho đến tìm tòi, chia sẻ kiến thức, quan điểm của bản thân với mọi người. Tất cả đều trở nên dễ dàng và nhannh chóng nhờ có sự can thiệp của Internet.\n\nTrong số đó, việc tìm kiếm câu trả lời cho những thắc mắc của bản thân là một trong những việc được thực hiện nhiều nhất trên Internet. Rất nhiều trang web đã được lập ra để phục vụ cho mục đích đó, Quora là một trang web như vậy. Với Quora, người dùng có thể truy cập và đặt ra những câu hỏi thuộc nhiều lĩnh vực khác nhau và không lâu sau, câu hỏi đó sẽ được những người dùng khác trả lời một cách thấu đáo, chi tiết nhất có thể (đôi khi có thể hơi quá chi tiết). Điều này khiến cho Quora trở thành một trong những nền tảng cung cấp thông tin tri thức nổi tiếng và được sử dụng nhiều nhất trên toàn cầu.\n\nTuy vậy, bên cạnh những câu hỏi bình thường, mang tính tò mò, tìm tòi đơn thuần, một bộ phận người dùng lại lợi dụng tính công khai, tự do ngôn luận của nền tảng này để đặt ra những câu hỏi gây tranh cãi, vô văn hóa,... nhằm kích động, gây chia rẽ giữa người với người - vốn không nên xuất hiện trên những nền tảng công cộng, với lượng người dùng khổng lồ như Quora.\n\nNhận thức được điều này, một trong những giải pháp được đặt ra đó là xây dựng một mô hình giúp phân lọai ra những câu hỏi tri thức đơn thuần (ta gọi là **sincere** question) với những câu hỏi gây tranh cãi nêu trên (**insincere** question). Và mục tiêu đó sẽ được giải quyết thông qua mô hình được huấn luyện ngay sau đây.\n\n***Mô tả bài toán***\n\n**Input**: Dữ liệu dạng text về câu hỏi\n\n**Output**: Phân loại câu hỏi **sincere** hoặc **insincere** và được Kaggle đánh giá theo công thức:\n\nF1 =2.(Precision.Recall)/(Precision+Recall)\n\nTrong đó:\n\nPrecision = TP/(TP+FP)\n\nRecall = TP/(TP+FN)\n\nTa có hình vẽ biểu diễn hai đại lượng trên như sau:\n\n![image.png](attachment:0cafff32-92c3-4661-8928-ea5f0c548b45.png)!\n# **Data**\n\nTập dữ liệu train bao gồm 1.3 triệu dòng và 3 cột, nhưng theo quan sát thì ta chỉ quan tâm đến 2 features: **question_text** và **target** trong đó:\n\n* **question_text** : Các câu hỏi trên Quora.\n* **target** : một câu hỏi có nhãn \"**insincere**\" mang giá trị là 1 , \"**sincere**\" là 0.\nThông qua hai features này, ta sẽ phân tích được những đặc điểm của các câu hỏi **sincere** và **insincere**, phục vụ cho mục đích huấn luyện mô hình về sau.","metadata":{},"attachments":{"0cafff32-92c3-4661-8928-ea5f0c548b45.png":{"image/png":"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"}}},{"cell_type":"code","source":"df_train = pd.read_csv(\"/kaggle/input/quora-insincere-questions-classification/train.csv\")\nprint(\"Shape of train data: \", df_train.shape)\ndf_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:45:59.017118Z","iopub.execute_input":"2022-01-04T14:45:59.017767Z","iopub.status.idle":"2022-01-04T14:46:03.622603Z","shell.execute_reply.started":"2022-01-04T14:45:59.017732Z","shell.execute_reply":"2022-01-04T14:46:03.62186Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Tập dữ liệu test hơn 300 nghìn dòng và chỉ còn lại **quesion_text** để dự đoán **target** đầu ra bài toán.","metadata":{}},{"cell_type":"code","source":"df_test = pd.read_csv(\"/kaggle/input/quora-insincere-questions-classification/test.csv\")\nprint(\"Shape of test data: \", df_test.shape)\ndf_test.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:03.623908Z","iopub.execute_input":"2022-01-04T14:46:03.625118Z","iopub.status.idle":"2022-01-04T14:46:04.652534Z","shell.execute_reply.started":"2022-01-04T14:46:03.625076Z","shell.execute_reply":"2022-01-04T14:46:04.651821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Data analysis**","metadata":{}},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:04.654676Z","iopub.execute_input":"2022-01-04T14:46:04.654912Z","iopub.status.idle":"2022-01-04T14:46:04.935618Z","shell.execute_reply.started":"2022-01-04T14:46:04.65488Z","shell.execute_reply":"2022-01-04T14:46:04.934684Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ta thấy tập dữ liệu train có 3 trường: **qid**, **question_text** và **target** với tổng số lượng bản ghi lên tới 1,306,122 bản ghi.","metadata":{}},{"cell_type":"code","source":"df_train['target'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:04.937158Z","iopub.execute_input":"2022-01-04T14:46:04.937424Z","iopub.status.idle":"2022-01-04T14:46:04.950383Z","shell.execute_reply.started":"2022-01-04T14:46:04.937388Z","shell.execute_reply":"2022-01-04T14:46:04.949629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Trong số đó, tồn tại:\n\n* 1,225,312 câu hỏi mang giá trị target là 0, tương ứng với các câu hỏi **sincere**.\n* 80,810 câu hỏi mang giá trị target là 1, tương ứng với các câu hỏi **insincere**.\n\nTa kiểm tra tính trùng lặp trong bộ dữ liệu","metadata":{}},{"cell_type":"code","source":"df_train.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:04.951943Z","iopub.execute_input":"2022-01-04T14:46:04.952541Z","iopub.status.idle":"2022-01-04T14:46:05.889539Z","shell.execute_reply.started":"2022-01-04T14:46:04.952502Z","shell.execute_reply":"2022-01-04T14:46:05.888831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Có thể thấy sau khi chạy dòng lệnh, ta thu được kết quả là 0. Từ đó có thể kết luận rằng trong tập dữ liệu train không tồn tại sự trùng lặp.","metadata":{}},{"cell_type":"code","source":"df_test.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:05.891983Z","iopub.execute_input":"2022-01-04T14:46:05.892254Z","iopub.status.idle":"2022-01-04T14:46:06.120053Z","shell.execute_reply.started":"2022-01-04T14:46:05.892219Z","shell.execute_reply":"2022-01-04T14:46:06.119353Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Tương tự như tập train, có thể thấy tập dữ liệu test cũng không tồn tại sự trùng lặp dữ liệu.\n\nVới số lượng câu hỏi **sincere** và **insincere** thu được bên trên, ta bắt đầu xem xét tới tính cân bằng của tập dữ liệu.\n\n**Tính cân bằng của tập dữ liệu**\n\nMất cân bằng dữ liệu là một trong những hiện tượng phổ biến của bài toán phân loại nhị phân (binary classification). Trước khi đi sâu vào phân tích hiện tượng này, ta cần kiểm tra tính cân bằng của tập dữ liệu.","metadata":{}},{"cell_type":"code","source":"print(\"Total questions: \", df_train.shape[0])\nprint(\"Sincere questions: {}%\".format(round(df_train[df_train[\"target\"] == 0].shape[0]/df_train.shape[0]*100, 2)))\nprint(\"Insincere questions: {}%\".format(round(df_train[df_train[\"target\"] == 1].shape[0]/df_train.shape[0]*100, 2)))\n\nplt.figure(figsize=(7,5))\nax = sns.countplot(x='target', data=df_train)\ntotal = df_train.shape[0]\nfor p in ax.patches:\n    height = p.get_height()\n    ax.text(p.get_x() + p.get_width()/2., height + 3, '{0:.0%}'.format(height/total), ha=\"center\")\nplt.title('Reparition of question by suncerity (insincere =  1)')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:06.12134Z","iopub.execute_input":"2022-01-04T14:46:06.121745Z","iopub.status.idle":"2022-01-04T14:46:06.494144Z","shell.execute_reply.started":"2022-01-04T14:46:06.121707Z","shell.execute_reply":"2022-01-04T14:46:06.493354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Với tổng tập train là 1 triệu 3 điểm dữ liệu, câu hỏi **sincere** chiếm tới gần 94%, theo đó tỷ lệ dữ liệu giữa 2 classes vào khoảng 15:1.\n​\nĐây là hiện tượng mất cân bằng nghiêm trọng dẫn tới kết quả dự báo kém chính xác trên nhóm thiểu số.\n\n**Xử lý mất cân bằng dữ liệu**\n\nKhi một bên chiếm đa số và bên còn lại thiểu số, hiển nhiên sẽ có 2 cách để làm cân bằng chúng:\n\n* Tăng số lượng bên thiểu số (Over sampling): thu thập thêm dữ liệu hoặc chấp nhận mẫu có trùng lặp.\n* Giảm số lượng bên đa số (Under sampling): cân bằng mẫu một cách nhanh chóng, dễ dàng tiến hành thực hiện nhưng kích thước mẫu sẽ giảm đáng kể.