{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/train.csv',\n                       low_memory=False, \n                       nrows=10**6)\nlectures = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/lectures.csv')\nquestions = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/questions.csv')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# step1 熟悉数据\n在建立模型之前，最重要的是去熟悉数据，熟悉数据有几个目的：\n1. 了解数据的含义，以根据经验初步筛选你模型中需要用到的数据列和不需要的数据列。\n    \n    本题的数据含义见我的熟悉数据notebook：https://www.kaggle.com/zcatherine/riiid\n   \n   \n2. 思考数据间的关系，以便选取合适的模型和参数。\n   \n   比如：本题是一个二分类的概率预测，我用MLP写了一个简单的模型，那么这种问题的优化器通常会选择RMSProp，激活函数选择sigmoid的组合比较合适；有的特征单独看和结果没什么关系，但是两个或多个特征组合在一起会对结果产生较大的影响(e.g. XOR)，那么就要在模型中加入非线性结构。\n   \n   \n3. 对数据进行处理，包括填补nan和把一些列处理成你想要的特征。\n   \n   关于nan的是否填补，填补为哪些值（平均数？众数？0？INF？pre预测结果？）也是一个需要思考的问题。就好像本题的训练集中，上一个题目是否有解释这一列有true、false、nan三种值，我一开始就不确定要不要把nan填补成false，但是后来考虑到这些nan代表着第一个题目，或许也有它的意义，然后写出本文中的feature3之后发现它和false关于作答是否正确的平均值和方差还是有区别，所以最终还是没有去填补它。"},{"metadata":{},"cell_type":"markdown","source":"# step2 选择模型\n熟悉好数据之后就要做特征选择（处理）和选择模型了，本题目前最好用的模型应当是讨论区提到的SANT+：http://arxiv.org/abs/2010.12042\n\n但是不走科研新成果捷径的话（毕竟新手有时候没办法看懂新论文后就立马能写出来那个代码），新手去挑选目前已学习到的各种模型（lstm\\RNN\\RandomForest\\NLP\\MLP...），应当以什么作为挑选准则呢？\n\n（个人认为特征处理和模型选择是分不开的，所以我把它们放到一起考虑）\n\n我也在这样的一个摸索阶段。\n\n有人说，这些模型都差不多，最重要的还是数据清洗、特征选择和特征处理。\n\n但是我认为，模型的内部结构决定了能够造出来的模型的几何形状的空间，起码这一点还是要明确（也就是说，比如你去拿一个分类模型去做一个线性的预测，一个几何空间是离散值，另一个是线，根本就对不上；或者就好比一个最多产生二次函数的模型拿去预测三维空间的值，就不能适用。非数学专业，描述不太准确，全靠感觉，实在抱歉，欢迎指正）。\n\n我的思考过程：\n\n这个题目学生做题正确率确实是个监督学习下的二分类的题目，但是学生做题水平会随着时间提高，也要考虑时间序列的因素（LSTM,RNN啥的），我个人才疏学浅，目前也只是学了一些模型的原理，还没怎么写过代码，所以就拿那个可以用非线性面分割空间，模型简单又有现成的接口可以直接用的MLP开刀吧。"},{"metadata":{},"cell_type":"markdown","source":"# step3 特征处理\n对于lecture文件，我认为有意义的东西包括lecture对应的tag跟part，question也是一样。tag的含义是知识点，那么一定是预测学生是否能做对题目（基本等同于是否掌握知识点）的最重要的信息，我需要要把处理的重心放在这里，但它可能也是最难的部分？tag跟part似乎要结合在一起更有意义，但是应该怎么实现呢？\n\n我去翻找了其他人对tag做的工作，发现了一个想法相投的伙计https://www.kaggle.com/yanamal/questions-tags-and-lectures-riiid-metadata-eda\n还发现了一个做了好看的聚类分析的大兄弟：https://www.kaggle.com/spacelx/2020-r3id-clustering-question-tags\n\nYana Malysheva的工作非常有趣，他得出了一些有指导意义的结论，说明tags在不同part的划分具有意义，包括：\n* part5和part6会问一些相似问题（具有相同tag）；\n* part1-4包含相同的tag（和听力理解有关）；\n* 1-4与7只考察综合知识（多tag），5只考察某个知识点（单tag）；\n* tag162使得part6区分于part5；\n* part3-4的每个问题都会包含[74, 82, 161, 106, 136, 157, 113]其中的一个\n\n此外，他还探索了tag的含义，比如那些没出现在讲座里的标签，可能是虚的内容（比如听力能力），并提出了关于part3-4中tag跟难度的关系猜想。\n\n-\n\n我想一定有人可以通过这些信息来将tag和part做出合适的好的处理，但是我最初的模型恐怕不适合体量太大，就先将tag和part分开做一下处理好了，另外，我认为一个知识点可以迁移运用，其实也不会那么拘泥于涉及的part。相同tag对不同part，区分更多的可能是一些解题技巧。\n\n最终，我想对L&Q的tag做的处理是，计算出一个用户是否学习过question涉及的tag，对于包含多个tag的question，计算学过的tag所占的比率（这个特征确实不太好，我还在思考要怎么去做改进，比如是否要去掉那些没有单独出现或没有在lecture出现的tag，或者再进一步，把那些看过解释的问题的tag也加入进来）\n\n我的第一个处理步骤是，提取用户从lecture中学到的tags："},{"metadata":{"trusted":true},"cell_type":"code","source":"# 准备用户看过的lectures具有的tag的表user_lec\ndef add_user_lec(data,lectures):\n    # 从数据中分出lecture行，选择需要的信息并对id重命名以便merge\n    data = data[data['content_type_id'] == 1]\n    data = data[['user_id','content_id']].copy()\n    data.rename(columns = {\"content_id\": \"lecture_id\"}, inplace = True)\n\n    data = data.merge(lectures,'left')\n    \n    # 构造空的表来存储用户学的tags\n    col = ['user_id','lec_tag']\n    user_lec = pd.DataFrame(columns = col)\n    \n    # 将不同用户看过的tag以list形式存起来\n    user_id = data['user_id'].unique()\n    idx = 0\n    for uid in user_id:\n        tmptdf = data[data[col[0]] == uid]\n        tlist = tmptdf['tag'].tolist()\n        user_lec.loc[idx] = {col[0]:uid, col[1]:tlist}\n        idx += 1\n    \n    return user_lec\n\nuser_lec = add_user_lec(train_df,lectures)\nuser_lec.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"接下来是对question表的处理："},{"metadata":{"trusted":true},"cell_type":"code","source":"# 简化问题表（tags列含有nan）\ntqdf = questions[['question_id','part','tags']].copy()\ntqdf.fillna(0,inplace = True)\n# 将tags由string和int（填补的0）类型变为int类型的list\ntqdf['tags'] = tqdf['tags'].apply(lambda tgs:  [int(x) for x in str(tgs).split()])\ntqdf.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"接下来是根据我对数据的理解，提取一些有用的特征和分布，我认为影响一个*学生*对于一个*问题*是否能答对，除了看他是不是学过，还要看学生水平和问题难度。\n\n因此，我用一个学生的答题正确率和标准差来表征学生水平，问题难度同理。\n\n此外，学生是否看过上一个问题的解释也对学生是否能做对当前题目会有影响（我想到了高中做阅读的场景，做了一个题，学了几个单词或者学了个做题方法，下一个题用上了。或者同一篇阅读做一个题目就看一个答案可能有点过分）"},{"metadata":{"trusted":true},"cell_type":"code","source":"import string\n\n# 选取问题集，删去多余列,填补缺失答案（应当没有缺失，但是我又被这个scoring error给人整傻了还是加上吧）\ntdf = train_df[train_df['content_type_id'] == 0].copy()\ntdf = tdf.drop(['content_type_id','row_id'],1)\ntdf['answered_correctly'] = tdf['answered_correctly'].apply(lambda x: 0 if str(x) == 'nan' else x)\n    \n# 构造新的features\nfeatures = tdf[['user_id','content_id','answered_correctly','prior_question_had_explanation']].copy()\nfeatures.rename(columns = {\"content_id\": \"question_id\"}, inplace=True)\n\n# 不同用户的做题正确率（表征用户水平）\nfeature1 = features.groupby('user_id').agg({'answered_correctly': [\n                                                'mean',\n                                                'std'\n                                            ]}).reset_index().copy()\nfeature1.columns = ['user_id','u_min','u_std']\n\n# 不同问题的做题正确率（表征题目难度）\nfeature2 = features.groupby('question_id').agg({'answered_correctly': [\n                                                