05b_modeling-with-extracted-features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import ast\n",
    "from tqdm import tqdm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\dsi\\lib\\site-packages\\tqdm\\std.py:670: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n"
     ]
    }
   ],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs = pd.read_csv('./data/ten_g_500_modeling_12filt_uncomp.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'genre',\n",
       " 'track_name',\n",
       " 'preview_url',\n",
       " 'location',\n",
       " 'filename',\n",
       " 'artist',\n",
       " 'artist_uri',\n",
       " 'album',\n",
       " 'release_date',\n",
       " 'danceability',\n",
       " 'energy',\n",
       " 'key',\n",
       " 'loudness',\n",
       " 'mode',\n",
       " 'speechiness',\n",
       " 'acousticness',\n",
       " 'instrumentalness',\n",
       " 'liveness',\n",
       " 'valence',\n",
       " 'tempo',\n",
       " 'energy_calc',\n",
       " 'rms_calc',\n",
       " 'mfcc_calc',\n",
       " 'mean_energy',\n",
       " 'std_energy',\n",
       " 'min_energy',\n",
       " 'max_energy',\n",
       " 'mfcc_mean',\n",
       " 'mfcc_std',\n",
       " 'mfcc_min',\n",
       " 'mfcc_max',\n",
       " 'max_rms',\n",
       " 'std_rms',\n",
       " 'median_rms',\n",
       " 'min_rms']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "songs.columns[:36].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4999/4999 [01:08<00:00, 72.57it/s]\n",
      "100%|██████████| 4999/4999 [01:10<00:00, 70.70it/s]\n",
      "100%|██████████| 4999/4999 [14:00<00:00,  5.95it/s]\n",
      "100%|██████████| 4999/4999 [00:00<00:00, 16018.86it/s]\n",
      "100%|██████████| 4999/4999 [00:00<00:00, 13471.39it/s]\n",
      "100%|██████████| 4999/4999 [00:00<00:00, 18510.66it/s]\n"
     ]
    }
   ],
   "source": [
    "songs['energy_calc'] = songs['energy_calc'].progress_apply(lambda x: np.array(ast.literal_eval(x)))\n",
    "songs['rms_calc'] = songs['rms_calc'].progress_apply(lambda x: np.array(ast.literal_eval(x)))\n",
    "songs['mfcc_calc'] = songs['mfcc_calc'].progress_apply(lambda x: np.array(ast.literal_eval(x)))\n",
    "songs['mfcc_mean'] = songs['mfcc_mean'].progress_apply(lambda x: np.array(ast.literal_eval(x)))\n",
    "songs['mfcc_min'] = songs['mfcc_min'].progress_apply(lambda x: np.array(ast.literal_eval(x)))\n",
    "songs['mfcc_max'] = songs['mfcc_max'].progress_apply(lambda x: np.array(ast.literal_eval(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = songs.columns[24:].drop(['mfcc_mean', 'mfcc_std', 'mfcc_min', 'mfcc_max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = songs[features]\n",
    "y = songs['genre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def running_models(X, y, scaled=False, c=10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=182)\n",
    "    \n",
    "    # MODELING WITH SCALED FEATURES #\n",
    "    \n",
    "    # Scale the features\n",
    "    sc = StandardScaler()\n",
    "    X_train_sc = sc.fit_transform(X_train)\n",
    "    X_test_sc = sc.transform(X_test)\n",
    "    \n",
    "    # Creating a df to display results\n",
    "    model_df = pd.DataFrame(columns=['train_score', 'test_score'])\n",
    "    \n",
    "    model_sc = SVC(C=c, kernel='rbf', degree=3)\n",
    "    model_sc.fit(X_train_sc, y_train)\n",
    "    train_score = model_sc.score(X_train_sc, y_train)\n",
    "    test_score = model_sc.score(X_test_sc, y_test)\n",
    "    \n",
    "    row = {'model': type(model_sc).__name__,\n",
    "           'train_score' : train_score,\n",
    "          'test_score' : test_score,\n",
    "           'C': c,\n",
    "          'scaled': True}\n",
    "    model_df = model_df.append(row, ignore_index=True)\n",
    "    \n",
    "    # MODELING WITH UNSCALED FEATURES #\n",
    "    \n",
    "    model = SVC(C=c, kernel='rbf', degree=3)\n",
    "    model.fit(X_train, y_train)\n",
    "    train_score = model.score(X_train, y_train)\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    \n",
    "    row = {'model': type(model).__name__,\n",
    "       'train_score' : train_score,\n",
    "      'test_score' : test_score,\n",
    "       'C': c,\n",
    "      'scaled': False}\n",
    "    model_df = model_df.append(row, ignore_index=True)\n",
    "    return model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>train_score</th>\n",
       "      <th>test_score</th>\n",
