{"cells":[{"metadata":{},"cell_type":"markdown","source":"This is an example that shows how to use xgboost to handle audio classification. But it need to be modifid from multi-class to multi-label"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\nimport librosa\n#from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom xgboost import XGBClassifier\nfrom lightgbm import LGBMClassifier\n\nfrom sklearn.metrics import accuracy_score\nfrom scipy.stats import skew\nSAMPLE_RATE = 45100\n\n#from sklearn.model_selection import KFold, RepeatedKFold\nfrom tqdm import tqdm, tqdm_pandas\n\ntqdm.pandas()\nimport scipy\ndata_path = '../input/'\nss = pd.read_csv(os.path.join(data_path, 'sample_submission.csv'))","execution_count":31,"outputs":[{"output_type":"stream","text":"['train_noisy.csv', 'test', 'train_curated', 'train_noisy', 'sample_submission.csv', 'train_curated.csv']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"#loading data\naudio_train_files = os.listdir('../input/train_curated/')\naudio_test_files = os.listdir('../input/test/')\n\ntrain = pd.read_csv('../input/train_curated.csv')\nsubmission = pd.read_csv('../input/sample_submission.csv')","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#function from EDA kernel: https://www.kaggle.com/codename007/a-very-extensive-freesound-exploratory-analysis\ndef clean_filename(fname, string):   \n    file_name = fname.split('/')[1]\n    if file_name[:2] == '__':        \n        file_name = string + file_name\n    return file_name\n\n#returns mfcc features with mean and standard deviation along time\ndef get_mfcc(name, path):\n    b, _ = librosa.core.load(path + name, sr = SAMPLE_RATE)\n    assert _ == SAMPLE_RATE\n    try:\n        ft1 = librosa.feature.mfcc(b, sr = SAMPLE_RATE, n_mfcc=20)\n        ft2 = librosa.feature.zero_crossing_rate(b)[0]\n        ft3 = librosa.feature.spectral_rolloff(b)[0]\n        ft4 = librosa.feature.spectral_centroid(b)[0]\n        ft1_trunc = np.hstack((np.mean(ft1, axis=1), np.std(ft1, axis=1), skew(ft1, axis = 1), np.max(ft1, axis = 1), np.min(ft1, axis = 1)))\n        ft2_trunc = np.hstack((np.mean(ft2), np.std(ft2), skew(ft2), np.max(ft2), np.min(ft2)))\n        ft3_trunc = np.hstack((np.mean(ft3), np.std(ft3), skew(ft3), np.max(ft3), np.min(ft3)))\n        ft4_trunc = np.hstack((np.mean(ft4), np.std(ft4), skew(ft4), np.max(ft4), np.min(ft4)))\n        return pd.Series(np.hstack((ft1_trunc, ft2_trunc, ft3_trunc, ft4_trunc)))\n    except:\n        print('bad file')\n        return pd.Series([0]*115)","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#preparing data\ntrain_data = pd.DataFrame()\ntrain_data['fname'] = train['fname']\ntest_data = pd.DataFrame()\ntest_data['fname'] = audio_test_files\n\ntrain_data = train_data['fname'].apply(get_mfcc, path='../input/train_curated/')\nprint('done loading train mfcc')\ntest_data = test_data['fname'].apply(get_mfcc, path='../input/test/')\nprint('done loading test mfcc')\n\ntrain_data['fname'] = train['fname']\ntest_data['fname'] = audio_test_files","execution_count":9,"outputs":[{"output_type":"stream","text":"done loading train mfcc\ndone loading test mfcc\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data['label'] = train['labels'].apply(lambda x:x.split(',')[0])\ntest_data['label'] = np.zeros((len(audio_test_files)))","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data.head()","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"            0           1       ...                fname            label\n0 -505.089749  180.441064       ...         0006ae4e.wav             Bark\n1 -666.170512   89.343013       ...         0019ef41.wav         Raindrop\n2 -738.851927   24.688094       ...         001ec0ad.wav  Finger_snapping\n3 -429.256141   89.789210       ...         0026c7cb.wav              Run\n4 -697.901080    7.609523       ...         0026f116.wav  Finger_snapping\n\n[5 rows x 117 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>...