Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e2542daa-bbf1-4f7f-883c-24ec91959378",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn \n",
    "import matplotlib.pyplot as plt \n",
    "import datetime as dt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold, GridSearchCV\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.cluster import KMeans\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc6bbda-7f75-4b56-85dd-e6f9c18ccfcd",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6cd53e34-08ac-4c56-89cf-a973f2ffc8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data from csv into a dataframe\n",
    "spotify_df = pd.read_csv('spotify_songs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b10cc828-607d-452b-9aac-3b905ade0327",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genres:\n",
      " ['pop' 'rap' 'rock' 'latin' 'r&b' 'edm']\n",
      "subgenres:\n",
      " ['dance pop' 'post-teen pop' 'electropop' 'indie poptimism' 'hip hop'\n",
      " 'southern hip hop' 'gangster rap' 'trap' 'album rock' 'classic rock'\n",
      " 'permanent wave' 'hard rock' 'tropical' 'latin pop' 'reggaeton'\n",
      " 'latin hip hop' 'urban contemporary' 'hip pop' 'new jack swing'\n",
      " 'neo soul' 'electro house' 'big room' 'pop edm'\n",
      " 'progressive electro house']\n"
     ]
    }
   ],
   "source": [
    "# Determine genres and subgenres\n",
    "genres = spotify_df['playlist_genre'].unique()\n",
    "subgenres = spotify_df['playlist_subgenre'].unique()\n",
    "print(\"genres:\\n\", genres)\n",
    "print(\"subgenres:\\n\", subgenres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d60ffc19-7fa6-47cd-9646-517866184768",
   "metadata": {},
   "outputs": [],
   "source": [
    "spotify_df['popularity_discrete'] = np.where(spotify_df['track_popularity'] >= 75, 'popular', 'unpopular')\n",
    "# Convert to datetime format to be able to do computations like sorting\n",
    "spotify_df['track_album_release_date_dt'] = pd.to_datetime(spotify_df['track_album_release_date'])\n",
    "# Determine relevant features\n",
    "features = np.array(['track_name', 'track_popularity', 'track_album_release_date_dt'] + spotify_df.columns[9:-1].to_list())\n",
    "# Filter the dataframe by relevant features\n",
    "filtered_spotify_df = spotify_df[features].sort_values('track_popularity').reset_index(drop=True)\n",
    "# Create a dictionary of dataframes for each subgenre\n",
    "dict_df = {}\n",
    "genre_dict = {}\n",
    "for g in genres:\n",
    "    genre_dict[g] = filtered_spotify_df[filtered_spotify_df['playlist_genre'] == g].reset_index(drop = True).drop(columns = ['playlist_genre', 'playlist_subgenre'])\n",
    "    \n",
    "for sg in subgenres:\n",
    "    dict_df[sg] = filtered_spotify_df[filtered_spotify_df['playlist_subgenre'] == sg].reset_index(drop = True).drop(columns = ['playlist_genre', 'playlist_subgenre'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bfd4a2f9-733e-4f8f-86e9-6ab4283b13f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9d64b148b0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# explore relationship between popularity and, e.g., danceability\n",
    "graph_df = filtered_spotify_df.sort_values('danceability')\n",
    "plt.plot(graph_df['danceability'], graph_df['track_popularity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4e001572-fa2b-4ea8-9a61-03848741ef78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_balancing(df, under = True):\n",
    "    #undersample the bigger label\n",
    "    if under:\n",
    "        min_label = df['popularity_discrete'].value_counts().idxmin()\n",
    "        max_label = df['popularity_discrete'].value_counts().idxmax()\n",
    "        \n",
    "        smaller_df = df[df['popularity_discrete'] == min_label]\n",
    "        larger_df = df[df['popularity_discrete'] == max_label]\n",
    "        \n",
    "        match_count = df['popularity_discrete'].value_counts().min()\n",
    "        \n",
    "        sampled_df = larger_df.sample(n=match_count, replace=True, ignore_index=True)\n",
    "        \n",
    "        return pd.concat([sampled_df, smaller_df], ignore_index = True)\n",
    "        \n",
    "    #oversample the smaller label\n",
    "    else:\n",
    "        min_label = df['popularity_discrete'].value_counts().idxmin()\n",
    "        max_label = df['popularity_discrete'].value_counts().idxmax()\n",
    "        \n",
    "        smaller_df = df[df['popularity_discrete'] == min_label]\n",
    "        larger_df = df[df['popularity_discrete'] == max_label]\n",
    "        \n",
    "        match_count = df['popularity_discrete'].value_counts().max()\n",
    "        \n",
    "        sampled_df = smaller_df.sample(n=match_count, replace=True, ignore_index=True)\n",
    "        \n",
    "        return pd.concat([sampled_df, larger_df], ignore_index = True)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e5966466-bf96-41b0-b66d-9844fa9717bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize function\n",
    "def normalize(df):\n",
    "    for c in df.columns:\n",
    "        if df[c].dtype == int or df[c].dtype == float:\n",
    "            df[c] /= df[c].abs().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d02747bf-3eb7-4356-a6a7-bbef68fefc52",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlation_matrix(features):\n",
    "    data = features.corr().abs() \n",
    "\n",
    "    # plotting the heatmap \n",
    "    hm = sn.heatmap(data = data) \n",
    "\n",
    "    # displaying the plotted heatmap \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "54c50b6e-03d1-421f-9249-e529807ef79c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_features_labels(df, test_size = 0.7):\n",
    "    X = df.drop(columns = ['track_name', 'key', 'mode', 'track_album_release_date_dt', 'track_popularity', 'popularity_discrete'])\n",
    "    y = df['track_popularity']\n",
    "    y_discrete = df['popularity_discrete']\n",
    "    dX_train, dX_test, dy_train, dy_test = train_test_split(X, y_discrete, test_size=test_size, shuffle = True)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle = True)\n",
    "    \n",
    "    return X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a4918ef7-9de6-4128-a9bb-caf8be914925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dance pop', 'post-teen pop', 'electropop', 'indie poptimism',\n",
       "       'hip hop', 'southern hip hop', 'gangster rap', 'trap',\n",
       "       'album rock', 'classic rock', 'permanent wave', 'hard rock',\n",
       "       'tropical', 'latin pop', 'reggaeton', 'latin hip hop',\n",
       "       'urban contemporary', 'hip pop', 'new jack swing', 'neo soul',\n",
       "       'electro house', 'big room', 'pop edm',\n",
       "       'progressive electro house'], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgenres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dc7658fd-d174-4c2e-b378-858d8802a58e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# take a subgenre dataframe\n",
    "df = genre_dict['pop']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a253972c-db2d-4855-bf91-e4849a10981c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f3b6ae-1563-4413-bb8c-3a818676949a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc05a51-51f4-4a9a-9c64-8277920963bb",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Hyperparameterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5548ab2e-ed25-4c50-922c-c9f28e8dd545",
   "metadata": {},
   "outputs": [],
   "source": [
    "def runTuneTest(learner, parameters, X,y, verbose = False):\n",
    "    # Apply KFold with 5 folds\n",
    "    kf = KFold(n_splits = 5) \n",
    "    X = X.to_numpy()\n",
    "    if type(y) == list: #list\n",
    "        y = np.array(y_transformed_discrete)\n",
    "    else: # dataframe\n",
    "        y = y.to_numpy()\n",
    "    score_sum = 0\n",
    "    for train, test in kf.split(X, y):\n",
    "        # Determine hyperparameters using wrapper class GridSearchCV\n",
    "        gs_wrapper = GridSearchCV(learner, parameters, cv = 3)\n",
    "        gs_wrapper.fit(X[train], y[train])\n",
    "        # Print parameter settings that give the best results on the hold out data\n",
    "        score_sum += gs_wrapper.best_score_\n",
    "\n",
    "        if verbose:\n",
    "            print(\"best score: \", gs_wrapper.best_score_)\n",
    "            print(\"best estimator: \", gs_wrapper.best_estimator_)\n",
    "        \n",
    "    return score_sum / 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b4bf772-75e6-46ef-9cc9-d9449c11efb6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Supervised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a9800e1-7d43-4ac8-b703-9d51a82d7802",
   "metadata": {
    "tags": []
   },
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "613eb766-9543-4160-851c-e17c47461959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5967184801381693\n"
     ]
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier()\n",
    "neigh.fit(dX_train, dy_train)\n",
    "print(neigh.score(dX_test, dy_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "303b165d-499e-4d56-b82e-4c5f3b50e5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def KNNPipe(X, y_discrete, verbose = False):\n",
    "    score = runTuneTest(KNeighborsClassifier(), {}, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e9f862b-e535-4dd1-a9ae-06ea8157a18e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b4bee250-d0bb-44e7-9ce7-8435cf541c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linearSVMPipe(X, y_discrete, verbose = False):\n",
    "    grid_values = {\n",
    "        'dual': [True, False],\n",
    "        'fit_intercept': [True, False],\n",
    "    }    \n",
    "    score = runTuneTest(LinearSVC(), grid_values, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "fe79a173-f438-44fa-a3a6-4bb267453d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6448049305192163"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearSVMPipe(X, y_discrete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a9696941-c314-4e98-a548-17ac64ddf919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6450777202072538\n"
     ]
    }
   ],
   "source": [
    "clf = make_pipeline(StandardScaler(), LinearSVC(dual=False))\n",
    "clf.fit(dX_train, dy_train)\n",
    "print(clf.score(dX_test, dy_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9c5876-9a9d-40d1-a719-bbfbc854abfd",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "302abe2b-733b-4aa7-95c3-2ef60334b310",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def logRegPipe(X, y_discrete, verbose = False):\n",
    "    grid_values = {\n",
    "        'tol': [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1],\n",
    "        'C': [0.1, 0.25, 0.5, 1, 1.5, 2],\n",
    "        'fit_intercept': [True, False]\n",
    "    }\n",
    "    score = runTuneTest(LogisticRegression(), grid_values, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "78aaaeb9-bc98-4892-8a9f-3fac7fce4221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6478269940454814"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logRegPipe(X, y_discrete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bf2aaa4a-437f-4667-ac9d-3d7a6c84c67f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5647668393782384\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(C = 0.1)\n",
    "lr.fit(dX_train, dy_train)\n",
    "print(lr.score(dX_test, dy_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "8a810dbc-f07f-4d62-a754-8d374a74531c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['danceability', 'energy', 'loudness', 'speechiness',\n",
       "       'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',\n",
       "       'duration_ms'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "38b68abb-109d-4188-a9e0-1e5bdf825921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.16920415,  0.35191601, -0.18369237, -0.05455951, -0.19967178,\n",
       "         0.56485665,  0.25100778, -0.14828137,  0.10465928,  0.08840239]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "584cfca6-de67-486b-a738-442fd7b6214b",
   "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>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>danceability</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003663</td>\n",
       "      <td>0.091296</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.056479</td>\n",
       "      <td>0.013312</td>\n",
       "      <td>0.077134</td>\n",
       "      <td>0.371762</td>\n",
       "      <td>0.243543</td>\n",
       "      <td>0.202079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>energy</th>\n",
       "      <td>0.003663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651193</td>\n",
       "      <td>0.022414</td>\n",
       "      <td>0.573924</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0.192436</td>\n",
       "      <td>0.354627</td>\n",
       "      <td>0.144826</td>\n",
       "      <td>0.007782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loudness</th>\n",
       "      <td>0.091296</td>\n",
       "      <td>0.651193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.372230</td>\n",
       "      <td>0.222591</td>\n",
       "      <td>0.120273</td>\n",
       "      <td>0.270189</td>\n",
       "      <td>0.051253</td>\n",
       "      <td>0.174491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>speechiness</th>\n",
       "      <td>0.029248</td>\n",
       "      <td>0.022414</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.065713</td>\n",
