From fbf38fbca0fd9f1db5d3833ee2f550d3c0fa64a7 Mon Sep 17 00:00:00 2001 From: Yong Choi Date: 2020年12月10日 14:43:10 +0900 Subject: [PATCH 1/3] bug fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit SAVE_FILE_NM 불일치 수정 --- .../4.1.7.CNN_Classification.ipynb | 60 ++++++++++--------- 1 file changed, 33 insertions(+), 27 deletions(-) diff --git a/4.TEXT_CLASSIFICATION/4.1.7.CNN_Classification.ipynb b/4.TEXT_CLASSIFICATION/4.1.7.CNN_Classification.ipynb index fa024cd..adfdc4b 100644 --- a/4.TEXT_CLASSIFICATION/4.1.7.CNN_Classification.ipynb +++ b/4.TEXT_CLASSIFICATION/4.1.7.CNN_Classification.ipynb @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 9, "metadata": { "scrolled": true }, @@ -208,17 +208,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "./data_out/cnn_classifier_eng -- Folder create complete \n", - "\n", - "Train on 22500 samples, validate on 2500 samples\n", - "Epoch 1/2\n", - "22016/22500 [============================>.] - ETA: 0s - loss: 0.6737 - accuracy: 0.6266\n", - "Epoch 00001: val_accuracy improved from -inf to 0.76520, saving model to ./data_out/cnn_classifier_eng/weights.01-0.77.h5\n", - "22500/22500 [==============================] - 30s 1ms/sample - loss: 0.6716 - accuracy: 0.6295 - val_loss: 0.5728 - val_accuracy: 0.7652\n", - "Epoch 2/2\n", - "22016/22500 [============================>.] - ETA: 0s - loss: 0.3865 - accuracy: 0.8302\n", - "Epoch 00002: val_accuracy improved from 0.76520 to 0.87560, saving model to ./data_out/cnn_classifier_eng/weights.02-0.88.h5\n", - "22500/22500 [==============================] - 27s 1ms/sample - loss: 0.3849 - accuracy: 0.8310 - val_loss: 0.3106 - val_accuracy: 0.8756\n" + "./data_out/cnn_classifier_en -- Folder already exists \n", + "\n" ] } ], @@ -252,9 +243,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2\n", + "44/44 [==============================] - ETA: 0s - loss: 0.6709 - accuracy: 0.5958\n", + "Epoch 00001: val_accuracy improved from -inf to 0.77560, saving model to ./data_out/cnn_classifier_en\\weights.h5\n", + "44/44 [==============================] - 4s 84ms/step - loss: 0.6709 - accuracy: 0.5958 - val_loss: 0.5596 - val_accuracy: 0.7756\n", + "Epoch 2/2\n", + "44/44 [==============================] - ETA: 0s - loss: 0.3790 - accuracy: 0.8400\n", + "Epoch 00002: val_accuracy improved from 0.77560 to 0.87760, saving model to ./data_out/cnn_classifier_en\\weights.h5\n", + "44/44 [==============================] - 3s 76ms/step - loss: 0.3790 - accuracy: 0.8400 - val_loss: 0.3092 - val_accuracy: 0.8776\n" + ] + } + ], "source": [ "history = model.fit(train_input, train_label, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS,\n", " validation_split=VALID_SPLIT, callbacks=[earlystop_callback, cp_callback])" @@ -269,12 +275,12 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -291,14 +297,14 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -322,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -343,11 +349,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "SAVE_FILE_NM = 'weight.h5'\n", + "SAVE_FILE_NM = 'weights.h5'\n", "\n", "model.load_weights(os.path.join(DATA_OUT_PATH, model_name, SAVE_FILE_NM))" ] @@ -361,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -371,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +407,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.12" }, "pycharm": { "stem_cell": { From 11b37bbde76af8864384ec9fb6541fe9e157b17d Mon Sep 17 00:00:00 2001 From: Yong Choi Date: 2020年12月10日 14:44:07 +0900 Subject: [PATCH 2/3] Create examples for 2.3.1 --- 2.NLP_PREP/2.3.1.1.nltk.ipynb | 339 ++++++++++++++++++++++++++++++++ 2.NLP_PREP/2.3.1.2.spacy.ipynb | 215 ++++++++++++++++++++ 2.NLP_PREP/2.3.1.3.koNLPy.ipynb | 245 +++++++++++++++++++++++ 3 files changed, 799 insertions(+) create mode 100644 2.NLP_PREP/2.3.1.1.nltk.ipynb create mode 100644 2.NLP_PREP/2.3.1.2.spacy.ipynb create mode 100644 2.NLP_PREP/2.3.1.3.koNLPy.ipynb diff --git a/2.NLP_PREP/2.3.1.1.nltk.ipynb b/2.NLP_PREP/2.3.1.1.nltk.ipynb new file mode 100644 index 0000000..b3a14bb --- /dev/null +++ b/2.NLP_PREP/2.3.1.1.nltk.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading collection 'all-corpora'\n", + "[nltk_data] | \n", + "[nltk_data] | Downloading package abc to\n", + "[nltk_data] | C:\\Users\\sk8er\\AppData\\Roaming\\nltk_data...\n", + "[nltk_data] 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"[nltk_data] Package punkt is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "\n", + "nltk.download('all-corpora')\n", + "nltk.download('punkt')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Natural', 'language', 'processing', '(', 'NLP', ')', 'is', 'a', 'subfield', 'of', 'computer', 'science', ',', 'information', 'engineering', ',', 'and', 'artificial', 'intelligence', 'concerned', 'with', 'the', 'interactions', 'between', 'computers', 'and', 'human', '(', 'natural', ')', 'languages', ',', 'in', 'particular', 'how', 'to', 'program', 'computers', 'to', 'process', 'and', 'analyze', 'large', 'amounts', 'of', 'natural', 'language', 'data', '.']