Factorization Machine

factorization_machine.ipynb

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We will predict this column.\n    dataset['target'] = dataset.groupby('user id')['movie id'].shift(-1)\n    # create a column that represents average movie rating given by user till that time (represented by row)\n    dataset['mean_rate'] = dataset.groupby('user id')['rating'].cumsum() / dataset['sample_num']\n    \n    # do a left join with movies dataframe and bring all the genre representations (0/1 binary values for each movie representing its category) here.\n    dataset = dataset.merge(items[['movie id'] + GENRES], on='movie id', how='left')\n    \n    # For each genre column (19 total) creates another column (total 19 more). This column represents a given user's mean score (float value) for a given genre till that time (represented by row).\n    # Note that we also update the genre columns such that each column now has cumulative sum, i.e. the corresponding number of movies that the user has watched in that genre so far.\n    for genre in GENRES:\n        dataset[f'{genre}_rate'] = dataset[genre]*dataset['rating']\n        dataset[genre] = dataset.groupby('user id')[genre].cumsum()\n        dataset[f'{genre}_rate'] = dataset.groupby('user id')[f'{genre}_rate'].cumsum() / dataset[genre]\n    \n    # Next we normalize the scores for movies in each genre such that we divide it by the number of movies that the user has watched so far.\n    dataset[GENRES] = dataset[GENRES].apply(lambda x: x / dataset['sample_num'])\n    # do a left-join on users data and get more information on users\n    dataset = dataset.merge(users, on='user id', how='left')\n    \n    occupations_categoricals = dataset['occupation'].unique().tolist()\n\n    dataset['gender'] = (dataset['gender'] == 'M').astype(int) # change gender to 0/1 integer\n    dataset = pd.concat([dataset.drop(['occupation'], axis=1), pd.get_dummies(dataset['occupation'], prefix=\"\", prefix_sep=\"\")], axis=1) # get occupation dummy variables and drop occupation column\n    dataset.drop('zip code', axis=1, inplace=True)\n    \n    COLD_START_TRESH = 5 # take the rows AFTER each user has watched at least 4 movies\n    # filter using threshold and remove null target rows\n    filtred_data = dataset[(dataset['sample_num'] >= COLD_START_TRESH) &\n                           ~(dataset['target'].isna())].sort_values('timestamp')\n    \n    continuous_cols = ['age', 'gender', 'mean_rate'] + GENRES + [gen+\"_rate\" for gen in GENRES] # 41\n    categoricals = occupations_categoricals# already dummy encoded\n    df_wide_without_cross = filtred_data[continuous_cols + categoricals]\n    \n    TEST_SIZE = 0.2 # size of test set\n    X_train_wide_wo_cross, X_test_wide_wo_cross = df_wide_without_cross[:int(len(df_wide_without_cross)*(1-TEST_SIZE))], df_wide_without_cross[int(len(df_wide_without_cross)*(1-TEST_SIZE)):]\n\n    filtered_train_data, filtered_test_data = filtred_data[:int(len(filtred_data)*(1-TEST_SIZE))], filtred_data[int(len(filtred_data)*(1-TEST_SIZE)):]\n    y_train, y_test = filtered_train_data['target'], filtered_test_data['target']\n    \n    # target\n    target_train = torch.Tensor(y_train.values).long().to(device)\n    target_test = torch.Tensor(y_test.values).long().to(device)\n    target_test_sparse = y_test.apply(lambda x: idx_to_sparse(x, items['movie id'].nunique() + 1)) # to calculate mean rank over test set during training\n    \n    # tensor with continuous features\n    X_train_wide_wo_cross_tensor = torch.Tensor(X_train_wide_wo_cross.fillna(0).values).to(device)\n    X_test_wide_wo_cross_tensor = torch.Tensor(X_test_wide_wo_cross.fillna(0).values).to(device)\n    \n    return X_train_wide_wo_cross_tensor, X_test_wide_wo_cross_tensor, target_train, target_test, target_test_sparse, items['movie id'].nunique() + 1\n\nclass FM(nn.Module):\n    def __init__(self, input_dim, n_class, k=5):\n        super().__init__()\n        # Initially we fill V with random values sampled from Gaussian distribution\n        self.V = nn.Parameter(torch.randn(input_dim, k),requires_grad=True)\n        self.linear_layer = nn.Linear(input_dim, n_class, device=device)\n        \n    def forward(self, x):\n        square_of_sum = torch.matmul(x, self.V.to(device)).pow(2).sum(1, keepdim=True) #S_1^2\n        sum_of_square = torch.matmul(x.pow(2), self.V.to(device).pow(2)).sum(1, keepdim=True) # S_2\n        \n        out_inter = 0.5 * (square_of_sum - sum_of_square)\n        out_lin = self.linear_layer(x)\n        out = out_inter + out_lin\n        \n        return out\n\ndef run_gradient_descent_fm(model,\n    learning_rate=1e-3,\n    weight_decay=0.01,\n    num_epochs=10):\n    loss_fn = nn.CrossEntropyLoss(ignore_index=PAD_IDX) # the model doesn't need to predict padding index\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n    \n    iters, train_losses, test_losses, mean_test_ranks = [], [], [], []\n    \n    # training\n    n = 0 # the number of iterations\n    for epoch in range(num_epochs):\n        model.train()\n        y_logits = model(X_train_wide_wo_cross_tensor)\n        loss_train = loss_fn(y_logits, target_train)\n\n        # Backpropagation\n        optimizer.zero_grad() # a clean up step for PyTorch\n        loss_train.backward() # compute updates for each parameter\n        optimizer.step() # make the updates for each parameter\n\n        # save the current training information\n        if n%100 == 0:\n            pred_train = torch.softmax(y_logits, dim=1).argmax(dim=1)\n            acc = accuracy_fn(y_true=target_train, y_pred=pred_train)\n            \n            model.eval()\n            