       "      <th>C</th>\n",
       "      <th>model</th>\n",
       "      <th>scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.862097</td>\n",
       "      <td>0.6088</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SVC</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.603894</td>\n",
       "      <td>0.5648</td>\n",
       "      <td>10.0</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_score  test_score     C model  scaled\n",
       "0     0.862097      0.6088  10.0   SVC     1.0\n",
       "1     0.603894      0.5648  10.0   SVC     0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "running_models(X, y, scaled=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting, it looks like **unscaled data leads to less overfitting**. With an accuracy of $\\approx$ 56.5%, this `SVC` model is outperforming the model using Spotify's features by about 6%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Digging deeper into the model**\n",
    "\n",
    "Let's recreate the less overfit model above and further explore the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=182)\n",
    "\n",
    "# Scale the features\n",
    "sc = StandardScaler()\n",
    "X_train_sc = sc.fit_transform(X_train)\n",
    "X_test_sc = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC(C=10)\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55866667, 0.55466667, 0.584     , 0.536     , 0.5540721 ])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(model, X_train, y_train, n_jobs=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6038943718324886"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5648"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = './models/SVC_10g_model.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learned how to pickle from https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = list(set(y_preds))\n",
    "numbers = list(range(len(categories)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = X_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.loc[:, 'genre'] = y_test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.loc[:, 'preds'] = y_preds.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(font_scale=1.5)\n",
    "plot_confusion_matrix(model, X_test, y_test, \n",
    "                      xticks_rotation='90', \n",
    "                      values_format='.1%', \n",
    "                      normalize='true', ax=ax)\n",
    "plt.grid(False)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./images/extracted_10g_confmatrix.png', dpi=72);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting that **classical is now much less accurately predicted as classical** as compared to the 5-genre model using my features: now down to 30.4% from 95%.\n",
    "\n",
    "Also, what this confusion matrix is good at showing us is **relationships between genres**. Note how hip hop, rap, r&b, and pop music seem to get confused for each other often. While "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_wrong = results.copy()[results['genre'] != results['preds']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "songs_wrong = songs.loc[results_wrong.index, ['track_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_wrong['track_name'] = songs_wrong.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What songs were predicted wrong?\n",
    "\n",
    "Let's dig in to what our model is having trouble predicting - we'll get more into this in the final notebook, but is there anything we can glean, initially?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>mean_energy</th>\n",
       "      <th>std_energy</th>\n",
       "      <th>min_energy</th>\n",
       "      <th>max_energy</th>\n",
       "      <th>max_rms</th>\n",
       "      <th>std_rms</th>\n",
       "      <th>median_rms</th>\n",
       "      <th>min_rms</th>\n",
       "      <th>mfcc_mean_00</th>\n",
       "      <th>mfcc_mean_01</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc_min_05</th>\n",
       "      <th>mfcc_min_06</th>\n",
       "      <th>mfcc_min_07</th>\n",
       "      <th>mfcc_min_08</th>\n",
       "      <th>mfcc_min_09</th>\n",
       "      <th>mfcc_min_10</th>\n",
       "      <th>mfcc_min_11</th>\n",
       "      <th>genre</th>\n",
       "      <th>preds</th>\n",
       "      <th>track_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3195</th>\n",
       "      <td>24.843471</td>\n",