\n" + ] + } + ], + "source": [ + "from nltk.tokenize import word_tokenize\n", + "\n", + "sentence = \"Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.\"\n", + "\n", + "print(word_tokenize(sentence))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.', 'Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.']\n" + ] + } + ], + "source": [ + "from nltk.tokenize import sent_tokenize\n", + "\n", + "paragraph = \"Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.\"\n", + "\n", + "print(sent_tokenize(paragraph))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/2.NLP_PREP/2.3.1.2.spacy.ipynb b/2.NLP_PREP/2.3.1.2.spacy.ipynb new file mode 100644 index 0000000..80465d0 --- /dev/null +++ b/2.NLP_PREP/2.3.1.2.spacy.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: spacy in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (2.3.4)\n", + "Requirement already satisfied: setuptools in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy) (51.0.0.post20201207)\n", + "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy) (3.0.5)\n", + "Requirement already satisfied: thinc<7.5.0,>=7.4.1 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy) (7.4.4)\n", + "Requirement already satisfied: requests<3.0.0,>=2.13.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy) (2.25.0)\n", + "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy) (1.0.5)\n", + "Requirement 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"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (3.0.5)\n", + "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.8.0)\n", + "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.0.0)\n", + "Requirement already satisfied: numpy>=1.15.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.16.6)\n", + "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (2.0.5)\n", + "Requirement already satisfied: plac<1.2.0,>=0.9.6 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (1.1.3)\n", + "Requirement already satisfied: blis<0.8.0,>=0.4.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from spacy<2.4.0,>=2.3.0->en_core_web_sm==2.3.1) (0.7.4)\n", + "[+] Download and installation successful\n", + "You can now load the model via spacy.load('en_core_web_sm')\n", + "[x] Couldn't link model to 'en'\n", + "Creating a symlink in spacy/data failed. Make sure you have the required\n", + "permissions and try re-running the command as admin, or use a virtualenv. You\n", + "can still import the model as a module and call its load() method, or create the\n", + "symlink manually.\n", + "C:\\Users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages\\en_core_web_sm -->\n", + "C:\\Users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages\\spacy\\data\\en\n", + "[!] Download successful but linking failed\n", + "Creating a shortcut link for 'en' didn't work (maybe you don't have admin\n", + "permissions?), but you can still load the model via its full package name: nlp =\n", + "spacy.load('en_core_web_sm')\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "이 작업을 수행할 수 있는 권한이 없습니다.\n" + ] + } + ], + "source": [ + "!python -m spacy download en" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#nlp = spacy.load('en')\n", + "nlp = spacy.load('en_core_web_sm')\n", + "\n", + "sentence = \"Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.\"\n", + "\n", + "doc = nlp(sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Natural', 'language', 'processing', '(', 'NLP', ')', 'is', 'a', 'subfield', 'of', 'computer', 'science', ',', 'information', 'engineering', ',', 'and', 'artificial', 'intelligence', 'concerned', 'with', 'the', 'interactions', 'between', 'computers', 'and', 'human', '(', 'natural', ')', 'languages', ',', 'in', 'particular', 'how', 'to', 'program', 'computers', 'to', 'process', 'and', 'analyze', 'large', 'amounts', 'of', 'natural', 'language', 'data', '.']\n" + ] + } + ], + "source": [ + "word_tokenized_sentence = [token.text for token in doc]\n", + "print(word_tokenized_sentence)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.']\n" + ] + } + ], + "source": [ + "sentence_tokenized_list = [sent.text for sent in doc.sents]\n", + "print(sentence_tokenized_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/2.NLP_PREP/2.3.1.3.koNLPy.ipynb b/2.NLP_PREP/2.3.1.3.koNLPy.ipynb new file mode 100644 index 0000000..5cc871a --- /dev/null +++ b/2.NLP_PREP/2.3.1.3.koNLPy.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "java version \"1.8.0_271\"\n", + "Java(TM) SE Runtime Environment (build 1.8.0_271-b09)\n", + "Java HotSpot(TM) 64-Bit Server VM (build 25.271-b09, mixed mode)\n" + ] + } + ], + "source": [ + "!java -version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing c:\\tensorflow-ml-nlp-tf2\\wheels\\jpype1-1.2.0-cp36-cp36m-win_amd64.whl\n", + "Requirement already satisfied: typing-extensions in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from JPype1==1.2.0) (3.7.4.3)\n", + "JPype1 is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n" + ] + } + ], + "source": [ + "!pip install ../wheels/JPype1-1.2.0-cp36-cp36m-win_amd64.whl" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: konlpy in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (0.5.2)\n", + "Requirement already satisfied: colorama in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from konlpy) (0.4.4)\n", + "Requirement already satisfied: beautifulsoup4==4.6.