with torch.inference_mode():\n                test_logits = model(X_test_wide_wo_cross_tensor)\n                test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n                loss_test = loss_fn(test_logits, target_test)\n                test_acc = accuracy_fn(y_true=target_test,y_pred=test_pred)\n            \n            # calculate mean rank on test set\n            softmax = nn.Softmax(dim=0)\n            preds_wnd = softmax(test_logits.float()).cpu().detach().numpy()\n            ranks_wnd = pd.DataFrame(preds_wnd).apply(lambda x: pd.Series(rankdata(-x)), axis=1)\n            ranks_target_wnd = (ranks_wnd.values * target_test_sparse).sum(axis=1)\n            mean_rank_wnd = ranks_target_wnd.mean()\n            \n            print(f\"Epoch: {epoch} | Loss: {loss_train:.5f}, Acc: {acc:.2f}% | Test Loss: {loss_test:.5f}, Test Acc: {test_acc:.2f}% Test mean rank: {mean_rank_wnd:.0f}\")\n            \n            iters.append(n)\n            train_losses.append(float(loss_train))\n            test_losses.append(float(loss_test))\n            mean_test_ranks.append(mean_rank_wnd)\n            \n            # increment the iteration number\n        n += 1\n    \n    # plotting\n    plt.figure(figsize=(12, 8), dpi=100)\n    plt.title(f\"Training Curve (lr={learning_rate})\")\n    plt.plot(iters, train_losses, label=\"Train Loss\")\n    plt.plot(iters, test_losses, label=\"Test Loss\")\n    plt.xlabel(\"Iterations\")\n    plt.ylabel(\"Loss\")\n    plt.legend(loc='best')\n    plt.show()\n    \n    plt.figure(figsize=(12, 8), dpi=100)\n    plt.plot(iters, mean_test_ranks, label=\"Test Rank\")\n    plt.xlabel(\"Iterations\")\n    plt.ylabel(\"Mean Rank on testset\")\n    plt.legend(loc='best')\n    plt.show()\n    \n    return model, iters, train_losses, test_losses","metadata":{"execution":{"iopub.status.busy":"2022-11-06T20:43:37.072212Z","iopub.execute_input":"2022-11-06T20:43:37.072511Z","iopub.status.idle":"2022-11-06T20:43:37.104923Z","shell.execute_reply.started":"2022-11-06T20:43:37.072485Z","shell.execute_reply":"2022-11-06T20:43:37.103989Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"X_train_wide_wo_cross_tensor, X_test_wide_wo_cross_tensor, target_train, target_test, target_test_sparse,n_classes = load_and_process_data_fm()","metadata":{"execution":{"iopub.status.busy":"2022-11-06T20:43:37.106291Z","iopub.execute_input":"2022-11-06T20:43:37.106808Z","iopub.status.idle":"2022-11-06T20:43:47.576119Z","shell.execute_reply.started":"2022-11-06T20:43:37.106773Z","shell.execute_reply":"2022-11-06T20:43:47.575200Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"fm_model = FM(input_dim=X_train_wide_wo_cross_tensor.shape[1], n_class=n_classes)","metadata":{"execution":{"iopub.status.busy":"2022-11-06T20:43:47.577735Z","iopub.execute_input":"2022-11-06T20:43:47.578134Z","iopub.status.idle":"2022-11-06T20:43:47.586428Z","shell.execute_reply.started":"2022-11-06T20:43:47.578096Z","shell.execute_reply":"2022-11-06T20:43:47.585462Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"fm_model_trained, iters, train_losses, test_losses = run_gradient_descent_fm(fm_model, num_epochs=1000, weight_decay=0, learning_rate=0.03)","metadata":{"execution":{"iopub.status.busy":"2022-11-06T20:43:47.588177Z","iopub.execute_input":"2022-11-06T20:43:47.588602Z","iopub.status.idle":"2022-11-06T20:45:34.783968Z","shell.execute_reply.started":"2022-11-06T20:43:47.588565Z","shell.execute_reply":"2022-11-06T20:45:34.783036Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Epoch: 0 | Loss: 9.84589, Acc: 0.11% | Test Loss: 8.87657, Test Acc: 0.24% Test mean rank: 842\nEpoch: 100 | Loss: 6.04683, Acc: 1.47% | Test Loss: 6.94882, Test Acc: 0.73% Test mean rank: 842\nEpoch: 200 | Loss: 5.94247, Acc: 1.73% | Test Loss: 7.06234, Test Acc: 0.73% Test mean rank: 842\nEpoch: 300 | Loss: 5.89036, Acc: 1.84% | Test Loss: 7.16583, Test Acc: 0.72% Test mean rank: 842\nEpoch: 400 | Loss: 5.87478, Acc: 1.85% | Test Loss: 7.28036, Test Acc: 0.62% Test mean rank: 842\nEpoch: 500 | Loss: 5.88775, Acc: 1.79% | Test Loss: 7.41003, Test Acc: 0.60% Test mean rank: 842\nEpoch: 600 | Loss: 6.03344, Acc: 1.64% | Test Loss: 7.66681, Test Acc: 0.54% Test mean rank: 842\nEpoch: 700 | Loss: 6.16944, Acc: 1.46% | Test Loss: 7.89027, Test Acc: 0.54% Test mean rank: 842\nEpoch: 800 | Loss: 6.37742, Acc: 1.43% | Test Loss: 8.26573, Test Acc: 0.48% Test mean rank: 842\nEpoch: 900 | Loss: 6.65704, Acc: 1.26% | Test Loss: 8.66080, Test Acc: 0.55% Test mean rank: 842\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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ANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATs1qaK+qeVV1UlVdUVW3VdXlVfWqqqpp43avqk9W1bKqurWqvllVO430b1VV76yqG6vqlqo6t6q23/BHBAAAAJMz2zPtL0/yp0lelGT34eeXJTlmakBV/WaSrya5LMlvJXlkkpOS/GJkO29J8qwk/yPJfkl2SPLR9V49AAAArEfVWpu9nVd9Osn1rbXnj7Sdm+S21tpzh5/PSXJHa+0P1rCNRUl+kuSI1tpHhm0PTfLdJE9orX19LepYmGTZsmXLsnDhwnU9LAAAAPiVli9fnkWLFiXJotba8jWNm+2Z9guSHFhVuyVJVe2ZZN8k5w0/b5bkGUm+X1Wfr6obquobVfXfR7axd5L5Sc6famitXZbkqiRPWN1Oq2rLqlo49UqyzeQPDQAAANbNbIf2k5Ock+SyqrojybeTnNZaO2vYv12SrZO8Isnnkvx2ko8l+WhV7TccsyTJitbazdO2ff2wb3VemWTZyOvHEzkaAAAAmKDNZ3n/hyU5MskRSb6T5FFJTquqa1prH8jdv1T4RGvtLcP3F1fVE5McneSfxtzvG5KcOvJ5mwjuAAAAdGa2Q/spSU5urZ0z/HxJVe2cwUz4B5L8V5I7k1w67XvfzWAZfZJcl2SLqlo8bbZ9+2HfL2mt3Z7k9qnP025WDwAAAF2Y7eXxC5KsnNZ2V4Z1tdZWJPlmkodMG7Nbkh8N338ryR1JDpzqrKqHJNkpyYWTLxkAAAA2jNmeaf9UkuOr6qoMlsfvleS4JGeMjDklyYeq6itJ/jHJwRk83u23kqS1tqyq3pfk1Kq6KcnyJG9PcuHa3DkeAAAAejXbof2YDJ65/q4Mbjp3TZJ3JzlxakBr7WNVdXQGS+bfluR7SX6vtfbVke28JIMZ+3OTbJnk80leuCEOAAAAANaXWX1Oey88px0AAIANaWN5TjsAAACwBkI7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