</th>\n      <th>77</th>\n      <th>78</th>\n      <th>79</th>\n      <th>80</th>\n      <th>81</th>\n      <th>82</th>\n      <th>83</th>\n      <th>84</th>\n      <th>85</th>\n      <th>86</th>\n      <th>87</th>\n      <th>88</th>\n      <th>89</th>\n      <th>90</th>\n      <th>91</th>\n      <th>92</th>\n      <th>93</th>\n      <th>94</th>\n      <th>95</th>\n      <th>96</th>\n      <th>97</th>\n      <th>98</th>\n      <th>99</th>\n      <th>100</th>\n      <th>101</th>\n      <th>102</th>\n      <th>103</th>\n      <th>104</th>\n      <th>105</th>\n      <th>106</th>\n      <th>107</th>\n      <th>108</th>\n      <th>109</th>\n      <th>110</th>\n      <th>111</th>\n      <th>112</th>\n      <th>113</th>\n      <th>114</th>\n      <th>fname</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-505.089749</td>\n      <td>180.441064</td>\n      <td>15.068468</td>\n      <td>10.800259</td>\n      <td>-9.600303</td>\n      <td>4.576918</td>\n      <td>8.881082</td>\n      <td>-6.049447</td>\n      <td>-4.160875</td>\n      <td>4.630835</td>\n      <td>1.992888</td>\n      <td>0.560607</td>\n      <td>-0.090579</td>\n      <td>3.867188</td>\n      <td>3.349992</td>\n      <td>1.606435</td>\n      <td>1.705743</td>\n      <td>1.694710</td>\n      <td>5.780792</td>\n      <td>9.075267</td>\n      <td>67.172105</td>\n      <td>30.846796</td>\n      <td>23.057665</td>\n      <td>10.070208</td>\n      <td>16.333535</td>\n      <td>9.430675</td>\n      <td>8.262834</td>\n      <td>15.407742</td>\n      <td>10.851452</td>\n      <td>7.908980</td>\n      <td>8.867473</td>\n      <td>6.861082</td>\n      <td>7.182113</td>\n      <td>6.911124</td>\n      <td>6.462065</td>\n      <td>6.216631</td>\n      <td>7.572919</td>\n      <td>7.139398</td>\n      <td>6.598127</td>\n      <td>9.187224</td>\n      <td>...</td>\n      <td>30.056971</td>\n      <td>28.153337</td>\n      <td>43.951801</td>\n      <td>-736.838738</td>\n      <td>26.702456</td>\n      <td>-70.810188</td>\n      <td>-18.172710</td>\n      <td>-70.888891</td>\n      <td>-17.465429</td>\n      <td>-11.334218</td>\n      <td>-45.869613</td>\n      <td>-31.710518</td>\n      <td>-16.412176</td>\n      <td>-29.836455</td>\n      <td>-18.176302</td>\n      <td>-20.893256</td>\n      <td>-16.819023</td>\n      <td>-13.571973</td>\n      <td>-18.081521</td>\n      <td>-24.448299</td>\n      <td>-18.228786</td>\n      <td>-10.615964</td>\n      <td>-14.110874</td>\n      <td>0.022583</td>\n      <td>0.010618</td>\n      <td>0.751250</td>\n      <td>0.065918</td>\n      <td>0.000488</td>\n      <td>1167.300725</td>\n      <td>808.083240</td>\n      <td>2.531639</td>\n      <td>6093.896484</td>\n      <td>376.831055</td>\n      <td>745.175777</td>\n      <td>266.495106</td>\n      <td>3.284192</td>\n      <td>3017.984339</td>\n      <td>412.778244</td>\n      <td>0006ae4e.wav</td>\n      <td>Bark</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-666.170512</td>\n      <td>89.343013</td>\n      <td>11.708950</td>\n      <td>44.341037</td>\n      <td>19.341729</td>\n      <td>23.879220</td>\n      <td>-3.023549</td>\n      <td>22.180218</td>\n      <td>11.925984</td>\n      <td>16.667709</td>\n      <td>-2.438549</td>\n      <td>5.341580</td>\n      <td>-0.705911</td>\n      <td>-6.309737</td>\n      <td>-6.273408</td>\n      <td>7.822523</td>\n      <td>2.538993</td>\n      <td>5.940230</td>\n      <td>4.128231</td>\n      <td>3.514395</td>\n      <td>111.003256</td>\n      <td>34.160382</td>\n      <td>27.391266</td>\n      <td>21.582060</td>\n      <td>16.461118</td>\n      <td>11.123822</td>\n      <td>10.640620</td>\n      <td>15.637882</td>\n      <td>9.534221</td>\n      <td>7.353127</td>\n      <td>6.592269</td>\n      <td>5.949958</td>\n      <td>9.484252</td>\n      <td>8.613264</td>\n      <td>7.694590</td>\n      <td>6.456034</td>\n      <td>5.835454</td>\n      <td>4.659840</td>\n      <td>5.759114</td>\n      <td>5.000570</td>\n      <td>...