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from konlpy) (4.6.0)\n", + "Requirement already satisfied: JPype1>=0.7.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from konlpy) (1.2.0)\n", + "Requirement already satisfied: tweepy>=3.7.0 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages 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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (1.22)\n", + "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (2.6)\n", + "Requirement already satisfied: certifi>=2017年4月17日 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (2020年12月5日)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (3.0.4)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests>=2.0.0->requests-oauthlib>=0.7.0->tweepy>=3.7.0->konlpy) (1.22)\n", + "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages (from requests[socks]>=2.11.1->tweepy>=3.7.0->konlpy) (1.7.1)\n" + ] + } + ], + "source": [ + "!pip install konlpy" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import konlpy" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from konlpy.tag import Okt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "okt = Okt()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['한글', '자연어', '처리', '는', '재밌다', '이제', '부터', '열심히', '해야지', 'ᄒᄒᄒ']\n", + "['한글', '자연어', '처리', '는', '재밌다', '이제', '부터', '열심히', '하다', 'ᄒᄒᄒ']\n" + ] + } + ], + "source": [ + "text = \"한글 자연어 처리는 재밌다 이제부터 열심히 해야지ᄒᄒᄒ\"\n", + "print(okt.morphs(text))\n", + "print(okt.morphs(text, stem=True)) # 형태소 단위로 나눈 후 어간을 추출" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['한글', '자연어', '처리', '이제']\n", + "['한글', '한글 자연어', '한글 자연어 처리', '이제', '자연어', '처리']\n" + ] + } + ], + "source": [ + "print(okt.nouns(text))\n", + "print(okt.phrases(text))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('한글', 'Noun'), ('자연어', 'Noun'), ('처리', 'Noun'), ('는', 'Josa'), ('재밌다', 'Adjective'), ('이제', 'Noun'), ('부터', 'Josa'), ('열심히', 'Adverb'), ('해야지', 'Verb'), ('ᄒᄒᄒ', 'KoreanParticle')]\n", + "['한글/Noun', '자연어/Noun', '처리/Noun', '는/Josa', '재밌다/Adjective', '이제/Noun', '부터/Josa', '열심히/Adverb', '해야지/Verb', 'ᄒᄒᄒ/KoreanParticle']\n" + ] + } + ], + "source": [ + "print(okt.pos(text))\n", + "print(okt.pos(text, join=True)) # 형태소와 품사를 붙여서 리스트화" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from konlpy.corpus import kolaw\n", + "from konlpy.corpus import kobill" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'대한민국헌법\\n\\n유구한 역사와 전통에 '" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kolaw.open('constitution.txt').read()[:20]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'지방공무원법 일부개정법률안\\n\\n(정의화의원 대표발의 )\\n\\n 의 안\\n 번 호\\n\\n9890\\n\\n발의연월일 : 2010. 11. 12. \\n\\n발 의 자 : 정의화.이명수.김을동 \\n\\n이사철.여상규.안규백\\n\\n황영철.박영아.김정훈\\n\\n김학송 의원(10인)\\n\\n제안이유 및 주요내용\\n\\n 초등학교 저학년의 경우에도 부모의 따뜻한 사랑과 보살핌이 필요\\n\\n한 나이이나, 현재 공무원이 자녀를 양육하기 위하여 육아휴직을 할 \\n\\n수 있는 자녀의 나이는 만 6세 이하로 되어 있어 초등학교 저학년인 \\n\\n자녀를 돌보기 위해서는 해당 부모님은 일자리를 그만 두어야 하고 \\n\\n이는 곧 출산의욕을 저하시키는 문제로 이어질 수 있을 것임.\\n\\n 따라서 육아휴직이 가능한 자녀의 연령을 만 8세 이하로 개정하려\\n\\n는 것임(안 제63조제2항제4호).\\n\\n- 1 -\\n\\n\\x0c법률 제 호\\n\\n지방공무원법 일부개정법률안\\n\\n지방공무원법 일부를 다음과 같이 개정한다.\\n\\n제63조제2항제4호 중 "만 6세 이하의 초등학교 취학 전 자녀를"을 "만 \\n\\n8세 이하(취학 중인 경우에는 초등학교 2학년 이하를 말한다)의 자녀를"\\n\\n로 한다.\\n\\n부 칙\\n\\n이 법은 공포한 날부터 시행한다.\\n\\n- 3 -\\n\\n\\x0c신 ·구조문대비표\\n\\n현 행\\n\\n개 정 안\\n\\n제63조(휴직) 1 (생 략)\\n\\n제63조(휴직) 1 (현행과 같음)\\n\\n 2 공무원이 다음 각 호의 어\\n\\n 2 -------------------------\\n\\n느 하나에 해당하는 사유로 휴\\n\\n----------------------------\\n\\n직을 원하면 임용권자는 휴직\\n\\n----------------------------\\n\\n을 명할 수 있다. 다만, 제4호\\n\\n-------------.---------------\\n\\n의 경우에는 대통령령으로 정\\n\\n----------------------------\\n\\n하는 특별한 사정이 없으면 휴\\n\\n----------------------------\\n\\n직을 명하여야 한다.\\n\\n--------------.\\n\\n 1. ∼ 3. (생 략)\\n\\n 1. ∼ 3. (현행과 같음)\\n\\n 4. 만 6세 이하의 초등학교 취\\n\\n 4. 만 8세 이하(취학 중인 경우\\n\\n학 전 자녀를 양육하기 위하\\n\\n에는 초등학교 2학년 이하를 \\n\\n여 필요하거나 여자공무원이 \\n\\n말한다)의 자녀를 ----------\\n\\n임신 또는 출산하게 되었을 \\n\\n---------------------------\\n\\n때\\n\\n---------------------------\\n\\n 5.⋅6. (생 략)\\n\\n 3⋅4 (생 략)\\n\\n--------\\n\\n 5.⋅6. (현행과 같음)\\n\\n 3⋅4 (현행과 같음)\\n\\n- 5 -\\n\\n\\x0c지방공무원법 일부개정법률안 등 비용추계서 미첨부사유서\\n1. 재정수반요인\\n\\n개정안에서 「국가공무원법」 제71조제2항제4호 중 국가공무원의 육아\\n\\n휴직 가능 자녀의 연령을 만6세 이하에서 만8세 이하로 하고, 「지방공\\n\\n무원법」 제63조제2항제4호 중 지방공무원의 육아휴직 가능 자녀의 연\\n\\n령을 만6세 이하에서 만8세 이하로 하고, 「교육공무원법」 제44조제1항\\n\\n제7조 중 교육공무원의 육아휴직 가능 자녀의 연령을 만6세 이하에서 \\n\\n만8세 이하로 하고, 「남녀고용평등과 일.가정 양립지원에 관한 법률」 \\n\\n제19조제1항 중 근로자 육아휴직 가능 자녀연령을 만6세 이하에서 만\\n\\n8세 이하로 조정함에 따라 추가 재정소요가 예상됨.\\n\\n2. 미첨부 근거 규정\\n「의안의 비용추계에 관한 규칙」 제3조제1항 단서 중 제1호(예상되는 비용이 연평균 10억원 미만\\n이거나 한시적인 경비로서 총 30억원 미만인 경우)에 해당함.\\n\\n3. 미첨부 사유\\n\\n개정안에서 국가.지방.교육공무원 및 근로자가 육아휴직을 신청할 \\n\\n수 있는 자녀의 연령을 만6세 이하에서 만8세 이하로 상향조정함에 \\n\\n따라 추가 재정소요가 예상된다. 동 법률 개정안이 2011년에 시행된다\\n\\n고 가정한 경우, 2010년 현재 자녀의 연령이 7세이고 육아휴직을 신청\\n\\n- 7 -\\n\\n\\x0c- 8 -\\n\\n하지 않은 국가.