       "      <td>0.082135</td>\n",
       "      <td>0.025878</td>\n",
       "      <td>0.144688</td>\n",
       "      <td>0.148293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acousticness</th>\n",
       "      <td>0.056479</td>\n",
       "      <td>0.573924</td>\n",
       "      <td>0.372230</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017643</td>\n",
       "      <td>0.113166</td>\n",
       "      <td>0.201813</td>\n",
       "      <td>0.144975</td>\n",
       "      <td>0.002897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instrumentalness</th>\n",
       "      <td>0.013312</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0.222591</td>\n",
       "      <td>0.065713</td>\n",
       "      <td>0.017643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>0.070359</td>\n",
       "      <td>0.027587</td>\n",
       "      <td>0.167361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liveness</th>\n",
       "      <td>0.077134</td>\n",
       "      <td>0.192436</td>\n",
       "      <td>0.120273</td>\n",
       "      <td>0.082135</td>\n",
       "      <td>0.113166</td>\n",
       "      <td>0.026470</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015487</td>\n",
       "      <td>0.044943</td>\n",
       "      <td>0.026665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valence</th>\n",
       "      <td>0.371762</td>\n",
       "      <td>0.354627</td>\n",
       "      <td>0.270189</td>\n",
       "      <td>0.025878</td>\n",
       "      <td>0.201813</td>\n",
       "      <td>0.070359</td>\n",
       "      <td>0.015487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>0.145501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempo</th>\n",
       "      <td>0.243543</td>\n",
       "      <td>0.144826</td>\n",
       "      <td>0.051253</td>\n",
       "      <td>0.144688</td>\n",
       "      <td>0.144975</td>\n",
       "      <td>0.027587</td>\n",
       "      <td>0.044943</td>\n",
       "      <td>0.001502</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_ms</th>\n",
       "      <td>0.202079</td>\n",
       "      <td>0.007782</td>\n",
       "      <td>0.174491</td>\n",
       "      <td>0.148293</td>\n",
       "      <td>0.002897</td>\n",
       "      <td>0.167361</td>\n",
       "      <td>0.026665</td>\n",
       "      <td>0.145501</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  danceability    energy  loudness  speechiness  acousticness  \\\n",
       "danceability          1.000000  0.003663  0.091296     0.029248      0.056479   \n",
       "energy                0.003663  1.000000  0.651193     0.022414      0.573924   \n",
       "loudness              0.091296  0.651193  1.000000     0.000220      0.372230   \n",
       "speechiness           0.029248  0.022414  0.000220     1.000000      0.050714   \n",
       "acousticness          0.056479  0.573924  0.372230     0.050714      1.000000   \n",
       "instrumentalness      0.013312  0.012171  0.222591     0.065713      0.017643   \n",
       "liveness              0.077134  0.192436  0.120273     0.082135      0.113166   \n",
       "valence               0.371762  0.354627  0.270189     0.025878      0.201813   \n",
       "tempo                 0.243543  0.144826  0.051253     0.144688      0.144975   \n",
       "duration_ms           0.202079  0.007782  0.174491     0.148293      0.002897   \n",
       "\n",
       "                  instrumentalness  liveness   valence     tempo  duration_ms  \n",
       "danceability              0.013312  0.077134  0.371762  0.243543     0.202079  \n",
       "energy                    0.012171  0.192436  0.354627  0.144826     0.007782  \n",
       "loudness                  0.222591  0.120273  0.270189  0.051253     0.174491  \n",
       "speechiness               0.065713  0.082135  0.025878  0.144688     0.148293  \n",
       "acousticness              0.017643  0.113166  0.201813  0.144975     0.002897  \n",
       "instrumentalness          1.000000  0.026470  0.070359  0.027587     0.167361  \n",
       "liveness                  0.026470  1.000000  0.015487  0.044943     0.026665  \n",
       "valence                   0.070359  0.015487  1.000000  0.001502     0.145501  \n",
       "tempo                     0.027587  0.044943  0.001502  1.000000     0.003692  \n",
       "duration_ms               0.167361  0.026665  0.145501  0.003692     1.000000  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "16801af6-7ef2-458c-8dce-557523f154c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "predictions = lr.predict(dX_test)\n",
    "confmat = confusion_matrix(dy_test, predictions)\n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=confmat, display_labels=lr.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3e8293-e07f-43dc-96a7-d1aa864315ad",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "4ddbd256-6e62-4988-a70f-57ef906c26cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6733870967741935\n",
      "0.6347150259067358\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(max_depth=2)\n",
    "dt.fit(dX_train, dy_train)\n",
    "print(dt.score(dX_train, dy_train))\n",
    "print(dt.score(dX_test, dy_test))\n",
    "predictions = dt.predict(dX_test)\n",
    "#print(f1_score(dy_test, predictions, pos_label='popular'))\n",
    "# care about F score\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "851ba3e1-3758-4b13-9790-145ef7a8f85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def DTPipe(X, y_discrete, verbose = False):\n",
    "    features = len(X.columns)\n",
    "    grid_values = {\n",
    "        'max_depth': [\n",
    "            i for i in range(1, features)\n",
    "        ],\n",
    "        'min_samples_split': [\n",
    "            2,\n",
    "            5,\n",
    "            10,\n",
    "            50,\n",
    "            100,\n",
    "            200,\n",
    "            300,\n",
    "            317\n",
    "        ]\n",
    "    }\n",
    "    score = runTuneTest(DecisionTreeClassifier(), grid_values, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "47b57644-7515-49b5-9a34-5651d4ad799a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6587031393754084"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTPipe(X, y_discrete)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e088a709-f52b-44ff-afb9-958ca4e390a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "# Visualize the Decision Tree\n",
    "plt.figure(figsize=(12, 8))\n",
    "plot_tree(dt, filled=True, feature_names=dX_train.columns, class_names=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "122d0690-f811-4c32-a551-31aa22b44a5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "predictions = dt.predict(dX_test)\n",
    "confmat = confusion_matrix(dy_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "81438a51-defd-4fb7-97c0-cd425adedec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[414, 176],\n",
       "       [247, 321]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confmat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0960ca53-8015-4a3a-bd99-3d24a9f07fdf",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "6d40d653-7794-495c-9a29-a86182ee842d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9939516129032258\n",
      "0.6485319516407599\n",
      "      (rf) feature | weight\n",
      "======================================\n",
      "  instrumentalness = 0.132\n",
      "          loudness = 0.106\n",
      "            energy = 0.103\n",
      "       speechiness = 0.102\n",
      "      danceability = 0.100\n",
      "           valence = 0.097\n",
      "      acousticness = 0.095\n",
      "          liveness = 0.094\n",
      "             tempo = 0.088\n",
      "       duration_ms = 0.084\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['popular', 'unpopular'], dtype=object)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(dX_train, dy_train)\n",
    "print(rf.score(dX_train, dy_train))\n",
    "print(rf.score(dX_test, dy_test))\n",
    "predictions = rf.predict(dX_test)\n",
    "#print(f1_score(dy_test, predictions, pos_label='popular'))\n",
    "\n",
    "print(\"%18s | %s\" % (\"(rf) feature\", \"weight\"))\n",
    "print(\"======================================\")\n",
    "pairs = []\n",
    "for i in range(len(rf.feature_names_in_)):\n",
    "    pairs.append((abs(rf.feature_importances_[i]), rf.feature_names_in_[i]))\n",
    "pairs.sort()\n",
    "for i in reversed(range(len(rf.feature_names_in_))):\n",
    "    print(\"%18s = %.3f\" % (pairs[i][1], pairs[i][0]))\n",
    "rf.classes_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a9e6dc7d-6911-493f-8e94-b82dee984167",
   "metadata": {},
   "outputs": [],
   "source": [
    "def RFPipe(X, y_discrete, verbose = False):\n",
    "    features = len(X.columns)\n",
    "    grid_values = {\n",
    "        'max_depth': [\n",
    "            i for i in range(1, features)\n",
    "        ],\n",
    "        'n_estimators': [\n",
    "            1,\n",
    "            10,\n",
    "            100\n",
    "        ]\n",
    "    }\n",
    "    score = runTuneTest(RandomForestClassifier(), grid_values, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f8d1b695-4c72-4f73-9100-a38cabac5b0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best score:  0.6848072562358277\n",
      "best estimator:  RandomForestClassifier(max_depth=5)\n",
      "best score:  0.693121693121693\n",
      "best estimator:  RandomForestClassifier(max_depth=8)\n",
      "best score:  0.6848072562358277\n",
      "best estimator:  RandomForestClassifier(max_depth=7)\n",
      "best score:  0.671201814058957\n",
      "best estimator:  RandomForestClassifier(max_depth=5)\n",
      "best score:  0.6661570611150444\n",
      "best estimator:  RandomForestClassifier(max_depth=9)\n",
      "average score:  0.68001901615347\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.68001901615347"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFPipe(X, y_discrete, verbose = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb4c9e0-3b87-47e4-8361-c9bf13aed4e2",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8ef9d6f8-7b4f-4501-8772-50604ffbf6ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9435483870967742\n",
      "0.6580310880829016\n"
     ]
    }
   ],
   "source": [
    "gb = GradientBoostingClassifier()\n",
    "gb.fit(dX_train, dy_train)\n",
    "print(gb.score(dX_train, dy_train))\n",
    "print(gb.score(dX_test, dy_test))\n",
    "predictions = gb.predict(dX_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f8c67470-b9b7-44d6-b51f-d36eebb1ad1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def BoostingPipe(X, y_discrete, verbose = False):\n",
    "    grid_values = {\n",
    "        'learning_rate': [0.1, 0.5, 0.9],\n",
    "        'n_estimators': [1,10,100],\n",
    "        'subsample': [0.1, 0.5, 0.9]\n",
    "    }\n",
    "    score = runTuneTest(GradientBoostingClassifier(), grid_values, X, y_discrete, verbose)\n",
    "    if verbose:\n",
    "        print(\"average score: \", score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9a2df43d-5b6b-45c8-a5fe-e182db084834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6718581449673887"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BoostingPipe(X, y_discrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ffcfe0-0d8d-4664-adaf-406e3afd510c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Unsupervised"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfa4a56-b106-45cf-a7d0-a56275d9cb3e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8bacb3ea-8eef-4077-8807-c3b849433492",
   "metadata": {},
   "outputs": [],
   "source": [
    "def KMeanPipe(X, clusters = 8):\n",
    "    km = KMeans(n_clusters = clusters)\n",
    "    km.fit(X)\n",
    "    km_labels = pd.Series(km.labels_)\n",
    "    plt.scatter(km_labels, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "23cf3882-89d3-4423-8f06-3dae13c8e4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KMeanPipe(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98572291-d3bc-4760-af59-876fb349f09e",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "51e64e73-3752-4033-b7fa-d547bffbcbfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e5260ce9-ed94-4866-b11a-6e08a9bf3b8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame()\n",
    "results['genres'] = genres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c7c9cb6c-f4f4-4c8a-b8ee-a25e8fa41ba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████| 6/6 [19:46<00:00, 197.80s/it]\n"
     ]
    }
   ],
   "source": [
    "uknns = []\n",
    "usvms = []\n",
    "ulrs = []\n",
    "udts = []\n",
    "urfs = []\n",
    "uboos = []\n",
    "oknns = []\n",
    "osvms = []\n",
    "olrs = []\n",
    "odts = []\n",
    "orfs = []\n",
    "oboos = []\n",
    "\n",
    "for g in tqdm(genres):\n",
    "    df = genre_dict[g]\n",
    "    normalize(df)\n",
    "    under_df = data_balancing(df, under = True)\n",
    "    over_df = data_balancing(df, under = False)\n",
    "    X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "    \n",
    "    # KMeanPipe(X)\n",
    "    uknns.append(KNNPipe(X, y_discrete))\n",
    "    usvms.append(linearSVMPipe(X, y_discrete))\n",
    "    ulrs.append(logRegPipe(X, y_discrete))\n",
    "    udts.append(DTPipe(X, y_discrete))\n",
    "    urfs.append(RFPipe(X, y_discrete))\n",
    "    uboos.append(BoostingPipe(X, y_discrete))\n",
    "    \n",
    "    X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(over_df)\n",
    "    \n",