'mean',\n                                                'std'\n                                                ]}).reset_index().copy()\nfeature2.columns = ['question_id','q_min','q_std']\n\n# 是否看过上个题目答案解释和做题正确率的关系分布\nfeatures['prior_question_had_explanation'] = features['prior_question_had_explanation'].astype(str)\nfeature3 = features.groupby('prior_question_had_explanation').agg({'answered_correctly': [\n                                                                    'mean',\n                                                                    'std'\n                                                                    ]}).reset_index().copy()\nfeature3.columns = ['prior_question_had_explanation','e_min','e_std']\n\n# 看一下nan和false的区别\nfeature3.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"上面准备好了处理过的lecture跟question表还有自己选取的几个特征，接下来就要拼接它们来构造训练集了。\n\nSANT+对于SANT的改进就是使用了ET和LT，这两个时间在SANT+的论文中有图片可以解释，这里的ET数据集本身就已经给出，实际上我是想处理出来一个LT跟一个用户加入平台的总时间的，但是这有点复杂，所以我就还没有实现，只先简单的用了一下给出的时间戳：\n\n![image.png](attachment:image.png)","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"# 这处耗费CPU\n# 找a和b的交集（不知道用set快还是这个快，但是肯定这个稳一点）\ndef csnm(a,b):\n    ans = 0\n    for i in a:\n        if i in b:\n            ans += 1\n    return ans\n\n# 将标签转化为学习过的比例（考虑改进，加入遇到过且看过解释的标签）\ndef trans_tags(data):\n    # 时间戳也加了进来，如果距离上一个题目间隔时间不过长（代表着不遗忘），越往后可能学的越多做的越好\n    cols = ['timestamp','user_id','content_id','task_container_id','prior_question_elapsed_time','prior_question_had_explanation']\n    data = data[cols].copy()\n    \n    # 防止explanation里的nan被fill\n    data['prior_question_had_explanation'] = data['prior_question_had_explanation'].astype(str)\n    data.fillna(0, inplace = True)\n    \n    # 方便merge\n    data.rename(columns = {\"content_id\": \"question_id\"}, inplace=True)\n    \n    # 合并fetures，选择left，保留data所有的内容\n    data = pd.merge(data,feature1,'left',sort = False)\n    data = pd.merge(data,feature2,'left',sort = False)\n    data = pd.merge(data,feature3,'left',sort = False)\n    \n    # 表征都用0.5填补nan\n    data.fillna(0.5, inplace = True)\n    \n    # 合并question和lecture\n    data = pd.merge(data,tqdf,'left',sort = False)\n    data = pd.merge(data,user_lec,'left',sort = False)\n    \n    # 将问题的tag的nan用[-1]填补，讲座的tag用[0]填补以区分它们\n    data['tags'] = data['tags'].apply(lambda x:  [-1] if str(x) == 'nan' else x)\n    data['lec_tag'] = data['lec_tag'].apply(lambda x: [0] if str(x) == 'nan' else x)\n    \n    data.fillna(0, inplace = True)\n    \n    # 计算学习过的比率，去除无用列\n    tlst = data.apply(lambda x: csnm(x['tags'],x['lec_tag'])/(len(x['tags'])+1e-3), axis = 1)\n    data = data.drop(['tags','lec_tag'],1)\n    data[\"learned_ratio\"] = tlst\n    \n    # 这里是因为发现nan跟true的结果接近，最后觉得还是保留做一下尝试\n    data['prior_question_had_explanation'] = data['prior_question_had_explanation'].apply(lambda x: 'True' if x == 'nan' or x == '<NA>' else x)\n    \n    return data\n\ntdf = trans_tags(tdf)\ntdf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tdf.describe()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# step4 数据scale\n在选择好特征之后，还有一些有用的列值没有经过处理，他们的数值量级比处理过的量级大得多，这会影响调参效率和结果，所以还要对这些列值做一个归一化、标准化之类的处理，另外part1-7也可以做一个onehot编码，考虑到后续test数据的不完整性，这里对part编码后保存encoder直接对后续test数据进行处理："},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder,MinMaxScaler,OneHotEncoder\nimport tensorflow as tf\n\n# 数据scale\ndef enc_scl(data):\n    # 类别编码\n    le = LabelEncoder()\n    le = le.fit(['True', 'False'])\n    data['prior_question_had_explanation'] = le.transform(data['prior_question_had_explanation'])\n    \n    # 使用MinMaxScaler归入区间[0,1]\n    sclcol = ['timestamp','task_container_id', 'prior_question_elapsed_time']\n    min_max_scaler = MinMaxScaler()\n    data[sclcol] = min_max_scaler.fit_transform(data[sclcol])\n                                                   \n    return data\n\ntdf = enc_scl(tdf)\n\n# onehot编码\nenc = OneHotEncoder()\n# 因为要输入二维向量所以要进行reshape\ntpart = tdf['part'].values.reshape(-1, 1)\n# fit并保存到one_hot_enc\none_hot_enc = enc.fit(tpart)\n# 转化后要进行toarray来获取trans之后的二维数组\ntpart = one_hot_enc.transform(tpart).toarray()\n# 构建新的dataframe保存结果\npartcol = ['1','2','3','4','5','6','7']\ntpart = pd.DataFrame(columns = partcol,data = tpart)\n# 去掉part列，合并新的dataframe\ntdf = tdf.drop('part',1)\ntdf = tdf.join(tpart)\n\n# 构造训练集\ntrain_x = tdf.drop(['user_id','question_id'],1)\ntrain_y = train_df['answered_correctly'].copy()\ntrain_x.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# step5 构造模型\nstep2中说明过选择MLP模型的理由，这里就不再重复，如何使用keras写模型可以参考https://keras.io/guides/functional_api/\n\n首先还是构造一个序列模型，叠层的时候考虑到层数多了容易收敛过快+过拟合，这里就只构造了一个两层模型，节点数的选择确实很困难，我尝试了多次调整，但是差别不大，就随便选了一个数（感觉输入shape的2倍好用的）。\n\n至于激活函数的选择，一般情况下relu的表现都不错，二分类概率问题最后选用sigmoid，经过尝试发现这样确实是合适的组合。\n\n后面模型metrics中的AUC函数是从CSDN上找的，大家可以通过他的用户名搜到来源。