{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Recurrent Neural Network of Events and Streams\n",
    "\n",
    "The purpose of this notebook is to train a recurrent neural network using data of tracks that have spiked in the past. In this preliminary analysis, only two event variables will be used: playlists and syncs.\n",
    "\n",
    "Because we have syncs data for only a small subset of tracks, the intersection between tracks with syncs and trending tracks will likely be small, or empty, so clearly we can't generate a dataset based on intersection. But adding the two sets of tracks together is also problematic, because only a small subset of tracks will have non-zero sync information and it's unclear whether such sparse information will have an impact on the results.\n",
    "\n",
    "One approach to handle this is to simply train a RNN on the combined dataset without using sync information, and then another RNN on the same dataset using sync information, and evaluate the errors of both on a test dataset. But the test dataset will likely be composed mostly of tracks without sync information, so comparison of the test errors will give you a general sense of whether incorporating syncs makes the algorithm worse, but not a sense of whether it makes predictions for syncs better.\n",
    "\n",
    "Another approach proceeds hierarchically in the following way.\n",
    "\n",
    "1. Train a RNN using all tracks with spikes but excluding any that have had syncs.\n",
    "2. Evaluate the RNN on the tracks with syncs and compute the test error.\n",
    "3. Split the sync tracks into training and test sets, and train another RNN with sync information using the training set, including the prediction of the first RNN as an additional input.\n",
    "4. Evaluate the second RNN on the test set and compare the test error to that computed in 2. If it's significantly lower, then incorporating sync information makes a difference.\n",
    "\n",
    "This notebook explores both approaches. It constructs models with and without sync information and then compares them on a test set as well as the set of sync tracks. It then constructs a hierarchical model and evaluates against the set of sync tracks. Then it compares that model against the other two models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from snowflake.sqlalchemy import URL\n",
    "from sqlalchemy import create_engine, Table, MetaData\n",
    "from sqlalchemy.sql import select\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define utility functions\n",
    "def sample_to_daily(df, group_keys, cols_to_pad=None, cols_to_interpolate=None):\n",
    "    \"\"\"Sample to daily time step.\"\"\"\n",
    "    df_resampled = df.groupby(group_keys).resample('D').asfreq()\n",
    "    df_resampled[group_keys] = df_resampled[group_keys].fillna(method='pad')\n",
    "    \n",
    "    if cols_to_pad:\n",
    "        df_resampled[cols_to_pad] = df_resampled[cols_to_pad].fillna(method='pad')\n",
    "    \n",
    "    if cols_to_interpolate:\n",
    "        df_resampled[cols_to_interpolate] = \\\n",
    "            df_resampled[cols_to_interpolate].interpolate(method='linear')\n",
    "    \n",
    "    return df_resampled.fillna(0).drop(group_keys, axis=1).reset_index()\n",
    "\n",
    "def diff_vars(df, var_names, group_keys):\n",
    "    \"\"\"Difference variables.\"\"\"\n",
    "    df_grouped = df.groupby(group_keys)\n",
    "    for var in var_names:\n",
    "        df['diff_' + var] = df_grouped[var].diff()\n",
    "    return df\n",
    "\n",
    "def lag_vars(df, var_names, group_keys, lag_period=1):\n",
    "    \"\"\"Lag variables.\"\"\"\n",
    "    df_grouped = df.groupby(group_keys)\n",
    "    for var in var_names:\n",
    "        df['lag' + str(lag_period) + '_' + var] = df_grouped[var].shift(lag_period)\n",
    "    return df\n",
    "\n",
    "def load_data(engine, table_name, date_col='download_activity_date'):\n",
    "    \"\"\"Load time-series data.\"\"\"\n",
    "    table = Table(table_name, MetaData(), autoload=True, autoload_with=engine)\n",
    "    df = pd.read_sql(select([table]), engine, parse_dates=[date_col], index_col=date_col)\n",
    "    return df.fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# establish Snowflake connection\n",
    "user = ''\n",
    "password = ''\n",
    "\n",
    "engine = create_engine(URL(\n",
    "    user=user,\n",
    "    password=password,\n",
    "    account='orchard',\n",
    "    database='dev_engineering',\n",
    "    schema='events_streams',\n",
    "    role='dev_engineering',\n",
    "    warehouse='dev_performance_warehouse'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and prepare data\n",
    "table_name = 'spikes_and_events'\n",
    "spikes_and_events = load_data(engine, table_name)\n",
    "\n",
    "group_keys = ['labelid', 'subaccountid', 'isrc']\n",
    "spikes_and_events = sample_to_daily(spikes_and_events, group_keys)\n",
    "spikes_and_events = shift_vars(spikes_and_events, ['total_streams'], group_keys)\n",
    "spikes_and_events = diff_vars(\n",
    "    spikes_and_events, ['total_streams', 'prev_total_streams', 'num_playlists'], group_keys)\n",
    "\n",
    "spikes_and_events.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN without Syncs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we'll build a recurrent neural network using two predictors (difference in number of playlists today and yesterday's difference in total streams) and one output (difference in total streams today).\n",
    "\n",
    "The first step is to group the DataFrame by track (defined by label, subaccount, and ISRC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tracks is 3446\n"
     ]
    }
   ],
   "source": [
    "# group by tracks\n",
    "spikes_and_events_grouped = spikes_and_events.groupby(group_keys)\n",
    "\n",
    "print('Number of tracks is', len(spikes_and_events_grouped))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we split the tracks into three groups: training, validation, and evaluation. The first set (comprising 70% of the data) is used to train the model. The second (15%) is used to select the best sets of weights produced during training. The last set (15%) is withheld to evaluate the overall error and make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training tracks is 2412\n",
      "Number of validation tracks is 517\n",
      "Number of evaluation tracks is 517\n"
     ]
    }
   ],
   "source": [
    "RANDOM_STATE = 0\n",
    "\n",
    "track_keys = list(spikes_and_events_grouped.groups.keys())\n",
    "\n",
    "# split into train and test\n",
    "track_keys_train, track_keys_test = train_test_split(\n",
    "    track_keys, test_size=0.3, random_state=RANDOM_STATE)\n",
    "\n",
    "# split test further into validation and evaluation\n",
    "track_keys_validate, track_keys_eval = train_test_split(\n",
    "    track_keys_test, test_size=0.5, random_state=RANDOM_STATE)\n",
    "\n",
    "print('Number of training tracks is', len(track_keys_train))\n",
    "print('Number of validation tracks is', len(track_keys_validate))\n",
    "print('Number of evaluation tracks is', len(track_keys_eval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow's dataset API is then used to produce three Dataset objects corresponding to the three sets above. The first step is to convert the track keys above into objects that can be converted into Tensor objects for passing into a generator which returns the predictors and output over the various sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zip into separate tuples\n",
    "labelid_train, subaccountid_train, isrc_train = zip(*track_keys_train)\n",
    "labelid_validate, subaccountid_validate, isrc_validate = zip(*track_keys_validate)\n",
    "labelid_eval, subaccountid_eval, isrc_eval = zip(*track_keys_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a generator function which can take a set of tracks defined by the tuples above, as well as predictor and output variable names, and yield tracks from the DataFrame one at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_tracks(labelids, subaccountids, isrcs, x_vars, y_vars):\n",
    "    # decode string arguments\n",
    "    isrcs = [s.decode('utf-8') for s in isrcs]\n",
    "    x_vars = [s.decode('utf-8') for s in x_vars]\n",
    "    y_vars = [s.decode('utf-8') for s in y_vars]\n",
    "    \n",
    "    # iterate over tracks\n",
    "    for key in zip(labelids, subaccountids, isrcs):\n",
    "        track = spikes_and_events_grouped.get_group(key)\n",
    "        yield track[x_vars].values, track[y_vars].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This generator function is used to create the three Dataset objects for training, validation, and evaluation. The datasets are batched, making sure to pad the time series to account for varying-length tracks. Both the training and validation sets are repeated a certain number of times because they will be used to train and validate across several epochs. The training data is additionally shuffled to produce the best weights as possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = ['diff_num_playlists', 'diff_prev_total_streams']\n",
    "y_var = ['diff_total_streams']\n",
    "\n",
    "output_shapes = (tf.TensorShape([None, len(x_vars)]), tf.TensorShape([None, len(y_var)]))\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "NUM_EPOCHS = 5\n",
    "\n",
    "# prepare training data\n",
    "dataset_train = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_train, subaccountid_train, isrc_train, x_vars, y_var))\n",
    "dataset_train = dataset_train.shuffle(buffer_size=10000)\n",
    "dataset_train = dataset_train.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_train = dataset_train.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_validate, subaccountid_validate, isrc_validate, x_vars, y_var))\n",
    "dataset_validation = dataset_validation.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_validation = dataset_validation.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_eval, subaccountid_eval, isrc_eval, x_vars, y_var))\n",
    "dataset_evaluation = dataset_evaluation.padded_batch(BATCH_SIZE, output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is an example of iterating over the training dataset. As you can see, the first training batch has the correct dimensions. There are two features over 128 (maximum) timesteps for 100 batches. There is one output for the same number of timesteps and batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features for first training batch have shape of (100, 128, 2)\n",
      "Output for first training batch has shape of (100, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "# iterate over training dataset\n",
    "iterator = dataset_train.make_one_shot_iterator()\n",
    "next_element = iterator.get_next()\n",
    "with tf.Session() as sess:\n",
    "    first_training_sample = sess.run(next_element)\n",
    "\n",
    "print('Features for first training batch have shape of', first_training_sample[0].shape)\n",
    "print('Output for first training batch has shape of', first_training_sample[1].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create the model. There are several ways to do this, using either the low- or high-level TensorFlow API. The code below uses Keras which runs on top of TensorFlow allowing you to specify the inputs, hidden layers, and outputs of the model in a high-level way. This example model has two hidden layers of LSTM cells with 10 neurons each (chosen arbitrarily). The final layer is a dense linear layer corresponding to the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Keras model\n",
    "inputs = layers.Input(shape=(None, len(x_vars)))\n",
    "hidden_layer_1 = layers.LSTM(10, activation='tanh', return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.LSTM(10, activation='tanh', return_sequences=True)(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "LEARNING_RATE = 0.01\n",
    "\n",
    "rnn_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=LEARNING_RATE),\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a summary of the model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_19 (InputLayer)        (None, None, 2)           0         \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, None, 10)          520       \n",
      "_________________________________________________________________\n",
      "lstm_12 (LSTM)               (None, None, 10)          840       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, None, 1)           11        \n",
      "=================================================================\n",
      "Total params: 1,371\n",
      "Trainable params: 1,371\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rnn_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do the parameters correspond to? To find out, we can inspect the shapes of the weight matrices for each layer, as shown below. The first layer is the input and has no parameters. The second layer, corresponding to an LSTM with 10 neurons, has three weight matrices. The first is the weights that map from the input to the hidden state for all four gates in an LSTM: 2 x 10 x 4 = 2 x 40. The second is the weights mapping previous hidden states to new states: 10 x 40. The final weights for the second layer are the biases for all gates: 10 x 4 = 40. The second LSTM layer has shapes computed similarly. The final layer of the overall network is a dense linear layer with 10 weights mapping from hidden state to output, plus a bias term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of weight matrices in layer 1 is 0\n",
      "Number of weight matrices in layer 2 is 3\n",
      " . Shape of weights 1 is (2, 40)\n",
      " . Shape of weights 2 is (10, 40)\n",
      " . Shape of weights 3 is (40,)\n",
      "Number of weight matrices in layer 3 is 3\n",
      " . Shape of weights 1 is (10, 40)\n",
      " . Shape of weights 2 is (10, 40)\n",
      " . Shape of weights 3 is (40,)\n",
      "Number of weight matrices in layer 4 is 2\n",
      " . Shape of weights 1 is (10, 1)\n",
      " . Shape of weights 2 is (1,)\n"
     ]
    }
   ],
   "source": [
    "num_layers = len(rnn_model.layers)\n",
    "\n",
    "for i in range(num_layers):\n",
    "    weights = rnn_model.layers[i].get_weights()\n",
    "    num_weights = len(weights)\n",
    "    print('Number of weight matrices in layer', i + 1, 'is', num_weights)\n",
    "    \n",
    "    for j in range(num_weights):\n",
    "        print(' . Shape of weights', j + 1, 'is', weights[j].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to train. The number of training epochs corresponds to the number of times the datasets were repeated above. The number of steps is the number of batches to process each epoch, which we set to the total number of batches. The mean squared error and mean absolute error for training and validation sets are reported at the end of each epoch. These metrics are computed over all batches and timesteps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "25/25 [==============================] - 15s 588ms/step - loss: 1274508.7862 - mean_absolute_error: 171.0977 - val_loss: 2194218.0430 - val_mean_absolute_error: 197.4260\n",
      "Epoch 2/5\n",
      "25/25 [==============================] - 10s 418ms/step - loss: 1252714.6300 - mean_absolute_error: 169.5872 - val_loss: 2194086.1523 - val_mean_absolute_error: 198.1358\n",
      "Epoch 3/5\n",
      "25/25 [==============================] - 10s 419ms/step - loss: 1383161.1881 - mean_absolute_error: 173.6345 - val_loss: 2193858.5716 - val_mean_absolute_error: 198.7661\n",
      "Epoch 4/5\n",
      "25/25 [==============================] - 10s 397ms/step - loss: 1331133.0944 - mean_absolute_error: 173.7620 - val_loss: 2193652.7526 - val_mean_absolute_error: 198.0124\n",
      "Epoch 5/5\n",
      "25/25 [==============================] - 10s 394ms/step - loss: 1249654.2895 - mean_absolute_error: 168.6795 - val_loss: 2193704.0742 - val_mean_absolute_error: 197.4429\n"
     ]
    }
   ],
   "source": [
    "# train and test on validation set\n",
    "steps_per_epoch = int(np.ceil(len(track_keys_train)/BATCH_SIZE))\n",
    "validation_steps = int(np.ceil(len(track_keys_validate)/BATCH_SIZE))\n",
    "\n",
    "history = rnn_model.fit(\n",
    "    dataset_train, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_validation, validation_steps=validation_steps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize training and validation MAE\n",
    "training_mae = history.history['mean_absolute_error']\n",
    "val_mae = history.history['val_mean_absolute_error']\n",
    "\n",
    "epochs = range(1, len(training_mae) + 1)\n",
    "plt.plot(epochs, training_mae, 'r--', label='Training MAE')\n",
    "plt.plot(epochs, val_mae, 'b-', label='Validation MAE')\n",
    "plt.xticks(epochs)\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is then evaluated on the evaluation set to produce overall MSE and MAE statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 2s 370ms/step\n",
      "Mean squared error on evaluation set = 5292593.2578125\n",
      "Mean absolute error on evaluation set = 226.32123311360678\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "eval_steps = int(np.ceil(len(track_keys_eval)/BATCH_SIZE))\n",
    "\n",
    "mse_eval, mae_eval = rnn_model.evaluate(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Mean squared error on evaluation set =', mse_eval)\n",
    "print('Mean absolute error on evaluation set =', mae_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can also be used to create predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of predictions is (517, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "# get predictions on evaluation set\n",
    "predictions = rnn_model.predict(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Shape of predictions is', predictions.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a plot of the actual difference in streams (blue) with predicted difference in streams (red) on an example track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predictions\n",
    "track_index = 16\n",
    "\n",
    "label, subaccount, isrc = track_keys_eval[track_index]\n",
    "\n",
    "df_track = spikes_and_events_grouped.get_group(track_keys_eval[track_index])\n",
    "num_steps = df_track.shape[0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Label = {}, Subaccount = {}, ISRC = {}'.format(int(label), int(subaccount), isrc))\n",
    "plt.plot(range(num_steps), df_track['diff_total_streams'], 'b', label='Truth')\n",
    "plt.plot(range(num_steps), predictions[track_index, :num_steps, 0], 'r', label='RNN')\n",
    "plt.xticks([])\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, save the computational graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write computational graph\n",
    "writer = tf.summary.FileWriter('.')\n",
    "writer.add_graph(tf.get_default_graph())\n",
    "writer.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After opening up TensorBoard and saving the graph as an image, this is what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=4412x1489 at 0x12AC3FBE0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "display(Image.open('lstm_graph.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, below are the weights for the input mapping of the first gate in the first LSTM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2457756 , -0.43758094, -0.05566861, -0.66099316,  0.35392419,\n",
       "         0.57820445, -0.4743521 , -0.03477478,  0.347595  ,  0.17137934],\n",
       "       [-0.44920373,  0.17996255,  0.21610825,  0.5035527 , -0.07148753,\n",
       "         0.08993571, -0.09215409,  0.4863802 , -0.23598579, -0.04874202]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnn_model.layers[1].get_weights()[0][:, :10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN with Syncs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if we include sync predictors in the model? To do this, we'll just define new datasets with the additional predictors, then train a similar model that also uses those predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars_with_syncs = ['diff_num_playlists', 'diff_prev_total_streams', 'has_sync', 'sync_fee']\n",
    "\n",
    "output_shapes_with_syncs = (\n",
    "    tf.TensorShape([None, len(x_vars_with_syncs)]), tf.TensorShape([None, len(y_var)]))\n",
    "\n",
    "# prepare training data\n",
    "dataset_train_with_syncs = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes_with_syncs,\n",
    "    args=(labelid_train, subaccountid_train, isrc_train, x_vars_with_syncs, y_var))\n",
    "dataset_train_with_syncs = dataset_train_with_syncs.shuffle(buffer_size=10000)\n",
    "dataset_train_with_syncs = dataset_train_with_syncs.padded_batch(BATCH_SIZE, output_shapes_with_syncs)\n",
    "dataset_train_with_syncs = dataset_train_with_syncs.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation_with_syncs = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes_with_syncs,\n",
    "    args=(labelid_validate, subaccountid_validate, isrc_validate, x_vars_with_syncs, y_var))\n",
    "dataset_validation_with_syncs = dataset_validation_with_syncs.padded_batch(BATCH_SIZE, output_shapes_with_syncs)\n",
    "dataset_validation_with_syncs = dataset_validation_with_syncs.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation_with_syncs = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes_with_syncs,\n",
    "    args=(labelid_eval, subaccountid_eval, isrc_eval, x_vars_with_syncs, y_var))\n",
    "dataset_evaluation_with_syncs = dataset_evaluation_with_syncs.padded_batch(BATCH_SIZE, output_shapes_with_syncs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model creation is the same, except the number of input dimensions is now greater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Keras model\n",
    "inputs = layers.Input(shape=(None, len(x_vars_with_syncs)))\n",
    "hidden_layer_1 = layers.LSTM(10, activation='tanh', return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.LSTM(10, activation='tanh', return_sequences=True)(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "rnn_model_with_sync_predictors = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model_with_sync_predictors.compile(optimizer=tf.train.AdamOptimizer(learning_rate=LEARNING_RATE),\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "25/25 [==============================] - 11s 446ms/step - loss: 1794917.4487 - mean_absolute_error: 181.7018 - val_loss: 2194181.5208 - val_mean_absolute_error: 197.7244\n",
      "Epoch 2/5\n",
      "25/25 [==============================] - 11s 424ms/step - loss: 1250209.1206 - mean_absolute_error: 168.9863 - val_loss: 2193944.5156 - val_mean_absolute_error: 197.9810\n",
      "Epoch 3/5\n",
      "25/25 [==============================] - 10s 396ms/step - loss: 2342748.0656 - mean_absolute_error: 182.4962 - val_loss: 2193870.2943 - val_mean_absolute_error: 199.1621\n",