    "    # KMeanPipe(X)\n",
    "    oknns.append(KNNPipe(X, y_discrete))\n",
    "    osvms.append(linearSVMPipe(X, y_discrete))\n",
    "    olrs.append(logRegPipe(X, y_discrete))\n",
    "    odts.append(DTPipe(X, y_discrete))\n",
    "    orfs.append(RFPipe(X, y_discrete))\n",
    "    oboos.append(BoostingPipe(X, y_discrete))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "560949d2-00ec-42ad-bdd3-33bcdbccd477",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████████████████████████████████▎           | 18/24 [19:17<06:36, 66.09s/it]/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan]\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968: RuntimeWarning: invalid value encountered in cast\n",
      "  results[\"rank_%s\" % key_name] = np.asarray(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan]\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968: RuntimeWarning: invalid value encountered in cast\n",
      "  results[\"rank_%s\" % key_name] = np.asarray(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan]\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968: RuntimeWarning: invalid value encountered in cast\n",
      "  results[\"rank_%s\" % key_name] = np.asarray(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan]\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968: RuntimeWarning: invalid value encountered in cast\n",
      "  results[\"rank_%s\" % key_name] = np.asarray(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:776: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
      "Traceback (most recent call last):\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 767, in _score\n",
      "    scores = scorer(estimator, X_test, y_test)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 429, in _passthrough_scorer\n",
      "    return estimator.score(*args, **kwargs)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/base.py\", line 666, in score\n",
      "    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_classification.py\", line 226, in predict\n",
      "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
      "  File \"/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/neighbors/_base.py\", line 749, in kneighbors\n",
      "    raise ValueError(\n",
      "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 4, n_neighbors = 5\n",
      "\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:953: UserWarning: One or more of the test scores are non-finite: [nan]\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_search.py:968: RuntimeWarning: invalid value encountered in cast\n",
      "  results[\"rank_%s\" % key_name] = np.asarray(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "/scratch/knerr/venvs/cs66/lib/python3.10/site-packages/sklearn/model_selection/_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
      "  warnings.warn(\n",
      "100%|███████████████████████████████████████████████| 24/24 [25:21<00:00, 63.40s/it]\n"
     ]
    }
   ],
   "source": [
    "uknns = []\n",
    "usvms = []\n",
    "ulrs = []\n",
    "udts = []\n",
    "urfs = []\n",
    "uboos = []\n",
    "oknns = []\n",
    "osvms = []\n",
    "olrs = []\n",
    "odts = []\n",
    "orfs = []\n",
    "oboos = []\n",
    "\n",
    "for sg in tqdm(subgenres):\n",
    "    df = dict_df[sg]\n",
    "    normalize(df)\n",
    "    under_df = data_balancing(df, under = True)\n",
    "    over_df = data_balancing(df, under = False)\n",
    "    X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "    \n",
    "    # KMeanPipe(X)\n",
    "    uknns.append(KNNPipe(X, y_discrete))\n",
    "    usvms.append(linearSVMPipe(X, y_discrete))\n",
    "    ulrs.append(logRegPipe(X, y_discrete))\n",
    "    udts.append(DTPipe(X, y_discrete))\n",
    "    urfs.append(RFPipe(X, y_discrete))\n",
    "    uboos.append(BoostingPipe(X, y_discrete))\n",
    "    \n",
    "    X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(over_df)\n",
    "    \n",
    "    # KMeanPipe(X)\n",
    "    oknns.append(KNNPipe(X, y_discrete))\n",
    "    osvms.append(linearSVMPipe(X, y_discrete))\n",
    "    olrs.append(logRegPipe(X, y_discrete))\n",
    "    odts.append(DTPipe(X, y_discrete))\n",
    "    orfs.append(RFPipe(X, y_discrete))\n",
    "    oboos.append(BoostingPipe(X, y_discrete))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9aa35b1d-2430-43ba-a142-c11a7742ce7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results['under_KNN'] = uknns\n",
    "results['under_Linear_SVM'] = usvms\n",
    "results['under_Logistic_Regression'] = ulrs\n",
    "results['under_Decisiton_Tree'] = udts\n",
    "results['under_Random_Forest'] = urfs\n",
    "results['under_Gradient_Boost'] = uboos\n",
    "\n",
    "results['over_KNN'] = oknns\n",
    "results['over_Linear_SVM'] = osvms\n",
    "results['over_Logistic_Regression'] = olrs\n",
    "results['over_Decisiton_Tree'] = odts\n",
    "results['over_Random_Forest'] = orfs\n",
    "results['over_Gradient_Boost'] = oboos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "22d69144-d80e-4028-bebf-bbb5a75c69aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('genre_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "id": "fe971101-a356-4dde-a42c-04890b94f0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_num = results.drop(columns = ['Subgenres']).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "0d2c19e9-2f9e-46ef-ae42-1418d607f54c",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgenre_mean = []\n",
    "for c in results_num.T.columns:\n",
    "    subgenre_mean.append(results_num.T[c].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "bfafbccc-f72a-42c0-b61a-f8332153a7e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "under_KNN                    0.574492\n",
       "under_Linear_SVM             0.631322\n",
       "under_Logistic_Regression    0.646285\n",
       "under_Decisiton_Tree         0.652142\n",
       "under_Random_Forest          0.693940\n",
       "under_Gradient_Boost         0.684306\n",
       "over_KNN                     0.826223\n",
       "over_Linear_SVM              0.692658\n",
       "over_Logistic_Regression     0.693646\n",
       "over_Decisiton_Tree          0.859827\n",
       "over_Random_Forest           0.920944\n",
       "over_Gradient_Boost          0.935580\n",
       "dtype: float64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_num.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "b6432e79-ccec-4f25-bcf3-6b60a7690601",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_num['subgenre_mean'] = subgenre_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "010338bf-eb17-4c97-8804-c9c0ff8715df",
   "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>under_KNN</th>\n",
       "      <th>under_Linear_SVM</th>\n",
       "      <th>under_Logistic_Regression</th>\n",
       "      <th>under_Decisiton_Tree</th>\n",
       "      <th>under_Random_Forest</th>\n",
       "      <th>under_Gradient_Boost</th>\n",
       "      <th>over_KNN</th>\n",
       "      <th>over_Linear_SVM</th>\n",
       "      <th>over_Logistic_Regression</th>\n",
       "      <th>over_Decisiton_Tree</th>\n",
       "      <th>over_Random_Forest</th>\n",
       "      <th>over_Gradient_Boost</th>\n",
       "      <th>subgenre_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dance pop</th>\n",
       "      <td>0.576981</td>\n",
       "      <td>0.642090</td>\n",
       "      <td>0.638305</td>\n",
       "      <td>0.630087</td>\n",
       "      <td>0.662270</td>\n",
       "      <td>0.645888</td>\n",
       "      <td>0.718284</td>\n",
       "      <td>0.659992</td>\n",
       "      <td>0.663176</td>\n",
       "      <td>0.788409</td>\n",
       "      <td>0.874205</td>\n",
       "      <td>0.880222</td>\n",
       "      <td>0.698326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>post-teen pop</th>\n",
       "      <td>0.574918</td>\n",
       "      <td>0.622641</td>\n",
       "      <td>0.628551</td>\n",
       "      <td>0.632496</td>\n",
       "      <td>0.660114</td>\n",
       "      <td>0.644724</td>\n",
       "      <td>0.637152</td>\n",
       "      <td>0.630533</td>\n",
       "      <td>0.635151</td>\n",
       "      <td>0.702579</td>\n",
       "      <td>0.785711</td>\n",
       "      <td>0.788326</td>\n",
       "      <td>0.661908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>electropop</th>\n",
       "      <td>0.616469</td>\n",
       "      <td>0.658021</td>\n",
       "      <td>0.665899</td>\n",
       "      <td>0.652455</td>\n",
       "      <td>0.682997</td>\n",
       "      <td>0.681090</td>\n",
       "      <td>0.717872</td>\n",
       "      <td>0.666230</td>\n",
       "      <td>0.664778</td>\n",
       "      <td>0.800480</td>\n",
       "      <td>0.869890</td>\n",
       "      <td>0.888706</td>\n",
       "      <td>0.713741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indie poptimism</th>\n",
       "      <td>0.580561</td>\n",
       "      <td>0.634362</td>\n",
       "      <td>0.640210</td>\n",
       "      <td>0.625005</td>\n",
       "      <td>0.682315</td>\n",
       "      <td>0.658903</td>\n",
       "      <td>0.841130</td>\n",
       "      <td>0.656543</td>\n",
       "      <td>0.654148</td>\n",
       "      <td>0.881471</td>\n",
       "      <td>0.956789</td>\n",
       "      <td>0.962937</td>\n",
       "      <td>0.731198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hip hop</th>\n",
       "      <td>0.693152</td>\n",
       "      <td>0.710116</td>\n",
       "      <td>0.705042</td>\n",
       "      <td>0.694060</td>\n",
       "      <td>0.716196</td>\n",
       "      <td>0.709272</td>\n",
       "      <td>0.797738</td>\n",
       "      <td>0.704755</td>\n",
       "      <td>0.707199</td>\n",
       "      <td>0.844232</td>\n",
       "      <td>0.886456</td>\n",
       "      <td>0.922428</td>\n",
       "      <td>0.757554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>southern hip hop</th>\n",
       "      <td>0.590765</td>\n",
       "      <td>0.621934</td>\n",
       "      <td>0.618903</td>\n",
       "      <td>0.615873</td>\n",
       "      <td>0.663059</td>\n",
       "      <td>0.651371</td>\n",
       "      <td>0.936468</td>\n",
       "      <td>0.690443</td>\n",
       "      <td>0.694113</td>\n",
       "      <td>0.948471</td>\n",
       "      <td>0.982492</td>\n",
       "      <td>0.992049</td>\n",
       "      <td>0.750495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gangster rap</th>\n",
       "      <td>0.576747</td>\n",
       "      <td>0.602218</td>\n",
       "      <td>0.618817</td>\n",
       "      <td>0.625202</td>\n",
       "      <td>0.659073</td>\n",
       "      <td>0.657527</td>\n",
       "      <td>0.906717</td>\n",
       "      <td>0.702193</td>\n",
       "      <td>0.704249</td>\n",
       "      <td>0.931201</td>\n",
       "      <td>0.972658</td>\n",
       "      <td>0.978376</td>\n",
       "      <td>0.744581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trap</th>\n",
       "      <td>0.607738</td>\n",
       "      <td>0.646695</td>\n",
       "      <td>0.641248</td>\n",
       "      <td>0.670351</td>\n",
       "      <td>0.685493</td>\n",
       "      <td>0.685546</td>\n",
       "      <td>0.804555</td>\n",
       "      <td>0.700403</td>\n",
       "      <td>0.692854</td>\n",
       "      <td>0.849021</td>\n",
       "      <td>0.928808</td>\n",
       "      <td>0.949663</td>\n",
       "      <td>0.738531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>album rock</th>\n",
       "      <td>0.536949</td>\n",
       "      <td>0.590917</td>\n",
       "      <td>0.617901</td>\n",
       "      <td>0.610494</td>\n",
       "      <td>0.625309</td>\n",
       "      <td>0.622751</td>\n",
       "      <td>0.877952</td>\n",
       "      <td>0.608597</td>\n",
       "      <td>0.618091</td>\n",
       "      <td>0.826421</td>\n",
       "      <td>0.942451</td>\n",
       "      <td>0.976706</td>\n",
       "      <td>0.704545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>classic rock</th>\n",
       "      <td>0.553126</td>\n",
       "      <td>0.563428</td>\n",
       "      <td>0.611336</td>\n",
       "      <td>0.597661</td>\n",
       "      <td>0.611336</td>\n",
       "      <td>0.611336</td>\n",
       "      <td>0.850467</td>\n",
       "      <td>0.616413</td>\n",
       "      <td>0.622343</td>\n",
       "      <td>0.820536</td>\n",
       "      <td>0.942771</td>\n",
       "      <td>0.965150</td>\n",
       "      <td>0.697158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>permanent wave</th>\n",
       "      <td>0.578944</td>\n",
       "      <td>0.600893</td>\n",
       "      <td>0.622769</td>\n",
       "      <td>0.630546</td>\n",
       "      <td>0.641348</td>\n",
       "      <td>0.634900</td>\n",
       "      <td>0.764238</td>\n",
       "      <td>0.642144</td>\n",
       "      <td>0.647694</td>\n",
       "      <td>0.819629</td>\n",
       "      <td>0.901872</td>\n",
       "      <td>0.932139</td>\n",
       "      <td>0.701426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hard rock</th>\n",
       "      <td>0.508658</td>\n",
       "      <td>0.549062</td>\n",
       "      <td>0.609812</td>\n",
       "      <td>0.597691</td>\n",
       "      <td>0.606926</td>\n",
       "      <td>0.646320</td>\n",
       "      <td>0.934557</td>\n",
       "      <td>0.660056</td>\n",
       "      <td>0.657891</td>\n",
       "      <td>0.909716</td>\n",
       "      <td>0.974904</td>\n",