\n\n模型的参数我进行了初始化，也是选取比较通用的he_normal, loss是二分类的交叉验证方法，优化器我试了一下自适应学习率的adadelta（ https://keras.io/zh/optimizers/#adadelta ），最终发现还是rmsprop效果好："},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Dense, Activation, Dropout, advanced_activations\nfrom keras import backend as K\nimport keras\n\nmodel = Sequential()\nmodel.add(Dense(36, input_shape=(18,)))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1, activation='sigmoid'))\n\n# AUC函数来源：AI_盲（CSDN）\n# PFA, prob false alert for binary classifier\ndef binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)):\n    y_pred = K.cast(y_pred >= threshold, 'float32')\n    # N = total number of negative labels\n    N = K.sum(1 - y_true)\n    # FP = total number of false alerts, alerts from the negative class labels\n    FP = K.sum(y_pred - y_pred * y_true)\n    return FP/N\n\n\n# P_TA prob true alerts for binary classifier\ndef binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)):\n    y_pred = K.cast(y_pred >= threshold, 'float32')\n    # P = total number of positive labels\n    P = K.sum(y_true)\n    # TP = total number of correct alerts, alerts from the positive class labels\n    TP = K.sum(y_pred * y_true)\n    return TP/P\n\n# AUC for a binary classifier\ndef auc(y_true, y_pred):\n    ptas = tf.stack([binary_PTA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)\n    pfas = tf.stack([binary_PFA(y_true,y_pred,k) for k in np.linspace(0, 1, 1000)],axis=0)\n    pfas = tf.concat([tf.ones((1,)) ,pfas],axis=0)\n    binSizes = -(pfas[1:]-pfas[:-1])\n    s = ptas*binSizes\n    return K.sum(s, axis=0)\n'''\nmodel.compile(optimizer='adadelta',\n              loss='binary_crossentropy',\n              metrics=[auc])\n'''\nkeras.initializers.he_normal(seed=None)\nmodel.compile(optimizer='rmsprop',\n              loss='binary_crossentropy',\n              metrics=[auc])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# step6 模型训练\n模型训练要考虑的参数：\n\nbatch_size：太大或者太小都会训练很慢，适中的batch_size可以提高训练速度（还记得mini-batch那一课么？），这里考虑到训练数据集有100w数据，就选择了4800（微调了一下）。\n\nepochs：    作为一个训练轮次，当然可以自适应，但是我不希望太多以致太慢，也不希望太少看不出效果，影响我的效率，于是设置为30。\n\n其他参数不做过多解释，但是在文档中都可以找到具体含义，新手就是要多学习文档。"},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(train_x, train_y, batch_size=4800, epochs=30, verbose=1,validation_split=0.2,\n          shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# step7 模型预测与结果提交\n题外话：终于训练好一个模型之后就要开始预测和提交模型啦，这里的env卡了我好久，就是之前直接写在另一个notebook里的模型一直过不去卡了我30多version，直到我开始写这份notebook用阉割版模型一点一点加上来才拿到了分数，但是明明两个notebook没有什么区别，我的这个模型训练结果是0.5几，另一个一直scoring error的模型结果是0.7几，我还没找到差在哪了，还是挺懵的。还有就是这个模型有时候改一点点突然就跑得很慢，我也没弄明白到底是为什么。\n\n------------ 另一个notebook用了.copy()之后也跑出分了[doge]，得分0.692，但我还是找不出两个notebook的差别在哪，哭泣 ------------\n![image.png](attachment:image.png)\n\n\n预测的时候也还是要处理test集，就像处理train集一样","attachments":{"image.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA9EAAAB6CAYAAAC4A9DDAAAgAElEQVR4Ae29+3NWRdb2//wbb8236vtOzQ9WTdXUODWWU5SPpcUMBQ8jygijojAoCoIIBkFOEwkHo2AUD4EoGGA4BMMpmHAIIDz/Wr/16bhuVzq9773vYw5cP6R637t7d69effXqdfXqvfNf/+c3/3/Qn3QgDAgDwoAwIAwIA8KAMCAMCAPCgDAgDJRj4L+kpHIlSUfSkTAgDAgDwoAwIAwIA8KAMCAMCAPCABgQiVYkXicRhAFhQBgQBoQBYUAYEAaEAWFAGBAGKmJAJLqiorTrpF0nYUAYEAaEAWFAGBAGhAFhQBgQBoQBkWiRaO04CQPCgDAgDAgDwoAwIAwIA8KAMCAMVMTAnCTRg4MnwoWLlzSIFQdRu2HaDRMGhAFhQBgQBoQBYUAYEAaEAWGgOxjIkui/LlkWrt+4Ef73f/932h/3yOv04LRCon/7uyfCsc8HwuTkvSj7rVu3w0e7906Tmd/cp3+U+/hAX+C5XL927todRkdHw7PPLY75r61ZG0avXAmPHj0KDx8+DMPDZ8Pivy2tPfvCipVhZGSkln/q9A+1Z3P16153gC49S8/CgDAgDAgDwoAwIAwIA8KAMNAODNQl0Qf6DtbIYTsaq1pHKyT64KEjYeLWrfDmW+9EYtzf/1kYn5gIq1a/GvtCym/uQ5wh0Ldv3w5vb3x3Rl/JP3vuXBj44njMg0hDoE8ODYXf/+HJAGEeGxsLx49/FfP/9Oe/hPPnL0RiTVnyr1+/EehP1b6rnCa2MCAMCAPCgDAgDAgDwoAwIAwIA3MXA02R6H++sibcvPlTuDQyEqOxRGRPnBwKTz29KJJFyOeRT/pjlJeILaR2+wc7phFJHw2GxO7b31vLh3RCVqmf54kW9/b21fLrAYrIryetyDo+Ph62be+Jz5Pym/vUs2z5ijA2djNb/7r1GyJJJqUsxHjfvt5Ijk0GT/gjQR8fn9bXw0f6I5F+fvGSSvJbvUrn7qTR2GhshAFhQBgQBoQBYUAYEAaEgccXA02TaIgvx6YhzFu3bg937tyJ0V3ABNGcnJwMO3buivkQasobGd20+b14nJp8ypNyvJr7/IaYUn7Le9t+jSaPj4eV/1jdMBFFFgj/ipdejs9Cmm+MjYVPPzsa/vjkU+HfvQciieaYdjoRiFafOXsuypDm8XvN6+visfe+vkPxWU/YLcp9//79WKYbx+BzMure4zu5NfYae2FAGBAGhAFhQBgQBoQBYaD9GKhLov070Q8ePKhFcz1ZtEE5dep0PMrMb4gnvy2PCO7Vq9dqx6K//mYwRpkhmpThGPTuPfvCq6+9EX/76C75EFyix5B1q7NKunbdm/Ho9qHDR6Y91/PhrnDv3tQ700TROdKd1mdHt/f3fjwjj2g2+kA/vBO96JnnYhnTy569+8OFCxfju9FHjw2IROsDaTMwlOJNv9tv3KRT6VQYEAaEAWFAGBAGhAFhoBMYqEuii96JNrJoR6QRzBNfvqzNby+wv8d1va9v+7qoI9eerzt3DfHmfWVIrJF1yvGuNMfLiUBbFD0S9G0fTJMXon3t2vV43DtXP/eIbl+5ejUMffd9jUQTkYegE8XmvWl02K0PshXJqfsyHsKAMCAMCAPCgDAgDAgDwoAwIAy0BwNtI9H1ItHPPPt8JJv2ga40Eg2ZfXHlqkA5BrZVEs0HvSDAtOMJNHXnSK0n+AYsiHG6EZB7Jzq+8/zLV8vtnehdH+2pEXI+dMbHxfROdHsAa+OjVPoUBoQBYUAYEAaEAWFAGBAGhIHZwEDTJJp3lu0L1xyz5jeEkU7wHvLdu3fD+9t7Iolt5p1oH6luJBLNO88Q6G8HZxJoZCPCjGz2PvbGTVvi+9j+2DZkmPem3928tUaGedY+QjY4eDJGmfnXVhcvjYTh4TOxXPp17qXL/h43D1IyPhsDrTZlYIQBYUAYEAaEAWFAGBAGhAFhQBhoHQN1SbR/J5prO5YMqeVDYPzmnWL+IK3Nfp07/V/OrUSiiT6ncvPbSDmR6akvh0/W/k/05wNfRFJsgOJDYXwZHFJs9yzd8M6mSLCL/k/06lfWhMuXf6z9n+j0/0hbPUpbB690KB0KA8KAMCAMCAPCgDAgDAgDwkC3MZAl0WVCNBIZLqtrruVDnCHQRNnnmmySRwZCGBAGhAFhQBgQBoQBYUAYEAaEgdnFgEi0vhytzQJhQBgQBoQBYUAYEAaEAWFAGBAGhIGKGBCJrqgo7fbM7m6P9C/9CwPCgDAgDAgDwoAwIAwIA8LAXMBAUyR6LgguGTSBhAFhQBgQBoQBYUAYEAaEAWFAGBAGuo0BkWhFonVsQxgQBoQBYUAYEAaEAWFAGBAGhAFhoCIGRKIrKqrbuxtqTztqwoAwIAwIA8KAMCAMCAPCgDAgDMw9DIhEi0Rrx0kYEAaEAWFAGBAGhAFhQBgQBoQBYaAiBkSiM4r665Jl8X9g+/8vrR2gubcDpDHRmNTDwLbtPeHBgwfx/8Hzv+frlVWesCQMCAPCgDAgDAgDwoAwUBUDlUj004v+O/z2d0/MihP6xyefCvxV7VA7yhmJPtB3sKvttkN21aHJLwxMx8CFi5eCSPR0nQgjjenDb8iwuaq1oTH9CW/S11zAAPOW+csfG6zM60bkYi2x56/fuBHwFRt5XmUX1jzQurCwxrOZ+VlIop96elF0FH76aTxGZVNjAan+aPfeMDZ2Mzx69KjQqdj10Z7w888/F+Yj9M5du8Po6Gh49rnF0SBR96Z3twQM1r1797KGjvwrV6+Ghw8fFjrIK156OSA/9TSiHJFoTYxG8KKycxsv3SbRy5avCOf+cz7aJmzj5cs/htWvrGnIBhVhCtt47POBMDl5Lzpzt27djnbYl1/7r7fCjbGxaJdxFI8f/yr8/g9PtqV9386f/vyXMDIyEvb3ftxQ3b29fbEPvq75cj0f1wbGHgyABfAINsBIVZ2vf3NDmJycrJEHSERuTS1ab0+dOj3t2ZS8LF3293Dq9A/h/v37WV+jqpwLqRzz/OMDfdPm+fYPdlQaM08UjfCR3r59O6xa/Wqtjno+VD0b0opsc2WM/vnKmjA+Pp71LavIiI7nG4muN6ZlfcbW//jj6LR5fPfu3YAeefa1NWvD6JUr0b7gkw8Pnw2L/7a0hjXKFOGNDW6PU7u+fv1GeH7xkml1lMk5W/nzcV0o01Ur60aVMYXvHT/+dVxbPJaQq8yHgp9+/c1gbU27NDISnynrUyfyC0n0hQsXw9Wr18KZM2ezxqKv71A0yvv29RY6aCgCQ8OkKtq5xyCfPXcuDHxxvDZZKIvBH/ru+5imu4Vbt30Q7ty5Ez797GhAmTnFUO/g4MnYdm7Bzz1j9xbihLC+KZ3bhE/j0/7x6SaJxu7gQNAmiwR/XGPjyGt1fA8eOhImbt0Kb771Tqyvv/+zMD4xUXOOzXaxOLEIUo7yPNdq2+nz727eGgnZyn+sbqhuFthGbXLa9mz9Nv0WrWedlotxxbl8edUrlXXO2LOZjBMNJlgXGyEArL9jY2Oh3jjXW28Za7++ex2hz2vXroVz5/4TIOE+b6FcQ4bR/8ZNWyr3D52zQfb+9p44z4980h/n8etr/1W5Dq8/fCn+7F49H8owXmRD2i2bydTNdD6TaAgrG2FsplbVWdmYltUzNU+vB/z9tCxrHAT65NBQtC8vrFgZ7QUbd1a2Ht6sjKUQdkgRa5vdm+up6Xe21oV6+kE2NvLZzGzEB2l13fAypWPKbwINo6NXwrr1G6aNcxUfKvo941NrGvg7f/5C9Lsa6Z+Xr5XrQhJNJ6kYUKQLLuSYSVwvAmGLKmScskXgQoEs0F6RLPQ8nzN0yEX0w0/QnAJ6enbGes2hzZUpujeXJ0SRzLrffvIlnS4MneLEd+s4N/Zp95594dXX3qgtDKkNtR1eIm/suueiyUXYI2Ln+5LayK1bt8cIC46W1YHzzCKDXeP0DhEYNja5pm2/ydmIbKljTnsQNdYLIp5pRAI9WJTBp35t4HQTMuX0Qr85sWT5bB40QkxMH62kRWtDujOODtCFtYVjiWOITnKnE8ryrR7Gnw3kteverNVteUUpYz94YqhW/o216+PGC1gpesbf5+RA6gP4fK6L1lsiSZB+P8b+WZwhcIgj5O934xofA3JadKoj3eyBPDJ3LPpGpA3/gjHlD6cQ3yiVHbLDXO/ZsXNGXlrWfhNlAS/mFFIvp/4YCytTNcW3gsRv2vxefLbMh6pnQ2iznbJV7YOVAy/oweu5/9OjMeADhsrG1OpJ7abdr5qmNt2e2/DOptrpTMbcnwJCtqnTBVOnOsAd+LMxrmp7aYOTIZBWa7csLRvTsuc5wVAUuUfvkGtsmNXj504Z3uwZS3s+3BV12MhGoT3baEpbrCeef3Aylr7aqQ0/ppyiAf9p8K5oXSiTJ103wDbt8VyuTq9XynDCDYLMmoJsQ0PfzZDNTghdvDQSGIsymSy/1XXD6iFNx5Tf2CTTsS9bxYdKfTpO6fgx8/V1+rqQRFvDOWPBhAR45hQweF98ebxmDHh2alG9GZ0cFuB6i+iZs/koTc7Q2WQGELTL4sUOiwc1BvbateuBaHkKOutXvTQHXitPfd4B5Bo5WGDtuTTfHBD6w7GFNN+iMugozeO36a4s/3GVrVW9dlJvkm3q/TPDddW50KrebL6SpgbX53X6mqOqbPqx8FpbkGyiwxs2bIw2E8eQkzeeeFvZshTn5ebNn2pRPO/oR6fsq6+jjUQH2CcIy+69++IrNJAxZMDRMxtUVTZsLNEHotEm4zPPPh9JbhqRYH5ZGVJ+W3v+Pk4+evh374EY0cDB5Lc5ODxHX3EccNyw+/z2DrWvrxPXZuPNJlsbrH+siUT+0TvRXu/sDw+fiX1Gbv7oPxvM5kCX5Vs7zaS0ZWMAsUcuW7Oq1EdEAkLAX24DoN56u2Tp8kiicfp5NiUOOGq2MUI+G+5+A6iKfM2WwZHD8fLz0J/qSHHq5xbjBoFGfhtTrrlnY9qsXDzn24acmKOc4q5KG9TlT8KU+VC+n6kNabdsVeT3ZZAd55ux4z66x6ZhQ/ldNqZWF+tLESm0MvVSxsHWMisH6QM/zH30hi3AJphsb298N/7euXN3lJUUfxB9U0dV22vtNZKWjWlZXcxJbLHNY/rFhmfuuTWvr4u6wf8mvwxvvg4IFPOoLEjmn2nlOsUPc5e5Yqc2sG3YJMaQPGwFfU8DiEXrQpls1Ev9zHEww9rJCWDkytXp7QJliOQa3qiDDUvWorJ2q+S3um5YG7kxxR8ieErfsfvodOv7+U3dnA8VZXMbw+CT+Vx1Y9hka0faFInGcYO8Dgx8GQeeI0cYAzvq4RfVHBBM8AiCK1dmANLyc4aOyAPvSeNA8TxOAcoz4AB0QMWuMIPnQWf1lqX1ZC57VvkLI3qpcVw44+gXgyrjavPfiD9p+s5OWT1+EwCyyUJgz+DoUR9RJRZO/jgq6zcCrWy9lGgkTtuhw78e1TZn6YOeD+Mx2dM/DEeHAB3QLxZdylg55MQhJJ+2qsqGE0G/sMEmI/aWDQM2V+1oLm3+z/IXa2UoW2ST431HLNMoZvoc7+qyGVF14aSPfkwbIZLWR8OGJzNsHkRH/tOjtX5Gx3FiorYm4pThMNiGAM+8uHJVjXCV5Vv7zaQR/yeG4vFP1sq+g4cbIhCbt2wNn/R/GscaHEPoGGfGu2y9BR84irwPSVk2SHDEwZnpjY0QHG/KQkJTXNXrcytjmo5BdMQmJmqkJsWbnzNGDPx7ymyCQYTQSz2Zq+TRNoECfCojYuDH465KPSnp5JkyH8r6mbMhPN+qbPTBz0OuqbNKfyjDXCHwwrW9UkI/+V02ptZGzre0vCopfUhJNGPFBtXyF16q9QUyiG1ALuY+RPTzgS+irWc+YAPIo82qtreKfGmZsjFNy6e/IWifHT0W5ynrFb6/3+CkPG1gUxlP5vGiZ56L/SrDm2+L8cRGmZ30eUXXreIJ+4TNYRzAEfPMNofTtZkxY31L8ZpbF4rk9ffT9TFGVCcm4mmXXJ20a2t17nQBuiaA6dto9rrVdcPazY0p8xcfiD6gY14dwZfxpw/q+VCc7sEuslHFiSDqgxfSf2u3W2lTJBrQ+l12hI0KHzwxY1HNAcE6h9EgYlwUScgZOiYqjhNH0qweDyyLgNtuts+z8mVpPZnLnlX+wiFfGsuFMZZmm2ZjPKOBP3O2RjqQgYV46qOMY3EzEltq0YmqMmLf2Mk9emwg1mfPYR+JGOLg7Nm7/xd7PLXwYtfKSHQV2SAJOBI4H9aupZAsjhwTcUQOrtMPzBTZZMYpda69g50+l1sfTI5Opbm1oco9CCKOAtF/PrRJtMV/bK4sv5X+oFc2vYmwMD6t6i0ed/zlPfxG11uwwweKiESY3g4f6a/hyJx9ZGylz1WeZdOK44/g1HDnN1ZSvHnZuMYJJK3SVqNlaJsxQ1dsMJiu8L0aqQsSZxse9hwy1/OhyC+yIdTRLtlMnkZTT5yRxaKG1FM2ptZWq3MgR6Kr3GMzCcKGDYAEsAGKzUWuKrbX5G80LRvTRutjo4ATAbnXC9hAZePAxqUMb9Y2/fdRYLvf6dQTZ6LnrG1+I2zf/qmNLCKmZifAnZer2fkJ9+F7EMx1qxu7Aj5zddIu9py2wZuV9bK067od60bRmFK34QN5683HnA/FPCeQit6wVWw4svlim2nt0kGVepoi0bkdEJTCANMJHDgDhE9t8E0wlJiC0fJIc4qlfpTld4A9sHBOfJt23QjYcuA1uWjL6rTUFl57zu5bajuW9Ac57L6lphcmhd3zKfdpvyz/cZWtVb12Um+S7fE6zs3iSzTKH81msxCnFSwwj7ETFp1lF5b35HCoOO5ndqZeSlSAzUeIiDlgVh7bTFt+J5/3YbGLtFtGoqvIRt3s1vs+0n66a48zRdSSo8omH6m31/4+i6I/durzcs81GolO62vmt9l4s8nUQQQjjURzsoB1iugUY+QjTjgF9JVjzDxblt+MnP4Zxv7770/VsNLI0Tf+vSU480ft/PtnZevtlve2hW++HaxF2+grTip1IiPP+/e1cbjRm80V3492XxPNYAPL5gpt0jYy0FaKUy9bzg9hLP3pglbkRT/MHfv3nhyLZ87lSEtRO/F48fj4jJN+Odl9X+vZENpqh2xFMle5b5t4R49+Hu0g9tWeKxtTK5eOtd2vmjL/za+zZ3KRaD6oZ5Fojw/mASc48ZVZL6ijiu21thpNy8a0rD76hm6tnMcjG4Dksy5ZPhtjpp8yvNkzvM7D2mVRYLvfjRQucuLk0IzNYTuCzxrN+oYsxnW8XLl1wecXXbM2Ms/t5Ja3Mbk603mKvbKAIW14DBW1WfV+K+uGtVE0ptgQ1gHmAWX9fKziQ1n9lvKRafToNz8sr9NpUySaSePP4tsE9cbMBM8BgTwmlj82YeV96hVr91G6fxeJXVoWwlxUhGc86KyOsrRI5rLnlL8wIpcax4U1jrlFr1NjjBEn8sMChJ1kx5T3nIww0S62isXPPjxFFBpnqspRJHtV5tvBmQSaus122Zd1aYOIAe+1kldGoqvIRnQrR3aRDVtsbdN/dG9HL03n7PbTf5x87DlEjTy+4Ioe9v97ingSNYX8rX/r7ZiPLSf6jrNG3RBRc9Ss7k6npl9PommT14lsTHG2/DvRhgnWLeQmH0yYc12W7/uEblv9OnesI/kft8hgDh199G3iYCIr44H8YDuNblr5dL2FoLJBxBFyxnrHzqlXGcxXwPnmGB/H8mxMi+q2NtqVQnDwQcAt85Rjtv5favLKAkfNwSn5kH1OEuCXpH6I6YUxJs/L2I6vc9sR/PTr3NQN7vDBfJtc41gybsjm81LZUx/KMG7z2NsQ6sHRZ0zty+FFsvk2233N2BCQwK76/pWNqcmR8y0tr0qaI9Fx02Jiomb/0neiwTz2Db3Rhr1fyxzgdxXbSzlIE7j1pLZM5rIxtefBEXgCV3aP1MsOfvz3Kszu27u5bBISGbTN0zK8UT9lsPW5dcXL0alriDsEPv3YFWsyY8aJG2RkM5Hf