      "Epoch 4/5\n",
      "25/25 [==============================] - 10s 388ms/step - loss: 1250459.5719 - mean_absolute_error: 170.1407 - val_loss: 2193623.5091 - val_mean_absolute_error: 199.0697\n",
      "Epoch 5/5\n",
      "25/25 [==============================] - 10s 401ms/step - loss: 1281923.8775 - mean_absolute_error: 170.4616 - val_loss: 2193580.0299 - val_mean_absolute_error: 200.8969\n"
     ]
    }
   ],
   "source": [
    "rnn_model_with_sync_predictors.fit(\n",
    "    dataset_train_with_syncs, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_validation_with_syncs, validation_steps=validation_steps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 1s 242ms/step\n",
      "Mean squared error on evaluation set = 5292665.645833333\n",
      "Mean absolute error on evaluation set = 229.69273630777994\n"
     ]
    }
   ],
   "source": [
    "mse_eval, mae_eval = rnn_model_with_sync_predictors.evaluate(\n",
    "    dataset_evaluation_with_syncs, steps=eval_steps)\n",
    "\n",
    "print('Mean squared error on evaluation set =', mse_eval)\n",
    "print('Mean absolute error on evaluation set =', mae_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the MAE is not very different than before, because there are few sync tracks. But how does this model compare to the other when evaluated only on sync tracks? To determine this, we start by creating a generator for sync tracks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sync tracks\n",
    "sync_tracks = spikes_and_events_grouped.filter(\n",
    "    lambda x: x['has_sync'].nonzero()[0].size > 0)\n",
    "sync_tracks_grouped = sync_tracks.groupby(group_keys)\n",
    "\n",
    "def gen_sync_tracks(x_vars, y_vars):\n",
    "    # decode string arguments\n",
    "    x_vars = [s.decode('utf-8') for s in x_vars]\n",
    "    y_vars = [s.decode('utf-8') for s in y_vars]\n",
    "    \n",
    "    # iterate over tracks\n",
    "    for _, track in sync_tracks_grouped:\n",
    "        yield track[x_vars].values, track[y_vars].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create two datasets of sync tracks: one using sync information, the other not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sync tracks without sync information\n",
    "dataset_syncs = tf.data.Dataset.from_generator(\n",
    "    gen_sync_tracks, (tf.float32, tf.float32), output_shapes, args=(x_vars, y_var))\n",
    "dataset_syncs = dataset_syncs.padded_batch(1, output_shapes)\n",
    "\n",
    "# sync tracks with sync information\n",
    "dataset_syncs_with_sync_predictors = tf.data.Dataset.from_generator(\n",
    "    gen_sync_tracks, (tf.float32, tf.float32), output_shapes_with_syncs, args=(x_vars_with_syncs, y_var))\n",
    "dataset_syncs_with_sync_predictors = dataset_syncs_with_sync_predictors.padded_batch(\n",
    "    1, output_shapes_with_syncs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then evaluate. Interestingly, the model without sync predictors outperforms the model with sync predictors by a small margin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8/8 [==============================] - 1s 78ms/step\n",
      "8/8 [==============================] - 1s 83ms/step\n",
      "Mean squared error of model without sync predictors = 4526.483562648296\n",
      "Mean absolute error on model without sync predictors = 27.186033368110657\n",
      "Mean squared error of model with sync predictors = 4671.3163776397705\n",
      "Mean absolute error on model with sync predictors = 31.337857961654663\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "mse_eval_1, mae_eval_1 = rnn_model.evaluate(\n",
    "    dataset_syncs, steps=len(sync_tracks_grouped))\n",
    "mse_eval_2, mae_eval_2 = rnn_model_with_sync_predictors.evaluate(\n",
    "    dataset_syncs_with_sync_predictors, steps=len(sync_tracks_grouped))\n",
    "\n",
    "print('Mean squared error of model without sync predictors =', mse_eval_1)\n",
    "print('Mean absolute error on model without sync predictors =', mae_eval_1)\n",
    "print('Mean squared error of model with sync predictors =', mse_eval_2)\n",
    "print('Mean absolute error on model with sync predictors =', mae_eval_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we create a standard linear regression model to compare to. Conveniently (and elegantly), Keras can be used again by just changing the layers of the model. Notice that when creating the datasets, we don't need to batch, because each track will essentially be considered its own batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare training data\n",
    "dataset_train_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_train, subaccountid_train, isrc_train, x_vars, y_var))\n",
    "dataset_train_lr = dataset_train_lr.shuffle(buffer_size=10000)\n",
    "dataset_train_lr = dataset_train_lr.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_validate, subaccountid_validate, isrc_validate, x_vars, y_var))\n",
    "dataset_validation_lr = dataset_validation_lr.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_eval, subaccountid_eval, isrc_eval, x_vars, y_var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Keras model\n",
    "inputs = layers.Input(shape=(2,))\n",
    "outputs = layers.Dense(1, activation='linear')(inputs)\n",
    "\n",
    "model_lr = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "model_lr.compile(optimizer=tf.train.AdamOptimizer(learning_rate=LEARNING_RATE),\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model has a lot fewer parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 2)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 3         \n",
      "=================================================================\n",
      "Total params: 3\n",
      "Trainable params: 3\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_lr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "2412/2412 [==============================] - 8s 3ms/step - loss: 1318841.0066 - mean_absolute_error: 180.0990 - val_loss: 2957915.6257 - val_mean_absolute_error: 231.9920\n",
      "Epoch 2/5\n",
      "2412/2412 [==============================] - 7s 3ms/step - loss: 1327027.8823 - mean_absolute_error: 180.9708 - val_loss: 2954612.5986 - val_mean_absolute_error: 233.2837\n",
      "Epoch 3/5\n",
      "2412/2412 [==============================] - 7s 3ms/step - loss: 1326962.4800 - mean_absolute_error: 181.8802 - val_loss: 2958791.9901 - val_mean_absolute_error: 233.6616\n",
      "Epoch 4/5\n",
      "2412/2412 [==============================] - 7s 3ms/step - loss: 1321505.4414 - mean_absolute_error: 182.7897 - val_loss: 2958355.6878 - val_mean_absolute_error: 236.8090\n",
      "Epoch 5/5\n",
      "2412/2412 [==============================] - 8s 3ms/step - loss: 1325595.2401 - mean_absolute_error: 183.5587 - val_loss: 2951524.2356 - val_mean_absolute_error: 235.8641\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "model_lr.fit(\n",
    "    dataset_train_lr, epochs=NUM_EPOCHS, steps_per_epoch=len(track_keys_train),\n",
    "    validation_data=dataset_validation_lr, validation_steps=len(track_keys_validate));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, evaluate. As can be seen, the RNN outperforms linear regression in terms of mean absolute error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517/517 [==============================] - 2s 3ms/step\n",
      "Mean squared error on evaluation set = 6328908.408741432\n",
      "Mean absolute error on evaluation set = 259.35876629200385\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "mse_eval, mae_eval = model_lr.evaluate(dataset_evaluation_lr, steps=len(track_keys_eval))\n",
    "\n",
    "print('Mean squared error on evaluation set =', mse_eval)\n",
    "print('Mean absolute error on evaluation set =', mae_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then make predictions on the example track and compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict\n",
    "predictions_lr = model_lr.predict(df_track[x_vars].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Label = {}, Subaccount = {}, ISRC = {}'.format(int(label), int(subaccount), isrc))\n",
    "plt.plot(range(num_steps), df_track['diff_total_streams'], 'b', label='Truth')\n",
    "plt.plot(range(num_steps), predictions[track_index, :num_steps, 0], 'r', label='RNN')\n",
    "plt.plot(range(num_steps), predictions_lr[:num_steps, 0], 'r--', label='LR')\n",
    "plt.xticks([])\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding More Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To achieve better performance, we'll add additional data. In the dataset loaded below, we add campaign data with the variable \"ad_campaigns\", which indicates the number of active campaigns on a given day. Another non-event streaming variable is also added, \"new_daily_listeners\",  which indicates the number of new listeners on a given day. Some context variables are added as well, including genre, days since release relative to the earlieast release containing the ISRC, day of week, and month of year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and prepare data\n",
    "table_name = 'input_data_jpc_with_playlists_popularity_v2'\n",
    "spikes_and_events_more_data = load_data(engine, table_name)\n",
    "\n",
    "group_keys = ['labelid', 'subaccountid', 'isrc']\n",
    "\n",
    "# sample to daily\n",
    "vars_to_pad = ['genre']\n",
    "vars_to_interpolate = ['days_since_release']\n",
    "spikes_and_events_more_data = sample_to_daily(\n",
    "    spikes_and_events_more_data, group_keys, vars_to_pad, vars_to_interpolate)\n",
    "\n",
    "# difference\n",
    "vars_to_diff = [\n",
    "    'num_playlists', 'playlists_popularity', 'streams',\n",
    "    'passive_streams', 'active_streams', 'collection_streams']\n",
    "spikes_and_events_more_data = diff_vars(\n",
    "    spikes_and_events_more_data, vars_to_diff, group_keys)\n",
    "\n",
    "# lag variables\n",
    "vars_to_lag = [\n",
    "    'sync_event', 'sync_payment', 'ad_campaigns', 'diff_streams',\n",
    "    'diff_passive_streams', 'diff_active_streams', 'diff_collection_streams']\n",
    "for lag_period in [1, 2]:\n",
    "    spikes_and_events_more_data = lag_vars(\n",
    "        spikes_and_events_more_data, vars_to_lag, group_keys, lag_period=lag_period)\n",
    "\n",
    "spikes_and_events_more_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# index by track\n",
    "spikes_and_events_more_data_indexed = spikes_and_events_more_data.set_index(group_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add day of week and month of year\n",
    "spikes_and_events_more_data_indexed['day'] = \\\n",
    "    spikes_and_events_more_data_indexed['download_activity_date'].apply(lambda x: x.dayofweek)\n",
    "\n",
    "spikes_and_events_more_data_indexed['month'] = \\\n",
    "    spikes_and_events_more_data_indexed['download_activity_date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_name = 'user_streams_for_input_data'\n",
    "table = Table(table_name, MetaData(), autoload=True, autoload_with=engine)\n",
    "user_streams_data = pd.read_sql(select([table]), engine, index_col=group_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_streams_data['log_streams'] = np.log(user_streams_data['streams'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>streams</th>\n",
       "      <th>log_streams</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labelid</th>\n",
       "      <th>subaccountid</th>\n",
       "      <th>isrc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28323</th>\n",
       "      <th>31404</th>\n",
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       "      <td>41959252993</td>\n",
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       "      <td>4.859812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510</th>\n",
       "      <th>0</th>\n",
       "      <th>USXDR1800670</th>\n",
       "      <td>1420074648</td>\n",
       "      <td>122</td>\n",
       "      <td>4.804021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20005</th>\n",
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       "      <th>USFV71201975</th>\n",
       "      <td>701846997</td>\n",
       "      <td>264</td>\n",
       "      <td>5.575949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18407</th>\n",
       "      <th>0</th>\n",
       "      <th>QMDA71418683</th>\n",
       "      <td>9572341948</td>\n",
       "      <td>137</td>\n",
       "      <td>4.919981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26510</th>\n",
       "      <th>0</th>\n",
       "      <th>USXDR1800652</th>\n",
       "      <td>58004827578</td>\n",
       "      <td>196</td>\n",
       "      <td>5.278115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user_id  streams  log_streams\n",
       "labelid subaccountid isrc                                           \n",
       "28323   31404        AUZN31700128  41959252993      129     4.859812\n",
       "26510   0            USXDR1800670   1420074648      122     4.804021\n",
       "20005   0            USFV71201975    701846997      264     5.575949\n",
       "18407   0            QMDA71418683   9572341948      137     4.919981\n",
       "26510   0            USXDR1800652  58004827578      196     5.278115"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_streams_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_streams_data.hist(column='log_streams', bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# discretize\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "\n",
    "n_bins = 95\n",
    "\n",
    "enc_quantile = KBinsDiscretizer(n_bins=n_bins, strategy='quantile', encode='ordinal')\n",
    "log_streams_quantile_binned = enc_quantile.fit_transform(\n",
    "    user_streams_data['log_streams'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[45.]\n",
      " [37.]\n",
      " [86.]\n",
      " ...\n",
      " [42.]\n",
      " [77.]\n",
      " [22.]]\n"
     ]
    }
   ],
   "source": [
    "print(log_streams_quantile_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  4  6  8 10 12 14 16 18 20 22 23 25 26 28 29 31 32 33 35 36 37 38 39\n",
      " 40 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64\n",
      " 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88\n",
      " 89 90 91 92 93 94]\n"
     ]
    }
   ],
   "source": [
    "unique_bin_labels = np.unique(log_streams_quantile_binned).astype(int)\n",
    "print(unique_bin_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.61512052 4.62497281 4.63472899 4.6443909  4.65396035 4.66343909\n",
      " 4.67282883 4.68213123 4.69134788 4.70048037 4.7095302  4.71849887\n",
      " 4.72738782 4.73619845 4.74493213 4.75359019 4.76217393 4.77068462\n",
      " 4.77912349 4.78749174 4.79579055 4.80402104 4.81218436 4.82028157\n",
      " 4.82831374 4.83628191 4.84418709 4.85203026 4.8598124  4.86753445\n",
      " 4.87519732 4.88280192 4.8978398  4.90527478 4.91265489 4.91998093\n",
      " 4.92725369 4.94164242 4.94875989 4.95582706 4.9698133  4.97673374\n",
      " 4.99043259 5.00394631 5.01063529 5.02388052 5.0369526  5.04985601\n",
      " 5.06259503 5.07517382 5.08759634 5.09986643 5.11799381 5.12989871\n",
      " 5.14749448 5.16478597 5.18178355 5.19849703 5.22035583 5.24174702\n",
      " 5.26269019 5.28320373 5.3082677  5.33753808 5.36129217 5.39362755\n",
      " 5.42495002 5.46383181 5.50125821 5.54517744 5.59471138 5.65248918\n",
      " 5.72031178 5.80211838 5.90263333 6.04025471 6.25382881 6.67329797]\n"
     ]
    }
   ],
   "source": [
    "bin_edges = enc_quantile.bin_edges_[0][unique_bin_labels]\n",
    "print(bin_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "le.fit(unique_bin_labels)\n",
    "streams_labels = le.transform(\n",
    "    log_streams_quantile_binned.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[28 21 69 ... 25 60 10]\n"
     ]
    }
   ],
   "source": [
    "print(streams_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_streams_data['streams_labels'] = streams_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>streams</th>\n",
       "      <th>log_streams</th>\n",
       "      <th>streams_labels</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labelid</th>\n",
       "      <th>subaccountid</th>\n",
       "      <th>isrc</th>\n",
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       "      <th>31404</th>\n",
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       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>0</th>\n",
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       "      <td>196</td>\n",
       "      <td>5.278115</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       user_id  streams  log_streams  \\\n",
       "labelid subaccountid isrc                                              \n",
       "28323   31404        AUZN31700128  41959252993      129     4.859812   \n",
       "26510   0            USXDR1800670   1420074648      122     4.804021   \n",
       "20005   0            USFV71201975    701846997      264     5.575949   \n",
       "18407   0            QMDA71418683   9572341948      137     4.919981   \n",
       "26510   0            USXDR1800652  58004827578      196     5.278115   \n",
       "\n",
       "                                   streams_labels  \n",
       "labelid subaccountid isrc                          \n",
       "28323   31404        AUZN31700128              28  \n",
       "26510   0            USXDR1800670              21  \n",
       "20005   0            USFV71201975              69  \n",
       "18407   0            QMDA71418683              35  \n",
       "26510   0            USXDR1800652              60  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_streams_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "track_user_streams_matrix = user_streams_data.groupby(group_keys).apply(\n",
    "    lambda x: pd.Series([sum(x['streams_labels'] == i) for i in range(len(le.classes_))]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1242</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>USA370305388</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA370305389</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <th>0</th>\n",
       "      <th>USA370375414</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0   1   2   3   4   5   6   7   8   9  ...  \\\n",
       "labelid subaccountid isrc                                                 ...   \n",
       "894     0            USA371202809   0   0   0   0   0   0   0   0   0   0 ...   \n",
       "1227    0            USA370688328   0   0   0   0   0   0   0   1   0   1 ...   \n",
       "1242    0            USA370305388   1   0   0   0   1   0   1   0   0   0 ...   \n",
       "                     USA370305389   1   1   0   0   1   0   1   0   0   1 ...   \n",
       "1821    0            USA370375414   6   8   3   2   4  10   8   5   6   8 ...   \n",
       "\n",
       "                                   68  69  70  71  72  73  74  75  76  77  \n",
       "labelid subaccountid isrc                                                  \n",
       "894     0            USA371202809   0   0   0   0   0   0   0   0   0   0  \n",
       "1227    0            USA370688328   0   0   0   0   0   0   0   0   0   0  \n",
       "1242    0            USA370305388   0   0   2   0   1   1   2   4   3  12  \n",
       "                     USA370305389   5   2   3   2   3   3   3   7  10  20  \n",
       "1821    0            USA370375414   1   4   6   5   6   7   9   7   4   2  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_user_streams_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "svd = TruncatedSVD(n_components=10)\n",
    "track_user_streams_reduced = svd.fit_transform(track_user_streams_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(track_user_streams_matrix.values, vmin=0, vmax=100, aspect='auto', cmap=plt.cm.Greys)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(track_user_streams_reduced, vmin=-80, vmax=80, aspect='auto', cmap=plt.cm.RdBu)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import BayesianGaussianMixture\n",
    "\n",
    "n_components = 3\n",
    "\n",
    "gmm = BayesianGaussianMixture(n_components=n_components, random_state=0).fit(\n",
    "    track_user_streams_matrix.values)\n",
    "cluster_labels = gmm.predict(track_user_streams_matrix.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1409), (1, 2260), (2, 3340)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, sum(cluster_labels == i)) for i in range(n_components)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 156183.15081753573), (1, 18102.92290138951), (2, 12600.877199477647)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, sum(gmm.means_[i] * np.exp(bin_edges))) for i in range(n_components)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>USA370375414</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0  1  2  3  4   5  6  7  8  9     ...      \\\n",
       "labelid subaccountid isrc                                            ...       \n",
       "894     0            USA371202809  0  0  0  0  0   0  0  0  0  0     ...       \n",
       "1227    0            USA370688328  0  0  0  0  0   0  0  1  0  1     ...       \n",
       "1242    0            USA370305388  1  0  0  0  1   0  1  0  0  0     ...       \n",
       "                     USA370305389  1  1  0  0  1   0  1  0  0  1     ...       \n",
       "1821    0            USA370375414  6  8  3  2  4  10  8  5  6  8     ...       \n",
       "\n",
       "                                   69  70  71  72  73  74  75  76  77  \\\n",
       "labelid subaccountid isrc                                               \n",
       "894     0            USA371202809   0   0   0   0   0   0   0   0   0   \n",
       "1227    0            USA370688328   0   0   0   0   0   0   0   0   0   \n",
       "1242    0            USA370305388   0   2   0   1   1   2   4   3  12   \n",
       "                     USA370305389   2   3   2   3   3   3   7  10  20   \n",
       "1821    0            USA370375414   4   6   5   6   7   9   7   4   2   \n",
       "\n",
       "                                   user_group  \n",
       "labelid subaccountid isrc                      \n",
       "894     0            USA371202809           1  \n",
       "1227    0            USA370688328           1  \n",
       "1242    0            USA370305388           2  \n",
       "                     USA370305389           2  \n",
       "1821    0            USA370375414           0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_user_streams_matrix['user_group'] = cluster_labels\n",
    "track_user_streams_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_and_events_more_data_indexed['user_group'] = track_user_streams_matrix['user_group']\n",
    "spikes_and_events_more_data_indexed['user_group'].fillna(2, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>download_activity_date</th>\n",