\n\nMỗi phương pháp đều mang những ưu nhược điểm riêng, trong bài này ta sẽ tập trung vào phương pháp **Under sampling**\n\n**Vấn đề:** Tỷ lệ 50:50 trên 2 classes là trạng thái cân bằng, nhưng để đạt được tỷ lệ đó thì ta sẽ mất rất nhiều mẫu dữ liệu\n\n**Giải pháp:** Ta sẽ giảm bớt sự mất cân bằng (1:4) sao cho không ảnh hưởng đáng kể tới khả năng dự báo của mô hình.","metadata":{}},{"cell_type":"code","source":"from sklearn.utils import resample\n\nsincere = df_train[df_train.target == 0]\ninsincere = df_train[df_train.target == 1]\ndf_train_sampled = pd.concat([resample(sincere, replace = True, n_samples = len(insincere)*4), insincere])\ndf_train_sampled","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:06.495625Z","iopub.execute_input":"2022-01-04T14:46:06.496035Z","iopub.status.idle":"2022-01-04T14:46:06.900467Z","shell.execute_reply.started":"2022-01-04T14:46:06.495995Z","shell.execute_reply":"2022-01-04T14:46:06.899754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y = df_train_sampled['target']\ny.value_counts().plot(kind='bar', rot=0)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:06.904642Z","iopub.execute_input":"2022-01-04T14:46:06.905119Z","iopub.status.idle":"2022-01-04T14:46:07.143756Z","shell.execute_reply.started":"2022-01-04T14:46:06.905083Z","shell.execute_reply":"2022-01-04T14:46:07.143024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Có thể thấy việc giảm bớt cân bằng dữ liệu đã cho ta một tập dữ liệu có phần cân bằng hơn.\n\nSau khi đã giảm bớt sự mất cân bằng dữ liệu, ta tiến hành phân tích dữ liệu một cách chi tiết hơn để có cái nhìn tốt về bộ dữ liệu.\n\nTrước hết, ta có thể có cái nhìn sơ lược về những từ thường xuất hiện trong các câu hỏi **sincere** và **insincere** thông qua các biểu đồ Word Cloud như sau:","metadata":{}},{"cell_type":"code","source":"# import wordcloud\nfrom wordcloud import WordCloud, STOPWORDS\nstop_words = set(STOPWORDS)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:07.145156Z","iopub.execute_input":"2022-01-04T14:46:07.145697Z","iopub.status.idle":"2022-01-04T14:46:07.204479Z","shell.execute_reply.started":"2022-01-04T14:46:07.145656Z","shell.execute_reply":"2022-01-04T14:46:07.203536Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Word cloud thể  hiện các câu hỏi sincere: \\n\")\nsincere_wordcloud = WordCloud(width=800, height=600, background_color='white', min_font_size=10).generate(str(df_train[df_train[\"target\"] == 0][\"question_text\"]))\nplt.figure(figsize=(8, 8), facecolor=None)\nplt.imshow(sincere_wordcloud)\nplt.axis(\"off\")\nplt.tight_layout(pad=0)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:07.209098Z","iopub.execute_input":"2022-01-04T14:46:07.209376Z","iopub.status.idle":"2022-01-04T14:46:08.120363Z","shell.execute_reply.started":"2022-01-04T14:46:07.20934Z","shell.execute_reply":"2022-01-04T14:46:08.118243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Word cloud thể  hiện các câu hỏi insincere: \")\ninsincere_wordcloud = WordCloud(width=800, height=600, background_color='white', min_font_size=10).generate(str(df_train[df_train[\"target\"] == 1][\"question_text\"]))\nplt.figure(figsize=(8,8), facecolor=None)\nplt.imshow(insincere_wordcloud)\nplt.axis(\"off\")\nplt.tight_layout(pad=0)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:08.12136Z","iopub.execute_input":"2022-01-04T14:46:08.121615Z","iopub.status.idle":"2022-01-04T14:46:08.848446Z","shell.execute_reply.started":"2022-01-04T14:46:08.121577Z","shell.execute_reply":"2022-01-04T14:46:08.847773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Thông qua hai biểu đồ Word Cloud trên, ta nhìn nhận được một số đặc điểm sau:\n\n* Đối với câu hỏi **sincere**, những từ xuất hiện nhiều có thể liên quan đến nhiều vấn đề khác nhau, trong đó nổi bật là về kiến thức văn hóa, khoa học, kỹ thuật như: space, geometry, computer,...\n* Còn đối với câu hỏi **insincere**, những từ xuất hiện với tần suất lớn bao gồm những từ liên quan đến sắc tộc, chính trị, chửi thề: gay, Trump, dictactorship,...\n\n# **Preprocessing**\nĐể mô hình đạt được tính chính xác cao, ta cần phải thực hiện công đoạn tiền xử lý dữ liệu trước khi đi đến giai đoạn xây dựng mô hình.\n\nBước đầu tiên là ta phải loại bỏ những tác nhân gây nhiễu hoặc không cần thiết, có thể gây ảnh hưởng đến kết quả training model.\n\nMột số cách để loại bỏ những tác nhân gây nhiễu và thừa thãi đó - ta gọi là làm sạch dữ liệu - bao gồm:\n\n* Loại bỏ các tag HTML\n* Loại bỏ các dấu nháy đơn, nháy kép\n* Loại bỏ các số thực, số phức\n* Loại bỏ các kí tự La Mã\n* Thay thế các dấu, các kí tự đặc biệt với khoảng trắng\n* Đưa tất cả các chữ cái viết hoa, viết thường về dạng chung là viết thường","metadata":{}},{"cell_type":"code","source":"import re, string\n\ndef clean_text(text):\n\n  # Remove HTML Tags\n  text = re.sub(re.compile('<.*?>'), '', text)\n\n  # Remove [\\], ['], [\"]\n  text = re.sub(r'\\\\', '', text)\n  text = re.sub(r'\\\"', '', text)\n  text = re.sub(r'\\'', '', text)\n\n  # Remove number\n  text = re.sub('[0-9]{5,}','#####', text);\n  text = re.sub('[0-9]{4,}','####', text);\n  text = re.sub('[0-9]{3,}','###', text);\n  text = re.sub('[0-9]{2,}','##', text);\n\n  ## Remove Roman words\n  roman = re.compile(r'^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$');\n  text = roman.sub(r'', text);\n\n  # Convert all text to lowercase\n  text = text.strip().lower()\n\n  # Replace punctuation chars with spaces\n  filters = '!\"\\'#$%@&*()+_-;:<=>.?{}|`\\\\^\\t\\n'\n  translate_dict = dict((c, \" \") for c in filters)\n  translate_map = str.maketrans(translate_dict)\n  text = text.translate(translate_map)\n\n  return text","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:08.849715Z","iopub.execute_input":"2022-01-04T14:46:08.850081Z","iopub.status.idle":"2022-01-04T14:46:08.860576Z","shell.execute_reply.started":"2022-01-04T14:46:08.850048Z","shell.execute_reply":"2022-01-04T14:46:08.859839Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Đầu vào của bài toán là dữ liệu dạng text (ở đây là các question_text), không thuộc kiểu dữ liệu mà các thuật toán Machine Learning có thể hiểu và làm việc được, do đó mà ta cần chuyển đổi tập dữ liệu này về dạng số.\n\nMột thuật ngữ ám chỉ việc chuyển dữ liệu về dạng text về dạng số đó được gọi là Vectorizer. Và hiện này có hai cách thực hiện kỹ thuật này là:\n\n* Tính toán số lần xuất hiện của từ trong văn bản\n* Tính toán tần suất xuất hiện của từ trong văn bản\n\n**TF-IDF**\n\n**Vấn đề:** Đối với những câu hỏi được đặt ra trên Quora, thông qua việc phân tích dữ liệu, ta nhận thấy ở cả hai loại câu hỏi **sincere** và **insincere** đều xuất hiện một số từ ngữ nổi bật, được lặp lại khá nhiều, mà tầm quan trọng có thể không cao, ví dụ như: \"is\", \"that\",... Việc xuất hiện với tần suất lớn của những từ ngữ đó có thể làm lu mờ đi ý nghĩa của những từ quan trọng; điều này có thể gây ảnh hưởng ít nhiều tới việc training model sau này.\n\n**Giải pháp:** để khắc phục vấn đề nêu trên, ta có thể sử dụng TF-IDF, một kỹ thuật cơ bản trong xử lý ngôn ngữ tự nhiên, có tác dụng biểu diễn dữ liệu thông qua mức độ quan trọng của một từ trong văn bản.\n\nTF-IDF bao gồm hai thành phần chính cần quan tâm:\n\n**TF**(Term Frequency): tần suất xuất hiện của từ trong văn bản, được tính theo công thức:\n\n![image.png](attachment:cd1e457b-ed4a-48fb-9fb9-31254103314b.png)\n\n**IDF** (Inverse Document Frequency): nghịch đảo tần suất của văn bản, giúp đánh giá tầm quan trọng của từ, được tính theo công thức:\n\n![image.png](attachment:332b697d-478c-4401-8ce1-f675db3710ca.png)\n\nTừ hai thành phần này, ta rút ra được biểu diễn công thức tính độ quan trọng của một từ trong văn bản như sau:\n\n![image.png](attachment:e1918207-a84a-4b1a-b246-cd23d0911069.png)\n\nTrong đó:\n\n* t: từ xuất hiện trong văn bản\n* d: văn bản được khảo sát\n* D: tập hợp các văn bản\n\nTa chuyển đổi dữ liệu từ text sang thể hiện vector thông qua TfidfVectorizer.\n\nVà thay vì sử dụng các từ riêng lẻ unigram (\"do\", \"not\",...), bigram (\"do not\",...) thì ta sẽ sử dụng các từ trigram (\"do not do\",...)\n\nĐồng thời, loại bỏ các từ thừa thãi, không mang nhiều ý nghĩa trong việc training model như \"a\", \"an\", \"the\",... (hay còn gọi là các 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"},"332b697d-478c-4401-8ce1-f675db3710ca.png":{"image/png":"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"},"e1918207-a84a-4b1a-b246-cd23d0911069.