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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "uq9k8YYUKjnp"
},
"outputs": [],
"source": [
"import os\n",
"import urllib.request\n",
"import zipfile\n",
"import json\n",
"import pandas as pd\n",
"import time\n",
"import torch\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"from sklearn.model_selection import train_test_split\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "L5h3Tsa0LIoo"
},
"outputs": [],
"source": [
"def unzip_archive(filepath, dir_path):\n",
" with zipfile.ZipFile(f\"{filepath}\", 'r') as zip_ref:\n",
" zip_ref.extractall(dir_path)\n",
"\n",
"unzip_archive(os.getcwd() + '/data/raw/spotify_million_playlist_dataset.zip', os.getcwd() + '/data/raw/playlists')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"def make_dir(directory):\n",
" if os.path.exists(directory):\n",
" shutil.rmtree(directory)\n",
" os.makedirs(directory)\n",
" else:\n",
" os.makedirs(directory)\n",
" \n",
"directory = os.getcwd() + '/data/raw/data'\n",
"make_dir(directory)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"cols = [\n",
" 'name',\n",
" 'pid',\n",
" 'num_followers',\n",
" 'pos',\n",
" 'artist_name',\n",
" 'track_name',\n",
" 'album_name'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qyCujIu8cDGg",
"outputId": "0964ace3-2916-49e3-eebf-2e08e61d95d9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mpd.slice.188000-188999.json\t100/1000\t10.0%"
]
}
],
"source": [
"\n",
"directory = os.getcwd() + '/data/raw/playlists/data'\n",
"df = pd.DataFrame()\n",
"index = 0\n",
"# Loop through all files in the directory\n",
"for filename in os.listdir(directory):\n",
" # Check if the item is a file (not a subdirectory)\n",
" if os.path.isfile(os.path.join(directory, filename)):\n",
" if filename.find('.json') != -1 :\n",
" index += 1\n",
"\n",
" # Print the filename or perform operations on the file\n",
" print(f'\\r{filename}\\t{index}/1000\\t{((index/1000)*100):.1f}%', end='')\n",
"\n",
" # If you need the full file path, you can use:\n",
" full_path = os.path.join(directory, filename)\n",
"\n",
" with open(full_path, 'r') as file:\n",
" json_data = json.load(file)\n",
"\n",
" temp = pd.DataFrame(json_data['playlists'])\n",
" expanded_df = temp.explode('tracks').reset_index(drop=True)\n",
"\n",
" # Normalize the JSON data\n",
" json_normalized = pd.json_normalize(expanded_df['tracks'])\n",
"\n",
" # Concatenate the original DataFrame with the normalized JSON data\n",
" result = pd.concat([expanded_df.drop(columns=['tracks']), json_normalized], axis=1)\n",
" \n",
" result = result[cols]\n",
"\n",
" df = pd.concat([df, result], axis=0, ignore_index=True)\n",
" \n",
" if index % 50 == 0:\n",
" df.to_parquet(f'{os.getcwd()}/data/raw/data/playlists_{index % 1000}.parquet')\n",
" del df\n",
" df = pd.DataFrame()\n",
" if index % 100 == 0:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import pyarrow.parquet as pq\n",
"\n",
"def read_parquet_folder(folder_path):\n",
" dataframes = []\n",
" for file in os.listdir(folder_path):\n",
" if file.endswith('.parquet'):\n",
" file_path = os.path.join(folder_path, file)\n",
" df = pd.read_parquet(file_path)\n",
" dataframes.append(df)\n",
" \n",
" return pd.concat(dataframes, ignore_index=True)\n",
"\n",
"folder_path = os.getcwd() + '/data/raw/data'\n",
"df = read_parquet_folder(folder_path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"directory = os.getcwd() + '/data/raw/mappings'\n",
"make_dir(directory)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def create_ids(df, col, name):\n",
" # Create a dictionary mapping unique values to IDs\n",
" value_to_id = {val: i for i, val in enumerate(df[col].unique())}\n",
"\n",
" # Create a new column with the IDs\n",
" df[f'{name}_id'] = df[col].map(value_to_id)\n",
" df[[f'{name}_id', col]].drop_duplicates().to_csv(os.getcwd() + f'/data/raw/mappings/{name}.csv')\n",
" # df = df.drop(col, axis=1)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df = create_ids(df, 'artist_name', 'artist')\n",
"df = create_ids(df, 'pid', 'playlist')\n",
"df = create_ids(df, 'track_name', 'song')\n",
"df = create_ids(df, 'album_name', 'album')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"df['artist_count'] = df.groupby(['playlist_id','artist_id'])['song_id'].transform('nunique')\n",
"df['album_count'] = df.groupby(['playlist_id','artist_id'])['album_id'].transform('nunique')\n",
"df['song_count'] = df.groupby(['playlist_id','artist_id'])['song_id'].transform('count')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df['playlist_songs'] = df.groupby(['playlist_id'])['pos'].transform('max')\n",
"df['playlist_songs'] += 1"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df['artist_percent'] = df['artist_count'] / df['playlist_songs']\n",
"df['song_percent'] = df['song_count'] / df['playlist_songs']\n",
"df['album_percent'] = df['album_count'] / df['playlist_songs']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>pid</th>\n",
" <th>num_followers</th>\n",
" <th>pos</th>\n",
" <th>artist_name</th>\n",
" <th>track_name</th>\n",
" <th>album_name</th>\n",
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" <td>R. Kelly</td>\n",
" <td>Ignition - Remix</td>\n",
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" <td>I Want It That Way</td>\n",
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" <td>Bye Bye Bye</td>\n",
" <td>No Strings Attached</td>\n",
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" <td>5</td>\n",
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" <td>4</td>\n",
" <td>Bowling For Soup</td>\n",
" <td>1985</td>\n",
" <td>A Hangover You Don't Deserve</td>\n",
" <td>112</td>\n",
" <td>5</td>\n",
" <td>207</td>\n",
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" <td>Spice Girls</td>\n",
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" <td>Spice</td>\n",
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" <td>This Is Why I'm Hot</td>\n",
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" <td>throwbacks</td>\n",