0KkZh/aqenVVLVhN+72q6tWTKQsAAAAYZ6b9NUm2Xk37gmEfAAAAMAHjhPZK0lbTvmeSm9atHAAAAGDK5ms7sKp+mkFYb0m+X1WjwX1eBrPvfzvZ8gAAAGDuWuvQnuTFGcyyn5HBMvhlI30rklzZWrtwcqUBAADA3LbWob219oEkqaorknyttXbneqsKAAAAmPk17a21f0qyc1W9rqrOrqrtkqSqnl5VD594hQAAADBHjfPIt/2SXJLkcUkOzd13kt8zyQmTKw0AAADmtnHuHn9ykr9qrR2UwbXsU/4hyeMnUhUAAAAwVmh/RJKPrab9hiS/sW7lAAAAAFPGCe03J1m6mva9kvznOlUDAAAArDJOaD8nyV9X1ZIMntm+WVU9KcmbkvzdJIsDAACAuWyc0P6XSS5LcnUGN6G7NMlXklyQ5HWTKw0AAADmtrV+TvuU1tqKJC+oqhMzuL596yTfbq39YNLFAQAAwFw249A+pbV2dZKrq2pekkdU1X1aaz+dXGkAAAAwt43znPbTqur5w/fzkvxTkn/JIMD/1kSrAwAAgDlsnGvan53kX4fvn5XkQUkemuQtSV4/oboAAABgzhsntP9GkuuG738nyYdba99PckYG17gDAAAAEzBOaL8+ycOGS+MPTvLFYfuCJHdNqjAAAACY68a5Ed2ZST6c5NoMntN+/rD9cRk8Cg4AAACYgHEe+fbaqvr3JDsm+fvW2u3DrruSnDzJ4gAAAGAum3For6r/meRDI2F9ytlJfn8iVQEAAABjXdN+ZpJFq2nfZtgHAAAATMA4ob0yuJZ9ugckWbZu5QAAAABT1np5fFV9O4Ow3pJ8qaruHOmel2SXJJ+bbHkAAAAwd83kmvaPD/98VJLPJ7llpG9FkiuTnDuJogAAAIAZhPbW2glJUlVXJjlnNTeiAwAAACZonGva/yHJ/aY+VNVjq+q0qvrjyZUFAAAAjBPa/1+S/ZOkqpYkOT/JY5O8vqpePcHaAAAAYE4bJ7TvkeSfh+8PS3JJa+2JSY5M8rwJ1QUAAABz3jihfX6SqevZn5rkk8P3lyVZOomiAAAAgPFC+3eSHF1VT05yUO5+zNsOSW6cVGEAAAAw140T2l+e5E+SfDnJ2a21fx22/27uXjYPAAAArKOZPKc9SdJa+3JV/UaSha21n450vSfJzydWGQAAAMxx48y0J0kl2buq/qSqthm2rYjQDgAAABMz45n2qto5g+vYd0qyZZIvJvlZBsvmt0xy9CQLBAAAgLlqnJn2tya5KMl9ktw20v6xJAdOoigAAABgjJn2JE9O8sTW2oqqGm2/Msn9J1EUAAAAMN5M+2ZJ5q2m/QEZLJMHAAAAJmCc0P6FJC8e+dyqauskJyT57CSKAgAAAMZbHv/nST5fVZcm2SrJ/0uya5L/SnL4BGsDAACAOW2c57T/uKr2TPKcJHsm2TrJ+5Kc1Vq77Vd+GQAAAFhr4zzy7SlJLmitnZXkrJH2zavqKa21r0yyQAAAAJirxrmm/R+TbLua9kXDPgAAAGACxgntlaStpv2+SW5dt3IAAACAKWu9PL6qPjp825K8v6puH+mel+SRSS6YYG0AAAAwp83kmvZlwz8rg+exj950bkWSryc5fUJ1AQAAwJy31qG9tfaHSVJVVyZ5U2vNUngAAABYj8Z55NsJ66MQAAAA4J7GuREdAAAAsAEI7QAAANApoR0AAAA6JbQDAABAp2Z8I7okqaoDkxyYZLtMC/6ttf81gboAAABgzptxaK+q1yR5dZKLklybpE26KAAAAGC8mfajkzyvtfbBSRcDAAAA3G2ca9q3SHLBpAsBAAAA7mmc0P7eJEdMuhAAAADgnsZZHr9Vkj+uqqcm+bckd4x2ttaOm0RhAAAAMNeNE9ofmeTi4fs9pvW5KR0AAABMyIxDe2tt//VRCAAAAHBP41zTvkpVPaCqHjCpYgAAAIC7zTi0V9VmVfXqqlqW5EdJflRVN1fVq6pqnX4JAAAAANxtnGvaX5/k+UlekeRrw7Z9k7w2g5vUHT+RygAAAGCOGye0H5Xkj1prnxxp+7eq+s8k74rQDgAAABMxznL2bZNctpr2y4Z9AAAAwASME9r/NcmLVtP+omEfAAAAMAHjLI9/WZLPVNVTk1w4bHtCkh2T/M6kCgMAAIC5bsYz7a21f0qyW5KPJVk8fH00yUNaa//fJIsDAACAuWycmfa01q6JG84BAADAeuW56gAAANApoR0AAAA6JbQDAABAp8a6pp0Nr7WW2+64a7bLAAAA2Cjca/68VNVsl7HOZhzaq+rw1trZa+g7pbX2F+teFtPddsddedirPz/bZQAAAGwULj3xaVmwxcY/Tz3O8vi/qaqnT2+sqrckee66lwQAAAAk4y2PPzLJ2VX1zNbaV5Okqt6e5NAk+0+yOO52r/nzcumJT5vtMgAAADYK95o/b7ZLmIgZh/bW2meq6oVJPllVByV5fpL/lmT/1tr3J10gA1W1SSztAAAAYO2NlQJba/+vqhYn+VqSnyTZr7X2w0kWBgAAAHPdWoX2qjp1DV0/SfIvSV44dVe+1tpxkykNAAAA5ra1nWnfaw3tP0yycKS/rXNFAAAAQJK1DO2tNTeYAwAAgA1snEe+AQAAABvAjG9EV1X3TvKKJAcm2S7Tgn9r7UGTKQ0AAADmtnHuHv/eJPsl+WCSa7MO17FX1bwkr03y3CRLklyT5P1JXtda+6XtVtXfJvmTJC9prZ020r5tkrcneVaSlUnOTfJnrbVbxq0NAAAAZts4of3pSZ7RWvvaBPb/8iR/muSoJN9J8pgkZyZZluRtowOr6pAkj88g2E93VpKlSQ5KMn+4jfckOWICNQIAAMCsGCe0/zTJTRPa/xOTfKK19pnh5yur6vAkjx0dVFX3z2Am/WlJPjOtb/ckByfZp7V20bDtmCSfraqXttZWF/IBAACge+PciO5VSU6sqgUT2P8FSQ6sqt2SpKr2TLJvkvOmBlTVZhksxT+ltfad1WzjCUlungrsQ+dnsEz+cavbaVVtWVULp15JtpnAsQAAAMBEjTPT/udJfjPJ9VV1ZZI7Rjtba4+ewbZOzuA575dV1V1J5iU5vrV21siYlye5M9OWy49YkuSGaTXcWVU3DftW55VJXjODOgEAAGCDGye0f3yC+z8syZEZXHv+nSSPSnJaVV3TWvtAVe2d5M+SPHp1N6ZbB29IcurI522S/HiC2wcAAIB1NuPQ3lo7YYL7PyXJya21c4afL6mqnTOYCf9Akidn8Fi5q6pq6jvzkry5ql7cWntgkuuGY1apqs2TbDvsW90x3J7k9pHxkzoeAAAAmJhxZtonaUEG156Puit3X2v/wQyuTx/1+WH7mcPPFyZZXFV7t9a+NWw7YLiNb0y8YgAAANhAZhzah89Wf0kGS9t3SrLFaH9rbdsZbO5TSY6vqqsyWB6/V5Ljkpwx3NaNSW6ctv87klzXWvvecMx3q+pzSU6vqqMzeOTbO5Kc487xAAAAbMzGuXv8azII1h9KsiiDa8M/msGM+WtnuK1jknwkybuSfDfJm5K8O4M71M/EkUkuS/KlJJ9N8tUkfzzDbQAAAEBXaqb3d6uqy5Mc21r7TFX9LMmjWmuXV9WxSR7fWjtifRS6Pg0f+7Zs2bJlWbhw4WyXAwAAwCZu+fLlWbRoUZIsaq0tX9O4cWbalyS5ZPj+lgxm25Pk00meMcb2AAAAgNUYJ7T/OMnS4fvLk/z28P0+GbkjOwAAALBuxgntH0ty4PD925OcVFU/SPJ3Gd5ADgAAAFh34zyn/RUj7z9UVT9K8sQkP2itfWqSxQEAAMBcts7PaW+tfT3J15Okqh7TWrtonasCAAAAZr48vqq2rqp7TWt7VFV9Ksk3JlYZAAAAzHFrHdqrasequjDJsiTLqurUqlpQVX+XQVi/NYNl8gAAAMAEzGR5/ClJtkryZ0kOHf755AwC+2+21n48+fIAAABg7ppJaH9KkkNba1+vqg8nuS7JWa2109ZLZQAAADDHzeSa9u2TXJEkrbUbkvw8yXnroygAAABg5jeiWznt/YoJ1gIAAACMmMny+Ery/apqw89bJ/l2VY0G+bTWtp1UcQAAADCXzSS0/+F6qwIAAAD4JWsd2ltrH1ifhQAAAAD3NNNr2gEAAIANRGgHAACATgntAAAA0CmhHQAAADoltAMAAECnZvLItyRJVc1L8rwkBybZLtOCf2vtgIlUBgAAAHPcjEN7krdmENo/k+Tfk7RJFgQAAAAMjBPafz/JYa21z066GAAAAOBu41zTviLJDyddCAAAAHBP44T2Nyf5s6qqSRcDAAAA3G2c5fH7Jtk/ydOr6jtJ7hjtbK0dOonCAAAAYK4bJ7TfnORjE64DAAAAmGbGob219ofroxAAAADgnsa5ph0AAADYAMZZHp+qenaSw5LslGSL0b7W2qMnUBcAAADMeTOeaa+qY5OcmeT6JHsl+eckNyZ5UJLzJlodAAAAzGHjLI9/YZI/bq0dk8Ez29/YWjsoyduSLJpkcQAAADCXjRPad0pywfD9bUm2Gb7/YJLDJ1EUAAAAMF5ovy7JtsP3VyV5/PD9LklqEkUBAAAA44X2f0jyu8P3ZyZ5S1V9McmH4vntAAAAMDHj3D3+jzMM+621d1bVjUmemOSTSd49wdoAAABgTptxaG+trUyycuTzOUnOmWRRAAAAwHjL41NVT66q/1tVF1bV/Ydtf1BV+062PAAAAJi7xnlO++8l+XwGd47fK8mWw65FSf5ycqUBAADA3DbOTPtfJTm6tfaCJHeMtH8tyaMnUhUAAAAwVmh/SJKvrKZ9WZLF61QNAAAAsMq4z2l/8Gra903yH+tWDgAAADBlnNB+epK3VtXjkrQkO1TVkUnelORvJlkcAAAAzGXjPKf95AzC/peSLMhgqfztSd7UWnv7BGsDAACAOW2c57S3JK+vqlMyWCa/dZJLW2u3TLo4AAAAmMvGmWlPkrTWViS5dIK1AAAAACPWOrRX1RlrM6619r/GLwcAAACYMpOZ9ucl+VGSbyep9VINAAAAsMpMQvvfJDk8yS5Jzkzyf1trN62XqgAAAIC1f+Rba+1/J1ma5I1JnpXk6qr6cFU9rarMvAMAAMCEzeg57a2121trZ7fWDkrysCTfSfKuJFdW1dbro0AAAACYq2YU2qdZmaRlcH37vMmUAwAAAEyZUWivqi2r6vCq+mKS7yd5RJIXJdnJc9oBAABgsmbyyLd3Jfn9JFcnOSPJ4a21/1pfhQEAAMBcN5O7xx+d5Kok/5FkvyT7re7+c621QydTGgAAAMxtMwntf5fBNewAAADABrDWob219rz1WAcAAAAwzbrcPR4AAABYj4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQqVkN7VU1r6pOqqorquq2qrq8ql5VVTXsn19Vf11Vl1TVrVV1TVX9XVXtMG0721bVWVW1vKpurqr3VdXWs3NUAAAAMBmzPdP+8iR/muRFSXYffn5ZkmOG/QuSPDrJScM/D03ykCSfnLads5I8PMlBSZ6Z5ClJ3rOeawcAAID1qlprs7fzqk8nub619vyRtnOT3NZae+4avrNPkn9OsnNr7aqq2j3JpUn2aa1dNBxzcJLPJnlAa+2atahjYZJly5Yty8KFC9f5uAAAAOBXWb58eRYtWpQki1pry9c0brZn2i9IcmBV7ZYkVbVnkn2TnPcrvrMoSUty8/DzE5LcPBXYh85PsjLJ41a3garasqoWTr2SbLNORwEAAADrweazvP+TkyxMcllV3ZVkXpLjW2tnrW5wVW2V5K+TnD3ym4glSW4YHddau7Oqbhr2rc4rk7xmAvUDAADAejPbM+2HJTkyyREZXLN+VJKXVtVR0wdW1fwkH05SGVwHvy7ekMGM/dTrAeu4PQAAAJi42Z5pPyXJya21c4afL6mqnTOYCf/A1KCRwL5zkgOmrfe/Lsl2oxutqs2TbDvs+yWttduT3D4yft2PBAAAACZstmfaF2Rw7fmouzJS10hg3zXJU1trN04bf2GSxVW190jbAcNtfGPiFQMAAMAGMtsz7Z9KcnxVXZXkO0n2SnJckjOSVYH9IxksnX9mknlVNXWd+k2ttRWtte9W1eeSnF5VRyeZn+QdSc5ZmzvHAwAAQK9m+5Fv22TwDPZDMljifk2Ss5Oc2FpbUVUPTHLFGr6+f2vty8PtbJtBUH9WBjP35yY5trV2y1rW4ZFvAAAAbDBr+8i3WQ3tvRDaAQAA2JA2lue0AwAAAGsgtAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBOCe0AAADQKaEdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATs1qaK+qeVV1UlVdUVW3VdXlVfWqqqqRMVVVJ1bVtcMx51fVrtO2s21VnVVVy6vq5qp6X1VtveGPCAAAACZntmfaX57kT5O8KMnuw88vS3LMyJiXJTk2ydFJHpfk1iSfr6qtRsacleThSQ5K8swkT0nynvVdPAAAAKxP1VqbvZ1XfTrJ9a2154+0nZvkttbac4cz7tckeXNr7U3D/kVJrk/yvNbaOVW1e5JLk+zTWrtoOObgJJ9N8oDW2jVrUcfCJMuWLVuWhQsXTvgoAQAA4J6WL1+eRYsWJcmi1tryNY2b7Zn2C5IcWFW7JUlV7Zlk3yTnDft3SbIkyflTX2itLUvyjSRPGDY9IcnNU4F96PwkKzOYmQcAAICN0uazvP+TkyxMcllV3ZVkXpLjW2tnDfuXDP+8ftr3rh/pW5LkhtHO1tqdVXXTyJh7qKotk2w50rTN2EcAAAAA68lsz7QfluTIJEckeXSSo5K8tKqOWs/7fWWSZSOvH6/n/QEAAMCMzXZoPyXJya21c1prl7TWPpjkLRmE6iS5bvjn9tO+t/1I33VJthvtrKrNk2w7Mma6NyRZNPJ6wLocBAAAAKwPsx3aF2Rw7fmou3J3XVdkELwPnOoc3jTucUkuHDZdmGRxVe09so0Dhtv4xup22lq7vbW2fOqV5GfreiAAAAAwabN9TfunkhxfVVcl+U6SvZIcl+SMJGmttao6LclfVdUPMgjxJ2VwR/mPD8d8t6o+l+T0qjo6yfwk70hyztrcOR4AAAB6Nduh/ZgMQvi7Mljifk2Sdyc5cWTMG5PcO4Pnri9O8tUkB7fWfjEy5sgMgvqXMpi5PzeDZ7sDAADARmtWn9PeC89pBwAAYEPaWJ7TDgAAAKyB0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB0SmgHAACATgntAAAA0CmhHQAAADoltAMAAECnhHYAAADolNAOAAAAnRLaAQAAoFNCOwAAAHRKaAcAAIBObT7bBfRk+fLls10CAAAAc8Da5s9qra3nUvpXVfdP8uPZrgMAAIA55wGttf9cU6fQnqSqKskOSX4227X8Gttk8MuFB6T/WmFdOd+ZS5zvzCXOd+YS5zu/zjZJrmm/IphbHp9k+Be0xt9s9GLwu4Ukyc9aa9bys0lzvjOXON+ZS5zvzCXOd9bCrz0v3IgOAAAAOiW0AwAAQKeE9o3L7UlOGP4JmzrnO3OJ8525xPnOXOJ8Z525ER0AAAB0ykw7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbRvRKrqf1fVlVX1i6r6RlU9drZrgpmoqldW1Ter6mdVdUNVfbyqHjJtzFZV9c6qurGqbqmqc6tq+2ljdqqqz1TVz4fbOaWqNt+wRwNrr6peUVWtqk4baXOus8moqvtX1f8dns+3VdUlVfWYkf6qqhOr6tph//lVteu0bWxbVWdV1fKqurmq3ldVW2/4o4E1q6p5VXVSVV0xPJcvr6pXVVWNjHG+M1FC+0aiqp6T5NQMHhnx6CT/muTzVbXdrBYGM7NfkncmeXySg5LMT/KFqrr3yJi3JHlWkv8xHL9Dko9OdVbVvCSfSbJFkicmOSrJ85KcuP7Lh5mrqn2S/EmSf5vW5Vxnk1BV90nytSR3JHl6kocl+fMkPx0Z9rIkxyY5Osnjktyawc8xW42MOSvJwzP4/4dnJnlKkves7/phhl6e5E+TvCjJ7sPPL0tyzMgY5zsT5ZFvG4mq+kaSb7bWXjT8vFmSq5O8vbV28qwWB2OqqvsluSHJfq21r1TVoiQ/SXJEa+0jwzEPTfLdJE9orX29qp6e5NNJdmitXT8cc3SSv05yv9baitk4Flid4azJvyR5YZK/SnJxa+3FznU2JVV1cpIntdaevIb+SnJNkje31t40bFuU5Pokz2utnVNVuye5NMk+rbWLhmMOTvLZJA9orV2zAQ4Ffq2q+nSS61trzx9pOzfJba215zrfWR/MtG8EqmqLJHsnOX+qrbW2cvj5CbNVF0zAouGfNw3/3DuD2ffRc/2yJFfl7nP9CUkumQoxQ59PsjCD31hDT96Z5DOttfOntTvX2ZT8bpKLqurvh5dxfLuqXjDSv0uSJbnn+b4syTdyz/P95qkAM3R+kpUZzFRCLy5IcmBV7ZYkVbVnkn2TnDfsd74zca6L2zj8RpJ5GfyGbtT1SR664cuBdTdcLXJakq+11v592LwkyYrW2s3Thl8/7Jsas7r/FjIyBmZdVf1+Bpcz7bOabuc6m5IHZbBc+NQk/yeDc/5tVbWitfaB3H2+ru58Hj3fbxjtbK3dWVU3xflOX07O4Jenl1XVXRn8jH58a+2sYb/znYkT2oHZ8s4ke2Tw22nYpFTVjknemuSg1tovZrseWM82S3JRa+0vh5+/XVV7ZHA97wdmryxYLw5LcmSSI5J8J8mjkpxWVdcMf0kFE2d5/Mbhv5LclWT7ae3bJ7luw5cD66aq3pHBTVf2b639eKTruiRbVNXiaV8ZPdevy+r/W0j890A/9k6yXZJ/qao7q+rODG42d+zw/fVxrrPpuDaD63NHfTfJTsP3U+frr/o55roM/ptZZfikhG3jfKcvpyQ5ubV2TmvtktbaBzO4segrh/3OdyZOaN8IDG829K0kB061DZcWH5jkwtmqC2Zq+AiUdyQ5JMkBrbUrpg35VgZ3Hx491x+SwQ9+U+f6hUkeMe3JCQclWZ5f/qERZsuXkjwigxmYqddFGdwteOq9c51NxdeSPGRa225JfjR8f0UGQWT0fF+YwbW7o+f74qrae2QbB2Tws+o31kPNMK4FGVx7Puqu3J2rnO9MnOXxG49Tk3ygqi5K8s9JXpzk3knOnM2iYIbemcFysv+W5GdVNXXd1rLW2m2ttWVV9b4kpw6v61qe5O1JLmytfX049gsZBJYPVtXLMrj263VJ3tlau31DHgysSWvtZ0n+fbStqm5NcuPUPRyc62xC3pLkgqr6yyQfTvLYJH88fKW11qrqtCR/VVU/yCDUnJTBHbY/Phzz3ar6XJLTh09JmJ/kHUnOcSdtOvOpJMdX1VUZLI/fK8lxSc5InO+sHx75thGpqhcl+YsMfnC7OMmxrTW/jWOjUVVr+h+cP2ytvX84Zqskb05yeJItM7hb9gtba6uWi1XVzkn+JslvZfDs0w8keUVr7c71Vjyso6r6coaPfBt+dq6zyaiqZyZ5Q5JdMwgpp7bWTh/pryQnZBDkFyf5agbn+/dHxmybQXB5VgYzmedm8LPOLRvoMODXqqptMgjhh2SwxP2aJGcnOXHqUZzOdyZNaAcAAIBOuaYdAAAAOiW0AwAAQKeEdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QDAxFXVlVX14tmuAwA2dkI7AGzkqur9VfXx4fsvV9VpG3Dfz6uqm1fTtU+S92yoOgBgU7X5bBcAAPSnqrZora0Y9/uttZ9Msh4AmKvMtAPAJqKq3p9kvyR/VlVt+HrgsG+Pqjqvqm6pquur6oNV9Rsj3/1yVb2jqk6rqv9K8vlh+3FVdUlV3VpVV1fVu6pq62HfbyU5M8mikf29dth3j+XxVbVTVX1iuP/lVfXhqtp+pP+1VXVxVf3B8LvLquqcqtpmZMyzh7XcVlU3VtX5VXXv9fTXCQBdENoBYNPxZ0kuTHJ6kqXD19VVtTjJPyT5dpLHJDk4yfZJPjzt+0clWZHkSUmOHratTHJskocP+w9I8sZh3wVJXpxk+cj+3jS9qKraLMknkmybwS8VDkryoCQfmjb0N5P89yTPHL72S/KK4TaWJjk7yRlJdk/yW0k+mqR+7d8KAGzELI8HgE1Ea21ZVa1I8vPW2nVT7VX1oiTfbq395Ujb/8og0O/WWvv+sPkHrbWXTdvmaSMfr6yqv0ryt0le2FpbUVXLBsPu3t9qHJjkEUl2aa1dPdz//0zynarap7X2zeG4zZI8r7X2s+GYDw6/e3wGvxDYPMlHW2s/Go6/ZC3/agBgo2WmHQA2fXsm2X+4NP2WqrolyWXDvt8cGfet6V+sqqdW1Zeq6j+r6mdJPpjkvlW1YAb73z3J1VOBPUlaa5cmuXnYN+XKqcA+dG2S7Ybv/zXJl5JcUlV/X1UvqKr7zKAGANgoCe0AsOnbOsmnkjxq2mvXJF8ZGXfr6JeG18N/Osm/Jfm9JHsn+d/D7i3WQ513TPvcMvxZpbV2VwbL6p+e5NIkxyT5XlXtsh7qAIBuCO0AsGlZkWTetLZ/yeCa9Ctbaz+c9rr1lzexyt4Z/Kzw5621rw+X0e+wFvub7rtJdqyqHacaquphSRZnEMDXShv4WmvtNUn2Gu77kLX9PgBsjIR2ANi0XJnkcVX1wKr6jeFN4N6ZwU3gzq6qfarqN6vqaVV1ZlX9qsD9wyTzkxxTVQ+qqj/I3TeoG93f1lV14HB/q1s2f34G15+fVVWPrqrHJvm7JP/UWrtobQ6qqh5XVX9ZVY+pqp2SHJrkfhn8QgAANllCOwBsWt6U5K4MZrB/kmSn1to1GdwRfl6SL2QQoE/L4JrylWvaUGvtX5Mcl+TlSf49yZFJXjltzAUZ3JjuQ8P9vWzaZtJaa0n+W5KfZrAc//wk/5HkOTM4ruVJnpLks0m+n+R1GawAOG8G2wCAjU4N/n8UAAAA6I2ZdgAAAOiU0A4AAACdEtoBAACgU0I7AAAAdEpoBwAAgE4J7QAAANApoR0AAAA6JbQDAABAp4R2AAAA6JTQDgAAAJ0S2gEAAKBTQjsAAAB06v8H5xzpoPgjZvcAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]}]}