       "      <th>genre</th>\n",
       "      <th>days_since_release</th>\n",
       "      <th>is_spiking</th>\n",
       "      <th>sync_event</th>\n",
       "      <th>sync_payment</th>\n",
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       "      <th>playlists_popularity</th>\n",
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       "      <th>...</th>\n",
       "      <th>lag2_sync_event</th>\n",
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       "      <th>lag2_diff_active_streams</th>\n",
       "      <th>lag2_diff_collection_streams</th>\n",
       "      <th>day</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2018-08-07</td>\n",
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       "      <th>USA371202809</th>\n",
       "      <td>2018-08-08</td>\n",
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       "    <tr>\n",
       "      <th>USA371202809</th>\n",
       "      <td>2018-08-09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2698.0</td>\n",
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       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  download_activity_date  genre  \\\n",
       "labelid subaccountid isrc                                         \n",
       "894     0            USA371202809             2018-08-05    1.0   \n",
       "                     USA371202809             2018-08-06    1.0   \n",
       "                     USA371202809             2018-08-07    1.0   \n",
       "                     USA371202809             2018-08-08    1.0   \n",
       "                     USA371202809             2018-08-09    1.0   \n",
       "\n",
       "                                   days_since_release  is_spiking  sync_event  \\\n",
       "labelid subaccountid isrc                                                       \n",
       "894     0            USA371202809              2694.0         0.0         0.0   \n",
       "                     USA371202809              2695.0         0.0         0.0   \n",
       "                     USA371202809              2696.0         0.0         0.0   \n",
       "                     USA371202809              2697.0         0.0         0.0   \n",
       "                     USA371202809              2698.0         0.0         0.0   \n",
       "\n",
       "                                   sync_payment  ad_campaigns  num_playlists  \\\n",
       "labelid subaccountid isrc                                                      \n",
       "894     0            USA371202809           0.0           0.0            1.0   \n",
       "                     USA371202809           0.0           0.0            0.0   \n",
       "                     USA371202809           0.0           0.0            2.0   \n",
       "                     USA371202809           0.0           0.0            2.0   \n",
       "                     USA371202809           0.0           0.0            3.0   \n",
       "\n",
       "                                   playlists_popularity  new_daily_listeners  \\\n",
       "labelid subaccountid isrc                                                      \n",
       "894     0            USA371202809             11.375000                  2.0   \n",
       "                     USA371202809              0.000000                  0.0   \n",
       "                     USA371202809             49.000000                  2.0   \n",
       "                     USA371202809             15.694444                  5.0   \n",
       "                     USA371202809             29.294643                  4.0   \n",
       "\n",
       "                                      ...      lag2_sync_event  \\\n",
       "labelid subaccountid isrc             ...                        \n",
       "894     0            USA371202809     ...                  0.0   \n",
       "                     USA371202809     ...                  0.0   \n",
       "                     USA371202809     ...                  0.0   \n",
       "                     USA371202809     ...                  0.0   \n",
       "                     USA371202809     ...                  0.0   \n",
       "\n",
       "                                   lag2_sync_payment  lag2_ad_campaigns  \\\n",
       "labelid subaccountid isrc                                                 \n",
       "894     0            USA371202809                0.0                0.0   \n",
       "                     USA371202809                0.0                0.0   \n",
       "                     USA371202809                0.0                0.0   \n",
       "                     USA371202809                0.0                0.0   \n",
       "                     USA371202809                0.0                0.0   \n",
       "\n",
       "                                   lag2_diff_streams  \\\n",
       "labelid subaccountid isrc                              \n",
       "894     0            USA371202809                2.0   \n",
       "                     USA371202809                1.0   \n",
       "                     USA371202809               -2.0   \n",
       "                     USA371202809               -2.0   \n",
       "                     USA371202809                2.0   \n",
       "\n",
       "                                   lag2_diff_passive_streams  \\\n",
       "labelid subaccountid isrc                                      \n",
       "894     0            USA371202809                        3.0   \n",
       "                     USA371202809                        0.0   \n",
       "                     USA371202809                       -1.0   \n",
       "                     USA371202809                       -2.0   \n",
       "                     USA371202809                        2.0   \n",
       "\n",
       "                                   lag2_diff_active_streams  \\\n",
       "labelid subaccountid isrc                                     \n",
       "894     0            USA371202809                      -1.0   \n",
       "                     USA371202809                       1.0   \n",
       "                     USA371202809                      -1.0   \n",
       "                     USA371202809                       0.0   \n",
       "                     USA371202809                       0.0   \n",
       "\n",
       "                                   lag2_diff_collection_streams  day  month  \\\n",
       "labelid subaccountid isrc                                                     \n",
       "894     0            USA371202809                           0.0    6      8   \n",
       "                     USA371202809                           0.0    0      8   \n",
       "                     USA371202809                           0.0    1      8   \n",
       "                     USA371202809                           0.0    2      8   \n",
       "                     USA371202809                           0.0    3      8   \n",
       "\n",
       "                                   user_group  \n",
       "labelid subaccountid isrc                      \n",
       "894     0            USA371202809         1.0  \n",
       "                     USA371202809         1.0  \n",
       "                     USA371202809         1.0  \n",
       "                     USA371202809         1.0  \n",
       "                     USA371202809         1.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes_and_events_more_data_indexed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before training the model, categorical variables should be one-hot encoded to transform them into a one-of-K scheme."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat_var in ['month', 'day', 'genre', 'user_group']:\n",
    "    groups = pd.get_dummies(\n",
    "        spikes_and_events_more_data_indexed[cat_var], prefix=cat_var)\n",
    "    spikes_and_events_more_data_indexed[list(groups.columns)] = groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the final columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['download_activity_date', 'genre', 'days_since_release', 'is_spiking',\n",
       "       'sync_event', 'sync_payment', 'ad_campaigns', 'num_playlists',\n",
       "       'playlists_popularity', 'new_daily_listeners', 'streams',\n",
       "       'passive_streams', 'active_streams', 'collection_streams',\n",
       "       'diff_num_playlists', 'diff_playlists_popularity', 'diff_streams',\n",
       "       'diff_passive_streams', 'diff_active_streams',\n",
       "       'diff_collection_streams', 'lag1_sync_event', 'lag1_sync_payment',\n",
       "       'lag1_ad_campaigns', 'lag1_diff_streams', 'lag1_diff_passive_streams',\n",
       "       'lag1_diff_active_streams', 'lag1_diff_collection_streams',\n",
       "       'lag2_sync_event', 'lag2_sync_payment', 'lag2_ad_campaigns',\n",
       "       'lag2_diff_streams', 'lag2_diff_passive_streams',\n",
       "       'lag2_diff_active_streams', 'lag2_diff_collection_streams', 'day',\n",
       "       'month', 'user_group', 'month_1', 'month_8', 'month_9', 'month_10',\n",
       "       'month_11', 'month_12', 'day_0', 'day_1', 'day_2', 'day_3', 'day_4',\n",
       "       'day_5', 'day_6', 'genre_1.0', 'genre_2.0', 'genre_3.0', 'genre_4.0',\n",
       "       'genre_5.0', 'genre_6.0', 'genre_7.0', 'genre_8.0', 'genre_9.0',\n",
       "       'genre_11.0', 'genre_12.0', 'genre_13.0', 'genre_14.0', 'genre_15.0',\n",
       "       'genre_16.0', 'genre_18.0', 'genre_19.0', 'genre_20.0', 'genre_21.0',\n",
       "       'genre_25.0', 'genre_27.0', 'user_group_0.0', 'user_group_1.0',\n",
       "       'user_group_2.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes_and_events_more_data_indexed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the data back to the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_and_events_more_data_indexed.to_sql(\n",
    "    'input_data_jpc_final',\n",
    "    engine,\n",
    "    if_exists='replace',\n",
    "    index=True,\n",
    "    index_label=None, \n",
    "    chunksize=20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next create the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of tracks is 7942\n"
     ]
    }
   ],
   "source": [
    "track_keys = spikes_and_events_more_data_indexed.index.unique()\n",
    "\n",
    "num_tracks = len(track_keys)\n",
    "print('Number of tracks is', num_tracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training tracks is 5559\n",
      "Number of validation tracks is 1191\n",
      "Number of evaluation tracks is 1192\n"
     ]
    }
   ],
   "source": [
    "RANDOM_STATE = 0\n",
    "\n",
    "# split into train and test\n",
    "track_idxs_train, track_idxs_test = train_test_split(\n",
    "    range(num_tracks), test_size=0.3, random_state=RANDOM_STATE)\n",
    "\n",
    "# split test further into validation and evaluation\n",
    "track_idxs_validate, track_idxs_eval = train_test_split(\n",
    "    track_idxs_test, test_size=0.5, random_state=RANDOM_STATE)\n",
    "\n",
    "print('Number of training tracks is', len(track_idxs_train))\n",
    "print('Number of validation tracks is', len(track_idxs_validate))\n",
    "print('Number of evaluation tracks is', len(track_idxs_eval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = [\n",
    "    'lag1_diff_streams', 'lag2_diff_streams',\n",
    "    'lag1_diff_passive_streams', 'lag2_diff_passive_streams',\n",
    "    'lag1_diff_active_streams', 'lag2_diff_active_streams',\n",
    "    'lag1_diff_collection_streams', 'lag2_diff_collection_streams',\n",
    "    'diff_num_playlists', 'diff_playlists_popularity',\n",
    "    'ad_campaigns', 'lag1_ad_campaigns', 'lag2_ad_campaigns',\n",
    "    'sync_event', 'sync_payment',\n",
    "    'lag1_sync_event', 'lag2_sync_event', 'lag1_sync_payment', 'lag2_sync_payment',\n",
    "    'new_daily_listeners',\n",
    "    'days_since_release',\n",
    "    'day_0', 'day_1', 'day_2', 'day_3', 'day_4', 'day_5', 'day_6',\n",
    "    'month_1', 'month_8', 'month_9','month_10', 'month_11', 'month_12',\n",
    "    'genre_1.0', 'genre_2.0', 'genre_3.0', 'genre_4.0', 'genre_5.0',\n",
    "    'genre_6.0', 'genre_7.0', 'genre_8.0', 'genre_9.0', 'genre_11.0',\n",
    "    'genre_12.0', 'genre_13.0', 'genre_14.0', 'genre_15.0', 'genre_16.0',\n",
    "    'genre_18.0', 'genre_19.0', 'genre_20.0', 'genre_21.0', 'genre_25.0', 'genre_27.0',\n",
    "    'user_group_0.0', 'user_group_1.0', 'user_group_2.0'\n",
    "]\n",
    "y_vars = ['diff_streams']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_tracks(indices):\n",
    "    # iterate over tracks\n",
    "    for idx in indices:\n",
    "        track = spikes_and_events_more_data_indexed.loc[track_keys[idx]]\n",
    "        yield track[x_vars].values, track[y_vars].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_shapes = (\n",
    "    tf.TensorShape([None, len(x_vars)]), tf.TensorShape([None, len(y_vars)]))\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "NUM_EPOCHS = 20\n",
    "\n",
    "# prepare training data\n",
    "dataset_train = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes, args=[track_idxs_train])\n",
    "dataset_train = dataset_train.shuffle(buffer_size=10000)\n",
    "dataset_train = dataset_train.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_train = dataset_train.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes, args=[track_idxs_validate])\n",
    "dataset_validation = dataset_validation.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_validation = dataset_validation.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes, args=[track_idxs_eval])\n",
    "dataset_evaluation = dataset_evaluation.padded_batch(BATCH_SIZE, output_shapes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create and train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Keras model\n",
    "LEARNING_RATE = 0.001\n",
    "\n",
    "inputs = layers.Input(shape=(None, len(x_vars)))\n",
    "hidden_layer_1 = layers.LSTM(\n",
    "    128, activation='tanh', return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.Dense(64, activation='linear')(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "rnn_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=LEARNING_RATE),\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, None, 58)          0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, None, 128)         95744     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, None, 64)          8256      \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, None, 1)           65        \n",
      "=================================================================\n",
      "Total params: 104,065\n",
      "Trainable params: 104,065\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "56/56 [==============================] - 83s 1s/step - loss: 10661868.3309 - mean_absolute_error: 306.9945 - val_loss: 8777181.6562 - val_mean_absolute_error: 329.0542\n",
      "Epoch 2/20\n",
      "56/56 [==============================] - 91s 2s/step - loss: 10700219.3460 - mean_absolute_error: 303.9995 - val_loss: 8757010.7083 - val_mean_absolute_error: 323.2765\n",
      "Epoch 3/20\n",
      "56/56 [==============================] - 90s 2s/step - loss: 10627990.4503 - mean_absolute_error: 296.7206 - val_loss: 8717610.3333 - val_mean_absolute_error: 315.0273\n",
      "Epoch 4/20\n",
      "56/56 [==============================] - 60s 1s/step - loss: 10585577.3147 - mean_absolute_error: 289.4084 - val_loss: 8673435.3177 - val_mean_absolute_error: 308.9865\n",
      "Epoch 5/20\n",
      "56/56 [==============================] - 62s 1s/step - loss: 10539964.9386 - mean_absolute_error: 285.1507 - val_loss: 8626235.8021 - val_mean_absolute_error: 305.6953\n",
      "Epoch 6/20\n",
      "56/56 [==============================] - 64s 1s/step - loss: 10498590.2472 - mean_absolute_error: 283.6503 - val_loss: 8582986.1849 - val_mean_absolute_error: 303.8861\n",
      "Epoch 7/20\n",
      "56/56 [==============================] - 65s 1s/step - loss: 14537265.1942 - mean_absolute_error: 296.2733 - val_loss: 8546420.6562 - val_mean_absolute_error: 303.9450\n",
      "Epoch 8/20\n",
      "56/56 [==============================] - 62s 1s/step - loss: 10437184.1708 - mean_absolute_error: 283.9913 - val_loss: 8507760.6693 - val_mean_absolute_error: 304.0705\n",
      "Epoch 9/20\n",
      "56/56 [==============================] - 60s 1s/step - loss: 10417785.6629 - mean_absolute_error: 283.3109 - val_loss: 8476702.7448 - val_mean_absolute_error: 301.7048\n",
      "Epoch 10/20\n",
      "56/56 [==============================] - 63s 1s/step - loss: 10506678.0458 - mean_absolute_error: 285.0034 - val_loss: 8443676.9896 - val_mean_absolute_error: 303.1564\n",
      "Epoch 11/20\n",
      "56/56 [==============================] - 68s 1s/step - loss: 10358164.6334 - mean_absolute_error: 282.4439 - val_loss: 8416255.3698 - val_mean_absolute_error: 302.6953\n",
      "Epoch 12/20\n",
      "56/56 [==============================] - 49s 877ms/step - loss: 10321294.7969 - mean_absolute_error: 282.4369 - val_loss: 8384321.0495 - val_mean_absolute_error: 301.8577\n",
      "Epoch 13/20\n",
      "56/56 [==============================] - 48s 854ms/step - loss: 10291906.6590 - mean_absolute_error: 282.1708 - val_loss: 8354252.1276 - val_mean_absolute_error: 301.7394\n",
      "Epoch 14/20\n",
      "56/56 [==============================] - 52s 923ms/step - loss: 10264706.4771 - mean_absolute_error: 281.5573 - val_loss: 8330273.2005 - val_mean_absolute_error: 303.4374\n",
      "Epoch 15/20\n",
      "56/56 [==============================] - 49s 866ms/step - loss: 10256920.1501 - mean_absolute_error: 283.1309 - val_loss: 8306135.2604 - val_mean_absolute_error: 299.8820\n",
      "Epoch 16/20\n",
      "56/56 [==============================] - 47s 838ms/step - loss: 10222211.8728 - mean_absolute_error: 279.7766 - val_loss: 8279688.5625 - val_mean_absolute_error: 297.8676\n",
      "Epoch 17/20\n",
      "56/56 [==============================] - 47s 838ms/step - loss: 10198524.8326 - mean_absolute_error: 278.2959 - val_loss: 8253095.0833 - val_mean_absolute_error: 300.4379\n",
      "Epoch 18/20\n",
      "56/56 [==============================] - 48s 865ms/step - loss: 10179920.4621 - mean_absolute_error: 279.8013 - val_loss: 8228067.4349 - val_mean_absolute_error: 300.5463\n",
      "Epoch 19/20\n",
      "56/56 [==============================] - 46s 825ms/step - loss: 10175129.1133 - mean_absolute_error: 279.5322 - val_loss: 8204818.0104 - val_mean_absolute_error: 297.7540\n",
      "Epoch 20/20\n",
      "56/56 [==============================] - 47s 847ms/step - loss: 10132185.4308 - mean_absolute_error: 280.5129 - val_loss: 8183291.9453 - val_mean_absolute_error: 302.3822\n"
     ]
    }
   ],
   "source": [
    "steps_per_epoch = int(np.ceil(len(track_idxs_train)/BATCH_SIZE))\n",
    "validation_steps = int(np.ceil(len(track_idxs_validate)/BATCH_SIZE))\n",
    "\n",
    "history = rnn_model.fit(\n",
    "    dataset_train, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_validation, validation_steps=validation_steps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_mae = history.history['mean_absolute_error']\n",
    "val_mae = history.history['val_mean_absolute_error']\n",
    "\n",
    "epochs = range(1, len(training_mae) + 1)\n",
    "plt.plot(epochs, training_mae, 'r--', label='Training MAE')\n",
    "plt.plot(epochs, val_mae, 'b-', label='Validation MAE')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('MAE');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 4s 347ms/step\n",
      "Mean squared error on evaluation set = 11013258.151041666\n",
      "Mean absolute error on evaluation set = 283.05222574869794\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "eval_steps = int(np.ceil(len(track_idxs_eval)/BATCH_SIZE))\n",
    "mse_eval, mae_eval = rnn_model.evaluate(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Mean squared error on evaluation set =', mse_eval)\n",
    "print('Mean absolute error on evaluation set =', mae_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict for all tracks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of predictions is (7942, 175, 1)\n"
     ]
    }
   ],
   "source": [
    "max_steps = max(\n",
    "    len(spikes_and_events_more_data_indexed.loc[key]) for key in track_keys)\n",
    "\n",
    "output_shapes_explicit = (\n",
    "    tf.TensorShape([max_steps, len(x_vars)]), tf.TensorShape([max_steps, len(y_vars)]))\n",
    "\n",
    "dataset_full = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes, args=[list(range(num_tracks))])\n",
    "dataset_full = dataset_full.padded_batch(BATCH_SIZE, output_shapes_explicit)\n",
    "\n",
    "total_steps = int(np.ceil(num_tracks/BATCH_SIZE))\n",
    "\n",
    "predictions = rnn_model.predict(dataset_full, steps=total_steps)\n",
    "print('Shape of predictions is', predictions.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find tracks of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2018-12-26</td>\n",
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       "      <td>40906.0</td>\n",
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       "      <td>27833.0</td>\n",
       "      <td>10253.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                  download_activity_date  diff_num_playlists  \\\n",
       "labelid subaccountid isrc                                                      \n",
       "7514    0            ES6100676604             2018-12-24                 1.0   \n",
       "                     ES6100676613             2018-12-24                 1.0   \n",
       "                     ES6100676618             2018-12-24                 1.0   \n",
       "11223   0            ES5770603837             2018-12-24                 1.0   \n",
       "11678   0            PLA811300078             2018-12-24                 1.0   \n",
       "14835   0            ES64A0827601             2018-12-24                 1.0   \n",
       "18805   230          US89R0692703             2018-12-24                 1.0   \n",
       "        20005        CASD10700381             2018-12-24                 1.0   \n",
       "19510   0            US3Z41500515             2019-01-01                 1.0   \n",
       "21349   9869         ES60C1000106             2018-12-24                 1.0   \n",
       "21384   0            GBBLG0100312             2019-01-17                 1.0   \n",
       "21786   0            BRRGE1404419             2018-12-24                 1.0   \n",
       "22240   0            USS9T1700026             2019-01-21                 1.0   \n",
       "        24591        USS9T1800026             2018-12-01                 1.0   \n",
       "22389   13109        USVIC0845611             2018-10-31                 1.0   \n",
       "24915   0            QM4TW1833174             2018-11-27                 1.0   \n",