       "      <td>0.989873</td>\n",
       "      <td>0.720456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tropical</th>\n",
       "      <td>0.569048</td>\n",
       "      <td>0.615397</td>\n",
       "      <td>0.644603</td>\n",
       "      <td>0.654127</td>\n",
       "      <td>0.700635</td>\n",
       "      <td>0.697937</td>\n",
       "      <td>0.921697</td>\n",
       "      <td>0.677191</td>\n",
       "      <td>0.669494</td>\n",
       "      <td>0.931098</td>\n",
       "      <td>0.967493</td>\n",
       "      <td>0.987998</td>\n",
       "      <td>0.753060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latin pop</th>\n",
       "      <td>0.621643</td>\n",
       "      <td>0.689831</td>\n",
       "      <td>0.685808</td>\n",
       "      <td>0.663108</td>\n",
       "      <td>0.705712</td>\n",
       "      <td>0.694852</td>\n",
       "      <td>0.723827</td>\n",
       "      <td>0.692151</td>\n",
       "      <td>0.692874</td>\n",
       "      <td>0.825078</td>\n",
       "      <td>0.875214</td>\n",
       "      <td>0.878338</td>\n",
       "      <td>0.729036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reggaeton</th>\n",
       "      <td>0.601871</td>\n",
       "      <td>0.665927</td>\n",
       "      <td>0.652063</td>\n",
       "      <td>0.673679</td>\n",
       "      <td>0.705266</td>\n",
       "      <td>0.705382</td>\n",
       "      <td>0.720736</td>\n",
       "      <td>0.680230</td>\n",
       "      <td>0.677386</td>\n",
       "      <td>0.808590</td>\n",
       "      <td>0.871481</td>\n",
       "      <td>0.877186</td>\n",
       "      <td>0.719983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latin hip hop</th>\n",
       "      <td>0.607672</td>\n",
       "      <td>0.665160</td>\n",
       "      <td>0.666984</td>\n",
       "      <td>0.666104</td>\n",
       "      <td>0.692539</td>\n",
       "      <td>0.680691</td>\n",
       "      <td>0.730273</td>\n",
       "      <td>0.676363</td>\n",
       "      <td>0.680704</td>\n",
       "      <td>0.778483</td>\n",
       "      <td>0.842886</td>\n",
       "      <td>0.873367</td>\n",
       "      <td>0.713436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban contemporary</th>\n",
       "      <td>0.636012</td>\n",
       "      <td>0.653234</td>\n",
       "      <td>0.650176</td>\n",
       "      <td>0.643391</td>\n",
       "      <td>0.679618</td>\n",
       "      <td>0.669810</td>\n",
       "      <td>0.730119</td>\n",
       "      <td>0.659647</td>\n",
       "      <td>0.662144</td>\n",
       "      <td>0.799539</td>\n",
       "      <td>0.887493</td>\n",
       "      <td>0.896447</td>\n",
       "      <td>0.713969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hip pop</th>\n",
       "      <td>0.574780</td>\n",
       "      <td>0.628125</td>\n",
       "      <td>0.632125</td>\n",
       "      <td>0.630345</td>\n",
       "      <td>0.651239</td>\n",
       "      <td>0.636104</td>\n",
       "      <td>0.678846</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.670385</td>\n",
       "      <td>0.762821</td>\n",
       "      <td>0.827564</td>\n",
       "      <td>0.832436</td>\n",
       "      <td>0.682898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new jack swing</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.422222</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.544444</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.711111</td>\n",
       "      <td>0.987708</td>\n",
       "      <td>0.865250</td>\n",
       "      <td>0.856169</td>\n",
       "      <td>0.992028</td>\n",
       "      <td>0.999336</td>\n",
       "      <td>0.999336</td>\n",
       "      <td>0.724060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neo soul</th>\n",
       "      <td>0.606926</td>\n",
       "      <td>0.633766</td>\n",
       "      <td>0.643146</td>\n",
       "      <td>0.661616</td>\n",
       "      <td>0.707215</td>\n",
       "      <td>0.697835</td>\n",
       "      <td>0.938202</td>\n",
       "      <td>0.683970</td>\n",
       "      <td>0.679193</td>\n",
       "      <td>0.919250</td>\n",
       "      <td>0.976425</td>\n",
       "      <td>0.992559</td>\n",
       "      <td>0.761675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>electro house</th>\n",
       "      <td>0.608081</td>\n",
       "      <td>0.624747</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.773232</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.957127</td>\n",
       "      <td>0.771705</td>\n",
       "      <td>0.780014</td>\n",
       "      <td>0.948143</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.995133</td>\n",
       "      <td>0.800219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>big room</th>\n",
       "      <td>0.634432</td>\n",
       "      <td>0.732234</td>\n",
       "      <td>0.715385</td>\n",
       "      <td>0.796337</td>\n",
       "      <td>0.865568</td>\n",
       "      <td>0.861172</td>\n",
       "      <td>0.946332</td>\n",
       "      <td>0.812120</td>\n",
       "      <td>0.813813</td>\n",
       "      <td>0.957550</td>\n",
       "      <td>0.983911</td>\n",
       "      <td>0.993013</td>\n",
       "      <td>0.842656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop edm</th>\n",
       "      <td>0.622145</td>\n",
       "      <td>0.668525</td>\n",
       "      <td>0.652683</td>\n",
       "      <td>0.634410</td>\n",
       "      <td>0.661450</td>\n",
       "      <td>0.656777</td>\n",
       "      <td>0.729665</td>\n",
       "      <td>0.689415</td>\n",
       "      <td>0.691810</td>\n",
       "      <td>0.818429</td>\n",
       "      <td>0.891960</td>\n",
       "      <td>0.903561</td>\n",
       "      <td>0.718402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>progressive electro house</th>\n",
       "      <td>0.710185</td>\n",
       "      <td>0.710185</td>\n",
       "      <td>0.749074</td>\n",
       "      <td>0.728704</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.806481</td>\n",
       "      <td>0.977688</td>\n",
       "      <td>0.807441</td>\n",
       "      <td>0.811830</td>\n",
       "      <td>0.972669</td>\n",
       "      <td>0.987659</td>\n",
       "      <td>0.997978</td>\n",
       "      <td>0.843417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           under_KNN  under_Linear_SVM  \\\n",
       "dance pop                   0.576981          0.642090   \n",
       "post-teen pop               0.574918          0.622641   \n",
       "electropop                  0.616469          0.658021   \n",
       "indie poptimism             0.580561          0.634362   \n",
       "hip hop                     0.693152          0.710116   \n",
       "southern hip hop            0.590765          0.621934   \n",
       "gangster rap                0.576747          0.602218   \n",
       "trap                        0.607738          0.646695   \n",
       "album rock                  0.536949          0.590917   \n",
       "classic rock                0.553126          0.563428   \n",
       "permanent wave              0.578944          0.600893   \n",
       "hard rock                   0.508658          0.549062   \n",
       "tropical                    0.569048          0.615397   \n",
       "latin pop                   0.621643          0.689831   \n",
       "reggaeton                   0.601871          0.665927   \n",
       "latin hip hop               0.607672          0.665160   \n",
       "urban contemporary          0.636012          0.653234   \n",
       "hip pop                     0.574780          0.628125   \n",
       "new jack swing              0.000000          0.422222   \n",
       "neo soul                    0.606926          0.633766   \n",
       "electro house               0.608081          0.624747   \n",
       "big room                    0.634432          0.732234   \n",
       "pop edm                     0.622145          0.668525   \n",
       "progressive electro house   0.710185          0.710185   \n",
       "\n",
       "                           under_Logistic_Regression  under_Decisiton_Tree  \\\n",
       "dance pop                                   0.638305              0.630087   \n",
       "post-teen pop                               0.628551              0.632496   \n",
       "electropop                                  0.665899              0.652455   \n",
       "indie poptimism                             0.640210              0.625005   \n",
       "hip hop                                     0.705042              0.694060   \n",
       "southern hip hop                            0.618903              0.615873   \n",
       "gangster rap                                0.618817              0.625202   \n",
       "trap                                        0.641248              0.670351   \n",
       "album rock                                  0.617901              0.610494   \n",
       "classic rock                                0.611336              0.597661   \n",
       "permanent wave                              0.622769              0.630546   \n",
       "hard rock                                   0.609812              0.597691   \n",
       "tropical                                    0.644603              0.654127   \n",
       "latin pop                                   0.685808              0.663108   \n",
       "reggaeton                                   0.652063              0.673679   \n",
       "latin hip hop                               0.666984              0.666104   \n",
       "urban contemporary                          0.650176              0.643391   \n",
       "hip pop                                     0.632125              0.630345   \n",
       "new jack swing                              0.533333              0.544444   \n",
       "neo soul                                    0.643146              0.661616   \n",
       "electro house                               0.666667              0.773232   \n",
       "big room                                    0.715385              0.796337   \n",
       "pop edm                                     0.652683              0.634410   \n",
       "progressive electro house                   0.749074              0.728704   \n",
       "\n",
       "                           under_Random_Forest  under_Gradient_Boost  \\\n",
       "dance pop                             0.662270              0.645888   \n",
       "post-teen pop                         0.660114              0.644724   \n",
       "electropop                            0.682997              0.681090   \n",
       "indie poptimism                       0.682315              0.658903   \n",
       "hip hop                               0.716196              0.709272   \n",
       "southern hip hop                      0.663059              0.651371   \n",
       "gangster rap                          0.659073              0.657527   \n",
       "trap                                  0.685493              0.685546   \n",
       "album rock                            0.625309              0.622751   \n",
       "classic rock                          0.611336              0.611336   \n",
       "permanent wave                        0.641348              0.634900   \n",
       "hard rock                             0.606926              0.646320   \n",
       "tropical                              0.700635              0.697937   \n",
       "latin pop                             0.705712              0.694852   \n",
       "reggaeton                             0.705266              0.705382   \n",
       "latin hip hop                         0.692539              0.680691   \n",
       "urban contemporary                    0.679618              0.669810   \n",
       "hip pop                               0.651239              0.636104   \n",
       "new jack swing                        0.777778              0.711111   \n",
       "neo soul                              0.707215              0.697835   \n",
       "electro house                         0.750000              0.755556   \n",
       "big room                              0.865568              0.861172   \n",
       "pop edm                               0.661450              0.656777   \n",
       "progressive electro house             0.861111              0.806481   \n",
       "\n",
       "                           over_KNN  over_Linear_SVM  \\\n",
       "dance pop                  0.718284         0.659992   \n",
       "post-teen pop              0.637152         0.630533   \n",
       "electropop                 0.717872         0.666230   \n",
       "indie poptimism            0.841130         0.656543   \n",
       "hip hop                    0.797738         0.704755   \n",
       "southern hip hop           0.936468         0.690443   \n",
       "gangster rap               0.906717         0.702193   \n",
       "trap                       0.804555         0.700403   \n",
       "album rock                 0.877952         0.608597   \n",
       "classic rock               0.850467         0.616413   \n",
       "permanent wave             0.764238         0.642144   \n",
       "hard rock                  0.934557         0.660056   \n",
       "tropical                   0.921697         0.677191   \n",
       "latin pop                  0.723827         0.692151   \n",
       "reggaeton                  0.720736         0.680230   \n",
       "latin hip hop              0.730273         0.676363   \n",