" <td>143005</td>\n",
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" <td>192</td>\n",
" <td>DEV</td>\n",
" <td>Bass Down Low</td>\n",
" <td>The Night The Sun Came Up</td>\n",
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"</div>"
],
"text/plain": [
" name pid num_followers pos artist_name \\\n",
"212 throwbacks 143005 2 0 R. Kelly \n",
"213 throwbacks 143005 2 1 Backstreet Boys \n",
"214 throwbacks 143005 2 2 *NSYNC \n",
"215 throwbacks 143005 2 3 Fountains Of Wayne \n",
"216 throwbacks 143005 2 4 Bowling For Soup \n",
".. ... ... ... ... ... \n",
"400 throwbacks 143005 2 188 JoJo \n",
"401 throwbacks 143005 2 189 Spice Girls \n",
"402 throwbacks 143005 2 190 MiMS \n",
"403 throwbacks 143005 2 191 Rihanna \n",
"404 throwbacks 143005 2 192 DEV \n",
"\n",
" track_name album_name \\\n",
"212 Ignition - Remix Chocolate Factory \n",
"213 I Want It That Way Original Album Classics \n",
"214 Bye Bye Bye No Strings Attached \n",
"215 Stacy's Mom Welcome Interstate Managers \n",
"216 1985 A Hangover You Don't Deserve \n",
".. ... ... \n",
"400 Too Little, Too Late - Radio Version Too Little, Too Late \n",
"401 Wannabe - Radio Edit Spice \n",
"402 This Is Why I'm Hot Music Is My Savior \n",
"403 Disturbia Good Girl Gone Bad \n",
"404 Bass Down Low The Night The Sun Came Up \n",
"\n",
" artist_id playlist_id song_id album_id artist_count album_count \\\n",
"212 108 5 203 152 1 1 \n",
"213 109 5 204 153 1 1 \n",
"214 110 5 205 154 1 1 \n",
"215 111 5 206 155 1 1 \n",
"216 112 5 207 156 1 1 \n",
".. ... ... ... ... ... ... \n",
"400 199 5 390 293 1 1 \n",
"401 200 5 391 294 1 1 \n",
"402 201 5 392 295 1 1 \n",
"403 115 5 393 296 3 3 \n",
"404 179 5 394 264 2 1 \n",
"\n",
" song_count playlist_songs artist_percent song_percent album_percent \n",
"212 1 193 0.005181 0.005181 0.005181 \n",
"213 1 193 0.005181 0.005181 0.005181 \n",
"214 1 193 0.005181 0.005181 0.005181 \n",
"215 1 193 0.005181 0.005181 0.005181 \n",
"216 1 193 0.005181 0.005181 0.005181 \n",
".. ... ... ... ... ... \n",
"400 1 193 0.005181 0.005181 0.005181 \n",
"401 1 193 0.005181 0.005181 0.005181 \n",
"402 1 193 0.005181 0.005181 0.005181 \n",
"403 3 193 0.015544 0.015544 0.015544 \n",
"404 2 193 0.010363 0.010363 0.005181 \n",
"\n",
"[193 rows x 18 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['playlist_id'] == 5]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>playlist_id</th>\n",
" <th>artist_id</th>\n",
" <th>artist_percent</th>\n",
" </tr>\n",
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],
"text/plain": [
" playlist_id artist_id artist_percent\n",
"0 0 0 0.571429\n",
"1 0 0 0.571429\n",
"2 0 0 0.571429\n",
"3 0 0 0.571429\n",
"4 0 0 0.571429"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"artists = df.loc[:,['playlist_id','artist_id','album_id','album_percent']]\n",
"artists.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"X = artists.loc[:,['playlist_id','artist_id','album_id']]\n",
"y = artists.loc[:,'album_percent']\n",
"\n",
"# Split our data into training and test sets\n",
"X_train, X_val, y_train, y_val = train_test_split(X,y,random_state=0, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def prep_dataloaders(X_train,y_train,X_val,y_val,batch_size):\n",
" # Convert training and test data to TensorDatasets\n",
" trainset = TensorDataset(torch.from_numpy(np.array(X_train)).long(), \n",
" torch.from_numpy(np.array(y_train)).float())\n",
" valset = TensorDataset(torch.from_numpy(np.array(X_val)).long(), \n",
" torch.from_numpy(np.array(y_val)).float())\n",
"\n",
" # Create Dataloaders for our training and test data to allow us to iterate over minibatches \n",
" trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
" valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False)\n",
"\n",
" return trainloader, valloader\n",
"\n",
"batchsize = 64\n",
"trainloader,valloader = prep_dataloaders(X_train,y_train,X_val,y_val,batchsize)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"class NNColabFiltering(nn.Module):\n",
" \n",
" def __init__(self, n_playlists, n_artists, embedding_dim_users, embedding_dim_items, n_activations, rating_range):\n",
" super().__init__()\n",
" self.user_embeddings = nn.Embedding(num_embeddings=n_playlists,embedding_dim=embedding_dim_users)\n",
" self.item_embeddings = nn.Embedding(num_embeddings=n_artists,embedding_dim=embedding_dim_items)\n",
" self.fc1 = nn.Linear(embedding_dim_users+embedding_dim_items,n_activations)\n",
" self.fc2 = nn.Linear(n_activations,1)\n",
" self.rating_range = rating_range\n",
"\n",
" def forward(self, X):\n",
" # Get embeddings for minibatch\n",
" embedded_users = self.user_embeddings(X[:,0])\n",
" embedded_items = self.item_embeddings(X[:,1])\n",
" # Concatenate user and item embeddings\n",
" embeddings = torch.cat([embedded_users,embedded_items],dim=1)\n",
" # Pass embeddings through network\n",
" preds = self.fc1(embeddings)\n",
" preds = F.relu(preds)\n",
" preds = self.fc2(preds)\n",
" # Scale predicted ratings to target-range [low,high]\n",
" preds = torch.sigmoid(preds) * (self.rating_range[1]-self.rating_range[0]) + self.rating_range[0]\n",
" return preds"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def train_model(model, criterion, optimizer, dataloaders, device, num_epochs=5, scheduler=None):\n",
" model = model.to(device) # Send model to GPU if available\n",
" since = time.time()\n",
"\n",
" costpaths = {'train':[],'val':[]}\n",
"\n",
" for epoch in range(num_epochs):\n",
" print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
" print('-' * 10)\n",
"\n",
" # Each epoch has a training and validation phase\n",
" for phase in ['train', 'val']:\n",
" if phase == 'train':\n",
" model.train() # Set model to training mode\n",
" else:\n",
" model.eval() # Set model to evaluate mode\n",
"\n",
" running_loss = 0.0\n",
"\n",
" # Get the inputs and labels, and send to GPU if available\n",
" index = 0\n",