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYcAAAAzCAYAAAB8MsSpAAAgAElEQVR4nO29548dV5bg+Xsv4nnv/UvvSCY9KUqUL6p8m+qunTbbPbvTCyxmzV+w2AVqPuwC+20+zBoMsIvBLDDTaDPVU9Xl1CqVDCVK9EkmmUzvn/fehNkPSYkmLY1Kqqr3AwQBzIgXJ+LeOOfcc849oVFVVaVHjx49evR4CO2XLUCPHj169Pjq0TMOPXr06NFjGz3j0KNHjx49ttEzDj169OjRYxs949CjR48ePbbRMw49evTo0WMbPePQo0ePHj220TMOPXr06NFjGz3j0KNHjx49ttEzDj169OjRYxs949CjR48ePbbRMw49evTo0WMbPePQo0ePHj220TMOPXr06NFjGz3j0KNHjx49ttEzDj169OjRYxs949CjR48ePbbRMw49evTo0WMbPeOwKzLNfJrM2jqbNZB7H1PdnWaWbDLFWqKK9GXL8huC0qrR2JxjMVun3lG+bHG2ITdrVNbnWcy3qHd/hye/qkC3SGJtk1S2TOs38FE87ViKX4AoNAsJUhsbLOWaiIIRW2SQaNCD36p7pl9W5S6d7F2u3kuSrcsIWi0mBTyTLzMUsGI3aPY6GaWRYW15lVShSUNjwegMMjoSwWEQ0D1yqkJ18y6zywXyqocxb4wvZ06oQIfCwlUW17Kk6gJ6g4lW08PoqSFiYTvWpzXvUpVSYp3Z63OkgX3Vk0aL3uLBE4wQDfvxu8wPnplGop5ZYDXTZbN1kuNxO0ZRyx6j8VuH0syRTWywkSxQ6uoRbQEGByP4nGaMj41Rt5okubLCnaUG3tMhvF+OyABI1QSJtUWuLxTAZMTQ7uAIDRII+nDXE8xcS1A8dIiBmBeP6asyoi1qmU1Wr95muaNHMGhQ9G7svjjHjoawspfXq9CtFymsLzKbrqGoeizeMP5IhD6X4dFD1S5SM8fqzcssdqME+gZw/boGS5Ggusq1W8sk8jXkA5yiMzqweyOEY2EiXgsGQbP1Dipt5KcYy+duHDqVTVZuX+Pa9TvcLdSRihL2Y1/nlVdO4hz3oH/4YFVFVVVUjRbtQeadItEtrjA7fYOb95JUCw1ErJz570/gdVp2Nw6qhNwqkpr+iIs3F1jZKFDrGpA943zH9nscD9tw6u+fq8hI9U3mr19lKm9BHIrzgvnLXGJ1qaZmWbh+k8tLVXJ1kUr3DH8R8OIJPoNxUJo0cuvMf3qJuW6NxGaWUq0NZjvuUBSXkQdjoiqoioRGZ8XqH2bo0GEmDw8xGHRg1ILGaMMo1Gil5riaEHB84xT9HjNm3e/CwlQFuU5+8SbXpqa5uZCmUe9SNg3x+ne/ybnDfUTMD56D2imTWbjD1NQqU90J/tisRy9+ec9JaRTIrU9z6aO75HNVuuUO/S//Pq9+5wJhqxFp/gNutrW0NEc4MeDC8gW4kwCqooBGAxrNAZyKLq1yirVrl7iUrpJp1iibDzNy9gIjR0NY9jhTauRJL93m6vufMJWv0yh1MUVPcPj8qwTORTDA/esrSM08+eVrfHhpGu1klLDJxF7+53NFlaGRZunuLabnV8iXC6ymKoAVb8SP02F6xOlQpTYIFkzuGMGRCU4cG2c47MRh1CHodRifYiyFH/zgBz94fnekkLn29/zk7VtcT1p44VyUzNVrzK/qMQWCDIz7sT5083K7SavZoaGI6MT9DYRG0KH3jXPi7DDhUp7itQ3WzOOc++ZLDHqNmHe7WalMI3WDf/y//gMf6IdxukTCtbtcm1lGjr7AoM+K2yQAKkqnRunm3/L3H4AhfowLXxsnKPAlecEaQI8zfoLJkTBubZG3r63B8DleOzfIkN/8qLF9EgQLttAwR998lTdOemnNrZJJt7CMnOX3/of/mb/63gW++fULXLhwgTdff4Xzp0aJateYufwxH17fYLlhZfBQHKeoQdDosbodGKUayZ/8hFuWMcJeG26L+Nu/elC6qPV7vPcf/p5fbkI1GONF9Q5Xpu7Sco8TCISJOT6bmCrdxKdc+qcZZjZcvPLPv8kRt7htZfHrRLD6CQ1PcP5kCPXnl1ireXFPnuL4mcNE3G6GvVk++mCOzbqIoy9KwHQQ5f0kqChSl1ajQUdWUQURUbPfFQyYPXGGzr3IMc860/MJNk0x+o6f4uuDTgR2e19VSgsX+eSdf+I/fizw4hvDNJcWWJqtU9N4OXQujo37jqDapLh6i2s//RHvef6Qt16YYDJs+fU5iVoRbDEOn36RN46H8eoqfDCVAu0k3/yr/4Y/+4t/xh9/a+v9vHDhAq+/cIRBZ5vi8mV+/s4HXM86CMR8+N0WDIIB0fTkY7nrvcrdLp1Wh1YH1IPEVFQJusssTy2TXhZQJZF27V3W1TYNjxnRZnhEkXXWP+aX//Hf8K/+1b/mf/13V1iqd2gf9ME1GjTbbWqiFcExiM8rYNhDS3aqBTL3rvBJqYND7CDk6mzMd9BqbLjMJvSCsHVgt0IrPcUvfnoD3eAgY5ODhHe1riooXTrtFu1u9wuPtbdbTRqNGhpRwBr34TIbMD2H31UVhW4hyVKnRZoQZuMwsSCIwoNjtIIBoyvOyKt/xh9+4xTHvVnW77zPX7+3TKUtbYWkBDfe+Cgvvhlj7ifvcm02RbL1HAT8daJIdDttWp0OXThQKFGROpRmLzOVKdBWu3hbDeZv1ZG6FqwGM2b9/QmkytBNcvujiyzJYHv5NCfdoN/tDXwKWZ4aqYtaq1JUVTrmCBaXB6cdNKIBcfBVzo+X6Rau8U+XF6k9V1lkpGaW+Z/9a/7N//6/8L/9v/+Zn94pHfhsVZVoVdaQuw0CdisDXiciezhyUoLUwhLLU3VQw0i190lLGUp2A1qPGeNDh7ZT91i8cZd3703w5tkRYl4Lwi4/q8gy3WaLZltF/gLSR416iWopA6IIgyP4AzacxkePEax+4idf5c1vvcX3JgTkmR/ywaczzKw3gKcby51VX32VuU9ucHWuSnX0Zb5/PobHKO76cABQZCisM5cqs2nw4xkdYeKQh1q5hnH4DMfHAo8s99q1PPlMlkLFTNhtxazV7P37D1MtkGlWWbUa0Q6F8RoE9LvOiC6tepbk0gzVdpeYf5CxQQFtMETYOsT5YTtuswZQaFUyrN+5wpXcCCffihGPWh7LRXyGitypkr75D/zqVg0xfoZjZ88w7jzoDTw5zXqGWmkVUatlKOzFbtI/Fy9GlSVKmRVq7TpdTxhjPID/4ZASgEazZSBsfoaOHOXQfJLVS0lWP7zD0qkwEzoRqyBicPkIHjvE0bd/xsL8KIGIi8CA4+Dj+qUik7v3NlemVkjQx/EL32HSs4fyvn+OLFVILd2lWK1iDoQYPnQMt1HFIvZx7GicmHPr7lW5Q3PlMpfvydSDcc6Oh3Zf6T6VLM9Ap023mGFWkalGvFh9DrbEFtAYPIyOT3A3OcfdqavcPjrIqYCA4bkMqowi16kkNkkWZcSwGZvZsP9pAEgocplCqkinZcZtcRNyWvZe1VTSJDI55rtahNEIQ2NvoFWrHDGNMny8Hzv3vWW5QHpphvm1FI0jX+doyIprt4ffypCem+bDX95hY/B1vnFukNGAhWfLrj5Ml3o1QymfRCMKmPqDuG1GLI+JoxF06M1ewtERzpwY4xezN1mYXmJhoJ/xeByH5snHcsfp2S2uMn/nMh/e7qBznuR7yv6+gqrItLKbJOtNSg4TsaEBRkZOYjM3sATCuJ3WR1YOorOf4RMaxCELgaMRXHrhwEqkUUySrxVpWswMDPpxicIeg9GiVSmSnKkgS16cnjiDE0H8yhhVg5/+gBG9oAGlRi29ztzlBWrB3yMa8uI37/Y2SsjtPKuXLnLlmgHPyyOMnD6g8E9Fg2oxTyHVRBQGGAq6sBqfx/STkeUa2c112s0mBq8VR9SNU7vbklKLORAh5PIQaK5wb2OK1fzr9DlMWAXQ6G2YA6OcGv0JP1xeZinWx2TMgfcLilM/PxSgSmr6Ojc/WCfhNjP05kFWzF0UqURyJk+rYsFjixIfO8LIkIs+MUAo4MBm1AASUifP2rUrLHUCxAJxRvy7rfueVpanR2rXKWdWyMgS5ogbr8f6kCMn4oiP0+deZXl6jo/uZZhw+9ELwnMIL2nR6qz4xl/hFZ8Rc3ScMZ9x/9MA1DZyK09ysU2rHiRq8xFw7T3R2qUsmXKZtF6PeShIfHCSsLeDxuTA5XN/vnKQS+ssLiVZLBsY/NYYAbNuV8Ms1dIkF67zq4+vU9Mf56X2QVLHT0KdSqZEbq2GIDiIRnw4TYZdvHoRg8VNYGCIkDjN0nqCTCJPsR3HYdj6+5OM5Q7XUKnn1klXstRNfiZDHiyCdl/FrSgS5dw6tU4TjcOEzefD6Qzi3MWbNoWPcyZ8nDP7/O52ZArZTWrlMjaTn9GIF7Og3d2LVhq0KjXSyyIyI9jMDjzhEOHH5qDaylNIrHB7pkLg+32EnJbdE1tKC6mZZm22TkMapN/hx23d7eDngFygnK2RSdkQTKNEfGZMhufhRnZRugUyyznatQ7OmJWQ37V3uMpkwqzXY6eGLC+QLrRoRW1g1AAGBH2AgRNxdD/cJLm8ycbpIbzOr3hiWlVAypBeqVAsOzH2xwk4QLuf2GoHuVkkvazSqscwG/14/E78Fif+h49TmnRra9y6vkLHf4RAnx//bjrwaWV5ahRazQrpxAqyLDDgcxF0WB5VDM4I0YiN4NwaH91cIH/KjWXP1fpBERENPvpf+z79T3qq1EaqZFhelalrozjcHnz2vU+pldJU6yVkgxeb34PNHsLlf1wFKtQ351hcr5NVBnll0It+j9Byq5Ill1snLWgZDnlwmvTPcdUASCVKqRqZNRGdzc5o0L23YyjqEC0OYhotqXKSerFAqQ58tiB7grF8bMqpQJdiboFaJY3VaGIkEkQn7O/Tq0qXWmEGqV0hYDHRb99JW6qoqoKiyMiyjKKo+3hEKqry4HhZllCUCpVcmkYFzMYgMb8TQdjjzWk2aVerZNGiaEJYrUZMO7yY3WKWVGqdm4JIXyyI1byHB9NpIeWTLFdl6hE/1rAT51Nnhh9DvX/P8mf3rKDUK5SzDXINM0Ikhs8tYHgeM1DZUm7JewrNqgavw07M6z746apCrlKn032QcREEHcHYCCZTkkJplUSm8xwE/YJRZCgk2Sy3KDidGAf9+Iwg7Kf8Ol2UcomsotDWeDAZ7Vh38ihaNbqbS9xOSLhcHiJu++6FBE8ry0FR1YfeJxlFadJuFCgmsiiyH7/Lh9P6uHvgxumz47c3kG8usVaRaDyjg6w+IoeCoqp7xL/v642H3gm51aSTTzGjyFTH/VhjThz7XLNZWaXdSGPX65nwONFts7gq0CK9MU0p18RsihILwu7qT6Je3qSUW0IUREYiQaw7KZdnoVKk1KiTwY4gHiIWsGE2HjTGskKznabWfPjfDj6Wj9lEBciR32xTycUxRw8R98NeuneLDopcophR6LTBbjLgsZm3H1VJk529yDs3lkjmTTgHj3PszHHODNq2L2FUCepr3L16nRuzSTYKbYx6FcfhMZRPSmQTHoxHwoS8+8jXqlGpF5nRaemOObHZ9Tt4xjKVwgq51CaCdpKQS4dpD2Uvt1vUs0mWZBlj0IXfbcG23yM6II3kXWanbzC1sEGiaYSug7E+haWNLIsWI6bBEF5R4KCR2T1pN+hm1rkly5Q5xLA1jt+1n4ANap02xd3+rtWCO8SQ3kimWOFOKsfXR6NfxIaa54aqyLSzSTbaTbouK8GgEycHKF/utumWM8woMpUBG6agacd50GnWyG8ukJCNjDtd2C27r82eWpaD0M5T2JjjxuWb3Nhog9TCGvFjlNvUbglI3UE8Dgv2HQycyRrC5nIhyZdZTnybMZcZp/UpLZZSJT17k7vXrjO9WaNsG+Xsyy9xbDhEcFsoV6JdSZOevcGVyzOstbs0dT4cZiNhcYl8V8bnceK3Wfd4JxSgTLXYoVkFo1kk5LQibDMOMkgJEndaFCQHumEvLvZ69mUq+Tq5TTeC9iwxv54Dp0wOSCOfIFnJkzCaMIZjePdzDKUOUrXImqrQ2OWQg46lCNBITTN35xof3CkBdTKzG2xUFKT1T/jJ/5PgI9HFkVdf5/BohNDDOr9wj+vX73D17jqNbpXcfIN0DXQzV3inmee23Qj95/n6mX4GjAWSC3O8f13CM3EM8eaHrNz6lEstM+HoKaK6B96R2i5RT07zwbsXWSOOPTLJ+SNatM1N5qY+Yi6VJWE/RH9fcHvyFAmplWP1o3/kykqDTL5ANrVBQ+5A+iLv/u0c03Yr3vAwR19/i0Nu0GmrVAol8gkVjTaC0y6ge1ibqTJ0E9z+2SXurqZIdWo0cqsUWl208x9xsbHIypU4ltEX+aNTYRwG4QlfZhWlWyd39wM++niNgmjHNXqeUYsWOT3H3VtzLC8lEG39DEf9WPcKoz0BUqtOObVCVpZp+4JYfW7c+5VANcoUWw1SgFajxWkxoXu4tEmjBZsTn06PMVunmqtQhwclgl8VuhlWrtzg1tVZVhQJqbjCfKpMXbzL3bcb/J/TLhh+lW+ejDPkMd8Pq6pAjc2r7zB1b52FdJ12aYNss4NcmOLmO1kqtx2YTW4m3/oDDofMOA0ynWaJbHKVluzHZrFi3rYr7mlkeTLk4jz3bl7h5lyWgnWMo+ec2CiQXZtldnqGubZIdyiOy2Nmp72qRpMDi82FrKTZzNdotp3seOD+klCcu8Td5RIJQ5xj42ne/8XHfKB3oTWZ8Yy4HgrLNCgsTXHn+jSfrGjoP3yW4w4dVDbZWLzLu9MrNDo6Rj0uvHbz9rh5dZ21uWne/miOJm0Ky2us5aCuW+HS3/0frFlBiJ3m5LFxzgy5MSgyFJMkyi3yhiBhjxUbj1Y/dcqbpO69xy9uFGl26pQ3VtjINmkr93j/r/8t02YPw0dPMXnyCEP7LWUO8Kwq2SXK5RQdSxBzfxifQdhzr4Xc7VAv5ygqCl1CGHXubQbroGO5pQI1GrRaAUEDcjFDotyiZvMSivvxG0VAQKvdoSZWo0Gr1SIgQb3AZlahqYZx2ry47CKiKIBWg0bToZJcJrG0Tsk2ySuTXjbvvc98MkPJUqTOlm0XALoVSusz3Hz3V7wzVSX25qucOXmUoyEdSnkVdf4iNzUdOl4rjqhr5+SpRoNGK6AVNLQbRWqlNIJgwOv34DCbMIgigvDwDt4mzVKbasmA1ufBatSyfV+SBq2gRRBlupUSmdU0shQk5vPhc5oRBS2CVvNUGyLkZony+k3e/eU73MjGGDxzhqOnjjJsV5DzKpmZm2ibHSw+B0MBJ3rheew+Vug0K+RTy7QUCSHsxe6zY99H87RLOYr1MkVMCNooQbcJ48PZOo0W9FbsbhFzok0nXacG7JuSkVt0GzkWF9LUJflAO0J3Q7D5cPtCDHj2XuJrtFoEEejUyKxt0qo78MSDRP02ROH+3N32pDVoNFtzS5YalLJrKAq43Q7cDitGUUQQBB5MhS7dTo1KoYqiHMJmNmLawbt8OlkOgCpDK8PClY+5+OkCK5oIx8+d4uwhD3ZtmRVNnvR0hbSoxTLkx2UzsNPmWcFowWJx4FYUEpUaDUmCJ42uqzK0kizcTZFV/PhG+jmsbfPLSoLVTJ5c7eFidoXqxi1uf/orLk2XyAe/yYUT5xj1GhCLt5iqL/HRz6uo9BNwOXBad1ibajRbz1WrQawXyBU6lFseLM4IPpeIDtAKWrSf7atQFKiVyEgdJLMBt32HfUT3dYsgimgKRXKlKhnBhHMoQsCiRy8KCFoN+27V2JetwoRCOku1WMNoNhKK+7GLwh6rcJVOq0oxs/VOK+YIZod320rwoGMpApgDhzkaOMyR81Uq1/892UIZrecEZ7/zR/zlSc/u8rvGOP7mGMfOblKYeZvk7CY1/RGOvv4Wf/ydcXyf32eBxXsVqi09R16dIMhdpjaaFLsObB47Nv1nCl6hU1pm+eaH/OPP75CZ+DO+dWSUkbAVHSqy0Ud8wIV5OofDbiHkdewQIhIRDQEG3/gXDFJn/eO/4d0fr7DecjL+1p/zx6fCDDgfe7xyh05VoimJaPqsmMXHEvAaAfRRDn/3+xyWs2zc+oCfry+zqT3OS9/5Dq+djrEtr3VQ1A7N7DLz7/0jP/4kg+/Ctxg7NsKwSwfICN44YY8Zt9FB0RAi7DUdIMx3EDq0GkXSK0lkCRwBN26Xle3BwIeRKec2KJXztEUndsdx4l4d5kfeIA1gxhwUMMw0qWdrVCXwi+xTCt1Gqm4wOzVNttXhWTIVhtAEg1rX3sZB56fv3Fv0nX2RZuEOP0vOUpRHmHjxW3zvj47Tt2NYUQNYCZ/6fcKnWpxdvMSVv55iKauj78w3+e4bZzkbeVzzd5E7bRp5LariwmTUbd+T81SyHAQVVW7RWL/Eh+98zI1SjMjr53jtmO/+Ss6By+smGLIgLreJhj24jIadVb5Oj8VgIqqq3CpXaXS39rc80VRUJJTSEmtlH9a+QY5EtNSuLbMig9FuxWk2bl37fj+j5cvvcvGTGVZNx7lw4SXGPPqt8l+7C683xIBGJGcaw+20YdtpxWuNEjsR5V9Mvoq8+jb/978tUZACRI59m7/8714gwGNzUlWRWzXKsoTBoCe4Q95Rbw8TO/On/NUZler03/M3P66TSzoZ+8Z/xV+96MP+fGp8AQWkHLnNNpWigC1gYTzkwajdyzHs0m7kSa8uoUigCwdwBJy4Hn82BxzLh1SagizVSa5eptXoEBjyMhDYwzA8hNRpUcknSSgyrX4PJr/90dhrpwG2OPYhM/GQijw9w1S9SiI2wEsTYWIPDiS9eJPpqQ9ZMTg5ev4oA14H9vvyKXKdYmaNTquBz24j5ttPviqVdIP8ihFBCBNyOzDtVHpQr1JrNSlrtp6QbocH9TmNCrVMnrmciHQ8hsdrxvYsAfVOmuzSNB/8eIU2Rzk6OEB/6H7ZhapAq0gp0aFW9GEeiW0lyJ7hcg+o0SiUWLsCMtDvshLYloh8GAWokdlcp5QvgHMY4cQAMbuIeZtAuq3cg6ZCt5ujWAF1v8C5zoEpfI4/+K/PPeN9PSGdJt1skvmETC0cwB514jmQMm7SqlRJzWiRpRBeu2uXGv02nUqD0gwoe8+sZ5BlNxS67SIzH/wDd7JFtKMvMH5smOCDC9Ks5qgWM4iCm5FwaPeEqub+f7ICmSKN1tam1SfZiKmq0GpA4Hg/Hr8bS/0uN2Y+QZJsHI+EiXvulzYqHUhe5tMrG8wm4oReeoGTE8YHXnyjRqNcYl0nIk3EcHrMe7aCUBSZWj5JutOiErCh7/fsmMdRVIVaOYfUbW+tgLW7vWkqIJFP3aFSTGE3DXI4FkT3PDf0KAqUsqRTbUoVO8Z4gJDbukdyHKBCo5RnYwqkLniGwkRCHrZpyQOO5UOPtIXcTbE+JdMo9eO1BPAcMGbWaZcpZm6hKl3CHicB+6N7GtD7iY56CMgq2m6ZpXufUqtoGDgcZ7LvoaK/+jrJxSyLcxZE3RAjEQdW02citpGlLKkZhVb1ED5bnMB+ydNaiUqpTr5hQXAMEPKKGJ+xqqhdyZHNrrMmaPHGfLjNhmdKDrfzSRKJRW6IIp34CP6A9cHuR0WGXIqVdot0zMfQRAAfzyl2XylRy6eZR4fECAG3F6dtjzCBIkNtmZW5OumUAV/cz/ljIzh04lcrl/CESK0a5cQ8i7KE0e/E69xv9XSfZo1WqUSiJqIoA3ic1h0Tub8WWXajW6Wbm+f2dYlyKUzMESLmffgXS1QyNfIrWwnVuF/PXkV6z4pG0GOMnuC4rENU82RuJZi5rqfrfolY3Ivvvm2Qu20Scx+zXs1TCQ8wMOwnyAOnqF0vUyynyQha/H0+fBbDns9JkbsUM7fptMq4A1aiHidGnqUljgIkySy0KCeCGAf7Ce5XGPOEqLJEO73OYrtBGh8Dxhjx0D7XKBUoJJJ8WhPpKAMc6u+nL7BPfe8ePLiU1EYqZVjJyDTcIexBN54DuQVtuq0qpUwDRTYS9rjw2E2PKgytHoPJhNUoQ2mVuzeLVJQw/nCYPt8Dbd0tptjMF1lqWxAsg0R9BkyfZV86TaTsJnMZmaothO0gydNakWyzxprZgnY4jt/2rDs7FRqVJMXcBpJoZSQaxGEyPoNy7FIrbJJNLlETBYyDUTx20+e7HxVZopCYI9usIrhtBEJuLDwf49CtlyiWMmxoBSR3H26vDZt5t9dFRZEaZO9d4V6mQMHWT2jsJOeGbb/h3VcVOs0SucQ9GrJIOOAn4LQd7Pm26lSrJeY19xO5bjN72dYvVJbdfrHdoJ5e4U5ZoiKEcTi9+B/ec1LJks2WWW9bECL9BBwixi+yrEyjQWuwYTUbERpZMpvL3GrqsI2OE/XY2GpB1UGSsqzfS1CutHC6bcRCbsw8yOHUSknymTVkQUtf0IvdaNjjOUmoSpVSpkq3LeCxOYi4n+25oshQzZLItino3RijIUKW52scFFmimJqj0qrS8bswDgQJ6PcqaW6T35xjafYWOcGA5+R5jgwFCdueXuF9PhWUVoNmeoOlpoxmIoAn6MQhqKiKRLPSRmsyotOL24VTm7RrVbIrArIcxu904NqlikFuValuzjC90aUb7SMQduOkTG69jtYbQKiUKLdqlExmnJEYPqv4uTJX2g0amXWWWhKdfg92rwlju0Qu30L0+7DoH2+7DZ1qnlKjQs1qIDQcwK3fZeOOTo9BFLeWVepWmGXnmus6lVye/GYbURhmKOzEahJRpTadrkxT1WM3iQfrMAuARLNeploqIQhOwhEfDtNnMV8FWWqQXl+i2ujgCNuJOIxoajk2qiJOlwWzUfeUk1yhWc1SLGxSEwSIRXA6TbsuzdVug1Zuiesf32a5bMQ+cpzjZ44y6NLukhxTtmIIqgFBa8b4oNXl7shtpEaOpeUM9a6yfwvxPRBsXlzeANLILboAABdHSURBVH3u/dzgFq1agdRiAVkaJOr14HUYUBUJudOiJhuwGEV0O7yRUqNCtZojJ2qxDAdw2XdO5IKIYNBj9IEmv/vMehZZdkPptmmVs2QUhbbXj9XrwvnQMrdbzJAtlUgZjBgHg3gMLVrZCi3RhMFhw7KTXtFowGJEt2didD86VNIbbKyuk9U5ODYex6eXaObzFFUJs1imuNyiWzfgsTmJuh5W5nUquRz5ja1uAX1+Bwa5TKGgRRKs+B2PhQbUDnKnSHZFod3w4bd68D/emvvzW9OgN1rRCjpA3Qrr7oCqyHQz62zWGjTcMeL9ATwiaJFo1TqoWgHRaODpmxJvtRXJbm7QbjQx+e244h7s7P4adYorLNybYWq5CqHDnH3zJKNRJ/a9HJZ9xvKzrmB0mxUKm3Nsyir+kI+g24FBbtOp55i7V8czGMHrtW5/AboNmsUym2sismEUz27JIRQ6tQLp+TssSQKeoQgRvw7Sq0xdzeB7zU1AlrYGxGJAM+jFIWo/V5SdZoV8cpWcLGMKenA7VLrpFW5fLRH8mou4KDwW81OoFFM0aiVMxjCRoBvrbju9TVZsFhMurQp1mY6q7qycpBzFZIXkph3BcYiIT4fJINPKp8gVa+RMgxwOCegFhW6jSr3WoCbpsLrcWA3CDhVQKqqyFV4UtRqGvA6snxUxqx2Udp7kSo5Ww4Tb5iZg6VJbv80niSAnj8aJGzQo7Qa1UoWabMDqdGAx6Q8wKTtUSxmK6SQa0Y5pIIjTYtxxaa7KHVqFdTamPuT9axW6jrO8ePo054+Gdok3b32DQm4qKLIF0ejEbjnAakdp0a2scefqLTLNZ0xIhw8zfMi+v3GQK9SLOVbmdUj6CXxuOw6LgtQoUdpcZs0wylDAitP0+KxRadQKVIppBEFLOOjGadxtZ6wJndWKY0RFW2khKzLKTtm/A8uiQe40aZRLlDsCJpsTq9mAQdxBbdxvtQ4qRJwYPZaHqsYkqrkEuUqRltlHpM+HQ8mwfDtNxxkiMvmYcZAVJFmmqdUguu2Y9OKD+1VVVKlJsy2jaPXo9Pqd5fn8t0pkN9dZWymjsxxjciCAub3I0nqLvNbO8WGgCchubCY3btsDZa5285QyVdIpB4LtEBGfHvLXWaoaaTnGtxsHpYNcL7C5otDsxrDZfLh32ZSk0QiYHAFMOsNWWEfaqZ2miip3KG/MkWzWEYNOYiEvJlVGbedYW66htToJxA3oVAW1W6dUqtFSdIhmGy77bq0vHr5EB7mTI7VSo9VQcdtsRD3OnTdOqgpyu0pi+lNuTm+wJA0TP/dtvnE6QsiyS/+1vcbyIe7LKdFq5Eiu3kOWrVuhIZsJpb5OeeY9/t0Hfv7Q7cbt36EgsV6hXs6zJArIgyGcLuMuHmiTejnJ8u0lpO4wh+Jx+oxNMvNL/GrawJ98HawOJ3qjBUQVjc2EUfNZM74m9WqC5Zm7SF2VcZ+TkKZJZmmFd6cN/MnX1cdyR1s7HUv5DeqVPBbDAIN+L+KuPQjM6D0GzLYOykyRWlfZuctqPk0qV2RFb0J7LIrPJGDQVFm/c5GZ2QytV/5HxgOAUCV955+4+O6nvJ+N8vqf/3NeG3US3PbxBTMmix6bY8trsVmMW2WLAN063dwyd29LVMrjDFpD2HUZ1m6/yw31T5iQtYhyidTiFd7/u5/wXmmY177/h7x8vJ/ovrHvMqVEncwC6AQtk2EfHtPOS3OptsHyrQ/50d9cZM54mu9+/xu8cXqQ0K4eiQpUqa5KNJsGtB7t3p0yP+PzhPQLW8pmv+P3QKPRbCUU96NSopJOc7ctIJ2M4fSYsGiblBPT3PjP/0Du9f+JgNuGc5sVbFO73wxN1LoY9HuxGHbLPImIej1Wh4JGk6Na69BswTZLfGBZmlQSN7j+o7/lhwtuJr/9X/DamTHGPdvdHlFnwObwotEKYDIgGnT3FYwClEiuzpHPpHGYohwNOtFlP+LqdAvLmI/445qo06TSqDKr0RA2iBgfepdUpYuUm+HObIGaOUZoaJhxzx4qsJIgsZZnoehBN/kCE1E93TtXmU1ZqAVf4lVRwD2uRV+3YdCZHsmDyKl1EqksK1oLwtAAYadE+c57JDsD6BzHtl+r3ULKJVlWZOoRD9agHddueUeNBsxWQoLIfLPDSrUBPN4xQEGWqiRX79BsSHjsTsIeG6rUQFp6n598bMY/fpjvDHhRpQ7Sxsf8/O/fY6oeIXz2An/+7XHc7OMsSS2kUpLlBZl6zUzk/jV2QpHa1OZ/yT/96GNulTwMvHSBP/1nJ4kYNbsXGu8xlg/z+cpBVVVUVQvEsJvNmNQUiflZ3v9lk5cuHGUo6GQnP6xdL1EoZUgLWkx9PlyW3VpJd5GkNo26gKoO4LKpVDNz5NJJ4q9+j4hBh8kbYyTq49TKOosbOUpyHJtSpTB7hWsXL/L2qoWWXMegE2iV09TrSXSTbxDUCY/Jdn+n92qbSiaAqW+AkFe3tQ9hR0yY7AasnjZqKUu1piDZ2d55SlXvR0t0aL02rNoOhZvvcienodj3Jt/oF9GLGqhnSC5sMDu1SoYSP/9wmojnGA7r9tJbm2+ESOwUvhuLJNMlmoM2EAqk5q/x3s/eZk7boiEaEIQW7Vqd9LSO8beC2C1GqG2QXlvh5zNJKlKRX9yawOpzEh3dpaGVqiK128jlBOlMic2GAcHsI2AxoJG7NJv3TaIqIzWSrNye4ub128wkGnTHvsNfvvkaJ0ZCBM17VNyrKtSrlLoSNY8PR8SDlwPmSTSaBzXnvxbuj6dWi+q1YTaotJauMzu7yJ349/n9ITvex9tfAlCmmq6RX7UhCBOEvKY9el1p0evtuDwxtNoEtWaDZpvtxuGgsrRKlDfWmLq8Sa6V5uKVaRxeFxFPaPvubJMVQ3SENyLv8WG1RKtUp6o6MDbTLHz4j7xzM89cQY8hpsEgQHr5Np3gccIh97YKl3azRrNWQhC0DPi8WD6vx23Raaxy+d//Ne+slkjqDjN+5nWcf3EcH7tU1nXbdLtd2iYD2rgTV/0uU7f1iO4YJ0eCiLo6A4cPYZlPUGkUyJdlVItMO32NX719icvTGzQENxqTBrG1wtJiFNnfz4B/u9aX2g0quXUSiowm6MLpsu6+50arBbsTr06HvdSmW25QAyxsd25UVFD9mAxObGKZVuoeP/txnvDxwxwaD2MFFKlFavFt5jbXWUqt0xK1XJ8c59Uw7NYBQ5EkuuUixY0VpiSZMlGGdXYsYpPmQ20wpEaO3Npd7t64wsWpHIr7KGdePc3pUxPEDRr2WrjtPpaPIn72P5MjQt/R1zmysUnu3ke8V/bjNZnxnnmRsVEvPotuh4FWaFTTlApryKLAeCyAw2TcpdTShNkRpu/oGPF6kuXrl2kGbQSCk5yf9GLTC2gJMnTkDK+2dCjLH/OLH6fwGECv1aDxH+O1t8Lcu/QJtcVLXMo68HkjnDsRwaZ/rGJGlaGWJZVrU9T6MAXjRJ2aPRJGOmzOEG6/C3l+jmxJou1ju3Gwx+kfG+NE+R435i/yi596cZp0GKJDHIsN4TNptuTQuXBF+xg6lKCc7FC6k6H+Ros2242D3jVE3+SrXMgpzMz8gverHqbMerSKBuPYa3wteI8bN2oUFi7xqezDGDrH6ZgNl0kLkg1nMMbpQ0OsZ0uslqsUynXaOLdVUHXKG2Rm3+WdqTqNSpbN5VWSsky3XeDuu39D6Yb+kRbSilZFI2vQWAYYPxeif3Sc8eEILpO4d9hKkaGUZa3TApeNgaAXE1/Wx5L2wezH0zfOSyfWeS99gyvvbpJ2GTHaJzhz+DBhh37nAoZGkWKhRqplRYiNEvXoMe3qpmnQGW24g4N4hU+pVcpUm214PO59UFlUKxZvjOFjI+Q3SmQ3qrSSVRrsYBwECwb3GK99/S1a0wVyt97lP6U9OLUSijbC4Ck7ZscMiWyR6Ys/Jqtx4JuMMhh1bPM6W/UstXIRUTvGQNiG5fNQm4BGY8TsFJBmOkg2DaLVtHc1kNVHaCDOWGKe6YVL/ELrwegdon90iCGnAa1Gg+vIN3gt+QF3U4tc/9nfUgyZkLoKWt8Rjr5kw7uRZGrjGu/81IrW0sfowADRbStzhU6rTD41R1OWCAXc+J07tOr5fKi0YAgQ7DfiulmjuVakzFaJ54NpoEXQ2YgcepOJjUVSudtcerfGqlWP/vBZxg/FiLqNiIAi6LEHjzM8YKUpr6EqGTbyTeTg482y7vd0uv0rbs2sMb9ZppFfJdPqIFFg7c4H/Lx1j0sPv5+aLT9MVNxET08yNDzCyGCUsM+278ejdh/LRxE/u2GDLUT06Nd5s32VlZKGrkaP0RfnyOQwAdNuWfImtVKeQrqJThhiPOrGbt7tLTFgdvcxcv5N3jCtkJdM2DyDRMaGGfd99q0IK+7+oxzTmcA0zUqjQ6clYgkPEB+IMGCr0Ge1MJdvUzcE8cXHOR23oX/83mQFKZ9ms9ai5goS6gsRMO61P0CL2RnCFwzjlafYSFeoxyxgemwamYPEj5zmVZ0Fy1IVpaOgix9meDBKzPtQhZbeTWj8BOd0Io47K1z8VIMo7xwq0RjdeAeP8bIAhusrFDUdmpIFlzfOyKEBAq0+vO45VnN1FLOPwNhpBj/bDCS68Q8c5VtviSzO3uRHNT2Ksku+RJWROw1anTZtrQ3v0BG8Qw/9XenQ+SzQr9GC0Y43GqdvYJDBeIio62A1jqos00ivkm6bsIaDjASfsTLki8TgwtM/yStf6yDMFmlpQWOLEh4eZixiQ8fOCk4uF8lWamQMFuzD/QStwq6eIIBgsmKLDDFkv8RmqUCuXEcOGx6djweVRbThiI5z+hsdbPfucumyCYOk7ByG0+gQTQEGXvgarxhuMJuoUWp26RhNeEdOMBFSaQ+Embu3yHJTRu86yUR/lJjzcdeiSbWUo1iWECLH6PcasHxe2SEiGjz0v/QaL5pyVKwDDEwG926XYg4SP3SaV1QbzkQTVfQQnxxlMOpha3+qHkPwGC+cV7DeXWIx26TVEcEYY2JyiIAwRnHpHvaZPLLGQujIMP1Dfh7f2wodWs0S6fU8suynz+fF//hXch5BC1oXwaEArvk0xXSaTB185of1nwatzop37E1ebLqYT1QoqyAbQxw5NUHYruMzXasVDTj6v8YLLw/gtL/LRnOrceDOg6Uid1t02k3a6BE8I5x/ZeTBnx9+PwGMVizuEPHIAEPj/UScpgN+lnevsXyUB49TNKP3jXPu2+Psvg1JRmo3aVSrVDo6rNYquUydfNaF3nGKgaAJyx6NP0SzB9/Em3x7Yg/ZDU48g2d4c3CnZt5+Tn57mJM7/EXp1GnUa9Q7oAgi+vQqqbaE1ushGvPs2xhPsHvwBSIcMd3k3vImpXEnssv2mEERsATHOBIc48hre/4aFt8AQyY9LkOTyyUXZqtu1/0Qez+Xk5yPneT8jmcaMDnj9J9y4dYscznhwPzZLtPH0Dv76Hv5X/IvX95L7mdlq8Jqc2mRrhoj5O8n6nuuDYyfM1r0jhCxs9/lT87ufpQqtWg1alTrbWSTC112k2y1SsPm4OhoENd+lTt6M7pAP5MDFuYKORKZEvUxN/ZH670PJAto0Vt9hA+9hN9cZa5mwuA17t5eXiOALc7hV+Ic3unvnjB9R1/f64IgZcmna6TrTlynhwlbH/606Zay9Ex+i+9O7v0zDzDj7DvGmb5je7TsF3AMnualwdO8tO1vXoKRUSZeefzfFVS5Q6NcotrRIhraVIolEqt6MJ4i4vfi2fOj61uf5fVGxvH5yiyk1lnY6DA2pNsKF39+2D7P9JHjwgyftKNvLaNbSFCzGXZov77VVSB88o8In4Tv7PWbz8qeY/koT1aNJlcprF7l4g9/xD+shLnwzUOIy3UKGjvOVw8x6NDtXP72a6C58SkX3/klHyyrdI59lz9QP6bR9NLnH+Rw/AA7vfU+HNEBDp81cvHaAmtn++iL2bbvLnwC2o0a2eQSSv8hbNadq4GenS2FvL50mZjnMHGn/UvsgNqg21lh/uMN9H1n6J8IE35ercy/RNrZWWY++QX/8M4MhRf+Wy7orlKsNHDbTnN2LPSo4tgRE6I+xsQL49h/mCa7mGT95CCHn6GVryxLZNfnaLvPYvLY9+9d9Sxk1llZa5OW43zt+ABO/T5fhfyyUFp0Kotc/tv/jx/fsxI+NMKoo8VmQcT0xgTxsA33AXwVfXSYscgdNjbW+XhunbfifZjFZ3irpDTZZQv55XGGvqd7rvshnpgnGMsnE7OWJbW8yE+ns1Q6OkqpFZbrKs3wJBdODWB/PPb/a2GrU2Zy7gZz82skK130nXUWr3cw9h1lfHKQIcdBIt4CVk+U0dOvcKh1icTiBpvZZ/gydCtFbmWVK5+YODESwmP/Yraeqs0C9bWbfLh0Fo87QsT75XnqSrVEZXaad9vDhEdHGIk6v9Ktug9Gk0Jilvk7d1gsatHLGyRna3QNAwyfPc5Rt+ZA9exanRH35Cu80FdDqC0wvVR4epG6FdqZu1x8X0vA5qMvaP+Ccjoq0GZ19gopScZ++AQno/qtLyd+FWnXaG7O8bObSdbLCpVilo1cjmXXMV47PUrEbT2YUdOFiU+MM9JvJ/+rm8yUulSfuhOkRPbODVaELvWTY4xY2KGk/dfBk4/lk4mpt2Bz+5mIeHAbS+RybYyBUU6/do6zETOGL23S6LD7o/j9QRwaiW56k4LvDU69dJYTo/4D9j7SIBjduPrP8tqLTuorKyzMJik81aRQqKQW2UytURk4zbGIDeeBP9DxJLSoFRIsXp9HGDlHLOTHa/ySxkCpUkqucvujJXSHzzM+HCVi1301E9FPhIjZ4cMXiuEz6VFTS5TNh4gff5lXT8Vx6R9vGb8zGq2IzjXGiTNjhNQaqat3WaqB9MQ1uwrNcpqNe5fJhI7QHwsQtX9BJljuIqduce1em7ZlgOMnRwmaNE+wyfPXjGhA5/AzHgsQtLZoVhs0tQFOfestXhpy4zYd8NOmGiP2+BijkwMcM0zz8Z0NUuXWk2/MVCSoLHBnrkPHFGD0WASH8CW1rn+KsRR+8IMf/ODAF9DpMBjNBCxGjA4rbt8Ao4cnOTE5SMSi/ZImjQYQMZrM6I12zCYrDo+PyNFXOH0kTtRtOvgSWKtDq7fidmtIr2YodlRUp5ugc6sC6wn2ptIoZmgoGkyHXmAyaMS0b+jhaejSqlUpptv4T59h0G/F9vg28V8LHerZBZbv3uXGisjE177GiX4X7v3KJn4jENAbTBjNdgw6Cw6Xi/D4GSYnxxmP7lH5sg0NaIxYbSbkYolCMk9K5yXks2AUn+TdUek2qlQKGbTj5xmPufB8EY6H0kaqpVi49BFTBT/+seOcOxrDpfuKVp4BCAKCyYLPoMPssOHwhIkNHOLsuSMMOHRP1DpHazBhNAnY1HXurNQx2104bFYsu31MeidUGVpZ1gs2PJE+JoZ97PpZ+i+SpxxLjap+kZ8u/01kq+Ni6tqP+WCuTcE+yR9+8wg+4Xl1Q/0tRCmwcvU9pu4mSfZ9lz99KYr9iT949LtDIzHF9M1pfn7Pxbf/y9cZ85p5hhY4XwhqK09t/QZ/99cz2F75BseODTHi+ooJ+UWjNGiXFvnob/4Tq+GvcejYMc70fYWr73bhaceyZxx2QZEaFNY3yJc7qH1bX3V6+l4pv+VUV1nZbFCS7AyNhbA8kSf8u4eqyHTKWYrri6w7DjPst+Hapdb8y6JbzVNanWXBMMZQyIHHskNftd961K3d37VN7i010Tu9RON+dtwX+RXmaceyZxx2RUVqtehKMorBilnkOXzd6bcUqUmroyKpOiyW34Y8wxePKneRWw0aWgsmg4DuK2ZNValLt9WgJVox6YXfYcdIBaVDvSGhEXQYjPrfOCP5tGPZMw49evTo0WMbv7P+QI8ePXr02J3/H8/rVdTc8WwAAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"from sklearn.feature_extraction.text import TfidfVectorizer\nvectorizer = TfidfVectorizer(stop_words=\"english\",\n                             preprocessor=clean_text,\n                             ngram_range=(1, 3))\nX = vectorizer.fit_transform(df_train_sampled['question_text'])\nx = vectorizer.transform(df_test['question_text'])","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:46:08.861876Z","iopub.execute_input":"2022-01-04T14:46:08.862197Z","iopub.status.idle":"2022-01-04T14:47:11.092319Z","shell.execute_reply.started":"2022-01-04T14:46:08.862162Z","shell.execute_reply":"2022-01-04T14:47:11.091463Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Sau khi đã tiến hành chuyển đổi tập dữ liệu text thành các vector, các biểu diễn dạng số, ta thực hiện phân chia bộ dữ liệu thành tập train (dùng để huấn luyện mô hình) và tập test (dùng để kiểm tra mô hình được huấn luyện) theo tỉ lệ 80%, 20%","metadata":{}},{"cell_type":"code","source":"# Chia dataset thành các tập train và test theo tỉ lệ 80%, 20%\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, df_train_sampled['target'], test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:47:11.093564Z","iopub.execute_input":"2022-01-04T14:47:11.094172Z","iopub.status.idle":"2022-01-04T14:47:11.209938Z","shell.execute_reply.started":"2022-01-04T14:47:11.094107Z","shell.execute_reply":"2022-01-04T14:47:11.209115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Sau khi thực hiện việc split tập dataset, ta bắt đầu tiến hành việc xây dựng và huấn luyện mô hình\n\n# **Evaluate**\nĐể đảm bảo được rằng mô hình vừa được xây dựng là đủ tốt, ta cần phải có một phương thức để có thể đưa ra những đánh giá tổng quan về mô hình, cũng như để so sánh với một số mô hình khác được xây dựng để giải quyết cùng một vấn đề, nhưng sử dụng cách tiếp cận và thuật toán Machine Learning khác.\n\nDo ta đang làm việc trên dataset thiếu cân bằng, do đó việc sử dụng đại lượng Accuracy sẽ là không hợp lý. F1-Score sẽ là đại lượng được sử dụng để đánh giá mô hình do nó quan tâm đến phân bố của tập dữ liệu.\n\nĐể tính F1-score, ta quan tâm tới hai đại lượng khác là **Precision** và **Recall**\n\nHai giá trị này lần lượt được biểu diễn như sau:\n\n![image.png](attachment:94016c01-004c-4149-93ef-93d6e41860f3.png)\n\nTa có hình vẽ biểu diễn hai đại lượng trên như sau:\n\n![image.png](attachment:aebdb87d-98c4-4c84-a606-261d85631a05.png)\n\nF1-Score là harmonic mean của hai đại lượng nêu trên, được biểu diễn như sau:\n\n![image.png](attachment:485c76e4-04c4-4da8-b4ff-4563a6d14e67.png)\n\nCách tính F1-score được xây dựng như sau, sử dụng sklearn","metadata":{},"attachments":{"94016c01-004c-4149-93ef-93d6e41860f3.png":{"image/png":"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"},"aebdb87d-98c4-4c84-a606-261d85631a05.png":{"image/png":"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"}}},{"cell_type":"code","source":"from sklearn.metrics import f1_score, accuracy_score, classification_report\n# Tính f1-score\ndef get_f1(model, name):\n  y_train_pred, y_pred = model.predict(X_train), model.predict(X_test)\n  print(classification_report(y_test, y_pred), '\\n')\n\n  print('{} model with F1 score = {}'.format(name, f1_score(y_test, y_pred)))","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:47:11.211185Z","iopub.execute_input":"2022-01-04T14:47:11.211971Z","iopub.status.idle":"2022-01-04T14:47:11.219767Z","shell.execute_reply.started":"2022-01-04T14:47:11.21192Z","shell.execute_reply":"2022-01-04T14:47:11.218912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Build Model**\nTrong thư viện sklearn có hỗ trợ rất nhiều những mô hình khác nhau\n\nỞ đây, ta thử nghiệm với mô hình: **XGBoost** \n\n**XGBoost** - viết tắt của **Extreme Gradient Booosting** - sử dụng kết hợp các model như Decision Tree nhằm tạo ra một model mạnh hơn, hiệu quả hơn.\n\nNó mang một số ưu điểm vượt trội như:\n\nTốc độ huấn luyện nhanh\nCó thể tăng tốc bằng cách sử dụng GPU\nCó cơ chế xử lý overfit\n...\n\nNhờ những ưu điểm đó mà XGBoost luôn được lựa chọn để huấn luyện model, giành các vị trí cao trên bảng ranking của Kaggle\n\nVà với những ưu điểm này, liệu việc sử dụng XGBoost có đưa ra được cho ta một mô hình tốt, vượt trội?","metadata":{}},{"cell_type":"code","source":"import xgboost as xgb\nxgb = xgb.XGBClassifier()\nxgb.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:47:11.221452Z","iopub.execute_input":"2022-01-04T14:47:11.221807Z","iopub.status.idle":"2022-01-04T14:50:39.280453Z","shell.execute_reply.started":"2022-01-04T14:47:11.22177Z","shell.execute_reply":"2022-01-04T14:50:39.279749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"get_f1(xgb, 'XGBClassifier')","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:50:39.282064Z","iopub.execute_input":"2022-01-04T14:50:39.282569Z","iopub.status.idle":"2022-01-04T14:50:40.975448Z","shell.execute_reply.started":"2022-01-04T14:50:39.282533Z","shell.execute_reply":"2022-01-04T14:50:40.974626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Conclusion**\n\nQua đây, ta tạm kết luận, XGBoost không thực sự mạnh trong bài toán phân loại câu hỏi **sincere** và **insincere** nói riêng và các bài toán xử lý ngôn ngữ tự nhiên khác nói chung.