       "      <td>43.473087</td>\n",
       "      <td>0.711391</td>\n",
       "      <td>312.387702</td>\n",
       "      <td>0.781110</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>0.156280</td>\n",
       "      <td>0.037275</td>\n",
       "      <td>-141.366699</td>\n",
       "      <td>133.685287</td>\n",
       "      <td>...</td>\n",
       "      <td>-20.908154</td>\n",
       "      <td>-35.137718</td>\n",
       "      <td>-27.743557</td>\n",
       "      <td>-29.407875</td>\n",
       "      <td>-26.499763</td>\n",
       "      <td>-27.540306</td>\n",
       "      <td>-25.050533</td>\n",
       "      <td>rap</td>\n",
       "      <td>r&amp;b</td>\n",
       "      <td>Let Me Love You</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>26.590519</td>\n",
       "      <td>29.747921</td>\n",
       "      <td>0.191029</td>\n",
       "      <td>273.605072</td>\n",
       "      <td>0.731016</td>\n",
       "      <td>0.110927</td>\n",
       "      <td>0.180929</td>\n",
       "      <td>0.019316</td>\n",
       "      <td>-133.751358</td>\n",
       "      <td>115.526764</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.578063</td>\n",
       "      <td>-50.545280</td>\n",
       "      <td>-39.140114</td>\n",
       "      <td>-38.253895</td>\n",
       "      <td>-32.995163</td>\n",
       "      <td>-34.234432</td>\n",
       "      <td>-34.955311</td>\n",
       "      <td>rap</td>\n",
       "      <td>r&amp;b</td>\n",
       "      <td>Only You (feat. The Notorious B.I.G. &amp; Mase) -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3165</th>\n",
       "      <td>47.097539</td>\n",
       "      <td>23.124728</td>\n",
       "      <td>1.186404</td>\n",
       "      <td>163.884424</td>\n",
       "      <td>0.565762</td>\n",
       "      <td>0.077898</td>\n",
       "      <td>0.299036</td>\n",
       "      <td>0.048137</td>\n",
       "      <td>-74.971954</td>\n",
       "      <td>114.833939</td>\n",
       "      <td>...</td>\n",
       "      <td>-24.593081</td>\n",
       "      <td>-79.668434</td>\n",
       "      <td>-25.795635</td>\n",
       "      <td>-51.206230</td>\n",
       "      <td>-33.526154</td>\n",
       "      <td>-35.183311</td>\n",
       "      <td>-32.016739</td>\n",
       "      <td>rap</td>\n",
       "      <td>r&amp;b</td>\n",
       "      <td>Lean Back (feat. Lil Jon, Eminem, Mase &amp; Remy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>29.673278</td>\n",
       "      <td>31.338210</td>\n",
       "      <td>0.298578</td>\n",
       "      <td>281.688644</td>\n",
       "      <td>0.741737</td>\n",
       "      <td>0.099919</td>\n",
       "      <td>0.205723</td>\n",
       "      <td>0.024149</td>\n",
       "      <td>-104.038696</td>\n",
       "      <td>130.311005</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.830107</td>\n",
       "      <td>-53.566849</td>\n",
       "      <td>-32.964211</td>\n",
       "      <td>-33.709805</td>\n",
       "      <td>-41.523914</td>\n",
       "      <td>-34.184483</td>\n",
       "      <td>-26.018078</td>\n",
       "      <td>rap</td>\n",
       "      <td>r&amp;b</td>\n",
       "      <td>How Could You</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3335</th>\n",
       "      <td>60.966544</td>\n",
       "      <td>56.715459</td>\n",
       "      <td>0.013328</td>\n",
       "      <td>335.565174</td>\n",
       "      <td>0.809568</td>\n",
       "      <td>0.142688</td>\n",
       "      <td>0.282822</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>-79.955498</td>\n",
       "      <td>124.234909</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.862773</td>\n",
       "      <td>-69.910767</td>\n",
       "      <td>-28.862019</td>\n",
       "      <td>-38.025276</td>\n",
       "      <td>-47.551888</td>\n",
       "      <td>-29.548595</td>\n",
       "      <td>-38.075996</td>\n",
       "      <td>rap</td>\n",
       "      <td>r&amp;b</td>\n",
       "      <td>Panini - DaBaby Remix</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean_energy  std_energy  min_energy  max_energy   max_rms   std_rms  \\\n",
       "3195    24.843471   43.473087    0.711391  312.387702  0.781110  0.116883   \n",
       "3321    26.590519   29.747921    0.191029  273.605072  0.731016  0.110927   \n",
       "3165    47.097539   23.124728    1.186404  163.884424  0.565762  0.077898   \n",
       "3245    29.673278   31.338210    0.298578  281.688644  0.741737  0.099919   \n",
       "3335    60.966544   56.715459    0.013328  335.565174  0.809568  0.142688   \n",
       "\n",
       "      median_rms   min_rms  mfcc_mean_00  mfcc_mean_01  ...  mfcc_min_05  \\\n",
       "3195    0.156280  0.037275   -141.366699    133.685287  ...   -20.908154   \n",
       "3321    0.180929  0.019316   -133.751358    115.526764  ...   -15.578063   \n",
       "3165    0.299036  0.048137    -74.971954    114.833939  ...   -24.593081   \n",
       "3245    0.205723  0.024149   -104.038696    130.311005  ...   -15.830107   \n",
       "3335    0.282822  0.005102    -79.955498    124.234909  ...    -5.862773   \n",
       "\n",
       "      mfcc_min_06  mfcc_min_07  mfcc_min_08  mfcc_min_09  mfcc_min_10  \\\n",
       "3195   -35.137718   -27.743557   -29.407875   -26.499763   -27.540306   \n",
       "3321   -50.545280   -39.140114   -38.253895   -32.995163   -34.234432   \n",
       "3165   -79.668434   -25.795635   -51.206230   -33.526154   -35.183311   \n",
       "3245   -53.566849   -32.964211   -33.709805   -41.523914   -34.184483   \n",
       "3335   -69.910767   -28.862019   -38.025276   -47.551888   -29.548595   \n",
       "\n",
       "      mfcc_min_11  genre  preds  \\\n",
       "3195   -25.050533    rap    r&b   \n",
       "3321   -34.955311    rap    r&b   \n",
       "3165   -32.016739    rap    r&b   \n",
       "3245   -26.018078    rap    r&b   \n",
       "3335   -38.075996    rap    r&b   \n",
       "\n",
       "                                             track_name  \n",
       "3195                                    Let Me Love You  \n",
       "3321  Only You (feat. The Notorious B.I.G. & Mase) -...  \n",
       "3165  Lean Back (feat. Lil Jon, Eminem, Mase & Remy ...  \n",
       "3245                                      How Could You  \n",
       "3335                              Panini - DaBaby Remix  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_wrong[(results_wrong['preds'] == 'r&b') & (results_wrong['genre'] == 'rap')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my conclusions, I want to listen to some of the songs that were misclassified and determine what might be causing the misclassifications. **Is there something that I can hear as a feature to separate the genres?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_preds = results_wrong.groupby('preds').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style.use('fivethirtyeight')\n",
    "results_wrong['genre'].value_counts(normalize=True).sort_values().plot(kind='barh', title='Extracted Features - Percent Predicted Wrong', fontsize=16, figsize=(9,6))\n",
    "plt.xlabel(\"Percentage\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('./images/incorrect_preds_10g_extracted.png', dpi=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classical and pop are near the top of the list of \"most incorrectly predicted\". These genres are more generic genres with many subgenres underneath. It makes sense that the model wasn't able to predict well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_wrong[results_wrong['genre'] == 'rap']['preds'].value_counts(normalize=True).sort_values().plot(kind='barh', figsize=(8,6))\n",
    "plt.xlabel(\"Percentage\")\n",
    "plt.title(\"If Rap is Predicted Wrong, It's Predicted As:\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrary to the last model, **when rap is predicted wrong, it is most likely to be predicted as hip hop**. This means our 10-genre model more closely relates rap with hip hop. As a refresher, when our 5-genre model predicted rap wrong, it predicted it as r&b 100% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_wrong[results_wrong['genre'] == 'r&b']['preds'].value_counts(normalize=True).sort_values().plot(kind='barh', figsize=(8,6))\n",
    "plt.title(\"If R&B is Predicted Wrong, It's Predicted As:\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrary to the 5-genre model, **when R&B is predicted wrong, it's predicted to be rap only 35% of the time**, down from 84% of the time in the 5-genre model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Neural Network\n",
    "\n",