</td>\n      <td>19.719279</td>\n      <td>21.692740</td>\n      <td>15.149624</td>\n      <td>-910.170514</td>\n      <td>-90.945900</td>\n      <td>-89.207539</td>\n      <td>-8.936872</td>\n      <td>-36.500116</td>\n      <td>-1.623992</td>\n      <td>-46.019010</td>\n      <td>-12.112696</td>\n      <td>-17.606534</td>\n      <td>1.238040</td>\n      <td>-20.600297</td>\n      <td>-13.990914</td>\n      <td>-29.632725</td>\n      <td>-23.618353</td>\n      <td>-20.555851</td>\n      <td>-17.757781</td>\n      <td>-14.184980</td>\n      <td>-6.114079</td>\n      <td>-7.112447</td>\n      <td>-12.825725</td>\n      <td>0.064744</td>\n      <td>0.077409</td>\n      <td>3.144545</td>\n      <td>0.463379</td>\n      <td>0.003418</td>\n      <td>4408.953082</td>\n      <td>1302.057143</td>\n      <td>0.453779</td>\n      <td>7611.987305</td>\n      <td>1808.789062</td>\n      <td>1930.296240</td>\n      <td>805.582988</td>\n      <td>1.882097</td>\n      <td>5449.869552</td>\n      <td>938.740016</td>\n      <td>0019ef41.wav</td>\n      <td>Raindrop</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-738.851927</td>\n      <td>24.688094</td>\n      <td>8.369442</td>\n      <td>10.640992</td>\n      <td>5.429327</td>\n      <td>4.093085</td>\n      <td>0.947911</td>\n      <td>0.124441</td>\n      <td>-0.381000</td>\n      <td>4.122988</td>\n      <td>1.677409</td>\n      <td>-0.168981</td>\n      <td>0.747731</td>\n      <td>1.216025</td>\n      <td>0.094377</td>\n      <td>-1.380938</td>\n      <td>-2.286875</td>\n      <td>-0.766164</td>\n      <td>-0.674148</td>\n      <td>-0.179731</td>\n      <td>113.765049</td>\n      <td>14.708152</td>\n      <td>22.546999</td>\n      <td>6.149522</td>\n      <td>5.795745</td>\n      <td>4.910861</td>\n      <td>4.428245</td>\n      <td>4.356917</td>\n      <td>5.998273</td>\n      <td>6.643124</td>\n      <td>3.508556</td>\n      <td>4.376543</td>\n      <td>2.779258</td>\n      <td>2.899820</td>\n      <td>2.409793</td>\n      <td>3.117367</td>\n      <td>3.568433</td>\n      <td>2.863102</td>\n      <td>2.675586</td>\n      <td>2.733805</td>\n      <td>...</td>\n      <td>10.958717</td>\n      <td>7.620899</td>\n      <td>12.061928</td>\n      <td>-785.183479</td>\n      <td>5.142378</td>\n      <td>-87.062206</td>\n      <td>-13.400298</td>\n      <td>-19.598588</td>\n      <td>-8.785371</td>\n      <td>-16.494646</td>\n      <td>-15.657153</td>\n      <td>-24.264134</td>\n      <td>-2.844079</td>\n      <td>-11.527364</td>\n      <td>-16.598262</td>\n      <td>-7.364427</td>\n      <td>-4.040663</td>\n      <td>-5.106069</td>\n      <td>-12.913259</td>\n      <td>-16.852777</td>\n      <td>-5.813879</td>\n      <td>-7.242315</td>\n      <td>-9.925327</td>\n      <td>0.294251</td>\n      <td>0.077837</td>\n      <td>-0.160418</td>\n      <td>0.475098</td>\n      <td>0.098145</td>\n      <td>6334.939803</td>\n      <td>689.305899</td>\n      <td>-2.475258</td>\n      <td>7159.790039</td>\n      <td>3445.312500</td>\n      <td>2637.285418</td>\n      <td>245.334940</td>\n      <td>-0.103139</td>\n      <td>3585.775617</td>\n      <td>1910.926095</td>\n      <td>001ec0ad.wav</td>\n      <td>Finger_snapping</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-429.256141</td>\n      <td>89.789210</td>\n      <td>-3.267959</td>\n      <td>25.943623</td>\n      <td>-10.381607</td>\n      <td>5.995649</td>\n      <td>-5.471482</td>\n      <td>7.434913</td>\n      <td>0.204388</td>\n      <td>11.546948</td>\n      <td>1.724260</td>\n      <td>5.590036</td>\n      <td>2.697353</td>\n      <td>4.828801</td>\n      <td>4.021077</td>\n      <td>4.495891</td>\n      <td>1.704539</td>\n      <td>0.973502</td>\n      <td>-0.313691</td>\n      <td>4.303538</td>\n      <td>92.774764</td>\n      <td>35.785890</td>\n      <td>19.374310</td>\n      <td>11.614118</td>\n      <td>14.438876</td>\n      <td>10.936619</td>\n      <td>11.434155</td>\n      <td>7.377882</td>\n      <td>8.426847</td>\n      <td>6.186598</td>\n      <td>6.702710</td>\n      <td>5.934732</td>\n      <td>5.659035</td>\n      <td>5.009476</td>\n      <td>4.884049</td>\n      <td>4.892464</td>\n      <td>4.612518</td>\n      <td>4.396953</td>\n      <td>4.451472</td>\n      <td>4.430248</td>\n      <td>...