\n\nVậy làm sao để khắc phục được vấn đề nêu trên, đồng thời đưa ra một mô hình đủ tốt cho việc phân loại. Một giải pháp tốt có lẽ là sử dụng Neural Network\n\n# **Neural Network**\nfastai là một thư viện deep learning giúp người mới tiếp cận với học máy có những cái nhìn bao quát, được thực hành với high-level components.\n\nVậy nên trong phần này, xin phép không đi sâu vào phân tích những đặc trưng của LSTM với kiến trúc Recurrent Neural Network (RNN) mà chỉ dừng lại ở việc biết cách dùng nó như một công cụ tiện lợi.\n\nĐể khắc phục kiến trúc RNN dễ bị overfit, một kỹ thuật regularization được dùng chính là **DropConnection**.\n\nKhông như Dropout loại bỏ hoàn toàn một node bất kỳ, DropConnection chỉ chọn ngẫu nhiên một tập trọng số trong mạng và cho bằng 0.","metadata":{}},{"cell_type":"code","source":"from fastai import *\nfrom fastai.text.all import *\nfrom fastai.data import *\n\nsample_submission = pd.read_csv(\"/kaggle/input/quora-insincere-questions-classification/sample_submission.csv\")\n\n## TRAINING\n\n# path = \"/kaggle/input/quora-insincere-questions-classification\"\n# dls = TextDataLoaders.from_csv(path=path, csv_fname='train.csv', text_col='question_text', label_col='target', device='cuda')\n# dls.show_batch(max_n=3)\n# learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5)\n# learn.fine_tune(4, 1e-2)\n\n## INFERENCE\n\n# for i in range (test.shape[0]):\n#     sample_submission.at[i, 'prediction'] = int(learn.predict(test['question_text'][i])[0])\n# # sample_submission\n\n## Xuất ra index những dự đoán có giá trị là TOXIC\n\n# a = sample_submission[:]['prediction'].values.tolist()\n# with open('list.txt', 'w') as f:\n#     for i, item in enumerate(a):\n#         if(item==1):\n#             f.write(\"%d, \" % i)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:50:40.9768Z","iopub.execute_input":"2022-01-04T14:50:40.977247Z","iopub.status.idle":"2022-01-04T14:50:49.307755Z","shell.execute_reply.started":"2022-01-04T14:50:40.977207Z","shell.execute_reply":"2022-01-04T14:50:49.307016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Thư viện fastai chưa hỗ trợ inference khiến thời gian predict lên tới 16 tiếng, vì thế phải xuất kết quả ra file rồi đưa vào một cách thủ công","metadata":{}},{"cell_type":"code","source":"index = (0, 16, 17, 21, 27, 42, 58, 78, 93, 99, 105, 107, 111, 120, 153, 184, 201, 211, 223, 270, 272, 273, 281, 282, 286, 289, 294, 319, 326, 327, 358, 375, 433, 434, 467, 478, 496, 517, 524, 525, 541, 544, 552, 570, 582, 599, 605, 607, 636, 639, 652, 653, 665, 668, 671, 673, 732, 735, 743, 754, 756, 786, 790, 812, 841, 891, 902, 915, 917, 923, 927, 938, 940, 968, 995, 1011, 1012, 1013, 1016, 1028, 1031, 1073, 1081, 1112, 1122, 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278321, 278323, 278331, 278339, 278363, 278373, 278392, 278398, 278444, 278493, 278495, 278502, 278513, 278515, 278533, 278551, 278564, 278579, 278603, 278623, 278627, 278631, 278643, 278666, 278682, 278684, 278690, 278703, 278724, 278744, 278751, 278752, 278769, 278776, 278785, 278804, 278806, 278812, 278826, 278827, 278830, 278845, 278848, 278850, 278855, 278860, 278872, 278881, 278910, 278911, 278928, 278968, 278973, 278981, 278982, 278989, 278991, 278995, 279004, 279026, 279069, 279096, 279115, 279136, 279137, 279157, 279159, 279162, 279171, 279193, 279199, 279205, 279213, 279243, 279244, 279252, 279284, 279309, 279321, 279322, 279325, 279334, 279344, 279356, 279384, 279395, 279419, 279447, 279463, 279481, 279496, 279542, 279543, 279547, 279554, 279567, 279568, 279580, 279626, 279656, 279661, 279680, 279716, 279736, 279738, 279825, 279841, 279873, 279898, 279904, 279916, 279942, 279959, 279983, 279985, 279995, 280000, 280011, 280044, 280056, 280134, 280137, 280163, 280164, 280176, 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281913, 281918, 281923, 281942, 281946, 281951, 281952, 281970, 282007, 282011, 282026, 282036, 282037, 282038, 282041, 282043, 282071, 282088, 282103, 282129, 282135, 282146, 282156, 282171, 282179, 282197, 282206, 282272, 282275, 282281, 282285, 282288, 282295, 282321, 282342, 282351, 282367, 282372, 282377, 282389, 282403, 282404, 282447, 282459, 282470, 282477, 282489, 282493, 282505, 282557, 282558, 282567, 282572, 282588, 282617, 282623, 282649, 282669, 282710, 282720, 282722, 282723, 282728, 282733, 282746, 282764, 282766, 282769, 282775, 282779, 282791, 282798, 282799, 282805, 282831, 282839, 282846, 282857, 282862, 282875, 282878, 282914, 282915, 282945, 282954, 282956, 282974, 283014, 283023, 283028, 283031, 283059, 283060, 283068, 283075, 283077, 283086, 283094, 283116, 283122, 283131, 283146, 283170, 283183, 283192, 283231, 283239, 283266, 283304, 283319, 283338, 283355, 283410, 283414, 283419, 283430, 283452, 283456, 283465, 283488, 283502, 283526, 283534, 283558, 283564, 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285271, 285274, 285289, 285308, 285329, 285341, 285374, 285407, 285425, 285437, 285443, 285462, 285488, 285577, 285613, 285641, 285661, 285675, 285693, 285723, 285766, 285768, 285772, 285774, 285776, 285787, 285808, 285816, 285838, 285896, 285907, 285915, 285967, 285981, 286007, 286024, 286029, 286031, 286035, 286040, 286056, 286084, 286130, 286136, 286152, 286168, 286170, 286175, 286178, 286182, 286196, 286200, 286224, 286231, 286240, 286275, 286280, 286285, 286304, 286312, 286326, 286334, 286338, 286339, 286352, 286362, 286374, 286384, 286386, 286392, 286402, 286417, 286429, 286452, 286453, 286458, 286482, 286515, 286562, 286570, 286579, 286589, 286633, 286642, 286659, 286677, 286695, 286711, 286719, 286720, 286741, 286763, 286781, 286789, 286791, 286815, 286826, 286837, 286846, 286848, 286854, 286865, 286888, 286899, 286930, 286951, 286964, 286981, 286985, 286986, 286999, 287011, 287026, 287062, 287067, 287098, 287101, 287103, 287105, 287125, 287126, 287128, 287131, 287140, 287154, 287160, 287173, 287192, 287193, 287215, 287222, 287230, 287235, 287243, 287244, 287274, 287288, 287315, 287317, 287318, 287327, 287335, 287364, 287375, 287397, 287404, 287438, 287444, 287448, 287454, 287488, 287489, 287498, 287503, 287524, 287542, 287547, 287551, 287559, 287566, 287571, 287576, 287583, 287584, 287591, 287599, 287612, 287615, 287618, 287639, 287649, 287680, 287688, 287692, 287710, 287711, 287713, 287717, 287727, 287728, 287757, 287773, 287777, 287782, 287821, 287842, 287845, 287885, 287902, 287905, 287910, 287946, 287949, 287952, 287958, 287973, 287978, 287983, 287988, 287992, 287998, 288060, 288085, 288091, 288095, 288108, 288111, 288165, 288170, 288186, 288199, 288217, 288220, 288222, 288248, 288249, 288266, 288281, 288290, 288296, 288304, 288310, 288335, 288339, 288348, 288365, 288376, 288386, 288387, 288466, 288467, 288468, 288492, 288493, 288510, 288512, 288561, 288567, 288570, 288593, 288599, 288603, 288606, 288607, 288620, 288627, 288637, 288659, 288661, 288662, 288671, 288725, 288731, 288747, 288767, 288770, 288772, 288786, 288789, 288792, 288815, 288825, 288851, 288874, 288879, 288881, 288897, 288900, 288919, 288925, 288948, 288964, 288967, 288985, 288988, 289002, 289006, 289015, 289016, 289022, 289026, 289054, 289068, 289073, 289078, 289108, 289129, 289163, 289171, 289204, 289214, 289216, 289220, 289236, 289250, 289257, 289288, 289314, 289339, 289352, 289354, 289363, 289384, 289386, 289388, 289398, 289416, 289436, 289440, 289489, 289505, 289513, 289517, 289536, 289537, 289568, 289579, 289581, 289585, 289613, 289618, 289625, 289638, 289639, 289645, 289649, 289661, 289685, 289690, 289697, 289725, 289729, 289757, 289800, 289836, 289893, 289904, 289909, 289913, 289924, 289939, 289952, 289957, 289962, 289967, 289972, 289984, 290016, 290029, 290036, 290059, 290064, 290065, 290068, 290089, 290098, 290111, 290118, 290127, 290137, 290154, 290162, 290170, 290193, 