지방.교육공무원 및 근로자가 대상이 된다.\\n\\n대상연령의 확대됨에 따라 육아휴직신청자의 수가 어느 정도 늘어날 \\n\\n것으로 예상된다. 이 경우 발생하는 비용은 현행법에 따르면 월50만원\\n\\n이나 현재 관련법령 개정이 추진되고 있으며, 이에 따라 2011년에는 \\n\\n육아휴직자가 지급받는 월급여액에 비례하여 육아휴직급여가 지급되\\n\\n기 때문에 법령개정을 가정하고 추계한다. 이러한 경우 육아휴직급여\\n\\n액은 육아휴직자가 지급받는 월급여의 40%에 해당한다. 육아휴직자가 \\n\\n발생한 경우 발생하는 비용은 대체인력 고용인건비와 육아휴직자가 \\n\\n받는 월급여액의 40%이다. 이와 대비하여 육아휴직자에게 지급하던 \\n\\n임금은 더 이상 발생하지 않는다. 따라서 실제 발생하는 순비용은 육\\n\\n아휴직자에게 지급하던 월 급여액과 연령 확대에 따라 발생하는 비용\\n\\n인 육아휴직자가 받던 월급여액의 40%와 대체인력 고용인건비의 차\\n\\n액인데 이 값이 0보다 크면 추가 재정소요는 발생하지 않는다고 볼 \\n\\n수 있다.\\n\\n추가비용 발생여부를 정확하게 알아보기 위하여 비용에 대한 수리모\\n\\n델을 만들고 이에 따라 비용발생 여부를 알아보기로 하자. 모델에 사\\n\\n용되는 변수를 다음과 같이 정의한다.\\n\\n발생비용 : ×ばつp×ばつX + ×ばつ육아휴직급여액 - ×ばつP\\n\\nN\\n\\nP\\n\\n: 육아휴직대상자의 수\\n\\n: 육아휴직대상자의 월급여액\\n\\n\\x0cp\\n\\nX\\n\\n: 육아휴직자가 발생한 경우 대체 고용할 확률\\n\\n: 대체 고용한 인력에게 지급하는 월급여액\\n\\n위의 수식에서 육아휴직급여액은 육아휴직자 월급여액의 40%까지 지\\n\\n급할 예정이므로 육아휴직급여액은 ×ばつ40%이다. 육아휴직자가 발생한 \\n\\n경우 대체 고용할 확률 p는 고용노동부의 육아휴직 관련 자료를 이용\\n\\n한다. 고용노동부에 따르면 2011년의 경우 육아휴직급여 대상자는 \\n\\n40,923명이며, 육아휴직에 따른 대체인력 고용 예상인원은 2,836명이\\n\\n다. 2007년부터 2011년까지의 현황을 정리하면 다음의 [표]와 같다.\\n\\n[표] 육아휴직급여 수급자의 수 및 대체인력 고용 현황: 2007~2011년\\n\\n(단위: 명, % )\\n\\n2007\\n\\n2008\\n\\n2009\\n\\n2010\\n\\n2011\\n\\n평균\\n\\n육아휴직급여 수급자(A)\\n\\n21,185\\n\\n29,145\\n\\n35,400\\n\\n41,291\\n\\n43,899\\n\\n34,184\\n\\n대체인력 채용(B)\\n\\n796\\n\\n1,658\\n\\n1,957\\n\\n2,396\\n\\n2,836\\n\\n1,929\\n\\n비 율(B/A)\\n\\n3.8\\n\\n5.7\\n\\n5.5\\n\\n5.8\\n\\n6.5\\n\\n5.6\\n\\n자료: 고용노동부 자료를 바탕으로 국회예산정책처 작성\\n\\n위의 [표]의 자료에 따라 육아휴직자가 발생한 경우 대체 고용할 확률 \\n\\np의 값은 5.6%라고 가정한다. 그리고 비용이 발생한다고 가정하여 위\\n\\n의 수식을 다시 작성하면 다음의 수식과 같다.\\n\\n×ばつp×ばつX + ×ばつ육아휴직급여액 - ×ばつP> 0\\n\\n(1)\\n\\n- 9 -\\n\\n\\x0c- 10 -\\n\\n×ばつX + ×ばつ40% - ×ばつP> 0\\n\\n×ばつX> 0.6P\\n\\nX> ×ばつP\\n\\n(2)\\n\\n(3)\\n\\n(5)\\n\\n위의 수식에 육아휴직자가 받는 월 급여액을 대입하여 대체고용인력\\n\\n자에게 지급하는 월 급여액을 추정하여 보자. 육아휴직자가 월 200만\\n\\n원을 받는다고 가정하면, 대체고용인력자에게 육아휴직자가 받는 월 \\n\\n급여액의 10.7배에 달하는 월 21,428,571원 이상을 지급해야 추가 비용\\n\\n이 발생한다. 대체고용인력자에게 육아휴직자보다 더 많은 월급여액을 \\n\\n주지는 않을 것이고 그리고 10여배 이상 월급을 주지도 않을 것이기 \\n\\n때문에 추가 비용이 발생한다고 보기 힘들다. 위의 수식에서 대체인력 \\n\\n고용확률 p를 20%로 가정하더라도(이 경우 X> ×ばつP) 200만원 받는 \\n\\n육아휴직자 대체인력에게 월 600만원 이상을 지급해야 추가 비용이 \\n\\n발생한다.\\n\\n행정안전부의 통계자료(행정안전부 통계연감)에서는 지방공무원의 육\\n\\n아휴직 현황자료를 보여주고 있다. 여기서 육아휴직자가 발생한 경우 \\n\\n대체인력을 주로 임용대기자 또는 일용직을 활용하는 것으로 보인다. \\n\\n따라서 공무원의 경우에도 [표]에서 보여주는 일반기업체의 대체인력 \\n\\n고용확률과 차이는 크지 않을 것으로 보인다.\\n\\n이상의 논의를 바탕으로 육아휴직기간을 만6에서 만8세로 연장하더라\\n\\n도 법률 개정에 따른 추가 비용은 발생하지 않을 것으로 예상된다.\\n\\n\\x0c4. 작성자\\n\\n국회예산정책처 법안비용추계1팀\\n\\n팀 장 정 문 종\\n\\n예산분석관 김 태 완\\n\\n(02-788-4649, tanzania@assembly.go.kr)\\n\\n- 11 -\\n\\n\\x0c'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kobill.open('1809890.txt').read()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 0bd38ca9f725719391d2f6623f67cab41dcc99d6 Mon Sep 17 00:00:00 2001 From: Yong Choi Date: 2020年12月10日 15:23:30 +0900 Subject: [PATCH 3/3] bug fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 변수명 불일치로 인한 오류 제거(train_lenght --> train_length) --- .../4.2.2.EDA&preprocessing.ipynb | 173 ++++++++++-------- 1 file changed, 94 insertions(+), 79 deletions(-) diff --git a/4.TEXT_CLASSIFICATION/4.2.2.EDA&preprocessing.ipynb b/4.TEXT_CLASSIFICATION/4.2.2.EDA&preprocessing.ipynb index 94df0de..9c491fb 100644 --- a/4.TEXT_CLASSIFICATION/4.2.2.EDA&preprocessing.ipynb +++ b/4.TEXT_CLASSIFICATION/4.2.2.EDA&preprocessing.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "metadata": { "pycharm": { "is_executing": false @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": { "pycharm": { "is_executing": false @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": { "pycharm": { "is_executing": false @@ -60,9 +60,9 @@ "output_type": "stream", "text": [ "파일 크기 : \n", - "ratings_test.txt 4.89MB\n", - "ratings.txt 19.52MB\n", - "ratings_train.txt 14.63MB\n" + "ratings.txt 19.72MB\n", + "ratings_test.txt 4.94MB\n", + "ratings_train.txt 14.78MB\n" ] } ], @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 4, "metadata": { "pycharm": { "is_executing": false @@ -152,7 +152,7 @@ "4 6483659 사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ... 1" ] }, - "execution_count": 27, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": { "pycharm": { "is_executing": false @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": { "pycharm": { "is_executing": false @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": { "pycharm": { "is_executing": false @@ -216,37 +216,45 @@ "Name: document, dtype: int64" ] }, - "execution_count": 30, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_lenght.head()" + "train_length.head()" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages\\ipykernel_launcher.py:11: MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n", + " # This is added back by InteractiveShellApp.init_path()\n" + ] + }, { "data": { "text/plain": [ "Text(0, 0.5, 'Number of review')" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -267,7 +275,7 @@ "# alpha: 그래프 색상 투명도\n", "# color: 그래프 색상\n", "# label: 그래프에 대한 라벨\n", - "plt.hist(train_lenght, bins=200, alpha=0.5, color= 