2Dkvi+k3XRd8mdw1ayPfxsC+oDd04G0M+vhheDiuGeAL229BN3DveRjrYu6daGSDYLJe0oecHLl76de5G1036o0p5BobwnrAesjrAWaLbT2s50OZvDzLK0KMCSek7H4306ZINAIykXFgch8L8R0oAlfu2IR/jusiQ8ekZZISyicKzK4GIEyf53e6qOfKpPeKZE7L6ffCIlsaz4U5nt0k0WCIY7ojI5ejfcI+soD5o7vYKmyWHSPlHVH/ldZ6OOQ5f0rFrm1h5dmir4li18pIdJls1IFjbiQolZW2WQzpN/YZPfi+Ux77zS60lSFSafVwzB1nhjz04207tpzFksWXfuPw2JdM7flOpRAHe9/PdE5qzpTpjTLIztpomyTIlGICHXnZy/KtX818nTuVjY0O3zZ1EyUDp+bIWHukOHeQQ8aTvqV49mVz663/4nr6YTEcLTYgGOuyun077bj2OGTccPq9M4becDzNz0AHkGr8klQvlCEfXaWyQXboXyNf50Yv2AT0Bc7AfO5DTkV1E9lC1qLgQpkPBT7ACWOS+lhVZUv10M7fdpw47V/ZmEJy/Py166rkBztrz1jqTzl6vTHmbDji6NP3X/U29XXu1L7ZPOU+dRetC7RBHhtxjejUy5aOqdUDxsjzr1hMlz2PR+r2dj+dC2V4sw2HonXF5OtUCnHj1Ql/xNj6jX1CJ2af+Jd82AXyy9aFMnltQ4S6mesQZuY6p6x41udzP5LuX45zk8+6gT3OzVNrG1vAWglnop92vyw1PFrfG103ysaUUzXgGNnTutP1MLfmsNZgd8Hd9g8681pNmY7ILyXRVSpptAwDiROVGsBG6+lUeZHohUmmOoUX1Tu38dJtEr2Q8YCTU+87Fp3se46gdbI91T2357XGR+PTbgzkSDIkArLU7rZU38LHr/C08Md4Vkj0XDceRqLZDWQSzHV5Jd/Cn6ga48bH2O8SW8RQemxcj3NFZyLR83fs5gqGJIcwJAwIA8KAMNAuDIhE/0ZgaheYVI+wJAwIA53CgEi0sNUpbKleYUsYEAaEAWGgUQyIRItEK9IuDAgDwoAwIAwIA8KAMCAMCAPCgDBQEQMi0RUV1ejuhMprR0sYEAaEAWFAGBAGhAFhQBgQBoSBhYcBkWiRaO04CQPCgDAgDAgDwoAwIAwIA8KAMCAMVMSASHRFRWkHaeHtIGlMNabCgDAgDAgDwoAwIAwIA8KAMNAoBkSiRaK14yQMCAPCgDAgDAgDwoAwIAwIA8KAMFARA3OSROsrrNoNanQ3SOWFGWFAGBAGhAFhQBgQBoQBYUAY6AYGsiTa/59k/ley/V2/cSOQ12nBWiHRv/3dE+HY5wNhcvJelPvWrdvho917azKTf+ST/pj/6NGjcGNsLLy2Zm0tP+3bzl27w+joaHj2ucWxDGVHr1wJPPvw4cMwPHw2LP7b0trzL6xYGUZGRmr5p07/UHs2rVu/NcmFAWFAGBAGhAFhQBgQBoQBYUAYmF8YqEuiD/QdrJHDbg5sKyT64KEjYeLWrfDmW+8ECHN//2dhfGIirFr9auxLz4e7wu3bt8P723vC7//wZBgcPBlGR69kiS7Pnz13Lgx8cTw+C5GGQJ8cGorPQpjHxsbC8eNfxfw//fkv4fz5C5FYU5b869dvBPrTTf2prfk1CTVeGi9hQBgQBoQBYUAYEAaEAWFg/mCgKRL9z1fWhJs3fwqXRkZiNJaI7ImTQ+GppxdFsphGeyG12z/YMY1IEh0mSkyUG1K7b39vLR/SCVmlfiK+RJV7e/tq+fUARuTXk1ZkHR8fD9u298TnU4JOZJn8rVu3z6h/3foNkSST0ibEeN++3kiOTQZfH0SdunxfDx/pj0T6+cVLZtRvdSidPxNGY6WxEgaEAWFAGBAGhAFhQBgQBh5vDDRNoiG+HJuGMENA79y5E6O+AAqiOTk5GXbs3BXzOT5NeSOjmza/Fwk0+ZQnhVBzn98QU8pveW/br9Hk8fGw8h+rGyaiyALhX/HSy7W6IefITVtLli6PR7pzJJ0o9pmz52plKe//1ry+LnDEva/vULzvCTv1f3ygL9y/fz+W6cYxeC+brqePlfQhfQgDwoAwIAwIA8KAMCAMCAPCQDswUJdE27vQpA8ePKhFcz1ZNCFOnTodjzLzG+LJb8sjgnv16rXaseivvxmMUWYjshyD3r1nX3j1tTfiMz66Sx31osXWRi5du+7NeJT70OEjNVloxwg+kfNvvh2M0XTItq/Djm7v7/142n3KENVGH+iFd6IXPfNcLGN62bN3f7hw4WJ8N/rosQGR6GTzwetZ1zJkwoAwIAwIA8KAMCAMCAPCgDAwnzBQl0QXvRNtZNGOSNNhT3wvXLw07Ug1+f4e1/wVKcrXRZlce0XP2n2IN+8rQ2KNrJPH9RdfHo8RYo6hX7w0EqPg6XFu3p2+du16WLZ8RaGcRLevXL0ahr77vkaiicjfu3cvRuV55xodduuDbNZ3pTJCwoAwIAwIA8KAMCAMCAPCgDAgDHQGA20j0fUi0c88+3wkm/aBrjQSDbF9ceWqQDkGulUSzQe9IMC04wl0DkS7PtoTSe7yF16aRpYhxsjhn8m9Ex3fef7lq+X2TjR12nN86IyPi+md6M4A2PSsVPoVBoQBYUAYEAaEAWFAGBAGhIFuYKBpEs07y7wzDEklistvCCNCczT67t278QvY5DfzTrSPVDcSiSZyDIH+drA+gY5yv789fsmbfnhlQ4b511fvbt467T51j43djF/0JsrMv7Yikj08fCaWS7/OvXTZ3+PmQUrGfVu61kQXBoQBYUAYEAaEAWFAGBAGhAFhYP5goC6J9u9Ec23HkiG1fAiM3xyJ5g/S2uzXudP/5dxKJJrocyo3vz0p54g1X/3mS9p8/CuNVtVC2vkAACAASURBVPOhMD4+BilOwbzhnU2RYBf9n+jVr6wJly//WPs/0en/kU7r0+/5M1k0VhorYUAYEAaEAWFAGBAGhAFhQBjIkugyYDQSGS6ra67lQ5wh0Gl0eq7JKXk0eYUBYUAYEAaEAWFAGBAGhAFhQBjoPgZEovXl6BnRdk3E7k9E6Vw6FwaEAWFAGBAGhAFhQBgQBuYHBkSiRaJFooUBYUAYEAaEAWFAGBAGhAFhQBgQBipioCkSrR2S+bFDonHSOAkDwoAwIAwIA8KAMCAMCAPCgDDQXgyIRFfcbRDw2gs86VP6FAaEAWFAGBAGhAFhQBgQBoSB+YgBkWiRaB3bEAaEAWFAGBAGhAFhQBgQBoQBYUAYqIgBkeiKipqPOySSWTt7woAwIAwIA8KAMCAMCAPCgDAgDLQXAyLRGRL91yXL4v/ATv+/tMDXXvBJn9JnJzGwbXtPePDgQfy/8fzv+U62pbqFZWFAGBAGhAFhQBgQBh4fDFQi0U8v+u/w2989MStO6B+ffCrw101QGok+0Hewq+12s49q6/GZ5I/7WF+4eCmIRAvvrcwDvyHD5qrWBuGpFTzp2dnBD/OW+csfG6zM60bGgrXEnr9+40bAV2zkeZWdnXHvlN61Liys8WwGJ4Uk+qmnF0VH4aefxmNUNjUWkOqPdu8NY2M3w6NHjwqdil0f7Qk///xzYT5C79y1O4yOjoZnn1scDRJ1b3p3S8Bg3bt3L2voyL9y9Wp4+PBhoYO84qWXA/JTTyPKEYnWxGgELyo7t/HSbRK9bPmKcO4/56NtwjZevvxjWP3KmoZsUBGmsI3HPh8Ik5P3ojN369btaId9+bX/eivcGBuLdhlH8fjxr8Lv//BkW9r37fzpz38JIyMjYX/vxw3V3dvbF/vg65ov1/NxbWDswQBYAI9gA4xU1fn6NzeEycnJGnmAROTW1KL19tSp09OeLSIvn352tCliU7Uf86kc8/zjA33T5vn2D3ZUGjNPFI3wkd6+fTusWv1qrY56PlQ9G9JJ+9atMfrnK2vC+Ph41resIgM6nm8kut6YlvUZW//jj6PT5vHdu3cDeuTZ19asDaNXrkT7gk8+PHw2LP7b0hrWKFMPb3AAnvN4nU8b3/NxXSgb81bWDcbOj6VdX79+Izy/eEnEBXzv+PGv49risZTKleOQrciW1t/q70ISfeHCxXD16rVw5szZrLHo6zsUjfK+fb2FDhrGFkPD5CjauWexOHvuXBj44nhtwlEWgz/03fcxTXcLt277INy5cyew6EL2c0qg3sHBk7Ht3IKfe8buLcQJYX1TOrcJn8an/ePTTRKN3cGBoE0WCf64xsaR1+r4Hjx0JEzcuhXefOudWF9//2dhfGKi5hyb7WJxYqGhHOV5rtW20+ff3bw1ErKV/1jdUN0ssI3a5LTt2fpt+i1azzotF+OKI/Lyqlcq65yxZzMZJxpMsC42QgBYf8fGxkK9ca633jLWfn3P6QjZWPOLCHbumflyDzKM/jdu2lJ5zNA5G2Tvb++J8/zIJ/1xHr++9l+V6/D6wZfiz+7V86EM4zkb0mn7ZvJ1Op3PJBrCykYYm6lV9VRvTKvUwfPXrl0P+PtpedY4CPTJoaFoX15YsTLaCzburGw9vFEGe0pQ7Jlnn689Y8/Oh9T0O1vrQj0dIRsb+WxmNuKDtLpueJnYhLk0MhLwV7jPbwINo6NXwrr1GwrHvIhDtlM2L2cz14Ukmk5SIaBIF1w6xiSuF4GwRRUyTtkicKFAFmivSBZ6ns8ZOuQi+uEnaK7jPT07Y73m0ObKFN2byxOiSGbdbz/5kk4Xhk5x4ru1q4192r1nX3j1tTdqC0NqQ20X9f79+3G3NhdNLsLeqdM/TOtLaiO3bt0eIyw4WlYHzvP58xfi0UMcFSIwbGxyTdt+k7MR2VLHnPYgQ6wXRDyp10ck0IPtSPvUrw2cbkIm8lO9MIacWLJ8Ng8aISamj1bSorWBzdyvvxmsRXvRAbqwtnAscSLQSe50Qlm+1cP4s4G8dt2btbotryhl7AdPDNXKv7F2fdx4AStFz/j7nBxIfQCfz3XRekvUAdLvxzh9Ft1duHAhOnmtRAfTest+42NATotOdaSbPRBb5LPoG5E28M2Y8odTiG+UtgvZYa737Ng5Iy8ta7/BEngxp5d6OfXHWFiZqim+FSR+0+b34rNlPlQ9G1LFvlWVq5lyOOHoweu5/9OjMeADmSsbU2sztZt2v2qa2nR7bsM7m2qnMxlzfwoI2aZOF0yd6gB34M/GuKrtpQ1OhkBard2ytN6Ylj1LPicYiuYmeodcY8OsLj93yvDGM+