    "I've seen many people posting on the usefulness of CNNs for instrument and genre classification, and I wanted to try it out. Since Mel Frequency Cepstral Coefficients are used to generate spectrograms, they can be passed similarly to image data through a CNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_df = songs[['mfcc_calc', 'genre']].explode('mfcc_calc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cnn_sequential_prep(data, column, rename_suffix, n_windows, drop=True):\n",
    "    '''\n",
    "    Picks sequential rows from data on a given column.\n",
    "    Dataframe passed should include a calculation column\n",
    "    and a categorical column.\n",
    "    '''\n",
    "    grouped = data.groupby(data.index).apply(lambda x: x.iloc[:n_windows]) # Group data by index\n",
    "    grouped_tag = grouped.droplevel(0)[column].progress_apply(pd.Series) # Turns each item in array into its own column\n",
    "    grouped_tag = grouped_tag.rename(columns = lambda x: rename_suffix + str(x)) # Rename columns so I know what they are\n",
    "    final = pd.concat([grouped_tag[:], grouped.droplevel(0)[:]], axis=1) # Concatenate with original dataframe\n",
    "    if drop:\n",
    "        final.drop(columns=column)\n",
    "    \n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some of my songs ended up being longer than 30 seconds, \n",
    "# so I had to reduce them all to the minimum number of windows (30 seconds)\n",
    "cnn_df = songs['mfcc_calc'].apply(lambda x: x[:2584]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4999/4999 [00:41<00:00, 120.33it/s]\n"
     ]
    }
   ],
   "source": [
    "# Prepping the 2d mfcc_calcs to be passed through a CNN requires them\n",
    "# to be separated into columns\n",
    "for i in tqdm(range(len(cnn_df))):\n",
    "    cnn_df[i] = np.array([coef for window in cnn_df[i] for coef in window])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4999/4999 [00:08<00:00, 618.70it/s]  \n"
     ]
    }
   ],
   "source": [
    "# Let's split the lists into their own columns!\n",
    "cnn_df = cnn_df.progress_apply(pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_df = pd.concat([cnn_df[:], songs[['genre']]], axis=1) # Adding genre to this df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cnn_df.drop(columns='genre')\n",
    "# Thanks joris on SO: https://stackoverflow.com/questions/32011359/convert-categorical-data-in-pandas-dataframe/32011969\n",
    "y_cat = cnn_df['genre'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = to_categorical(y_cat) # One Hot Encoding y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=182)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling data for CNN\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data needs to be reshaped to be the size of the mfcc spectrogram, which is 2584 x 12 x 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0], 2584, 12, 1)\n",
    "X_test = X_test.reshape(X_test.shape[0], 2584, 12, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 2.3382 - acc: 0.0990 - val_loss: 2.2975 - val_acc: 0.1064\n",
      "Epoch 2/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 2.2958 - acc: 0.1198 - val_loss: 2.2624 - val_acc: 0.1648\n",
      "Epoch 3/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 2.2391 - acc: 0.1630 - val_loss: 2.1749 - val_acc: 0.2040\n",
      "Epoch 4/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 2.1663 - acc: 0.1998 - val_loss: 2.0752 - val_acc: 0.1960\n",
      "Epoch 5/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 2.0847 - acc: 0.2107 - val_loss: 1.9900 - val_acc: 0.2400\n",
      "Epoch 6/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 2.0106 - acc: 0.2315 - val_loss: 1.8603 - val_acc: 0.2872\n",
      "Epoch 7/30\n",
      "15/15 [==============================] - 1s 40ms/step - loss: 1.9024 - acc: 0.2777 - val_loss: 1.7699 - val_acc: 0.3320\n",
      "Epoch 8/30\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 1.8122 - acc: 0.2961 - val_loss: 1.6846 - val_acc: 0.3272\n",
      "Epoch 9/30\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 1.7288 - acc: 0.3065 - val_loss: 1.6133 - val_acc: 0.3560\n",
      "Epoch 10/30\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 1.6730 - acc: 0.3270 - val_loss: 1.5717 - val_acc: 0.3744\n",
      "Epoch 11/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.6021 - acc: 0.3628 - val_loss: 1.5208 - val_acc: 0.3656\n",
      "Epoch 12/30\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 1.5562 - acc: 0.3620 - val_loss: 1.4733 - val_acc: 0.3872\n",
      "Epoch 13/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.5155 - acc: 0.3668 - val_loss: 1.4213 - val_acc: 0.3952\n",