" for (inputs,labels) in dataloaders[phase]:\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # Zero the weight gradients\n",
" optimizer.zero_grad()\n",
"\n",
" # Forward pass to get outputs and calculate loss\n",
" # Track gradient only for training data\n",
" with torch.set_grad_enabled(phase == 'train'):\n",
" outputs = model.forward(inputs).view(-1)\n",
" loss = criterion(outputs, labels)\n",
"\n",
" # Backpropagation to get the gradients with respect to each weight\n",
" # Only if in train\n",
" if phase == 'train':\n",
" loss.backward()\n",
" # Update the weights\n",
" optimizer.step()\n",
"\n",
" # Convert loss into a scalar and add it to running_loss\n",
" running_loss += np.sqrt(loss.item()) * labels.size(0)\n",
" print(f'\\r{running_loss} {index} {index / len(dataloaders[phase])}', end='')\n",
" index +=1\n",
"\n",
" # Step along learning rate scheduler when in train\n",
" if (phase == 'train') and (scheduler is not None):\n",
" scheduler.step()\n",
"\n",
" # Calculate and display average loss and accuracy for the epoch\n",
" epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
" costpaths[phase].append(epoch_loss)\n",
" print('{} loss: {:.4f}'.format(phase, epoch_loss))\n",
"\n",
" time_elapsed = time.time() - since\n",
" print('Training complete in {:.0f}m {:.0f}s'.format(\n",
" time_elapsed // 60, time_elapsed % 60))\n",
"\n",
" return costpaths"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0/2\n",
"----------\n",
"910724978601.7391 123493 100.00%\n",
"train loss: 115229.4395\n",
"227700857865.127 30873 100.00%\n",
"val loss: 115239.3512\n",
"Epoch 1/2\n",
"----------\n",
"910727409277.4519 123493 100.00%\n",
"train loss: 115229.7471\n",
"227700857865.127 30873 100.00%\n",
"val loss: 115239.3512\n",
"Epoch 2/2\n",
"----------\n",
"910734475316.9005 123493 100.00%\n",
"train loss: 115230.6411\n",
"227700857865.127 30873 100.00%\n",
"val loss: 115239.3512\n",
"Training complete in 71m 54s\n"
]
}
],
"source": [
"dataloaders = {'train':trainloader, 'val':valloader}\n",
"n_playlists = X.loc[:,'playlist_id'].max()+1\n",
"n_artists = X.loc[:,'artist_id'].max()+1\n",
"n_albums = X.loc[:,'album_id'].max()+1\n",
"model = NNColabFiltering(\n",
" n_playlists,\n",
" n_artists,\n",
" embedding_dim_users=50,\n",
" embedding_dim_items=50,\n",
" n_activations = 100,\n",
" rating_range=[0.,n_albums]\n",
")\n",
"criterion = nn.MSELoss()\n",
"lr=0.001\n",
"n_epochs=10\n",
"wd=1e-3\n",
"optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=wd)\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"costpaths = train_model(model,criterion,optimizer,dataloaders, device, n_epochs, scheduler=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
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mzZoym80aOnSoFi5cqKZNmyorK0s+Pj4KDAws079OnTrKysq64Fg5OTk6deqUXnvtNd1777365ptv1KNHD/Xs2VNr1qy5aA0TJ06U1Wp1PSIjIytyigAA4BrYuD9Xw2anqtThVM/W9fR8lyYymUxGlwUAAIBqzMvoAho3bqy0tDTZbDbNmzdPAwcOvGQodikOh0OS1K1bN40aNUqS1KpVK61bt07Tpk3THXfcccHXjR07VqNHj3Y9t9vthG0AALixnZk2DZm5SYUlDt0dHarXe7WUhwchGwAAAIxleNDm4+OjRo3ObvOIi4vTpk2bNHnyZPXp00dFRUXKy8src1Zbdna2wsLCLjhW7dq15eXlpaZNm5Zpb9KkiX744YeL1mA2m2U2m69+MgAA4Jo7cDxfAz/YpJOFJWrXMEhT+sXK29Pwk/QBAAAA47eO/i+Hw6HCwkLFxcXJ29tbK1eudB1LT0/XoUOHFB8ff8HX+vj4qG3btkpPTy/TvmfPHjVo0OCa1g0AAK69bHuBHpm+QcdPFapJXYv+ObCNfL09jS4LAAAAkGTwGW1jx47Vfffdp/r16+vkyZOaPXu2Vq9ereXLl8tqtWrIkCEaPXq0goKCZLFYNGLECMXHx5e542h0dLQmTpyoHj16SJKeeeYZ9enTR7fffrs6deqkZcuW6csvv9Tq1asNmiUAAKgIeaeLNGD6RmWcOKOGwTX04eB2svp5G10WAAAA4GJo0JaTk6MBAwbo6NGjslqtatmypZYvX6577rlHkvTWW2/Jw8NDSUlJKiwsVGJiot57770yY6Snp8tms7me9+jRQ9OmTdPEiRP1hz/8QY0bN9b8+fPVsWPH6zo3AABQcU4XlWjwzE1Kzz6p0ACzPhrSXiEBXPYBAAAA7sXkdDqdRhfhbux2u6xWq2w2mywWi9HlAABQrRWVOPT4h5u1Zs8xWXy9NHfoLWocFmB0WRfEGsL98R0BAIArUd41hNtdow0AAOAch8OpP87dqjV7jsnP21MzHm3ntiEbAAAAQNAGAADcktPp1IQvd+rLrZny8jBp6iOximtQy+iyAAAAgIsiaAMAAG7p7W/36sPkgzKZpDd7x+jOxqFGlwQAAABcEkEbAABwOzN/3K/JK/dKkl56sJm6tapncEUAAADAbyNoAwAAbmXRliOa8OUuSdKozjerf3xDYwsCAAAAyomgDQAAuI3vdufoT3O3SpIG3dJQf7i7kcEVAQAAAOVH0AYAANzCpgO5euqTFJU4nOreKlzjHmgqk8lkdFkAAABAuRG0AQAAw/101K7BMzepoNihu6JD9cbvYuThQcgGAACAyoWgDQAAGOrgr/ka8MFGnSwoUZsGtTTl4Vh5e7JEuV7Wrl2rrl27Kjw8XCaTSYsWLXIdKy4u1pgxY9SiRQv5+/srPDxcAwYMUGZmZpkxcnNz1a9fP1ksFgUGBmrIkCE6depUmT7btm3TbbfdJl9fX0VGRmrSpEnn1TJ37lxFR0fL19dXLVq00FdffVXmuNPp1Lhx41S3bl35+fmpc+fO2rt3b8V9GAAAAFeJVSwAADBMjr1A/adv1LGThYoOC9D0QW3l5+NpdFnVSn5+vmJiYjRlypTzjp0+fVqpqal64YUXlJqaqgULFig9PV0PPvhgmX79+vXTzp07tWLFCi1ZskRr167VE0884Tput9uVkJCgBg0aKCUlRW+88YYmTJig999/39Vn3bp1euihhzRkyBBt2bJF3bt3V/fu3bVjxw5Xn0mTJumdd97RtGnTtGHDBvn7+ysxMVEFBQXX4JMBAAC4fCan0+k0ugh3Y7fbZbVaZbPZZLFYjC4HAIAqyXa6WH3eT9burJOqH1RD84bGK9Tia3RZV6WyryFMJpMWLlyo7t27X7TPpk2b1K5dOx08eFD169fXTz/9pKZNm2rTpk1q06aNJGnZsmW6//77lZGRofDwcE2dOlXPPfecsrKy5OPjI0l69tlntWjRIu3evVuS1KdPH+Xn52vJkiWu9+rQoYNatWqladOmyel0Kjw8XH/84x/1pz/9SZJks9lUp04dzZw5U3379i3XHCv7dwQAAIxR3jUEZ7QBAIDr7kxRqYbM2qTdWScVEmDWx0PaV/qQrbqw2WwymUwKDAyUJCUnJyswMNAVsklS586d5eHhoQ0bNrj63H777a6QTZISExOVnp6uEydOuPp07ty5zHslJiYqOTlZkrR//35lZWWV6WO1WtW+fXtXnwspLCyU3W4v8wAAALhWCNoAAMB1VVzq0O8/SdHmgycU4OulDwe3U/3gGkaXhXIoKCjQmDFj9NBDD7l+k5uVlaXQ0NAy/by8vBQUFKSsrCxXnzp16pTpc+75b/X57+P//boL9bmQiRMnymq1uh6RkZGXNWcAAIDLQdAGAACuG4fDqWfmbtV36cfk6+2hGYPaqkldtu9VBsXFxerdu7ecTqemTp1qdDnlNnbsWNlsNtfj8OHDRpcEAACqMC+jCwAAANWD0+nUS0t2aVFaprw8TJraL05tGgYZXRbK4VzIdvDgQa1atarMdUnCwsKUk5NTpn9JSYlyc3MVFhbm6pOdnV2mz7nnv9Xnv4+fa6tbt26ZPq1atbpo7WazWWaz+XKmCwAAcMU4ow0AAFwX76z8WTPXHZAkvdk7Rp2iQy/9AriFcyHb3r179e233yo4OLjM8fj4eOXl5SklJcXVtmrVKjkcDrVv397VZ+3atSouLnb1WbFihRo3bqxatWq5+qxcubLM2CtWrFB8fLwkKSoqSmFhYWX62O12bdiwwdUHAADAaARtAADgmvsw+YDe+naPJGlC16bq1qqewRXhnFOnTiktLU1paWmSzt50IC0tTYcOHVJxcbF69eqlzZs365NPPlFpaamysrKUlZWloqIiSVKTJk1077336vHHH9fGjRv1448/avjw4erbt6/Cw8MlSQ8//LB8fHw0ZMgQ7dy5U3PmzNHkyZM1evRoVx1PP/20li1bpjfffFO7d+/WhAkTtHnzZg0fPlzS2Tuijhw5Un/961+1ePFibd++XQMGDFB4ePgl75IKAABwPZmcTqfT6CLcDbd9BwCg4nyRdkQj56TJ6ZSevvsmjbrnZqNLumYq4xpi9erV6tSp03ntAwcO1IQJExQVFXXB13333Xe68847JUm5ubkaPny4vvzyS3l4eCgpKUnvvPOOatas6eq/bds2DRs2TJs2bVLt2rU1YsQIjRkzpsyYc+fO1fPPP68DBw7opptu0qRJk3T//fe7jjudTo0fP17vv/++8vLy1LFjR7333nu6+eby/5mqjN8RAAAwXnnXEARtF8ACDACAirE6PUePzdqsEodTA+Ib6MUHm8lkMhld1jXDGsL98R0BAIArUd41BFtHAQDANZFyMFdDP05RicOpB2PCNaFr1Q7ZAAAAAII2AABQ4XZn2fXojE0qKHbojptD9LffxcjDg5ANAAAAVRtBGwAAqFCHc09rwPSNsheUKLZ+oKY+EisfL5YcAAAAqPpY9QIAgApz7GShHpm+QTknC9W4ToA+GNRWNXy8jC4LAAAAuC4I2gAAQIWwnSnWgA826uCvpxUZ5KcPh7RTYA0fo8sCAAAArhuCNgAAcNXOFJXq8Vmb9dNRu2rXNOujwe1Vx+JrdFkAAADAdUXQBgAArkpxqUPDZ6dq44FcBfh66cPB7dSwtr/RZQEAAADXHUEbAAC4Yg6HU3+et00rd+fI7OWh6QPbqmm4xeiyAAAAAEMQtAEAgCvidDr18tJdWrjliDw9THqvX6zaRQUZXRYAAABgGII2AABwRaZ897Nm/HhAkvRGr5a6u0kdYwsCAAAADEbQBgAALtvH6w/qb9/skSSNe6CpesZGGFwRAAAAYDyCNgAAcFmWbMvUC1/skCSNuKuRBneMMrgiAAAAwD0QtAEAgHJbu+eYRs1Jk9Mp9WtfX6PvudnokgAAAAC3QdAGAADKJfXQCT35UYqKS516oGVdvdStuUwmk9FlAQAAAG6DoA0AAPymPdkn9eiMTTpTXKrbbqqtv/duJU8PQjYAAADgvxG0AQCASzqce1r9p2+Q7UyxWtcP1D/6x8nHiyUEAAAA8L9YJQMAgIs6drJQ/advULa9UDfXqakZg9qqho+X0WUBAAAAbomgDQAAXJC9oFiDZmzUgV9Pq16gnz4c3F6BNXyMLgsAAABwWwRtAADgPAXFpXps1mbtzLQr2N9HHz/WXmFWX6PLAgAAANwaQRsAACijpNSh4bO3aOP+XAWYvTRrcDtF1fY3uiwAAADA7RG0AQAAF4fDqTHzt+vbn7Ll4+Whfw5so+b1rEaXBQAAAFQKBG0AAECS5HQ69epXP2l+aoY8PUya8nCsOtwQbHRZAAAAQKVB0AYAACRJU9fs079+2C9JmpTUUvc0rWNwRQAAAEDlQtAGAAA0e8MhTVqWLkl6vksTJcVFGFwRAAAAUPkQtAEAUM19tf2onlu0XZI0rNONeuy2GwyuCAAAAKicCNoAAKjGvt97TE9/tkVOp/RQu/r6U0Jjo0sCAAAAKi2CNgAAqqm0w3l68qMUFZc6dX+LMP21e3OZTCajywIAAAAqLYI2AACqoZ9zTmrQjI06XVSqjo1q660+reTpQcgGAAAAXA2CNgAAqpmME6f1yL82Ku90sWIiA/WP/nEye3kaXRYAAABQ6RkatE2dOlUtW7aUxWKRxWJRfHy8vv76a9fxgoICDRs2TMHBwapZs6aSkpKUnZ1d7vGHDh0qk8mkt99++xpUDwBA5fPrqUINmL5RWfYCNQqtqRmD2srf7GV0WQAAAECVYGjQFhERoddee00pKSnavHmz7rrrLnXr1k07d+6UJI0aNUpffvml5s6dqzVr1igzM1M9e/Ys19gLFy7U+vXrFR4efi2nAABApXGyoFiDZmzSL8fzVS/QTx8Naacgfx+jywIAAACqDEN/hd21a9cyz1955RVNnTpV69evV0REhKZPn67Zs2frrrvukiTNmDFDTZo00fr169WhQ4eLjnvkyBGNGDFCy5cvV5cuXX6zjsLCQhUWFrqe2+32K5wRAADuqaC4VE98mKLtR2wK9vfRR0Paqa7Vz+iyAAAAgCrFba7RVlpaqs8++0z5+fmKj49XSkqKiouL1blzZ1ef6Oho1a9fX8nJyRcdx+FwqH///nrmmWfUrFmzcr33xIkTZbVaXY/IyMirng8AAO6ipNShP3y6Rcm//KqaZi/NfLSdbgipaXRZAAAAQJVjeNC2fft21axZU2azWUOHDtXChQvVtGlTZWVlycfHR4GBgWX616lTR1lZWRcd7/XXX5eXl5f+8Ic/lLuGsWPHymazuR6HDx++0ukAAOBWnE6nxi7Yrm92ZcvHy0P/HNBGLSKsRpcFAAAAVEmGX/24cePGSktLk81m07x58zRw4ECtWbPmisZKSUnR5MmTlZqaKpPJVO7Xmc1mmc3mK3pPAADc2Wtf79bclAx5mKT/e6i14m8MNrokAAAAoMoy/Iw2Hx8fNWrUSHFxcZo4caJiYmI0efJkhYWFqaioSHl5eWX6Z2dnKyws7IJjff/998rJyVH9+vXl5eUlLy8vHTx4UH/84x/VsGHDaz8ZAADcyLQ1+/SPtb9Ikl5LaqnEZhf+9xMAAABAxTA8aPtfDodDhYWFiouLk7e3t1auXOk6lp6erkOHDik+Pv6Cr+3fv7+2bdumtLQ01yM8PFzPPPOMli9ffr2mAACA4T7beEivfb1bkvSX+6PVuw3XHwUAAACuNUO3jo4dO1b33Xef6tevr5MnT2r27NlavXq1li9fLqvVqiFDhmj06NEKCgqSxWLRiBEjFB8fX+aOo9HR0Zo4caJ69Oih4OBgBQeX3RLj7e2tsLAwNW7c+HpPDwAAQyzbcVR/WbhdkjT0jhv1xO03GlwRAAAAUD0YGrTl5ORowIABOnr0qKxWq1q2bKnly5frnnvukSS99dZb8vDwUFJSkgoLC5WYmKj33nuvzBjp6emy2WxGlA8AgNtZ9/Nx/eHTNDmcUt+2kRpzL79oAgAAAK4Xk9PpdBpdhLux2+2yWq2y2WyyWCxGlwMAQLlsy8jTQ++vV35Rqe5tFqYp/WLl6VH+mwPh6rGGcH98RwAA4EqUdw3hdtdoAwAAl+/nnFMaNGOT8otKdWujYE1+qBUhGwAAAHCdEbQBAFDJHck7owHTNyg3v0gtI6z6R/82Mnt5Gl0WAAAAUO0QtAEAUIn9eqpQ/advUKatQDeE+Gvmo+1U02zoJVgBAACAaougDQCASupUYYkenblJvxzLV7jVVx8Paa8gfx+jywIAAACqLYI2AAAqocKSUj3x4WZty7CpVg1vfTikvcID/YwuCwAAAKjWCNoAAKhkSh1OPf1pmtbt+1X+Pp6a+Wg7NQqtaXRZAAAAQLVH0AYAQCXidDr13MLtWrYzSz6eHnp/QBvFRAYaXRYAAAAAEbQBAFCpTFqers82HZaHSXrnoVa6tVFto0sCAAAA8G8EbQAAVBLvr92nqav3SZIm9myhe5vXNbgiAAAAAP+NoA0AgErg882H9epXuyVJz94XrT5t6xtcEaqKtWvXqmvXrgoPD5fJZNKiRYvKHF+wYIESEhIUHBwsk8mktLS088bIyspS//79FRYWJn9/f8XGxmr+/Pll+uTm5qpfv36yWCwKDAzUkCFDdOrUqTJ9tm3bpttuu02+vr6KjIzUpEmTznuvuXPnKjo6Wr6+vmrRooW++uqrq/4MAAAAKgpBGwAAbm75ziw9O3+bJOnJ22/Q0DtuNLgiVCX5+fmKiYnRlClTLnq8Y8eOev311y86xoABA5Senq7Fixdr+/bt6tmzp3r37q0tW7a4+vTr1087d+7UihUrtGTJEq1du1ZPPPGE67jdbldCQoIaNGiglJQUvfHGG5owYYLef/99V59169bpoYce0pAhQ7RlyxZ1795d3bt3144dOyrgkwAAALh6JqfT6TS6CHdjt9tltVpls9lksViMLgcAUI2t23dcg2ZsUlGJQ73bROj1pJYymUxGl4WLqOxrCJPJpIULF6p79+7nHTtw4ICioqK0ZcsWtWrVqsyxmjVraurUqerfv7+rLTg4WK+//roee+wx/fTTT2ratKk2bdqkNm3aSJKWLVum+++/XxkZGQoPD9fUqVP13HPPKSsrSz4+PpKkZ599VosWLdLu3WfP5uzTp4/y8/O1ZMkS1/t06NBBrVq10rRp08o1x8r+HQEAAGOUdw3BGW0AALip7Rk2PfFhiopKHEpoWkev9mhByAa3dMstt2jOnDnKzc2Vw+HQZ599poKCAt15552SpOTkZAUGBrpCNknq3LmzPDw8tGHDBlef22+/3RWySVJiYqLS09N14sQJV5/OnTuXee/ExEQlJydftLbCwkLZ7fYyDwAAgGuFoA0AADe079gpDZyxUacKS9ThhiC981BreXnyzzbc0+eff67i4mIFBwfLbDbrySef1MKFC9WoUSNJZ6/hFhoaWuY1Xl5eCgoKUlZWlqtPnTp1yvQ59/y3+pw7fiETJ06U1Wp1PSIjI69usgAAAJfAih0AADdz1HZGA6ZvVG5+kZrXs+ifA9rI19vT6LKAi3rhhReUl5enb7/9Vps3b9bo0aPVu3dvbd++3ejSNHbsWNlsNtfj8OHDRpcEAACqMC+jCwAAAP9xIr9I/adv1JG8M7qhtr9mPtpOAb7eRpcFXNS+ffv07rvvaseOHWrWrJkkKSYmRt9//72mTJmiadOmKSwsTDk5OWVeV1JSotzcXIWFhUmSwsLClJ2dXabPuee/1efc8Qsxm80ym81XN0kAAIBy4ow2AADcRH5hiQbN3KSfc06prtVXHw5pp9o1CQjg3k6fPi1J8vAou6z09PSUw+GQJMXHxysvL08pKSmu46tWrZLD4VD79u1dfdauXavi4mJXnxUrVqhx48aqVauWq8/KlSvLvM+KFSsUHx9f8RMDAAC4AgRtAAC4gcKSUg39OEVbD+epVg1vfTSknSJq1TC6LFQDp06dUlpamtLS0iRJ+/fvV1pamg4dOiRJys3NVVpamnbt2iVJSk9PV1pamuu6aNHR0WrUqJGefPJJbdy4Ufv27dObb76pFStWuO5e2qRJE9177716/PHHtXHjRv34448aPny4+vbtq/DwcEnSww8/LB8fHw0ZMkQ7d+7UnDlzNHnyZI0ePdpV69NPP61ly5bpzTff1O7duzVhwgRt3rxZw4cPv06fFgAAwKURtAEAYLBSh1Oj52zV93uPq4aPp2Y82k6NQgOMLgvVxObNm9W6dWu1bt1akjR69Gi1bt1a48aNkyQtXrxYrVu3VpcuXSRJffv2VevWrTVt2jRJkre3t7766iuFhISoa9euatmypT788EPNmjVL999/v+t9PvnkE0VHR+vuu+/W/fffr44dO+r99993Hbdarfrmm2+0f/9+xcXF6Y9//KPGjRunJ554wtXnlltu0ezZs/X+++8rJiZG8+bN06JFi9S8efNr/jkBAACUh8npdDqNLsLd2O12Wa1W2Ww2WSwWo8sBAFRhTqdTf1m4Q59uPCRvT5NmDGqnjjfVNrosXCHWEO6P7wgAAFyJ8q4hOKMNAAAD/e2bdH268ZBMJmly39aEbAAAAEAlRtAGAIBB/vX9L5ry3T5J0ivdW+j+FnUNrggAAADA1SBoAwDAAPNSMvTXpT9Jkp5JbKyH29c3uCIAAAAAV4ugDQCA62zFrmyNmb9NkvRYxyj9/s4bDa4IAAAAQEUgaAMA4Dpa/8uvGjY7VaUOp5JiI/SX+5vIZDIZXRYAAACACkDQBgDAdbLjiE2Pz9qsohKHOjepo9eTWsjDg5ANAAAAqCoI2gAAuA72H8/XoBkbdbKwRO2jgvTuw63l5ck/wwAAAEBVwgofAIBrLMtWoEf+tUHHTxWpWbhF/xzYRr7enkaXBQAAAKCCEbQBAHAN5Z0u0oAPNuhI3hlF1fbXrMHtZPH1NrosAAAAANcAQRsAANdIfmGJBs3YpD3Zp1THYtaHg9updk2z0WUBAAAAuEYI2gAAuAaKShwa+nGK0g7nyernrY+GtFdkUA2jywIAAABwDRG0AQBQwUodTo3+PE3f7z0uP29PzXi0rW6uE2B0WQAAAACuMYI2AAAqkNPp1LgvdmjJtqPy9jRpWv84xdavZXRZAAAAAK4DgjYAACrQWyv26JMNh2QySX/v3Up33BxidEkAAAAArhOCNgAAKsgHP+zXO6t+liS93K25usaEG1wRAAAAgOuJoA0AgAqwcEuGXlqyS5L0p4Sb9UiHBgZXBAAAAOB