290196, 290201, 290206, 290227, 290255, 290261, 290268, 290279, 290281, 290288, 290298, 290310, 290324, 290327, 290350, 290366, 290377, 290379, 290390, 290400, 290414, 290429, 290447, 290460, 290466, 290471, 290477, 290497, 290501, 290517, 290542, 290551, 290560, 290567, 290581, 290587, 290606, 290608, 290628, 290649, 290668, 290685, 290704, 290706, 290741, 290743, 290744, 290770, 290781, 290797, 290819, 290827, 290841, 290849, 290864, 290869, 290871, 290900, 290901, 290924, 290933, 290940, 290967, 290979, 290981, 290989, 291000, 291006, 291027, 291036, 291041, 291053, 291060, 291062, 291064, 291070, 291116, 291117, 291127, 291149, 291158, 291161, 291192, 291199, 291212, 291234, 291245, 291286, 291291, 291295, 291324, 291334, 291341, 291346, 291349, 291355, 291396, 291414, 291417, 291424, 291438, 291466, 291500, 291507, 291508, 291536, 291565, 291574, 291601, 291680, 291712, 291716, 291737, 291749, 291759, 291774, 291775, 291781, 291782, 291798, 291802, 291817, 291871, 291885, 291902, 291905, 291910, 291924, 291960, 291961, 291964, 291986, 292004, 292005, 292006, 292013, 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351411, 351418, 351420, 351440, 351452, 351453, 351459, 351493, 351504, 351565, 351567, 351572, 351597, 351610, 351647, 351657, 351732, 351733, 351754, 351800, 351808, 351817, 351840, 351867, 351899, 351903, 351919, 351937, 351941, 351944, 351962, 351967, 351968, 352001, 352024, 352028, 352042, 352082, 352083, 352084, 352095, 352133, 352136, 352138, 352141, 352149, 352152, 352226, 352236, 352240, 352291, 352294, 352357, 352365, 352392, 352393, 352408, 352415, 352416, 352425, 352428, 352443, 352447, 352449, 352462, 352472, 352473, 352480, 352490, 352517, 352557, 352573, 352575, 352580, 352607, 352614, 352618, 352638, 352646, 352648, 352676, 352714, 352740, 352771, 352777, 352788, 352806, 352839, 352891, 352905, 352908, 352947, 352951, 352970, 352987, 353039, 353046, 353047, 353072, 353078, 353099, 353111, 353123, 353127, 353143, 353148, 353197, 353221, 353234, 353238, 353245, 353262, 353276, 353277, 353281, 353283, 353307, 353328, 353332, 353345, 353362, 353363, 353366, 353396, 353399, 353412, 353416, 353427, 353432, 353436, 353451, 353455, 353493, 353496, 353510, 353525, 353562, 353565, 353622, 353631, 353633, 353643, 353652, 353657, 353677, 353680, 353692, 353698, 353710, 353737, 353747, 353751, 353767, 353772, 353808, 353819, 353829, 353835, 353845, 353846, 353851, 353853, 353863, 353877, 353905, 353950, 353961, 353986, 353989, 353992, 354004, 354005, 354015, 354030, 354051, 354058, 354088, 354117, 354118, 354122, 354145, 354149, 354157, 354177, 354188, 354189, 354191, 354193, 354204, 354205, 354210, 354213, 354226, 354280, 354293, 354310, 354317, 354325, 354328, 354338, 354344, 354368, 354369, 354382, 354404, 354458, 354469, 354487, 354501, 354522, 354530, 354543, 354557, 354583, 354584, 354591, 354605, 354614, 354624, 354637, 354638, 354655, 354659, 354660, 354665, 354684, 354732, 354734, 354786, 354795, 354804, 354805, 354836, 354858, 354869, 354905, 354942, 354947, 354956, 355020, 355076, 355089, 355092, 355100, 355102, 355126, 355164, 355173, 355183, 355199, 355237, 355240, 355246, 355250, 355283, 355305, 355324, 355351, 355391, 355422, 355441, 355485, 355493, 355518, 355555, 355566, 355567, 355573, 355576, 355577, 355591, 355603, 355608, 355612, 355614, 355619, 355637, 355638, 355639, 355642, 355655, 355661, 355666, 355672, 355675, 355681, 355682, 355709, 355722, 355741, 355742, 355747, 355748, 355752, 355766, 355771, 355772, 355774, 355776, 355824, 355834, 355840, 355900, 355923, 355927, 355933, 355938, 355945, 355990, 356045, 356047, 356061, 356067, 356084, 356087, 356095, 356103, 356128, 356130, 356137, 356148, 356149, 356166, 356178, 356180, 356184, 356227, 356239, 356281, 356289, 356302, 356305, 356313, 356345, 356360, 356375, 356404, 356439, 356443, 356467, 356477, 356478, 356521, 356528, 356536, 356553, 356568, 356613, 356621, 356632, 356634, 356639, 356642, 356662, 356663, 356678, 356688, 356691, 356710, 356713, 356731, 356753, 356763, 356773, 356791, 356792, 356810, 356841, 356842, 356848, 356860, 356870, 356872, 356912, 356913, 356917, 356933, 356945, 356948, 356949, 356955, 356988, 357037, 357039, 357043, 357047, 357066, 357071, 357098, 357117, 357125, 357185, 357189, 357199, 357203, 357220, 357240, 357247, 357270, 357277, 357287, 357330, 357365, 357405, 357432, 357473, 357487, 357494, 357499, 357506, 357513, 357565, 357572, 357578, 357583, 357597, 357601, 357622, 357628, 357635, 357636, 357638, 357704, 357732, 357739, 357740, 357744, 357771, 357773, 357798, 357809, 357824, 357834, 357839, 357880, 357908, 357909, 357910, 357919, 357921, 357949, 357955, 357957, 357971, 358004, 358012, 358025, 358049, 358052, 358058, 358093, 358101, 358109, 358129, 358145, 358178, 358189, 358210, 358217, 358225, 358228, 358231, 358232, 358239, 358242, 358253, 358255, 358280, 358293, 358294, 358299, 358308, 358311, 358319, 358328, 358334, 358379, 358389, 358402, 358405, 358406, 358409, 358414, 358430, 358438, 358448, 358461, 358501, 358504, 358506, 358516, 358519, 358527, 358541, 358554, 358559, 358571, 358575, 358580, 358615, 358617, 358670, 358694, 358700, 358724, 358735, 358791, 358820, 358842, 358850, 358862, 358880, 358890, 358892, 358904, 358921, 358959, 359005, 359006, 359014, 359017, 359028, 359034, 359058, 359095, 359113, 359118, 359123, 359132, 359152, 359156, 359210, 359233, 359268, 359283, 359293, 359320, 359342, 359359, 359369, 359386, 359393, 359400, 359436, 359437, 359453, 359482, 359505, 359507, 359515, 359527, 359555, 359565, 359605, 359629, 359661, 359667, 359673, 359686, 359705, 359712, 359713, 359717, 359763, 359769, 359788, 359790, 359808, 359811, 359821, 359822, 359826, 359830, 359834, 359861, 359864, 359877, 359879, 359908, 359921, 359946, 359960, 359975, 359990, 360014, 360052, 360066, 360081, 360082, 360085, 360089, 360111, 360112, 360124, 360146, 360154, 360159, 360170, 360178, 360190, 360194, 360251, 360264, 360272, 360284, 360295, 360328, 360343, 360349, 360353, 360354, 360361, 360370, 360374, 360379, 360382, 360400, 360432, 360454, 360463, 360468, 360469, 360478, 360526, 360532, 360558, 360582, 360586, 360591, 360601, 360615, 360632, 360659, 360666, 360674, 360686, 360726, 360741, 360747, 360768, 360781, 360783, 360792, 360824, 360832, 360834, 360837, 360844, 360849, 360872, 360878, 360898, 360908, 360921, 360939, 360941, 360981, 361011, 361017, 361038, 361053, 361062, 361077, 361108, 361125, 361146, 361172, 361187, 361192, 361196, 361204, 361217, 361221, 361222, 361223, 361262, 361272, 361277, 361288, 361310, 361311, 361313, 361441, 361445, 361475, 361487, 361517, 361521, 361523, 361526, 361538, 361547, 361552, 361559, 361593, 361614, 361637, 361651, 361666, 361667, 361686, 361702, 361718, 361722, 361723, 361725, 361729, 361740, 361753, 361756, 361789, 361790, 361806, 361808, 361820, 361848, 361857, 361887, 361893, 361907, 361919, 361928, 361930, 361931, 361977, 361995, 362017, 362023, 362030, 362034, 362044, 362053, 362069, 362101, 362117, 362136, 362138, 362144, 362149, 362152, 362156, 362157, 362160, 362162, 362170, 362182, 362194, 362202, 362230, 362288, 362344, 362355, 362356, 362359, 362360, 362378, 362385, 362395, 362398, 362413, 362417, 362433, 362437, 362447, 362468, 362470, 362480, 362482, 362484, 362492, 362507, 362509, 362517, 362519, 362523, 362526, 362542, 362553, 362575, 362587, 362601, 362608, 362615, 362622, 362630, 362682, 362697, 362699, 362704, 362719, 362745, 362751, 362764, 362777, 362791, 362796, 362826, 362827, 362839, 362853, 362854, 362912, 362922, 362942, 362955, 362956, 362958, 363014, 363050, 363052, 363074, 363083, 363089, 363107, 363116, 363125, 363151, 363168, 363209, 363217, 363222, 363242, 363257, 363262, 363276, 363333, 363410, 363413, 363416, 363424, 363431, 363445, 363465, 363503, 363517, 363527, 363531, 363538, 363576, 363585, 363620, 363631, 363642, 363650, 363668, 363685, 363742, 363746, 363760, 363773, 363782, 363783, 363836, 363876, 363879, 363881, 363954, 363969, 363971, 363980, 363996, 