</td>\n      <td>17.188587</td>\n      <td>13.986233</td>\n      <td>22.290690</td>\n      <td>-599.918235</td>\n      <td>9.567578</td>\n      <td>-95.874499</td>\n      <td>-22.919304</td>\n      <td>-61.543827</td>\n      <td>-35.481633</td>\n      <td>-45.069870</td>\n      <td>-29.306123</td>\n      <td>-32.703857</td>\n      <td>-9.403415</td>\n      <td>-38.398746</td>\n      <td>-16.327934</td>\n      <td>-19.206187</td>\n      <td>-20.469787</td>\n      <td>-19.553214</td>\n      <td>-12.346420</td>\n      <td>-15.052636</td>\n      <td>-16.458089</td>\n      <td>-14.184880</td>\n      <td>-10.601411</td>\n      <td>0.109377</td>\n      <td>0.054297</td>\n      <td>0.507787</td>\n      <td>0.255371</td>\n      <td>0.014160</td>\n      <td>5201.838784</td>\n      <td>1383.682232</td>\n      <td>-1.224612</td>\n      <td>7385.888672</td>\n      <td>656.762695</td>\n      <td>2419.062894</td>\n      <td>659.047645</td>\n      <td>-0.610302</td>\n      <td>4120.688332</td>\n      <td>783.909282</td>\n      <td>0026c7cb.wav</td>\n      <td>Run</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-697.901080</td>\n      <td>7.609523</td>\n      <td>-2.702084</td>\n      <td>3.314518</td>\n      <td>1.075505</td>\n      <td>6.472180</td>\n      <td>1.506116</td>\n      <td>0.979527</td>\n      <td>0.642133</td>\n      <td>3.388375</td>\n      <td>0.368710</td>\n      <td>1.285987</td>\n      <td>0.282099</td>\n      <td>1.307120</td>\n      <td>2.080976</td>\n      <td>1.527842</td>\n      <td>-0.266796</td>\n      <td>1.709230</td>\n      <td>-0.576489</td>\n      <td>1.723662</td>\n      <td>154.435596</td>\n      <td>20.669898</td>\n      <td>14.534752</td>\n      <td>9.112657</td>\n      <td>6.169057</td>\n      <td>16.440526</td>\n      <td>5.880632</td>\n      <td>3.174800</td>\n      <td>4.244987</td>\n      <td>8.043572</td>\n      <td>5.713224</td>\n      <td>3.745933</td>\n      <td>4.225375</td>\n      <td>4.302439</td>\n      <td>5.589612</td>\n      <td>4.765563</td>\n      <td>3.869477</td>\n      <td>4.340709</td>\n      <td>4.579368</td>\n      <td>4.241854</td>\n      <td>...</td>\n      <td>17.325322</td>\n      <td>11.848079</td>\n      <td>19.295846</td>\n      <td>-751.576898</td>\n      <td>0.000000</td>\n      <td>-77.692454</td>\n      <td>-7.878032</td>\n      <td>-22.091396</td>\n      <td>-9.486487</td>\n      <td>-18.894384</td>\n      <td>-6.771509</td>\n      <td>-17.096250</td>\n      <td>0.000000</td>\n      <td>-20.151914</td>\n      <td>-3.825721</td>\n      <td>-20.549645</td>\n      <td>-11.527579</td>\n      <td>-7.810526</td>\n      <td>-7.992131</td>\n      <td>-18.882413</td>\n      <td>-6.015112</td>\n      <td>-24.874474</td>\n      <td>-3.763872</td>\n      <td>0.018467</td>\n      <td>0.048999</td>\n      <td>2.862872</td>\n      <td>0.230957</td>\n      <td>0.000000</td>\n      <td>904.659284</td>\n      <td>1973.363115</td>\n      <td>2.011765</td>\n      <td>7956.518555</td>\n      <td>0.000000</td>\n      <td>440.434445</td>\n      <td>987.991755</td>\n      <td>2.283926</td>\n      <td>4182.347219</td>\n      <td>0.000000</td>\n      <td>0026f116.wav</td>\n      <td>Finger_snapping</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def extract_features(files, path):\n    features = {}\n    cnt = 0\n    for f in tqdm(files):\n        features[f] = {}\n        fs, data = scipy.io.wavfile.read(os.path.join(path, f))\n        abs_data = np.abs(data)\n        diff_data = np.diff(data)\n        def calc_part_features(data, n=2, prefix=''):\n            f_i = 1\n            for i in range(0, len(data), len(data)//n):\n                features[f]['{}mean_{}_{}'.format(prefix, f_i, n)] = np.mean(data[i:i + len(data)//n])\n                features[f]['{}std_{}_{}'.format(prefix, f_i, n)] = np.std(data[i:i + len(data)//n])\n                features[f]['{}min_{}_{}'.format(prefix, f_i, n)] = np.min(data[i:i + len(data)//n])\n                features[f]['{}max_{}_{}'.format(prefix, f_i, n)] = np.max(data[i:i + len(data)//n])\n        features[f]['len'] = len(data)\n        if features[f]['len'] > 0:\n            n = 1\n            calc_part_features(data, n=n)\n            calc_part_features(abs_data, n=n, prefix='abs_')\n            calc_part_features(diff_data, n=n, prefix='diff_')\n\n            n = 2\n            calc_part_features(data, n=n)\n            calc_part_features(abs_data, n=n, prefix='abs_')\n            