6I2gDAOAqrdqdrT/N3SZJGnxrlIZ1amRwRQAAAACMQNAGAMBV2Lg/V099nKpSh1M9W9fT812ayGQyGV0WAAAAAAMQtAEAcIV2Zto0ZOYmFZY4dHd0qF7v1VIeHoRsAAAAQHVF0AYAwBU4cDxfAz/YpJOFJWrXMEhT+sXK25N/VgEAAIDqjJ8IAAC4TNn2Aj0yfYOOnypUk7oW/XNgG/l6expdFgAAAACDEbQBAHAZbKeLNWD6RmWcOKMGwTU0a3BbWf28jS4LAAAAgBsgaAMAoJxOF5Vo8KxNSs8+qdAAsz4e0l6hAb5GlwUAAADATRC0AQBQDkUlDj31capSDp6QxddLHw1pr8igGkaXBQAAAMCNELQBAPAbHA6n/jR3q9bsOSY/b0/NeLSdGocFGF0WAAAAADdD0AYAwCU4nU69+OVOLd6aKS8Pk6Y+Equ4BrWMLgsAAACAGyJoAwDgEt7+dq9mJR+UySS92TtGdzYONbokAAAAAG7K0KBt6tSpatmypSwWiywWi+Lj4/X111+7jhcUFGjYsGEKDg5WzZo1lZSUpOzs7IuOV1xcrDFjxqhFixby9/dXeHi4BgwYoMzMzOsxHQBAFTPzx/2avHKvJOmlB5upW6t6BlcEAAAAwJ0ZGrRFRETotddeU0pKijZv3qy77rpL3bp1086dOyVJo0aN0pdffqm5c+dqzZo1yszMVM+ePS863unTp5WamqoXXnhBqampWrBggdLT0/Xggw9erykBAKqIRVuOaMKXuyRJozrfrP7xDY0tCAAAAIDbMzmdTqfRRfy3oKAgvfHGG+rVq5dCQkI0e/Zs9erVS5K0e/duNWnSRMnJyerQoUO5xtu0aZPatWungwcPqn79+uV6jd1ul9Vqlc1mk8ViueK5AAAqp+925+jxDzerxOHUoFsaanzXpjKZTEaXhUqANYT74zsCAABXorxrCLe5Rltpaak+++wz5efnKz4+XikpKSouLlbnzp1dfaKjo1W/fn0lJyeXe1ybzSaTyaTAwMCL9iksLJTdbi/zAABUT5sP5OqpT1JU4nCqW6twjXuAkA0AAABA+RgetG3fvl01a9aU2WzW0KFDtXDhQjVt2lRZWVny8fE5LyCrU6eOsrKyyjV2QUGBxowZo4ceeuiSaePEiRNltVpdj8jIyKuZEgCgkvrpqF2DZ25SQbFDnRqH6G+/i5GHByEbAAAAgPIxPGhr3Lix0tLStGHDBj311FMaOHCgdu3addXjFhcXq3fv3nI6nZo6deol+44dO1Y2m831OHz48FW/PwCgcjn062kN+GCj7AUlatOglt7rFydvT8P/mQQAAABQiXgZXYCPj48aNWokSYqLi9OmTZs0efJk9enTR0VFRcrLyytzVlt2drbCwsIuOea5kO3gwYNatWrVb15/w2w2y2w2X/VcAACVU87JAj0yfYOOnSxUdFiApg9qKz8fT6PLAgAAAFDJuN2v6h0OhwoLCxUXFydvb2+tXLnSdSw9PV2HDh1SfHz8RV9/LmTbu3evvv32WwUHB1+PsgEAlZTtTLEGTN+oQ7mnVT+ohj4c3E5WP2+jywIAAABQCRl6RtvYsWN13333qX79+jp58qRmz56t1atXa/ny5bJarRoyZIhGjx6toKAgWSwWjRgxQvHx8WXuOBodHa2JEyeqR48eKi4uVq9evZSamqolS5aotLTUdT23oKAg+fj4GDVVAIAbOlNUqiEzN2l31kmFBJj18ZD2CrX4Gl0WAAAAgErK0KAtJydHAwYM0NGjR2W1WtWyZUstX75c99xzjyTprbfekoeHh5KSklRYWKjExES99957ZcZIT0+XzWaTJB05ckSLFy+WJLVq1apMv++++0533nnnNZ8TAKByKC516PefpGjzwRMK8PXSh4PbqX5wDaPLAgAAAFCJmZxOp9PoItyN3W6X1WqVzWb7zeu7AQAqH4fDqdGfp2lRWqZ8vT300ZD2atswyOiyUAWwhnB/fEcAAOBKlHcN4XbXaAMA4FpyOp16ackuLUrLlJeHSVP7xRGyAQAAAKgQBG0AgGrl/1b9rJnrDkiS/va7GHWKDjW2IAAAAABVBkEbAKDa+Cj5gP6+Yo8kaULXpureup7BFQEAAACoSgjaAADVwuKtmRq3eKck6em7b9KgW6MMrggAAABAVUPQBgCo8lan52j0nDQ5ndKA+AYa2fkmo0sCAAAAUAURtAEAqrSUgyf01MepKnE49WBMuCZ0bSaTyWR0WQAAAACqIII2AECVlZ51UoNnbtKZ4lLdcXOI/va7GHl4ELIBAAAAuDYI2gAAVdLh3NPqP32DbGeKFVs/UFMfiZWPF//sAQAAALh2+IkDAFDlHDtZqEemb1DOyUI1rhOgDwa1VQ0fL6PLAgAAAFDFEbQBAKoU25liDfhgow7+eloRtfz04ZB2CqzhY3RZAAAAAKoBgjYAQJVRUFyqx2dt1k9H7apd00cfD2mvOhZfo8sCAAAAUE0QtAEAqoTiUoeGz07VxgO5CjB7adbgdmpY29/osgAAAABUIwRtAIBKz+Fwasy8bfr2pxyZvTw0fVBbNQu3Gl0WUCmsXbtWXbt2VXh4uEwmkxYtWlTm+IIFC5SQkKDg4GCZTCalpaVdcJzk5GTddddd8vf3l8Vi0e23364zZ864jufm5qpfv36yWCwKDAzUkCFDdOrUqTJjbNu2Tbfddpt8fX0VGRmpSZMmnfc+c+fOVXR0tHx9fdWiRQt99dVXV/0ZAAAAVBSCNgBApeZ0OvXXpT9pwZYj8vQw6b1+sWoXFWR0WUClkZ+fr5iYGE2ZMuWixzt27KjXX3/9omMkJyfr3nvvVUJCgjZu3KhNmzZp+PDh8vD4z1KzX79+2rlzp1asWKElS5Zo7dq1euKJJ1zH7Xa7EhIS1KBBA6WkpOiNN97QhAkT9P7777v6rFu3Tg899JCGDBmiLVu2qHv37urevbt27NhRAZ8EAADA1TM5nU6n0UW4G7vdLqvVKpvNJovFYnQ5AIBLmPLdz3pjebok6e+9Y9QzNsLgilCdVfY1hMlk0sKFC9W9e/fzjh04cEBRUVHasmWLWrVqVeZYhw4ddM899+jll1++4Lg//fSTmjZtqk2bNqlNmzaSpGXLlun+++9XRkaGwsPDNXXqVD333HPKysqSj8/ZG5g8++yzWrRokXbv3i1J6tOnj/Lz87VkyZIy792qVStNmzatXHOs7N8RAAAwRnnXEJzRBgCotD5ef9AVso17oCkhG2CAnJwcbdiwQaGhobrllltUp04d3XHHHfrhhx9cfZKTkxUYGOgK2SSpc+fO8vDw0IYNG1x9br/9dlfIJkmJiYlKT0/XiRMnXH06d+5c5v0TExOVnJx80foKCwtlt9vLPAAAAK4VgjYAQKW0ZFumXvji7HaxEXc10uCOUQZXBFRPv/zyiyRpwoQJevzxx7Vs2TLFxsbq7rvv1t69eyVJWVlZCg0NLfM6Ly8vBQUFKSsry9WnTp06Zfqce/5bfc4dv5CJEyfKarW6HpGRkVcxWwAAgEsjaAMAVDpr9xzTqDlpcjqlfu3ra/Q9NxtdElBtORwOSdKTTz6pRx99VK1bt9Zbb72lxo0b64MPPjC4Omns2LGy2Wyux+HDh40uCQAAVGFeRhcAAMDlSD10Qk9+lKLiUqe6tKyrl7o1l8lkMrosoNqqW7euJKlp06Zl2ps0aaJDhw5JksLCwpSTk1PmeElJiXJzcxUWFubqk52dXabPuee/1efc8Qsxm80ym82XOy0AAIArwhltAIBKY0/2SQ2euUlnikt120219VbvVvL0IGQDjNSwYUOFh4crPT29TPuePXvUoEEDSVJ8fLzy8vKUkpLiOr5q1So5HA61b9/e1Wft2rUqLi529VmxYoUaN26sWrVqufqsXLmyzPusWLFC8fHx12RuAAAAl4sz2gAAlcLh3NPqP32D8k4Xq3X9QE17JE4+Xvy+CLhap06d0s8//+x6vn//fqWlpSkoKEj169dXbm6uDh06pMzMTElyBWphYWEKCwuTyWTSM888o/HjxysmJkatWrXSrFmztHv3bs2bN0/S2bPb7r33Xj3++OOaNm2aiouLNXz4cPXt21fh4eGSpIcfflgvvviihgwZojFjxmjHjh2aPHmy3nrrLVdtTz/9tO644w69+eab6tKliz777DNt3rxZ77///vX6uAAAAC7J5HQ6nUYX4W647TsAuJfjpwr1u2nJ2n88XzfXqanPn4xXYA2f334hcJ1VxjXE6tWr1alTp/PaBw4cqJkzZ2rmzJl69NFHzzs+fvx4TZgwwfX8tdde05QpU5Sbm6uYmBhNmjRJHTt2dB3Pzc3V8OHD9eWXX8rDw0NJSUl65513VLNmTVefbdu2adiwYdq0aZNq166tESNGaMyYMWXed+7cuXr++ed14MAB3XTTTZo0aZLuv//+cs+3Mn5HAADAeOVdQxC0XQALMABwHycLitX3/fXamWlXvUA/zX/qFoVZfY0uC7gg1hDuj+8IAABcifKuIdhzAwBwWwXFpXps1mbtzLQr2N9HHz/WnpANAAAAgNsiaAMAuKWSUoeGz96iDftzFWD20qzB7RRV29/osgAAAADgogjaAABux+Fwasz87fr2p2z5eHnonwPbqHk9q9FlAQAAAMAlEbQBANyK0+nUq1/9pPmpGfL0MGnKw7HqcEOw0WUBAAAAwG8iaAMAuJWpa/bpXz/slyS9ntRS9zStY3BFAAAAAFA+VxS0HT58WBkZGa7nGzdu1MiRI/X+++9XWGEAgOrn042HNGlZuiTp+S5N1CsuwuCKAPfFegwAAMD9XFHQ9vDDD+u7776TJGVlZemee+7Rxo0b9dxzz+mll16q0AIBANXDV9uP6rmF2yVJwzrdqMduu8HgigD3xnoMAADA/VxR0LZjxw61a9dOkvT555+refPmWrdunT755BPNnDmzIusDAFQDP+w9rpGfpcnhlB5qV19/SmhsdEmA22M9BgAA4H6uKGgrLi6W2WyWJH377bd68MEHJUnR0dE6evRoxVUHAKjy0g7n6YmPNquo1KH7W4Tpr92by2QyGV0W4PZYjwEAALifKwramjVrpmnTpun777/XihUrdO+990qSMjMzFRzMneEAAOXzc85JPTpjo04Xlapjo9p6q08reXoQsgHlwXoMAADA/VxR0Pb666/rH//4h+6880499NBDiomJkSQtXrzYtYUBAIBLOZJ3Rv2nb9SJ08WKiQzUP/rHyezlaXRZQKXBegwAAMD9mJxOp/NKXlhaWiq73a5atWq52g4cOKAaNWooNDS0wgo0gt1ul9Vqlc1mk8ViMbocAKhyfj1VqN9NS9Yvx/PVKLSmPn8yXkH+PkaXBVy1672GqMrrsWuFdR4AALgS5V1DXNEZbWfOnFFhYaFrUXfw4EG9/fbbSk9PZ1EHALikkwXFGjRjk345nq96gX76aEg7QjbgCrAeAwAAcD9XFLR169ZNH374oSQpLy9P7du315tvvqnu3btr6tSpFVogAKDqKCgu1RMfpmj7EZuC/H304ZB2qmv1M7osoFJiPQYAAOB+vK7kRampqXrrrbckSfPmzVOdOnW0ZcsWzZ8/X+PGjdNTTz1VoUUCACq/klKH/vDpFiX/8qtqmr0069F2ujGkptFlAZUW6zH343Q6daa41OgyAACo1vy8PWUyGXeDtSsK2k6fPq2AgABJ0jfffKOePXvKw8NDHTp00MGDByu0QABA5ed0OvWXhdv1za5s+Xh56J8D2qhFhNXosoBKjfWY+zlTXKqm45YbXQYAANXarpcSVcPniuKuCnFFW0cbNWqkRYsW6fDhw1q+fLkSEhIkSTk5OVxUFgBwnteW7dbnmzPkYZL+76HWir8x2OiSgEqP9RgAAID7uaKIb9y4cXr44Yc1atQo3XXXXYqPj5d09reprVu3rtACAQCV27Q1+/SPNb9Ikl5LaqnEZmEGVwRUDazH3I+ft6d2vZRodBkAAFRrft6ehr6/yel0Oq/khVlZWTp69KhiYmLk4XH2xLiNGzfKYrEoOjq6Qou83rjtOwBUjDmbDmnM/O2SpL/cH60nbr/R4IqAa+t6ryGq8nrsWmGdBwAArkR51xBXvGk1LCxMYWFhysjIkCRFRESoXbt2VzocAKCKWbbjqMYuOBuyDb3jRkI24BpgPQYAAOBerugabQ6HQy+99JKsVqsaNGigBg0aKDAwUC+//LIcDkdF1wgAqGTW/Xxcf/g0TQ6n1KdNpMbc29jokoAqh/UYAACA+7miM9qee+45TZ8+Xa+99ppuvfVWSdIPP/ygCRMmqKCgQK+88kqFFgkAqDy2ZeTp8Q83q6jUoXubhemVHs0Nvb02UFWxHgMAAHA/V3SNtvDwcE2bNk0PPvhgmfYvvvhCv//973XkyJEKK9AIXLsDAK7Mzzmn1PsfycrNL9ItNwbrg0Ft5WvwxUiB6+l6riGq+nrsWmGdBwAArkR51xBXtHU0Nzf3ghfYjY6OVm5u7pUMCQCo5DLzzmjA9A3KzS9Sywir3h/QhpANuIZYjwEAALifKwraYmJi9O67757X/u6776ply5blHmfq1Klq2bKlLBaLLBaL4uPj9fXXX7uOFxQUaNiwYQoODlbNmjWVlJSk7OzsS47pdDo1btw41a1bV35+furcubP27t1b/skBAC5bbn6R+k/foExbgW4I8dfMR9uppvmK77cDoBwqaj0GAACAinNFPwVNmjRJXbp00bfffqv4+HhJUnJysg4fPqyvvvqq3ONERETotdde00033SSn06lZs2apW7du2rJli5o1a6ZRo0Zp6dKlmjt3rqxWq4YPH66ePXvqxx9/vGRt77zzjmbNmqWoqCi98MILSkxM1K5du+Tr63sl0wUAXMKpwhI9OmOj9h3LV7jVVx8Paa8gfx+jywKqvIpajwEAAKDiXNEZbXfccYf27NmjHj16KC8vT3l5eerZs6d27typjz76qNzjdO3aVffff79uuukm3XzzzXrllVdUs2ZNrV+/XjabTdOnT9ff//533XXXXYqLi9OMGTO0bt06rV+//oLjOZ1Ovf3223r++efVrVs3tWzZUh9++KEyMzO1aNGiK5kqAOASCktK9eRHm7U1w6ZaNbz14ZD2Cg/0M7osoFqoqPUYAAAAKs4V3QzhYrZu3arY2FiVlpZe9mtLS0s1d+5cDRw4UFu2bFFWVpbuvvtunThxQoGBga5+DRo00MiRIzVq1Kjzxvjll1904403asuWLWrVqpWr/Y477lCrVq00efLkC753YWGhCgsLXc/tdrsiIyO5SC4AXEKpw6nhs1P19Y4s+ft4avbjHRQTGWh0WYCh3OFC+1ezHqsO3OE7AgAAlc81vRlCRdq+fbtq1qwps9msoUOHauHChWratKmysrLk4+NTJmSTpDp16igrK+uCY51rr1OnTrlfI0kTJ06U1Wp1PSIjI69uUgBQxTmdTj23cLu+3pElH08PvT+gDSEbAAAAgGrP8KCtcePGSktL04YNG/TUU09p4MCB2rVr13WtYezYsbLZbK7H4cOHr+v7A0BlM2l5uj7bdFgeJumdh1rp1ka1jS4JAAAAAAxn+C3hfHx81KhRI0lSXFycNm3apMmTJ6tPnz4qKipSXl5embPasrOzFRYWdsGxzrVnZ2erbt26ZV7z31tJ/5fZbJbZbL76yQBANfD+2n2aunqfJOnVHi10b/O6v/EKAAAAAKgeLito69mz5yWP5+XlXU0tkiSHw6HCwkLFxcXJ29tbK1euVFJSkiQpPT1dhw4dct1Z639FRUUpLCxMK1eudAVrdrvddbYcAODqfL75sF79arckacy90erbrr7BFQHVz/VYjwEAAODKXFbQZrVaf/P4gAEDyj3e2LFjdd9996l+/fo6efKkZs+erdWrV2v58uWyWq0aMmSIRo8eraCgIFksFo0YMULx8fHq0KGDa4zo6GhNnDhRPXr0kMlk0siRI/XXv/5VN910k6KiovTCCy8oPDxc3bt3v5ypAgD+x/KdWXp2/jZJ0hO336Chd9xgcEVA9VTR6zEAAABUnMsK2mbMmFGhb56Tk6MBAwbo6NGjslqtatmypZYvX6577rlHkvTWW2/Jw8NDSUlJKiwsVGJiot57770yY6Snp8tms7me//nPf1Z+fr6eeOIJ5eXlqWPHjlq2bJl8fX0rtHYAqE6S9/2qEZ9ukcMp9W4TobH3RctkMhldFlAtVfR6DAAAABXH5HQ6nUYX4W647TsA/MeOIzb1fX+9ThWWKKFpHb3XL1ZenobfSwdwS6wh3B/fEQAAuBLlXUPwkxIA4KJ+OXZKAz/YqFOFJepwQ5Deeag1IRsAAAAAXAQ/LQEALuio7Yz6T9+oX/OL1LyeRf8c0Ea+3p5GlwUAAAAAbougDQBwnhP5RRowfaOO5J3RDbX9NfPRdgrw9Ta6LAAAAABwawRtAIAy8gtLNGjmJu3NOaUwi68+HNJOtWuajS4LAAAAANweQRsAwKWwpFRDP07R1sN5CqzhrY+GtFNErRpGlwUAAAAAlQJBGwBAklTqcGr0nK36fu9x1fDx1IxBbXVTnQCjywIAAACASoOgDQAgp9OpF77YoaXbj8rb06R/9I9T6/q1jC4LAAAAACoVgjYAgN78Zo9mbzgkk0ma3Le1brspxOiSAAAAAKDSIWgDgGruX9//one/+1mS9Er3Frq/RV2DKwIAAACAyomgDQCqsfkpGfrr0p8kSc8kNtbD7esbXBEAAAAAVF4EbQBQTX27K1t/nr9NkvRYxyj9/s4bDa4IAAAAACo3gjYAqIY2/PKrhs1OVanDqaTYCP3l/iYymUxGlwUAAAAAlRpBGwBUMzuO2PTYrM0qLHGoc5M6ej2phTw8CNkAAAAA4GoRtAFANbL/eL4Gzdiok4UlahcVpHcfbi0vT/4pAAAAAICKwE9XAFBNZNkK9Mi/Nuj4qSI1C7foXwPbyNfb0+iyAAAAAKDKIGgDgGog73SRBnywQUfyziiqtr9mDW4ni6+30WUBcANr165V165dFR4eLpPJpEWLFpU5vmDBAiUkJCg4OFgmk0lpaWkXHcvpdOq+++674DiHDh1Sly5dVKNGDYWGhuqZZ55RSUlJmT6rV69WbGyszGazGjVqpJkzZ573HlOmTFHDhg3l6+ur9u3ba+PGjVc4cwAAgIpH0AYAVdzpohI9OnOT9mSfUh2LWR8ObqfaNc1GlwXATeTn5ysmJkZTpky56PGOHTvq9ddf/82x3n777QveWKW0tFRdunRRUVGR1q1bp1mzZmnmzJkaN26cq8/+/fvVpUsXderUSWlpaRo5cqQee+wxLV++3NVnzpw5Gj16tMaPH6/U1FTFxMQoMTFROTk5VzBzAACAimdyOp1Oo4twN3a7XVarVTabTRaLxehyAOCKFZU49NiHm7V2zzFZ/bw1d2i8bq4TYHRZQJVV2dcQJpNJCxcuVPfu3c87duDAAUVFRWnLli1q1arVecfT0tL0wAMPaPPmzapbt26Zcb7++ms98MADyszMVJ06dSRJ06ZN05gxY3Ts2DH5+PhozJgxWrp0qXbs2OEas2/fvsrLy9OyZcskSe3bt1fbtm317rvvSpIcDociIyM1YsQIPfvss+WaY2X/jgAAgDHKu4bgjDYAqKJKHU6N/jxNa/cck5+3p2Y82paQDcA1cfr0aT388MOaMmWKwsLCzjuenJysFi1auEI2SUpMTJTdbtfOnTtdfTp37lzmdYmJiUpOTpYkFRUVKSUlpUwfDw8Pde7c2dXnQgoLC2W328s8AAAArhWCNgCogpxOpyYs3qkl247K29Okaf3jFFu/ltFlAaiiRo0apVtuuUXdunW74PGsrKwyIZsk1/OsrKxL9rHb7Tpz5oyOHz+u0tLSC/Y5N8aFTJw4UVar1fWIjIy87PkBAACUF0EbAFRBb63Yo4/WH5TJJP29dyvdcXOI0SUBqKIWL16sVatW6e233za6lAsaO3asbDab63H48GGjSwIAAFUYQRsAVDEf/LBf76z6WZL0Urfm6hoTbnBFAKqyVatWad++fQoMDJSXl5e8vLwkSUlJSbrzzjslSWFhYcrOzi7zunPPz201vVgfi8UiPz8/1a5dW56enhfsc6HtqueYzWZZLJYyDwAAgGuFoA0AqpCFWzL00pJdkqQ/3nOz+ndoYHBFAKq6Z599Vtu2bVNaWprrIUlvvfWWZsyYIUmKj4/X9u3by9wddMWKFbJYLGratKmrz8qVK8uMvWLFCsXHx0uSfHx8FBcXV6aPw+HQypUrXX0AAACM5mV0AQCAirFqd7b+NHebJOnRWxtq+F2NDK4IQGVw6tQp/fzzz67n+/fvV1pamoKCglS/fn3l5ubq0KFDyszMlCSlp6dLOnsG2n8//lf9+vUVFRUlSUpISFDTpk3Vv39/TZo0SVlZWXr++ec1bNgwmc1mSdLQoUP17rvv6s9//rMGDx6sVatW6fPPP9fSpUtdY44ePVoDBw5UmzZt1K5dO7399tvKz8/Xo48+es0+HwAAgMtB0AYAVcDG/bl66uNUlTqc6tm6nl7o0lQmk8nosgBUAps3b1anTp1cz0ePHi1JGjhwoGbOnKnFixeXCbL69u0rSRo/frwmTJhQrvfw9PTUkiVL9NRTTyk+Pl7+/v4aOHCgXnrpJVefqKgoLV26VKNGjdLkyZMVERGhf/3rX0pMTHT16dOnj44dO6Zx48YpKytLrVq10rJly867QQIAAIBRTE6n02l0Ee7GbrfLarXKZrNxHQ8Abm9Xpl193k/WyYIS3R0dqmn94+TtyZUBACOwhnB/fEcAAOBKlHcNwU9iAFCJHfw1XwM+2KiTBSVq1zBIU/rFErIBAAAAgEH4aQwAKqkce4Eemb5Bx08Vqkldi/45sI18vT2NLgsAAAAAqi2CNgCohGynizXgg406nHtGDYJraNbgtrL6eRtdFgAAAABUawRtAFDJnC4q0eBZm7Q766RCA8z6eEh7hQb4Gl0WAAAAAFR7BG0AUIkUlTj01MepSjl4QhZfL304pJ0ig2oYXRYAAAAAQARtAFBpOBxO/WnuVq3Zc0y+3h6a8WhbRYdxxzwAAAAAcBcEbQBQCTidTr345U4t3popLw+Tpj0Sp7gGQUaXBQAAAAD4LwRtAFAJTF65V7OSD8pkkt7sHaM7G4caXRIAAAAA4H8QtAGAm5u17oDe/navJOmlB5upW6t6BlcEAAAAALgQgjYAcGNfpB3R+MU7JUmjOt+s/vENjS0IAAAAAHBRBG0A4Ka+S8/RHz/fKkkadEtD/eHuRgZXBAAAAAC4FII2AHBDKQdz9dTHKSpxONWtVbjGPdBUJpPJ6LIAAAAAAJdA0AYAbuano3Y9OmOTCoodurNxiP72uxh5eBCyAQAAAIC7I2gDADdy6NfTGvDBRtkLStSmQS1N7Rcnb0/+qgYAAACAyoCf3gDATeScLNAj0zfo2MlCRYcFaPrAtvLz8TS6LAAAAABAORG0AYAbsJ0p1oDpG3Uo97TqB9XQh4PbyVrD2+iyAAAAAACXgaANAAx2pqhUj83apN1ZJxUSYNbHQ9or1OJrdFkAAAAAgMtE0AYABioudWjY7FRtOnBCAb5e+nBwO9UPrmF0WQAAAACAK0DQBgAGcTic+vO8bVq1O0e+3h76YFBbNalrMbosAAAAAMAVImgDAAM4nU69tGSXFm45Ii8Pk6b2i1PbhkFGlwUAAAAAuAoEbQBggHdX/ayZ6w5Ikv72uxh1ig41tiAAAAAAwFUzNGibOHGi2rZtq4CAAIWGhqp79+5KT08v02ffvn3q0aOHQkJCZLFY1Lt3b2VnZ19y3NLSUr3wwguKioqSn5+fbrzxRr388styOp3XcjoAUC4frT+oN1fskSSN79pU3VvXM7giAAAAAEBFMDRoW7NmjYYNG6b169drxYoVKi4uVkJCgvLz8yVJ+fn5SkhIkMlk0qpVq/Tjjz+qqKhIXbt2lcPhuOi4r7/+uqZOnap3331XP/30k15//XVNmjRJ//d//3e9pgYAF7R4a6bGfbFDkvSHu2/So7dGGVwRAAAAAKCieBn55suWLSvzfObMmQoNDVVKSopuv/12/fjjjzpw4IC2bNkii+XsBcJnzZqlWrVqadWqVercufMFx123bp26deumLl26SJIaNmyoTz/9VBs3bry2EwKAS1idnqPRc9LkdEr9OzTQqM43GV0SAAAAAKACudU12mw2myQpKOjsBcELCwtlMplkNptdfXx9feXh4aEffvjhouPccsstWrlypfbsObs1a+vWrfrhhx903333XbB/YWGh7HZ7mQcAVKSUgyf01MepKnE41TUmXC8+2Ewmk8nosgAAAAAAFchtgjaHw6GRI0fq1ltvVfPmzSVJHTp0kL+/v8aMGaPTp08rPz9ff/rTn1RaWqqjR49edKxnn31Wffv2VXR0tLy9vdW6dWuNHDlS/fr1u2D/iRMnymq1uh6RkZHXZI4Aqqf0rJMaPHOTzhSX6o6bQ/Tm72Lk4UHIBgAAAABVjdsEbcOGDdOOHTv02WefudpCQkI0d+5cffnll6pZs6asVqvy8vIUGxsrD4+Ll/7555/rk08+0ezZs5WamqpZs2bpb3/7m2bNmnXB/mPHjpXNZnM9Dh8+XOHzA1A9Hc49rf7TN8h2plix9QM19ZFY+Xi5zV+9AAAAAIAKZOg12s4ZPny4lixZorVr1yoiIqLMsYSEBO3bt0/Hjx+Xl5eXAgMDFRYWphtuuOGi4z3zzDOus9okqUWLFjp48KAmTpyogQMHntffbDaX2Z4KABXh2MlC9Z++QTknC9W4ToA+GNRWNXzc4q9dAAAAAMA1YOhPfE6nUyNGjNDChQu1evVqRUVd/O57tWvXliStWrVKOTk5evDBBy/a9/Tp0+ed8ebp6XnJO5UCQEWyFxRr4AcbdeDX04qo5acPh7RTYA0fo8sCAAAAAFxDhgZtw4YN0+zZs/XFF18oICBAWVlZkiSr1So/Pz9J0owZM9SkSROFhIQoOTlZTz/9tEaNGqXGjRu7xrn77rvVo0cPDR8+XJLUtWtXvfLKK6pfv76aNWumLVu26O9//7sGDx58/ScJoNopKC7VY7M2a9dRu2rX9NHHQ9qrjsXX6LIAAAAAANeYoUHb1KlTJUl33nlnmfYZM2Zo0KBBkqT09HSNHTtWubm5atiwoZ577jmNGjWqTP9zW0vP+b//+z+98MIL+v3vf6+cnByFh4frySef1Lhx467pfACguNSh4bNTtXF/rgLMXpo1uJ0a1vY3uiwAAAAAwHVgcjqdTqOLcDd2u11Wq1U2m00Wi8XocgBUEg6HU3+au1ULthyR2ctDHw5up/Y3BBtdFoDriDWE++M7AgAAV6K8awhufQcAFcDpdOqvS3/Sgi1H5Olh0pSHYwnZAAAAAKCaIWgDgArw3up9+uDH/ZKkN3q1VOemdQyuCAAAAABwvRG0AcBV+mTDQb2xPF2SNO6BpuoZG2FwRQAAAAAAIxC0AcBVWLrtqJ5ftEOSNOKuRhrcMcrgigAAAAAARiFoA4Ar9P3eYxo5Z4ucTqlf+/oafc/NRpcEAAAAADAQQRsAXIEth07oyY9SVFzqVJeWdfVSt+YymUxGlwUAAAAAMBBBGwBcpr3ZJ/XozE06XVSq226qrbd6t5KnByEbAAAAAFR3BG0AcBl2Z9nVf/pG5Z0uVqvIQE17JE4+XvxVCgAAAACQvIwuAADcne10sRZvy9T8lAylHc6TJN0UWlMzBrWVv5m/RgEAAAAAZ/ETIgBcQEmpQ2v3HtP8lCNasStbRaUOSZKnh0l3RYfq5W7NVcvfx+AqAQAAAADuhKANAP5LetZJzUs5rEVpmTp2stDVHh0WoF5xEerWqp5CAswGVggAAAAAcFcEbQCqvdz8Ii1OO6J5qRnaccTuag/y91G3VuFKio1Qs3ALdxUFAAAAAFwSQRuAaqm41KHvdudofmqGVu3OUXGpU5Lk9e+tob3iInRn41BudAAAAAAAKDeCNgDVys5Mm+alZGhxWqZ+zS9ytTevZ1FSbIQejAlXcE22hgIAAAAALh9BG4Aq79jJQn2RdkTzUjK0O+ukq712TbN6tA5XUlyEosMsBlYIAAAAAKgKCNoAVEmFJaVa9dPZraHfpR9TqePs1lAfTw/d07SOkuLq6fabQuTlydZQAAAAAEDFIGgDUGU4nU5ty7BpfmqGFm/NVN7pYtexmMhA9YqLUNeWdRVYw8fAKgEAAAAAVRVBG4BKL9teoIVbjmh+Sob25pxytdexmNWjdYR6xdVTo9AAAysEAAAAAFQH7JkCUCkVFJfqy62ZGjRjo+InrtRrX+/W3pxTMnt56MGYcM0a3E7rnr1bz94XTcgGAJewdu1ade3aVeHh4TKZTFq0aFGZ4wsWLFBCQoKCg4NlMpmUlpZW5nhubq5GjBihxo0by8/PT/Xr19cf/vAH2Wy2Mv0OHTqkLl26qEaNGgoNDdUzzzyjkpKSMn1Wr16t2NhYmc1mNWrUSDNnzjyv3ilTpqhhw4by9fVV+/bttXHjxor4GAAAACoEZ7QBqDScTqdSD+VpfmqGlmzNlL3gPz+gxTWopV5xEerSsq4svt4GVgkAlUt+fr5iYmI0ePBg9ezZ84LHO3bsqN69e+vxxx8/73hmZqYyMzP1t7/9TU2bNtXBgwc1dOhQZWZmat68eZKk0tJSdenSRWFhYVq3bp2OHj2qAQMGyNvbW6+++qokaf/+/erSpYuGDh2qTz75RCtXrtRjjz2munXrKjExUZI0Z84cjR49WtOmTVP79u319ttvKzExUenp6QoNDb2GnxIAAED5mJxOp9PoItyN3W6X1WqVzWaTxcKdCAGjZeadcW0N/eV4vqs93OqrnrER6hlbTzeE1DSwQgA4q7KvIUwmkxYuXKju3bufd+zAgQOKiorSli1b1KpVq0uOM3fuXD3yyCPKz8+Xl5eXvv76az3wwAPKzMxUnTp1JEnTpk3TmDFjdOzYMfn4+GjMmDFaunSpduzY4Rqnb9++ysvL07JlyyRJ7du3V9u2bfXuu+9KkhwOhyIjIzVixAg9++yz5ZpjZf+OAACAMcq7huCMNgBu6UxRqZbvzNK8lAz9uO+4zv1KwM/bU/c1D1NSXITibwiWh4fJ2EIBAOc5twD18jq71ExOTlaLFi1cIZskJSYm6qmnntLOnTvVunVrJScnq3PnzmXGSUxM1MiRIyVJRUVFSklJ0dixY13HPTw81LlzZyUnJ1+0lsLCQhUWFrqe2+32ipgiAADABRG0AXAbTqdTmw6c0PyUDC3dflSnCv+zNbRdVJB6xUXo/hZ1VdPMX10A4K6OHz+ul19+WU888YSrLSsrq0zIJsn1PCsr65J97Ha7zpw5oxMnTqi0tPSCfXbv3n3ReiZOnKgXX3zxquYEAABQXvy0CsBwh3NPa0HqES3YkqGDv552tUcG+aln6wglxUaofnANAysEAJSH3W5Xly5d1LRpU02YMMHociRJY8eO1ejRo13P7Xa7IiMjDawIAABUZQRtAAyRX1iir3dkaV7KYa3/JdfV7u/jqftb1FWvuAi1bRjE1lAAqCROnjype++9VwEBAVq4cKG8vf9zY5qwsLDz7g6anZ3tOnbuf8+1/Xcfi8UiPz8/eXp6ytPT84J9zo1xIWazWWaz+armBgAAUF4EbQCuG4fDqfX7f9X8lCP6esdRnS4qlSSZTNItNwYrKTZC9zYPUw0f/moCgMrEbrcrMTFRZrNZixcvlq+vb5nj8fHxeuWVV5STk+O6O+iKFStksVjUtGlTV5+vvvqqzOtWrFih+Ph4SZKPj4/i4uK0cuVK180aHA6HVq5cqeHDh1/jGQIAAJQPP80CuOYO/pqv+SkZmp96REfyzrjaGwbXUK+4CPWIjVC9QD8DKwSA6uvUqVP6+eefXc/379+vtLQ0BQUFqX79+srNzdWhQ4eUmZkpSUpPT5d09gy0sLAw2e12JSQk6PTp0/r4449lt9tdNxwICQmRp6enEhIS1LRpU/Xv31+TJk1SVlaWnn/+eQ0bNsx1ttnQoUP17rvv6s9//rMGDx6sVatW6fPPP9fSpUtdtY0ePVoDBw5UmzZt1K5dO7399tvKz8/Xo48+er0+LgAAgEsiaANwTZwsKNbSbUc1PzVDmw6ccLUHmL30QMzZraGx9WvJZGJrKAAYafPmzerUqZPr+bnrmQ0cOFAzZ87U4sWLywRZffv2lSSNHz9eEyZMUGpqqjZs2CBJatSoUZmx9+/fr4YNG8rT01NLlizRU089pfj4ePn7+2vgwIF66aWXXH2joqK0dOlSjRo1SpMnT1ZERIT+9a9/KTEx0dWnT58+OnbsmMaNG6esrCy1atVKy5YtO+8GCQAAAEYxOZ1Op9FFuBu73S6r1eq6NT2A8il1OLVu33HNS8nQ8p1ZKih2SJI8TFLHm0KUFFtPic3C5OvtaXClAHBtsIZwf3xHAADgSpR3DcEZbQCu2r5jpzQ/JUMLtxzRUVuBq/3GEH/1iotUj9b1FGb1vcQIAAAAAABUfgRtAK6I7XSxvtyWqfmpGdpyKM/VbvXzVteYuuoVF6mYCCtbQwEAAAAA1QZBG4ByKyl16Pufz24NXbErW0UlZ7eGenqYdMfNIeoVF6G7m4TK7MXWUAAAAABA9UPQBuA37ck+qfkpGVqw5YiOnSx0tTeuE6BecRHq1jpcoQFsDQUAAAAAVG8EbQAu6ER+kRZvPbs1dFuGzdVeq4a3urWqp15xEWoWbmFrKAAAAAAA/0bQBsCluNShNenHNC8lQyt3Z6u49OxNib08TOoUHapecRHq1DhUPl4eBlcKAAAAAID7IWgDoF2Zds1PzdAXaUd0/FSRq71ZuEVJsRHq1ipcwTXNBlYIAAAAAID7I2gDqqnjpwr1RVqm5qdkaNdRu6u9dk0fdW9VT0lxEWpS12JghQAAAAAAVC4EbUA1UlTi0Krd2ZqXckSr03NU4ji7NdTH00N3Nzm7NfT2m0Pk7cnWUAAAAAAALhdBG1DFOZ1O7Thi17yUw1q8NVMnThe7jsVEWJUUF6GuLcNVy9/HwCoBAAAAAKj8CNqAKirHXqBFaUc0LyVDe7JPudpDA8zqEVtPvWIjdFOdAAMrBAAAAACgaiFoA6qQguJSfftTtuanZGjNnmP6985Q+Xh5KLFZmJJi66ljo9ryYmsoAAAAAAAVjqANqOScTqfSDudpXkqGvtyaKXtBietYbP1A9YqLVJeWdWX18zawSgAAAAAAqj6CNqCSOmo7o4Vbzm4N/eVYvqu9rtVXPWPrqWdshG4MqWlghQAAAAAAVC8EbUAlcqaoVN/sytK8lAz98PNxOf+9NdTX20P3Na+rpNgIxd8YLE8Pk7GFAgAAAABQDRG0AW7O6XQq5eAJzUvJ0NJtR3Wy8D9bQ9s1DFKvuAjd1yJMAb5sDQUAAAAAwEgEbYCbyjhxWgtSj2hBaoYO/Hra1R5Ry089YyOUFFtPDYL9DawQAAAAAAD8N4I2wI2cLirR19vPbg1N/uVXV3sNH0/d3+Ls1tD2UUHyYGsoAAAAAABuh6ANMJjD4dTGA7mal5Khr7cfVX5RqetY/A3B6hUXoXubh8nfzH+uAAAAAAC4Mw8j33zixIlq27atAgICFBoaqu7duys9Pb1Mn3379qlHjx4KCQmRxWJR7969lZ2d/ZtjHzlyRI888oiCg4Pl5+enFi1aaPPmzddqKsBlO/hrvv6+Yo9uf+M79X1/vealZCi/qFQNgmto9D036/s/d9KnT3RQUlwEIRsAAAAAAJWAoT+9r1mzRsOGDVPbtm1VUlKiv/zlL0pISNCuXbvk7++v/Px8JSQkKCYmRqtWrZIkvfDCC+ratavWr18vD48L54QnTpzQrbfeqk6dOunrr79WSEiI9u7dq1q1al3P6QHnOVVYoq+2HdW8lAxtPJDraq9p9tIDLeuqV1yE4hrUksnE1lAAAAAAACobQ4O2ZcuWlXk+c+ZMhYaGKiUlRbfffrt+/PFHHThwQFu2bJHFYpEkzZo1S7Vq1dKqVavUuXPnC477+uuvKzIyUjNmzHC1RUVFXbSOwsJCFRYWup7b7farmRZQRqnDqeR9v2p+aoa+3nFUBcUOSZLJJHVsVFu94iKU0DRMfj6eBlcKAAAAAACuhlvtR7PZbJKkoKAgSWcDMJPJJLPZ7Orj6+srDw8P/fDDDxcN2hYvXqzExET97ne/05o1a1SvXj39/ve/1+OPP37B/hMnTtSLL75YwbNBdffLsVOan5qhhalHlGkrcLXfEOKvpNgI9Yytp7pWPwMrBAAAAAAAFcltgjaHw6GRI0fq1ltvVfPmzSVJHTp0kL+/v8aMGaNXX31VTqdTzz77rEpLS3X06NGLjvXLL79o6tSpGj16tP7yl79o06ZN+sMf/iAfHx8NHDjwvP5jx47V6NGjXc/tdrsiIyMrfpKo8mxnirV021HNSzms1EN5rnaLr5e6xoSrV1yEWkUGsjUUAAAAAIAqyG2CtmHDhmnHjh364YcfXG0hISGaO3eunnrqKb3zzjvy8PDQQw89pNjY2Iten006G9q1adNGr776qiSpdevW2rFjh6ZNm3bBoM1sNpc5aw64HKUOp77fe0zzU49o+c4sFZWc3RrqYZLuuDlESXER6tykjny92RoKAAAAAEBV5hZB2/Dhw7VkyRKtXbtWERERZY4lJCRo3759On78uLy8vBQYGKiwsDDdcMMNFx2vbt26atq0aZm2Jk2aaP78+dekflRPe7NPal5qhhZtOaJs+3+u8XdznZrqFReh7q3qKdTia2CFAAAAAADgejI0aHM6nRoxYoQWLlyo1atXX/KGBbVr15YkrVq1Sjk5OXrwwQcv2vfWW29Venp6mbY9e/aoQYMGFVM4qq2800VavDVT81MytDXD5moPrOGtbjHh6hUXqeb1LGwNBQAAAACgGjI0aBs2bJhmz56tL774QgEBAcrKypIkWa1W+fmdvUj8jBkz1KRJE4WEhCg5OVlPP/20Ro0apcaNG7vGufvuu9WjRw8NHz5ckjRq1CjdcsstevXVV9W7d29t3LhR77//vt5///3rP0lUesWlDq3dc0zzUjK08qccFZWe3Rrq5WHSnY1D1SuunjpFh8rsxdZQAAAAAACqM0ODtqlTp0qS7rzzzjLtM2bM0KBBgyRJ6enpGjt2rHJzc9WwYUM999xzGjVqVJn+57aWntO2bVstXLhQY8eO1UsvvaSoqCi9/fbb6tev3zWdD6qWn47aNT8lQ4vSjuj4qSJXe5O6FvWKi1C3VuGqXZNr+wEAAAAAgLNMTqfTaXQR7sZut8tqtcpms8lisRhdDq6jX08V6ou0TM1PzdDOTLurPdjfR91a1VNSXD01C7caWCEAwJ2xhnB/fEcAAOBKlHcN4RY3QwCMVFTi0HfpOZqXkqHvdueoxHE2e/b2NOnu6DrqFRehOxqHyNvz4ne6BQAAAAAAIGhDteR0OrUz0655KRlavDVTufn/2RraMsKqpNgIPRgTrlr+PgZWCQAAAAAAKhOCNlQrOScL9MWWs1tDd2eddLWHBJjVs3U9JcVF6OY6AQZWCAAAAAAAKiuCNlR5hSWlWvnT2a2ha/YcU+m/t4b6eHnonqZnt4be1qi2vNgaCgAAAAAArgJBG6okp9OprRk2zf/31lDbmWLXsdb1A5UUG6GuLcNlreFtYJUAAAAAAKAqIWhDlZJlK9DCLUc0L+Ww9h3Ld7WHWXzVM/bs1tAbQ2oaWCEAAAAAAKiqCNpQ6RUUl+qbXdmal5KhH/Ye0793hsrs5aF7m4epV1yEbrmxtjw9TMYWCgAAAAAAqjSCNlRKTqdTqYdOaF5KhpZsO6qTBSWuY20b1lJSbITub1lXFl+2hgIAAAAAgOuDoA2VypG8M1qYmqH5qUe0//h/tobWC/RTUmw99YyNUMPa/gZWCAAAAAAAqiuCNri900UlWrYjS/NTM7Ru369y/ntraA0fT93XvK6S4uqpQ1SwPNgaCgAAAAAADETQBrfkcDi16UCu5qVk6KvtR5VfVOo61uGGIPWKi9R9zcPkb+aPMAAAAAAAcA+kFHArh3NPa35qhuanZuhw7hlXe/2gGkqKjVDP2HqKDKphYIUAAAAAAAAXRtAGw50qLNFX249qfkqGNuzPdbXXNHupS4u6SoqLUNuGtWQysTUUAAAAAAC4L4I2GMLhcGr9L79qXkqGvt6RpTPFZ7eGmkzSrTfWVq+4CCU2C5Ofj6fBlQIAAAAAAJQPQRuuqwPH8zU/NUMLUo/oSN5/tobeUNtfSXER6tG6nsID/QysEAAAAAAA4MoQtOGasxcUa+m2s1tDNx884WoP8PVS15hw9YqLUOvIQLaGAgAAAACASo2gDddEqcOpH34+rvkpGVq+M0uFJQ5JkodJuu2mEPWKi9A9TevI15utoQAAAAAAoGogaEOF+jnnpOalHNGiLUeUZS9wtd8UWtO1NbSOxdfACgEAAAAAAK4NgjZcNdvpYi3elql5KRnaejjP1R5Yw1sP/ntraIt6VraGAgAAAACAKs3D6AJQOZWUOrRqd7aGfZKqtq98qxcW7dDWw3ny9DCpc5NQTe0Xqw1/uVsvdWuulhFcfw0AAHe1du1ade3aVeHh4TKZTFq0aFGZ4wsWLFBCQoKCg4NlMpmUlpZ23hgFBQUaNmyYgoODVbNmTSUlJSk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"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot the cost over training and validation sets\n",
"fig,ax = plt.subplots(1,2,figsize=(15,5))\n",
"for i,key in enumerate(costpaths.keys()):\n",
" ax_sub=ax[i%3]\n",
" ax_sub.plot(costpaths[key])\n",
" ax_sub.set_title(key)\n",
" ax_sub.set_xlabel('Epoch')\n",
" ax_sub.set_ylabel('Loss')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Save the entire model\n",
"torch.save(model, os.getcwd() + '/models/recommender.pt')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def generate_recommendations(artist_album, playlists, model, playlist_id, device, top_n=10, batch_size=1024):\n",
" model.eval()\n",
"\n",
"\n",
" all_movie_ids = torch.tensor(artist_album['artist_album_id'].values, dtype=torch.long, device=device)\n",
" user_ids = torch.full((len(all_movie_ids),), playlist_id, dtype=torch.long, device=device)\n",
"\n",
" # Initialize tensor to store all predictions\n",
" all_predictions = torch.zeros(len(all_movie_ids), device=device)\n",
"\n",
" # Generate predictions in batches\n",
" with torch.no_grad():\n",
" for i in range(0, len(all_movie_ids), batch_size):\n",
" batch_user_ids = user_ids[i:i+batch_size]\n",
" batch_movie_ids = all_movie_ids[i:i+batch_size]\n",
"\n",
" input_tensor = torch.stack([batch_user_ids, batch_movie_ids], dim=1)\n",
" batch_predictions = model(input_tensor).squeeze()\n",
" all_predictions[i:i+batch_size] = batch_predictions\n",
"\n",
" # Convert to numpy for easier handling\n",
" predictions = all_predictions.cpu().numpy()\n",
"\n",
" albums_listened = set(playlists.loc[playlists['playlist_id'] == playlist_id, 'artist_album_id'].tolist())\n",
"\n",
" unlistened_mask = np.isin(artist_album['artist_album_id'].values, list(albums_listened), invert=True)\n",
"\n",
" # Get top N recommendations\n",
" top_indices = np.argsort(predictions[unlistened_mask])[-top_n:][::-1]\n",
" recs = artist_album['artist_album_id'].values[unlistened_mask][top_indices]\n",
"\n",
" recs_names = artist_album.loc[artist_album['artist_album_id'].isin(recs)]\n",
" album, artist = recs_names['album_name'].values, recs_names['artist_name'].values\n",
"\n",
" return album.tolist(), artist.tolist() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 5.0609978643478826e-06\n",
"Recall: 5.0609978643478826e-06\n"
]
}
],
"source": [
"from torchmetrics import Precision, Recall\n",
"\n",
"precision = Precision(task=\"multiclass\", num_classes=num_classes).to(device) \n",
"recall = Recall(task=\"multiclass\", num_classes=num_classes).to(device) \n",
"\n",
"\n",
"model.eval()\n",
"with torch.no_grad():\n",
" for batch in dataloaders['val']:\n",
" inputs, targets = batch\n",
" inputs = inputs.to(device)\n",
" targets = targets.to(device)\n",
"\n",
" outputs = model(inputs)\n",
"\n",
" # For binary classification\n",
" preds = torch.argmax(outputs, dim=1)\n",
"\n",
" # Update metrics\n",
" precision(preds, targets)\n",
" recall(preds, targets)\n",
"\n",
"# Compute final metrics\n",
"final_precision = precision.compute()\n",
"final_recall = recall.compute()\n",
"\n",
"print(f\"Precision: {final_precision}\")\n",
"print(f\"Recall: {final_recall}\")"
]
}
],
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