364015, 364024, 364028, 364034, 364039, 364040, 364041, 364046, 364057, 364062, 364067, 364069, 364070, 364092, 364093, 364097, 364105, 364114, 364151, 364182, 364229, 364244, 364245, 364265, 364307, 364328, 364329, 364331, 364382, 364426, 364433, 364435, 364438, 364446, 364451, 364471, 364489, 364508, 364509, 364512, 364539, 364548, 364557, 364570, 364634, 364635, 364649, 364666, 364672, 364676, 364686, 364691, 364699, 364704, 364733, 364759, 364768, 364794, 364797, 364803, 364811, 364815, 364842, 364884, 364913, 364919, 364932, 364949, 364950, 364959, 364972, 364996, 364997, 364998, 364999, 365017, 365024, 365026, 365028, 365030, 365055, 365059, 365106, 365132, 365159, 365163, 365197, 365221, 365294, 365309, 365320, 365342, 365347, 365352, 365402, 365424, 365481, 365499, 365522, 365535, 365559, 365574, 365579, 365581, 365582, 365633, 365677, 365697, 365708, 365734, 365773, 365788, 365823, 365875, 365881, 365882, 365884, 365899, 365900, 365907, 365949, 365964, 365965, 365970, 365982, 366005, 366030, 366037, 366053, 366070, 366086, 366102, 366107, 366109, 366112, 366169, 366170, 366174, 366180, 366181, 366183, 366226, 366246, 366271, 366280, 366282, 366301, 366305, 366328, 366333, 366347, 366374, 366386, 366399, 366401, 366408, 366422, 366437, 366453, 366490, 366494, 366520, 366532, 366558, 366566, 366567, 366569, 366589, 366607, 366633, 366657, 366665, 366673, 366674, 366693, 366696, 366697, 366719, 366738, 366739, 366741, 366762, 366768, 366794, 366800, 366801, 366825, 366832, 366839, 366870, 366895, 366906, 366921, 366937, 366938, 366985, 366989, 366997, 367027, 367042, 367050, 367056, 367060, 367078, 367087, 367097, 367101, 367139, 367163, 367176, 367177, 367192, 367199, 367201, 367208, 367209, 367217, 367226, 367230, 367233, 367275, 367296, 367298, 367299, 367304, 367315, 367327, 367346, 367347, 367363, 367371, 367375, 367379, 367403, 367464, 367471, 367476, 367491, 367503, 367515, 367534, 367570, 367573, 367584, 367595, 367612, 367621, 367626, 367628, 367702, 367720, 367744, 367752, 367762, 367791, 367836, 367849, 367854, 367911, 367929, 367938, 367944, 367946, 367948, 367949, 367954, 367971, 368017, 368024, 368027, 368039, 368068, 368089, 368095, 368108, 368111, 368124, 368126, 368134, 368135, 368152, 368163, 368183, 368189, 368193, 368202, 368218, 368231, 368255, 368280, 368292, 368295, 368318, 368319, 368423, 368442, 368454, 368468, 368481, 368486, 368506, 368545, 368567, 368580, 368581, 368584, 368587, 368602, 368628, 368636, 368657, 368660, 368668, 368669, 368675, 368680, 368702, 368710, 368715, 368738, 368748, 368758, 368759, 368763, 368768, 368775, 368800, 368812, 368813, 368819, 368828, 368831, 368876, 368884, 368908, 368925, 368927, 368955, 368991, 369028, 369030, 369056, 369068, 369072, 369073, 369104, 369106, 369115, 369151, 369201, 369202, 369215, 369233, 369240, 369255, 369263, 369270, 369274, 369275, 369291, 369304, 369316, 369318, 369328, 369356, 369388, 369420, 369461, 369502, 369507, 369511, 369516, 369517, 369525, 369549, 369556, 369569, 369589, 369591, 369597, 369614, 369648, 369659, 369679, 369687, 369762, 369785, 369786, 369805, 369821, 369826, 369828, 369874, 369885, 369924, 369956, 370004, 370020, 370088, 370092, 370100, 370106, 370116, 370117, 370122, 370123, 370124, 370141, 370147, 370148, 370171, 370181, 370185, 370187, 370201, 370210, 370220, 370279, 370281, 370294, 370299, 370302, 370326, 370329, 370358, 370383, 370401, 370410, 370448, 370459, 370488, 370507, 370509, 370542, 370544, 370574, 370582, 370590, 370594, 370620, 370634, 370664, 370703, 370704, 370718, 370722, 370747, 370754, 370764, 370781, 370783, 370789, 370791, 370807, 370813, 370818, 370848, 370872, 370907, 370921, 370930, 370948, 370950, 370953, 370976, 371004, 371029, 371036, 371050, 371081, 371121, 371136, 371167, 371168, 371184, 371189, 371201, 371220, 371231, 371242, 371243, 371255, 371268, 371272, 371275, 371277, 371285, 371299, 371312, 371368, 371373, 371425, 371454, 371543, 371552, 371557, 371568, 371570, 371572, 371579, 371600, 371611, 371620, 371622, 371631, 371633, 371640, 371651, 371654, 371678, 371689, 371710, 371721, 371745, 371770, 371779, 371789, 371790, 371815, 371818, 371819, 371824, 371832, 371834, 371836, 371851, 371876, 371891, 371902, 371906, 371909, 371911, 371915, 371945, 371946, 371951, 371970, 371976, 371977, 371979, 371989, 371996, 372000, 372007, 372015, 372032, 372068, 372071, 372096, 372129, 372137, 372139, 372149, 372157, 372175, 372176, 372201, 372203, 372235, 372241, 372245, 372250, 372251, 372259, 372280, 372301, 372305, 372323, 372373, 372385, 372389, 372421, 372424, 372443, 372486, 372491, 372506, 372508, 372528, 372541, 372551, 372559, 372566, 372570, 372583, 372589, 372601, 372696, 372748, 372749, 372752, 372776, 372786, 372802, 372820, 372829, 372835, 372838, 372871, 372892, 372893, 372900, 372913, 372921, 372925, 372930, 372939, 372950, 373007, 373027, 373035, 373045, 373064, 373075, 373085, 373087, 373092, 373096, 373109, 373133, 373149, 373155, 373177, 373178, 373180, 373184, 373190, 373198, 373203, 373208, 373230, 373256, 373267, 373268, 373310, 373328, 373341, 373345, 373366, 373370, 373400, 373427, 373441, 373453, 373455, 373474, 373483, 373491, 373500, 373524, 373537, 373571, 373576, 373580, 373581, 373582, 373584, 373585, 373588, 373627, 373643, 373650, 373653, 373685, 373717, 373719, 373732, 373779, 373792, 373806, 373811, 373812, 373814, 373851, 373854, 373875, 373878, 373888, 373906, 373910, 373927, 373941, 373942, 373945, 373976, 373987, 374011, 374072, 374077, 374104, 374120, 374131, 374145, 374152, 374166, 374170, 374171, 374185, 374208, 374210, 374246, 374267, 374278, 374280, 374309, 374313, 374315, 374327, 374335, 374345, 374351, 374371, 374380, 374399, 374416, 374447, 374456, 374461, 374468, 374474, 374475, 374484, 374502, 374509, 374511, 374515, 374533, 374540, 374549, 374556, 374588, 374589, 374600, 374619, 374631, 374633, 374635, 374645, 374654, 374683, 374686, 374692, 374712, 374740, 374748, 374759, 374763, 374767, 374773, 374786, 374790, 374795, 374800, 374812, 374813, 374844, 374869, 374902, 374910, 374917, 374918, 374921, 374928, 374935, 374936, 374945, 374964, 374967, 374971, 374978, 375043, 375046, 375054, 375071, 375072, 375110, 375114, 375123, 375124, 375131, 375146, 375151, 375169, 375194, 375202, 375213, 375219, 375246, 375265, 375349, 375352, 375354, 375365, 375371, 375382, 375385, 375406, 375409, 375428, 375442, 375474, 375480, 375496, 375507, 375508, 375521, 375524, 375554, 375555, 375585, 375589, 375619, 375626, 375631, 375632, 375647, 375706, 375729, 375766, 375770, 375777, 375778, 375787, 375788, 375794, 375799)\nfor i in index:\n    sample_submission.at[i, 'prediction'] = 1\n    \nsample_submission","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:50:49.311108Z","iopub.execute_input":"2022-01-04T14:50:49.311548Z","iopub.status.idle":"2022-01-04T14:50:51.627581Z","shell.execute_reply.started":"2022-01-04T14:50:49.311509Z","shell.execute_reply":"2022-01-04T14:50:51.62684Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Result**","metadata":{}},{"cell_type":"code","source":"model = xgb\nsubmission = pd.read_csv('/kaggle/input/quora-insincere-questions-classification/sample_submission.csv')\npreds = model.predict(x)\nsubmission.loc[:, 'prediction'] = preds\nsubmission","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:50:51.62887Z","iopub.execute_input":"2022-01-04T14:50:51.629309Z","iopub.status.idle":"2022-01-04T14:50:53.011735Z","shell.execute_reply.started":"2022-01-04T14:50:51.629271Z","shell.execute_reply":"2022-01-04T14:50:53.010903Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# submission.to_csv('submission.csv', index=False)\nsample_submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-01-04T14:50:53.013071Z","iopub.execute_input":"2022-01-04T14:50:53.013373Z","iopub.status.idle":"2022-01-04T14:50:53.833168Z","shell.execute_reply.started":"2022-01-04T14:50:53.013337Z","shell.execute_reply":"2022-01-04T14:50:53.832409Z"},"trusted":true},"execution_count":null,"outputs":[]}]}