calc_part_features(diff_data, n=n, prefix='diff_')\n\n            n = 3\n            calc_part_features(data, n=n)\n            calc_part_features(abs_data, n=n, prefix='abs_')\n            calc_part_features(diff_data, n=n, prefix='diff_')\n        cnt += 1\n    features = pd.DataFrame(features).T.reset_index()\n    features.rename(columns={'index': 'fname'}, inplace=True)\n    return features\n\npath = os.path.join(data_path, 'train_curated')\ntrain_files = train.fname.values\ntrain_features = extract_features(train_files, path)\n\npath = os.path.join(data_path, 'test')\ntest_files = ss.fname.values\ntest_features = extract_features(test_files, path)","execution_count":13,"outputs":[{"output_type":"stream","text":"\n\n\n\n  0%|          | 0/4970 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n\n\n\n  0%|          | 4/4970 [00:00<03:09, 26.19it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n\n\n\n  0%|          | 11/4970 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0.0\n1 -666.170512   89.343013  11.708950   ...      75.109294   25.900444      0.0\n2 -738.851927   24.688094   8.369442   ...     334.738651    0.924074      0.0\n3 -429.256141   89.789210  -3.267959   ...     581.425611  646.524825      0.0\n4 -697.901080    7.609523  -2.702084   ...     649.458686    0.000000      0.0\n\n[5 rows x 154 columns]","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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>...</th>\n      <th>114</th>\n      <th>fname</th>\n      <th>label</th>\n      <th>abs_max_1_1</th>\n      <th>abs_max_1_2</th>\n      <th>abs_max_1_3</th>\n      <th>abs_mean_1_1</th>\n      <th>abs_mean_1_2</th>\n      <th>abs_mean_1_3</th>\n      <th>abs_min_1_1</th>\n      <th>abs_min_1_2</th>\n      <th>abs_min_1_3</th>\n      <th>abs_std_1_1</th>\n      <th>abs_std_1_2</th>\n      <th>abs_std_1_3</th>\n      <th>diff_max_1_1</th>\n      <th>diff_max_1_2</th>\n      <th>diff_max_1_3</th>\n      <th>diff_mean_1_1</th>\n      <th>diff_mean_1_2</th>\n      <th>diff_mean_1_3</th>\n      <th>diff_min_1_1</th>\n      <th>diff_min_1_2</th>\n      <th>diff_min_1_3</th>\n      <th>diff_std_1_1</th>\n      <th>diff_std_1_2</th>\n      <th>diff_std_1_3</th>\n      <th>len</th>\n      <th>max_1_1</th>\n      <th>max_1_2</th>\n      <th>max_1_3</th>\n      <th>mean_1_1</th>\n      <th>mean_1_2</th>\n      <th>mean_1_3</th>\n      <th>min_1_1</th>\n      <th>min_1_2</th>\n      <th>min_1_3</th>\n      <th>std_1_1</th>\n      <th>std_1_2</th>\n      <th>std_1_3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-505.089749</td>\n      <td>180.441064</td>\n      <td>15.068468</td>\n      <td>10.800259</td>\n      <td>-9.600303</td>\n      <td>4.576918</td>\n      <td>8.881082</td>\n      <td>-6.049447</td>\n      <td>-4.160875</td>\n      <td>4.630835</td>\n      <td>1.992888</td>\n      <td>0.560607</td>\n      <td>-0.090579</td>\n      <td>3.867188</td>\n      <td>3.349992</td>\n      <td>1.606435</td>\n      <td>1.705743</td>\n      <td>1.694710</td>\n      <td>5.780792</td>\n      <td>9.075267</td>\n      <td>67.172105</td>\n      <td>30.846796</td>\n      <td>23.057665</td>\n      <td>10.070208</td>\n      <td>16.333535</td>\n      <td>9.430675</td>\n      <td>8.262834</td>\n      <td>15.407742</td>\n      <td>10.851452</td>\n      <td>7.908980</td>\n      <td>8.867473</td>\n      <td>6.861082</td>\n      <td>7.182113</td>\n      <td>6.911124</td>\n      <td>6.462065</td>\n      <td>6.216631</td>\n      <td>7.572919</td>\n      <td>7.139398</td>\n      <td>6.598127</td>\n      <td>9.187224</td>\n      <td>...