B9NjZWez7cFdcTzz+IitNXO7Xhx5RNPmRNg3dF64LpoyhN1w2wTXuUz9Xp9UoZTrhBkFlTkG1o6LsZstkJoYuXRiJ5LZIlvd/quuHrQ8/0zTZ/+Y1NMh37snbNvGDDN8ch2ymbtddsWkiircKcsWBC4siYU8DgffHl8Zox4NmpRfVmdHJYgIsWUYzimbP5KE3O0NlkBhC0y+LFDosHNQaWXTOi5SnorF/10hx4rTz1eQeQa+RggbXn0nxzQOgPxxbSfDMe6CjN47fpriz/cZWtVb12Um+Sber9M8N11bnQqt5svpJ2k0T7drleuuzvcdOPhdfyINlEhzds2BhtJjaQKJwn3la2LMV5uXnzp8BCSVnv6Een7Kuvo41EB9gniPPuvfviKzSQMWTA0TMbVFU2bCzRB6LRJiNRBEhuGpFgflkZUn5be/4+Tj56+HfvgRjRwMHktzk4PEdfcRxw3LD7/PYOta+vE9dm480mWxusf6yJRP7RO4u/d/aHh8/EPiM3f/Qf58Ac6LJ8a6eZlLZsDCD2yGVrVpX62PWHEPCX2wCot94uWbo8kmicfp5NiQPtM87gcsPbGwsd9SpyNloGRw5n2c9Df6ojxamfW4wbBBpnzsaUa+7ZmDYqjy/v24acmKOc4s4/U3RNXf4kTJkP5fuZ2pC0jZx9S8u08zey43wzdtSL7sGORbjKxtRkYX0pIoVWpl7KONhaZuUgCOCHuY/esAXYBJPt7Y3vxt87d+6OspPiD6Jv6qhqe629RtJGxjRXL5uy2GKbx/SLDc9c2TWvr4u6wf8mvwxvlGGTj7ptQ8pvvubaaNe9FD/MXeaKndrAthEEZAzJw1bQ9zSAWLQulMlJvdTPHAczrJ2cAEauXJ3eLlCGSK7hjTrYsGQtKmu3Sn6r64a1wSYKttEHPvGHCJ7Sd9YFdLr1/embuvU4ZLtkMxlbSZsi0ThugH1g4Ms48Bw5whjYUQ+/qOaAYAJHEFy5MgOQlp8zdEQeeE8aB4rncQowhgYcgA6o2BVm8DzorN6ytJ7MZc8qf2FELzWOC2ccvcGtMq42/434k9Z7ZydXp98EgGzibFo5HD3qI6rEwskfR2X9RqCVrZcSjcRpO3T416Pa5ix90PNhuHbtWjj9w3B0CNAB/WLRpYyVQ04cQvJpq6psOBH0CxtsMlrEgc1VI/W0+T/LX6yVoWyRTY73HbFMo5jpc7yry2ZE1YgqffRj2giRtD4aNjyZYfMgOvKfHq31MzqOExO1NRGnDIfBNgR45sWVq2qEqyzf2m8mjfg/MRSPf7JW9h083BCB2Lxla/ik/9M41uAYQsdpMMa7bL0FHziKvA9JWTZIcJaNBLF+sxHCcc/cel/W31bGNB0DiALziblBuyne/JwxYuDfU2YTDCKEXsrkLsunbQIF+FRGxMCPx11ZHeSnpJN7ZT6U9TNnQ6zNevbNyhSl9MHPQ67pb1H59D5zhcAL9+2VEvrJ77IxtbqawZo9S0ofUhLNWLFBtfyFl2p9gThgG5CLuQ8R/Xzgi2jrmQ/YAPKos6rt9XJUva4ypvXqgqB9dvRYgCCzXuH7+w1OnqUNbCrjCQle9MxzsV9leONZCDlReuqmDWyCEdl6cpHXKp6wT6xljAM4Yp7Z5nC6NjNmrG8pXnPrQpnc5KfrI/YEGwQ+c3V6m5Q7XYCuIZ9V2i4r0+q6YfWjS9YdW/u4z/zFB6IP6JhXR+i3RarLOGS7ZDMZW0mbItGA1u+yI0Ds1OCJGYtqDggmMEaDiDEKs3s+zRk6JiqOE0fSrKwHlu1e2HFGn2fly9J6Mpc9q/yFQ740lgtjLM02zcZ4cuST70oY6UAGFuKpjzKOxc1IbKlFJ6rKiH1jJ/fosYFYnz2HfSRiiIOzZ+/+X+zxVOQXu1ZGoqvIBknAkcD5sHYthWRx5JiII3JwnX5gpsgmM06pc+0d7PS53PpgcnQqza0NVe5BJnEUiP7zoU125v3H5sryW+kPemXTmwgL49Oq3uJxx1/ew290vQU7fKDIjkRyjNtOabQqV6M6YtOK44/g1HDnN1ZSvBkRQU6ucQJJG223SnnaZszQFaQih7Eq9UDivO3hGWSu50ORX2RD0jZz9i0t0+7fnjijJ0+2ysbUZGkVazkSXeUem0kQNmwAmyNsgGJzkauK7TX5G00bGdMqdbNRwImA3OsFbKCycWDjUoa3XHsc0fcfocqVadc9T5yJnrO2+Y2wffunNrKImJqdAHe+/WbnJ9zn3Ln/xLludWNXwGeuTm+TwJuV9bK067od6waY9pF9k426DR/c8/ORZ3wgNKeHdshmsrSaNkWiczsgdIoBBpA4cAYIn1LGC4wSUzD6fK9Yu0/97Gr4HWAPLJwT36ZdNwK23KBZ+7RldVpqC689Z/cttR1L+oMcdt9S0wuTwu75lPu0X5b/uMrWql47qTfJ9ngd52bxJRrlj2azWYjTChaYx9gJi86yC8sOPA4Vx/3MztRLiQqw+Qj5MAfMymObacvv+nJUDrtIu2Ukuops1M1uve8j7ae79jhTRC05qmzykXp77e9zusgfO/V5uecajUSn9TXz22y82WTqIIKRRqI5WcA6RXSKMfIRJ4gHfeXoO8+W5Tcjp3+Gsf/++1M1rMSI6/h4pQg+/94SnPmjdjFa8ss7g2Xr7Zb3toVvvh2sRdvoK05qrHPr9lrkyq93XIMR34dOXPOuMhtYNleYn4wZTj/tpTjlPvmUy/khjKU/XdCKzOiHuWP/3pNj8cy5HGkpaiceLx4fn3HSLye772s9G1LFvhXJ0677yACGjh79PNpBO9VA/WVjajKkY233q6bMf/Pr7JlcJJoP6lkk2uODecAJTnxl1gvqqGJ7ra1G03pjWqUu+uY/ZObxyAYg+axLVtfhI/01/ZThjY091ij/DQleIekWiUZmuMiJk0MzNoftCL5FySlrXMf6SppbF3x+0TVrI/PcTm55G5OrM52n2CMLGJoc5lsUtVn1fivrhrXBK1r4IxbZt/vYN+Yw84B7fj6ClzIO2Q7ZTJZW06ZINJPGn8W3CeqNmQmWAwJ5KMofm7DyPvWKtfso3b+LxC4tC2EuKsIzHnRWR1laJHPZc8pfGJFLjePCGsfcotepMcbBI/KDkcdOEhnhPScjTLSLrWLxM6eBKDSLBkexyuSyY07fDs4k0Dxrtsu+rEsbRAxwSsgrI9FVZCO6lSO7yIYttrbpP7q3o5fWN3b76T9OPvYcokYeR3rRw/5/TxFPnCvI3/q33o752HKi7zhr1A0RTR1Za6NTqenXk2ja4nUiG1M2E9hJRxfoxDDBuoXc5IMJc67L8n1f0C3OpR1783lF1+mXTGMdyf+4RQZz6OijrwsHE1kZD+QH22l008qn6y0ElQ0ijpAz1jt2Tr3KkPMVcuu91duJFIKDD8IYMU85Zuv/pSavLHCsFF2Tj6PPSQLkTP0Q0wtjTJ6XFwecOVhlfttzONPozb7ObUfw069zUze4wwezZy3lv5cwbshm90hT2VMfyjBu89jbEMNqPfvm2+rUNWNDQAK76vtXNqYmT6tYy5HouGkxMVGzf+k70WAe+8aYIoe9XwsB5XcV20s5SBO49aTW+lWU1htT/ww4Ak/gyt/3soMf/70Ks/v2bi6bhLyKYJunZXgzTJl9xL5j43yk0svSiWtIHmSPeQo3sTaYs4wZJ27oB5uJ/MbOWRlS02+6LvgyuWvWRr6NgX1Bb6x33sawzv4wPBzXDPCFXizoBu49DzO92aut1h6yQdRZL+mD3S9LW103aIv+5HwFyDX2jfWA9ZDXA8wWp3LldFtFtrSeTv1uikQjDBMZB6boYyEmcE4B5OWOTdgzlhYZOiYtk5TjTkSB2dUAhPacT9NF3ecVXRfJXFRe9xcW6dJ4Lqzx7CaJBjsc0x0ZuRztE/aRBcwf3cVWYbPsGCnviPqvtNbDH8+lUTt+28LKs0VfE8WulZHoMtmoA8c8R4KsbRZD+o19Rg++75TBfrMLbWWIVFqfOeaOM0Me+vG2HVuOA8PiS59xeOxLpvZ8p1JIjb3v5/VvzpTpjTLIztpomyTIlGICHXnZy/KtX818nTuVDXLv26ZuomTgNOfI4Nzh4DKe9C3Fs8lGmltvGVMbs9yHxez5ovXe8tudehwybjj94Ivj6rSF3nA8zc9AB5Bq5CTf64Uy5HMvlROyA5Yb+To3Dig2AX2BN/SHHqvWTWQLWYuCC2U+VJENof0Uq/XwkMrbrt92nDjtX9mYQnL8/LXrquQHO2vPWOpPOXq9MeZsOEIS6PevYzr1de7Uvtk85T51F60LtEEeG3GN6NPLVuQ3gzHyiCT7un+VPY9H6vZ2P50LZXgDU+DI1oT0eS9LJ64h8rw6kRJ3+g0pNbuOjHxrBLuAHGXrQpmstiFCv5nrEGbmOqeseNbncz+Sbnei1+utaEyxBayVcCb6WSaT5Rsere+Nrhu2iVTkK3CqBhzT91zdJkeOj1WRzZ7vdFpKojshAAOJE5UawE601UyduUFrph49s7DImMZzfo5nt0n0QsYJC2K971h0su85gtbJ9lT3/JzvGjeNW7MYyJFkSARkqdk69dzji0fhaeGP/ayQ6LluVIxEsxvIJJjr8kq+hT9RNcaNj7HfJbaIofTYuB7nis5Eoufv2M0VDEkOYUgYEAaEAWGgXRgQif6NwNQuMKkeYUkYEAY6hQGRaGGrU9hSvcKWMCAMCAPCQKMYEIkWiVakXRgQBoQBYUAYEAaEAWFAGBAGhAFhoCIGRKIrKqrR3QmV146WMCAMCAPCgDAgDAgDwoAwIAwIAwsPAyLRItHacRIGhAFhQBgQBoQBYUAYEAaEAWFAGKiIAZHoiorSDtLC20HSmGpMhQFhQBgQBoQBYUAYEAaEAWGgUQyIRItEa8dJGBAGhAFhQBgQBoQBYUAYEAaEAWGgIgbmJInWV1i1G9TobpDKCzPCgDAgDAgDwoAwIAwIA8KAMNANDGRJtP8/yfyvZPu7fuNGIK/TgrVCon/7uyfCsc8HwuTkvSj3rVu3w0e799ZkJv/jA33T8rd/sKOWn/Zt567dYXR0NDz73OJY5rU1a8PolSvh0aNH4eHDh2F4+GxY/LeltedfWLEyjIyM1PJPnf6h9mxat35rkgsDwoAwIAwIA8KAMCAMCAPCgDAwvzBQl0Qf6DtYI4fdHNhWSPS+fb1h4tatsGHDxgBh7u//LIxPTIRVq1+Nfdm2vSdArN/f3hPzj3zSH8u/vvZfM/rK82fPnQsDXxyPeRBpCPTJoaHw+z88GSDMY2Nj4fjxr2L+n/78l3D+/IVIrClL/vXrNwL96ab+1Nb8moQaL42XMCAMCAPCgDAgDAgDwoAwMH8w0BSJ/ucra8LNmz+FSyMjMRpLRPbEyaHw1NOLIlmEfEJOiQYTsYXUptFeosOQWaLct2/fDvv299aIJqQTskr9PE89vb19tfx6ANu4aUvo6dlZK4us4+PjAfLMc19/MxjrRUZ+L1u+IoyN3czWv279hkiSSSkLMYakQ45NBk/4Ieq05ft6+Eh/JNLPL15Se8aeVTp/JorGSmMlDAgDwoAwIAwIA8KAMCAMCANgoGkSDfHl2DRkdOvW7eHOnTsx6kulEM3JycmwY+euWrSX8kZGN21+LxJo8ilPCqHmPr8hppTf8t62X6PJ4+Nh5T9WN0REiRYPDHwZrly9WjuG7kkvZPjy5R8jUc9F3Ylinzl7LsqAXOnfmtfXBY649/UdinmesKMXjo3fv38/lunGMfhUPv2eOWbSiXQiDAgDwoAwIAwIA8KAMCAMCAOtYKAuibZ3oUkfPHhQi+Z6smiNnzp1Oh5l5jfEk9+WRwT36tVrtWPRaTSYY9C79+wLr772RnzGE13q4D1kIryQdauzLL1w8VKMchPF5ui2lafui5dGItGHuEOUb4yNhZRE29Ht/b0f1561Oohqow/0wjvRi555LpYxvezZuz9cuHAxvht99NiASHRmA8J0qVQGTBgQBoQBYUAYEAaEAWFAGBAG5hMG6pLolFhax4ws2hFp7nviC4Hlt5Un9fe45s/n+2tfF/dz7fnyRddEog8eOhKj3BYFp26On//442ggkmwfUUv72vPhrnDt2vV43Luo/hUvvRyj3EPffV8j0UTk7927F8k57VNvtz7IViSn7ssoCQPCgDAgDAgDwoAwIAwIA8KAMNAeDLSNRNeLRD/z7PORbNoHutJINEefX1y5KlCOgW2FRKfvRNs7z0aSaZsj3H988qnY1pKly2MkOn3nGmKcbgTk3omO7zz/8tVyeyd610d7ahsEkHg+LqZ3otsDWE186VEYEAaEAWFAGBAGhAFhQBgQBmYTA02TaN5Z5ii0vRPNbwgjneGd6Lt37077Anaj70T7SHUjkWhk4pj2m2+9E2WzD5i9vfHdKFv6de6+g4fjUXH/dW7IMEe83928tUaG6ZcR8sHBk/Hr3PxrK46GDw+fieXSr3MvXfb3uHmQkvHZHHC1LYMjDAgDwoAwIAwIA8KAMCAMCAPCQPMYqEui/TvRXNuxZEgtRJXfHI3m79vBwaa/zk1d/n85txKJ5gvhRJv5oBcyp3VD+u3L4bl8wMSHwvgyOKQ4BdeGdzZFgl30f6JXv7Km9rEy9JL+H+m0Pv1uHrzSnXQnDAgDwoAwIAwIA8KAMCAMCAPdxkCWRJcJ0UhkuKyuuZYPcYZAE9Gea7JJHhkIYUAYEAaEAWFAGBAGhAFhQBgQBmYXAyLR+nK0NguEAWFAGBAGhAFhQBgQBoQBYUAYEAYqYkAkuqKitNszu7s90r/0LwwIA8KAMCAMCAPCgDAgDAgDcwEDTZHouSC4ZNAEEgaEAWFAGBAGhAFhQBgQBoQBYUAY6DYGRKIVidaxDWFAGBAGhAFhQBgQBoQBYUAYEAaEgYoYEImuqKhu726oPe2oCQPCgDAgDAgDwoAwIAwIA8KAMDD3MCASLRKtHSdhQBgQBoQBYUAYEAaEAWFAGBAGhIGKGBCJzijqr0uWxf+Bzf+RvnDxksCU0ZF2xObejpjGZPqYbNveEx48eBD/Xzz/e176ma4f6UP6EAaEAWFAGBAGhAFhoDkMVCLRTy/67/Db3z0xK07oH598KvDXzQE2En2g72BX2+1mH9VWcxNGept/emMj7HEl0c88+7xsWBs2Af2GDJurWhvmnx2Q7W7/mP3+D0+Gp55eNG9sDPOW+csfG6zM60ZwwVpiz1+/cSPgKzbyvMq2H4OzqVOtC7M/nrPBET3mCkk0hhGD89NP4zEqmxoLSPVHu/eGsbGb4dGjR4VOxa6P9oSff/65MB9hdu7aHUZHR8Ozzy2OBom6N727JUaB7927lzV05F+5ejU8fPiw0EFe8dLLUf5Go8ki0bM/MTxIda3xaAUD3SbRy5avCOf+cz7aJmzj5cs/htWvrGmLs4VtPPb5QJicvBeduVu3bkc77PWz+G9Lw9FjA+H27dsdPUnzpz//JYyMjIT9vR831Lfe3r7YBy/zfLmej2sDROf48a8iaQCPN8bGwtp/vVV5zNa/uSFMTk7WyAMkIremFq23p06dnvasJy9g6McfR6fl3717N/yzTfNlvuAqlZN5/vGBvmnzfPsHOyqNmSeKRvhIsQerVr9aq6OeDwU+wAl4YbzADzgyOV9f+68wPHwm5s3HDSXwNT4+nvUtrY/1Uvo830h02ZjW62/ZPH1tzdoweuVKxAs++fDw2cA65Oushzc4AM95vM6nje/5uC74scldt7JuMHZ+LO36+vUb4fnFSyIu4HvHj38d15Z6Nj/HIatwxFyfOnGvkERfuHAxXL16LZw5czZrLPr6DkWjvG9f7zTj6oXEmcTQMDmKDC3KOHvuXBj44nhtwlEWgz/03fcxTXcLt277INy5cyd8+tnRwl1Q6h0cPBnbzi34Xs70eiFOiLSP+i1i+rhgoJskGruDA0GbLBL8cY2NI69VnR88dCRM3LoV3nzrnVhff/9nYXxiouYcY7uuXbsW2xwZuZwlO63KYM+/u3lrdLRX/mN1Q/1igW3UJlubs53O9tqA04Ej8vKqVyrrHMywGY4TjWPEutgIAWD9HRsbC/XGud56y1j79d2P4RRerwf8CH9/IV1DhtH/xk1bKvcRnbNB9v72njjPj3zSH+c95LUZ3eBL8WfP1vOhDONgDbxga7A54IjnTTY2R5CxyLeztuZiOp9JNISVDQ42U6vqtmxMy+qpN09Z4yDQJ4eGIl5eWLEy2gs2XqzeenijDBgiKDZfT06ZfufiXEA2NvKZr434IK2uGzb2pGzCXBoZCfgr9ptAw+jolbBu/YYaTvwzXBdxyDKOmNbTyd+FJJpO0zDCpgsuHWMS14tA2KIKGadsEbhQIAu0VySGm+dzhg65iH74CZpTUE/PzlivObS5MkX35vKEKJJZ90WKhYE8BnDiu7WrjX3avWdfePW1N2oLQ2pDbYf3/v37cbc2F00uGstTp3+Y1pfURmI3qZ/nU7KKXcNRIQLDxibXtO03ORuRLXXMaROixnpBBIt6fUQCPdiOtE/92sDpJmQiP9UL/eHEkuWzedAIMSnSaSP3i9YGTm59/c1gLdqLDtCF1Y1jiROBTnKnE8ryrR7Gnw3ktevewLhWmwAAEENJREFUrNVteUXp+fMXwuCJoVr5N9aujxsvW7dur90repb7nBxIfYC0fNF6S9QB0u/H2D9LZLSViKCvq9Fr5grktOhURzp/II/IypyjLSJt4Jsx5Q+nEN8olQOyw1zv2bFzRl5a1n6DJfCCjNyjXk79MRZWpmqKbwWJ37T5vfhsmQ8FLugnZM3aYK6DI36bf1Y0F+yZTqQ44ejB67n/06Mx4AOZKxtTkym1m3a/apradHtuwzubaqczGXMfwUe2qdMFU6c6wB34szGuantpg5MhkFZrtywtG9Oy5+vNU/TOJhg2zOrxc6cMbzwD3mdjY7Xnw11xPfH8g6g4+LdTG35MOZWBrOkrDM3OhXTdANu0h05ydXq9UoYTbhBkOzEyNPTdDNnshNDFSyORzNoYlaWtrhu+fvRM32zzl9/YJNOxL2vXzAs2fHMc0mxQq/PY2molLSTRVmnOWDAhcWTMKQBYX3x5vGYMeHZqUb0ZnRwW4KJFFKN45mw+SpNTkE1mAEG7LF7ssHhQY2CvXbseiJanoLN+1Utz4LXy1OcdQK6RgwXWnkvzzQGhPxxbSPPNeKCjNI/fpruy/MdVtlb12km9Sbap988M11XnQqt6s/lK2k0S7dvleumyv8dNPxZey4NkE9nZsGFjtJnYQE7eeOJtZctSnJebN38KLJRp2dT2YZ8gzrv37ouv0EDGkAFHz2xQVdmwsUQfiEZbu0QRILlpRAI5rAxpKpfl4eSjh3/3HoiOOg4mv83B4Tn6iuOA44bd57d3qK2uTqVm480mWzusf6yJRO1Y4Fn8vbPP0Vd0jNz8cY1zYA50Wb6100zq8Q+xRy5bs6rUR0QCQsBfbgOg3nq7ZOnySKJx+nk2JQ4QNcbY8tONkyryNVsGRw5n2c9Df6ojxakn0YwbBBpH08aUa+7ZmDYrF8/5tiEn5iinuKvSBnX5kzBlPpTvZyR2X30d/SyzEdZm0Vyw/E6kyI7zzdhRP7rHplmEq2xMTSbWF8aevtq9RlLGwdYyew6CAH6Y++gNWwCeTba3N74bf+/cuTu2SYo/aDJUtb3WXiNp1TEtqrORebrm9XVRN/jf1FeGN8qwyYcNsA0pv/laJFM77qf4Ye4yV+zUBraNICBjSB62gjFNA4jNzgXqpX7mOJhh7eQEMHLl6vR2gTJEcg1v1MGGJWtRO3TT6rphMrCJgm30gU/8IYKn9J11AZ1ufX/6pm4VDtnqPDYZW0mbItE4boB9YODLOPAcOcIY2JEsv6jmgGACRxBcuTIDkJafUxCRB96TxoHieZwCjKEBB6ADKnaFGTwPOqu3LK0nc9mzys9HA6UX6WW2MOAXgyoy2Pw34k9a752dXJ1+EwCyCZm2cjh61EdUiYWTP47K+o1AK1svJRqJ03bo8NQxy7RsavvoF4suDpV3qnAIzUGuKhtOBP3CBlu7FnFgc9VIPW3+z/IXa2Uom8plz8f7jlimUcz0Od7VZTOiakSVPvoxbYRImoyGDU9m2DyIjvynR2v9jI7jxERtTcQpw2GwDQGeeXHlqhrhKsu39ptJI/5PDMXjn6yVfQcPN0QgNm/ZGj7p/zSONTiG0HEajPEuW2/BB44i70NSlg0SnGUjQTh+nx09FnC8mQf4FH7jpKy/rYxpOgYQBeaTkZoUb37OGDHw7ymzCQYRQi9lcpfl0zaBAnwqI2Lgx+OurA7yU9LJvTIfyvr5Qc+H8dWQ0z8MR1JhNsLazc0Fy6uX0gc/D7mmv/We8XnMFQIv3LNXSugnv8vG1OrJ+ZaWVyWlDymJZqzYoFr+wku1vkAcsA3IxdwH258PfBFtPfMBG0AebVa1