'r', label='word')\n", + "plt.hist(train_length, bins=200, alpha=0.5, color= 'r', label='word')\n", "plt.yscale('log', nonposy='clip')\n", "# 그래프 제목\n", "plt.title('Log-Histogram of length of review')\n", @@ -279,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": { "pycharm": { "is_executing": false @@ -301,19 +309,19 @@ } ], "source": [ - "print('리뷰 길이 최대 값: {}'.format(np.max(train_lenght)))\n", - "print('리뷰 길이 최소 값: {}'.format(np.min(train_lenght)))\n", - "print('리뷰 길이 평균 값: {:.2f}'.format(np.mean(train_lenght)))\n", - "print('리뷰 길이 표준편차: {:.2f}'.format(np.std(train_lenght)))\n", - "print('리뷰 길이 중간 값: {}'.format(np.median(train_lenght)))\n", + "print('리뷰 길이 최대 값: {}'.format(np.max(train_length)))\n", + "print('리뷰 길이 최소 값: {}'.format(np.min(train_length)))\n", + "print('리뷰 길이 평균 값: {:.2f}'.format(np.mean(train_length)))\n", + "print('리뷰 길이 표준편차: {:.2f}'.format(np.std(train_length)))\n", + "print('리뷰 길이 중간 값: {}'.format(np.median(train_length)))\n", "# 사분위의 대한 경우는 0~100 스케일로 되어있음\n", - "print('리뷰 길이 제 1 사분위: {}'.format(np.percentile(train_lenght, 25)))\n", - "print('리뷰 길이 제 3 사분위: {}'.format(np.percentile(train_lenght, 75)))" + "print('리뷰 길이 제 1 사분위: {}'.format(np.percentile(train_length, 25)))\n", + "print('리뷰 길이 제 3 사분위: {}'.format(np.percentile(train_length, 75)))" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": { "pycharm": { "is_executing": false @@ -323,23 +331,23 @@ { "data": { "text/plain": [ - "{'whiskers': [,\n", - " ],\n", - " 'caps': [,\n", - " ],\n", - " 'boxes': [],\n", - " 'medians': [],\n", - " 'fliers': [],\n", - " 'means': []}" + "{'whiskers': [,\n", + " ],\n", + " 'caps': [,\n", + " ],\n", + " 'boxes': [],\n", + " 'medians': [],\n", + " 'fliers': [],\n", + " 'means': []}" ] }, - "execution_count": 33, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -357,14 +365,14 @@ "# labels: 입력한 데이터에 대한 라벨\n", "# showmeans: 평균값을 마크함\n", "\n", - "plt.boxplot(train_lenght,\n", + "plt.boxplot(train_length,\n", " labels=['counts'],\n", " showmeans=True)" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": { "pycharm": { "is_executing": false @@ -377,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": { "pycharm": { "is_executing": false @@ -390,26 +398,34 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 13, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " FutureWarning\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -428,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": { "pycharm": { "is_executing": false @@ -451,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": { "pycharm": { "is_executing": false @@ -464,26 +480,34 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\sk8er\\anaconda3\\envs\\pr_tensorflow\\lib\\site-packages\\ipykernel_launcher.py:4: MatplotlibDeprecationWarning: The 'nonposy' parameter of __init__() has been renamed 'nonpositive' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.\n", + " after removing the cwd from sys.path.\n" + ] + }, { "data": { "text/plain": [ "Text(0, 0.5, 'Number of reviews')" ] }, - "execution_count": 39, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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ceuihY7/OFFAAAGCuXHXVVdl///3nNvwlSVVl//33X/UopwAIAADMnXkOfwt25jMKgAAAAAMhAAIAAPOtarKPHbjyyivz8pe/fNVlPuQhD8mVV165Ex9wfAIgAADABC0XAK+99toVX3fGGWdkv/32m1JVHauAAgAATNAJJ5yQL37xiznyyCOz1157Ze+9986tbnWrnH/++fn85z+fRzziEbnoooty1VVX5clPfnI2b96cJDnkkEOydevWfOtb38qxxx6b+9znPvnwhz+cgw46KG9961uzzz777HJtRgABAAAm6G/+5m/yIz/yI/nEJz6R5z//+fn4xz+el7zkJfn85z+fJDnppJNy7rnnZuvWrXnpS1+ayy+//EbH+MIXvpAnPelJOe+887LffvvljW9840RqMwIIAAAwRUcdddQN7tX30pe+NG9+85uTJBdddFG+8IUvZP/997/Baw499NAceeSRSZJ73OMeufDCCydSiwAIAAAwRbe4xS2+//uZZ56Zf/3Xf81HPvKR3PzmN8/RRx+95L38bnazm33/9z322CPf+c53JlLL3EwBrapNVXXi9u3bZ10KAAAwYPvuu2+++c1vLrlv+/btudWtbpWb3/zmOf/88/PRj350TWubmxHA1tqWJFs2btz4hFnXAgAArCOtrenb7b///rn3ve+du971rtlnn31y29ve9vv7jjnmmLzyla/MXe5yl9zpTnfKPe95zzWtrdoad8a0bdy4sW3dunXWZQAAADPy2c9+Nne5y11mXcaaWOqzVtW5rbWNSz1/bqaAAgAAsDIBEAAAYCAEQAAAYO7M26VuS9mZzygAAgAAc2XvvffO5ZdfPtchsLWWyy+/PHvvvfeqXjc3q4ACAAAkycEHH5xt27bl0ksvnXUpU7X33nvn4IMPXtVrBEAAAGCu7LXXXjn00ENnXca6JACysqqdf+0cD7kDAMDuyDWAAAAAAyEAAgAADIQACAAAMBCuARyCXbmODwAAmBtGAAEAAAbCCCDrk9VHAQBg4owAAgAADIQACAAAMBACIAAAwEAIgAAAAANhERjmz67e9sIiMgAAzCkjgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBAWARmrezKwiQWJQEAACbACCAAAMBAGAHcHezqbQ1YHaO1AADMKSOAAAAAAyEAAgAADIQACAAAMBACIAAAwECs+wBYVbeoqq1V9dBZ1wIAALA7W/MAWFUnVdUlVfWZRe3HVNXnquqCqjphZNfTkpy2tlUCAADMn1mMAJ6c5JjRhqraI8nLkhyb5Igkx1XVEVX1wCT/keSStS4SAABg3qz5fQBba2dV1SGLmo9KckFr7T+TpKpOSfLwJLdMcot0ofA7VXVGa+26xcesqs1JNifJHe5whylWDwAAsPtaLzeCPyjJRSPb25L8dGvt+CSpql9PctlS4S9JWmsnJjkxSTZu3OhO3AAAAEtYLwFwRa21k2ddA4ylaudf2/zbBQAA07VeVgH9SpLbj2wf3LcBAAAwIeslAJ6T5PCqOrSqbprksUlOn3FNAAAAc2UWt4F4XZKPJLlTVW2rqse31q5NcnySdyX5bJLTWmvnrXVtAAAA82wWq4Aet0z7GUnO2NnjVtWmJJsOO+ywnT0EAADAXFsvU0B3WWttS2tt84YNG2ZdCgAAwLo0NwEQAACAlQmAAAAAAyEAAgAADIQACAAAMBBzEwCralNVnbh9+/ZZlwJrr2rnHwAADMbcBECrgAIAAKxsbgIgAAAAKxMAAQAABkIABAAAGAgBEAAAYCDmJgBaBRQAAGBlcxMArQIKAACwsrkJgAAAAKxMAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYiLkJgO4DCDupatceAADsNuYmALoPIAAAwMrmJgACAACwMgEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZibgJgVW2qqhO3b98+61IAAADWpbkJgK21La21zRs2bJh1KQAAAOvS3ARAAAAAVrbnrAsAdnNVO//a1iZXBwAAO2QEEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGYm4CYFVtqqoTt2/fPutSAAAA1qW5CYCttS2ttc0bNmyYdSkAAADr0twEQAAAAFYmAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAA7HnrAsABqxq51/b2uTqAAAYCCOAAAAAAyEAAgAADIQACAAAMBBzEwCralNVnbh9+/ZZlwIAALAuzU0AbK1taa1t3rBhw6xLAQAAWJfmJgACAACwMgEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZiz1kXALDmqnbt9a1Npg4AgDVmBBAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCDmJgBW1aaqOnH79u2zLgUAAGBdmpsA2Frb0lrbvGHDhlmXAsy7qp1/AADM0NwEQAAAAFYmAAIAAAyEAAgAADAQAiAAAMBACIAAAAADseesCwAYlF1ZCbS1ydUBAAySEUAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAj3AQTYXbiHIACwi4wAAgAADIQACAAAMBCmgAKwsl2ZepqYfgoA64gRQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBsAooANPlBvYAsG4YAQQAABgIARAAAGAgBEAAAICBEAABAAAGwiIwAKxfFpABgIkyAggAADAQAiAAAMBArOspoFV1lyRPTnJAkve21l4x45IA2F2YPgoAN7LmI4BVdVJVXVJVn1nUfkxVfa6qLqiqE5KktfbZ1toTkzw6yb3XulYAAIB5MospoCcnOWa0oar2SPKyJMcmOSLJcVV1RL/vYUnenuSMtS0TAABgvqx5AGytnZXkikXNRyW5oLX2n621a5KckuTh/fNPb60dm+R/LXfMqtpcVVurauull146rdIBAAB2a+vlGsCDklw0sr0tyU9X1dFJHpXkZllhBLC1dmKSE5Nk48aNLtwAAABYwnoJgEtqrZ2Z5MwZlwEAADAX1sttIL6S5PYj2wf3bQAAAEzIegmA5yQ5vKoOraqbJnlsktNnXBMAAMBcmcVtIF6X5CNJ7lRV26rq8a21a5Mcn+RdST6b5LTW2nlrXRsATETVzj8AYIrW/BrA1tpxy7SfkV241UNVbUqy6bDDDtvZQwDA7LmBPQBTtF6mgO6y1tqW1trmDRs2zLoUAJgNI48A7MDcBEAAAABWJgACAAAMhAAIAAAwEAIgAADAQMxNAKyqTVV14vbt22ddCgAAwLo0NwHQKqAAAAArW/P7AAIA65D7DwIMwtyMAAIAALAyARAAAGAgTAEFAFitXZkym5g2C8zM3IwAWgUUAHZTVTv/AGBV5iYAWgUUAABgZXMTAAEAAFiZawABgGEyhRQYIAEQANh9CXEAq2IKKAAAwEAYAQQA2J3syqin20/A4BkBBAAAGIi5CYDuAwgAALCyuQmA7gMIAACwsrkJgAAAAKzMIjAAAOyYxWdgLgiAAABD4b6JMHimgAIAAAyEAAgAADAQAiAAAMBAuAYQAIDp2tVrDy0iAxMz1ghgVe1ZVTdb1PagqvqDqrr7dEpbHTeCBwAAWNm4U0BPTfKKhY2q+v0k70zy3CQfraqHTqG2VXEjeAAAgJWNGwDvmeSMke0/TvKC1to+Sf4xyTMmXRgAAACTNW4A3D/J15Kkqn48yQ8leWW/7/VJjph8aQAAAEzSuAHw4iSH9L8fk+TLrbUv9tv7JLluwnUBAAAwYeOuAvr6JM+rqp9M8htJ/n5k392SfGHShQEAADBZ4wbAE5J8I8lPpVsM5q9H9t0j3SIxAAAArGNjBcDW2rVJ/mKZfY+aaEUAAABMxbj3AfznqnpCVd152gUBAAAwHeNOAd0vyd8k2a+qrkjywSRn94+Pt9a+N53yAAAAmJSxRgBba5uSHJBuwZdnJ7k6yVOSfDTJlVX1nqlVCAAAwESMOwKY1lpL8qkkn6qqtyW5X5Lf7H/+3HTKAwAAYFLGCoBVddck9+0f90ty23Rh8Owk/7f/OVNVtSnJpsMOO2zWpQAAAKxL1Q3s7eBJVdcl+U6S1yTZkuQjrbVvTLm2nbJx48a2devWWZdxY1WzrmDtjXFuLUt/rY7+Wh39tTr6a/WG1mf6a/X8N7k6u3qOwcBU1bmttY1L7Rt3CuipSe6T5IlJ7pXk7Ko6K8lZrbXLJlMmAAAA0zTuIjDHtdZun+TwJC9Ncst0q4JeXFX/UVWvmGKNAAAATMDYi8AkSWvtS0m+VFXnJvl4kkenuybwTkl+Z/LlAQDALtiVKbOmnjKHxl0E5p65fhGYe6e7L+AVST6U5I+zDhaBAQAAYGXjjgB+OMlX0gW9ZyQ5u7V23tSqAgAAYOLGDYB3bK1dOM1CAAAAmK6xAuBC+KuqWyW5a5LbJ3lHa+3rVbV3kmtaa9dNrUoAAAB22VirgFbVnlX1t0m2JflAkn9Ocmi/+41Jnjmd8gAAAJiUsQJgkuckeUKS45PcMcnockpvTbJpwnUBAAAwYeNeA/irSU5orb2mqvZYtO+L6UIhAAAA69i4I4D7pQt6S7lpksWhEAAAgHVm3AD4mSQPX2bfseluCg8AAMA6Nu4U0L9K8saq2ifJ65O0JEdW1SOT/HaSh02pvrFV1aYkmw477LBZlwIAALAujTUC2Fp7a5JfSfLzSd6RbhGYf0zy60n+d2vtXdMqcFyttS2ttc0bNmyYdSkAAADr0rgjgGmtnZbktKr60SQHJLkiyedaa21axQEAwMxU7fg5y/EnMuvU2AFwQWvt80k+P4VaAAAAmKJlA2BV/W6S17fWLu1/X0lrrb1isqUBAAAwSSuNAP59kq1JLu1/X0lLIgACAACsY8sGwNbaTZb6HQAAgN3TWMGuqm4x7UIAAACYrnFH9i6pqlOr6pFVdbOpVgQAAMBUjBsAn5rkB5O8IV0Y/Oeq+oWqWvUqogAAAMzGuDeCf1lr7f5Jbp/kmUl+JMmWJBdX1aur6oFTrBEAAIAJWNXiLq21/26tvbi19jNJDkny3CTHJHnHFGoDAABggnZqCmdVHZbkMf3jdkkummRRAAAATN7YI4BV9cNV9dSqOjfJ55Icn+TMJPdtrf3wlOoDAABgQsYaAayqf0tyjyRXJHlTkj9K8oHW2nVTrA0AAIAJGncK6HlJ/jzJe1pr35tiPQAAAEzJWAGwtfYb0y4EAACA6VrNNYA/0d8M/otVdXVV3b1vf05VHTu9EgEAAJiEsQJgH/DOTXcz+P+XZK+R3Vcn+b3JlwYAAMAkjTsC+NwkJ/c3g3/Oon2fSHLkBGsCAABgCsYNgHdOcmr/e1u07xtJbj2xigAAAJiKcQPgJUnuuMy+H0vyX5MpZ+dV1aaqOnH79u2zLgUAAGBdGjcAnpLkL6rqPiNtrap+NMnTkrx24pWtUmttS2tt84YNG2ZdCgAAwLo07n0A/yzJEUk+kORrfdtb0y0K8+4kfz350gAAAJikce8DeHWSh1bVA5I8IMkBSa5I8t7W2numWB8AAAATssMAWFV7J/lUkt9vrb0zyXunXhUAAAATt8NrAFtrVyXZL8l1U68GAACAqRl3EZjXJvmNaRYCAABzo2rnHzBF4y4C819JHl1V5yR5R5KLc8P7AbbW2ismXRwAAACTM24AfEH/83ZJ7rHE/pZEAAQAAFjHxl0FdNypogAAAKxTgh0AAMBACIAAAAADIQACAAAMhAAIAAAwEMsGwKq6Q1XttZbFAAAAMD0rjQB+KcndkqSq3ldVd16bkgAAAJiGlQLgd5LcvP/96CQ/MPVqAAAAmJqV7gP470leUlXv6bd/r6q+usxzW2vtaZMtDQAAgElaKQA+Icnzkzw8SUvygCRXL/PclkQABAAAWMeWDYCttfOTbEqSqrouySNaa/+2VoUBAAAwWSuNAI46NMly0z8BAADYDYwVAFtrX66qPavqMUnuk+TWSa5IcnaSN7XWrp1ijQAAAEzAWAGwqm6T5N1JfiLJhUkuTnKvJE9K8smqelBr7dJpFQkAAMCuW+k2EKNemGT/JPdsrd2xtXav1todk/x03/7CaRUIAADAZIwbAB+S5GmLF4FprZ2T5OlJfmHShQEAADBZ4wbAmyX55jL7vpnkppMpBwAAgGkZNwB+NMnTquoWo4399tP6/QAAAKxj494G4ilJ3p/koqp6d7pFYG6T5MFJKsnRU6kOAACAiRlrBLC19okkhyc5McmBSR6YLgC+MsnhrbVPTqtAAAAAJmPcEcC01i5LcsIUawEAAGCKxr0GEAAAgN2cAAgAADAQY08BnZWqekS6+wz+QJJXt9bePduKAAAAdk8zGQGsqpOq6pKq+syi9mOq6nNVdUFVnZAkrbW3tNaekOSJSR4zi3oBAGDNVO3aA1awwwBYVTerqmdU1U9O8H1PTnLMovfZI8nLkhyb5Igkx1XVESNP+dN+PwAAADthhwGwtXZ1kmck2W9Sb9paOyvJFYuaj0pyQWvtP1tr1yQ5JcnDq/O8JO9orX18qeNV1eaq2lpVWy+99NJJlQkAADBXxp0C+rEkd59mIUkOSnLRyPa2vu33kvx8kl+qqicu9cLW2omttY2ttY0HHnjglMsEAADYPY27CMxTk/xLVX03yRlJLk7SRp/QWvv2hGtbOO5Lk7x0GscGAAAYknED4Mf6ny9N8pJlnrPHLtbylSS3H9k+uG8DAABgAsYNgL+ZRSN+U3BOksOr6tB0we+xSX5lyu8JAAAwGGMFwNbayZN806p6XZKjkxxQVduSPLO19uqqOj7Ju9KNJp7UWjtvku8LAAAwZKu6EXx/W4Z7pJuqeVJr7WtVdViSi1tr3xz3OK2145