</td>\n      <td>412.778244</td>\n      <td>0006ae4e.wav</td>\n      <td>Bark</td>\n      <td>3593.0</td>\n      <td>3593.0</td>\n      <td>2.0</td>\n      <td>260.261663</td>\n      <td>211.867614</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>314.171332</td>\n      <td>282.656719</td>\n      <td>0.0</td>\n      <td>633.0</td>\n      <td>1.0</td>\n      <td>633.0</td>\n      <td>0.000006</td>\n      <td>1.0</td>\n      <td>-0.001971</td>\n      <td>-688.0</td>\n      <td>1.0</td>\n      <td>-688.0</td>\n      <td>43.599811</td>\n      <td>0.0</td>\n      <td>56.619833</td>\n      <td>310456.0</td>\n      <td>3593.0</td>\n      <td>3593.0</td>\n      <td>2.0</td>\n      <td>-1.881023</td>\n      <td>-2.097250</td>\n      <td>2.0</td>\n      <td>-2891.0</td>\n      <td>-2891.0</td>\n      <td>2.0</td>\n      <td>407.965955</td>\n      <td>353.239732</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-666.170512</td>\n      <td>89.343013</td>\n      <td>11.708950</td>\n      <td>44.341037</td>\n      <td>19.341729</td>\n      <td>23.879220</td>\n      <td>-3.023549</td>\n      <td>22.180218</td>\n      <td>11.925984</td>\n      <td>16.667709</td>\n      <td>-2.438549</td>\n      <td>5.341580</td>\n      <td>-0.705911</td>\n      <td>-6.309737</td>\n      <td>-6.273408</td>\n      <td>7.822523</td>\n      <td>2.538993</td>\n      <td>5.940230</td>\n      <td>4.128231</td>\n      <td>3.514395</td>\n      <td>111.003256</td>\n      <td>34.160382</td>\n      <td>27.391266</td>\n      <td>21.582060</td>\n      <td>16.461118</td>\n      <td>11.123822</td>\n      <td>10.640620</td>\n      <td>15.637882</td>\n      <td>9.534221</td>\n      <td>7.353127</td>\n      <td>6.592269</td>\n      <td>5.949958</td>\n      <td>9.484252</td>\n      <td>8.613264</td>\n      <td>7.694590</td>\n      <td>6.456034</td>\n      <td>5.835454</td>\n      <td>4.659840</td>\n      <td>5.759114</td>\n      <td>5.000570</td>\n      <td>...</td>\n      <td>938.740016</td>\n      <td>0019ef41.wav</td>\n      <td>Raindrop</td>\n      <td>4993.0</td>\n      <td>134.0</td>\n      <td>0.0</td>\n      <td>34.783239</td>\n      <td>16.208727</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>66.572596</td>\n      <td>20.219058</td>\n      <td>0.0</td>\n      <td>6905.0</td>\n      <td>0.0</td>\n      <td>32.0</td>\n      <td>-0.004072</td>\n      <td>0.0</td>\n      <td>-0.000397</td>\n      <td>-5490.0</td>\n      <td>0.0</td>\n      <td>-36.0</td>\n      <td>62.045847</td>\n      <td>0.0</td>\n      <td>1.491975</td>\n      <td>90616.0</td>\n      <td>4993.0</td>\n      <td>134.0</td>\n      <td>0.0</td>\n      <td>-0.614947</td>\n      <td>-0.836740</td>\n      <td>0.0</td>\n      <td>-3951.0</td>\n      <td>-129.0</td>\n      <td>0.0</td>\n      <td>75.109294</td>\n      <td>25.900444</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-738.851927</td>\n      <td>24.688094</td>\n      <td>8.369442</td>\n      <td>10.640992</td>\n      <td>5.429327</td>\n      <td>4.093085</td>\n      <td>0.947911</td>\n      <td>0.124441</td>\n      <td>-0.381000</td>\n      <td>4.122988</td>\n      <td>1.677409</td>\n      <td>-0.168981</td>\n      <td>0.747731</td>\n      <td>1.216025</td>\n      <td>0.094377</td>\n      <td>-1.380938</td>\n      <td>-2.286875</td>\n      <td>-0.766164</td>\n      <td>-0.674148</td>\n      <td>-0.179731</td>\n      <td>113.765049</td>\n      <td>14.708152</td>\n      <td>22.546999</td>\n      <td>6.149522</td>\n      <td>5.795745</td>\n      <td>4.910861</td>\n      <td>4.428245</td>\n      <td>4.356917</td>\n      <td>5.998273</td>\n      <td>6.643124</td>\n      <td>3.508556</td>\n      <td>4.376543</td>\n      <td>2.779258</td>\n      <td>2.899820</td>\n      <td>2.409793</td>\n      <td>3.117367</td>\n      <td>3.568433</td>\n      <td>2.863102</td>\n      <td>2.675586</td>\n      <td>2.733805</td>\n      <td>...</td>\n      <td>1910.926095</td>\n      <td>001ec0ad.wav</td>\n      <td>Finger_snapping</td>\n      <td>21248.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>18.028577</td>\n      <td>0.764901</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>334.253144</td>\n      <td>0.718669</td>\n      <td>0.0</td>\n      <td>19286.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.000418</td>\n      <td>0.0</td>\n      <td>0.000045</td>\n      <td>-25905.0</td>\n      <td>0.0</td>\n      <td>-6.0</td>\n      <td>185.995498</td>\n      <td>0.0</td>\n      <td>0.433791</td>\n      <td>66976.0</td>\n      <td>18134.