       "24954   20994        QM6DW1800109             2018-12-24                 1.0   \n",
       "26510   0            USXDR1800652             2018-12-14                 1.0   \n",
       "                     USXDR1800652             2018-12-26                 1.0   \n",
       "                     USXDR1800901             2018-11-27                 1.0   \n",
       "26525   0            MX1721600897             2018-12-25                 1.0   \n",
       "                     MX1721600899             2018-12-25                 1.0   \n",
       "\n",
       "                                   diff_playlists_popularity  diff_streams  \\\n",
       "labelid subaccountid isrc                                                    \n",
       "7514    0            ES6100676604                9115.380836       23453.0   \n",
       "                     ES6100676613                9061.585080       30486.0   \n",
       "                     ES6100676618               31169.133165       58930.0   \n",
       "11223   0            ES5770603837               22504.759325       36527.0   \n",
       "11678   0            PLA811300078               21026.244681       31595.0   \n",
       "14835   0            ES64A0827601               23270.103385       46881.0   \n",
       "18805   230          US89R0692703               20998.240430       35565.0   \n",
       "        20005        CASD10700381               21186.324388       36618.0   \n",
       "19510   0            US3Z41500515                5481.546188       23099.0   \n",
       "21349   9869         ES60C1000106               10330.874103       29151.0   \n",
       "21384   0            GBBLG0100312                6389.955467       38413.0   \n",
       "21786   0            BRRGE1404419                8195.805453       20761.0   \n",
       "22240   0            USS9T1700026                8187.703923       28580.0   \n",
       "        24591        USS9T1800026               14522.192912       90310.0   \n",
       "22389   13109        USVIC0845611               13886.825710       45161.0   \n",
       "24915   0            QM4TW1833174               51189.000000       52223.0   \n",
       "24954   20994        QM6DW1800109               16497.833032       32058.0   \n",
       "26510   0            USXDR1800652               15209.953571       20441.0   \n",
       "                     USXDR1800652               15690.891817       71653.0   \n",
       "                     USXDR1800901               17580.691394       33970.0   \n",
       "26525   0            MX1721600897                5548.128682       37780.0   \n",
       "                     MX1721600899                5708.835041       27833.0   \n",
       "\n",
       "                                   diff_passive_streams  \n",
       "labelid subaccountid isrc                                \n",
       "7514    0            ES6100676604               20090.0  \n",
       "                     ES6100676613               27065.0  \n",
       "                     ES6100676618               55209.0  \n",
       "11223   0            ES5770603837               34898.0  \n",
       "11678   0            PLA811300078               30129.0  \n",
       "14835   0            ES64A0827601               45378.0  \n",
       "18805   230          US89R0692703               35458.0  \n",
       "        20005        CASD10700381               36432.0  \n",
       "19510   0            US3Z41500515               22017.0  \n",
       "21349   9869         ES60C1000106               27596.0  \n",
       "21384   0            GBBLG0100312               37953.0  \n",
       "21786   0            BRRGE1404419               16726.0  \n",
       "22240   0            USS9T1700026               17188.0  \n",
       "        24591        USS9T1800026               89485.0  \n",
       "22389   13109        USVIC0845611               44740.0  \n",
       "24915   0            QM4TW1833174               52207.0  \n",
       "24954   20994        QM6DW1800109               30738.0  \n",
       "26510   0            USXDR1800652               16304.0  \n",
       "                     USXDR1800652               40906.0  \n",
       "                     USXDR1800901               20609.0  \n",
       "26525   0            MX1721600897               11229.0  \n",
       "                     MX1721600899               10253.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes_and_events_more_data_indexed.query(\n",
    "    'num_playlists > 1 & diff_num_playlists == 1 & diff_streams > 20000 & diff_playlists_popularity > 5000 &'\n",
    "    'diff_passive_streams > 10000')[\n",
    "        ['download_activity_date', 'diff_num_playlists', 'diff_playlists_popularity',\n",
    "         'diff_streams', 'diff_passive_streams']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-18</td>\n",
       "      <td>7572.833333</td>\n",
       "      <td>9228.333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1155.0</td>\n",
       "      <td>-637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-19</td>\n",
       "      <td>-8456.333333</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>-169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-20</td>\n",
       "      <td>-653.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>-660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>456.166667</td>\n",
       "      <td>575.166667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-22</td>\n",
       "      <td>140.833333</td>\n",
       "      <td>716.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-23</td>\n",
       "      <td>-96.500000</td>\n",
       "      <td>619.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>-91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-24</td>\n",
       "      <td>283.500000</td>\n",
       "      <td>903.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-25</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>1223.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-26</td>\n",
       "      <td>6128.000000</td>\n",
       "      <td>7351.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7903.0</td>\n",
       "      <td>6549.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-27</td>\n",
       "      <td>51189.000000</td>\n",
       "      <td>58540.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>60126.0</td>\n",
       "      <td>52223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-28</td>\n",
       "      <td>-52105.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6839.0</td>\n",
       "      <td>-53287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-29</td>\n",
       "      <td>-5568.500000</td>\n",
       "      <td>866.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>-5814.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>-166.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>-215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>774.0</td>\n",
       "      <td>-36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-02</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>779.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-03</td>\n",
       "      <td>-21.500000</td>\n",
       "      <td>661.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-04</td>\n",
       "      <td>-259.500000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>-294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-05</td>\n",
       "      <td>-390.333333</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>-449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>1.184523</td>\n",
       "      <td>23.541666</td>\n",
       "      <td>4.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>0.148810</td>\n",
       "      <td>23.690476</td>\n",
       "      <td>4.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>15.226190</td>\n",
       "      <td>38.916666</td>\n",
       "      <td>5.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>13.100001</td>\n",
       "      <td>52.016667</td>\n",
       "      <td>6.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>260.483333</td>\n",
       "      <td>312.500000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>13.883333</td>\n",
       "      <td>326.383333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>-214.383333</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>-293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>75.571429</td>\n",
       "      <td>187.571429</td>\n",
       "      <td>6.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>85.297618</td>\n",
       "      <td>272.869047</td>\n",
       "      <td>7.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>-31.347618</td>\n",
       "      <td>241.521429</td>\n",
       "      <td>6.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>-22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>1.395237</td>\n",
       "      <td>242.916666</td>\n",
       "      <td>9.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>311.440477</td>\n",
       "      <td>554.357143</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>71.183333</td>\n",
       "      <td>625.540476</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1251.0</td>\n",
       "      <td>241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>-245.457143</td>\n",
       "      <td>380.083333</td>\n",
       "      <td>8.0</td>\n",
       "      <td>817.0</td>\n",
       "      <td>-434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>-183.250000</td>\n",
       "      <td>196.833333</td>\n",
       "      <td>6.0</td>\n",
       "      <td>623.0</td>\n",
       "      <td>-194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-12</td>\n",
       "      <td>70.547620</td>\n",
       "      <td>267.380953</td>\n",
       "      <td>6.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>-25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-13</td>\n",
       "      <td>46.154761</td>\n",
       "      <td>313.535714</td>\n",
       "      <td>4.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>31.103572</td>\n",
       "      <td>344.639286</td>\n",
       "      <td>12.0</td>\n",
       "      <td>869.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>49.777381</td>\n",
       "      <td>394.416667</td>\n",
       "      <td>10.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>-13.266666</td>\n",
       "      <td>381.150001</td>\n",
       "      <td>11.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>-341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-17</td>\n",
       "      <td>30.308333</td>\n",
       "      <td>411.458334</td>\n",
       "      <td>11.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>8.062121</td>\n",
       "      <td>419.520455</td>\n",
       "      <td>13.0</td>\n",
       "      <td>886.0</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-19</td>\n",
       "      <td>45.666449</td>\n",
       "      <td>465.186904</td>\n",
       "      <td>13.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-20</td>\n",
       "      <td>95.999207</td>\n",
       "      <td>561.186111</td>\n",
       "      <td>12.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>-152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>-16.556818</td>\n",
       "      <td>544.629293</td>\n",
       "      <td>14.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>-142.345959</td>\n",
       "      <td>402.283334</td>\n",
       "      <td>13.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>-144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>-153.160715</td>\n",
       "      <td>249.122619</td>\n",
       "      <td>12.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>-185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>155.829762</td>\n",
       "      <td>404.952381</td>\n",
       "      <td>12.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-25</td>\n",
       "      <td>38.527380</td>\n",
       "      <td>443.479761</td>\n",
       "      <td>14.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QM4TW1833174</th>\n",
       "      <td>2019-01-26</td>\n",
       "      <td>-443.479761</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-790.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  download_activity_date  \\\n",
       "labelid subaccountid isrc                                  \n",
       "24915   0            QM4TW1833174             2018-11-06   \n",
       "                     QM4TW1833174             2018-11-07   \n",
       "                     QM4TW1833174             2018-11-08   \n",
       "                     QM4TW1833174             2018-11-09   \n",
       "                     QM4TW1833174             2018-11-10   \n",
       "                     QM4TW1833174             2018-11-11   \n",
       "                     QM4TW1833174             2018-11-12   \n",
       "                     QM4TW1833174             2018-11-13   \n",
       "                     QM4TW1833174             2018-11-14   \n",
       "                     QM4TW1833174             2018-11-15   \n",
       "                     QM4TW1833174             2018-11-16   \n",
       "                     QM4TW1833174             2018-11-17   \n",
       "                     QM4TW1833174             2018-11-18   \n",
       "                     QM4TW1833174             2018-11-19   \n",
       "                     QM4TW1833174             2018-11-20   \n",
       "                     QM4TW1833174             2018-11-21   \n",
       "                     QM4TW1833174             2018-11-22   \n",
       "                     QM4TW1833174             2018-11-23   \n",
       "                     QM4TW1833174             2018-11-24   \n",
       "                     QM4TW1833174             2018-11-25   \n",
       "                     QM4TW1833174             2018-11-26   \n",
       "                     QM4TW1833174             2018-11-27   \n",
       "                     QM4TW1833174             2018-11-28   \n",
       "                     QM4TW1833174             2018-11-29   \n",
       "                     QM4TW1833174             2018-11-30   \n",
       "                     QM4TW1833174             2018-12-01   \n",
       "                     QM4TW1833174             2018-12-02   \n",
       "                     QM4TW1833174             2018-12-03   \n",
       "                     QM4TW1833174             2018-12-04   \n",
       "                     QM4TW1833174             2018-12-05   \n",
       "...                                                  ...   \n",
       "                     QM4TW1833174             2018-12-28   \n",
       "                     QM4TW1833174             2018-12-29   \n",
       "                     QM4TW1833174             2018-12-30   \n",
       "                     QM4TW1833174             2018-12-31   \n",
       "                     QM4TW1833174             2019-01-01   \n",
       "                     QM4TW1833174             2019-01-02   \n",
       "                     QM4TW1833174             2019-01-03   \n",
       "                     QM4TW1833174             2019-01-04   \n",
       "                     QM4TW1833174             2019-01-05   \n",
       "                     QM4TW1833174             2019-01-06   \n",
       "                     QM4TW1833174             2019-01-07   \n",
       "                     QM4TW1833174             2019-01-08   \n",
       "                     QM4TW1833174             2019-01-09   \n",
       "                     QM4TW1833174             2019-01-10   \n",
       "                     QM4TW1833174             2019-01-11   \n",
       "                     QM4TW1833174             2019-01-12   \n",
       "                     QM4TW1833174             2019-01-13   \n",
       "                     QM4TW1833174             2019-01-14   \n",
       "                     QM4TW1833174             2019-01-15   \n",
       "                     QM4TW1833174             2019-01-16   \n",
       "                     QM4TW1833174             2019-01-17   \n",
       "                     QM4TW1833174             2019-01-18   \n",
       "                     QM4TW1833174             2019-01-19   \n",
       "                     QM4TW1833174             2019-01-20   \n",
       "                     QM4TW1833174             2019-01-21   \n",
       "                     QM4TW1833174             2019-01-22   \n",
       "                     QM4TW1833174             2019-01-23   \n",
       "                     QM4TW1833174             2019-01-24   \n",
       "                     QM4TW1833174             2019-01-25   \n",
       "                     QM4TW1833174             2019-01-26   \n",
       "\n",
       "                                   diff_playlists_popularity  \\\n",
       "labelid subaccountid isrc                                      \n",
       "24915   0            QM4TW1833174              -19603.333333   \n",
       "                     QM4TW1833174               -2337.333333   \n",
       "                     QM4TW1833174                   0.000000   \n",
       "                     QM4TW1833174                3674.833333   \n",
       "                     QM4TW1833174               73623.666667   \n",
       "                     QM4TW1833174              -66064.500000   \n",
       "                     QM4TW1833174              -10205.000000   \n",
       "                     QM4TW1833174                 121.000000   \n",
       "                     QM4TW1833174                -174.500000   \n",
       "                     QM4TW1833174                 863.500000   \n",
       "                     QM4TW1833174                -131.500000   \n",
       "                     QM4TW1833174                 -52.000000   \n",
       "                     QM4TW1833174                7572.833333   \n",
       "                     QM4TW1833174               -8456.333333   \n",
       "                     QM4TW1833174                -653.000000   \n",
       "                     QM4TW1833174                 456.166667   \n",
       "                     QM4TW1833174                 140.833333   \n",
       "                     QM4TW1833174                 -96.500000   \n",
       "                     QM4TW1833174                 283.500000   \n",
       "                     QM4TW1833174                 320.000000   \n",
       "                     QM4TW1833174                6128.000000   \n",
       "                     QM4TW1833174               51189.000000   \n",
       "                     QM4TW1833174              -52105.000000   \n",
       "                     QM4TW1833174               -5568.500000   \n",
       "                     QM4TW1833174                -166.500000   \n",
       "                     QM4TW1833174                   0.000000   \n",
       "                     QM4TW1833174                 -17.000000   \n",
       "                     QM4TW1833174                 -21.500000   \n",
       "                     QM4TW1833174                -259.500000   \n",
       "                     QM4TW1833174                -390.333333   \n",
       "...                                                      ...   \n",
       "                     QM4TW1833174                   1.184523   \n",
       "                     QM4TW1833174                   0.148810   \n",
       "                     QM4TW1833174                  15.226190   \n",
       "                     QM4TW1833174                  13.100001   \n",
       "                     QM4TW1833174                 260.483333   \n",
       "                     QM4TW1833174                  13.883333   \n",
       "                     QM4TW1833174                -214.383333   \n",
       "                     QM4TW1833174                  75.571429   \n",
       "                     QM4TW1833174                  85.297618   \n",
       "                     QM4TW1833174                 -31.347618   \n",
       "                     QM4TW1833174                   1.395237   \n",
       "                     QM4TW1833174                 311.440477   \n",
       "                     QM4TW1833174                  71.183333   \n",
       "                     QM4TW1833174                -245.457143   \n",
       "                     QM4TW1833174                -183.250000   \n",
       "                     QM4TW1833174                  70.547620   \n",
       "                     QM4TW1833174                  46.154761   \n",
       "                     QM4TW1833174                  31.103572   \n",
       "                     QM4TW1833174                  49.777381   \n",
       "                     QM4TW1833174                 -13.266666   \n",
       "                     QM4TW1833174                  30.308333   \n",
       "                     QM4TW1833174                   8.062121   \n",
       "                     QM4TW1833174                  45.666449   \n",
       "                     QM4TW1833174                  95.999207   \n",
       "                     QM4TW1833174                 -16.556818   \n",
       "                     QM4TW1833174                -142.345959   \n",
       "                     QM4TW1833174                -153.160715   \n",
       "                     QM4TW1833174                 155.829762   \n",
       "                     QM4TW1833174                  38.527380   \n",
       "                     QM4TW1833174                -443.479761   \n",
       "\n",
       "                                   playlists_popularity  num_playlists  \\\n",
       "labelid subaccountid isrc                                                \n",
       "24915   0            QM4TW1833174           2337.333333            2.0   \n",
       "                     QM4TW1833174              0.000000            0.0   \n",
       "                     QM4TW1833174              0.000000            0.0   \n",
       "                     QM4TW1833174           3674.833333            7.0   \n",
       "                     QM4TW1833174          77298.500000            4.0   \n",
       "                     QM4TW1833174          11234.000000            4.0   \n",
       "                     QM4TW1833174           1029.000000            3.0   \n",
       "                     QM4TW1833174           1150.000000            2.0   \n",
       "                     QM4TW1833174            975.500000            2.0   \n",
       "                     QM4TW1833174           1839.000000            4.0   \n",
       "                     QM4TW1833174           1707.500000            4.0   \n",
       "                     QM4TW1833174           1655.500000            4.0   \n",
       "                     QM4TW1833174           9228.333333            4.0   \n",
       "                     QM4TW1833174            772.000000            3.0   \n",
       "                     QM4TW1833174            119.000000            3.0   \n",
       "                     QM4TW1833174            575.166667            2.0   \n",
       "                     QM4TW1833174            716.000000            3.0   \n",
       "                     QM4TW1833174            619.500000            3.0   \n",
       "                     QM4TW1833174            903.000000            6.0   \n",
       "                     QM4TW1833174           1223.000000            2.0   \n",
       "                     QM4TW1833174           7351.000000            3.0   \n",
       "                     QM4TW1833174          58540.000000            4.0   \n",
       "                     QM4TW1833174           6435.000000            3.0   \n",
       "                     QM4TW1833174            866.500000            4.0   \n",
       "                     QM4TW1833174            700.000000            3.0   \n",
       "                     QM4TW1833174            700.000000            4.0   \n",
       "                     QM4TW1833174            683.000000            3.0   \n",
       "                     QM4TW1833174            661.500000            3.0   \n",
       "                     QM4TW1833174            402.000000            2.0   \n",
       "                     QM4TW1833174             11.666667            3.0   \n",
       "...                                                 ...            ...   \n",
       "                     QM4TW1833174             23.541666            4.0   \n",
       "                     QM4TW1833174             23.690476            4.0   \n",
       "                     QM4TW1833174             38.916666            5.0   \n",
       "                     QM4TW1833174             52.016667            6.0   \n",
       "                     QM4TW1833174            312.500000            7.0   \n",
       "                     QM4TW1833174            326.383333            6.0   \n",
       "                     QM4TW1833174            112.000000            6.0   \n",
       "                     QM4TW1833174            187.571429            6.0   \n",
       "                     QM4TW1833174            272.869047            7.0   \n",
       "                     QM4TW1833174            241.521429            6.0   \n",
       "                     QM4TW1833174            242.916666            9.0   \n",
       "                     QM4TW1833174            554.357143            6.0   \n",
       "                     QM4TW1833174            625.540476            7.0   \n",
       "                     QM4TW1833174            380.083333            8.0   \n",
       "                     QM4TW1833174            196.833333            6.0   \n",
       "                     QM4TW1833174            267.380953            6.0   \n",
       "                     QM4TW1833174            313.535714            4.0   \n",
       "                     QM4TW1833174            344.639286           12.0   \n",
       "                     QM4TW1833174            394.416667           10.0   \n",
       "                     QM4TW1833174            381.150001           11.0   \n",
       "                     QM4TW1833174            411.458334           11.0   \n",
       "                     QM4TW1833174            419.520455           13.0   \n",
       "                     QM4TW1833174            465.186904           13.0   \n",
       "                     QM4TW1833174            561.186111           12.0   \n",
       "                     QM4TW1833174            544.629293           14.0   \n",
       "                     QM4TW1833174            402.283334           13.0   \n",
       "                     QM4TW1833174            249.122619           12.0   \n",
       "                     QM4TW1833174            404.952381           12.0   \n",
       "                     QM4TW1833174            443.479761           14.0   \n",
       "                     QM4TW1833174              0.000000            0.0   \n",
       "\n",
       "                                   streams  diff_streams  \n",
       "labelid subaccountid isrc                                 \n",
       "24915   0            QM4TW1833174    340.0      -32231.0  \n",
       "                     QM4TW1833174    125.0        -215.0  \n",
       "                     QM4TW1833174    986.0         861.0  \n",
       "                     QM4TW1833174   6554.0        5568.0  \n",
       "                     QM4TW1833174  77419.0       70865.0  \n",
       "                     QM4TW1833174  11355.0      -66064.0  \n",
       "                     QM4TW1833174   1179.0      -10176.0  \n",
       "                     QM4TW1833174   1317.0         138.0  \n",
       "                     QM4TW1833174   1812.0         495.0  \n",
       "                     QM4TW1833174   1999.0         187.0  \n",
       "                     QM4TW1833174   1847.0        -152.0  \n",
       "                     QM4TW1833174   1792.0         -55.0  \n",
       "                     QM4TW1833174   1155.0        -637.0  \n",
       "                     QM4TW1833174    986.0        -169.0  \n",
       "                     QM4TW1833174    326.0        -660.0  \n",
       "                     QM4TW1833174    886.0         560.0  \n",
       "                     QM4TW1833174    997.0         111.0  \n",
       "                     QM4TW1833174    906.0         -91.0  \n",
       "                     QM4TW1833174   1028.0         122.0  \n",
       "                     QM4TW1833174   1354.0         326.0  \n",
       "                     QM4TW1833174   7903.0        6549.0  \n",
       "                     QM4TW1833174  60126.0       52223.0  \n",
       "                     QM4TW1833174   6839.0      -53287.0  \n",
       "                     QM4TW1833174   1025.0       -5814.0  \n",
       "                     QM4TW1833174    810.0        -215.0  \n",
       "                     QM4TW1833174    774.0         -36.0  \n",
       "                     QM4TW1833174    779.0           5.0  \n",
       "                     QM4TW1833174    901.0         122.0  \n",
       "                     QM4TW1833174    607.0        -294.0  \n",
       "                     QM4TW1833174    158.0        -449.0  \n",
       "...                                    ...           ...  \n",
       "                     QM4TW1833174    221.0         -16.0  \n",
       "                     QM4TW1833174    209.0         -12.0  \n",
       "                     QM4TW1833174    190.0         -19.0  \n",
       "                     QM4TW1833174    376.0         186.0  \n",
       "                     QM4TW1833174    432.0          56.0  \n",
       "                     QM4TW1833174    509.0          77.0  \n",
       "                     QM4TW1833174    216.0        -293.0  \n",
       "                     QM4TW1833174    296.0          80.0  \n",
       "                     QM4TW1833174    410.0         114.0  \n",
       "                     QM4TW1833174    388.0         -22.0  \n",
       "                     QM4TW1833174    391.0           3.0  \n",
       "                     QM4TW1833174   1010.0         619.0  \n",
       "                     QM4TW1833174   1251.0         241.0  \n",
       "                     QM4TW1833174    817.0        -434.0  \n",
       "                     QM4TW1833174    623.0        -194.0  \n",
       "                     QM4TW1833174    598.0         -25.0  \n",
       "                     QM4TW1833174    695.0          97.0  \n",
       "                     QM4TW1833174    869.0         174.0  \n",
       "                     QM4TW1833174    936.0          67.0  \n",
       "                     QM4TW1833174    595.0        -341.0  \n",
       "                     QM4TW1833174    602.0           7.0  \n",
       "                     QM4TW1833174    886.0         284.0  \n",
       "                     QM4TW1833174    919.0          33.0  \n",
       "                     QM4TW1833174    767.0        -152.0  \n",
       "                     QM4TW1833174    770.0           3.0  \n",
       "                     QM4TW1833174    626.0        -144.0  \n",
       "                     QM4TW1833174    441.0        -185.0  \n",
       "                     QM4TW1833174    704.0         263.0  \n",
       "                     QM4TW1833174    791.0          87.0  \n",
       "                     QM4TW1833174      1.0        -790.0  \n",
       "\n",
       "[82 rows x 6 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes_and_events_more_data_indexed.loc[\n",
    "    (24915, 0, 'QM4TW1833174')][\n",
    "    ['download_activity_date', 'diff_playlists_popularity',\n",
    "     'playlists_popularity', 'num_playlists', 'streams', 'diff_streams']].query(\n",
    "    'download_activity_date > \\'2018-11-05\\'')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predictions\n",
    "isrc = 'QM4TW1833174'\n",
    "\n",
    "min_date, max_date = '2018-11-01', '2018-12-31'\n",
    "min_dstreams, max_dstreams = -10000, 10000\n",
    "\n",
    "track_idx = [key[2] for key in track_keys].index(isrc)\n",
    "label, subaccount, isrc = track_keys[track_idx]\n",
    "\n",
    "df_track = spikes_and_events_more_data_indexed.loc[(label, subaccount, isrc)]\n",
    "num_steps = df_track.shape[0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Label = {}, Subaccount = {}, ISRC = {}'.format(int(label), int(subaccount), isrc))\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'], df_track['diff_streams'], 'b', label='Truth')\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'][:num_steps], predictions[track_idx, :num_steps, 0], 'k', label='RNN')\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True)\n",
    "plt.ylabel('difference in streams')\n",
    "plt.gca().tick_params(axis='x', labelrotation=70)\n",
    "\n",
    "# limit dates and streams ranges\n",
    "if min_date and max_date:\n",
    "    plt.xlim([min_date, max_date])\n",
    "if min_dstreams and max_dstreams:\n",
    "    plt.ylim([min_dstreams, max_dstreams])\n",
    "\n",
    "ax2 = plt.gca().twinx()\n",
    "ax2.plot(\n",
    "    df_track['download_activity_date'], df_track['diff_playlists_popularity'], 'r--')\n",
    "ax2.set_ylabel('difference in playlists popularity', color='r')\n",
    "ax2.tick_params('y', colors='r');\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(mdates.WeekdayLocator())\n",
    "plt.gca().xaxis.set_minor_locator(mdates.DayLocator())\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now compare to linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "7107/7107 [==============================] - 23s 3ms/step - loss: 875175853.3313 - mean_absolute_error: 668.8276 - val_loss: 27115985.9714 - val_mean_absolute_error: 673.8969\n",
      "Epoch 2/10\n",
      "7107/7107 [==============================] - 22s 3ms/step - loss: 5903940123.0111 - mean_absolute_error: 1666.0203 - val_loss: 42644775076.9968 - val_mean_absolute_error: 5528.7628\n",
      "Epoch 3/10\n",
      "7107/7107 [==============================] - 22s 3ms/step - loss: 14417818188.4699 - mean_absolute_error: 2341.0967 - val_loss: 312621973.7516 - val_mean_absolute_error: 840.0777\n",
      "Epoch 4/10\n",
      "7107/7107 [==============================] - 23s 3ms/step - loss: 130480087.1322 - mean_absolute_error: 982.8140 - val_loss: 40000938.2014 - val_mean_absolute_error: 467.2890\n",
      "Epoch 5/10\n",
      "7107/7107 [==============================] - 24s 3ms/step - loss: 405025017.0335 - mean_absolute_error: 971.1768 - val_loss: 1215707732.3237 - val_mean_absolute_error: 1198.1508\n",
      "Epoch 6/10\n",
      "7107/7107 [==============================] - 27s 4ms/step - loss: 772576516.5773 - mean_absolute_error: 1790.2996 - val_loss: 23137680859.7459 - val_mean_absolute_error: 5160.7412\n",
      "Epoch 7/10\n",
      "7107/7107 [==============================] - 23s 3ms/step - loss: 397150731.2114 - mean_absolute_error: 1139.2744 - val_loss: 18788732330.6822 - val_mean_absolute_error: 4025.3230\n",
      "Epoch 8/10\n",
      "7107/7107 [==============================] - 23s 3ms/step - loss: 428164546.9502 - mean_absolute_error: 1134.6377 - val_loss: 3832745613.0806 - val_mean_absolute_error: 2275.4134\n",
      "Epoch 9/10\n",
      "7107/7107 [==============================] - 22s 3ms/step - loss: 371326044.9153 - mean_absolute_error: 1542.7922 - val_loss: 52822547.4770 - val_mean_absolute_error: 626.1212\n",
      "Epoch 10/10\n",
      "7107/7107 [==============================] - 24s 3ms/step - loss: 3765441611.3715 - mean_absolute_error: 2453.0630 - val_loss: 5816215315.5346 - val_mean_absolute_error: 3192.35410s - loss: \n"
     ]
    }
   ],
   "source": [
    "# prepare training data\n",
    "dataset_train_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_train, subaccountid_train, isrc_train, x_vars, y_var))\n",
    "dataset_train_lr = dataset_train_lr.shuffle(buffer_size=10000)\n",
    "dataset_train_lr = dataset_train_lr.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_validate, subaccountid_validate, isrc_validate, x_vars, y_var))\n",
    "dataset_validation_lr = dataset_validation_lr.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation_lr = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(labelid_eval, subaccountid_eval, isrc_eval, x_vars, y_var))\n",
    "\n",
    "# create Keras model\n",
    "inputs = layers.Input(shape=(len(x_vars),))\n",
    "outputs = layers.Dense(1, activation='linear')(inputs)\n",
    "\n",
    "model_lr = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "model_lr.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.01),\n",
    "    loss='mse', metrics=['mae'])\n",
    "\n",
    "# train the model\n",
    "model_lr.fit(\n",
    "    dataset_train_lr, epochs=NUM_EPOCHS, steps_per_epoch=len(track_keys_train),\n",
    "    validation_data=dataset_validation_lr, validation_steps=len(track_keys_validate));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict\n",
    "predictions_lr = model_lr.predict(df_track[x_vars].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inject Artificial Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_and_events_more_data_indexed = \\\n",
    "    spikes_and_events_more_data.set_index(['labelid', 'subaccountid', 'isrc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_indices = spikes_and_events_more_data_indexed.index.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for keys in unique_indices:\n",
    "    is_spiking = spikes_and_events_more_data_indexed.loc[keys, 'is_spiking'].values\n",
    "    \n",
    "    spiking_indices = is_spiking.nonzero()[0]\n",
    "    if spiking_indices.any():\n",
    "        for idx in spiking_indices:\n",
    "            is_spiking[(idx - 3):idx] = 1\n",
    "    \n",
    "        spikes_and_events_more_data_indexed.loc[keys, 'is_spiking'] = is_spiking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>download_activity_date</th>\n",
       "      <th>genre</th>\n",
       "      <th>days_since_release</th>\n",
       "      <th>is_spiking</th>\n",
       "      <th>sync_event</th>\n",
       "      <th>sync_payment</th>\n",
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      "text/plain": [
       "                                  download_activity_date  genre  \\\n",
       "labelid subaccountid isrc                                         \n",
       "736     0            USA370988339             2018-08-28    7.0   \n",
       "                     USA370988339             2018-08-29    7.0   \n",
       "                     USA370988339             2018-08-30    7.0   \n",
       "                     USA370988339             2018-08-31    7.0   \n",
       "                     USA370988339             2018-09-01    7.0   \n",
       "\n",
       "                                   days_since_release  is_spiking  sync_event  \\\n",
       "labelid subaccountid isrc                                                       \n",
       "736     0            USA370988339              3192.0         0.0         0.0   \n",
       "                     USA370988339              3193.0         0.0         0.0   \n",
       "                     USA370988339              3194.0         0.0         0.0   \n",
       "                     USA370988339              3195.0         0.0         0.0   \n",
       "                     USA370988339              3196.0         0.0         0.0   \n",
       "\n",
       "                                   sync_payment  ad_campaigns  num_playlists  \\\n",
       "labelid subaccountid isrc                                                      \n",
       "736     0            USA370988339           0.0           0.0            0.0   \n",
       "                     USA370988339           0.0           0.0            0.0   \n",
       "                     USA370988339           0.0           0.0            0.0   \n",
       "                     USA370988339           0.0           0.0            0.0   \n",
       "                     USA370988339           0.0           0.0            0.0   \n",
       "\n",
       "                                   new_daily_listeners  streams  \\\n",
       "labelid subaccountid isrc                                         \n",
       "736     0            USA370988339                  0.0      0.0   \n",
       "                     USA370988339                  0.0      0.0   \n",
       "                     USA370988339                  0.0      0.0   \n",
       "                     USA370988339                  0.0      0.0   \n",
       "                     USA370988339                  0.0      0.0   \n",
       "\n",
       "                                   weekend_indicator  month  prev_streams  \\\n",
       "labelid subaccountid isrc                                                   \n",
       "736     0            USA370988339                  0      8           0.0   \n",
       "                     USA370988339                  0      8           0.0   \n",
       "                     USA370988339                  0      8           0.0   \n",
       "                     USA370988339                  0      8           0.0   \n",
       "                     USA370988339                  1      9           0.0   \n",
       "\n",
       "                                   diff_streams  diff_prev_streams  \\\n",
       "labelid subaccountid isrc                                            \n",
       "736     0            USA370988339           0.0               -3.0   \n",
       "                     USA370988339           0.0                0.0   \n",
       "                     USA370988339           0.0                0.0   \n",
       "                     USA370988339           0.0                0.0   \n",
       "                     USA370988339           0.0                0.0   \n",
       "\n",
       "                                   diff_num_playlists  \n",
       "labelid subaccountid isrc                              \n",
       "736     0            USA370988339                 0.0  \n",
       "                     USA370988339                 0.0  \n",
       "                     USA370988339                 0.0  \n",
       "                     USA370988339                 0.0  \n",
       "                     USA370988339                 0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spikes_and_events_more_data_indexed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training tracks is 4557\n",
      "Number of validation tracks is 977\n",
      "Number of evaluation tracks is 977\n"
     ]
    }
   ],
   "source": [
    "RANDOM_STATE = 0\n",
    "\n",
    "track_keys = range(len(unique_indices))\n",
    "\n",
    "# split into train and test\n",
    "track_keys_train, track_keys_test = train_test_split(\n",
    "    track_keys, test_size=0.3, random_state=RANDOM_STATE)\n",
    "\n",
    "# split test further into validation and evaluation\n",
    "track_keys_validate, track_keys_eval = train_test_split(\n",
    "    track_keys_test, test_size=0.5, random_state=RANDOM_STATE)\n",
    "\n",
    "print('Number of training tracks is', len(track_keys_train))\n",
    "print('Number of validation tracks is', len(track_keys_validate))\n",
    "print('Number of evaluation tracks is', len(track_keys_eval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_tracks(indices, x_vars, y_vars):\n",
    "    # decode string arguments\n",
    "    x_vars = [s.decode('utf-8') for s in x_vars]\n",
    "    y_vars = [s.decode('utf-8') for s in y_vars]\n",
    "    \n",
    "    # iterate over tracks\n",
    "    for idx in indices:\n",
    "        track = spikes_and_events_more_data_indexed.loc[unique_indices[idx]]\n",
    "        yield track[x_vars].values, track[y_vars].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = ['diff_num_playlists', 'diff_prev_streams', 'is_spiking']\n",
    "y_var = ['diff_streams']\n",
    "\n",
    "output_shapes = (tf.TensorShape([None, len(x_vars)]), tf.TensorShape([None, len(y_var)]))\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "NUM_EPOCHS = 10\n",
    "\n",
    "# prepare training data\n",
    "dataset_train = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_train, x_vars, y_var))\n",
    "dataset_train = dataset_train.shuffle(buffer_size=10000)\n",
    "dataset_train = dataset_train.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_train = dataset_train.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_validate, x_vars, y_var))\n",
    "dataset_validation = dataset_validation.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_validation = dataset_validation.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_eval, x_vars, y_var))\n",
    "dataset_evaluation = dataset_evaluation.padded_batch(BATCH_SIZE, output_shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = layers.Input(shape=(None, len(x_vars)))\n",
    "hidden_layer_1 = layers.LSTM(\n",
    "    256, activation='tanh', dropout=0.5, recurrent_dropout=0.5, unit_forget_bias=True,\n",
    "    return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.LSTM(\n",
    "    256, activation='tanh', dropout=0.5, recurrent_dropout=0.5, unit_forget_bias=True,\n",
    "    return_sequences=True)(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "rnn_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model.compile(optimizer='RMSprop',\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "46/46 [==============================] - 146s 3s/step - loss: 22389169.1095 - mean_absolute_error: 253.6034 - val_loss: 11174262.2188 - val_mean_absolute_error: 261.5929\n",
      "Epoch 2/10\n",
      "46/46 [==============================] - 143s 3s/step - loss: 22390455.4524 - mean_absolute_error: 253.8289 - val_loss: 11173528.6813 - val_mean_absolute_error: 261.7742\n",
      "Epoch 3/10\n",
      "46/46 [==============================] - 142s 3s/step - loss: 22391612.0228 - mean_absolute_error: 254.3815 - val_loss: 11172406.9375 - val_mean_absolute_error: 261.4345\n",
      "Epoch 4/10\n",