ZpPyPdNYarVlWbkmw67LDDdublAAAAc2+sVUCr6pZVdVqSzyT5xyR/meSH+t1/neSZ0ylvfK21La21zRs2bJh1KQAAAOvSuLeBeGGSn0nygCT7JqmRfWdk0U3dAQAAWH/GnQL6qCRPbq29v6oWr/b55SQ/PNmyAAAAmLRxRwD3SXL5Mvv2TfK9yZQDAADAtIwbAM9J8qvL7PulJB+eTDkAAABMy7hTQP8syXuq6l+TvD7dPQEfUlV/mC4A3m9K9Y3NKqAAAAArG2sEsLV2droFYG6W5O/TLQLz7CR3TPLzrbVzplbhmKwCCgAAsLKx7wPYWvtQkvtW1T5JbpXkytbat6dWGQAAABM17jWAo65K8t0k35lwLQAAAEzR2AGwqh5SVR9OFwC/luSqqvpwVf3C1KoDAABgYsYKgFX120m2JPlWkicn+eX+57eSnN7vBwAAYB0b9xrAP0nyqtba7y5qf2VVvTLJM5K8aqKVrZJVQAEAAFY27hTQ/ZO8eZl9b0xy68mUs/OsAgoAALCycQPg+5Pcf5l9909y1mTKAQAAYFqWnQJaVUeMbL40yT9W1f5J3pLkkiS3SfLIJMcm+a0p1ggAAMAErHQN4GeStJHtSvLb/aP12wvemWSPiVcHAADAxKwUAH92zaoAAABg6pYNgK21D6xlIQAAAEzXuLeB+L6q2jPJTRe3t9a+PZGKAAAAmIpxbwS/oapeXlVfTXJVkm8u8ZipqtpUVSdu37591qUAAACsS+OOAJ6c7nYP/5DkgiTXTKugndVa25Jky8aNG58w61oAAADWo3ED4AOS/HZr7XXTLAYAAIDpGfdG8P+VxDV+AAAAu7FxA+BTk/xpVd1hmsUAAAAwPWNNAW2tnVFVP5/kgqq6MMmVSzznqMmWBgAAwCSNFQCr6u+S/EGSc7JOF4EBAAB2UdXOv7a1ydXB1Iy7CMxvJXlGa+250ywGAACA6Rn3GsBvJzl3moUAAAAwXeMGwJck2Vy1K2PC0+VG8AAAACsbdwroAUl+OsnnqurM3HgRmNZae9oE61o1N4IHAABY2bgB8JeSXJtkryQPXGJ/SzLTAAgAAMDKxr0NxKHTLgQAAIDpGvcaQAAAAHZz494H8Hd39JzW2st3vRwAAACmZdxrAP9+hX0Ld3wUAAEAANaxsaaAttZusviR5NZJjkvyySRHTLNIAAAAdt24I4A30lq7MsmpVbUhyauSHD2hmgAAAJiCSSwC86UkGydwHAAAAKZolwJgVd0uyVPShUAAAADWsXFXAb001y/2suCmSfZNclWSR024LgAAACZs3GsAX5YbB8CrkmxL8s7W2uUTrWonVNWmJJsOO+ywWZcCAACwLlVri3Pd7m3jxo1t69atsy7jxqpmXcHa25VzS3+tjv5aHf21Ovpr9YbWZ/pr9fw3uTr6a3Vm1V9zlit2Z1V1bmttyXVaJrEIDAAAALuBZaeAVtX7VnGc1lp7wATqAQAAYEpWugZwnOv6bpfkZ3Lj6wMBAABYZ5YNgK21X15uX1XdIcnTkjw0yWVJXjT50gAAAJikcVcBTZJU1WFJnp7kcUku6X9/VWvtO1OoDQAAgAka9z6AP5bkGUl+OclFSZ6c5KTW2jVTrA0AAIAJWnEV0Kq6R1W9Kcmnktw9yW8lOby19krhDwAAYPey0iqg70jyoCSfTvLY1trr16wqAAAAJm6lKaAP7n8enORlVfWylQ7UWrvNxKoCAABg4lYKgM9esyoAAACYupVuAyEAAgAAzJEVF4EBAABgfsxNAKyqTVV14vbt22ddCgAAwLo0NwGwtbaltbZ5w4YNsy4FAABgXZqbAAgAAMDKBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgdhz1gUAAABzoGrnX9va5OpgRUYAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgZibAFhVm6rqxO3bt8+6FAAAgHVpbgJga21La23zhg0bZl0KAADAujQ3ARAAAICVCYAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAA7GuA2BV3bGqXl1Vb5h1LQAAALu7NQ+AVXVSVV1SVZ9Z1H5MVX2uqi6oqhOSpLX2n621x691jQAAAPNoFiOAJyc5ZrShqvZI8rIkxyY5IslxVXXE2pcGAAAwv9Y8ALbWzkpyxaLmo5Jc0I/4XZPklCQPH/eYVbW5qrZW1dZLL710gtUCAADMj/VyDeBBSS4a2d6W5KCq2r+qXpnkblX19OVe3Fo7sbW2sbW28cADD5x2rQAAALulPWddwEpaa5cneeKs6wAAAJgH62UE8CtJbj+yfXDfBgAAwISslwB4TpLDq+rQqrppkscmOX3GNQEAAMyVWdwG4nVJPpLkTlW1raoe31q7NsnxSd6V5LNJTmutnbfWtQEAAMyzNb8GsLV23DLtZyQ5Y2ePW1Wbkmw67LDDdvYQAAAAc229TAHdZa21La21zRs2bJh1KQAAAOvS3ARAAAAAViYAAgAADIQACAAAMBBzEwCralNVnbh9+/ZZlwIAALAuzU0AtAgMAADAyuYmAAIAALAyARAAAGAgBEAAAICBEAABAAAGQgAEAAAYiLkJgG4DAQAAu6mqXXswtrkJgG4DAQAAsLK5CYAAAACsTAAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGIi5CYDuAwgAALCyuQmA7gMIAACwsrkJgAAAAKxMAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIOYmALoRPAAADFTVzj8GZm4CoBvBAwAArGxuAiAAAAArEwABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZibgJgVW2qqhO3b98+61IAAADWpbkJgK21La21zRs2bJh1KQAAAOvS3ARAAAAAViYAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQMxNAKyqTVV14vbt22ddCgAAwLo0NwGwtbaltbZ5w4YNsy4FAABgXZqbAAgAAMDKBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABmJuAmBVbaqqE7dv3z7rUgAAANaluQmArbUtrbXNGzZsmHUpAAAA69LcBEAAAABWJgACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADMSesy5gJVV1iyQvT3JNkjNba6+dcUkAAAC7rTUfAayqk6rqkqr6zKL2Y6rqc1V1QVWd0Dc/KskbWmtPSPKwta4VAABgnsxiCujJSY4ZbaiqPZK8LMmxSY5IclxVHZHk4CQX9U/73hrWCAAAMHfWPAC21s5KcsWi5qOSXNBa+8/W2jVJTkny8CTb0oXAZIVaq2pzVW2tqq2XXnrpNMoGAADmUdXOP3ZD62URmINy/Uhf0gW/g5K8KckvVtUrkmxZ7sWttRNbaxtbaxsPPPDA6VYKAACwm1rXi8C01v4nyW/Mug4AAIB5sF5GAL+S5PYj2wf3bQAAAEzIegmA5yQ5vKoOraqbJnlsktNnXBMAAMBcmcVtIF6X5CNJ7lRV26rq8a21a5Mcn+RdST6b5LTW2nmrPO6mqjpx+/btky8aAABgDlRrbdY1TNTGjRvb1q1bZ13Gje2mqwTtkl05t/TX6uiv1dFfq6O/Vm9ofaa/Vs9/k6ujv1ZHf62ddZqlqurc1trGpfatlymgAAAATJkACAAAMBACIAAAwEAIgAAAAAMxNwHQKqAAAAArm5sA2Frb0lrbvGHDhlmXAgAAsC7NTQAEAABgZQIgAADAQAiAAAAAAyEAAgAADIQACAAAMBBzEwDdBgIAAGBlcxMA3QYCAABgZXMTAAEAAFiZAAgAADAQAiAAAMBACIAAAAADIQACAAAMRLXWZl3DRFXVpUm+PKHDHZDksgkdi9XR97Oj72dH38+W/p8dfT87+n529P3sDKHvf7i1duBSO+YuAE5SVW1trW2cdR1DpO9nR9/Pjr6fLf0/O/p+dvT97Oj72Rl635sCCgAAMBACIAAAwEAIgCs7cdYFDJi+nx19Pzv6frb0/+zo+9nR97Oj72dn0H3vGkAAAICBMAIIAAAwEAIgAADAQAiAy6iqY6rqc1V1QVWdMOt6hqSqLqyqT1fVJ6pq66zrmWdVdVJVXVJVnxlpu3VVvaeqvtD/vNUsa5xXy/T9s6rqK/25/4mqesgsa5xXVXX7qnp/Vf1HVZ1XVU/u2537U7ZC3zv3p6yq9q6qf6uqT/Z9/+y+/dCq+lj/986pVXXTWdc6b1bo+5Or6ksj5/2RMy51blXVHlX171X1tn570Oe9ALiEqtojycuSHJvkiCTHVdURs61qcH62tXbkkO/RskZOTnLMorYTkry3tXZ4kvf220zeyblx3yfJi/pz/8jW2hlrXNNQXJvkKa21I5LcM8mT+v+Nd+5P33J9nzj3p+3qJD/XWvvJJEcmOaaq7pnkeen6/rAkX0/y+NmVOLeW6/sk+eOR8/4TsypwAJ6c5LMj24M+7wXApR2V5ILW2n+21q5JckqSh8+4Jpi41tpZSa5Y1PzwJP/U//5PSR6xljUNxTJ9zxporX21tfbx/vdvpvuj4KA496duhb5nylrnW/3mXv2jJfm5JG/o2533U7BC37MGqurgJL+Q5B/77crAz3sBcGkHJbloZHtb/B/UWmpJ3l1V51bV5lkXM0C3ba19tf/9a0luO8tiBuj4qvpUP0XUFMQpq6pDktwtycfi3F9Ti/o+ce5PXT8N7hNJLknyniRfTHJla+3a/in+3pmSxX3fWls475/Tn/cvqqqbza7CufbiJE9Ncl2/vX8Gft4LgKxH92mt3T3dFNwnVdX9Zl3QULXuPjH+lXLtvCLJj6SbIvTVJC+YaTVzrqpumeSNSf6gtfaN0X3O/elaou+d+2ugtfa91tqRSQ5ON9vpzrOtaDgW931V3TXJ09N9Bz+V5NZJnja7CudTVT00ySWttXNnXct6IgAu7StJbj+yfXDfxhporX2l/3lJkjen+z8p1s7FVXW7JOl/XjLjegajtXZx/0fCdUn+Ic79qamqvdIFkNe21t7UNzv318BSfe/cX1uttSuTvD/JvZLsV1V79rv8vTNlI31/TD8lurXWrk7ymjjvp+HeSR5WVRemu6Tr55K8JAM/7wXApZ2T5PB+haCbJnlsktNnXNMgVNUtqmrfhd+TPCjJZ1Z+FRN2epJf63//tSRvnWEtg7IQPnqPjHN/KvrrP16d5LOttReO7HLuT9lyfe/cn76qOrCq9ut/3yfJA9Ndg/n+JL/UP815PwXL9P35I//gVOmuQXPeT1hr7emttYNba4ek+3v+fa21/5WBn/fVzXJhsX4J6hcn2SPJSa2158y2omGoqjumG/VLkj2T/Iu+n56qel2So5MckOTiJM9M8pYkpyW5Q5IvJ3l0a81iJRO2TN8fnW4KXEtyYZLfHrkmjQmpqvskOTvJp3P9NSF/ku5aNOf+FK3Q98fFuT9VVfUT6Ra72CPdAMBprbW/6P9/95R0UxD/Pcnj+hEpJmSFvn9fkgOTVJJPJHniyGIxTFhVHZ3kj1prDx36eS8AAgAADIQpoAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACMHFV9ayqalX1riX2vaGqzlzDWo7ua7nrWr3nalTVXarq7Kr6n77OQ2Zd01Kq6pZ9fb8+61oA2HkCIADT9KCq+qlZF7HOPT/JfkkeluReSdz/DoCpEQABmJYr0t3w+xmzLmSaqmrvXTzEnZO8p7X23tbaR2d5M+Lq7OrnAWAdEwABmJaW5DlJHlZVP77ck/rpopct0d6q6viR7Qur6u+q6oSq+mpVba+qF/Sh5SFVdV5VfbOq3lJVt1rirX6oqt7WT7X8r6p64hLved+q+kBVfbuqLq+qf6iqfUf2/3pf11FVdWZVfSfJH6/w2Y6sqvf2x/t6Vb22qm7b7zukqlqSH0nyh/1xz1zmOP9UVe8e2b5T//w3jbTdo287fKTt+Kr6QlVdXVUXVNUfLjrus6rqsqq6T1Wdk+SqJL/c7/vFqvp8VX2nqs5KF1QX1/Wwqjq379OvV9XHqur+y/UHALMnAAIwTa9P8oVMbhTwsUmOSvIbSf42yf9J8sIkf5nkz5I8Mcn9kzx3ide+OsmnkjwqyRlJXlFVD13YWVX3TvKvSb6W5JeS/EGShyR5zRLHel2SLf3+ty1VaFUdmOTMJDdP8itJfq+v7T1VddN0Uz3v1b/fv/S//+4yn/vsJPeqqj367fulC2v3GXnO/ZJc3Fr7Qv/+T0jyf5OcnmRTuu/iBVV1wqJj3zzJPyX5xyTHJPm3qrp7klOTfDJdf21Jctqiz/cjSd6Q5H398f9X3xe3XuYzALAO7DnrAgCYX62166rquUleXVV/3lr7/C4e8qokv9xa+16Sd1bVw9MFq8Nba19Kkqr6ySS/li4MjnpHa+1P+t/f1QeYP831Ae5vkny4tfaYhRdU1VeSvLeq7tpa+8zIsV7aWnvJDmp9Sv/zwa21b/TH+0KSjyb5xdba65J8tKquTvLV1tpHVzjW2UlumeRuSbYmuW+60Pb4qrpza+38vu3s/n1ukuRZSU5urS3U8e6q2pDk6VX14tbaVX37Pkn+T2vtrSOf+7Qkn0/y6NZaS/KOPrT+1UhNd0vyzdba6AjoGTvoEwBmzAggANP2/yX5ryRPn8CxzuzD34ILkly4EP5G2g7sA8uoNy/aflOSe1TVHlV183QjcKdV1Z4LjyQfTPLdJPdY9Nq3j1HrUUnevRD+kqS19rEkF+aGI3c71Fr7XJJL0oW8pBvte0eSj4+03Sd9AExycJIfSjfqN+rUJD+QZHRKbuuPtbj20/vwt+BNi57z6SQb+umpD6qqW6zmMwEwGwIgAFPVWrs23XTNx1XVD+/i4a5ctH3NMm2VZHEAvGSJ7T2THJDkVkn2SPLydIFv4XF1kr2S3H7Ray8eo9bbLfO8i7Nz0yTPTnLfqrp9kjukC6cLbXdJcmCuD4C3W6bOhe3R9/96a+2aRc/7wSzdX9/Xh9KHJ7ljupG/y6rqX/qprwCsUwIgAGvhpHQB4mlL7Lsqi8LaMou47KrbLLF9bZLL0oXIluSZSX5qicdJi17bsmNfXeI9k+S26VZIXa2z043y3S/Jf7TWLu/b7tu3fSPdNXsL750l3v+2/c/R91/qs3xtidfe6LO01t7eWrtvkv2TPD7Jz6e77hCAdUoABGDq+lsb/F2S38z1o1MLtiXZt6oOGml70BTKeOQS2+e21r7XWvufdNfm3am1tnWJx3/vxPt9LMmDF60i+lNJDkk3erdaZ6Ub5dvc/550AfCH0y0y8+HW2nV9+7Yk/51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" ] @@ -506,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": { "pycharm": { "is_executing": false @@ -540,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": { "pycharm": { "is_executing": false @@ -573,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 19, "metadata": { "pycharm": { "is_executing": false @@ -592,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 20, "metadata": { "pycharm": { "is_executing": false @@ -622,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 21, "metadata": { "pycharm": { "is_executing": false @@ -644,29 +668,13 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 22, "metadata": { "pycharm": { "is_executing": false } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/user/tf2/lib/python3.6/site-packages/jpype/_core.py:210: UserWarning: \n", - "-------------------------------------------------------------------------------\n", - "Deprecated: convertStrings was not specified when starting the JVM. The default\n", - "behavior in JPype will be False starting in JPype 0.8. The recommended setting\n", - "for new code is convertStrings=False. The legacy value of True was assumed for\n", - "this session. If you are a user of an application that reported this warning,\n", - "please file a ticket with the developer.\n", - "-------------------------------------------------------------------------------\n", - "\n", - " \"\"\")\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -683,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 23, "metadata": { "pycharm": { "is_executing": false @@ -706,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 24, "metadata": { "pycharm": { "is_executing": false @@ -719,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 25, "metadata": { "pycharm": { "is_executing": false @@ -751,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 26, "metadata": { "pycharm": { "is_executing": false @@ -773,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "pycharm": { "is_executing": true @@ -795,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "pycharm": { "is_executing": true @@ -814,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "pycharm": { "is_executing": true @@ -833,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "pycharm": { "is_executing": true @@ -868,6 +876,13 @@ "# 데이터 사전을 json 형태로 저장\n", "json.dump(data_configs, open(DATA_IN_PATH + DATA_CONFIGS, 'w'), ensure_ascii=False)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -886,7 +901,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.12" }, "pycharm": { "stem_cell": {

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