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>-0.479485</td>\n      <td>-0.497641</td>\n      <td>0.0</td>\n      <td>-21248.0</td>\n      <td>-6.0</td>\n      <td>0.0</td>\n      <td>334.738651</td>\n      <td>0.924074</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-429.256141</td>\n      <td>89.789210</td>\n      <td>-3.267959</td>\n      <td>25.943623</td>\n      <td>-10.381607</td>\n      <td>5.995649</td>\n      <td>-5.471482</td>\n      <td>7.434913</td>\n      <td>0.204388</td>\n      <td>11.546948</td>\n      <td>1.724260</td>\n      <td>5.590036</td>\n      <td>2.697353</td>\n      <td>4.828801</td>\n      <td>4.021077</td>\n      <td>4.495891</td>\n      <td>1.704539</td>\n      <td>0.973502</td>\n      <td>-0.313691</td>\n      <td>4.303538</td>\n      <td>92.774764</td>\n      <td>35.785890</td>\n      <td>19.374310</td>\n      <td>11.614118</td>\n      <td>14.438876</td>\n      <td>10.936619</td>\n      <td>11.434155</td>\n      <td>7.377882</td>\n      <td>8.426847</td>\n      <td>6.186598</td>\n      <td>6.702710</td>\n      <td>5.934732</td>\n      <td>5.659035</td>\n      <td>5.009476</td>\n      <td>4.884049</td>\n      <td>4.892464</td>\n      <td>4.612518</td>\n      <td>4.396953</td>\n      <td>4.451472</td>\n      <td>4.430248</td>\n      <td>...</td>\n      <td>783.909282</td>\n      <td>0026c7cb.wav</td>\n      <td>Run</td>\n      <td>29301.0</td>\n      <td>29301.0</td>\n      <td>34.0</td>\n      <td>156.123777</td>\n      <td>168.922580</td>\n      <td>34.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>34.0</td>\n      <td>560.072413</td>\n      <td>624.067002</td>\n      <td>0.0</td>\n      <td>7476.0</td>\n      <td>-19.0</td>\n      <td>4856.0</td>\n      <td>0.000029</td>\n      <td>-19.0</td>\n      <td>0.000829</td>\n      <td>-9124.0</td>\n      <td>-19.0</td>\n      <td>-6492.0</td>\n      <td>107.378732</td>\n      <td>0.0</td>\n      <td>116.508453</td>\n      <td>1125886.0</td>\n      <td>28566.0</td>\n      <td>28566.0</td>\n      <td>34.0</td>\n      <td>0.000040</td>\n      <td>-0.334592</td>\n      <td>34.0</td>\n      <td>-29301.0</td>\n      <td>-29301.0</td>\n      <td>34.0</td>\n      <td>581.425611</td>\n      <td>646.524825</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-697.901080</td>\n      <td>7.609523</td>\n      <td>-2.702084</td>\n      <td>3.314518</td>\n      <td>1.075505</td>\n      <td>6.472180</td>\n      <td>1.506116</td>\n      <td>0.979527</td>\n      <td>0.642133</td>\n      <td>3.388375</td>\n      <td>0.368710</td>\n      <td>1.285987</td>\n      <td>0.282099</td>\n      <td>1.307120</td>\n      <td>2.080976</td>\n      <td>1.527842</td>\n      <td>-0.266796</td>\n      <td>1.709230</td>\n      <td>-0.576489</td>\n      <td>1.723662</td>\n      <td>154.435596</td>\n      <td>20.669898</td>\n      <td>14.534752</td>\n      <td>9.112657</td>\n      <td>6.169057</td>\n      <td>16.440526</td>\n      <td>5.880632</td>\n      <td>3.174800</td>\n      <td>4.244987</td>\n      <td>8.043572</td>\n      <td>5.713224</td>\n      <td>3.745933</td>\n      <td>4.225375</td>\n      <td>4.302439</td>\n      <td>5.589612</td>\n      <td>4.765563</td>\n      <td>3.869477</td>\n      <td>4.340709</td>\n      <td>4.579368</td>\n      <td>4.241854</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0026f116.wav</td>\n      <td>Finger_snapping</td>\n      <td>28873.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>80.229081</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>644.484197</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>30680.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>-32629.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>476.771176</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>60638.0</td>\n      <td>28873.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.023005</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>-27956.