      "46/46 [==============================] - 155s 3s/step - loss: 22396014.9001 - mean_absolute_error: 254.6415 - val_loss: 11171281.6188 - val_mean_absolute_error: 262.0930\n",
      "Epoch 5/10\n",
      "46/46 [==============================] - 158s 3s/step - loss: 22387920.4847 - mean_absolute_error: 254.1846 - val_loss: 11169783.4062 - val_mean_absolute_error: 260.7107\n",
      "Epoch 6/10\n",
      "46/46 [==============================] - 182s 4s/step - loss: 22569553.7741 - mean_absolute_error: 258.7399 - val_loss: 11169545.8750 - val_mean_absolute_error: 262.2532\n",
      "Epoch 7/10\n",
      "46/46 [==============================] - 208s 5s/step - loss: 22384643.3094 - mean_absolute_error: 254.3173 - val_loss: 11167770.4313 - val_mean_absolute_error: 260.4975\n",
      "Epoch 8/10\n",
      "46/46 [==============================] - 202s 4s/step - loss: 22409982.9521 - mean_absolute_error: 255.9899 - val_loss: 11166687.3187 - val_mean_absolute_error: 262.1479\n",
      "Epoch 9/10\n",
      "46/46 [==============================] - 180s 4s/step - loss: 22397859.7683 - mean_absolute_error: 255.5522 - val_loss: 11165940.2875 - val_mean_absolute_error: 260.6522\n",
      "Epoch 10/10\n",
      "46/46 [==============================] - 125s 3s/step - loss: 22380841.9935 - mean_absolute_error: 254.2790 - val_loss: 11163865.5563 - val_mean_absolute_error: 260.9181\n"
     ]
    }
   ],
   "source": [
    "steps_per_epoch = int(np.ceil(len(track_keys_train)/BATCH_SIZE))\n",
    "validation_steps = int(np.ceil(len(track_keys_validate)/BATCH_SIZE))\n",
    "\n",
    "rnn_model.fit(\n",
    "    dataset_train, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_validation, validation_steps=validation_steps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of predictions is (977, 139, 1)\n"
     ]
    }
   ],
   "source": [
    "# get predictions on evaluation set\n",
    "eval_steps = int(np.ceil(len(track_keys_eval)/BATCH_SIZE))\n",
    "\n",
    "predictions = rnn_model.predict(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Shape of predictions is', predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predictions\n",
    "track_index = 2\n",
    "\n",
    "label, subaccount, isrc = unique_indices[track_keys_eval[track_index]]\n",
    "\n",
    "df_track = spikes_and_events_more_data_indexed.loc[\n",
    "    unique_indices[track_keys_eval[track_index]]]\n",
    "num_steps = df_track.shape[0]\n",
    "\n",
    "df_track_spiking = df_track[df_track['is_spiking'] != 0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Label = {}, Subaccount = {}, ISRC = {}'.format(int(label), int(subaccount), isrc))\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'], df_track['diff_streams'], 'b', label='Truth')\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'], predictions[track_index, :num_steps, 0], 'r', label='RNN')\n",
    "plt.plot(\n",
    "    df_track_spiking['download_activity_date'], df_track_spiking['diff_streams'], 'y', label='Spiking')\n",
    "plt.xticks([])\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-spiking Tracks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit to non-spiking tracks\n",
    "not_spiking_indices = [\n",
    "    idx for idx in unique_indices if not spikes_and_events_more_data_indexed.loc[idx].is_spiking.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "spikes_and_events_nonspiking_indexed = spikes_and_events_more_data_indexed.loc[not_spiking_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_indices_nonspiking = spikes_and_events_nonspiking_indexed.index.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit to tracks with higher streams\n",
    "idx = [\n",
    "    sum(spikes_and_events_nonspiking_indexed.loc[key]['streams'].values) > 5000\n",
    "        for key in unique_indices_nonspiking]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_indices_nonspiking_threshold = unique_indices_nonspiking[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# limit to ad campaigns\n",
    "idx = [\n",
    "    spikes_and_events_nonspiking_indexed.loc[key]['ad_campaigns'].values.any()\n",
    "        for key in unique_indices_nonspiking_threshold]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_indices_nonspiking_threshold = unique_indices_nonspiking_threshold[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HU5gGAzUlvFpRzy+f3cvbB5oG3Bf5SmeRawbaMxh9xhiaO60VYTt7df9OVRoM1JTQ0O61fnd4B9wXPgPZ2jZYmqgzhqGlrd29PLw54oA3hdXb6rEn8+ligFOXBgM1JTR0WGmIuraBwSDS9QzCh5aG9gyiP1g9uuUon793M4cbOqN+LlgByBfHM5+dFBFommgq02CgpoSGduuAU9/eM+A+f4QlrAetGcSQJmq037OmtTvq5wK8+39f5vZX9sf03KmgqaPvokE6qW/q0mCghq2xo4fV33uGtw82jvt7O+khJ10Uyhe2hPXA0UROCsPjkpjmGThXSKttiz4YdPb4qGruYk9N/I7BD+0ZxJKGU5ODBgM1bO8ca6Oxo4eNhwYWccdaX89g8JqBx+XqW6guQs8gKzUxphnIzXYwONY68L2Pp76tx35ubL2KqSA8GGiaaKrSYKCGraqpC4AjTbHlzkfCqRk4v0P1HndtIusAlZuWGNOZa7BnEMMBvc4OXrUxBBKAV/fW86X7tmD6XdN5MnFGEoEuBDiVaTBQw3a02QoGlXZQGE9Oeqg+QgE5dAbyYKOJXALTUhJiGlra0un0DGIIBnZ7Y+0Z/GtHDfdtqIy5XjEeQnsGWjOYujQYqGE72jIxwSAQMDR29BWQ+58l+yKsTRQ6eMdrz4pNS3LHlMZo7rLeuzZCIDoeJ63V0eOnrbv3OI8eqNre5zuqWqN+LsBT26uDPZux0tzZS3KCdSjRNNHUpcFADVtVs3V2WtnUOa5pi9buXnwBw4xpyfT4A7T1O7v3+/uGlroiLUfR6yfJ4yY1yRNTzaCla+Q9A+v50QeTo/Y+33E0+mBQ29rNTX/dyL1vHY76udFo6uyhMDMZt0tGrYDc1NFDc+fAlKAaOxoM1LA5aaLu3kDEIZ5jxakTLJieAQxMFUXqGYRe3KbHHyDJ4yIt0R3TpCgnJx5L3j+04B1LzcFJD+042hL1c6vs/6/9dR1RPzcaTZ29ZKUmkpoQW88rks/+bSM3/33z8R8YRwIBw0f/+CZP76iZkPfXYBCj7z66k39umphZqZ09vnEvKBpjONrcxZy8NMDqHYwXZyTRQicY9AtE/oAJLlLnjnTZy94ASQku0pI8Uee0u3v9eH0BMpI8tHl9UZ/51rd7SbIXbos279/d6w+mx2LpGdS0WO93oGGMg0FHD9mpCaQmuUelgGyMYcuRFrZVRh8Ap7KjLV28WlHPUxoMpo627l7+9PoBvvXIDlpjyAOPRHevn9P/53nuev3guL5vc2cvnT1+1pbmAONbN3CKxwsLM8JuO3x2MACwY0H4ZS99AZI8btISrTRRNIHUSRGVFaYD0fcO6tq8LCrKBKJPE1XbB/MFhelUNXfRFGEk1XCef2isg0FnD9mpiaQmRh9sI6ls6qLd66OhoyfivJLJzBgT8yzsfXYPblf1xMxJ0WAQg62VLRhjHSj++MqBcX3vA/UdNHf28uiWo+P6vk7KYe0cKxiM5/DSeidNVOj0DMIPEP5AAE8wGAgu6dcz8PmtNFGSh4AJX6voeJxg4PRKoq0b1Lf3UJqbSnqSJ+rnOsXjCxYXArCzOrregdMTOdbqHdPJYM2dvWSlJpCa6B6V5Sh2h0zQ21vbPuLXG0/3b6hk7Q+fjaloX2H/Wytq2+idgOVLNBjEwJl0dWZZHne8sj/YlR8Pzgdm05HmiBOwxopTLygrTCcnLTHYM6hs6uRI4/ACwy0vVPCDx3dG/d7OchDzC9IRgbp+aSJfwASDAVi1g/6jiZLs0UQQ3aUvnXpBWYEdDKIcUVTf7iUvPYnCzKSoZzBX28Xj8+1gEG3dwOkZABysH5vg3eML0O71kZOaaPW8RiHo7KnpC3oTEQx+9OSumBcmXL+/kbZuHxsORT9Lf1+d9W/t9Zsxr/NEcsIGg0MNHdz0lw3B4X7GGC675TX+OIw1ZDYdaaasIJ1vXrqErl4/t75YEdV7N3b08PzuYzG12/lyGAMv7K6N6TVi4QSDGVkplGSnBIPBTX/dwLV3vjWs1MuDGyu5b0Nl1PWOhg4vWakJJCe4yU5NHJA68AcMHnffR9klEvYe3l5raGlqonUtp2iKyM6IFqdX4hSBu3v9YamoSDq8Pjp7/ORnJFGYmRzM4Q+X0zNYOiOTomnJUdcNjrV0k5ls/ZvHKlXkDLvNSkskJXF0Csi7a9ooyU4hPclDxThfSrOh3cvtL+/n18/tjaku5/Te3twfQzCobScrNQGAXVH2AkfDCRsMHt9WzVM7anjePqDurmljy5Fm/nWc4o0xhk2Hm1g1K4uywgwuXDqdf24+OuQH589vHOS1ivqw29ffVR78skdjX207s3JSKcxM4rld4xgMWrpJ9LjITUu0g0Enhxo62F7Vyv76DjYeHnqJiu5efzDF5ZyxNnb08O1Hdhw3hdHQ3kNOWiIAeemJA3pEoTUDcHoG/dNEbtKdnkEUZ69Od39WTipJHhe1bV68Pj9n/eQF7nxt6BSh006rZ5AcU80g2w6CS2dkRh0Mqlu7WDsnF4itiPznNw5y0182DDlRz+k5ZacmxDyPo7/dNW0smp7J/IL0mHoGRxo7+a/7tnDnqweiXhPq5b11GGPl76O9prPX52ev/Zw3D8TSM+jgvIUFJLpd7KrRYDBunJEKTjB4YY/1e2tlS9g1c/s72NBJU2cvq2ZlA3D6vFzq2rwcaYx8YO/u9fP9x3Zx56t9Bw6nC1h+MPo1fipq2ykrSOe8RYW8srcu4pW/huOVvXVceevrwVrA8VQ1d1GclYKIMDM7lcqmLp7YZgXORI+L+zcM3a2uqG0PLivtHNQe23qUu14/eNygVt/uJS8tCYDctKTg6KLr73qbL/x9M929/vA0kUi/moGVJurrGUQfDKalJlCQmcSx1m7eOtBIbZuXt47zhe8LBokUZiZT29Yd1dlmdUs3RdNSAFgyYxr769qHnfsPBAzHWrzML0gnLz2Jg/XWZ25PTRu/fX4vP35qN398Zf+g7THG8PuX9vPUjhquu/OtAQFhe1ULnT2+YFHbKSCP9BrTXp910rC4KIOyGIJBrz/A//vbJh7cWMl3H9vJhb98mSe2VQ/7+S/uqSMz2YNL4PGtw38ewN5j7fgChjl5aWyraonuc9bZS327l4XTM5hfkD4hReQJCwYicpGI7BGRChH56ni//1Y7GLy4pw6fP8CLe+pwiXXgGKqLtsk+A15tB4M19uia8kFyhBsPN9HjD3Cgvu/M7KB9lrYhygXffPbrzC9M54LFBXT0+Hl9XwNbK5uDZySO7t7Bg0RLZy9f/McWyg818fm/bRrWWvtVTV3MyEoGoCQ7hR5fgHvePMTy4mlcuryIx7YcHfI9Q/fpTjsYvG0Hw9f31Ud8jqOxo4fcdLtnkJFEfbuXI42dPL+7loc2VfHolqNhPQOXS8LnGfgCJCW4SUuyg0EUZ68tXb24BDKSPBRmJHOstZsXdtcBHPfM0Zlw5tQMev0mrL50rLWb1/fVD3pAtoKBtc9PKplGwMDmw83DandjZw89/gDTM5OYk5fKQftaDF95YCs/e/odfv/SPr7/+K5gDaq/XdVtVDV3ccny6Ww60swNd70d7G21dPZy+S2v8atn99Jk9wycAnLnEJ+B4aiobccfMCycnkFZYTp1bd6oJp/95vkKNh9p5ldXr+K1r55HcVYK95UfGdZz/QHDy+/UccHiQk6bl8vjW6ujCt5Oiui600vxB0xU3+999db/w7z8dBYXZbL7REkTiYgbuAW4GFgCXCMiS8br/RvavVQ1d7FqVhYtXb28uKeODYeauHxlMdB3wI9k0+Fm0pM8zC+whhouKMwgI9kTPLD15+QODzd20usPYIwJBgZnKWhjDH9+4yCv7K0b8sN3uLGTHn+A+fnpnDE/j+QEF9ff9Tbv/+1rXPX7N4IH9T01bSz/9r94cU/kM+7vPb6Tho4ebjp7HuWHmvj1c3uH2FuWo3bPAKAkOxWwhgBetGw6V55cQpvXN2SKbU9NG0keF6W5qeystgJxuf3vf31fw5Dv3RASDHLTEqlv7wn26K4+ZSa9/vAC8sDRRCMrIGemJOByiXV23+oN9iIPN3YOeabuFLoL7JoBWCN7Nh5u4v2/fZVTf/gcH/7Dm9y3oTLi86tbuiiyA/Bp83JJ8rh4eufwak1OfWL6tBRm56ZxsL6Dww2dbD7SzFcuWsSrXzkP6OsR9/f0zhpE4DvvX8Y3LlnMmwca2W2nLjYdacIXMDy+rTq4LlFfz2BkwWC3fUa8aHoGZXadZri9g42Hm/jt83v5wKpi3nfSDIqzUrhk+XReragf1hDwLZXNNHX2cs6iAt67fAb76zsGPUPffKR5wMipnUdbSU10c8XqYtwuOW7PMZQTlOcVpLO4KIPaNu+4D6udqJ7BWqDCGLPfGNMD3AtcNl5vvrXKOhh9+pz5eFzCD5/chT9guObUWUzPTGbjEGdfGw83cdLMacEzUbdLWD0re9DRA28esA50voDhcKOVYmrr9pGdmsCu6lbavT7KDzXxzYd38LE73uLiX73Cb5/fyxPbqgfkxp0PzPyCdJIT3Hz5wkVcfcosrj9jDk2dvWw+YrX7iW3V9PoNt720D7CCzXV/eos133+GD/9hPfdvqOSms+fy1YsXceXJJfzmhQq2HBn83+z1+alt8zLDDgYzc1KC9128bDrr5uZSnJXC/YMc1MDKAy+cnsGy4mnsONpKVXMX1S3dzM1P41BDZ3ASm9cXXpj1BwxNnT3k2Gmi/Iwk2r0+Ht9Wzdy8NH70geX813sWcOHS6cHnDBxNZA8tjSFN1NzVS1ZKQvC9DzZ0cKC+g9Pn5WIMvHNs8ANVXZsXEchJSwwJBt188+Ht1LR086ULF7K2NIfvPrpzwCS+rh4/zZ29wTRRaqKHM8vyeXpHzbDOVp26TNG0ZObkpVHb5uXv5dayFO87qYgZWSksmp4RDKr9PbPzGKtnZZOfkcR7llqjmd62D26b7O9HZVMXr+61enXZqYmkJbrp8QcGHRZ5ywsVfP7eTTy8uWrQoZd7jrWR6HFRmptGmX3CtXeIfewwxvCdR3dSmJnMdy5bGtx+0bIiev2G50NSkd29fn73YgVfuX9r2GfNyQ6cVZbHhUsLcbuEx7cNHML99I4aLo8w2GTn0VYWTc8gMzmBZcXTePNAA3tq2vjQba/z8Tvf4tYX9w06omxfXTuJbhczs1NYbM9L2T3O18CYqGBQDIT23SrtbeNiW2ULItbZ1to5Oeyv6yAz2cOqmVmsnp01aDG03etjd01bMEXkOKU0m3eOtQ/oznb3+tl4uJmTZ1uP31/XEewVXLaymICxeiF/e+sw6UkefvSB5QD87Ol3+PQ9G3nfb14NS71U1PWdPQBc/645/OgDy7n53WV4XBL8Yj+3+xgusYa57TjawhPbanhxTx2LizJpaO9hbWkOnzu/DIBvv38pqQlu7nnz0KD761iLFZScYFCcZfUMFhZmMDc/HZdLuGrNTF7ZWx88OPS3u6aVhYUZLJmRSWVTF8/vss5w/9958wF4vaKBrh4/7/nFy3z/8V3B5zV19mCMlXeHvt9vHWjk/MUFiAifPa+Mr12yOPgcl4SniZzRRE6aKJoiZ0tXL9PsYFCYmRyse9x09jwgfBhkf/XtXrJTE/G4XRRmWsHssa3VbK9q5fMXlPGZc+fz86tOwhjDl+4LPzA5gwucNBHAhUsLOdrSzfaQReuMMdxXfoSHNoUH4pqQ55fmWrPG7379ECfPzg727M5dVED5waYBZ81VzV3sONrKu5dYQaAkO5UZ05J52057bDzcREl2Cm6X8PTOGpI8LlIS3aQkWj2vSPv3cEMnP396D09uq+Hz927mrJ+8wHO7BvZydte0MT8/HY/bxYxpKaQmutlbO/Cg6A8YPvXXDdxh1+Ke2XmMLUeaufmCMjKSE4KPWzUzi8LMJJ7cbuX/n9t1jPN+9iI/eWoPfy8/wovv9AWJF/fUsmpWNlmpieSmJ3H6vFzuK68Mq5ccbe7iS/dvtV4rJJAGAoad1a0snTENgHVzcth8pJkrfvcaB+o7qWnp4sdP7eYTf3o7YjDfV9tBaV4qHreLRfaclvEeUTSpC8gicqOIlItIeV1d3aio+fgBAAAe2ElEQVS97tbKFublp5Oe5OG8RQUAnLkgH4/bxepZ2VQ2dUWM4Pe+dRh/wHCu/RzHybOtukH/ILLlSDM9vgDXrJ0FwIH69uAQvytWFeMSeG5XLY9vreaylTO4Zu0snrr5LHZ850J+95HVVLd089f1fQfpitp2CjOTyAz5sANkJiewpjSb53fXUmMfLG48ax6piW5ufXEfP3pyF4umZ3DXJ9byry+cxT9uOo0kj/XFTU/y8N4VRTy+tXpAyqPd6+Ptg408vdNK/zhpopREN+cuzOe6M0qDj/3k2XOZl5/Gl+7fMuCsr67NS317D4uKMllin/X8+Y1DpCd5eN+KGeSlJ/L6vnr+8Mp+DjV08vi2vtFZTrE4N6SA7HDG3/fndsmAJayTPG5S7YNVNMtYt3T2MC3VCkDOAX1eflowTbenZvCz1vo2L/npfT0agAc3VZKVmsAHVpUAMDMnlf++dAlv7G8ISwH1ndn39cLOX1yISwj+f3T3+vmv+7bypfu38u1HdvYLJt14XEJuehKzc1OD/+73nzQj+JjzFhXgC5gBAfxZux1OMACrNlZ+sJFAwLD5SDNnLchn3dwcev2GbHv/9AXbgfv39y/vw+Ny8dKXz+H+m06jJDuFG+4u58dP7Q7+Xxtj2FXdyqIi62Docok1oihCz+CxrUd5cnsN33tsJw9urOR/n3mHOXlpfHB1SdjjXC7hoqXTeXFPHfeVH+HGv2wgMyWBv95wKoWZSfzptYMA7D3WxtbKFs5dmB987n++ewF17V5+8cw7gFWcvvnezfj8AT6wqpgtlc3BGpAza3rJDOvzvW5eLr1+w4LCDB7/3Lt4+gtn89MrV7DjaGvEVN/+unbm5VsnebnpSRRkJEU9yXCkJioYVAEzQ26X2NvCGGNuN8asMcasyc/P7393zLZVNbOi2Irg715SSIJbuHiZlWZwRgltPBSeNunu9fP7l/dz+rzcAT2DlTOz8LhkQN3gzQONiMC7FxeSm5bI/roODtZ34BJYXJTJoumZ/HX9IbwhAQOsL9Uly4s4Y34ut720L/jl2lfbHqxV9HfuwgJ217Txf/YZ/hWrivnQySU8trWayqYu/r/3Lgkrsob64OoSOnr8wZz//rp2vv7QNk79wbN86LY3+P7ju3AJzM1PCz7nT59YG9bm5AQ3P79qJbVtXr7z6I6w13eG9y2enhE8c9pb286qWVl43C5Om5fHS+/UcdtL+8hJS+RYqzf4RXDypqEFZIDMZE+wx9WfK2Q0USBgggvVJXlceKJcWbMlJE1UkGGdpZ+7sAC3S1hQmMGeY4N/YevaveRlWO1O8rjJSUvEGPjIqbOCZ9EAl68qRoRgTh5C53X09Qxy0hI5pTSHf+2o4UB9B1f9/g0e2FjJurk5tHT1hqUValq7gyuJltrrSbkELlleFHzMqplZTEtJGDBf5Ylt1czNTwsenMDq/R5r9fLiO7W0dftYNTOLi5ZZr+WMjXeCbf95HLVt3dy3oZIPnlxM0bQU1pTm8MCnTueqNSXc+uI+XrGD0dsHm6hr87Jubm7wuWUFGew42hK8pgRYAyl+9exeFk3P4LS5ufznP7awu6aNmy8oC5tv4rhoWRFeX4Av3b+VVTOzeOBTp/Ousjw+tm42r+yt551jbXzlga1kpyaEfaZXzcrmw2tn8afXDvD87mN85A9v8tbBRr5/xTKuPb0UY+Dld6yTVKcO5pzsnLMgn7/ecCr33rgumCK8YlUxc/PS+MUz7xAIGPYea+PWF/exfn8Dhxo7w/Z3WWF62KCT8TBRweBtoExE5ohIInA18Mh4vPGx1m6OtXpZXmIdlGbnpvHW1y/gvfaXZFlxJolu14Ai8n3lR6hr8/LZc+cPeM2URDfLiqfx+r4Gyg828sLuWuravKzf38Di6ZlMS01gbn6alSZq6KQ4O4VEj4tTSrPxBQzLijNZZgenUF+4YAH17T385Y1DGGOoqG1nfn7kYOD0cG57eT8l2SksKEznujPmIALnLyrgXWV5g+6TU0pzmJWTyv0bKtl5tJXLb3mNBzZUcvHyIu68bg0Pfvp0XvnKeWFnqZGsnJnFp86ex4Mbq8IObM7fC6dnkJ+RFDxLXmP3qM6Yl0tTZy8+v+H2j50MWPlb6FuKIjctMez3OQsLSIjwxQe7Z2CfJTvXP05KcCEipNorl7Z19wbPgIfSHJImWlxkDfu7YrWV0VxYmDHkOHZn9rGjMDMZj0v42LrSsMclJ7iZMS2FQw19dQOnAOwcSBwXLp3OO8faufhXL3OooZPbP3Yy/3vVSgDW728Ie/50O8WUnuShaFoyZ8zPC+57AI/bxVkL8nlhT11wfz21vZo3DzRyzSmzCHWKvQzJ7S9befJVs7K5cGlhsCYCBIfu9i+s3vnqQXz+AJ88a17Yv/l7ly8jLz2Ru+11tu558xAZyR4uXdEXsK5eO5MOr5/r7norWOt5ePNR9td3cPMFC7jtoyczvyCd5cXTeN+KGUSydk4OM3NSOHl2NnddvzbYg7lm7SwSPS5uuPttNh5u5r8vXUJuyP8XwJcvXEROWiLX31XO9qMt/OrqlVyxqoTlxdPITUsMDtLYcbQVl/QtWyIivKssj+SEvqDvcbv43Pll7K5p47/u28Klv3mVHz+1m6tvX48/YJhX0HeyNSsnLezzMB484/puNmOMT0Q+C/wLcAN3GmN2HOdpo8IZUrqipO/gm21/mME6g1teMo2/rD9EfXsP5y0qIDs1gdte2s/qWVmcNi93wGuC9YG7/eX9XHnbG2HbP2GnUubkpfH87jq6ff5gDndNaQ53v3Eo7Gwk1JrSHM5akM+vn9vLhkNNdPT4mW+PsOhvfkE6xVkpVDV3ccHiQkSEOXlp/O0/1gUXeBuMyyV8YHUxv3puLx+9403Skjw8/rnTmJmTOuTzIvnwqbP47QsVvFbRwKLpfYWw/Iyk4BdtSVEmL7XVcUqpdWZ/xvw8XALXnVHKmtIclhdP4/ndtXzm3PnB4ZnOc4umJXPB4kI+ftrsQdvgdgn2JQ6C6xA5abE0+5oGv3x2L3e8eoCnbj4z2M7+AgFj9QzsM9/c9CSe/c+zg/cvnJ7BfRsqBxz0wUp51Lf1BNNEAJetnIG3NxA8SIcqzUsNOxM82tJNblpi2MEE4MJl0/nxU7s5eXY2P/vQScEAPTs3lfX7G7j+XXMAKxg4hUiAP167JnjQDnXuwnwe3XKUO187wIfWzOSbD+9gSVFmWAoQYEGBNWpu/f5GMpM9zM1Lw+USrlhVzBz785yWOHBSX22rleq8eHlRsIfiSPK4+fCps/nN83vZeLiJJ7fVcM3amcGgAtaJyq+vWcVn/m8jH73jTU4pzeGxLUdZOiPTDkbCE587E3/A4Bqk5+t2CU9+/ixSE9xhj8lNT+Kyk2Zw34ZKzizL44pVA8uW01IT+PEHV3D3G4f45qVLgj1zl0s4e0E+L+yppbqli3vfPsLykqwB/1/9ve+kGfzm+b08uKmKcxfm89+XLmHH0Va2VbWEpT1n56bS2NFDa3fvgLTwWJmQYABgjHkCeGKs32d/XTv/3HyUR7cc5XBjJwFjzVZdUjTwTNzx0ytXcNtL+3hyWw0PbOwrzH3/8mWIRP7AferseSyankFuehLJHhebjjSzrbKFfzvFyobNzU/nH+WVtHt7uWqNte3CpdP5/uXLuPLkkoivCfC9y5byP0/uZmtliz1yKSvi40SEcxfl89f1h4O9BCCsyz2UD64u4ZfP7kWAv/77qTEFArCKzM6B6Qb7wLS7pjVYFANYNSuLNw80sNL+t8zMSeWJz58Z7PWcu6iA3z6/lyONndz56gFKc1ODqRqP28Ufr10zZBtE+lYtdSblOctIpyV5aOzoCeZtn9xWM2gwaPP6MIZgz6A/5yxwT00bv9u1j62Vzdz/qdMBay5DV68/mNaCvqJzJLNz03gyZHJUVXPfsNJQxVkpvPn188lMTgg7sK2bk8tTO2oIBAwiVs0gtLblpOf6u2R5EY9treb7j+/ijlcPUN/u5Y5rTxnQ63K5hDWzs3lhTx2rZmUH39vplQAhBeS+YPC9x3fR4wvwxXcviPj+Hz11Fr97oYJP/mUDPf4AHz51YJC/aNl0fvahFXz30Z3srm7D4xZ+fOWK4Hcx0XP8BEd6UuRD3U3nzONwYyc/vGL5oN/t8xcXRqxPnb0wnwc3VfGh296gw+vjp1euOG473C7hlo+sZu+xdi5dUYSIMDc/nfedFN6rKbXrPIcbOiNmDcbChAWD8dDh9XHxr16hxx/gtLm5XLJ8OoJQVpgelrPtb25+Oj+58iS+e9kyKmrbaff68LgkOMEskuy0RD4QUrw6td9BeK59VtTdG2C2fSaV6HHx0XWDn+GCdZC49aNW6sTnD0TMiTquPc2a7DLcABBqZk4qt3/M6nLPHSQVNVzr5uTy5Pbq4LDQXdVtfCrkQPjJs+Zx2crisDPA0APyuQut3tCH/7iemtZu7rvptEHP+iJxS99yFN5eq2fgHDDSEt28vLeOXr8hNy2RJ7dX84VBDlROnvp4weA7j+4IDjFt7LCWznDWMOrfYxhMaW4qTZ29tHT2Mi01gd3VrbxrfuTUXlbqwDP8dfNy+Hv5EXbXtFGclUJXrz9sJNJgkhPc/PHja7j9lf389F97+I8z5wZTqP2tKc2xg0HkE5L+o7VefqeOR7cc5eYLygb9TBVkJvPeFUU8vPkoa2ZnB/dpf1esKuGKVYOfNMVqXn46f//kaTE996yyfFxiFY9v+fDq4PpVx7NoeuagJyCOWTnWMeKQBoPRkZbk4ZYPr2Z5ybQBudfhSE5wj9p/RGjxdU5ebGfdQwUCgLLCDH70geOfnQzmPSFj9UfitHm5/L38CLuqWyk/2Ig/YHj/yr4zn5REd/AiOZGcVJJFbloiRxq7uPmCsgEF++Ox0kROz8BJE/X1DHr9hrl5aXx03Wy++9hO9oWM5AjljIqKdPAFyE9PIictkXeOtQfXDtpV3coZ8/OCtYTBCv79OScIhxo7KPKnUNvmDY5MGY5T7TWI1u9v4PT51t+R0lGRuFzCTWfP40Mnl0RMJTnOKsvnZ0/v4fR5kYOUU0Du9Prp7vXz3w9vZ05e2pA9IoAb3jWHR7cc5drTS4fV3skiOy2Rj66bTaEd0EaTMwLs4BhfiyLUpB5aOhouWFIYUyAYbbNy0oKjeZyaQbw6da7Vg1q/v4GHNh9lSVHmsM+awDo4/dspMzlnYX7Egv1xnx+yamlfmsg6UDm9kWvWzuLi5Vbwe2p75JnTzoqcg/UMRIQLlxZyyfLp/Om6U4C+pTa2VrXgcUlYemwozmfiYENncCRVNMHASc89vq2abz28I+w1hys3PWnQVAnA8pJpvP2NC4LXtOjP2bedPT42HW7mUEMnX75w4XHz6CtKslj/9fMHpEqmgu9etozPxPAZPZ60JA956UkcHsciclz3DCaTRI81u/BwY2dw0k+8KpqWQmluKveVV7LnWBvfCJkQNlxfvmhRzO8fumppsGeQYJ33ZCR7SHS7+KB9FrxqVhZPbq+O+IVuDll3ZzChPbHCzKTgRKHtVS0snJ5x3AOhwzkTPFTfgdttHZCXDlHXimTdHKtHlpns4QdXLBuT9MJQaa/g0NIef7AYPljKqT9n2K7qMzs3dVx7BhoMxtH8ggxEZFgFr6lu3dxc7n37CC4hLEU0Hlwho4l6+qWJbjp7Hu87qSiYDrl42XR++MRujjR2DiiaB1csHaRn0N/iokx2VrdijGFrZQuXLB9+2i05wU3RtGQONHTg9QUoyU5h2hBBKJL/OGsOBZlJXHt66bBrFaMpyePCJVbPoKWrl0SPNYtYxWZ2bipvHGfdrtEU/0elSeRb71vCLR9ePdHNGBdOEfuM+XnjnqZzh40mCh9aunB6Buct6hsZ8q751mTGLZV9kwyNMfjtYaUw/GCwpCiTitp29tW109LVG/WZ+ezcVA41dLLraCtLo0gROeYXZPDF9yyckEAAVtosLdFDZ4+f/XUdlOamRlX4V+Fm56RR3dI95GrAo0l7BuMo1uGaU9Hp83NJS3TzkVMjz6EYS66w0UThQ0v7K7EX3XMuMQnw73eXU36oicwUD8kJrmGnehYXZeILGB7caE2mX1EcedTNYEpz03h8WzXtXh+XrRy3pbpGVWqSm06vn4MNHcERdCo2pfZAkyONncEVXMeS9gzUmCjISGbLt94TXLJgPLmGGE3UX2ZyAhlJnrCL/Gw60kxGsocOr3/Yo4GA4CSv+zdUkuh2sWB6dEN0Z+em0dZtzW2IpWcwGaQmemjz9nK4oZM5+RoMRmJWjjOiaHyKyNozUGPmeENhx4pbJLiMcv80USRFWcnBtYA6vD4aO3r49zMX8smz5hFNkmNOXhrJCdalMVeUTBvyPSNxJhoBLC2eqsHATUVtOz3+QHBmsopNcLjxOBWRtWeg4k7oqqXBoaUJg3/UZ2SlcNRe8rmyyfpdkp2K2yXRTXZzCQvtyUSxjORxlmvITk1g+iQYDh2LtEQP++zLug41l0QdX3ZqAhnJnnFbo0iDgYo7Q40mimRGVgpH7ZqBc5GZmdmxjYJZYi+/vCKGYOAML106Y9qQ4/0ns5REd7Beo8FgZETEGlTQqMFAqZgMNZookuKsFBo7euju9XPE/uLFOhfEWQNoRUl0xWOw8u1nzM8Nu47AVONcWjQt0R22QqqKzezcNA6PU5pIawYq7oRNOuu3NlEkzjUDjjZ3UdnURXKCK3hFtWhdeXIJBRlJUc0eDnXPv6+L6XmThTMLeU5+2pTt3UwmZ87PIzPZgzFmzPenBgMVd0TCawYelwx6YR/ou5rY0eZujjRZM8Rj/eIlJ7hHbY2nqciZhRzvS66Ml6vXzuLqQZa4H22aJlJxJ2zVUl9gyHoB9F3O0+kZxFovUH09A51jMPVoMFBxJ3zVUj9Jx5k0VpiZjIh1DYHKpq64XztqLAV7BhoMphwNBiruuFyCca501nv8nkGix0VBRhLvHGujpauXmTnaM4iVEwx0JNHUo8FAxR23EEwT9fiPHwzAGl761oFGIPaRRMpan2lOXtq4LJ+gRpcGAxV3XP1GEw1nJvCMaSk0dFjXL5ipwSBmp8/P44X/OmfQy0yqyWtEwUBEfioiu0Vkq4g8JCJZ9vZSEekSkc32z20hzzlZRLaJSIWI/Fp0/JkaZe5+o4mGs2T4jJDrDZdoAVmdgEbaM3gGWGaMWQG8A3wt5L59xpiV9s9NIdtvBf4DKLN/LhphG5QK44pyNBFYaSKwLpw+1MVslIpXIwoGxpinjTE+++Z6YMgrVotIEZBpjFlvrOsS/hm4fCRtUKo/V9jaRIEh1yVyOMGgJDtFJ0upE9Jo1gyuB54MuT1HRDaJyEsicqa9rRioDHlMpb1NqVHjdhHSM/APu2YAWjxWJ67jVnlE5Fkg0pTKbxhjHrYf8w3AB9xj31cNzDLGNIjIycA/RWRptI0TkRuBGwFmzRr/i6SoqcmqGVh/D2doKfTVDLReoE5Uxw0GxpgLhrpfRK4DLgXOt1M/GGO8gNf+e4OI7AMWAFWEp5JK7G2DvfftwO0Aa9asMcdrq1Jgp4miHFqak5bIv62ZySXLx/9iPEpNBiMa/yUiFwFfBs42xnSGbM8HGo0xfhGZi1Uo3m+MaRSRVhFZB7wJfBz4zUjaoFR/bgmZgdwbGNZoIhHhx1euGOumKTVpjXQw8G+BJOAZu+i23h45dBbwXRHpBQLATcaYRvs5nwbuAlKwagxP9n9RpUYibNXSYdYMlDrRjSgYGGPmD7L9AeCBQe4rB5aN5H2VGkr4qqXDSxMpdaLTb4mKO+GjiYY3tFSpE51+S1TccUYT9foD+ANG00RKDYMGAxV3nIvYd3it+ZDOSppKqcFpMFBxx23PIK5ptS5yr9fiVer4NBiouOP0DKpbNBgoNVwaDFTcca53XGMHg4KM5KEerpRCg4GKQ3YsCPYMCjK1Z6DU8WgwUHHH5dQMWrpITnCRoRdaUeq4NBiouOMOqRkUZCTrktRKDYMGAxV3QmsGBVo8VmpYNBiouOMKGVqq9QKlhkeDgYo7Ts+grdunI4mUGiYNBiruuENqBDrHQKnh0WCg4k5ovVhrBkoNjwYDFXecNBFAQaamiZQaDg0GKu6EBQPtGSg1LBoMVNxxiQYDpaKlwUDFHadn4HEJ2amJE9wapaYGDQYq7jg9g/yMpOAKpkqpoY0oGIjIt0WkSkQ22z+XhNz3NRGpEJE9InJhyPaL7G0VIvLVkby/UpE4PQNNESk1fKOxgtcvjDE/C90gIkuAq4GlwAzgWRFZYN99C/BuoBJ4W0QeMcbsHIV2KAX0rVqarxPOlBq2sVrO8TLgXmOMFzggIhXAWvu+CmPMfgARudd+rAYDNWqc1JAuRaHU8I1GzeCzIrJVRO4UkWx7WzFwJOQxlfa2wbYrNWqcGciaJlJq+I4bDETkWRHZHuHnMuBWYB6wEqgGfj6ajRORG0WkXETK6+rqRvOlVRzrqxlomkip4TpumsgYc8FwXkhE/gA8Zt+sAmaG3F1ib2OI7ZHe+3bgdoA1a9aY4bRDKZf2DJSK2khHExWF3LwC2G7//QhwtYgkicgcoAx4C3gbKBOROSKSiFVkfmQkbVCqv5Uzs7jxrLmcNi93opui1JQx0gLyT0RkJWCAg8AnAYwxO0TkH1iFYR/wGWOMH0BEPgv8C3ADdxpjdoywDUqFSUl08/VLFk90M5SaUsSYqZF9WbNmjSkvL5/oZiil1JQhIhuMMWuG81idgayUUkqDgVJKKQ0GSiml0GCglFIKDQZKKaXQYKCUUgoNBkoppZhC8wxEpA44FOPT84D6UWzOWNP2ji1t79jS9o694bZ5tjEmfzgvOGWCwUiISPlwJ15MBtresaXtHVva3rE3Fm3WNJFSSikNBkoppU6cYHD7RDcgStresaXtHVva3rE36m0+IWoGSimlhnai9AyUUkoNIa6DgYhcJCJ7RKRCRL460e3pT0RmisgLIrJTRHaIyOft7Tki8oyI7LV/Zx/vtcaTiLhFZJOIPGbfniMib9r7+e/2hYsmDRHJEpH7RWS3iOwSkdMm8z4WkS/Yn4ftIvI3EUmeTPvYvt55rYhsD9kWcX+K5dd2u7eKyOpJ0t6f2p+HrSLykIhkhdz3Nbu9e0TkwsnQ3pD7vigiRkTy7Nujtn/jNhiIiBu4BbgYWAJcIyJLJrZVA/iALxpjlgDrgM/Ybfwq8Jwxpgx4zr49mXwe2BVy+8fAL4wx84Em4IYJadXgfgU8ZYxZBJyE1fZJuY9FpBj4HLDGGLMM6yJQVzO59vFdwEX9tg22Py/GutJhGXAj1nXTx9tdDGzvM8AyY8wK4B3gawD29+9qYKn9nN/Zx5LxdBcD24uIzATeAxwO2Txq+zdugwGwFqgwxuw3xvQA9wKXTXCbwhhjqo0xG+2/27AOUsVY7bzbftjdwOUT08KBRKQEeC/wR/u2AOcB99sPmWztnQacBdwBYIzpMcY0M4n3MdYVCFNExAOkAtVMon1sjHkZaOy3ebD9eRnwZ2NZD2T1u1zumIvUXmPM08YYn31zPdb12MFq773GGK8x5gBQgXUsGTeD7F+AXwBfxrqypGPU9m88B4Ni4EjI7Up726QkIqXAKuBNoNAYU23fVQMUTlCzIvkl1gcyYN/OBZpDvliTbT/PAeqAP9mprT+KSBqTdB8bY6qAn2Gd/VUDLcAGJvc+hsH351T4Hl4PPGn/PSnbKyKXAVXGmC397hq19sZzMJgyRCQdeAC42RjTGnqfsYZ7TYohXyJyKVBrjNkw0W2JggdYDdxqjFkFdNAvJTTJ9nE21tneHGAGkEaElMFkNpn25/GIyDew0rX3THRbBiMiqcDXgW+O5fvEczCoAmaG3C6xt00qIpKAFQjuMcY8aG8+5nT17N+1E9W+fs4A3i8iB7HSbudh5eOz7JQGTL79XAlUGmPetG/fjxUcJus+vgA4YIypM8b0Ag9i7ffJvI9h8P05ab+HInIdcCnwEdM3xn4ytnce1snBFvu7VwJsFJHpjGJ74zkYvA2U2aMwErGKQo9McJvC2Pn2O4Bdxpj/DbnrEeBa++9rgYfHu22RGGO+ZowpMcaUYu3P540xHwFeAK60HzZp2gtgjKkBjojIQnvT+cBOJuk+xkoPrRORVPvz4bR30u5j22D78xHg4/aol3VAS0g6acKIyEVY6c73G2M6Q+56BLhaRJJEZA5WYfatiWijwxizzRhTYIwptb97lcBq+7M9evvXGBO3P8AlWCMF9gHfmOj2RGjfu7C601uBzfbPJVh5+OeAvcCzQM5EtzVC288BHrP/nov1hakA7gOSJrp9/dq6Eii39/M/gezJvI+B7wC7ge3AX4CkybSPgb9h1TN67QPTDYPtT0CwRvXtA7ZhjZKaDO2twMq1O9+720Ie/w27vXuAiydDe/vdfxDIG+39qzOQlVJKxXWaSCml1DBpMFBKKaXBQCmllAYDpZRSaDBQSimFBgOllFJoMFBKKYUGA6WUUsD/Dx0M/ney8iydAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2Mc5TSlA6pRo7Ge4mxk5GLtNauq3Jqzj2WRm7PyomS6JaVEzuk2IachAANMack0yTOxNHtxM3x/Sz68wjMkZKMxEdKdxxVimmyuRtqotEK6ALK1kw7FPglBj2dgyycazNWYppSKjScezasJ9Oxj7ISVoZHeKXRhGSSBjnaatx7COkmFrmqUeKaXh2ABqzRIlx09/S+btpMtZ5yv++TdAYy+IIl/cHE+eCaPlzhjEqpUReqnIgTc+y18+RRAK3nl3C9l7Q2CfFqTLss2rsxybFTBrHfkoNO9eNfYaEPktjgTSOTLgjM+zREaNimqQYi7GPKinQIMXwZ9VkgA+cWjF60+4sbnYkz9mw0/mfcW4JUgKX9ycznm3kjvDn13Se/UGB5SzGxmonMPYpcKoMe1tSzKwZrJNe50YtAjauXO2wKCGEilCJI+FNUIqPuOdpkwxWMsNO5819hl3XJfI/O6BZMqklKBFjz+LGLFzuWNzvty/F0Fh8xrllAMDOhKxYy58zSDE8KaxpYtsf5OhmMdZXs+A8nQKnwrC35Tw1zH++Ugx3PElPvZDTHu5IUoz6f/0eB4VEGkcQQiCNhXFikmFPZ4hjb5hUc49hL0YkKLkkYjrG7kgxWTLCkcw19nlIMeqcZNgnZcWDFqQYfm9NZGp/UGAlS7C52sF2b3Cq9yhoE6fCsLeVoNTWBDEOXI+19710GfvpdJ7y/m1i7GlVEyRmGrt2nsbkPJ3+2k25CmRs0zhiTlMfY2+IimH30eQ87TnVHYntrnRiDAvpnagsKWYO1T5pzN82pWFvI0HJV4PHxQGTYgZ5id05rFpOI06HYR/Ozh6A9iaIsdcZ8gHNjNzQZFamsTi1Uoxt2OvGKi9KpIkamkkU6UiUPmPsR93ztCkVPufO01qtmHadp2TQCibFAP7xO3cpxjXsE4Y8thGwkJf1fnXRG+RYyRJsrGVV+4IcMwlOhWEnVjPrjkNtFRMbe50GptIv1O5AQgh0kvjUSjH8nkdJMQCQsLhyHQYZHz0qRstgzt8WPueptNk5RXG49+Deh08ykVLWNtrQztO02Vk+bymGrrm+miFLoqmlmFn8UblnwnTBGTuAEBkzIU6FYW8r/rytsMmx12lk7MqwA4q1BykGVrhjPy8QRwJJfPSomKaxwsMd3QQlX0XHUVKMj1kPqjru/DwuY/c9bzvccX6MvZPE2FztTFThkcpOA7P5o3wSl4veoMBKhxv2wNgnwekw7Cw2eRbnyrEZdvYCWxs1FyWyRL3kWRKd4uqOk0sxKtzRSG008R09KsafCl/4nKeFa9j9EzK1jQy0Twvnurs7cXQ7ZNg9q5cJnLKzgPqjk0bYWM0miooZjOiHaVBMIMUcDAospwnWVyspJhj2idDWnqc/I4R4Sgjx0TbONy344J9FjmmrmNik1wFMXDRQZ+yntR67vdenj7EbKcZ2nqr9TunzNqUYGjdp7At3VITBnoTdrOECZ5ZSCOF3nvYG9cncJCg1G3bOZOcpxXSSCOsTxorzds4kxZQ2qfGBwh0vdDMIEaSYSdEWY/85AK9t6VxTgw+0ozIIe3k5b8bO2lva7JUM+2lm7IMxUsygKJGQFMMTlNjEd9SoGC7F8NWdrbHbnwHKyHMj6xq0fl5iKY2wkiVWfRMCL2ZVZ+zNUVBk/LI4mqsUkyWKsU9m2E07Z5Fi7HDHZimm24mRxBEudENZgUnRimGXUv4+gMttnGsSDPIS73/4svU7gRjw070BHrh01fq7931mp9HwNy0vH3zimncwlaXEuz+9bX32ySd38dTuYe1YKSXe/dC2NiRNE9GAMdJO0pyN6MOHP38F1w4n22Hm/Q9fxi/f/3n88v2fx8PbvZHHPnDpKp5m5VKv7A/w0ceujvgL4NGn9/EIO6/bV+49uxgWph+SSBgphn0ej4mKeeraIT715G7tc+p7N42dDIslxUhbKhgnxWRJhOUsxsGwboC5safVQulo7F5HMtt8xWXs1K98gnLH4F4/x4c+f6V2XoKWYhKlY+/sDawNTN790