       "urban contemporary         0.730119         0.659647   \n",
       "hip pop                    0.678846         0.670000   \n",
       "new jack swing             0.987708         0.865250   \n",
       "neo soul                   0.938202         0.683970   \n",
       "electro house              0.957127         0.771705   \n",
       "big room                   0.946332         0.812120   \n",
       "pop edm                    0.729665         0.689415   \n",
       "progressive electro house  0.977688         0.807441   \n",
       "\n",
       "                           over_Logistic_Regression  over_Decisiton_Tree  \\\n",
       "dance pop                                  0.663176             0.788409   \n",
       "post-teen pop                              0.635151             0.702579   \n",
       "electropop                                 0.664778             0.800480   \n",
       "indie poptimism                            0.654148             0.881471   \n",
       "hip hop                                    0.707199             0.844232   \n",
       "southern hip hop                           0.694113             0.948471   \n",
       "gangster rap                               0.704249             0.931201   \n",
       "trap                                       0.692854             0.849021   \n",
       "album rock                                 0.618091             0.826421   \n",
       "classic rock                               0.622343             0.820536   \n",
       "permanent wave                             0.647694             0.819629   \n",
       "hard rock                                  0.657891             0.909716   \n",
       "tropical                                   0.669494             0.931098   \n",
       "latin pop                                  0.692874             0.825078   \n",
       "reggaeton                                  0.677386             0.808590   \n",
       "latin hip hop                              0.680704             0.778483   \n",
       "urban contemporary                         0.662144             0.799539   \n",
       "hip pop                                    0.670385             0.762821   \n",
       "new jack swing                             0.856169             0.992028   \n",
       "neo soul                                   0.679193             0.919250   \n",
       "electro house                              0.780014             0.948143   \n",
       "big room                                   0.813813             0.957550   \n",
       "pop edm                                    0.691810             0.818429   \n",
       "progressive electro house                  0.811830             0.972669   \n",
       "\n",
       "                           over_Random_Forest  over_Gradient_Boost  \\\n",
       "dance pop                            0.874205             0.880222   \n",
       "post-teen pop                        0.785711             0.788326   \n",
       "electropop                           0.869890             0.888706   \n",
       "indie poptimism                      0.956789             0.962937   \n",
       "hip hop                              0.886456             0.922428   \n",
       "southern hip hop                     0.982492             0.992049   \n",
       "gangster rap                         0.972658             0.978376   \n",
       "trap                                 0.928808             0.949663   \n",
       "album rock                           0.942451             0.976706   \n",
       "classic rock                         0.942771             0.965150   \n",
       "permanent wave                       0.901872             0.932139   \n",
       "hard rock                            0.974904             0.989873   \n",
       "tropical                             0.967493             0.987998   \n",
       "latin pop                            0.875214             0.878338   \n",
       "reggaeton                            0.871481             0.877186   \n",
       "latin hip hop                        0.842886             0.873367   \n",
       "urban contemporary                   0.887493             0.896447   \n",
       "hip pop                              0.827564             0.832436   \n",
       "new jack swing                       0.999336             0.999336   \n",
       "neo soul                             0.976425             0.992559   \n",
       "electro house                        0.972222             0.995133   \n",
       "big room                             0.983911             0.993013   \n",
       "pop edm                              0.891960             0.903561   \n",
       "progressive electro house            0.987659             0.997978   \n",
       "\n",
       "                           subgenre_mean  \n",
       "dance pop                       0.698326  \n",
       "post-teen pop                   0.661908  \n",
       "electropop                      0.713741  \n",
       "indie poptimism                 0.731198  \n",
       "hip hop                         0.757554  \n",
       "southern hip hop                0.750495  \n",
       "gangster rap                    0.744581  \n",
       "trap                            0.738531  \n",
       "album rock                      0.704545  \n",
       "classic rock                    0.697158  \n",
       "permanent wave                  0.701426  \n",
       "hard rock                       0.720456  \n",
       "tropical                        0.753060  \n",
       "latin pop                       0.729036  \n",
       "reggaeton                       0.719983  \n",
       "latin hip hop                   0.713436  \n",
       "urban contemporary              0.713969  \n",
       "hip pop                         0.682898  \n",
       "new jack swing                  0.724060  \n",
       "neo soul                        0.761675  \n",
       "electro house                   0.800219  \n",
       "big room                        0.842656  \n",
       "pop edm                         0.718402  \n",
       "progressive electro house       0.843417  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35270e7d-b4f7-4bfe-a63d-0ab809fec3ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91428e4e-86f1-413b-af23-b77f06c32076",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_matrix = filtered_spotify_df.drop(columns = ['track_name', 'playlist_genre', 'playlist_subgenre', 'popularity_discrete', 'track_album_release_date_dt'])\n",
    "normalize(cleaned_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3cecf750-6ccd-4db3-954b-50d022d7e5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
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       "      <td>0.363636</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0.409401</td>\n",
       "      <td>0.404469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32833 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       track_popularity  danceability  energy       key  loudness  mode  \\\n",
       "0                  0.00      0.456765   0.979  1.000000 -0.198222   0.0   \n",
       "1                  0.00      0.807731   0.741  0.090909 -0.106765   1.0   \n",
       "2                  0.00      0.818922   0.852  0.909091 -0.093373   1.0   \n",
       "3                  0.00      0.758901   0.705  0.636364 -0.152536   1.0   \n",
       "4                  0.00      0.760936   0.887  0.363636 -0.134128   0.0   \n",
       "...                 ...           ...     ...       ...       ...   ...   \n",
       "32828              0.99      0.631740   0.601  0.545455 -0.120909   0.0   \n",
       "32829              0.99      0.631740   0.601  0.545455 -0.120909   0.0   \n",
       "32830              0.99      0.631740   0.601  0.545455 -0.120909   0.0   \n",
       "32831              1.00      0.838250   0.588  0.545455 -0.137788   0.0   \n",
       "32832              1.00      0.838250   0.588  0.545455 -0.137788   0.0   \n",
       "\n",
       "       speechiness  acousticness  instrumentalness  liveness   valence  \\\n",
       "0         0.064270      0.012274          0.000000  0.416667  0.563068   \n",
       "1         0.067756      0.157948          0.033400  0.310241  0.812311   \n",
       "2         0.107407      0.439638          0.000001  0.080120  0.720484   \n",
       "3         0.054902      0.211268          0.000000  0.384538  0.546922   \n",
       "4         0.098584      0.085815          0.000000  0.108434  0.590313   \n",
       "...            ...           ...               ...       ...       ...   \n",
       "32828     0.161220      0.052515          0.000000  0.461847  0.461150   \n",
       "32829     0.161220      0.052515          0.000000  0.461847  0.461150   \n",
       "32830     0.161220      0.052515          0.000000  0.461847  0.461150   \n",
       "32831     0.100654      0.696177          0.000105  0.149598  0.517659   \n",
       "32832     0.100654      0.696177          0.000105  0.149598  0.517659   \n",
       "\n",
       "          tempo  duration_ms  \n",
       "0      0.774002     0.100139  \n",
       "1      0.396784     0.473971  \n",
       "2      0.392804     0.207744  \n",
       "3      0.338243     0.399063  \n",
       "4      0.405175     0.421983  \n",
       "...         ...          ...  \n",
       "32828  0.487533     0.316016  \n",
       "32829  0.487533     0.316016  \n",
       "32830  0.487533     0.316016  \n",
       "32831  0.409401     0.404469  \n",
       "32832  0.409401     0.404469  \n",
       "\n",
       "[32833 rows x 13 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ff247b2-2c4a-4733-8758-a67ddf768b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix(cleaned_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06affcd6-46c7-4fdb-8fef-c13e42d9ff2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['pop', 'rap', 'rock', 'latin', 'r&b', 'edm'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8bc51f4a-c1c3-46ef-baa0-33d113cbaa6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = dict_df['dance pop']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f196fadf-95e0-4059-8f20-3a56ad73da27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = dict_df['post-teen pop']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "65ca997f-de06-4460-998d-7456b7a5c174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['pop']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "94efae22-18e1-4174-a6b5-065341a895b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['rap']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb0ceee2-48a5-44b6-aa35-51527f563ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERIWnmHWPcvRoAUlNTQUA7N69Gw4ODhrHlEqlunDT19dX71coFACynoV7e40pU6agU6dO2a5vaPj3b3t5FVq5KVmyJNq1a4dVq1ahTJky+P333xEREZHna4KDgxEYqPmbR3v37NmIiIgkIZNuzYLCoq2AeHh4QKlUIjExEY0aNcp2/N3WttxUq1YNsbGxKFeuXL7uXaFCBZw5cwa9evVS7ztz5ky28/r374/u3bvjk08+QdmyZVGvXr08r6tUKqFUKjX2aWvXKBERFUMyaSErKCzaCoiZmRmCgoIwatQoqFQq1K9fH0+fPkVkZCTMzc3h7Oz83muEhISgbdu2cHJywmeffQYdHR1ER0fj8uXLmD59eq6vGzZsGAYMGIAaNWqgbt262Lx5My5evAhXV1eN8/z8/GBubo7p06dj6tSpH/2eiYiIqOiw2aQATZs2DZMmTUJYWBjc3d3RsmVL7N69G2XKlPmg1/v5+WHXrl3Yv38/atasiTp16mD+/PnvLfj8/f0RHByMoKAgVKtWDbdu3UJAQIBGlyoA6OjoICAgAJmZmRqtckRERLJUzEaPsqWtACkUCowYMQIjRozI8fg/B+pWrVo12z4/Pz/4+fnleo/cBvtOmjQJkyZNUn/fvHnzHLtZ7969i9atW6sHQRAREckWu0dJbl68eIGlS5fCz88Purq62LhxIw4ePIgDBw6oz3n69CkuXbqEDRs2YOfOnRKmJSIion+DRdt/gEKhwJ49ezBjxgy8evUKFSpUwLZt2+Dr66s+p3379jh9+jQGDRqE5s2bS5iWiIiogLCljeTGyMgIBw8ezPOc903vQUREJDvFbH0ADkQgIiIikgG2tBEREZE8sXuUiIiISAaKWdHG7lEiIiIiGWBLGxEREcmTTCbFLSgs2oiIiEie2D1KREREJANCFNyWT99//z1cXFxgaGiI2rVr4/Tp03mev2DBAlSoUAFGRkZwdHTEqFGj8OrVq3zdk0UbERERUT5s3rwZgYGBCA0Nxfnz51GlShX4+fkhOTk5x/M3bNiA8ePHIzQ0FDExMfjpp5+wefNmfP311/m6L4s2IiIikieVquC2fJg3bx4GDBiAPn36wMPDA0uXLoWxsTFWrlyZ4/nHjx9HvXr10KNHD7i4uKBFixbo3r37e1vn/olFGxEREclTARZt6enpePbsmcaWnp6e7ZYZGRk4d+6cxlKROjo68PX1xYkTJ3KMWbduXZw7d05dpN28eRN79uxB69at8/V2ORCB8uWZKn/970UpY8UUqSPkyWBAqNQR8pTwXYDUEXJ197ql1BHypNS7J3WEPN1M0958KelpUkfIUzWrslJHyFNpI2upI/xnhIWFYcoUzX9HQkNDMXnyZI19Dx8+RGZmJuzs7DT229nZ4dq1azleu0ePHnj48CHq168PIQTevHmDQYMGsXuUiIiIigmhKrAtODgYT58+1diCg4MLJGZERARmzpyJJUuW4Pz589i+fTt2796NadOm5es6bGkjIiIiWRKqglswXqlUQqlUvvc8Gxsb6Orq4v79+xr779+/j1KlSuX4mkmTJqFnz57o378/AMDT0xNpaWkYOHAgJkyYAB2dD2tDY0sbERER0QcyMDBA9erVER4ert6nUqkQHh4OHx+fHF/z4sWLbIWZrq4uAEDkY7oRtrQRERGRPEk0uW5gYCB69+6NGjVqoFatWliwYAHS0tLQp08fAECvXr3g4OCAsLAwAEC7du0wb948eHt7o3bt2oiLi8OkSZPQrl07dfH2IVi0ERERkTxJtIxVt27d8ODBA4SEhODevXuoWrUq9u7dqx6ckJiYqNGyNnHiRCgUCkycOBF3796Fra0t2rVrhxkzZuTrvizaiIiIiPJp6NChGDp0aI7HIiIiNL7X09NDaGgoQkM/bhYBFm1EREQkTwU4EEEOWLQRERGRPBWzBeNZtBEREZE8FbOijVN+EBEREckAW9qIiIhInvIxx9l/AVvactG4cWOMHDmyUO8REBCADh06FOo9iIiI/rMKcMF4OWDRRkRERCQD7B4lIiIieSpmU36wpe0DPHnyBL169UKJEiVgbGyMVq1a4caNG+rjkydPRtWqVTVes2DBAri4uKi/z8zMRGBgICwtLWFtbY2xY8dmW2+scePGGD58OMaOHQsrKyuUKlUKkydP1jgnJSUF/fv3h62tLczNzdG0aVNER0erj0dHR6NJkyYwMzODubk5qlevjrNnzwIAbt++jXbt2qFEiRIwMTFBpUqVsGfPnoL5kIiIiIqaUBXcJgMs2j5AQEAAzp49i507d+LEiRMQQqB169Z4/fr1B19j7ty5WL16NVauXIljx47h8ePH+PXXX7Odt2bNGpiYmODUqVOYM2cOpk6digMHDqiPd+nSBcnJyfj9999x7tw5VKtWDc2aNcPjx48BAP7+/vjkk09w5swZnDt3DuPHj4e+vj4AYMiQIUhPT8cff/yBS5cuYfbs2TA1Nf3IT4eIiIiKArtH3+PGjRvYuXMnIiMjUbduXQDA+vXr4ejoiB07dqBLly4fdJ0FCxYgODgYnTp1AgAsXboU+/bty3ael5eXepkLNzc3LF68GOHh4WjevDmOHTuG06dPIzk5GUqlEgDw7bffYseOHdi6dSsGDhyIxMREjBkzBhUrVlRf463ExER07twZnp6eAABXV9c8M6enpyM9PV1jn0qooKNgrU9ERFqA3aP0rpiYGOjp6aF27drqfdbW1qhQoQJiYmI+6BpPnz5FUlKSxjX09PRQo0aNbOd6eXlpfG9vb4/k5GQAWV2fqampsLa2hqmpqXq7desW4uPjAQCBgYHo378/fH19MWvWLPV+ABg+fDimT5+OevXqITQ0FBcvXswzd1hYGCwsLDS2pNQ7H/SeiYiICptQqQpskwMWbQVAR0cn2/Np+ek6fdfbrsy3FAoFVP//lyk1NRX29vaIiorS2GJjYzFmzBgAWc/XXblyBW3atMGhQ4fg4eGh7obt378/bt68iZ49e+LSpUuoUaMGvvvuu1yzBAcH4+nTpxqbvanjv3pfRERE9HFYtL2Hu7s73rx5g1OnTqn3PXr0CLGxsfDw8AAA2Nra4t69exqFW1RUlPprCwsL2Nvba1zjzZs3OHfuXL6yVKtWDffu3YOenh7KlSunsdnY2KjPK1++PEaNGoX9+/ejU6dOWLVqlfqYo6MjBg0ahO3bt2P06NFYsWJFrvdTKpUwNzfX2Ng1SkREWkMlCm6TAf4L/B5ubm5o3749BgwYgGPHjiE6OhpffPEFHBwc0L59ewBZoz4fPHiAOXPmID4+Ht9//z1+//13jeuMGDECs2bNwo4dO3Dt2jUMHjwYKSkp+cri6+sLHx8fdOjQAfv370dCQgKOHz+OCRMm4OzZs3j58iWGDh2KiIgI3L59G5GRkThz5gzc3d0BACNHjsS+fftw69YtnD9/HocPH1YfIyIikh2OHqV/WrVqFapXr462bdvCx8cHQgjs2bNH3ZXp7u6OJUuW4Pvvv0eVKlVw+vRpBAUFaVxj9OjR6NmzJ3r37g0fHx+YmZmhY8eO+cqhUCiwZ88eNGzYEH369EH58uXx+eef4/bt27Czs4Ouri4ePXqEXr16oXz58ujatStatWqFKVOmAMiadmTIkCFwd3dHy5YtUb58eSxZsqRgPiQiIqKiVsxa2hTinw9jEeWhZumGUkfIVcSwMlJHyJPBgFCpI+SprleA1BFytULfUuoIeWqeEit1hDzpKXSljpCrlPQ0qSPkqZpVWakj5OlFZvr7T5LQhXuRhXr9tKn+BXYtk5D1BXatwsIpP4iIiEieZDLqs6CwaCMiIiJ5kkm3ZkHhM21EREREMsCWNiIiIpInmYz6LCgs2oiIiEie2D1KRERERNqGLW1EREQkS3JZM7SgsGgjIiIieWL3KBERERFpG7a0ERERkTwVs5Y2Fm1EREQkT5zyg4iIiEgGillLG59pIyIiIpIBtrRRvvz18pHUEXIVsLSE1BHylPBdgNQR8nT84mqpI+SqYsXPpI6Qp6ev0qSOkCc3SwepI+TK0sBU6gh5ep75UuoIebr/8onUESQlillLG4s2IiIikqdiVrSxe5SIiIhIBtjSRkRERPLEFRGIiIiIZIDdo0RERESkbdjSRkRERPJUzFraWLQRERGRLAlRvIo2do8SERERyQBb2oiIiEie2D1KREREJAMs2oiIiIi0X3FbxorPtBWShIQEKBQKREVF5XrO6tWrYWlpWWSZiIiISL5YtEmoW7duuH79utQxiIiI5EklCm6TAXaPSsjIyAhGRkZSxyAiIpKn4rWKlbxb2rZu3QpPT08YGRnB2toavr6+SEtLQ0BAADp06IApU6bA1tYW5ubmGDRoEDIyMtSvValUCAsLQ5kyZWBkZIQqVapg69atGte/fPkyWrVqBVNTU9jZ2aFnz554+PChxjXmzJmDcuXKQalUwsnJCTNmzNC4xs2bN9GkSRMYGxujSpUqOHHihPrYP7tHJ0+ejKpVq+Lnn3+Gi4sLLCws8Pnnn+P58+cfnPvJkyfw9/eHra0tjIyM4ObmhlWrVgEAMjIyMHToUNjb28PQ0BDOzs4ICwv7uD8EIiIiKhKyLdqSkpLQvXt39O3bFzExMYiIiECnTp3UE+2Fh4er92/cuBHbt2/HlClT1K8PCwvD2rVrsXTpUly5cgWjRo3CF198gSNHjgAAUlJS0LRpU3h7e+Ps2bPYu3cv7t+/j65du6qvERwcjFmzZmHSpEm4evUqNmzYADs7O42cEyZMQFBQEKKiolC+fHl0794db968yfV9xcfHY8eOHdi1axd27dqFI0eOYNasWR+c+22W33//HTExMfjhhx9gY2MDAFi0aBF27tyJLVu2IDY2FuvXr4eLi8vH/UEQERFJRKhEgW1yINvu0aSkJLx58wadOnWCs7MzAMDT01N93MDAACtXroSxsTEqVaqEqVOnYsyYMZg2bRpev36NmTNn4uDBg/Dx8QEAuLq64tixY1i2bBkaNWqExYsXw9vbGzNnzlRfc+XKlXB0dMT169dhb2+PhQsXYvHixejduzcAoGzZsqhfv75GzqCgILRp0wYAMGXKFFSqVAlxcXGoWLFiju9LpVJh9erVMDMzAwD07NkT4eHhmDFjBtLT09+bOzExEd7e3qhRowYAaBRliYmJcHNzQ/369aFQKNSfW27S09ORnp6usU8IFRQK2db6RET0XyKTYqugyLZoq1KlCpo1awZPT0/4+fmhRYsW+Oyzz1CiRAn1cWNjY/X5Pj4+SE1NxZ07d5CamooXL16gefPmGtfMyMiAt7c3ACA6OhqHDx+GqalptnvHx8cjJSUF6enpaNasWZ45vby81F/b29sDAJKTk3Mt2lxcXNQF29vXJCcnAwDi4uLem/urr75C586dcf78ebRo0QIdOnRA3bp1AQABAQFo3rw5KlSogJYtW6Jt27Zo0aJFrtnDwsI0WicBwFRpA3Ojknm+ZyIiIip4si3adHV1ceDAARw/fhz79+/Hd999hwkTJuDUqVPvfW1qaioAYPfu3XBwcNA4plQq1ee0a9cOs2fPzvZ6e3t73Lx584Ny6uvrq79WKBQAslrTPuT8t695e/6H5G7VqhVu376NPXv24MCBA2jWrBmGDBmCb7/9FtWqVcOtW7fw+++/4+DBg+jatSt8fX2zPcv3VnBwMAIDAzX2VXSq/SFvm4iIqPAVs4EIsi3agKyCpl69eqhXrx5CQkLg7OyMX3/9FUBWS9nLly/VozNPnjwJU1NTODo6wsrKCkqlEomJiWjUqFGO165WrRq2bdsGFxcX6Oll/5jc3NxgZGSE8PBw9O/fv/De5Ds8PDzemxsAbG1t0bt3b/Tu3RsNGjTAmDFj8O233wIAzM3N0a1bN3Tr1g2fffYZWrZsicePH8PKyirbdZRKpboYfItdo0REpC3k8ixaQZFt0Xbq1CmEh4ejRYsWKFmyJE6dOoUHDx7A3d0dFy9eREZGBvr164eJEyciISEBoaGhGDp0KHR0dGBmZoagoCCMGjUKKpUK9evXx9OnTxEZGQlzc3P07t0bQ4YMwYoVK9C9e3eMHTsWVlZWiIuLw6ZNm/Djjz/C0NAQ48aNw9ixY2FgYIB69erhwYMHuHLlCvr161co7/lDcoeEhKB69eqoVKkS0tPTsWvXLri7uwMA5s2bB3t7e3h7e0NHRwe//PILSpUqxQl+iYiIZEC2RZu5uTn++OMPLFiwAM+ePYOzszPmzp2LVq1aYfPmzWjWrBnc3NzQsGFDpKeno3v37pg8ebL69dOmTYOtrS3CwsJw8+ZNWFpaolq1avj6668BAKVLl0ZkZCTGjRuHFi1aID09Hc7OzmjZsiV0dLJamyZNmgQ9PT2EhITgr7/+gr29PQYNGlSo7/t9uQ0MDBAcHIyEhAQYGRmhQYMG2LRpE4Csom/OnDm4ceMGdHV1UbNmTezZs0f9foiIiGSlmHWPKsTbOTL+QwICApCSkoIdO3ZIHeU/x6FEJakj5KqueTmpI+Qp4fUTqSPk6fjF1VJHyFXFip9JHSFPd54nSx0hT26WDu8/SSIqLf8nSF9HV+oIebr/Urt/rtx/eq1Qr/+4Y+6PCuWX1a9HCuxahUW2LW1ERERUzBWzljb2ixERERHJwH+ypW316tVSRyAiIqJCJopZS9t/smgjIiKiYqCYFW3sHiUiIiKSAba0ERERkSyxe5SIiIhIDopZ0cbuUSIiIiIZYEsbERERyVJx6x5lSxsRERHJklAV3JZf33//PVxcXGBoaIjatWvj9OnTeZ6fkpKCIUOGwN7eHkqlEuXLl8eePXvydU+2tBEREZEsSdXStnnzZgQGBmLp0qWoXbs2FixYAD8/P8TGxqJkyZLZzs/IyEDz5s1RsmRJbN26FQ4ODrh9+zYsLS3zdV8WbURERET5MG/ePAwYMAB9+vQBACxduhS7d+/GypUrMX78+Gznr1y5Eo8fP8bx48ehr68PAHBxccn3fdk9SkRERPIkFAW2paen49mzZxpbenp6tltmZGTg3Llz8PX1Ve/T0dGBr68vTpw4kWPMnTt3wsfHB0OGDIGdnR0qV66MmTNnIjMzM19vly1tlC82SgupI+RqSfkUqSPk6e51S6kj5Klixc+kjpCra9e2Sh0hT5+UbS11hDzFPf1L6giy5WpuL3WEPKWkp0kdQVIF2T0aFhaGKVOmaOwLDQ3F5MmTNfY9fPgQmZmZsLOz09hvZ2eHa9eu5Xjtmzdv4tChQ/D398eePXsQFxeHwYMH4/Xr1wgNDf3gjCzaiIiIqNgLDg5GYGCgxj6lUlkg11apVChZsiSWL18OXV1dVK9eHXfv3sU333zDoo2IiIj++4RKUWDXUiqVH1Sk2djYQFdXF/fv39fYf//+fZQqVSrH19jb20NfXx+6urrqfe7u7rh37x4yMjJgYGDwQRn5TBsRERHJkhRTfhgYGKB69eoIDw9X71OpVAgPD4ePj0+Or6lXrx7i4uKgUv19o+vXr8Pe3v6DCzaARRsRERFRvgQGBmLFihVYs2YNYmJi8NVXXyEtLU09mrRXr14IDg5Wn//VV1/h8ePHGDFiBK5fv47du3dj5syZGDJkSL7uy+5RIiIikiUhCq57ND+6deuGBw8eICQkBPfu3UPVqlWxd+9e9eCExMRE6Oj83S7m6OiIffv2YdSoUfDy8oKDgwNGjBiBcePG5eu+LNqIiIhIlqRcxmro0KEYOnRojsciIiKy7fPx8cHJkyc/6p7sHiUiIiKSAba0ERERkSwV5OhROWDRRkRERLIkhNQJihaLNiIiIpKl4tbSxmfaiIiIiGSALW2FLCAgACkpKdixY4fUUYiIiP5TiltLG4u2ApKQkIAyZcrgwoULqFq1qnr/woULIYpbpzsREVERKG7/vLJoK2QWFhZSRyAiIqL/AFk907Z3717Ur18flpaWsLa2Rtu2bREfH68+/ueff6J79+6wsrKCiYkJatSogVOnTqmP//DDDyhbtiwMDAxQoUIF/Pzzz+pjCQkJUCgUiIqKUu9LSUmBQqFQT5L35MkT+Pv7w9bWFkZGRnBzc8OqVasAAGXKlAEAeHt7Q6FQoHHjxgCyukc7dOigvqZKpcKcOXNQrlw5KJVKODk5YcaMGRoZtm/fjiZNmsDY2BhVqlTBiRMnND6HY8eOoUGDBjAyMoKjoyOGDx+OtLQ09fElS5bAzc0NhoaGsLOzw2effaY+tnXrVnh6esLIyAjW1tbw9fXVeC0REZFcCJWiwDY5kFXRlpaWhsDAQJw9exbh4eHQ0dFBx44doVKpkJqaikaNGuHu3bvYuXMnoqOjMXbsWPXirL/++itGjBiB0aNH4/Lly/jyyy/Rp08fHD58+IPvP2nSJFy9ehW///47YmJi8MMPP8DGxgYAcPr0aQDAwYMHkZSUhO3bt+d4jeDgYMyaNUt9rQ0bNqiXvXhrwoQJCAoKQlRUFMqXL4/u3bvjzZs3AID4+Hi0bNkSnTt3xsWLF7F582YcO3ZMPSvz2bNnMXz4cEydOhWxsbHYu3cvGjZsCABISkpC9+7d0bdvX8TExCAiIgKdOnVi9y0REcmSEIoC2+RAVt2jnTt31vh+5cqVsLW1xdWrV3H8+HE8ePAAZ86cgZWVFQCgXLly6nO//fZbBAQEYPDgwQCyFns9efIkvv32WzRp0uSD7p+YmAhvb2/UqFEDAODi4qI+ZmtrCwCwtrZGqVKlcnz98+fPsXDhQixevBi9e/cGAJQtWxb169fXOC8oKAht2rQBAEyZMgWVKlVCXFwcKlasiLCwMPj7+2PkyJEAADc3NyxatAiNGjXCDz/8gMTERJiYmKBt27YwMzODs7MzvL29AWQVbW/evEGnTp3g7OwMAPD09Mz1/aanpyM9PV1jn0qooKOQVa1PRET0nyCrf31v3LiB7t27w9XVFebm5uqiKTExEVFRUfD29lYXbP8UExODevXqaeyrV68eYmJiPvj+X331FTZt2oSqVati7NixOH78eL7yx8TEID09Hc2aNcvzPC8vL/XX9vb2AIDk5GQAQHR0NFavXg1TU1P15ufnB5VKhVu3bqF58+ZwdnaGq6srevbsifXr1+PFixcAgCpVqqBZs2bw9PREly5dsGLFCjx58iTXHGFhYbCwsNDYktPu5us9ExERFRahKrhNDmRVtLVr1w6PHz/GihUrcOrUKfXzahkZGTAyMvqoa+voZH0U73YVvn79WuOcVq1a4fbt2xg1ahT++usvNGvWDEFBQR98jw/NqK+vr/5aochqsn3bzZuamoovv/wSUVFR6i06Oho3btxA2bJlYWZmhvPnz2Pjxo2wt7dHSEgIqlSpgpSUFOjq6uLAgQP4/fff4eHhge+++w4VKlTArVu3cswRHByMp0+famwlTRw++P0SEREVJpVQFNgmB7Ip2h49eoTY2FhMnDgRzZo1g7u7u0YrkZeXF6KiovD48eMcX+/u7o7IyEiNfZGRkfDw8ADwd/dmUlKS+vi7gxLesrW1Re/evbFu3TosWLAAy5cvBwAYGBgAADIzM3N9D25ubjAyMkJ4ePgHvOOcVatWDVevXkW5cuWybW8z6OnpwdfXF3PmzMHFixeRkJCAQ4cOAcgqAuvVq4cpU6bgwoULMDAwwK+//prjvZRKJczNzTU2do0SERFJQzbPtJUoUQLW1tZYvnw57O3tkZiYiPHjx6uPd+/eHTNnzkSHDh0QFhYGe3t7XLhwAaVLl4aPjw/GjBmDrl27wtvbG76+vvjf//6H7du34+DBgwCyWsHq1KmDWbNmoUyZMkhOTsbEiRM1MoSEhKB69eqoVKkS0tPTsWvXLri7uwMASpYsCSMjI+zduxeffPIJDA0Ns033YWhoiHHjxmHs2LEwMDBAvXr18ODBA1y5cgX9+vX7oM9h3LhxqFOnDoYOHYr+/fvDxMQEV69exYEDB7B48WLs2rULN2/eRMOGDVGiRAns2bMHKpUKFSpUwKlTpxAeHo4WLVqgZMmSOHXqFB48eKB+D0RERHIilwEEBUU2zSY6OjrYtGkTzp07h8qVK2PUqFH45ptv1McNDAywf/9+lCxZEq1bt4anpydmzZoFXV1dAECHDh2wcOFCfPvtt6hUqRKWLVuGVatWqafmALIGNrx58wbVq1fHyJEjMX36dI0MBgYGCA4OhpeXFxo2bAhdXV1s2rQJQFbr1qJFi7Bs2TKULl0a7du3z/F9TJo0CaNHj0ZISAjc3d3RrVs39fNqH8LLywtHjhzB9evX0aBBA3h7eyMkJASlS5cGAFhaWmL79u1o2rQp3N3dsXTpUmzcuBGVKlWCubk5/vjjD7Ru3Rrly5fHxIkTMXfuXLRq1eqD709ERKQtituUHwrB+R4oH6qUqit1hFwd9NR//0kSuntduyda7pz2p9QRcnXt2lapI+Tpk7KtpY6Qp5R0zsX4b7ma20sdIU83nyW9/yQJpb+6U6jXj3EruP/33G/sKbBrFRbZtLQRERERFWeyeaaNiIiI6F1y6dYsKCzaiIiISJbkMlVHQWH3KBEREZEMsKWNiIiIZKm4TfnBoo2IiIhkqbjNf8HuUSIiIiIZYEsbERERyVJxG4jAoo2IiIhkqbg908buUSIiIiIZYEsbERERyVJxG4jAoo2IiIhkic+0EeXhWkrhLv77Mcoe1+4F45V696SOkKenr7R3UXFtX5D9z3jtXmi6YsXPpI6QK3M9I6kj5OnS4wSpI+TJ0tBE6giS4jNtRERERKR12NJGREREssTuUSIiIiIZKGbjENg9SkRERCQHbGkjIiIiWWL3KBEREZEMcPQoEREREWkdtrQRERGRLKmkDlDEWLQRERGRLAmwe5SIiIiItAxb2oiIiEiWVMVsojYWbURERCRLKnaP5q5x48YYOXJkIUUpXhISEqBQKBAVFSV1FCIiIlkSUBTYJgf5Ktq2b9+OadOmFciNFQoFduzYUSDXklJERAQUCgVSUlKkjkJERET/YfnqHrWysiqsHDnKyMiAgYFBkd6TiIiI5KG4Tfnxr7tHXVxcMHPmTPTt2xdmZmZwcnLC8uXL1edmZGRg6NChsLe3h6GhIZydnREWFqZ+LQB07NgRCoVC/f3kyZNRtWpV/PjjjyhTpgwMDQ3V5y9YsEAjS9WqVTF58mT19wqFAsuWLUPbtm1hbGwMd3d3nDhxAnFxcWjcuDFMTExQt25dxMfHa1znt99+Q7Vq1WBoaAhXV1dMmTIFb9680bjujz/+iI4dO8LY2Bhubm7YuXMngKwuziZNmgAASpQoAYVCgYCAAADA3r17Ub9+fVhaWsLa2hpt27bNdu93vW2xCw8PR40aNWBsbIy6desiNjb2g/MKITB58mQ4OTlBqVSidOnSGD58uPq1S5YsgZubGwwNDWFnZ4fPPvss1zxERETajt2j+TB37lzUqFEDFy5cwODBg/HVV1+pi4xFixZh586d2LJlC2JjY7F+/Xp1cXbmzBkAwKpVq5CUlKT+HgDi4uKwbds2bN++Pd/Pe02bNg29evVCVFQUKlasiB49euDLL79EcHAwzp49CyEEhg4dqj7/6NGj6NWrF0aMGIGrV69i2bJlWL16NWbMmKFx3SlTpqBr1664ePEiWrduDX9/fzx+/BiOjo7Ytm0bACA2NhZJSUlYuHAhACAtLQ2BgYE4e/YswsPDoaOjg44dO0Klyvv3ggkTJmDu3Lk4e/Ys9PT00Ldv3w/Ou23bNsyfPx/Lli3DjRs3sGPHDnh6egIAzp49i+HDh2Pq1KmIjY3F3r170bBhw3x9vkRERCSdjxo92rp1awwePBgAMG7cOMyfPx+HDx9GhQoVkJiYCDc3N9SvXx8KhQLOzs7q19na2gIALC0tUapUKY1rZmRkYO3atepz8qNPnz7o2rWrOo+Pjw8mTZoEPz8/AMCIESPQp08f9flTpkzB+PHj0bt3bwCAq6srpk2bhrFjxyI0NFR9XkBAALp37w4AmDlzJhYtWoTTp0+jZcuW6i7jkiVLwtLSUv2azp07a2RbuXIlbG1tcfXqVVSuXDnX9zBjxgw0atQIADB+/Hi0adMGr169gqGh4XvzJiYmolSpUvD19YW+vj6cnJxQq1YtAEBiYiJMTEzQtm1bmJmZwdnZGd7e3nl+nunp6UhPT9fYJ4SAQiGP30iIiOi/jd2j+eDl5aX+WqFQoFSpUkhOTgaQVehERUWhQoUKGD58OPbv3/9B13R2dv5XBds/89jZ2QGAuqXp7b5Xr17h2bNnAIDo6GhMnToVpqam6m3AgAFISkrCixcvcryuiYkJzM3N1e8zNzdu3ED37t3h6uoKc3NzdStjYmLiB78He3t7AFDf6315u3TpgpcvX8LV1RUDBgzAr7/+qu46bd68OZydneHq6oqePXti/fr1Gu8xJ2FhYbCwsNDYMjOf5fkaIiKioqIqwE0OPqpo09fX1/heoVCou/+qVauGW7duYdq0aXj58iW6du36Qc9QmZiYZA+powMhNGfQe/36dZ553rYG5bTvbcbU1FRMmTIFUVFR6u3SpUu4ceOG+nm6973P3LRr1w6PHz/GihUrcOrUKZw6dQpAVktiXj4mr6OjI2JjY7FkyRIYGRlh8ODBaNiwIV6/fg0zMzOcP38eGzduhL29PUJCQlClSpU8R70GBwfj6dOnGpuurnme+YmIiKhwFOrkuubm5ujWrRu6deuGzz77DC1btsTjx49hZWUFfX19ZGZmftB1bG1tkZSUpP7+2bNnuHXr1kfnq1atGmJjY1GuXLl/fY23o1vffS+PHj1CbGwsVqxYgQYNGgAAjh079nFh8WF5jYyM0K5dO7Rr1w5DhgxBxYoVcenSJVSrVg16enrw9fWFr68vQkNDYWlpiUOHDqFTp045XkupVEKpVGrsY9coERFpC7kMICgohVa0zZs3D/b29vD29oaOjg5++eUXlCpVSv3cl4uLC8LDw1GvXj0olUqUKFEi12s1bdoUq1evRrt27WBpaYmQkBDo6up+dMaQkBC0bdsWTk5O+Oyzz6Cjo4Po6GhcvnwZ06dP/6BrODs7Q6FQYNeuXWjdujWMjIxQokQJWFtbY/ny5bC3t0diYiLGjx9f6HlXr16NzMxM1K5dG8bGxli3bh2MjIzg7OyMXbt24ebNm2jYsCFKlCiBPXv2QKVSoUKFCh+di4iISAqq4lWzFd6C8WZmZpgzZw5q1KiBmjVrIiEhAXv27IGOTtYt586diwMHDsDR0fG9D8QHBwejUaNGaNu2Ldq0aYMOHTqgbNmyH53Rz88Pu3btwv79+1GzZk3UqVMH8+fP1xg08T4ODg7qAQJ2dnYYOnQodHR0sGnTJpw7dw6VK1fGqFGj8M033xR6XktLS6xYsQL16tWDl5cXDh48iP/973+wtraGpaUltm/fjqZNm8Ld3R1Lly7Fxo0bUalSpY/ORURERIVPIf75sBhRHpSGjlJHyJVSV//9J0lIqafd+Z6+SpM6Qq5KGJpKHSFPf8bvkTpCnipW1N45Gc31jKSOkKdLjxOkjpAnS8Psz4Frk4fPrhfq9X8r1aPArtX+3oYCu1Zh4YLxREREJEvFrdWJRRsRERHJklym6igohfZMGxEREREVHLa0ERERkSypitk0VCzaiIiISJaK2zNt7B4lIiIikgG2tBEREZEsFbeBCCzaiIiISJa4IgIRERERaR0WbURERCRLKigKbMuv77//Hi4uLjA0NETt2rVx+vTpD3rdpk2boFAo0KFDh3zfk0UbERERyZIowC0/Nm/ejMDAQISGhuL8+fOoUqUK/Pz8kJycnOfrEhISEBQUhAYNGuTzjllYtBERERHlw7x58zBgwAD06dMHHh4eWLp0KYyNjbFy5cpcX5OZmQl/f39MmTIFrq6u/+q+HIhA+VLfxl3qCLl6Jd5IHSFPN9PuSR0hT26WDlJHyFXc07+kjpAnbV6QHQCuXdsqdYRclS3fXuoIeTIxMJQ6Qp4yMrX7515hK8iBCOnp6UhPT9fYp1QqoVQqNfZlZGTg3LlzCA4OVu/T0dGBr68vTpw4kev1p06dipIlS6Jfv344evTov8rIljYiIiKSJVUBbmFhYbCwsNDYwsLCst3z4cOHyMzMhJ2dncZ+Ozs73LuX8y/nx44dw08//YQVK1Z81PtlSxsRERHJUkGuiBAcHIzAwECNff9sZfs3nj9/jp49e2LFihWwsbH5qGuxaCMiIqJiL6eu0JzY2NhAV1cX9+/f19h///59lCpVKtv58fHxSEhIQLt27dT7VKqsaYH19PQQGxuLsmXLflBGdo8SERGRLKkUBbd9KAMDA1SvXh3h4eF/51CpEB4eDh8fn2znV6xYEZcuXUJUVJR6+/TTT9GkSRNERUXB0dHxg+/NljYiIiKSJamWsQoMDETv3r1Ro0YN1KpVCwsWLEBaWhr69OkDAOjVqxccHBwQFhYGQ0NDVK5cWeP1lpaWAJBt//uwaCMiIiLKh27duuHBgwcICQnBvXv3ULVqVezdu1c9OCExMRE6OgXfmcmijYiIiGRJygXjhw4diqFDh+Z4LCIiIs/Xrl69+l/dk0UbERERyZLggvFEREREpG3Y0kZERESyJGX3qBRYtBEREZEsFbeijd2jRERERDLAoi0fGjdujJEjRwIAXFxcsGDBAknzEBERFWeiADc5YPfov3TmzBmYmJhIHYOIiKjYys9KBv8FLNr+JVtbW6kjEBERFWt8po0+yLvdoz169EC3bt00jr9+/Ro2NjZYu3YtgKx1ycLCwlCmTBkYGRmhSpUq2Lp1q/r8iIgIKBQKhIeHo0aNGjA2NkbdunURGxurcd3ffvsN1apVg6GhIVxdXTFlyhS8efMGACCEwOTJk+Hk5ASlUonSpUtj+PDh6tcuWbIEbm5uMDQ0hJ2dHT777LPC+GiIiIioELClrQD4+/ujS5cuSE1NhampKQBg3759ePHiBTp27AgACAsLw7p167B06VK4ubnhjz/+wBdffAFbW1s0atRIfa0JEyZg7ty5sLW1xaBBg9C3b19ERkYCAI4ePYpevXph0aJFaNCgAeLj4zFw4EAAQGhoKLZt24b58+dj06ZNqFSpEu7du4fo6GgAwNmzZzF8+HD8/PPPqFu3Lh4/foyjR48W5cdERERUoIpbSxuLtgLg5+cHExMT/Prrr+jZsycAYMOGDfj0009hZmaG9PR0zJw5EwcPHoSPjw8AwNXVFceOHcOyZcs0irYZM2aovx8/fjzatGmDV69ewdDQEFOmTMH48ePRu3dv9TWmTZuGsWPHIjQ0FImJiShVqhR8fX2hr68PJycn1KpVC0DWOmgmJiZo27YtzMzM4OzsDG9v7zzfV3p6OtLT0zX2qYQKOgo20BIRkfTkMoCgoPBf3wKgp6eHrl27Yv369QCAtLQ0/Pbbb/D39wcAxMXF4cWLF2jevDlMTU3V29q1axEfH69xLS8vL/XX9vb2AIDk5GQAQHR0NKZOnapxjQEDBiApKQkvXrxAly5d8PLlS7i6umLAgAH49ddf1V2nzZs3h7OzM1xdXdGzZ0+sX78eL168yPN9hYWFwcLCQmNLeH6rYD40IiIiyhe2tBUQf39/NGrUCMnJyThw4ACMjIzQsmVLAEBqaioAYPfu3XBwcNB4nVKp1PheX19f/bVCkTUsRqVSqa8zZcoUdOrUKdv9DQ0N4ejoiNjYWBw8eBAHDhzA4MGD8c033+DIkSMwMzPD+fPnERERgf379yMkJASTJ0/GmTNnYGlpmeN7Cg4ORmBgoMa+9u7Z701ERCQFjh6lf6Vu3bpwdHTE5s2b8fvvv6NLly7qAszDwwNKpRKJiYkaXaH5Va1aNcTGxqJcuXK5nmNkZIR27dqhXbt2GDJkCCpWrIhLly6hWrVq0NPTg6+vL3x9fREaGgpLS0scOnQoxyIQyCoo/1lUsmuUiIi0BZ9po3+tR48eWLp0Ka5fv47Dhw+r95uZmSEoKAijRo2CSqVC/fr18fTpU0RGRsLc3Fz9jNr7hISEoG3btnBycsJnn30GHR0dREdH4/Lly5g+fTpWr16NzMxM1K5dG8bGxli3bh2MjIzg7OyMXbt24ebNm2jYsCFKlCiBPXv2QKVSoUKFCoX1cRAREVEBYrNJAfL398fVq1fh4OCAevXqaRybNm0aJk2ahLCwMLi7u6Nly5bYvXs3ypQp88HX9/Pzw65du7B//37UrFkTderUwfz58+Hs7AwAsLS0xIoVK1CvXj14eXnh4MGD+N///gdra2tYWlpi+/btaNq0Kdzd3bF06VJs3LgRlSpVKtDPgIiIqKgUtxURFEIIuWQlLdDskxZSR8jVK/FG6gh5upl2T+oIebJSmkkdIVdxT/+SOkKePjHV7sm2r13b+v6TJFK2fHupI+TpaUbeA7akpu3/hD9Lu1mo15/h7F9g15pwe32BXauwsKWNiIiISAb4TBsRERHJEgciEBEREcmAdncOFzwWbURERCRLxa2ljc+0EREREckAW9qIiIhIlrgiAhEREZEMqIrZU23sHiUiIiKSAba0ERERkSwVr3Y2Fm1EREQkUxw9SkRERERahy1tREREJEvFbSACizbKl4UGBlJHyFWvl9q9sHNKeprUEfJkaWAqdQTZMtczkjpCnrR5Ufb4679JHSFPJg4NpY6QpzZ23lJHkFTxKtnYPUpEREQkC2xpIyIiIlkqbgMRWLQRERGRLPGZNiIiIiIZKF4lG59pIyIiIpIFtrQRERGRLPGZNiIiIiIZEMWsg5Tdo0REREQywJY2IiIikiV2jxIRERHJQHGb8oPdo0REREQywJY2IiIikqXi1c7GljZJuLi4YMGCBVLHICIikjUVRIFtcsCijYiIiEgG2D1KREREslTcRo+ypS2fli9fjtKlS0Ol0vyr0r59e/Tt2xfx8fFo37497OzsYGpqipo1a+LgwYN5XjMlJQX9+/eHra0tzM3N0bRpU0RHR6uPT548GVWrVsXPP/8MFxcXWFhY4PPPP8fz58/V56hUKsyZMwflypWDUqmEk5MTZsyYoT5+584ddO3aFZaWlrCyskL79u2RkJBQMB8KERGRBEQB/icHLNryqUuXLnj06BEOHz6s3vf48WPs3bsX/v7+SE1NRevWrREeHo4LFy6gZcuWaNeuHRITE/O8ZnJyMn7//XecO3cO1apVQ7NmzfD48WP1OfHx8dixYwd27dqFXbt24ciRI5g1a5b6eHBwMGbNmoVJkybh6tWr2LBhA+zs7AAAr1+/hp+fH8zMzHD06FFERkbC1NQULVu2REZGRiF8SkRERIVPVYCbHLB7NJ9KlCiBVq1aYcOGDWjWrBkAYOvWrbCxsUGTJk2go6ODKlWqqM+fNm0afv31V+zcuRNDhw7Ndr1jx47h9OnTSE5OhlKpBAB8++232LFjB7Zu3YqBAwcCyGpJW716NczMzAAAPXv2RHh4OGbMmIHnz59j4cKFWLx4MXr37g0AKFu2LOrXrw8A2Lx5M1QqFX788UcoFAoAwKpVq2BpaYmIiAi0aNEix/eanp6O9PR0jX0ZIhMGCt1//fkRERHRv8OWtn/B398f27ZtUxc069evx+effw4dHR2kpqYiKCgI7u7usLS0hKmpKWJiYnJtaYuOjkZqaiqsra1hamqq3m7duoX4+Hj1eS4uLuqCDQDs7e2RnJwMAIiJiUF6erq6iMzpHnFxcTAzM1Nf38rKCq9evdK4xz+FhYXBwsJCY/sxJffziYiIilJx6x5lS9u/0K5dOwghsHv3btSsWRNHjx7F/PnzAQBBQUE4cOAAvv32W5QrVw5GRkb47LPPcu2GTE1Nhb29PSIiIrIds7S0VH+tr6+vcUyhUKifqzMyMsozb2pqKqpXr47169dnO2Zra5vr64KDgxEYGKixL65KtzzvRUREVFTk0q1ZUFi0/QuGhobo1KkT1q9fj7i4OFSoUAHVqlUDAERGRiIgIAAdO3YEkFUw5fXAf7Vq1XDv3j3o6enBxcXlX+Vxc3ODkZERwsPD0b9//xzvsXnzZpQsWRLm5uYffF2lUqnusn2LXaNERETSYPfov+Tv74/du3dj5cqV8Pf3V+93c3PD9u3bERUVhejoaPTo0SPbSNN3+fr6wsfHBx06dMD+/fuRkJCA48ePY8KECTh79uwHZTE0NMS4ceMwduxYrF27FvHx8Th58iR++ukndVYbGxu0b98eR48exa1btxAREYHhw4fjzz///LgPgoiISCIqIQpskwO2tP1LTZs2hZWVFWJjY9GjRw/1/nnz5qFv376oW7cubGxsMG7cODx79izX6ygUCuzZswcTJkxAnz598ODBA5QqVQoNGzZUj/78EJMmTYKenh5CQkLw119/wd7eHoMGDQIAGBsb448//sC4cePQqVMnPH/+HA4ODmjWrFm+Wt6IiIi0iTxKrYKjEEIm5SVphcuubaWOkKteL1OkjpCnmJQ7UkfIk4vZh/+SUNRuPkuSOkKePCydpI6Qp0cZuf/iKLX4679JHSFPJg4NpY6QpzZ23lJHyNOvif8r1Ot/4dypwK617vb2ArtWYWFLGxEREcmSXNYMLSgs2oiIiEiW5DJVR0HhQAQiIiIiGWBLGxEREckS52kjIiIikgE+00ZEREQkA3ymjYiIiIi0DlvaiIiISJb4TBsRERGRDBS39QHYPUpERESUT99//z1cXFxgaGiI2rVr4/Tp07meu2LFCjRo0AAlSpRAiRIl4Ovrm+f5uWHRRkRERLKkgiiwLT82b96MwMBAhIaG4vz586hSpQr8/PyQnJyc4/kRERHo3r07Dh8+jBMnTsDR0REtWrTA3bt383VfFm1EREQkS6oC3NLT0/Hs2TONLT09Pcf7zps3DwMGDECfPn3g4eGBpUuXwtjYGCtXrszx/PXr12Pw4MGoWrUqKlasiB9//BEqlQrh4eH5er98po3yZZ7KUOoIuTLX1d5sAFDNqqzUEfL0PPOl1BFy5WpuL3WEPF16nCB1hDyZGGjv/xvaviB72t0/pI6QJ9NPGkkd4T8jLCwMU6ZM0dgXGhqKyZMna+zLyMjAuXPnEBwcrN6no6MDX19fnDhx4oPu9eLFC7x+/RpWVlb5ysiijYiIiGSpIOdpCw4ORmBgoMY+pVKZ7byHDx8iMzMTdnZ2Gvvt7Oxw7dq1D7rXuHHjULp0afj6+uYrI4s2IiIikqWCXBFBqVTmWKQVtFmzZmHTpk2IiIiAoWH+WsFZtBERERF9IBsbG+jq6uL+/fsa++/fv49SpUrl+dpvv/0Ws2bNwsGDB+Hl5ZXve3MgAhEREcmSEKLAtg9lYGCA6tWrawwieDuowMfHJ9fXzZkzB9OmTcPevXtRo0aNf/V+2dJGREREsiTVigiBgYHo3bs3atSogVq1amHBggVIS0tDnz59AAC9evWCg4MDwsLCAACzZ89GSEgINmzYABcXF9y7dw8AYGpqClNT0w++L4s2IiIikiWpFozv1q0bHjx4gJCQENy7dw9Vq1bF3r171YMTEhMToaPzd2fmDz/8gIyMDHz22Wca18lpdGpeWLQRERER5dPQoUMxdOjQHI9FRERofJ+QkFAg92TRRkRERLJUkKNH5YBFGxEREckSF4wnIiIiIq3DljYiIiKSJXaPEhEREcmAVKNHpcLuUSIiIiIZYNFWiBo3boyRI0dKHYOIiOg/SSVEgW1ywO5RIiIikiV5lFoFhy1thSQgIABHjhzBwoULoVAooFAokJCQgMuXL6NVq1YwNTWFnZ0devbsiYcPH6pf17hxYwwbNgwjR45EiRIlYGdnhxUrVqiXxzAzM0O5cuXw+++/q18TEREBhUKB3bt3w8vLC4aGhqhTpw4uX76skWnbtm2oVKkSlEolXFxcMHfu3CL7PIiIiOjjsGgrJAsXLoSPjw8GDBiApKQkJCUlwczMDE2bNoW3tzfOnj2LvXv34v79++jatavGa9esWQMbGxucPn0aw4YNw1dffYUuXbqgbt26OH/+PFq0aIGePXvixYsXGq8bM2YM5s6dizNnzsDW1hbt2rXD69evAQDnzp1D165d8fnnn+PSpUuYPHkyJk2ahNWrVxfVR0JERFSgVBAFtsmBQhS3memKUOPGjVG1alUsWLAAADB9+nQcPXoU+/btU5/z559/wtHREbGxsShfvjwaN26MzMxMHD16FACQmZkJCwsLdOrUCWvXrgUA3Lt3D/b29jhx4gTq1KmDiIgINGnSBJs2bUK3bt0AAI8fP8Ynn3yC1atXo2vXrvD398eDBw+wf/9+9b3Hjh2L3bt348qVKznmT09PR3p6usa+YZ69oavQLbDPqCDdfJMidYQ8vRZSLW38YZ5nvpQ6Qq5eqzKljpCnGyl3pY6QJxMDQ6kj5Orl6/T3nyShtLt/SB0hT6afNJI6Qp7SX90p1Ov7ODQpsGuduHu4wK5VWNjSVoSio6Nx+PBhmJqaqreKFSsCAOLj49XneXl5qb/W1dWFtbU1PD091fveLkibnJyscX0fHx/111ZWVqhQoQJiYmIAADExMahXr57G+fXq1cONGzeQmZnzP4hhYWGwsLDQ2C4+jf03b52IiKjACSEKbJMDDkQoQqmpqWjXrh1mz56d7Zi9vb36a319fY1jCoVCY59CoQAAqFSF23ITHByMwMBAjX3DPHsX6j2JiIgoZyzaCpGBgYFGK1a1atWwbds2uLi4QE+v4D/6kydPwsnJCQDw5MkTXL9+He7u7gAAd3d3REZGapwfGRmJ8uXLQ1c35+5OpVIJpVKpsU9bu0aJiKj4kcuzaAWF3aOFyMXFBadOnUJCQgIePnyIIUOG4PHjx+jevTvOnDmD+Ph47Nu3D3369Mm1izI/pk6divDwcFy+fBkBAQGwsbFBhw4dAACjR49GeHg4pk2bhuvXr2PNmjVYvHgxgoKCPvq+REREUhAF+J8csGgrREFBQdDV1YWHhwdsbW2RkZGByMhIZGZmokWLFvD09MTIkSNhaWkJHZ2P/6OYNWsWRowYgerVq+PevXv43//+BwMDAwBZrXxbtmzBpk2bULlyZYSEhGDq1KkICAj46PsSERFR4ePo0f+At6NHnzx5AktLy0K9V1+Xzwr1+h+Do0c/DkeP/nscPfrvcfToxynuo0dr2DcosGudTTpaYNcqLHymjYiIiGSJz7QRERERkdZhS9t/QOPGjWUzxwwREVFBKW7/9rFoIyIiIlli9ygRERERaR22tBEREZEsyWV+tYLCoo2IiIhkScVn2oiIiIi0X3FraeMzbUREREQywJY2IiIikiV2jxIRERHJALtHiYiIiEjrsKWN8iVNvJE6Qq5inv8pdYQ8lTayljpCnu6/fCJ1hFylpKdJHSFPloYmUkfIU0am9v5/28bOW+oIedL2BdlT/zwidQRJsXuUiIiISAbYPUpEREREWoctbURERCRL7B4lIiIikgF2jxIRERGR1mFLGxEREcmSECqpIxQpFm1EREQkS6pi1j3Koo2IiIhkSRSzgQh8po2IiIhIBtjSRkRERLLE7lEiIiIiGWD3KBERERFpnf9M0da4cWOMHDlSkntHRERAoVAgJSVFkvsTEREVRyohCmyTg/9M0VZUcioO69ati6SkJFhYWEgTioiIqBgSBfifHLBo+3+vX7/+1681MDBAqVKloFAoCjARERER0d9kWbSlpaWhV69eMDU1hb29PebOnatxXKFQYMeOHRr7LC0tsXr1agBAQkICFAoFNm/ejEaNGsHQ0BDr16/Ho0eP0L17dzg4OMDY2Bienp7YuHGj+hoBAQE4cuQIFi5cCIVCAYVCgYSEhBy7R7dt24ZKlSpBqVTCxcUlW0YXFxfMnDkTffv2hZmZGZycnLB8+fIPev9v82/ZsgUNGjSAkZERatasievXr+PMmTOoUaMGTE1N0apVKzx48ED9uoiICNSqVQsmJiawtLREvXr1cPv27Q+6JxERkbYRQhTYJgeyLNrGjBmDI0eO4LfffsP+/fsRERGB8+fP5/s648ePx4gRIxATEwM/Pz+8evUK1atXx+7du3H58mUMHDgQPXv2xOnTpwEACxcuhI+PDwYMGICkpCQkJSXB0dEx23XPnTuHrl274vPPP8elS5cwefJkTJo0SV00vjV37lzUqFEDFy5cwODBg/HVV18hNjb2g/OHhoZi4sSJOH/+PPT09NCjRw+MHTsWCxcuxNGjRxEXF4eQkBAAwJs3b9ChQwc0atQIFy9exIkTJzBw4EC2DhIRkWypIApskwPZTfmRmpqKn376CevWrUOzZs0AAGvWrMEnn3yS72uNHDkSnTp10tgXFBSk/nrYsGHYt28ftmzZglq1asHCwgIGBgYwNjZGqVKlcr3uvHnz0KxZM0yaNAkAUL58eVy9ehXffPMNAgIC1Oe1bt0agwcPBgCMGzcO8+fPx+HDh1GhQoUPyh8UFAQ/Pz8AwIgRI9C9e3eEh4ejXr16AIB+/fqpC8Vnz57h6dOnaNu2LcqWLQsAcHd3z/P66enpSE9P19iXKTKhq9D9oHxERERUcGTX0hYfH4+MjAzUrl1bvc/KyuqDC5131ahRQ+P7zMxMTJs2DZ6enrCysoKpqSn27duHxMTEfF03JiZGXTi9Va9ePdy4cQOZmZnqfV5eXuqvFQoFSpUqheTk5A++z7uvt7OzAwB4enpq7Ht7PSsrKwQEBMDPzw/t2rXDwoULkZSUlOf1w8LCYGFhobHFPL3xwfmIiIgKE7tH/wMUCkW2P4CcBhqYmJhofP/NN99g4cKFGDduHA4fPoyoqCj4+fkhIyOjUHLq6+trfK9QKKBSqf7V6992c/5z37vXW7VqFU6cOIG6deti8+bNKF++PE6ePJnr9YODg/H06VONzd3C7YPzERERFSZO+aHlypYtC319fZw6dUq978mTJ7h+/br6e1tbW41WpBs3buDFixfvvXZkZCTat2+PL774AlWqVIGrq6vGdYGskaLvtpblxN3dHZGRkdmuXb58eejqStu16O3tjeDgYBw/fhyVK1fGhg0bcj1XqVTC3NxcY2PXKBERaYvi1tImu2faTE1N0a9fP4wZMwbW1tYoWbIkJkyYAB2dv+vPpk2bYvHixfDx8UFmZibGjRuXrVUrJ25ubti6dSuOHz+OEiVKYN68ebh//z48PDzU57i4uODUqVNISEiAqakprKyssl1n9OjRqFmzJqZNm4Zu3brhxIkTWLx4MZYsWVIwH8K/cOvWLSxfvhyffvopSpcujdjYWNy4cQO9evWSLBMRERF9ONkVbUBWN2ZqairatWsHMzMzjB49Gk+fPlUfnzt3Lvr06YMGDRqgdOnSWLhwIc6dO/fe606cOBE3b96En58fjI2NMXDgQHTo0EHj2kFBQejduzc8PDzw8uVL3Lp1K9t1qlWrhi1btiAkJATTpk2Dvb09pk6dqjEIoagZGxvj2rVrWLNmDR49egR7e3sMGTIEX375pWSZiIiIPoZcRn0WFIWQS5sgaYVuzh2kjpCriJRrUkfIU2kja6kj5Omvl4+kjpCrlPQ0qSPkyczASOoIecrIfCN1hFw1s64kdYQ87UmOkjpCnlL/PCJ1hDzp27gW6vXNTQru+s/SbhbYtQqL7J5pIyIiIiqOWLRpoZkzZ8LU1DTHrVWrVlLHIyIi0grFbfSoLJ9p+68bNGgQunbtmuMxIyPt7oYhIiIqKnJZ6L2gsGjTQlZWVjmOSiUiIqLii0UbERERyZJcujULCos2IiIikqXiNgEGByIQERERyQBb2oiIiEiWOBCBiIiISAaKW/coizYiIiKSpeJWtPGZNiIiIiIZYEsbERERyVLxamcDIIgk8urVKxEaGipevXoldZRstDmbEMz3MbQ5mxDM9zG0OZsQzEcfTyFEMesQJq3x7NkzWFhY4OnTpzA3N5c6jgZtzgYw38fQ5mwA830Mbc4GMB99PD7TRkRERCQDLNqIiIiIZIBFGxEREZEMsGgjySiVSoSGhkKpVEodJRttzgYw38fQ5mwA830Mbc4GMB99PA5EICIiIpIBtrQRERERyQCLNiIiIiIZYNFGREREJAMs2oiIiIhkgEUbERERkQywaCMiIiKSARZtVGR69+6NP/74Q+oYuWrUqBHWrl2Lly9fSh3lPyElJUXqCGovX77Eixcv1N/fvn0bCxYswP79+yVMRQXlzZs3OHjwIJYtW4bnz58DAP766y+kpqZKnOxvQgho4wxbd+7cwZ9//qn+/vTp0xg5ciSWL18uYSrKDYs2KjJPnz6Fr68v3NzcMHPmTNy9e1fqSBq8vb0RFBSEUqVKYcCAATh58qTUkTTs3bsXx44dU3///fffo2rVqujRoweePHkiYTJg9uzZ2Lx5s/r7rl27wtraGg4ODoiOjpYwWZb27dtj7dq1ALKKydq1a2Pu3Llo3749fvjhB4nTZffs2TPs2LEDMTExUkfJkTblu337Njw9PdG+fXsMGTIEDx48AJD1dzIoKEjidMDatWvh6ekJIyMjGBkZwcvLCz///LPUsdR69OiBw4cPAwDu3buH5s2b4/Tp05gwYQKmTp0qcTrKRhAVoeTkZDF37lzh5eUl9PT0RMuWLcUvv/wiMjIypI4mhBDi9evXYtu2beLTTz8V+vr6wt3dXXzzzTfi3r17UkcTlStXFrt37xZCCHHx4kWhVCpFcHCwqFOnjggICJA0m4uLi4iMjBRCCLF//35haWkp9u3bJ/r16yeaN28uaTYhhLC2thaXL18WQgixYsUK4eXlJTIzM8WWLVtExYoVJU4nRJcuXcR3330nhBDixYsXws3NTejr6ws9PT2xdetWidNpd7727duLL774QqSnpwtTU1MRHx8vhBDi8OHDoly5cpJmmzt3rjA2NhZjx44Vv/32m/jtt9/EmDFjhLGxsZg3b56k2d6ytLQU165dE0IIsXDhQlG3bl0hhBD79u0TZcqUkTIa5YBFG0nm3LlzYujQocLQ0FDY2NiIkSNHiuvXr0sdS+3+/fti2rRpwtDQUOjr64v27duL8PBwyfKYmJiIW7duCSGECA0NFZ07dxZCZH2OdnZ2kuUSQghDQ0ORmJgohBBi+PDhYuDAgUIIIWJjY4WlpaWU0YQQQhgZGYnbt28LIbIKkMmTJwshhEhMTBRGRkZSRhNCCGFnZyeioqKEEEKsX79elCtXTqSlpYklS5aIqlWrSpxOu/NZWVmpi453i7Zbt25J/mfr4uIi1qxZk23/6tWrhYuLiwSJsnv350q7du3ErFmzhBBC3L59WxgaGkqYjHLC7lGSRFJSEg4cOIADBw5AV1cXrVu3xqVLl+Dh4YH58+dLHQ+nT59GaGgo5s6di5IlSyI4OBg2NjZo27atZF0uBgYG6ueyDh48iBYtWgAArKys8OzZM0kyvVWiRAncuXMHQFY3rq+vL4Cs53gyMzOljAYAKFeuHHbs2IE7d+5g37596s8uOTkZ5ubmEqfLenTAysoKQNbn17lzZxgbG6NNmza4ceOGxOm0O59Kpcrx79iff/4JMzMzCRL9LSkpCXXr1s22v27dukhKSpIgUXaVKlXC0qVLcfToURw4cAAtW7YEkPVMoLW1tcTpKBupq0YqPjIyMsTWrVtFmzZthL6+vqhevbr44YcfxNOnT9XnbN++XbKWmfv374tvv/1WVKpUSRgYGIjOnTuL33//XahUKvU5R48eFSYmJpLka9eunfDz8xNTp04V+vr64s8//xRCZHVjuLm5SZLprSFDhghnZ2fh6+srrK2txfPnz4UQQmzcuFF4e3tLmk0IIX755Rehr68vdHR0NLprZ86cKVq2bClhsixubm5i8+bNIjU1Vdja2qpbdKOiooS1tbXE6bQ7X9euXcWAAQOEEFktbTdv3hTPnz8XTZs2lfyxgUqVKokZM2Zk2z9t2jRRuXJlCRJld/jwYWFpaSl0dHREnz591PuDg4NFx44dJUxGOWHRRkXG2tpalChRQgwePFhcuHAhx3OePHkiWbeBvr6+qFixopgzZ45ITk7O8ZynT5+Kxo0bF3GyLLdv3xZt2rQRXl5e4scff1TvHzlypBg2bJgkmd7KyMgQ33zzjRg+fLg4f/68ev+8efPEihUrJEz2t6SkJHH+/HmRmZmp3nfq1CkRExMjYaos33//vdDT0xOWlpaiSpUq6oyLFi2S7O/bu7Q53507d4SHh4dwd3cXenp6ok6dOsLa2lpUqFBB3L9/X9JsW7duFbq6uupftqZOnSr8/PyEnp6e2L59u6TZ3vXmzRvx+PFjjX23bt2S/POj7BRCaOEYZPpP+vnnn9GlSxcYGhpKHSVHR48eRYMGDaSOQUXg2bNnOHToECpUqAB3d3ep4wAAzp49izt37qB58+YwNTUFAOzevRuWlpaoV6+exOm0O9+bN2+wefNmREdHIzU1FdWqVYO/vz+MjIwkzQUA586dw/z589Ujbd3d3TF69Gh4e3tLnIzkiEUbFZm+ffti4cKF2Z4zSUtLw7Bhw7By5UqJksnD+fPnoa+vD09PTwDAb7/9hlWrVsHDwwOTJ0+GgYGBZNnWrFkDGxsbtGnTBgAwduxYLF++HB4eHti4cSOcnZ0lywZkTUHSsGFDDB06FC9fvkSVKlWQkJAAIQQ2bdqEzp07S5rvnzIzM3Hp0iU4OzujRIkSUsfJRtvz0Yd79OgRQkJCcPjwYSQnJ0OlUmkcf/z4sUTJKCcs2qjI6OrqIikpCSVLltTY//DhQ5QqVQpv3ryRKFkWb29vKBSKbPsVCgUMDQ1Rrlw5BAQEoEmTJhKkA2rWrInx48ejc+fOuHnzJipVqoSOHTvizJkzaNOmDRYsWCBJLgCoUKECfvjhBzRt2hQnTpyAr68v5s+fj127dkFPTw/bt2+XLBsAlCpVCvv27UOVKlWwYcMGhIaGIjo6GmvWrMHy5ctx4cIFSfONHDkSnp6e6NevHzIzM9GoUSMcP34cxsbG2LVrFxo3bsx8uQgLC4OdnR369u2rsX/lypV48OABxo0bJ1GyLJmZmfj111/VLW0eHh5o37499PT0JM31VuvWrREXF4d+/frBzs4u28/A3r17S5SMciRh1ywVE0+fPhUpKSlCoVCIuLg48fTpU/X2+PFjsWbNGmFvby91TDF+/HhhYWEh6tevLwIDA0VgYKBo0KCBsLCwECNGjBDNmzcXOjo6YseOHZLkMzc3F3FxcUIIIWbNmiVatGghhBDi2LFj4pNPPpEk01vvTqkxduxY0bNnTyGEEJcvXxY2NjZSRhNCaE5J0rNnTzFu3DghRNZzglINLHmXg4ODOHPmjBBCiF9//VWULl1axMbGiokTJ6rnzZKSNudzdnZWzxH4rpMnT0o+rcbly5eFq6urMDY2Ft7e3sLb21uYmJgIFxcXcenSJUmzvWVqaqqezoW0n3aU+vSfZmlpCYVCAYVCgfLly2c7rlAoMGXKFAmSaXr48CFGjx6NSZMmaeyfPn06bt++jf379yM0NBTTpk1D+/btizyfEELddXHw4EG0bdsWAODo6IiHDx8WeZ53mZqa4tGjR3BycsL+/fsRGBgIADA0NNSKZcEcHR1x4sQJWFlZYe/evdi0aRMA4MmTJ1rxjOXb1mYA2LNnD7p06YLy5curHymQmjbnu3fvHuzt7bPtt7W1lXxajf79+6NSpUo4e/asuhv5yZMnCAgIwMCBA3H8+HFJ8wFAxYoVteL/UfowLNqo0B0+fBhCCDRt2hTbtm1Tz/cEZM095uzsjNKlS0uYMMuWLVtw7ty5bPs///xzVK9eHStWrED37t0xb948CdIBNWrUwPTp0+Hr64sjR46ol1+6desW7OzsJMn0VvPmzdG/f394e3vj+vXraN26NQDgypUrcHFxkTQbkNW95+/vD1NTUzg5Oam78/744w/1M4JSsrOzw9WrV2Fvb4+9e/eq/2xfvHgBXV1didNpdz5HR0dERkaiTJkyGvsjIyMl/7kSFRWlUbABWXMazpgxAzVr1pQw2d+WLFmC8ePHIyQkBJUrV4a+vr7GcW2Yx5D+xqKNCl2jRo0AZBUXTk5OOT43pg0MDQ1x/PhxlCtXTmP/8ePH1a0xKpVKspaZBQsWwN/fHzt27MCECRPUObdu3ZrjBJ5F6fvvv8fEiRNx584dbNu2TT0p57lz59C9e3dJswHA4MGDUatWLfXoRx2drHnFXV1dMX36dInTAX369EHXrl1hb28PhUKhnpz41KlTqFixosTptDvfgAEDMHLkSLx+/RpNmzYFAISHh2Ps2LEYPXq0pNnKly+P+/fvo1KlShr7k5OTs/2ckYqlpSWePXum/uzeEkJAoVBoxeTY9DcORKBCdfHiRVSuXBk6Ojq4ePFinud6eXkVUaqcTZ8+HTNnzsSAAQPUvwWfOXMGP/74I77++mtMmDAB8+fPx549e3DgwAFJs77r1atX0NXVzfYbMmWXkZGBW7duoWzZslrzIPhbW7duxZ07d9ClSxd88sknALJG5VpaWkrSHf9P2ppPCIHx48dj0aJFyMjIAJD1C9i4ceMQEhIiWS4gqyt57NixmDx5MurUqQMAOHnyJKZOnYpZs2ahfv366nOlatGqVasW9PT0MGLEiBwHIrz9pZu0A4s2KlQ6Ojq4d+8eSpYsCR0dHSgUCuT0V05bfqNbv349Fi9ejNjYWABZoyKHDRuGHj16AABevnypHk0qhZSUFGzduhXx8fEYM2YMrKyscP78edjZ2cHBwUGSTG8dPXoUy5Ytw82bN/HLL7/AwcEBP//8M8qUKaPxj5MUXrx4gWHDhmHNmjUAgOvXr8PV1RXDhg2Dg4MDxo8fL2m+d7169UornrPLjbbmS01NRUxMDIyMjODm5galUil1JHWLLgB1MfT259+730v588/Y2BgXLlxAhQoVJLk/5Y92/apJ/zm3bt2Cra2t+mtt9ebNG8ycORN9+/aFv79/rudJOVnnxYsX0axZM1haWiIhIQEDBgyAlZUVtm/fjsTERKxdu1aybNu2bUPPnj3h7++P8+fPIz09HUDWmpUzZ87Enj17JMsGAMHBwYiOjkZERIR6bUUA8PX1xeTJkyUv2jIzMzFz5kwsXboU9+/fVxeVkyZNgouLC/r168d872Fqaqo1z4m9dfjwYakjvFeNGjVw584dFm1yIdGoVSKtY2JiIm7duiV1jFw1a9ZMjBkzRgiRNUw/Pj5eCCFEZGSkcHZ2ljCZEFWrVhVr1qwRQmhmO3/+vLCzs5MymhBCCCcnJ3HixAkhhGa+GzduCDMzMymjCSGEmDJlinB1dRXr1q0TRkZG6nybNm0SderUkTiddudLTU0VEydOFD4+PqJs2bKiTJkyGhvlbcuWLcLDw0OsWrVKnD17VkRHR2tspF3Y0kaFaufOnR987qefflqISd6vWbNmOHLkiFaMdszJmTNnsGzZsmz7HRwccO/ePQkS/S02NhYNGzbMtt/CwgIpKSlFH+gfHjx4kG1SZyBrNQ5tGBizdu1aLF++HM2aNcOgQYPU+6tUqYJr165JmCyLNufr378/jhw5gp49e6oHSmiTV69e4eLFizmuNiD1zzwA6NatGwBoTE789jEWbXlshf7Goo0KVYcOHT7oPG344dCqVSuMHz8ely5dQvXq1WFiYqJxXOofsEqlEs+ePcu2//r16+ouaKmUKlUKcXFx2QreY8eOwdXVVZpQ76hRowZ2796NYcOGAfj7eaIff/wRPj4+UkYDANy9ezfH0YQqlQqvX7+WIJEmbc73+++/Y/fu3ZKvf5qTvXv3olevXjnOo6gNP/MA7X5shbJj0UaF6p+/WWqzwYMHA0CO87Bpww/YTz/9FFOnTsWWLVvUmRITEzFu3DjJ184cMGAARowYgZUrV0KhUOCvv/7CiRMnEBQUlG2yYinMnDkTrVq1wtWrV/HmzRssXLgQV69exfHjx3HkyBGp48HDwwNHjx7Ntkbr1q1btWJhcW3OV6JECY25H7XJsGHD0KVLF4SEhEg+l2JuPnRd4DZt2uDHH3/McSJjKkJS988S0YdJSUkRvr6+wtLSUujq6gpHR0ehr68vGjZsKFJTUyXNplKpxPTp04WJiYlQKBRCoVAIQ0NDMXHiRElzvSsuLk70799f1KxZU7i7uwt/f39x8eJFqWMJIYTYsWOHsLCwELNmzRLGxsbim2++Ef379xcGBgZi//79UsfT6nw///yz+Oyzz0RaWpqkOXJiZmamXnpO7t59FpSkwyk/qFAtWrQIAwcOhKGhIRYtWpTnucOHDy+iVO+nrdMaAFldjhcvXkRqaiqqVaumnuhUG2RkZCAuLg6pqanw8PCAqamp1JFk4+jRo5g6dSqio6PVf7YhISFo0aKF1NEAaG8+b29vxMfHQwgBFxeXbPMVnj9/XqJkWc+J1atXTytG134sMzMzREdHa8XjDsUZizYqVGXKlMHZs2dhbW2dbZmZdykUCty8ebMIk2Unh2kN6N9TqVSIi4vL8YHwnAZRkDy8b93i0NDQIkqS3YsXL9ClSxfY2trC09MzW0GpTb+ovg+LNu3Aoo3o/02dOhVr1qzB1KlTMWDAAFy+fBmurq7YvHkzFixYgBMnTkgdEeHh4QgPD8+x8Fi5cqVEqbJGYc6aNSvXbFIX5CdPnkSPHj1w+/btbJM7a8Pzim9lZGTk+Pk5OTlJlEiTtufTNj/99BMGDRoEQ0NDWFtba4xs1YZfVPODRZt24EAEkoT4x6zg2kCbpzUAsloUpk6diho1amjd1AbaPu3CoEGD1CNItTHfjRs30LdvXxw/flxjv9CSaRe0PZ+2rhQyYcIETJkyBePHj9dYHYHo32LRRkXqp59+wvz583Hjxg0AgJubG0aOHIn+/ftLnEy7pzUAgKVLl2L16tXo2bOn1FGy0eZpF4CsomPr1q1as0j3PwUEBEBPTw+7du3SyqJSm/NdvHgRvr6+sLCw0LqVQjIyMtCtWzcWbFRgWLRRkQkJCcG8efMwbNgw9dxYJ06cwKhRo5CYmIipU6dKmk+bpzUAsv4BqFu3rtQxcqTN0y4AQO3atREXF6e1RVtUVBTOnTuHihUrSh0lR9qcLzAwEAEBAZgzZw7MzMzU+1u3bq1eM1gqvXv3xubNm/H1119LmqMgfP3111r9/3hxwaKNiswPP/yAFStWoHv37up9n376Kby8vDBs2DDJi7aQkBD07t0bd+/ehUqlwvbt2xEbG4u1a9di165dkmYDsrogN2zYoBXznv3TtGnTEBISgjVr1sDY2FjqONkMGzYMo0ePxr1793J8INzLy0uiZFk8PDxynIBVW2hzPm1eKSQzMxNz5szBvn374OXlle3vXU5zQkrhxo0bOHz4cI7PK4aEhADIWr+XpMeBCFRkLC0tcebMGbi5uWnsv379OmrVqqUVyx1p67QGADBixAisXbsWXl5eWvcPgDZPuwAgx+4pbVqq59ChQ5g4cSJmzpyZY1Fpbm4uUbIs2pyvZMmS2LdvH7y9vTUelj9w4AD69u2LO3fuSJatSZMmuR5TKBQ4dOhQEabJ2YoVK/DVV1/BxsYGpUqVyjZYQur/d0kTizYqMsOGDYO+vn624iIoKAgvX77E999/L1EyedDmfwC0edoFALh9+3aexz90VvjC8rao/OezYtpSVGpzvv79++PRo0fYsmULrKyscPHiRejq6qJDhw5o2LAhFixYIFk2OXB2dsbgwYMxbtw4qaPQB2DRRoUqMDBQ/fWbN2+wevVqODk5oU6dOgCAU6dOITExEb169cJ3330nVUwNnNaAitr7ltJq1KhRESXJmTbne/r0KT777DOcPXsWz58/R+nSpXHv3j34+Phgz5492dYQlkJcXBzi4+PRsGFDGBkZqYtdbWBubo6oqChO5SETLNqoUOXVOvQuqVuKAO2f1oD+vbCwMNjZ2aFv374a+1euXIkHDx6wleE/QBtXCnn06BG6du2Kw4cPQ6FQ4MaNG3B1dUXfvn1RokQJzJ07V+qI6NevH2rWrKkxzRFpLxZtRP+vXr160NPTw/jx43Oc1qBKlSpFnqlTp04ffO727dsLMUl2VlZWuH79OmxsbFCiRIk8Ww4eP35chMmyc3FxwYYNG7KNvj116hQ+//xz3Lp1q8gzXbx48YPPlXqgBJD1vOeyZctw8+ZN/PLLL3BwcMDPP/+MMmXKoH79+lLH00q9evVCcnIyfvzxR7i7u6uft9u3bx8CAwNx5coVqSMiLCwM8+bNQ5s2bWS/akNxwNGjRP9PG6c1sLCwUH8thMCvv/4KCwsL1KhRAwBw7tw5pKSk5Ku4Kyjz589XT7Gg7c8N3bt3D/b29tn229raIikpSYJEQNWqVdWDIfKiDa2827ZtQ8+ePeHv74/z588jPT0dQFbX5MyZM7Fnz54izfO+dYzfJWXRsX//fuzbtw+ffPKJxn43N7f3PmdZVJYvXw5TU1McOXIkWze4QqFg0aZlWLRRkTp79iy2bNmCxMREZGRkaBwr6paif9LGaQ1WrVql/nrcuHHo2rUrli5dCl1dXQBZUwoMHjxYktF7vXv3zvFrbeTo6IjIyMhs699GRkaidOnSkmSSonXv35o+fTqWLl2KXr16YdOmTer99erVw/Tp04s8z/z58z/oPKmLjrS0tBynwHn8+DGUSqUEibKT099DYtFGRWjTpk3o1asX/Pz8sH//frRo0QLXr1/H/fv30bFjR6njYfbs2Rg7dqxWTmsAZD1/dezYMXXBBgC6uroIDAxE3bp18c0330iYTrsXZB8wYABGjhyJ169fo2nTpgCy1nEdO3YsRo8eLUkmqUes5kdsbGyOf4YWFhaSTNUjl0KjQYMGWLt2LaZNmwYgq4hUqVSYM2fOBz/vW5S0cXlB0sSijYrMzJkzMX/+fAwZMgRmZmZYuHAhypQpgy+//DLHrqui9vbB5aZNm2r80NKWgQhv3rzBtWvXUKFCBY39165dy1YkFTVtX5B9zJgxePToEQYPHqxu4TU0NMS4ceO0atLQq1ev5tgK/emnn0qUKEupUqUQFxcHFxcXjf3Hjh3jqMM8zJkzB82aNcPZs2eRkZGBsWPH4sqVK3j8+DEiIyOljqe2du1afPPNN+rlBcuXL48xY8Zo5ZJ5xR2LNioy8fHxaNOmDQDAwMAAaWlpUCgUGDVqFJo2bfreub4K2+HDhyW9//v06dMH/fr1Q3x8PGrVqgUg60H6WbNmoU+fPpJm0/YF2RUKBWbPno1JkyYhJiYGRkZGcHNz05ouqps3b6Jjx464dOmSxnNubz9HqYveAQMGYMSIEVi5ciUUCgX++usvnDhxAkFBQVqxQseff/6JnTt35ljwSjnptLm5OWJiYvDDDz/AzMwMqamp6NSpE4YMGaIV6xkDWZ/PpEmTMHToUPXawceOHcOgQYPw8OFDjBo1SuKEpEEQFREHBwdx8eJFIYQQnp6eYsOGDUIIIY4fPy7Mzc2ljKb2xx9/CH9/f1GnTh3x559/CiGEWLt2rTh69KjEyYTIzMwUs2fPFqVLlxYKhUIoFApRunRpMXv2bPHmzRtJsxkbG4sbN25ImkHO2rZtK9q3by8ePHggTE1NxdWrV8XRo0dFrVq1xB9//CF1PKFSqcT06dOFiYmJ+u+eoaGhmDhxotTRxMGDB4WxsbGoXLmy0NPTE1WrVhWWlpbCwsJCNGnSRNJsOjo64v79+9n2P3z4UOjo6EiQKDsXFxexZs2abPtXr14tXFxcJEhEeWFLGxWZhg0b4sCBA/D09ESXLl0wYsQIHDp0CAcOHECzZs2kjqcxQu7ChQuSj5D7Jx0dHYwdOxZjx47Fs2fPAEj/nN1b2rgge6dOnbB69WqYm5u/d3St1INgTpw4gUOHDsHGxgY6OjrQ0dFB/fr1ERYWhuHDh+PChQuS5lMoFJgwYQLGjBmDuLg4pKamwsPDA6amppLmArLWxAwKCsKUKVNgZmaGbdu2oWTJkvD390fLli0lzSZyGRmcmpoKQ0PDIk6Ts6SkpGxT4QBA3bp1JRtZTblj0UZFZvHixXj16hUAYMKECdDX18fx48fRuXNnTJw4UeJ02jdCLi/aUKy9O8+YNi7IbmFhoe5eNDc317ou23dlZmaqp0+xsbHBX3/9hQoVKsDZ2RmxsbESpwPWrVuHTp06wdjYGB4eHlLH0RATE4ONGzcCAPT09PDy5UuYmppi6tSpaN++Pb766qsiz/R2JRiFQoGQkBCNEaSZmZk4deoUqlatWuS5clKuXDls2bIFX3/9tcb+zZs3Z1snmqTHoo2KjJWVlfprHR0djB8/XsI02WnbCLl/KlOmTJ6Fx82bN4swTc7zjL274oDUC7K/O13K6tWri/z++VG5cmVER0ejTJkyqF27NubMmQMDAwMsX75cKx70HzVqFAYNGoRPP/0UX3zxBfz8/DRGMUvJxMRE/Rybvb094uPjUalSJQCQbAqfty2jQghcunQJBgYG6mMGBgaoUqUKgoKCJMn2T1OmTEG3bt3wxx9/qJ9pi4yMRHh4OLZs2SJxOvonFm1UpOLj47Fq1SrEx8dj4cKFKFmyJH7//Xc4OTmpf9BKRdtHyI0cOVLj+9evX+PChQvYu3cvxowZU+R55DLtApA1Inj79u2wtLTU2P/s2TN06NBB8iXUJk6ciLS0NADA1KlT0bZtWzRo0ADW1tYarb5SSUpKwt69e7Fx40Z07doVxsbG6NKlC/z9/XPsWitKderUwbFjx+Du7o7WrVtj9OjRuHTpErZv365e47iovR3U1KdPHyxcuFArWsZz07lzZ5w6dQrz58/Hjh07AADu7u44ffo0vL29pQ1H2Un7SB0VJxEREcLIyEj4+voKAwMDER8fL4QQIiwsTHTu3FnidELMnDlTeHh4iJMnTwozMzNx9OhRsW7dOmFraysWLVokdbxcLV68WAQEBEgdQ6spFIocHwi/f/++0NPTkyDR+z169EioVCqpY2STlpYm1q1bJ1q3bi0MDAyEq6urpHni4+NFdHS0EEKI1NRU8eWXXwpPT0/RqVMnkZCQIGk2ooLGtUepyPj4+KBLly4IDAyEmZmZeh2+06dPo1OnTvjzzz8lzSeEwMyZMxEWFoYXL14AAJRKJYKCgtSTY2qjmzdvomrVqurBCVLQ1gXZ3z53V7VqVRw6dEijiz4zMxN79+7FsmXLkJCQIEm+t/r27YuFCxeqn2t7Ky0tDcOGDcPKlSslSpazhw8fYtOmTVi6dCliYmIknZKkf//++OKLL9C4cWPJMsjNs2fP1K1/7/u5oc2thMWSxEUjFSMmJibi5s2bQgghTE1N1S1tt27dEkqlUspoGtLT08WVK1fEqVOnxPPnz6WO816zZ88Wzs7OkmZwdnYWkZGR2fafPHlS0mkDFAqF0NHRETo6OuqpKt7djI2NxU8//SRZvrdymxriwYMHQldXV4JE2b1tYWvVqpUwMDAQZcuWFRMnThQxMTGS5vr000+FUqkUn3zyiQgKChJRUVGS5pGDd/++vfv/yLvb2/2kXfhMGxUZS0tLJCUlZVv/8cKFC3BwcJAoVXYGBgZaN0IOALy9vbOt1HDv3j08ePAAS5YskTCZdi7IDmQ9dyeEULfo2traqo8ZGBigZMmSkj5Q/+zZMwghIITA8+fPNaaByMzMxJ49e1CyZEnJ8r31+eefY9euXTA2NkbXrl0xadIk+Pj4SB0LAPDbb7/hyZMn+OWXX7BhwwbMmzcPFStWhL+/P3r06JHtGVWCRquztk8qTppYtFGR+fzzzzFu3Dj88ssv6jX4IiMjERQUhF69ekkdT+t16NBB43sdHR3Y2tqicePGqFixojSh/p82LsgO/L2+p9TLfOXG0tISCoUCCoUC5cuXz3ZcoVBIvlIIkLXG7ZYtW7Rq1Oi7SpQogYEDB2LgwIH4888/sXHjRqxcuRIhISF48+aN1PG0TqNGjdRflylTBo6OjtlGpgshcOfOnaKORu/Boo2KzMyZMzFkyBA4OjoiMzMTHh4eyMzMRI8ePbRinjZtFxoaKnWEXGnjguzvWrNmDWxsbNTLqI0dOxbLly+Hh4cHNm7cKNni7YcPH4YQAk2bNsW2bds0nrkzMDCAs7OzpEXvW+vXr5c6wgd5/fo1zp49i1OnTiEhIQF2dnZSR9J6ZcqUQVJSUrYW3cePH6NMmTKSL6FGmjgQgYrcnTt3cOnSJaSmpsLb25sTOOZDZmYmduzYgZiYGABApUqV8Omnn0re+iGEwPjx47Fo0aJsC7KHhIRImg0AKlSogB9++AFNmzbFiRMn0KxZMyxYsAC7du2Cnp6e5Csi3L59G46OjtDR0ZE0x7sWLVqEgQMHwtDQEIsWLcrz3OHDhxdRqpwdPnwYGzZswLZt26BSqdCpUyf4+/ujadOmWj2psjbQ0dHB/fv3NR4dALL+Tnp4eKinoiHtwKKNSCbi4uLQunVr3L17FxUqVACQNSGwo6Mjdu/ejbJly0qcMGt5Hm1ckN3Y2BjXrl2Dk5MTxo0bh6SkJKxduxZXrlxB48aN8eDBA6kjIiUlBadPn0ZycnK27lwpHh8oU6YMzp49C2tr62zd3u9SKBRFPrHzuxwcHPD48WO0bNkS/v7+aNeundb8vdNmb1dtWLhwIQYMGJDjqg26urqIjIyUKiLlgN2jVGQ6d+6MWrVqZZv+Yc6cOThz5gx++eUXiZLJw/Dhw1G2bFmcPHlS3Y326NEjfPHFFxg+fDh2794tccKsAQmPHz9Gw4YNoVQq1SsiSM3U1BSPHj2Ck5MT9u/fr/4Hy9DQEC9fvpQ4HfC///0P/v7+SE1NzbbklkKhkKRoe3fyZG2eSHny5Mno0qVLtomTKW9yWrWB/saWNioytra2OHToEDw9PTX2X7p0Cb6+vrh//75EyeTBxMQEJ0+ezPb5RUdHo169ekhNTZUoWVbx2LVrVxw+fBgKhQI3btyAq6sr+vbtixIlSmDu3LmSZQMAf39/XLt2Dd7e3ti4cSMSExNhbW2NnTt34uuvv8bly5clzVe+fHm0bt0aM2fO1GjxkNLbwvZ9FAqF5H++9O/JYdUG+htb2qjIpKamavw295a+vr6kE8PKhVKpxPPnz7Ptz+1zLUqjRo2Cvr4+EhMT4e7urt7frVs3BAYGSv6P+vfff4+JEyfizp072LZtG6ytrQEA586dQ/fu3SXNBgB3797F8OHDtaZgA/5uiXkfbWhJpX/v3TV6SfuxaKMi4+npic2bN2d7MH3Tpk1aOS+atmnbti0GDhyIn376CbVq1QIAnDp1Sr2Qt5T279+Pffv24ZNPPtHY7+bmhtu3b0uU6m+WlpZYvHhxtv3aMJ0GAPj5+eHs2bNascbtW5y/q/g4e/YstmzZgsTERPVAorekHqRDmli0UZGZNGkSOnXqhPj4eI1pITZu3Mjn2T7AokWL0Lt3b/j4+EBfXx9A1hQH7du3x4IFCyTNlpaWlmMr0ePHj7XiofA//vgjz+MNGzYsoiQ5a9OmDcaMGYOrV6/C09NT/ef7ltRFOf13bdq0Cb169YKfnx/279+PFi1a4Pr167h//z46duwodTz6Bz7TRkVq9+7dmDlzJqKiomBkZAQvLy+EhoZqTPZIeYuLi1NP+eHu7o5y5cpJnAho3bo1qlevjmnTpsHMzAwXL16Es7MzPv/8c6hUKmzdulXSfDlNpfFut57Uc1HlNdWHQqGQPB/9d3l5eeHLL7/EkCFD1GtClylTBl9++SXs7e21pjWasrBoI9JiH/owOADMmzevEJPk7fLly2jWrBmqVauGQ4cO4dNPP8WVK1fw+PFjREZGSj4dydOnTzW+f/36NS5cuIBJkyZhxowZaNasmUTJiKRlYmKCK1euwMXFBdbW1oiIiICnpydiYmLQtGlTSZeho+zYPUpFLiMjI8e5qJycnCRKpL3k8jB45cqVcf36dXz33XcwMzNDamoqOnXqhCFDhuS4JmlRs7CwyLavefPmMDAwQGBgIM6dOydBqpy9evVKYw1SosJUokQJ9QAnBwcHXL58GZ6enkhJScGLFy8kTkf/xKKNisyNGzfQt29fHD9+XGP/27m82AWUnZweBrewsJDdcmR2dnaIjY2VOgYyMzMxc+ZMLF26FPfv38f169fh6uqKSZMmwcXFBf369ZM6Iv1HNWzYEAcOHICnpye6dOmCESNG4NChQzhw4ABboLUQizYqMgEBAdDT08OuXbtgb28veesQFayjR49i2bJluHnzJn755Rc4ODjg559/RpkyZVC/fn1Js128eFHjeyEEkpKSMGvWLFStWlWaUO+YMWMG1qxZgzlz5mDAgAHq/ZUrV8aCBQtYtFGhWbx4MV69egUAmDBhAvT19XH8+HF07txZdr+EFQd8po2KjImJCc6dO4eKFStKHYUK2LZt29CzZ0/4+/vj559/xtWrV+Hq6orFixdjz5492LNnj6T5dHR0oFAo8M8fd3Xq1MHKlSsl/ztZrlw5LFu2DM2aNVM/DO7q6opr167Bx8cHT548kTQf/Te9efMGGzZsgJ+fH+zs7KSOQx+ALW1UZDw8PPDw4UOpY1AhmD59OpYuXYpevXph06ZN6v316tXD9OnTJUyW5Z/LMOno6MDW1lZrnh27e/dujqOAVSoVXr9+LUEiKg709PQwaNAg9Wh00n4s2qjIzJ49G2PHjsXMmTNznIuKy6jIV2xsbI5znVlYWCAlJaXoA/2Ds7Oz1BHy5OHhgaNHj2bLuXXrVnh7e0uUioqDWrVqISoqSuv/H6EsLNqoyPj6+gJAtodbORBB/kqVKoW4uDi4uLho7D927JjWzPJ/5MgRfPvtt+pWBQ8PD4wZMwYNGjSQOBkQEhKC3r174+7du1CpVNi+fTtiY2Oxdu1a7Nq1S+p49B82ePBgBAYG4s6dO6hevTpMTEw0jnt5eUmUjHLCZ9qoyBw5ciTP45xgV77CwsKwbt06rFy5Es2bN8eePXtw+/ZtjBo1CpMmTcKwYcMkzbdu3Tr06dMHnTp1Qr169QAAkZGR+PXXX7F69Wr06NFD0nxA1kCOqVOnIjo6GqmpqahWrRpCQkLQokULqaPRf1huE0/zl2ntxKKNiD6aEAIzZ85EWFiYem4npVKJoKAgTJs2TeJ0WStHDBw4EKNGjdLYP2/ePKxYsYLP9FCx9b61gdltql1YtFGRe/HiRY4LE7MZXv4yMjIQFxeH1NRUeHh4wNTUVOpIALIKyCtXrmR72D8uLg6VK1dWT3mgDVJTU7NNPM3nPYkI4DNtVIQePHiAPn364Pfff8/xOJvh5c/AwABmZmYwMzPTmoINABwdHREeHp6taDt48CAcHR0lSvW3W7duYejQoYiIiNAoINlFRYVt7dq1eR7v1atXESWhD8GijYrMyJEjkZKSglOnTqFx48b49ddfcf/+fUyfPh1z586VOh59hDdv3mDKlClYtGgRUlNTAQCmpqYYNmwYQkNDs40ULmqjR4/G8OHDERUVhbp16wLIeqZt9erVWLhwoaTZAOCLL76AEAIrV66EnZ0dJ56mIjNixAiN71+/fo0XL17AwMAAxsbGLNq0DIs2KjKHDh3Cb7/9hho1akBHRwfOzs5o3rw5zM3NERYWhjZt2kgdkf6lYcOGYfv27ZgzZw58fHwAACdOnMDkyZPx6NEj/PDDD5Lm++qrr1CqVCnMnTsXW7ZsAZD1nNvmzZvRvn17SbMBQHR0NM6dO4cKFSpIHYWKmZwmbr5x4wa++uorjBkzRoJElBc+00ZFxtzcHBcvXoSLiwucnZ2xYcMG1KtXD7du3UKlSpW4OLGMWVhYYNOmTWjVqpXG/j179qB79+54+vSpRMnkoUmTJpgwYYJ6WhwiqZ09exZffPEFrl27JnUUegdb2qjIVKhQAbGxsXBxcUGVKlWwbNkyuLi4YOnSpbC3t5c6Hn0EpVKZbY42AChTpgwMDAyKPtA/nDlzBiqVCrVr19bYf+rUKejq6qJGjRoSJcvy448/YtCgQbh79y4qV66crTuZg3SoqOnp6eGvv/6SOgb9A4s2KjIjRoxAUlISACA0NBQtW7bEunXrYGBggDVr1kicjj7G0KFDMW3aNKxatQpKpRIAkJ6ejhkzZmDo0KESpwOGDBmCsWPHZiva7t69i9mzZ+PUqVMSJcvy4MEDxMfHo0+fPup9nCuLisLOnTs1vhdCICkpCYsXL1bPaUjag92jJJkXL17g2rVrcHJygo2NjdRx6CN07NgR4eHhUCqVqFKlCoCs57QyMjKyrYCxffv2Is9namqKixcvZlud4datW/Dy8sLz58+LPNO7PDw84O7ujrFjx+Y4EIFzZVFh+efkugqFAra2tmjatCnmzp3LXhAtw5Y2KlSBgYEffO68efMKMQkVJktLS3Tu3FljnzZMpfGWUqnE/fv3sxVtSUlJ0NOT/sfg7du3sXPnzhwXjScqTP+cE5C0m/Q/reg/7cKFCxrfnz9/Hm/evFGPkrt+/Tp0dXVRvXp1KeJRAVmyZAlUKpV63cKEhATs2LED7u7u8PPzkzgd0KJFCwQHB+O3336DhYUFACAlJQVff/01mjdvLnE6oGnTpoiOjmbRRkWCv0zLF4s2KlSHDx9Wfz1v3jyYmZlhzZo1KFGiBICs4eZ9+vTRikW76d9r3749OnXqhEGDBiElJQV16tSBvr4+Hj58iHnz5uGrr76SNN+3336Lhg0bwtnZGd7e3gCAqKgo2NnZ4eeff5Y0GwC0a9cOo0aNwqVLl+Dp6ZltIMKnn34qUTL6L+Iv0/LFZ9qoyDg4OGD//v2oVKmSxv7Lly+jRYsWHKkkYzY2Njhy5AgqVaqEH3/8Ed999x0uXLiAbdu2ISQkRCvW9kxLS8P69esRHR0NIyMjeHl5oXv37pJP/AvkvGj3WxyIQIVp3rx5iIiIyPWX6dGjR0uckN7FljYqMs+ePcODBw+y7X/w4IHkD4LTx3nx4gXMzMwAAPv370enTp2go6ODOnXqvHdB6qJiYmKC+vXrw8nJSb3u7dsl1aRuyeJzRSSVuXPnYv/+/eqCDQBKlCiB6dOno0WLFizatAyLNioyHTt2RJ8+fTB37lzUqlULQNY8WWPGjEGnTp0kTkcfo1y5ctixYwc6duyIffv2YdSoUQCA5ORkrVjs/ObNm+jYsSMuXbqkMZXGW2zJouKKv0zLC4s2KjJLly5FUFAQevTogdevXwPImsCxX79++OabbyRORx8jJCQEPXr0wKhRo9CsWTP1Ulb79+9XP0MmpREjRqBMmTIIDw9HmTJlcOrUKTx+/BijR4/Gt99+K3U8TJ06Nc/jISEhRZSEihv+Mi0vfKaNilxaWhri4+MBAGXLllWPOCR5u3fvHpKSklClShX1M1qnT5+Gubk5KlasKGk2Gxub/2vvXkKp6+I4jv+2HBPCwUAJZ7tkgjIykpSUIlEMlDJlYOCSXOvJyCkpIyETpUxl4G4q6qDMXIsiRShKJ7yDp/f0etA7eqy1+X5GZ9sGv9Hpd/Zae/21vr6uwsJCJSQkaGtrS3l5eVpfX1dHR8e7jdlf7c9iGw6HdXJyoujoaGVnZysUChlKhu/u8fFRnZ2dmp6e/vDHNN/PdqG0Afj2/H6/QqGQXNdVdna2pqamVFZWpqOjIxUUFFg59/b+/l7Nzc2qra1VU1OT6Tj45vgx7Q0sjwL49vLz87W3tyfXdVVcXKxgMKiYmBhNTEy8O3DXFvHx8fr165eqq6spbfjrYmNjmXHrAZQ2AN9ef3+/Hh4eJP3eP1ZVVaWSkhIlJydrbm7OcLrP3d3d6e7uznQMAJZgeRTAj3RzcyO/3/9uzqcJY2Njb67/Hdo9MzOj0tJSzc7OGkoGwCaUNgAwzHXdN9dRUVGRod09PT2RM/AA/GyUNgAAAA/4fHYKAOCvC4fDio6O1v7+vukoACxHaQMAg3w+nzIyMpjKAOB/UdoAwLC+vj719vbq5ubGdBQAFmNPGwAYVlRUpMPDQ4XDYWVmZr472JSJCAAkzmkDAONqamqsOHoEgN140gYAAOAB7GkDAMOysrJ0fX397u+3t7fWjtkC8PUobQBg2Onp6Ydvjz49Pen8/NxAIgA2Yk8bABgyPz8f+by0tKSEhITI9fPzs9bW1t5NSwDwc7GnDQAMiYr6vdjhOI7+/Cr2+XwKBAIaGRlRVVWViXgALENpAwDDXNfV9va2UlJSTEcBYDFKGwBY6Pb2VomJiaZjALAILyIAgGHDw8Oam5uLXNfX1yspKUlpaWna29szmAyATShtAGDY+Pi40tPTJUkrKytaXV3V4uKiKisr1dXVZTgdAFvw9igAGHZ5eRkpbQsLC2poaFBFRYUCgYCKi4sNpwNgC560AYBhfr9fZ2dnkqTFxUWVl5dLkl5fXz88vw3Az8STNgAwrK6uTo2NjcrNzdX19bUqKyslSTs7O8rJyTGcDoAtKG0AYNjo6KgCgYDOzs4UDAYVFxcnSbq4uFBra6vhdABswZEfAAAAHsCTNgCwwMHBgTY2NnR1daWXl5c39wYHBw2lAmATnrQBgGGTk5NqaWlRSkqKUlNT5ThO5J7jOAqFQgbTAbAFpQ0ADMvMzFRra6u6u7tNRwFgMUobABgWHx+v3d1dZWVlmY4CwGKc0wYAhtXX12t5edl0DACW40UEADAsJydHAwMD2tzcVEFBgXw+35v7bW1thpIBsAnLowBgmOu6n95zHEfHx8dfmAaArShtAAAAHsDyKAAY0N7erqGhIcXGxqq9vf3T/3McRyMjI1+YDICtKG0AYMDOzo7C4XDk82f+e2YbgJ+N5VEAAAAP4MgPAAAAD6C0AQAAeAClDQAAwAMobQAAAB5AaQMAAPAAShsAAIAHUNoAAAA84B+8GgBouW+gpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['rock']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "235507af-6fda-4371-984b-e2d3c034b2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['latin']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9df8aa34-310a-4437-beac-c87d9e5a9831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['r&b']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "630f4aee-76ac-4233-8dc2-315ab2afdfad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = genre_dict['edm']\n",
    "normalize(df)\n",
    "under_df = data_balancing(df, under = True)\n",
    "over_df = data_balancing(df, under = False)\n",
    "X, y, y_discrete, dX_train, dX_test, dy_train, dy_test, X_train, X_test, y_train, y_test = train_test_features_labels(under_df)\n",
    "correlation_matrix(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e69e9d5c-6310-4bb2-b826-c5eeddfbd4ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}