      "Epoch 14/30\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 1.4575 - acc: 0.4124 - val_loss: 1.3956 - val_acc: 0.4096\n",
      "Epoch 15/30\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 1.4446 - acc: 0.4094 - val_loss: 1.3656 - val_acc: 0.4232\n",
      "Epoch 16/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.3703 - acc: 0.4452 - val_loss: 1.3330 - val_acc: 0.4512\n",
      "Epoch 17/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.3459 - acc: 0.4567 - val_loss: 1.3213 - val_acc: 0.4448\n",
      "Epoch 18/30\n",
      "15/15 [==============================] - 1s 44ms/step - loss: 1.3106 - acc: 0.4601 - val_loss: 1.3043 - val_acc: 0.4680\n",
      "Epoch 19/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.2672 - acc: 0.4825 - val_loss: 1.2613 - val_acc: 0.4888\n",
      "Epoch 20/30\n",
      "15/15 [==============================] - 1s 45ms/step - loss: 1.2395 - acc: 0.4879 - val_loss: 1.2404 - val_acc: 0.4960\n",
      "Epoch 21/30\n",
      "15/15 [==============================] - 1s 68ms/step - loss: 1.2124 - acc: 0.5023 - val_loss: 1.2321 - val_acc: 0.4960\n",
      "Epoch 22/30\n",
      "15/15 [==============================] - 1s 50ms/step - loss: 1.1813 - acc: 0.5191 - val_loss: 1.2391 - val_acc: 0.5088\n",
      "Epoch 23/30\n",
      "15/15 [==============================] - 1s 46ms/step - loss: 1.1639 - acc: 0.5231 - val_loss: 1.2001 - val_acc: 0.5152\n",
      "Epoch 24/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.1258 - acc: 0.5351 - val_loss: 1.2018 - val_acc: 0.5184\n",
      "Epoch 25/30\n",
      "15/15 [==============================] - 1s 47ms/step - loss: 1.0957 - acc: 0.5479 - val_loss: 1.1901 - val_acc: 0.5192\n",
      "Epoch 26/30\n",
      "15/15 [==============================] - 1s 42ms/step - loss: 1.0527 - acc: 0.5663 - val_loss: 1.1852 - val_acc: 0.5288\n",
      "Epoch 27/30\n",
      "15/15 [==============================] - 1s 43ms/step - loss: 1.0294 - acc: 0.5724 - val_loss: 1.1667 - val_acc: 0.5408\n",
      "Epoch 28/30\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 1.0253 - acc: 0.5703 - val_loss: 1.1827 - val_acc: 0.5280\n",
      "Epoch 29/30\n",
      "15/15 [==============================] - 1s 41ms/step - loss: 0.9866 - acc: 0.5922 - val_loss: 1.1776 - val_acc: 0.5416\n",
      "Epoch 30/30\n",
      "15/15 [==============================] - 1s 38ms/step - loss: 0.9563 - acc: 0.6044 - val_loss: 1.1522 - val_acc: 0.5368\n"
     ]
    }
   ],
   "source": [
    "cnn_model = Sequential()\n",
    "\n",
    "cnn_model.add(Conv2D(filters=6,\n",
    "                     kernel_size=(4, 4),\n",
    "                     activation='relu',\n",
    "                     input_shape=(X_train.shape[1], X_train.shape[2], 1)))\n",
    "\n",
    "cnn_model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "cnn_model.add(Conv2D(filters=12,\n",
    "                     kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=(X_train.shape[1], X_train.shape[2], 1)))\n",
    "cnn_model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "cnn_model.add(Flatten())\n",
    "cnn_model.add(Dense(64, activation='relu'))\n",
    "cnn_model.add(Dropout(0.25))\n",
    "cnn_model.add(Dense(32, activation='relu'))\n",
    "cnn_model.add(Dropout(0.25))\n",
    "cnn_model.add(Dense(16, activation='relu'))\n",
    "cnn_model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "cnn_model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['acc'])\n",
    "\n",
    "hist = cnn_model.fit(X_train, y_train,\n",
    "                epochs=30,\n",
    "                batch_size=256,\n",
    "                validation_data=(X_test, y_test),\n",
    "                    use_multiprocessing=True,\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CNN model is not performing as well as the SVC model, it's **underperforming on testing data by about 3%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(hist.history['acc'], label=\"Training Acc\")\n",
    "plt.plot(hist.history['val_acc'], label=\"Testing Acc\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's next?\n",
    "\n",
    "Next up: Investigation! We've done some great modeling and are outperforming the baseline model (models using Spotify features) by $\\approx$ 5.9%, but I want to dig into the songs that we *aren't* predicting correctly and see if we can tell why."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}