LYeK5+/vF9rJ++H935mRx/72R3/uHrvZ3Zq4aTueQh5oTY871bO5Y3VTmNUzPsfvuztv6sHQ3zkUfv+73/kMg6ZXHbpygE+s7XnPe9R8NBTe3j86kFr5zsKjk1jF0LcI4S4Xwhx/9bW1kznesdHH8c3/+R78OQ1NYBtBqEe7r1/8Bl8y73v1Z9/dqeHv3zve/HbDzzpPWeTsf3On/1j/Pv//lDt+N//1Ba+9T++Dx+7dE1/9jf+0/340d/5VO3Y93xmB9/6U+/Dhx+9Wr8Wl2LyEp1qWT6N83SQl3j9T7wHP/9Hj4w9tiwlvu2n34c3/spH8MZf+Qj+8W880HislBJvuPe9uPcPPqM/+6k/eBhvuPe9I30Z/9evfwzf/8sf0r//3iefwrf+x/fhk5WhHec8zXlUTCQ0Uz4cFuhUPohoTFTMD//2J/E3/tP9tc9to8QZu8p2TZnEU5T2saOcff28RCeJ0c1iL2Mno7ySxZrt5zXnabMUc3Y5qRn2P3hoG9/6H9+Hjz9uJrC/+Z8/gH/9LjMGf+n9n8Prf+LdliHj4AXXLqxkyEuJvWpl0M8LfPvPvF+PlX/+Ww/W2qnLQ5QS326Nq4/VrvXQU3t4w73vxe9/csu6f8C/SiZfxUrlg7iwkuGypyb7E1cP8c0/+R6846OP17772T96GN/8k+/R4/Vyb4DX/+R78OsfvqSP+aHf/Di+5xf/xNs/R8HfeesH8UP/7eOtne8oODbDLqW8V0p5UUp5cXNzc6Zz0cO9sq8YqqX5VS/jtYMhrh3m6FURCpeuqEng6YZaGLZD0wy4a4c5rnj+hs7Hv7vcG4w8lq5tXau02Wsnnl6K2en1MShKXLpaXy24eHp/gEFe4vu/+nl45bMu4OqIfST3+jl2nfu/vD/AXj/HtcNmWeDp/QEeZ215rLr/3WpFYYV4egzOoCiRxiTFGEOrshArwx6N3hrv8v4Al64e1iagpr4vpEQSCQi25Z7LKEdHxRTIkgjdrG6AVdtVf51ZTmsTRzdrLiFBk8DZ5bQmxVy6oljhLlupXTsYWs/r6f0BhoXU74ELXn+HZC5dVjlXkTzf85XPxUtuO4Nr1Vjh49LsbqX652+9+jn40udu4AnPWKTxT0lGXM7ySTHkq6CdtFY6/r69cqDO+7TH6F+6coDDoXl2vX4OKe339umePV5nxaUrB62e7yhYyKgYeriUCOKTYtx9HOlnk07ZtLzs54Xl+CLQ+eg7KSUOBoWXrdGxNFDt9joaezq985SSSibRH8n59JzNVZzvpiO3cqNj+T3RPYy6Vq+fY2fPpH/T8tkXaui7x2FRMsYeaQNAMc2AiooZtZn1/iD3Zir2G/o+LyUioWrTFLqkwHRSTCeJ0M1i7xijMXtmKTVlCSZJUKrq5qwt1aUY3a/WGCqt8UrPrsnxyp2nSdXnJrtW/dxY7eDccqYnh4Hn+dGzvWmtg9vOLXvHh65u6ckX8UkxdL2VauJr6lu6R/97apMp+snHdG9Q4OkpKluOwuGwwG4/v+6+gAU17OrhkpHhA1sPtEbD7h/gTcvLYSG9UQ7uRDEoSuRlw7HVC0gDtd8gB/RzHhUTT8zY3Xuc5Nj11UyxS48e7B67bxkKdfyoDMCDYYFBUeLaQW6dxxdO6rtHLsXwWjGUhQigimNvvk9qs9vORimmUIzd3hrP1uDHSzFk2Ecx9sRKUIqE2VDEJ70NCok0irDSqZ/X7Vc6B3fU0ngcN+6zJNJ97oZ3prHyHdA5+CprqN83Uyt/fTXDTm9QW1FRfL8bFcQ/s453GHtT39I9jnpP6T4PPTtdHQwUi/fJPNNCv4vXOUO2rXDHXwTwHgDPF0I8KoT4622ctwn7zmAdeKSYQTXQthw221THo2l5qa5TN37u+fQL5DGU+tihh7E3OE+zKRj71hEM+8ZqB90sbuwPwAxO7gykexgVdkbPxW2XrwpnM2Ov14qxGHs02nlqVhZ2O5t8KXkpEUcCkRCg07p7ko5y+tKza+pTm7GbiSOOhJ7Mmxh7Ggt0s6R2Xro3+jsiItz4kU7dtFIlI53Fke7zumEXWMliM349EgqXdDZWOyhKiSuOzKcZO4WRls39CZixZhj7aJnL9x2NYV20zPNOu+N1FuhV7qAY+W7NG21FxXyLlPJWKWUqpbxdSvnTbZy3Cft9e7D2c6O9Nkoxu9ThDVJM9cBXWG1svfO8d8CYB8h/+qQNV9Kw2us4Esk52EmixlKu9fPb9zgKW9VA36wMu086cs9rLVv7Ngv3Yd85xl0ODwqTzONj7IOi1LJAwhKUOGMfV1KAnrPbzn5eevu+KCWSOEIcmWiYURq7W66Wnt1KlnjHmDbsy6n+27Iy7DSZN2nsaTVhuOfd0hOmbXAtw95vNnqAKmORJVRJ0y/FKMZuwjjpmVnviq7jE2NjzZ8lSu8rSTGF1Z/1Z0ljzWbsec1vYoie3T9SSjb2nHeajen9BhJwFHCmfj3lmMWUYjQLMVLMKm1O4Rhl6lzagb1xgFfnXF1KastLr2HvEWPPrZ/eY/fsY3l7bSmmQMadpxPWiiGDfjAsGp1kpi0DZHGEM8uJYoHDotEJuVUNdM48zEvgH7RSSv18KIvRJ8UspXFV6MyfRp/FPNzROE+XufN0Asa+UzPshbfvC6mMLJ8wLKkgd6SY3JFihoUJd2xglXEksJzFltSTRJGezP2OZHXMskeGcPuVfvrYaPO4N/LfKClmJYut8Qv435UsVvHwvH1uW4yPodlnAZhnSCSg24m9m3o33eNuP2cTj62xW5PfYLy8OCn4PbexAjgqFtOwV8ZLO3PyEisdelntJRd1NBmpJmchDZaVTlJj/V4phnRzZ1D52BpfnjW1lz43ztPJi4DRpAWM3yhhZ6+P9dUMQgj9whw0rAzIKPJ7GifFHA5LLWW4eqMuspZLpLFAFkf+wleFNFExUYS8yvzcHxS6zZEYHRVD7HLLI8X4+r4oJGIhEFWJUWUpLRY5LMc4TwtlIFc6zYy9m8VIWfhm4TD2Jlkqi4Xe8Jo7+HYcKUYbrX7daDVKMWyVSH3u7vClpKAY+8NC7ZFaUBiiMey8Vv5mQ8GuXs2wM6nLMw7oeC3FVL4I14A33SM31G79eHpGRSm17s7fo6Nih+n0k25aMg8spmF3dO1+XmK1elnd2t06YqR6yE01remBr3YSs9myNuz23/TzQof7URvIkBwMilpG42VntcDbW5diKsOeTsHYp2AJ23t9XVBJ1zZpYHM+vwTdZxNj5y/X9l5fRQo5Tu5hWSqmmvo3cbajYlSCEhXRIqPMo1dclKVkE5BpJ23c7Ot7rrEDisFbcex5aW0Q4pYkUMw3xnIa43BYehyHyrAnVpExlWFLz9zvSDZSDGDG7yAvdaiqZqMkMwzNGBzH2Hn9HVol5Q1SjJRq4qbrrHaSRo0dqDNgYvzaxzBGiqHjjRSTVPdiG/Cme+QTi7uq0b4xZg/akGK2ghRzdNDgNgajqL2sZKh3ev1Ka6uM65h43tVOUpvd9we2XMFnYlo1kIMxZ6UJABW7S396wHwCpr02a8nYSzYomndv59jeHUy8UcL23kAXVGp6UfixgMPYG7RrAn+5tvf61nG8LHJWxU17I0Hy0irbmxdSs9DllDN2/z1aLytnbYV5xtQOQlGWSGIVFaN+l1bUzbCQesKPRL2IGGnVTaug/WGBbpZUCVcmKiSKBJJIIBJNjF1FCNGz0hJTz8NG2XldUtIcx27KWCSjpJgqSag3yO13xWXsSYSzyyniSIyVYmxpqzkqhq5NxdJcA07j0w139I09w9hpdcOISEtSjKltHwz7VKCHcTDIdSQAMbl6uKNKqGli3wR7eWmzfgA4zG2DpdtCk4wn1ts9dpQUk1fhktp5mjY71Fxs7/XxwlvXatdrOnZaxk4MlOvnkxj2rd2BdRyvrJjGSoLwR4IYKYY2s3azELmTc1QbfC+3T4pxGXvpMnYmxXSzxPpbWgl0kkjXfXHlmP1+XjF2W8OnpKgmnwrVzaFnZcJNPWyUMX4T5mivLGvnZ2SiJsXkRoqhCfVgUFj9OHTet04SI4oE1lfqdV1cwz4u3JHel6XEOE/kMNvfAAAgAElEQVT5edx7PXClGM/YM4y97hNrJyqmj2ecW8LaUhIY+7QwenahB+GaK8WQ83S3rxm2EM16Mh2/5pFi+DUBM2B4JT+fg1Fdf1A71ifF0H24y+Jxhj0vSlzeH+B5N69Z1/NBSomdvYEx7B1i7A2GfbePys7hYFho/VyI5uuQIRFCsUrfcjgvFSPvNBgz+h6A3szaLMvHR8VQP7vVADnTBGxjUkqKY6ffnR1+8lL7XpYzu44PjwXvMgNo94uSYuJITVS0aQetEFTeQoMjmUsxHud132HsQD3AoEmC5PJf6koxVPGy8h0AxNirQANLiil0HwBVXRdH2th3pBguZ/mKiR1UlR3J76FXmP0JpRjGmOn9dp2n+w1j5ajYrt6vTc/9HycW1LAbFtJvYGE0yHf7OR6rUq9vPbM0NrWaGDuxMH3Nft1Y8/PZkQi2zqyPZZmyxDxpkPOaHQB0zZhxDtTL+wNICdx6dglnl9ORLOHagYoS2NBSDBmLep+QNn7rmSUA6oWm455xdrkxAodelFvPLDVKMYNChRb6GLsqjytZuKOKiqEVES8pAMDrQKV+pjYQzDO2+x5QxiwSTGMvpcMopY67VvVe6rH4ynlKzNovxdAGInl1fqpi2ZS3kDtSjC/mWjtPmfHe7+e2JDNSiiHnqSPFEGOvonLo+oNcZcMupXHdeUqGfa1eArjG2K14eD9jpzEKNDP2pjh27jh3JSM3RNIdK0fFdhWcsL6ahaiYacEfCs3ANSlmaHTsB59QRZLuuNAduSTl5xkW0mbsLEmHQh3vuNDVK4BeE2PfY8dajD2trlNfyqqfxNj97SXQamR9taM2Shjh2ad2b66Nl2J4uwFl6Om4Oy4sW9fmMMd0sb070KxptZNYUkwWq+3/XMZOxjbTUTHKsLtZiDGTTEa1YX9QmOxgzdjtvgcojt1o7LWomKLUhm7ZkWKIDXbSWK8o3AqPJMXEsZk4bMburw1EdXOWnUmY+p73q7vC9IU91s7vkWJqGnsidGQKkSmqLWMicuzx69u0ox7uqPo3Zn4HDp6QBjDG7vovGuLYd/b62gYYxl7qY/l2hXdc6OKyJ1t2GgyLElf2h9hY7VSVMoNhnxichfQGhZFQluqMnZwYDz6uKjA+c72LnifBATAGlJ+Hs8mew9hXshjrq6Z+hi9yBDBx47ecXcL+oEBeRXd0s1g54RwmYcr2TibF8ExSVda0eflHRtZo7M3OUzrvM9e7+p7oJbizMvY+RkLneua6mvQ+d3kfa0sJ1pYS5jzlUoz9kpIMxmvFFKXU5+2ykgKAX2fnbQCMEfQ9Y3NdiTiKjPNUuozdJCh1s9j6W83Yq3hv6i+7TcTYDSsuCsewj4gQWunUpZhuFuNcN/Vu5q5WWH6yweGTYlxnrFoxGI2fQiTTWDSuOGmbPf6uGcNul2zoprFXiun1TUIawIiIK8VUfX04LK1nxh2Zbhw7xcPzseLLlp0GVJJAv4tBipkc3AgdMI2dXqi8UC/ksJC47Xxl2J/YhRDA7ee73gQHQH1Gy0s6Dzc6rkN0Y61jpXnbsd62FLO+mlWV6UzCRFYVXXITKDqOYR8nxRjDnnmXv/axxO5dKcbH2NWxZMQPhrm+R/psVKEnOubBJ3axudqxnIPDslmKIR07YeGOAHSteS3FEGP3dE/TBMQzJgE3KsY4MtV561IMPStl2OsVKjupLVnYbTLOU7qeSoqihDR/6KeWYlJbiiEneJZE6DurPsBeYdH1feiz3AmtsTvVLbkUczBUjJ3Gr/FH2eN3fTWrFWEzGrsJGADUKszL2IcuY2/oW0/JC9VHA20DtB+itqqxx8oscszWrk2yrh4Mr9uG9Atn2C1mPMjNy9qpM+1nnFP68Cef3MX5bqaZms+QUVRDxiID+g4DItBLpdK8685Tm7FXx6Yqc1Av25MIWRzpl+eQfa5+Ntfo5iCGvrHWGbsZMWf3AGdAzVLMnesr+p7oHukz30tAKxg65pNP7mJjtWNJDUaKqbNUPfHpsr2qP6igGK8VA4xm7LqdlCBV1McKgZKFYhbHzqWSYVHqZ+Uydh7DvdIkxQyqnYD0+WyNvSn0U9eK6ZBT1oSbbqxmVrE4iuwC1GqW+0B8lQ+BSoqJJ5diev1Ch0imcaT9UdQG7jwFbAdmkxSzlMZejV2VaeaMvSGOnSdk9e33lGyAG8eu7iXX/eKOlaOA3ofNtQwba4o8tZH0dBQsnGGnB5HFkcXYV5nGTob9tnNqFu7nymG4MkJ66FcDnDuQBg4DItBLxRn7/sCUA/AeW9WSPmTRA2opay97eREwYDLGniUR1joJ1lcy7B7mjZsqbO/1EQngfNeNY/cY9mqA31ExHs5u6DOf7HPgHNPPS2ysZdaOUCQvZJ7sWh47rX5WjP2AGHslxTAnp4s6C6ukmKE9VmqGXZioGOU8LXWYH5XtjYSadH0FwXj0Cp/cST7spomeqFyNvSn0k+rm6PMSY98d6AnTLaMBqAmA2Cu9Kz5YzlNnzLnVHVXf5lqXz2IzSQ2qvqGJasOTfWqqO5oVAWXeutUyVR/mFmPPkghJJDyrIfPucb19f1DgGZUU48axA4rdHzir0FkcnnpFvNJhE9v1kWMWzrCTUd6o9G1e4wWwJZS1pUSHQa6vdMxy0jPIafciHvLFGZTtEB1oxk5V//YHuY42saJids2xAHS2YCdRWYhD56Wc1nm6tdfH5moHQghdfGmnofzo9l4fF1Y62phQZcEmjf3MUoJz1SRwMDRRMWeWU5zr+iNw9ocF0ljg1rPL+rP1FZJi1L3kpWwMd9TLfxbuCBgpxjD25qgYd3KhdvJcBcCJiqkSlGjCkFW441JqZBuakIipErjj0DfGjH8gtlYARVk6jN2vsWdxhKUkhhC2FLNOUsywyXmqPt9YzRqlGF7GItOkpp55ymUQ0tgTLd2U+jOSsoxh71f9aXIR9IqgLBFHqviYLyLoYGiK5RF8pXt71rtnJj5Aaf1ZHHkZu5eszKCL6xXxWqd2/8eNBTTs1WBd62i9D1APXFTOSCu9uTJ2G2sdlj3nM+xFtdmAkWJsPa7KLC1KPL1vG2saIHQtvvnGTk/p8TRAn+6RYVdSjHY+kQMutRn7uJrsapJRg7oplZuwtWuOJTTVuN7eG1jt5s7TlSypnEMew97P0c0SreNTu3gExSBXRtQnP1A/JB4phpe4jUc4T4ktn11OrRBQzdg9zlMtxbDM07KUWErNCm5YSGRxhCwRIxh7PUGJ+q3bifUKJC+lZqwAvBFCgKmbE0UqSWi/n+vchc3VzJoQavpxxZA31jojnae84Jq6psvYlfFVG2qbqBi9us2lFV2jrllJEaxQGT0qE+4okUZC1dz3hTv2Cx1lRFCle+1J6oC9e7qgV88YWT7OLLLWV6x+KY1wvpsh8WTLToPt3X61oXms37PrFfK4uIZ9tQO+5VcWx3rm5wkjxuhlWE7HSDGVsQXsCYJfl+LGSYoBTMz3uW6GiCUiXTvIMSwk1lcyPUBpSy5XiuF1sQHD3MfVZN9hmaR0r0263k6vr0MdCU01rrf2+thwVjm8jKovnI36qZupZ3Guq8IKN9Yy23lasVBftiX1h2tsrh0O0U0NI4xGhTsOc+3cW2ftrCUosWvnpdloAzAauyppq4zdsFB1W1K20lLnNY5Dmnx8CWuUoASoEgaltKNiBo0ae6T/fn9Y4On9oRqDa/aESe1QzD633pWRUTGpmSyJHPFnQddfrsrmDqptAG1/lClNAAAXuhmEMLHkXO9369G7KyACJShxuERkkKuMbXoHDGOvDHu1WuSkgpLuKCS0myUqW3Y1mylEcaenCJ8QQrfnehUCa2ujjdcKIT4hhHhICPGDbZyzCcRC1leUEXu62ve0k1YMOJfsRYuZ0euMdBYOdAiXbdiX0xhZHJn63ruD2vl6g1wNwjRGl9Xj3tLOFHOsbq9jIMjoLLGt8dTno6WYbcuwj9b1tvf6ut8ITduN7ez1sbGWWQyUl1FtZOws9pi3y3Ke6nDHOkt1pRhu2LneOjIqpm8SWzaYQ3nAVneAnflIjD1yomISLRUoKSaJBJKoWYoB6ntz8lDNVEsxnjh2n8aec8OeYJ9tu6b6Nbbi2IVQE5daYZl3xfeMKfQ2i1W7qSb7QK8iqwilqo0rlVGtMfZCFQajCQJQK63zXVNWgPcHL9ubVht8uASGpJuaFOPsJMXvkf9Okory79hSzJklRTh6gxz7/UL7UWYNUeTv4konwXIaL64UI4SIAfwYgK8D8CIA3yKEeNGs520Cl2IAs0Eu7QCTl0ZC6aSRlgQ2V40U40uvJsZODqRhYZaX3Y6psc11tC5jsxT1wHfQcXcrstpL4Y65EwdcvWSTOE/LUpUIWHekmKblH+n9HCOlmNWOxUD3h4V2MHODybHPNsPgEhF3nlIIHzEpHutcl2LIeZprbRwYFxVjoik22QTEJ3y+WgLqht1ExURVqd2yShaKkI6QYgBVqMwrxWSx5TytZZ76pJjS1M2hZ2UbdluKyWKzixN/V2g8c7jyHwCkkZFF8ioih1ZJVGuearhrjb2QqghabJuTjdXMVFW1DLtxniaxqK2AAFP+uSbFpLYU49oDt+TC+krHqiLaH5Y4X60kqY/ILjSRlUmxtdu33q+NtXq9nONCG4z9lQAeklJ+Rko5APBLAF7Xwnm9IKNMHUjSRieNtDNSM6jYlBBdXzVyiC+9uj+sQriYc4uWl93U7DRkG2sKAcu1BMFDIH3H6vYmMbJqIgIY66sx9mbDfvVgaC1Dl7MYK5mfJfT6KkpiwyvF2P1BJWF5WKRiNzlj4/4InB5j7OtMIuJOPsqm1LH67KWuSzGVxn441MwKgMWsXewP7Ha6mz530mq1lLuGPXKiYirGnkQ63JHkuiYpBlAlC/xSTMLCHctKYzfPe1wJ4261PR3ftzZL7OxPCrnsDUwoH7FZN2ig74TYAtD36l5b3Zc6r6o9b/wFA2Lsic2uuaHkDmQe7phE6lnkjhRDx5PRJTQxdhqr/N07u5zq50UyV78odUBAb1CgN8i1XVhnY+Uo4P4u9/6PG8n4Q8biNgCfZ78/CuDPtHBeL7RDaNWRYpK4eoDSeoG5HEDLOhoY73zgCeSFxJ976a0YFKowl2HsarBSeVmKS+YJQcS+94eFZqoqBNLekWVjNdODuVmKcRKUaINjj/P0ke0efvhdn9QhgNxYb6x18NsPPIknrh7i5Xeewz1f9hyn3XXG/sQ1O9uONHqeobo/KBALofuQvvvbb/0g1pYS/IOvfT5uP6/KJmywVRIdyw0XzzxV926Mgomdroc73sTukzs5pZT49//9IXz9y56BZ22sVIzdtPPaoSo9wZ3qLkskjZ1LPK4GzKWYUhqW33cZe5bYJSb6xqhpJ7zD2BWrtA2vWzenmyX46GNXtW7r9qtadcaaWZNGTSud/WGOMzLBj/7Op/CNL79NryK505NLMVQymLBMuRgV4bH9UYV1Hmrfhz5/BYCZVM4up1aCkmLsZgV06coB/uVvPagzQPlkTn346NP1SZPGnBvnr/qW9dGwwK1nl/SxB2ysbK528ORuH9/1Cx/AKHzxczfw7V/0TACKcPzjX/8Y9gc5Lvccxr7awed29vXvn9vZxw/95sfw3V9xN77g9rMjrzErjs15KoS4RwhxvxDi/q2trSOfZ1+zEJuxkzPSkmKSGF/y3A181Qtuwt03r1rZcwDwE//j0/ix+x4CAL28TJ3lJe2KY5Z4A3SSCKudRL8Ye4c5DocllivGzo+luHE61jB2FYFDmZaHTrjjcqpKDuwe1lOc7/vEU/iND1/Co0/v42W3n8XL7zinv3vdy56BlU6M935mB//2dx/Sn/MJiaPbqTtPjR/BZKjSspX68OJdF/AFt53FZ3d6eNuHLuG+B5+qnk+uVyeveeHN+Et/6nasdBK9I1RRSpQSlmG3SzcYIwhAM9prh7m1LOdOzr1+jh9+1yfxtg89BsCuMaJDQPcGxgBXsp1PY9dhlFJFxSSR0AxdhzsmdiKPWwDrzFKiw1oBE+K6tpTo8+eF1NvxASrS6HBoR2K5dXO++kU3Y3Otg7ws8doX34IzSwk6aWw5T4mxU6QWrSLVsynw1G4f/+Z3P4W3f/hSLcSW+mYUY+dx7FyK4Rt2EDhj7XHDzjKQ3XDHP3xoG//fhy7hczv7+ILbzuLld56zzkl+BgKtpOvOUyM7WhFZhSqZncbC6iMAePXzNnH3Tav49NZe4793f3oHP36fea8+8MjT+K8ffBQPXLqG599yBn/27g3v/QPA55/exzsfeLJx3+U20QZjfwzAHez326vPLEgp7wVwLwBcvHjxyJV29gc5ltNYL9E4AzZSjEkCetbGCn76r/1pagOSSFgbMpusvSo2N6ovL1WYmZ3KLYTJxqMaEStZgm7HvNQ7PRU3HkVCsya+wkjjCHu5qS2vShqYCIULKxm2PTHpZKB+43u+VBtRwvd/zfPx/V/zfPzYfQ/hLe/8BA6HBZbSGFvM6cuhMmL9daxJSul2FAONhYkBf+5Nq/iN7/lS5EWJu//3d5itB9mL8qV3b+BLq4FOETBkNJKqCBi/H8BEURDb5yn43JHGpRgygMaI5LilqkpJMsTO3kAbJHIS2lExio3zGjR5WaqNMCpGOSwkUoepLjH91hTA6uDh7Z4+9w6rIfL41UN9vYI5T8lPcrk3wC0Vo3Tr5vzVL74Lf/WL77KelYrRVjsmUYTLchbjyv5AT8Sm3G1h+X8orp8b5ISFHlLWK4H2XTXOUztL+8xyarVtYy3TkSc0xs4up7o/VLijLcVQ+37lu74YFxxHP2Aigwi0kl7pJFhKI2PYe3288JYzqo+SSBMn651mUTGAYuK/9X1fVrsmxz/7zY/jZ//oEUgpIYTQfqZf+Ot/BndWtYn0/a9muLw/qFYmUeOqeR5og7H/MYC7hRDPEkJkAN4A4NdbOK8XZDiMoRxAVBlvaSXFuAyKIISwNgXe3h1gp6roRktJWk6Ssywjxj403nZissQK6YEtZzG6qdn0l8eN07HcecrZUW9QWOF8gFqV+DS/AWOeTdBxtNXf7ziVHQm+TZJ1NA8Z9uqe9gdFbWnsi35wQ9QA6AxJUzLA9DUvN0v3e756qWmipbYSDLM2zFlv6u1h7Nt7fcVodUarLcUUpSosFjdExeSVFJNVzj7AMGoy7GToNlZVf5BTeGu3j7VOgqXUJCjlpdSTifqbekILrebSEc+5k0S6djx3npLRUvKgyRrV+9DuDbSxc6WYYYMU060IDkmUfILjpQkIPORvf+CRYnSCkmDvgb1ic7GcxXYJAZ1bEVv+ou1dJsU4kUOUb0AhoU3X8mFjtYNBUeqtMU0wRX0S2ljtQEpD5uhd3FwEwy6lzAF8N4B3Avg4gF+WUj4w63mbcFBFn5CBubI/RKdiYVk1QFzNk4N0zv0q5booJa4eDGtSjNLY1dKWD6bt3foORPRwtfOUsXtdIpe1F2A6b272pux2bPbd5FXv54XSekcadttQkNFzWdBK5YzikSk7LFSM7osSlHwvwQaL/90f5LVIBsA8C1otNTlPt/f6ON9NreqOhK7F2NVPVfBN/T1NXj0W/0wv0dZe3yl45UoxpR3HzlL+SSpQGrtJYuP+ERqDqj86OByWliOPJhiq7pgX0qruuLlWT2gx1RXN5NbUr4O81NnTXSbFLGcmG3Z/UOhnu131B+A4T5ksMnAYOy9k10li2x+VF1Z0jeoHc09kgM8wKYYifvgkezAoqrIN/rG9kqnt+GhVobdMzMzKmvYkpneAb5o+oICIjgpy4PLiJKB3gr9XXbYqsu/feQerSq9nltsQSkajFY1dSvmbUsrnSSmfI6X8oTbO2YTeIEc3TTRjv7I/MLUuYlVtTlfbS+oPjPRHHuutlqX28pLqsWdJpON36Vh6YJTmTTJHN0tUaOSwfixVM7SjeAxT2R/UkzGa4mqJMY2CW6tje6+Pc8xgErpZgsLZp5VKwtJg7XYSHFQOYnfy4e0c5EqucGOPAfOiasOeRGb7v6F9bb5UTbhhYS8PT1AyUgyXgyjk0rxclKsAwCPFSJ2gA5iyvQljlINKinE3pCBSwPsDMKsP7sgz0pJKrEli4f0b1SZbivGBO6C10dLkRYV9mhpJBTMy/VqYprqWG+7InKdZjL1qA49OErFM1UoGamDs27t9vYo9u5zqCTWvHMMpM7y9vnp2fOXK4W7qraNnskSTFENMKo09tR3Muo/6uRWeOwncxCN3vNrH2pPATlXptene2sRCZp5SvDigluI0MMkZ6W4zx0HSA2dGW5XWniX1ImCdxCQdlaVU2WXVrE1p3jZjT9Dr53oDbe6s7Gax3tg6q1K0aVcebowITeFSNAmNApcg6KdvAPqSttxjVbhntWxNfYxdtZP0UR8Doiifveo6aRTpmH17UrFj7cdJMZyxb+/29eYJdF86BHR3oCdqwCfFVJmnLCqGQiBJiskrKcatqaIkO9O2et+be3KrO0bClWLMRD6RFEPRU7nRvomNUhEtnkjH28Tj+gmjpJgVPn7Zu0LlN+qM3dxTb6BqCFG4o6x8GCTFUKXHg2Gd4HBwf4G6J8bYq/fU1bIpOU77ISop5un9IUrpH69NqLNw+x23jvW8g+sNx7aNxTTsWVwtfdVnfKMAcnoCfilmpdLWuMGkQW5lnuZmeUlRIVcOhihY3DigBhpJACvVhNPPlQZ3OCytY4kZxJWMYksxfsZOWimHL2bYhXEajh6ALgPyHUsJWiqZo4Gx7/a1H8J3DLE5w9iFl7HvMNkCgGNYGGNnTk4y0Lv9HLsVo+RtoDr1JK2p8xpjAjRvtBFH0JmRjVJMjbE7y3Vn5aauV6KQJtzRl6noJmv5QJPMIC91O7qp2tRkr9q1iSfS0cRx9WCIvepZdBzGblfh5BOr6dOO44/iIauEddYPBxVxSeMIsgoVpTo4fJLt9Udr3u52jly6WaneUzcCjLYdpAlLyVWxeW9nMOw7e/Wkv9qxu8Tum49tGwtp2JfTxIpK4RtA52VZKyHKQYydv0BPXj1EKWFJMXklT3QqZ1ReSlyq9k61DXusH9xyahxVn7+8XzuWmEGHrzAKzthdw169GE6JAHLqjsJSGlc7pY8eVL6kLTdDlSIR3K3KdDvXMvTYEtjrPK2M+J7W2Hkcu1s500wqcRNj90TFANBxw9zJSysKvtJJYjvTk0Ib9V6qVeYpJdDwqBjfTkPcOBpdfwC+XRoAS74oWIIS9SOvVeIma/mgJ8e81No3RYzt7A2UPMjKQvBx//gVFaHjauw83JH7cfhz7aSxdS9uETB13hhnlhJs7/XRqyYZ7ngeshUR1XVXmnezNOJuZNKrpBQhhE4O3HYiwGijcB0tF6uQ0G0moU6KCytZtZk7m7TX/Mb6zFKCjEXDjJJt2sYCGvZcD1x6yJmlm0rNHnxaFulw9FDjSOjNrt3lJdW/oIFGxnrdkVdIU1eMvfnYFd3euhxw4DPsa/4SAW7BpSbwtP/tXf+gcpO2AFMS1tyj0eF97Gajyimge/a9KNTevUNj2N2yCYfDAnv93Gqn7bwbLcXwNvBj11dUITDum+ARSbIy4r6oGJOgVGoGm2nWXUXFDO3knPNstUShsOuOxp5XzlnOxtdXbJ+KWzfHB10sLjdJQjxfo5vFWErV6vZgUGBrt68NMo17V4rJmRSTNRh2vncBXds3JjfWOioqZkjF4arVTqnKFlN1R+qTg2E+kkFzfwFgR0BRcuB2ry7F8GqtFBJK7223MzljjyOBC90MW3sDXWVzwxOWCagoPNrUWko5kt23jQU07MYArjQwYPdF41hO1eYYOz2Vcryxmmkm7pYipQmCrvO5ymhsOmxWn5stez83AWPnGwz0WDwtgQymW3FuEuepurZKkT4cFtjt514phtpEYWa8JKz/Hj1STOVz+Jw27PUXhdq7Z0XF2HHsW7v2EhqAxWipOifAnad2lUZqA28nSTHEaOn61PekyNSjYkrtPDXhjh7naRWNQkjjCOerevV8uzR1P8aI8Th2OsYnxaQjnnXGVj0D5hgkrGSK4NAOXju9AZ6zuQoAZtzzWjFsFelKMXx8dlIzMR8MC73idbGxosgFlXPmUieVLeb9qUr1Nhtad7z2mDyoa+lUexIvMyI1LMzG1W4fTRPuCJjnpCu9NjB2OnZnb4BrByqaqEmPbxsLZ9hJqwPMy8tZGMVKNzFaqntCjoz1lQ5j7HFVCIrF5jIG5DPWfLB3WcywngTW6sfyXeFHM3bSKOtSzKSMfXuvbyXIuOC7zwOwSsKado9+Cei8xqj6omLIeVpn7LRE9iVwcDnNF+5YSmmFLerJxZFiLu8P0Ovn3tUSRZ9Y1R2lRFnCCXdUDFtr7Kz4li8iZHt3oO+JwhlNuGNpxbHTMdseKSb1SIoELWcNSytGm7DM3pW9wxyXewM8/5Y1AIaxc1ZuhTvmTuZpA2OnZ+ojGxSyS2GFXIqhDVc4mToYFCOjVFb0FoHE2HMtu3GZlY9fd+zV+iidXIqhe9rZ69ckH++xVU6Du4qYNxbKsEsprRjlrsOAaRk5itF2mRSzsdrBxlrHYux0nmFplpc00D53eR9JJHCWZdhZbDY1UgwZGB43btqrfiZRhLyqdeJzGlHZBDcyZhLnKWDCELd3mweVu0Gwz7jak9d4w+57Metx7PWSArrUalO4Y5MU42HsfHm9uZpBSuDJa4f2M64MGG2vpzR2VJ/xBJpKislL2xCRFOON4VaTqntPWoopVGmFSNiM/XJvoNujpZhJGHvBnKeeiXilE+PS1QMUpcQLblWG3c/YjRRDhpfAJ+xOajbm7mknbEPE1G5f74bE91XltWIARVh6niACDndTb3sFbwIj+BiiPto9NO20VjVTSDH6nvYG3nfFf2x/5Ds4DyyUYaddWHR8tWsoSYoZxX1D4OgAACAASURBVNiriIEnrh1ic7UDVczL1HQH1OA+HKjlJWXyAcporK9m2sHG27CUqogKfux5J27cnYgMYy0rPdQ2iFkSWTsA6X6YwHkKqEF09WCIx69WTl/PkpHi0k0da59h54aibrRJPx4lxdTi2FmCEkkxJDnxdiZNUownKsZuA5NiVqnE89CKiiEphgypu5m10dgrKaYsdZgqwBh74cm6XCPDbvcn3Q+tUhJHiimlKVHhbnThA2fsJDX5JuLlNNZ9c8f5LrpZrMe9zdibpRgeacSLgPVGMfaqCNuV/aEtxRSllQAGqIm0yUGv74dKbzMphsbwchWO+diVA0vyoD6ionn8neZ9NCnMpO2vv2QdW/kYtvTYDlJMDW6BKB9jbyohSqDZ+VL18Llezndr3yUWkkZ64Dz29EG91kr13YqebEYc65FiALOfZ1NWZ52xT+g8rQbRJ57Y0+dyQZJFnbHzlYZ5of3JRyr64bGnDxrvg57HrleKIcZONbTNtRudpzwqpjLMQkC3gbeTTxSZtbqzGXvMo2LIuWk5Tx0phoc7erIuabWkahupPqS/pXT+OLYNO2AyaHW440gphhylKswzqzH2anx2zPPZWDV7clLoLcGKinGkGB5pxAMNdj1hk+49XbpyUIuKyVmtGHU9tSL3hcua+7HHK21wA5hn7r571K7dviFwroQ6DSgMWUuzYzT2vJT49FM9/ftxYKEMO9+wAEA93DGJ9CbUjc7T6m+pjrk1AFITXcOXl132N+uusWb6Hm+bOra+WxFvr641ftBcH4O0Wg5faJkPJOU8+MQ1fS4XbvgYXWu9gbE3sSkawOr4yaQYmkj7TIpZW0r0BtLAiHBHjxRz05ppAz+WTxRmdWfK0+ZMiomZxk7hiGmsElwKRxMmw+uL4d5Y7WCvn+PRpw+scUBG2s/Y7fBWHe444ll3ajKDY9g7ZlzSfW6uZTB1VOxz8yzQYSmbwx2TSGfqjpZi1HXyUtpRMZWPIY6Flpr6eaGqpHqS4AhL1TUoPLfXL9g9Jvpa61NIMdMydnqen3hiF1kcYW3ERET3/+AT13Sl1+PAghp2s/QC6s5IXwlRAtd/N9Y61tLIMPZI13txGZCv7C0/L9d2XUNKTESHZ1ZtpGqQ3noTa/XsU58h8WFzzQzA1Y5tMAn0gnIpJosjnFliLJ3dUxObonvlFSrd6wCmzGoaV/V9ErPR8JYnzrcxQckqKaAM0S1nl73H+hh7xiQHw9gja8IopNQaMCVw8QmJtGh/ASzV9598cte6JyGEVcOdR/2QMaLnPVm4oy0zUFYlgYiHPYYNoXEnDZ64RUXPCK4UQ23j74oLiyA4+x3kZbWZtd7+0L+5BkdUyZ2asQ+Nxu76VUxbq9XiIXeejpYXR4FW+Z94YhcbY0oEbLBjL6x0LKIyTyyYYR8txZAz8tAJP+NYdgY4sVqAa+zCysrjD96tzMZT19XvdW1XXzt1NPbqpbl60JzYs7GSeeLY60t/H+j6D+/0GnVACoWjl3Oryjrlg3XZiiBoYOxUMCz15w/oOHamsdPn2nm6W8+OtRi7h8nz6o63VqV6VZvNsWudRBsdn/OUM3Y3QYk0YDL+WVyvx97kPAWo7+1xEEdCZ9vypNJNx7BPUgSsbrRcNmpLhGmsnP9kcH2MvUmKsbO9Y90fvgxW954ANTYoKoikmJhJMfQejEpQUvdidinr9XO4PjcAzkq8mvwO65NfEomJVr8c1rMdIcPUjj2mUEdgwQz7gSPF0MMhI04PaL+fN2bruezbp8VxKabO2P2GnTupCG6JXJ9PAGCMvSFdf7faAYjQz4uR2YhuW6Ucre3xPV139gb17fNSbiiapRig+aX0STEAlVQ1GrvbTpIqltLIclrb1R2V0b313JL+jhsZIYQ2MB22WiJGXBQe5ynVihHCMm5JLLSExuPYm0rWSmlWToQ0EjisnmfM/u7MsspUpIl8Eucp9es1JsXwMdh1kvnWVzpVf5iStlbbWBYoZdoSfNneaSxGO0/ZvS97pJiUSTH0HoxL8adEpKJUuSbm3fOv0igUlSRP2seY98s0oHsa914BZuU2ybFtYqEMe8+RYuiBcqcnoFhhE6N1GTUfeNzg7jHdsJNE2pC4Xm2XEcWR0FJEk2xjipZVA3p/hPOU7QBE8BVc8oHqj9C9NqGbJVpq8BnX7hRSTNMy2hfHrj6PNHvd6dUz80i6cEMoObMmA/iMSoqhFHO7faZuCKCM66AqRkUbYlMOA1CPiiFwKWbIpJgaY+fbFbqTVRzpvWK5xk6ZiqSxTyLFGP3YOAapOB3A/VHVOKjGL7XPJ8XQvQ2rzFAOV/5MxjB2ntuxwqQYVXq3krqqa4x6D+xzKilGZ46ykE6Cj7HvWozd9tNNA77KH8fCz3czvboMjL0BWoohZ0nHZQ/Vcv8wryWMEFz2faGb1ZaXaRLp1Heqs+2WgXXP51v+1o7VUgxNSCTFqGv5ZA636JCpUDcZ0zAvcvOgUnWs63tFErLYOMqafBeasTdINXrSJcmAGXYqsMVrqnAkkagxK8OsjWEnxu6tZ+NIDybETqLgCUpUfTE3ESncsLpSDNVtrxXAYg5b3yrEaOzuBNTRUTHDCaSYOBJIImGkmKqt7qpWb9hcGSW3Pwh0r4d5ASnrk4obYpzF/F0ZvZrrZkyKySncMWKMPbfa2gRKRDJ7yfqkGO47s+WqThLrePhpHaeACUPm99aEqNoJbZJj28SCGXZHiknr7AFQ6cZNjJZmaEo5TuIIF7p1Nkcpy/QZXbPJsHO20Hhsxy/FXKm0RX/lRLtSoK5QN6EuSG0YNaiofk5Z+utZUIElHxN229nE6IVQcevUrxT2lyUR+sOitrkHRxpHNWbFnZwU3ULb4TVVoKTrAcZxnVcZkIAdFUP6dhyPlmJ8Nc0BU4SNX5ufg1YpbigjD28dTCDFAGosaP2YmHrHJhzuJuTNzlOSMytnceIa9nrIrvuuuDB75yZ6UjykXafYxKnfgzHGlhKRavaAyUSr3NHrMHYuxUxTJ8Z3T5MYa93nY/T4NjGTYRdCvF4I8YAQohRCXGyrUU1wo2JqCT+xcag1sQd3yzTAhC9xg0tZ6h3HsNdDGCtGxPS9ccd2nIloXLgjYELgTA3tyR4dMTQ3TJNjuXpRrh4MvSGd1LZReuQ6Y2VNoG3cAFuKGRQli2GvXzv2MHbLyVkl0pBPw7dqcJ8x39s2L+px7DZjt6UYkykpRz4PenbuOEgiEwnkY+w63HGCeuyAMuY1xp6qDZtTh8HTxOn2h7k/1R5aHbsTTzeLrZo6ieddccHHhl4RDMjHIPQ1rmnGPtrYEmN3t9HjhIoTkFEhod0pywm49zRJffVpJoG2MCtj/yiAbwLw+y20ZSwO3AdJUgyLPyc0sQdi1ryTeXlPwGYp9Jna1QWa3RP8jN3P1NyiZamWYkYlKKlzkENt1LZ/PpDjbnPEAKQdokZl0qmdeJpfuM0JDDvfjMJExcToD0uzz6qXsYuads+dnMNqpx962Xw6v37GziqP5BRAMek4shl75DhPKUyTdhpyN7K2r+l/oZNYmAQlx3CuV1IMbUQRifoxLrI4sjR2ANVmNPUknE2Hsfucp4AhUe4462axZcCtqJmGVTKXYmjiIX08jUyimnGejja2NF4PakSP3jtHStQOZqOx0+5nR2Xs1I+T7F/aNMHPE0ebripIKT8O4Fi2egKU85QXH3KdpzyZook9UIIDf/i1ZTp7kbgUc6Gb1fYZdcMd6bM1T9y463iidptwx/rj0DsA0fK8YaPuJkwixbi7SvkGK6VrN15nzSy3m8DZMo2ZLIlUfY8RtTTiSNQKNVH4N22Nl0QCK5kqUeuvQFk9Y5arAFSGvXKeRoJJMU0aeyUlUFhgkxTD78XtTxXHXneeqr/JMCzUPrwDZ2u6JnTSSFeR5CtMXxIOtYlqhTdKMYPC+p2fh/8Nj3PvxH4jucmkmESvCMyKxQ13HKd70wqz50gxWaK263PHEA8JzaqJWQiq7TSjFDOBvELHHscm1oSZDPtxw60jscpqRADOIGswfFEksNpJrFDEm9Y62kEIOCykOs/qUlILX6TPeVvo/95jq2PI4OuomIOhThTygepNABjJEH24qWqHrz28XY9dOcBf+5k/bjx2tZOglM2WvZslWO0kI5NLeC0ewlIa448+vY2PPHoVgN+wd5IYq8553QSlrHJy37S2VDsWMP1AY8Xa8EJr7JGeMIzGHlkrONLXk0hU++I2J+fctNZBltQ3L04jE0nCE5QA0/fbe30MczmZYU8iqyQtgOpZsDFZjVM6vxBKunJlK7pXkmLc6692UutvkgkY+6b2fRgpRjN2VgSMGPs4KWa1E2N7b4C/8fP3V+e179Mdv53ETFZrznt6lKgYALipuqdJjPVNa9Wxx6ixj70rIcTvALjF89WbpZRvm/RCQoh7ANwDAHfeeefEDeR41XPWrZTcF9yyhv/7G1+Cr3j+TQAmk2IA4Ee++WV43s1r+ve/+sV34U8987z+3ZZi1P//3muepwcjx01rS3jLX3opXvPCm/Vnf/er7taZgBznuhl+5Jtfhj9792bVXjOgRzGHM0upNgSjGKIP3/Cy25DGEe680G085jte9Uz1Mkm1ScRzb1qtHfPG174AwAjKDuBH//IX4tmbK43fu2GpAPC3v+I5eE71N89cX/E6Pv/pN75EO0YJblQMPfv/5y98gVVRk/Cn77qAf/K6F+NVz15XbXHC7gC7bK/F2NmES9dRNb5LXUjrHKv4Sfhfv+RZeNVzNmor2iQ2UTF1xk6GfaDjvMchs8arGkff85X2GPyiZ6/jn7zuxfgzz7qgP/tn3/QFNWND93owMIaX42+9+tn48y+71RzPvm/KrfiGlz0DaSRw27llHW+vwz1jnqDUnIHN8S2vvBNxFEFKiTPLqfUu/6u/9DI8yxmD1gqD/f9f/MWX4vbzyzgKXn/xdtx6dklvqjIK3/SK23Cum+JmZwzPE2MNu5TyNW1cSEp5L4B7AeDixYujLUQDvvbFt+BrX2zmGCEEvu2Lnql/t5l2s6H8mhfb89QdF7q4gxm+1GL+6jwvu+Nc4/lef/EO6/eX3Ha28dhvesXt+v8ZG9A+pyFhOYt1Esi0ztOz3RRveOXoifS5N63hTV/3wpHH8ImvCa950c0jv3f1bQB4xZ3n8Yo7R5/71c/brH2mqztWCUq0xP/Suze854gjge941V36d0uK4Rq7Y9jjMVLMKAnpro0V3LVRn+gSJsX4nKdAxdgnlWLYWKdx4Y7BNI6s+weAL/P0K12v1yDF3H3zGu5mhjRlk3XUsOI8u2zGIL1bB14pZvTKlfDszVX84Ne9wPudbwwmVX6CuxnIV7zgppHXGYWb1pasd3kU1lc7NRsxbyxUuOM48Nrd06YJc6RseTxJItCRr0MRAsNyJGNfycw2XtM6T08SKLcgiWZvuykpICfWojl0HHsh7Y02HOdpLIRfiqnK/m4foRxrEpukrLphp0JgfQwmlGKyCVeqk2CcFNN07UmlwbqGb6SYw2E5srLjUUE1iQA0lho5bZg13PEvCCEeBfAqAP9NCPHOdpp1NGQWY5/BsCfjl5dtgE9EIzcXyBLN2AdTauwnCVpjT8bLC+PAo2JyTz30cUhiY8DJd0DnjCNTIIxnRgKcoaowza29gTdaamTbq6xX+j/H+W6GSCgpxq2H3gROPmYdF3SvZHiTMdfn+QiTgKSng6HxMTSVBm4TPKHqRsCsUTG/BuDXWmrLzJhUipn0PKOWl23A3ii4+VEsZ6aWy7RSzEmCm5g1C9yomGnPyTfL4Bo7oAy8JcVYGq3Qf59X8fe+aKlR4Mba1dhVpqKq6Kk09smcp6Z9bTH2yik85vo8H2ESCKE2A9caviN1Tbub0aToaMa+eO/NUbBQUTHj0JoUQw6yOc/uvt2VfFhh1exoCb+IUowJJ21BiuEbbVRbrE0Du6SA0dgBNWlQNqsb7sgZ+7CQVUXK6aIdeCSMT0822adiMikmMZE+s5aFNZmn00kx04zHJBZaWoyrNpMGPq6cwFHhhhifdpyqu2xNiqHkmTnrcdwYjd6ZPdEMh5bwi8nYKQFs9lUQRa/Q1njTMnYuxfCSAgAxdjI8kc2wdc6Ekmt2eoOpE0+4tOPzN2yudbA1jRSTTMeaR4H6YH84nRQzzbXTOMLB0C6XoHNT5ibF3FiM/VTdpS/+fJbzzNt4Nm0i4WIli3WhLGLsi+gEaleK4YxdTs3EuBTDN9oA1KRhpBg4jN2JivFUwxwHbix9i5eN1Q52jiDFtLGKo3McTC3FTD4e0zjSJQUS1p/APKUYu3jZacfpMuwtaY3pEZaXs1wHGMfY1Xf7gwJ92i5tAZeUWYuGHVDLeGLsR5VihixBieSdyHJu2s49LT3MIMVwlu5j7CTFDPLJDLvZRGR2o6XDHSeUYo7yrmSOFKPOo34GKaYdnKq75Evc2Zyn0y8vj4KYaaKjmAo5Vg8GBfrVC7GIS0q3Rs6siIVAUWJiA8hBbchLxthjExUzGFEEjP7+2sEQvUEx9c7zfBLya+wdHA5VGWO3uqIPmo22MCZMETB/gpILd2eqSZDEUa1kgWbsR0zxH4cgxSwwJs08nfQ8x7FsI01zVFQMGf3eIDdx7AvIPNpm7FFktrCbtj+oDYO8rrFHQujyyG6CEhnlJI7wxNVDANNX7Ystjd1v2AHgiauHtY0ufGiTjZo488kYO7V/Oo1d6IJ+bhmPo+xoNAmOMgEtMk7VXfpKARzpPMckxQDmZRwVv0vfHQyKqYuAnSRo52lbUowQrKTALFKMHVMeR81FwCiiJ4sj7FZyxbTFndIxUTHkjN3t51Np7O0wdjLs/szTpuOneVeU89RUd1SfEcGZcxz7Ar43R8GpukurFMAMg5zv7jNv0GQ0SoqhbLxeXzF2Kni1aOi0rHNGkVBx7Hk5VRw5YEsx3qgYKtvLpBi+2TUfa9NGxcQTSDG6nRNJMW1q7EeVYqZznlKlUJexj6sTc1SQPbhRnKenKo7dKgXQUEJ0EkybTTcLyJiMchpp5+lQMfambf9OOsxOV+1MSpEQKiqmnD5BKWFSDIGMTBQJDChqgzF23m4+kUwrxaRjpBhemGsSKabNCdNl7OMmTGr/dIxd1P6faMM+J8Z+jKvwk4BTdZcRc0bOwtiPK9yRX2uU04hCIff7Bfp5sbAOoNalGBYVk005WWSWFGNnnkZu5qnj4HP/Py1j58bSx9h5dcppioC1KcWQBt525in/G8D0BT2/o5bRHQfD2Bfz3ZkWp+4uiQHMwl6O03lKDGKU06irwx0rKWZBGXubceyAMsBFiZmkmCFLUDIaO6sVE0U6M9IqtlX9/ZmlZOpxkliMvd7uNI5wvqvKAE+SzEVGq41xQfe6P5xMikmPIMVw1pwck/M0xLEvONIqU3CWGi/pMUox6QRMhQb7AUkxC5icBNgbILeBODIJStNOFnGkdtHJedlenaCEmrFXe53WGftRNii2wh0b+oLknWmqO7Y1LtI4gpSAEJNsyzf9u2JNbMeUoKQjhwJjX0ykSTR7hbtjlGLImIyuFUPO00qKWdDB2TZjj0UlxZTTSzFCCKRRhEEhtREne+MLR0zjyKuxH2WD4jiqM1YX0xj2tmUGLT1F4530NH6PKsWYTbErX9MRN5cehzbLLiwCTpXzFCA2ONvDO85wx3QCKWYpjSCE0j0pKmYR0X4cu9LCpTzaOdOq3ksc2fuwRsyY8cxInxRzlH0suUM0ajCcpNtPtINS3G4onymbMP7a6RGYMI/04eGjwPycp0cpVrbIOIWGPUIkjrRBkwaVZj0WjX0CKUYIgW6qKjwO8nJhWYepid2WFMM2hT6KYU9U6d0ijizpLvb8v0mKOcrO81x+OdGMfYLz0bOcirHz/j2uWjHBebrYSONo5od3lOXlrNca5zRSO7MX6OflwjqA6OU6ihH2IRJm79Cj6PZJJcUUZWkZWM6i5yHFEEsVAo2+IAp5nCpBqaVxocM7JyivrN+VKfR9X8KXlmLmFRUTnKeTQwjxFiHEg0KIjwghfk0I0bwx6DEhjcXMy63jlmLiSIydRLpZjP1