vVXkS8tUHdP0OftdZZ7SBjaV8YQEL3rmudivMrzRBieQiNJjA7AFbI4akTUZitJW8YR9Yi1jHMAR88w2h9O1mTFjfUvx2uxcSNdH7Ak2CHzm6vQ2KXe6AF1DPot01cj9VtcNawtdsu7Y2sd95i8+EH1Ax7w6Qr8tUl2VQ7Y6j03GVtKmSDSg9bvsCBAVPnhixqKaA4IJjNEgYozC7J5PcwpiouI4cSTNynpg2e6FHUXyeVa+LK0nc9mzyhdZFAbmFgbMNs3GuHDkk+9KGOlABhbiqY8yjsXNSGypRSeqyoh9YyeXD4hRX+651PZh18pIdBXZIAk4EjgfabuQLI4cE3Ekcsl1+oGZVC6rg3FKnWvvYKfP5dYHq6tTaW5tqHIPMomjQPSfD22yM+8/NleW30p/0Cub3kRYGJ9W9RaPO/7yHn6j6y3Y4UNi/mSG7xsEhEhjM8eWfT1Vrtm04vgjODXc+Y2VFG9GRNAf1ziBpFXaarQMbTNm6ApSkcNYlTohcd728EyZD0U+OoHw7dm7/xefbuZ3DJqVqYrc9cp44oyePNkqG1Ort9U5kCPRVe6xmQRhwwawOcIGqNnvKrbX5G80rTqmVeutN0/ZQGXjwMalDG+5Njmi7z9ClSvTrnueOBM9Z23zG2H79k9tZBExNTsB7nz7zc4FuM+5c/+Jc93qxq6Az1yd3iaBNyvrZWnXdTvWDTDtI/smG3UbPrjn5yPP+EBoTg9Wj3/O7nU7bYpE53ZAUAoDDCAxvgYIn1LGdxAlpmD0+TkFUT+7Gn4H2AML58S3adeNgK3eoNGW1WmpLbz2nN231HYs6Q9y2H1LTS9MCrvnU+6jl7L8x1W2VvXaSb1JtsfrODeLL9EofzSbzUI2/sAC8xg7YdFZdmHZgceh4rift39F10QF2HyEiJgDlivr7aK1W0aiq8jGjjK79b6P1J/u2uNMEbXkqLKXL5XL8jhd5I+d2n1L0+cajURbPa2kZuPNJlMXEYw0Es3JAtYpolOMkY84salAXzn6zrNl+a3Iy7Osid9/f6qGlRhxHR+vFMHn31uCM3/ULkZLfnlnsGy93fLetvDNt4O1aBt9xUk1Eo1+/AeSOP4NtrpBommXDSyLkDA/GTOcfvSW4o375FMu54cwlv50QSvjhn6YO/bvPZvRSzxePD4+46RfTnbfV/w77JXpJerixFDEke9Tbi74/E5d2ybe0aOfRztopxpor2xMTaZ0rO1+1ZT5b36dPZOLRPNBPYtEe3wwDzjBia/MekEdVWyvtdVoWnVMi+qtN0/ZACSfdcmeP3ykv6afMryxscdxbv8NCV4h6RaJRma4yImTQzM2h+0IvkXJKWtcx/pK2uxcYG1kntvJLW9jcnWm8xR7ZAFDk8N8Cy9fM9etrBvWHq9oYUsssm/3sW+sA8wD7vn5CF6qckj/nNXd7bQpEs2k8WfxbYJ6Y2YdyQGBPBTlj01YeZ/mFITS/btI7NKyEOaiItTlQefrrnddJHO9Z5Q3t6KPGg+Nh2Egt+hZXrtTHDwiPyxA2EkiI7znZISJ9rBVLH7mNBCFZtHgKFaZPHbM6dvB+gSaelLbh10rI9FVZCO6lSO7yIYttq/60n90b0cvrW/s9tN/nHzsOUSNPL7gih72/3uKeOJcQf7Wv/V2zKc/RN9x1qgbIpo6stZGp9KitYHXiWxM2UxgJx1doBPDBOsWcpMPJsy5Lsv3fUG3OJd27M3nFV2nX1mNdST/4xYZzKGjj74uHExkZTyQH2yn0U0rn2IOIsYGEUfIGesdO6deZTBfgZQx91+h5rcncFZ3u1MIDj4IY8Q85Zit/5eavLLAsVJ0TT6OPicJ8EtSP8T0whiT52XFASe6XmV+23M40+jN9GJH8NOvc1M3uMMHs2ct5b+XMG7IZvdIU9lTH8owbvMYO4X84MjXY+X8hpLP7+Q1Y0NAArvq+1c2piZTzre0vCppjkTHTYuJiZr9S9+J9linDXu/FgLK7yq2l3KQJnDrN5/KZLaxKhtTcASewJWv08sOfvz3Kszu27u5bBLyKoJtnpbhLbV/2HdsnI9Uelk6cQ3Jg+yBc7iJtcGcxR5x4oZ+sJnIb+yclSE1/TY6F1gb+TYG9gW9sd55G8M6+8PwcFwzwBd6saAbuPc8zPRmr7aafMgGUWe9pA92vyxtdd2gLfqT8xUg19g31gPWQ17jMVucylVPt63O47StZn43RaJpiImMA5P7WIgXpEgBuWMT/jmuixTEpGWSctyJKDC7GoAwfZ7f6aKeK5PeK5I5LaffImrCwNzHQDdJNHjgmC7/Xgr7hH1kAfNHd7FV2Cw7Rso7ov4rrfUwxXP+lIpd28Lqn01tH3atjESXyUYdOOZGgnx7XPNlURZD+k3/0YPvO2Ww3+xCWxkilVYPx9xxZshDP9620x8cGBZf+o3DY18ytec7lUJq7H0/0zmpOVOmN8ogO2ujbZIgU4oJdORlL8u3fjXzde5UNsi9b5u6iZKB05wjg3MHOSzCs8lGmmKOe/6L6+mHxXC0wD730SdjS3lfZ6euPQ4ZN5x+8MVxddpEbzie5megA0g1fgn5Xi+UIZ97qbyQHbDcyNe5q+qlqG4iW8haFFwo86HABzgBy0U+1mz6SXacOO1f2ZhCcvz8teuq5Ac7a89Y6k85er0x5mw4QhLAxK9jOvV17tS+2TzlPnUXrQu0QR4bcSnW6v32shWNKXOPPCLJvq5fZc/PU+r2dj+dC2V4w/5hf2xNSJ/3snTiGiLPqxMpcaffkFJ0gmzIyL+QxC4gR9m6UCarbYhQNzYQwowN5JQVz/p87kfS7U70er0VjSm2gLUSzkQ/y2SyfMOj9b3RdcM2kYp8BU4bgWP6nqvb5KhnZ4o4oj3bjbSURHdCCAYSJyo1gJ1oq5k66w1aM/XpmblPtDRGC3eMuk2iFzKWWBDrfceik33PEbROtqe6F65N0NhqbHMYyJFkSARkKVde94SjehgQnhY+PmaFRNcD3VzIMxLNbiCTYC7IJBkW/mTUGLd3jP0usUUMpeP26rib+hSJnr9j102cqC3hRBgQBoQBYaAbGBCJ/o2A1g2gqQ3hTBgQBlrBgEi08NMKfvSs8CMMCAPCgDDQTgyIRItEK9IuDAgDwoAwIAwIA8KAMCAMCAPCgDBQEQMi0RUV1c6dC9WlnTBhQBgQBoQBYUAYEAaEAWFAGBAG5icGRKJForXjJAwIA8KAMCAMCAPCgDAgDAgDwoAwUBEDItEVFaVdovm5S6Rx07gJA8KAMCAMCAPCgDAgDAgDwkA7MSASLRKtHSdhQBgQBoQBYUAYEAaEAWFAGBAGhIGKGBCJrqiodu5cqC7thAkDwoAwIAwIA8KAMCAMCAPCgDAwPzEgEi0SrR0nYUAYEAaEAWFAGBAGhAFhQBgQBoSBihgQia6oKO0Szc9dIo2bxk0YEAaEAWFAGBAGhAFhQBgQBtqJAZFokWjtOAkDwoAwIAwIA8KAMCAMCAPCgDAgDFTEwH/9f//3iaA/6UAYEAaEAWFAGBAGhAFhQBgQBoQBYUAYKMeASLQ2EbSJIgwIA8KAMCAMCAPCgDAgDAgDwoAwUBEDItEVFaUdmfIdGelIOhIGhAFhQBgQBoQBYUAYEAaEgYWOAZFokWjtOAkDwoAwIAwIA8KAMCAMCAPCgDAgDFTEgEh0RUUt9N0U9U87hsKAMCAMCAPCgDAgDAgDwoAwIAyUY+D/AXPTkYt0YkZlAAAAAElFTkSuQmCC"}}},{"metadata":{"trusted":true},"cell_type":"code","source":"import riiideducation\nenv = riiideducation.make_env()\niter_test = env.iter_test()\n\nfor (sample_test,sample_prediction_df) in iter_test:\n    # 更新用户看过的lecture包含的tags\n    user_lec = user_lec.append(add_user_lec(sample_test,lectures))\n    # 去除重复（实际上应该合并相同id的tags列表，但是我还没有搞）\n    user_lec = user_lec.drop_duplicates(['user_id'])\n    \n    # 选取问题集，进行数据预处理\n    sample_test = sample_test[sample_test['content_type_id'] == 0].copy()\n    test = trans_tags(sample_test)\n    test = enc_scl(test)\n    \n    tpart = test['part'].values.reshape(-1, 1)\n    tpart = one_hot_enc.transform(tpart).toarray()\n    partcol = ['1','2','3','4','5','6','7']\n    tpart = pd.DataFrame(columns = partcol,data = tpart)\n    test = test.drop('part',1)\n    test = test.join(tpart)\n    \n    test = test.drop(['user_id','question_id'],1)\n    \n    # 模型预测\n    res = model.predict(test)\n    \n    # 构造submission\n    sample_test['answered_correctly']=res\n    env.predict(sample_test[['row_id','answered_correctly']])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"最后，future works很多，也不止这个模型需要修改，我还有在考虑把tags处理一下之后也做一个onehot，目前也在学习transformer和sant还有sant+。最后还是想用利器sant+来实现的hhh，希望自己可以尽快学会！等我学会了再来给你们看成果~\n\n如果本文有什么问题，欢迎大家来指点我！\n\n---------------------- 致谢 --------------------------\n\n感谢 Shuhao Cao，我使用了.copy() 之后发现不仅仅是没有warning了，代码跑起来也快了很多，非常舒适"}],"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":4,"nbformat_minor":4}