BjsEiSzGesMFZEAmzKfJR+iSrpJi8lBZL97F3tYUbZ+zq86MYdl2WYIRjcmMaKaZlx6CJLJtcipkmt8LaatCJYptXrZhQUmA6vAvAS6SULwXwSQBvmr1Js0Ex9tkGh5Fi5j8IsljJLOOcVMqwL7bzdIm2xmtRijkcmrDEaZEmkd7MmhtZ3rwkMpORb2/RjSNIMTrDdaRhn4axExs9finG7F0w+bV5Px5fuGMw7BNDSvnbUsq8+vW9ACbbtnuOaEOK0VExxxBWmMbRRIOZGPsiO0/brtcxqxSTVoW8Ll05aNzViD5O48iKKZ8t3FH9bTxiMp/OsM9JY58m1PII4Y4xc1iniUounFeORptJXIuANgWt7wTwX5q+FELcA+AeALjzzjtbvKyNjdVs5qX+maUUaSyOFPEwLdZXM9xydmnscSudBHv9fKGdp2eWUmRxhPWVdvo1joTZEOIIfXJuOcX7Hr4MAHj25or+3NbYjQE/t5zqz9dXMiSRwO3nlo/Ubv7Th9vOLyONxUQrgrWlBFkSWaUIZkE6gVREoCzZaa5NKwF+/+srHdxyZmluNZCofW2NvZOOsYZdCPE7AG7xfPVmKeXbqmPeDCAH8Nam80gp7wVwLwBcvHhxtrCVEfjh138hMOPYOL+S4b6//+V4xtnpX9pp8YNf90KrXkkTltMYW7v9hXaenu2muO8ffDluOTN+IpsESoqZrAqhD//h216BTz/VAwA8c71rndf9/799wxda2vtrXngzfu8ffDluOsK9uPVRfNhY7eC+v//luHWCMbi2lOK+v//luLk1wz65Zv/szVX8wRu/Andc6I491j0/j475ri9/Dv7KF82P8P3pu85P3c5FxljDLqV8zajvhRB/DcDXA/gqKeXcDPakONtNxx80AW4/fzwDYLWTABO8jyudBL0Fd54CwG1HYLhNiITAISuvOy1uWlvCTWt1w+xLUDrXtZlzFIkjjxGSfcZldU5z/jb7dRopBsDUxjL1rFiW0hhLc5Q+hRA3jFEHZpRihBCvBfBGAK+WUu6306QAH5azWO2gtMDO07YRCegdpSZx9E0KkgNGhSPOAjJsR5mMjgOTRO3MAnpWbUVHBdQxa8/+ewBrAN4lhPiQEOInWmhTgAfdNMbuYa42bg6GHQAlKLW/oxT5YUc5N2cBSTBNWafXG/PO0vSVQQ5oFzMxdinlc9tqSMBodDuJNmKLqrG3jUiI2iYZbWAS5+YsoLaeVMM2rRQzLTK9IggEZV4IPbsg4DU0ghSjwA1vm1IMMel5SRFk0Oc1ccyKeUsxSWDsc0ewEAsCnpEXpBgFbhhblWLmzNjjE66x6wzhOUsxJ3ViOw0IFmJBwGtoBMauwDXqNmWDSEsl8zZsJ/M56nDHud1/lZR0Qu//NCD07ILAkmIWdKONtmGFJba4rI895XvbhFkRzOX0M4MybNsq/VA7f2Dsc8cJHVoBLrhhX9St8doGtwtt9gmdd27hftHJZuyJrrg43xXLvCaOgGDYFwZdLsXcIPUuxmHeUszcomLm7JycFbpa5JylmHlNHAHBsC8MLCkmvBAA5i/FzCtqY5LqjtcT0+ygdBRkQYqZO4KFWBDYGnt4bICdFdomY597HLveyOJkGrZ5SzFJkGLmjmAhFgQrHR4VE5yngJ0Z2qrGToZ9XpmnJ5yxt703rYtUx/EH8zMvhJ5dECyHOPYarASlOUgx89bYT6phn2YHpaOdv17dMaBdBAuxIOimIfPUBXeetmkkdQLRnAzbSU9QmndJgRDuOH8EC7EgSOKo9b0tFx1kF7I4anWDBjrVvKSCdMKyvdcLJuV/zglKIQhgbgg9u0AgB2rQ2BX4RtOtnveYasWc1CJY2TFJMaFWzPxwMkdWgBcrVSx7kGIU5pX6P//qjiebsc+9uqNna7yAdhEsxAKBHKhBilEgZt22AZp7VMwJd57OW4qhlVCoFTM/hJ5dIHSzGJE4uU634wYZxrYlgxs9QSmbc4KS3sw6SDFzw0yGXQjxT4UQH6l2T/ptIcQz2mpYQB3dLEYniee2k/uigaJi2i4vO++SAkIIxJE4sRP0vKs7ZiHcce6Y9cm9RUr5UinlFwJ4O4B/1EKbAhrQzZIgwzCQ3WnbQM7beQqoSeOkMvbjC3cMY3lemKlnpZTX2K8rAORszQkYBcXYw8tAiOalsetwx/kZ3vQEM3YdtTPHOH4hQkmBeWKmPU8BQAjxQwC+A8BVAF8x4rh7ANwDAHfeeeesl70h8cpnXQixvwwkmbS9ipm3FAMAX/LcDbz09nNzO/8seM7mKu6+aRXPvWl1btf4s3dvntj7Pw0Ya9iFEL8D4BbPV2+WUr5NSvlmAG8WQrwJwHcD+D9955FS3gvgXgC4ePFiYPZHwHe86i58x6uudytODuYlmZhwx/lNovd+x8W5nXtW3HxmCe/6/lfP9Rr/6TtfOdfz3+gYa9illK+Z8FxvBfCbaDDsAQFtg5h126uY49DYAwLmiVmjYu5mv74OwIOzNScgYHKQAV5EKSYgYJ6YVWP/50KI5wMoAXwWwN+avUkBAZOBiHr7jF39DIw9YFExk2GXUv7FthoSEDAtdEmBOWnsUTDsAQuKEGIRsLCYV4KSCBp7wIIjGPaAhYXW2BesCFhAwLwRDHvAwmJuUkxg7AELjmDYAxYW5OScX62Y8HoELCbCyA1YWJjqjm1LMfbPgIBFQxi6AQsLk6DUrmQSicDYAxYbYeQGLCxM3fT5OE+Dxh6wqAiGPWBhMb/qjiEqJmCxEQx7wMJCV3eckxQTGHvAoiIY9oCFhd5oI8SxBwRYCIY9YGExLynGRMUEwx6wmAiGPWBhMa/NrIMUE7DoJkPATgAABtlJREFUCIY9YGERz42xh3DHgMVGGLkBCwtdUiBstBEQYCEY9oCFhdHY2zXAIoQ7Biw4WjHsQogfEEJIIcRGG+cLCJgERNRDdceAABszvxFCiDsAfA2Az83enICAyRHNLfOUfgbDHrCYaOON+NcA3ghAtnCugICJEc+5VkzQ2AMWFbNuZv06AI9JKT/cUnsCAibGvKo7JlU0TGDsAYuKsXueCiF+B8Atnq/eDOAfQskwYyGEuAfAPQBw5513TtHEgAA/Xn7HedzzZc/Gy+883+p5X3DrGv7mq5+NL3rOeqvnDQg4Lggpj6agCCG+AMDvAtivProdwCUAr5RSPjHqby9evCjvv//+I103ICAg4EaFEOIDUsqL444by9ibIKX8nwBuYhd8BMBFKeX2Uc8ZEBAQEDA7Qhx7QEBAwCnDkRm7CynlXW2dKyAgICDg6AiMPSAgIOCUIRj2gICAgFOGYNgDAgICThmCYQ8ICAg4ZQiGPSAgIOCU4cgJSjNdVIgtAJ894p9vAFikWPnQ3vlj0doc2jtfnOb2PlNKuTnuoOti2GeBEOL+STKvTgpCe+ePRWtzaO98EdobpJiAgICAU4dg2AMCAgJOGRbRsN97vRswJUJ7549Fa3No73xxw7d34TT2gICAgIDRWETGHhAQEBAwAgtl2IUQrxVCfEII8ZAQ4gevd3tcCCHuEELcJ4T4mBDiASHE91afXxBCvEsI8anqZ7s7Q8wIIUQshPgTIcTbq9+fJYR4X9XP/0UIkV3vNhKEEOeEEL8ihHhQCPFxIcSrTnL/CiH+XjUWPiqE+EUhxNJJ6l8hxM8IIZ4SQnyUfebtT6Hwb6t2f0QI8YoT0t63VOPhI0KIXxNCnGPfvalq7yeEEF973O1tajP77geEEFIIsVH93kofL4xhF0LEAH4MwNcBeBGAbxFCvOj6tqqGHMAPSClfBOCLAPydqo0/COB3pZR3Q21OctImpe8F8HH2+78A8K+llM8F8DSAv35dWuXHvwHwW1LKFwB4GVS7T2T/CiFuA/B3ofYpeAmAGMAbcLL69+cAvNb5rKk/vw7A3dW/ewD8+DG1kePnUG/vuwC8REr5UgCfBPAmAKjevTcAeHH1N/+hsiPHjZ9Dvc0QQtwBtQPd59jH7fSxlHIh/gF4FYB3st/fBOBN17tdY9r8NgBfDeATAG6tPrsVwCeud9tYG2+Henm/EsDbAQioZInE1+/Xua1nATyMyjfEPj+R/QvgNgCfB3ABqkT22wF87UnrXwB3AfjouP4E8JMAvsV33PVsr/PdXwDw1ur/lo0A8E4ArzoJfVx99itQ5OQRABtt9vHCMHaYl4TwaPXZiYQQ4i4ALwfwPgA3Sykfr756AsDN16lZPvwogDcCKKvf1wFckVLm1e8nqZ+fBWALwM9W0tFPCSFWcEL7V0r5GIB/BcXIHgdwFcAHcHL7l9DUn4vwDn4ngHdU/z+x7RVCvA7AY1LKDztftdLmRTLsCwMhxCqA/wrg+6SU1/h3Uk3DJyIUSQjx9QCeklJ+4Hq3ZUIkAF4B4MellC8H0IMju5yw/j0P4HVQE9IzAKzAsyQ/yThJ/TkOQog3Q8mhb73ebRkFIUQXwD8E8I/mdY1FMuyPAbiD/X579dmJghAihTLqb5VS/mr18ZNCiFur728F8NT1ap+DLwHwDdV+tb/0/7d396xRRFEYx/+ncSGVphCLFBoRW7FKYSFoYYKksggEjJBPIan8AoKFYGNlkcIgslgmWvuCaCK+YEIEtxCsbGxSPBbnLo4hCwEX5mZ4frCwu7PF4WHuWfbcWYYcx9wDjkfE8O5aNeU8AAaSXpbXa2SjrzXfq8CupJ+S9oAnZOa15js0Ks9q12BE3AKuA4vlywjqrfcs+WX/vqy9KeBtRJxiTDUfpcb+GjhXrig4Rm6K9Fuu6R8REcBD4JOku41DfWCpPF8iZ++tk3Rb0pTytoYLwHNJi8AL4Eb5WE31/gC+R8T58tYV4COV5kuOYGYiYqKcG8N6q8y3YVSefeBmuXJjBvjVGNm0JiKukePEeUm/G4f6wEJE9CLiDLkh+aqNGpskbUk6Kel0WXsD4GI5v8eTcRsbCf+xATFH7nrvACtt13NAfZfIn62bwLvymCPn1hvAV2AdmGy71gNqvww8K8+nyQWwDTwGem3X16jzAvCmZPwUOFFzvsAd4DPwAXgE9GrKF1gl5/97pcEsj8qT3Fi/X9bfFnm1Tw31bpNz6eGae9D4/Eqp9wswW0vG+45/4+/m6Vgy9j9Pzcw65iiNYszM7BDc2M3MOsaN3cysY9zYzcw6xo3dzKxj3NjNzDrGjd3MrGPc2M3MOuYPXVyV2+Lh+GIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot some tracks\n",
    "tidx = 17\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(\n",
    "    spikes_and_events_nonspiking_indexed.loc[\n",
    "        unique_indices_nonspiking_threshold[tidx]]['diff_streams'].values)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(\n",
    "    spikes_and_events_nonspiking_indexed.loc[\n",
    "        unique_indices_nonspiking_threshold[tidx]]['diff_prev_streams'].values)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(spikes_and_events_nonspiking_indexed.loc[\n",
    "    unique_indices_nonspiking_threshold[tidx]]['ad_campaigns'].values);\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(spikes_and_events_nonspiking_indexed.loc[\n",
    "    unique_indices_nonspiking_threshold[tidx]]['diff_num_playlists'].values);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_tracks(indices, x_vars, y_vars):\n",
    "    # decode string arguments\n",
    "    x_vars = [s.decode('utf-8') for s in x_vars]\n",
    "    y_vars = [s.decode('utf-8') for s in y_vars]\n",
    "    \n",
    "    # iterate over tracks\n",
    "    for idx in indices:\n",
    "        track = spikes_and_events_nonspiking_indexed.loc[unique_indices_nonspiking_threshold[idx]]\n",
    "        yield track[x_vars].values, track[y_vars].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = ['diff_num_playlists', 'diff_prev_streams',\n",
    "          'ad_campaigns', 'new_daily_listeners',\n",
    "          'weekend_indicator', 'month_0', 'month_1', 'month_2', 'month_3',\n",
    "          'month_4', 'genre_0', 'genre_1', 'genre_2', 'genre_3', 'genre_4', 'genre_5', 'genre_6',\n",
    "          'genre_7', 'genre_8', 'genre_9', 'genre_10', 'genre_11', 'genre_12', 'genre_13', 'genre_14',\n",
    "          'genre_15', 'genre_16', 'genre_17', 'genre_18', 'genre_19', 'genre_20']\n",
    "y_var = ['diff_streams']\n",
    "\n",
    "output_shapes = (tf.TensorShape([None, len(x_vars)]), tf.TensorShape([None, len(y_var)]))\n",
    "\n",
    "BATCH_SIZE = 20\n",
    "NUM_EPOCHS = 10\n",
    "\n",
    "# prepare training data\n",
    "dataset_train = tf.data.Dataset.from_generator(\n",
    "    gen_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(list(range(len(unique_indices_nonspiking_threshold))), x_vars, y_var))\n",
    "dataset_train = dataset_train.shuffle(buffer_size=10000)\n",
    "dataset_train = dataset_train.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_train = dataset_train.repeat(NUM_EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = layers.Input(shape=(None, len(x_vars)))\n",
    "hidden_layer_1 = layers.LSTM(\n",
    "    256, activation='tanh', dropout=0.5, recurrent_dropout=0.5, unit_forget_bias=True,\n",
    "    return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.LSTM(\n",
    "    256, activation='tanh', dropout=0.5, recurrent_dropout=0.5, unit_forget_bias=True,\n",
    "    return_sequences=True)(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "rnn_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model.compile(optimizer='RMSprop',\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "21/21 [==============================] - 64s 3s/step - loss: 293557288.7664 - mean_absolute_error: 1506.8791\n",
      "Epoch 2/10\n",
      "21/21 [==============================] - 35s 2s/step - loss: 293548818.1667 - mean_absolute_error: 1511.1845\n",
      "Epoch 3/10\n",
      "21/21 [==============================] - 32s 2s/step - loss: 294197266.8199 - mean_absolute_error: 1517.9240\n",
      "Epoch 4/10\n",
      "21/21 [==============================] - 33s 2s/step - loss: 294952445.3021 - mean_absolute_error: 1523.1667\n",
      "Epoch 5/10\n",
      "21/21 [==============================] - 34s 2s/step - loss: 293954578.9129 - mean_absolute_error: 1520.6455\n",
      "Epoch 6/10\n",
      "21/21 [==============================] - 30s 1s/step - loss: 295470746.9494 - mean_absolute_error: 1524.4805\n",
      "Epoch 7/10\n",
      "21/21 [==============================] - 34s 2s/step - loss: 293430908.9911 - mean_absolute_error: 1515.8422\n",
      "Epoch 8/10\n",
      "21/21 [==============================] - 34s 2s/step - loss: 295583616.9122 - mean_absolute_error: 1517.6066\n",
      "Epoch 9/10\n",
      "21/21 [==============================] - 31s 1s/step - loss: 293411105.3795 - mean_absolute_error: 1502.2736\n",
      "Epoch 10/10\n",
      "21/21 [==============================] - 35s 2s/step - loss: 293410736.4792 - mean_absolute_error: 1502.6334\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x16eba02b0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps_per_epoch = int(np.ceil(len(unique_indices_nonspiking_threshold)/BATCH_SIZE))\n",
    "\n",
    "rnn_model.fit(\n",
    "    dataset_train, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of predictions is (419, 139, 1)\n"
     ]
    }
   ],
   "source": [
    "# get predictions\n",
    "predictions = rnn_model.predict(dataset_train, steps=steps_per_epoch)\n",
    "\n",
    "print('Shape of predictions is', predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predictions\n",
    "track_index = 17\n",
    "\n",
    "label, subaccount, isrc = unique_indices_nonspiking_threshold[track_index]\n",
    "\n",
    "df_track = spikes_and_events_nonspiking_indexed.loc[\n",
    "    unique_indices_nonspiking_threshold[track_index]]\n",
    "num_steps = df_track.shape[0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Label = {}, Subaccount = {}, ISRC = {}'.format(int(label), int(subaccount), isrc))\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'], df_track['diff_streams'], 'b', label='Truth')\n",
    "plt.plot(\n",
    "    df_track['download_activity_date'], predictions[track_index, :num_steps, 0], 'r', label='RNN')\n",
    "plt.xticks([])\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic Tracks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense if an RNN solution can track spikes well based on events, we create a set of synthetic tracks in which spikes are always matched to events. This should provide an upper bound on performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoregressive Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by creating a simple first-order autoregressive model\n",
    "\n",
    "$y_t = Ay_{t-1} + \\epsilon_t$\n",
    "\n",
    "where $y$ is the difference in streams, and $\\epsilon$ is zero-mean normally-distributed noise with variance $\\sigma^2$. The values for $A$ and $\\sigma$ were chosen arbitrarily to be 0.5 and 10, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.vector_ar import var_model\n",
    "\n",
    "A = 0.5\n",
    "sigma = 10\n",
    "\n",
    "track_process = var_model.VARProcess(\n",
    "    coefs=np.array([[[A]]]), coefs_exog=None, sigma_u=np.array([[sigma ** 2]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We simulate 10,000 tracks from this model with 120 steps each (three months), sampling different initial values for each track from a uniform distribution from -100 to 100 (down 100 streams to up 100 streams)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_tracks = 10000\n",
    "n_steps_per_track = 120\n",
    "\n",
    "offset = np.zeros((n_tracks, n_steps_per_track + 1, 1))\n",
    "offset[:, 0, 0] = np.random.uniform(-100, 100, n_tracks)\n",
    "\n",
    "simulated_tracks = pd.DataFrame(\n",
    "    columns=['trackid', 'diff_prev_streams', 'diff_streams'])\n",
    "\n",
    "for i in range(n_tracks):\n",
    "    simulated_track_i = track_process.simulate_var(\n",
    "        steps=n_steps_per_track + 1, offset=offset[i])\n",
    "    \n",
    "    simulated_tracks = simulated_tracks.append(pd.DataFrame(\n",
    "        {'trackid': i + 1,\n",
    "         'diff_prev_streams': simulated_track_i[:-1, 0],\n",
    "         'diff_streams': simulated_track_i[1:, 0]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now inject spikes at arbitrary times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulated_tracks['has_event'] = 0\n",
    "\n",
    "simulated_tracks_array = simulated_tracks.values\n",
    "\n",
    "spikes = np.random.uniform(100, 200, n_tracks)\n",
    "spike_pos = np.random.randint(40, 100, n_tracks)\n",
    "\n",
    "idx = 0\n",
    "for i in range(n_tracks):\n",
    "    simulated_tracks_array[idx + spike_pos[i], 2] += spikes[i]\n",
    "    simulated_tracks_array[idx + spike_pos[i] + 1, 1] = simulated_tracks_array[idx + spike_pos[i], 2]\n",
    "    simulated_tracks_array[(idx + spike_pos[i] - 3):(idx + spike_pos[i] + 1), 3] = 1\n",
    "    idx += 120\n",
    "\n",
    "simulated_tracks = pd.DataFrame(\n",
    "    simulated_tracks_array, columns=['trackid', 'diff_prev_streams', 'diff_streams', 'has_event'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting DataFrame looks like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trackid</th>\n",
       "      <th>diff_prev_streams</th>\n",
       "      <th>diff_streams</th>\n",
       "      <th>has_event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>21.3647</td>\n",
       "      <td>5.25963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5.25963</td>\n",
       "      <td>13.5225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5225</td>\n",
       "      <td>-5.84043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-5.84043</td>\n",
       "      <td>-1.95182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.95182</td>\n",
       "      <td>11.5058</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  trackid diff_prev_streams diff_streams has_event\n",
       "0       1           21.3647      5.25963         0\n",
       "1       1           5.25963      13.5225         0\n",
       "2       1           13.5225     -5.84043         0\n",
       "3       1          -5.84043     -1.95182         0\n",
       "4       1          -1.95182      11.5058         0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulated_tracks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the autocorrelation for the first track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.acorr(\n",
    "    simulated_tracks[simulated_tracks['trackid'] == 1]['diff_streams']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we train an RNN on just this simple data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training tracks is 7000\n",
      "Number of validation tracks is 1500\n",
      "Number of evaluation tracks is 1500\n"
     ]
    }
   ],
   "source": [
    "RANDOM_STATE = 0\n",
    "\n",
    "# group by tracks\n",
    "simulated_tracks_grouped = simulated_tracks.groupby('trackid')\n",
    "track_keys = list(simulated_tracks_grouped.groups.keys())\n",
    "\n",
    "# split into train and test\n",
    "track_keys_train, track_keys_test = train_test_split(\n",
    "    track_keys, test_size=0.3, random_state=RANDOM_STATE)\n",
    "\n",
    "# split test further into validation and evaluation\n",
    "track_keys_validate, track_keys_eval = train_test_split(\n",
    "    track_keys_test, test_size=0.5, random_state=RANDOM_STATE)\n",
    "\n",
    "print('Number of training tracks is', len(track_keys_train))\n",
    "print('Number of validation tracks is', len(track_keys_validate))\n",
    "print('Number of evaluation tracks is', len(track_keys_eval))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_simulated_tracks(track_ids, x_vars, y_vars):\n",
    "    # decode string arguments\n",
    "    x_vars = [s.decode('utf-8') for s in x_vars]\n",
    "    y_vars = [s.decode('utf-8') for s in y_vars]\n",
    "    \n",
    "    # iterate over tracks\n",
    "    for tid in track_ids:\n",
    "        df_track = simulated_tracks_grouped.get_group(tid)\n",
    "        yield df_track[x_vars].values, df_track[y_vars].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vars = ['diff_prev_streams', 'has_event']\n",
    "y_var = ['diff_streams']\n",
    "\n",
    "output_shapes = (tf.TensorShape([None, len(x_vars)]), tf.TensorShape([None, len(y_var)]))\n",
    "\n",
    "BATCH_SIZE = 100\n",
    "NUM_EPOCHS = 10\n",
    "\n",
    "# prepare training data\n",
    "dataset_train = tf.data.Dataset.from_generator(\n",
    "    gen_simulated_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_train, x_vars, y_var))\n",
    "dataset_train = dataset_train.shuffle(buffer_size=10000)\n",
    "dataset_train = dataset_train.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_train = dataset_train.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare validation data\n",
    "dataset_validation = tf.data.Dataset.from_generator(\n",
    "    gen_simulated_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_validate, x_vars, y_var))\n",
    "dataset_validation = dataset_validation.padded_batch(BATCH_SIZE, output_shapes)\n",
    "dataset_validation = dataset_validation.repeat(NUM_EPOCHS)\n",
    "\n",
    "# prepare evaluation data\n",
    "dataset_evaluation = tf.data.Dataset.from_generator(\n",
    "    gen_simulated_tracks, (tf.float32, tf.float32), output_shapes,\n",
    "    args=(track_keys_eval, x_vars, y_var))\n",
    "dataset_evaluation = dataset_evaluation.padded_batch(BATCH_SIZE, output_shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Keras model\n",
    "inputs = layers.Input(shape=(None, len(x_vars)))\n",
    "hidden_layer_1 = layers.LSTM(10, activation='tanh', return_sequences=True)(inputs)\n",
    "hidden_layer_2 = layers.LSTM(10, activation='tanh', return_sequences=True)(hidden_layer_1)\n",
    "outputs = layers.Dense(1, activation='linear')(hidden_layer_2)\n",
    "\n",
    "LEARNING_RATE = 0.01\n",
    "\n",
    "rnn_model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
    "rnn_model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=LEARNING_RATE),\n",
    "    loss='mse', metrics=['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training error does go down across epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "70/70 [==============================] - 34s 493ms/step - loss: 307.1010 - mean_absolute_error: 9.8807 - val_loss: 279.5575 - val_mean_absolute_error: 9.4003\n",
      "Epoch 2/10\n",
      "70/70 [==============================] - 27s 380ms/step - loss: 270.1286 - mean_absolute_error: 9.3217 - val_loss: 259.8578 - val_mean_absolute_error: 9.2259\n",
      "Epoch 3/10\n",
      "70/70 [==============================] - 28s 403ms/step - loss: 253.0048 - mean_absolute_error: 9.2010 - val_loss: 244.1968 - val_mean_absolute_error: 9.1181\n",
      "Epoch 4/10\n",
      "70/70 [==============================] - 30s 429ms/step - loss: 237.9791 - mean_absolute_error: 9.0987 - val_loss: 230.6533 - val_mean_absolute_error: 9.0499\n",
      "Epoch 5/10\n",
      "70/70 [==============================] - 27s 383ms/step - loss: 227.1531 - mean_absolute_error: 9.0734 - val_loss: 219.7964 - val_mean_absolute_error: 9.0102\n",
      "Epoch 6/10\n",
      "70/70 [==============================] - 24s 339ms/step - loss: 213.1705 - mean_absolute_error: 8.9625 - val_loss: 203.8334 - val_mean_absolute_error: 8.8680\n",
      "Epoch 7/10\n",
      "70/70 [==============================] - 25s 358ms/step - loss: 198.1665 - mean_absolute_error: 8.8366 - val_loss: 191.5209 - val_mean_absolute_error: 8.7880\n",
      "Epoch 8/10\n",
      "70/70 [==============================] - 25s 356ms/step - loss: 187.1379 - mean_absolute_error: 8.7773 - val_loss: 181.5018 - val_mean_absolute_error: 8.7406\n",
      "Epoch 9/10\n",
      "70/70 [==============================] - 24s 350ms/step - loss: 177.4853 - mean_absolute_error: 8.7227 - val_loss: 172.3998 - val_mean_absolute_error: 8.6861\n",
      "Epoch 10/10\n",
      "70/70 [==============================] - 25s 362ms/step - loss: 168.9936 - mean_absolute_error: 8.6756 - val_loss: 164.4078 - val_mean_absolute_error: 8.6406\n"
     ]
    }
   ],
   "source": [
    "# train and test on validation set\n",
    "steps_per_epoch = int(np.ceil(len(track_keys_train)/BATCH_SIZE))\n",
    "validation_steps = int(np.ceil(len(track_keys_validate)/BATCH_SIZE))\n",
    "\n",
    "rnn_model.fit(\n",
    "    dataset_train, epochs=NUM_EPOCHS, steps_per_epoch=steps_per_epoch,\n",
    "    validation_data=dataset_validation, validation_steps=validation_steps);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 3s 226ms/step\n",
      "Mean squared error on evaluation set = 164.7074971516927\n",
      "Mean absolute error on evaluation set = 8.661465263366699\n"
     ]
    }
   ],
   "source": [
    "# evaluate\n",
    "eval_steps = int(np.ceil(len(track_keys_eval)/BATCH_SIZE))\n",
    "\n",
    "mse_eval, mae_eval = rnn_model.evaluate(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Mean squared error on evaluation set =', mse_eval)\n",
    "print('Mean absolute error on evaluation set =', mae_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of predictions is (1500, 120, 1)\n"
     ]
    }
   ],
   "source": [
    "# get predictions on evaluation set\n",
    "predictions = rnn_model.predict(dataset_evaluation, steps=eval_steps)\n",
    "\n",
    "print('Shape of predictions is', predictions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot predictions\n",
    "track_index = 4\n",
    "\n",
    "tid = track_keys_eval[track_index]\n",
    "\n",
    "df_track = simulated_tracks_grouped.get_group(tid)\n",
    "num_steps = df_track.shape[0]\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.title('Track ID = {}'.format(tid))\n",
    "plt.plot(range(num_steps), df_track['diff_streams'], 'b', label='Truth')\n",
    "plt.plot(range(num_steps), predictions[track_index, :num_steps, 0], 'r', label='RNN')\n",
    "plt.xticks([])\n",
    "plt.legend(loc='best')\n",
    "plt.grid(True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "events_streams",
   "language": "python",
   "name": "events_streams"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}