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>649.458686</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Functions from LightGBM baseline: https://www.kaggle.com/opanichev/lightgbm-baseline\n# Construct features set\nX = train_data.drop(['label', 'fname'], axis=1)\nfeature_names = list(X.columns)\nX = X.values\nlabels = np.sort(np.unique(train_data.label.values))\nnum_class = len(labels)\nc2i = {}\ni2c = {}\nfor i, c in enumerate(labels):\n    c2i[c] = i\n    i2c[i] = c\ny = np.array([c2i[x.split(',')[0]] for x in train_data.label.values])","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":27,"outputs":[{"output_type":"execute_result","execution_count":27,"data":{"text/plain":"78"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.1, random_state=10, shuffle = True)\nclf = LGBMClassifier(max_depth=5, learning_rate=0.05, n_estimators=1000,\n                    n_jobs=-1, random_state=0, reg_alpha=0.2, \n                    colsample_bylevel=0.5, colsample_bytree=0.5)\nclf.fit(X_train, y_train)\nprint(accuracy_score(clf.predict(X_val), y_val))\n#more functions from LightGBM baseline: https://www.kaggle.com/opanichev/lightgbm-baseline","execution_count":33,"outputs":[{"output_type":"stream","text":"0.45472837022132795\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"p = clf.predict_proba(test_data.drop(['label', 'fname'], axis =1).values)","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"p.shape","execution_count":26,"outputs":[{"output_type":"execute_result","execution_count":26,"data":{"text/plain":"(1120, 78)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(labels)):\n    submission[i2c[i]] = p[:, i]","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.head()","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"          fname        ...          Zipper_(clothing)\n0  000ccb97.wav        ...                   0.010350\n1  0012633b.wav        ...                   0.012359\n2  001ed5f1.wav        ...                   0.011453\n3  00294be0.wav        ...                   0.010675\n4  003fde7a.wav        ...                   0.095865\n\n[5 rows x 81 columns]","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>fname</th>\n      <th>Accelerating_and_revving_and_vroom</th>\n      <th>Accordion</th>\n      <th>Acoustic_guitar</th>\n      <th>Applause</th>\n      <th>Bark</th>\n      <th>Bass_drum</th>\n      <th>Bass_guitar</th>\n      <th>Bathtub_(filling_or_washing)</th>\n      <th>Bicycle_bell</th>\n      <th>Burping_and_eructation</th>\n      <th>Bus</th>\n      <th>Buzz</th>\n      <th>Car_passing_by</th>\n      <th>Cheering</th>\n      <th>Chewing_and_mastication</th>\n      <th>Child_speech_and_kid_speaking</th>\n      <th>Chink_and_clink</th>\n      <th>Chirp_and_tweet</th>\n      <th>Church_bell</th>\n      <th>Clapping</th>\n      <th>Computer_keyboard</th>\n      <th>Crackle</th>\n      <th>Cricket</th>\n      <th>Crowd</th>\n      <th>Cupboard_open_or_close</th>\n      <th>Cutlery_and_silverware</th>\n      <th>Dishes_and_pots_and_pans</th>\n      <th>Drawer_open_or_close</th>\n      <th>Drip</th>\n      <th>Electric_guitar</th>\n      <th>Fart</th>\n      <th>Female_singing</th>\n      <th>Female_speech_and_woman_speaking</th>\n      <th>Fill_(with_liquid)</th>\n      <th>Finger_snapping</th>\n      <th>Frying_(food)</th>\n      <th>Gasp</th>\n      <th>Glockenspiel</th>\n      <th>Gong</th>\n      <th>...</th>\n      <th>Harmonica</th>\n      <th>Hi-hat</th>\n      <th>Hiss</th>\n      <th>Keys_jangling</th>\n      <th>Knock</th>\n      <th>Male_singing</th>\n      <th>Male_speech_and_man_speaking</th>\n      <th>Marimba_and_xylophone</th>\n      <th>Mechanical_fan</th>\n      <th>Meow</th>\n      <th>Microwave_oven</th>\n      <th>Motorcycle</th>\n      <th>Printer</th>\n      <th>Purr</th>\n      <th>Race_car_and_auto_racing</th>\n      <th>Raindrop</th>\n      <th>Run</th>\n      <th>Scissors</th>\n      <th>Screaming</th>\n      <th>Shatter</th>\n      <th>Sigh</th>\n      <th>Sink_(filling_or_washing)</th>\n      <th>Skateboard</th>\n      <th>Slam</th>\n      <th>Sneeze</th>\n      <th>Squeak</th>\n      <th>Stream</th>\n      <th>Strum</th>\n      <th>Tap</th>\n      <th>Tick-tock</th>\n      <th>Toilet_flush</th>\n      <th>Traffic_noise_and_roadway_noise</th>\n      <th>Trickle_and_dribble</th>\n      <th>Walk_and_footsteps</th>\n      <th>Water_tap_and_faucet</th>\n      <th>Waves_and_surf</th>\n      <th>Whispering</th>\n      <th>Writing</th>\n      <th>Yell</th>\n      <th>Zipper_(clothing)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>000ccb97.wav</td>\n      <td>0.026983</td>\n      <td>0.010287</td>\n      <td>0.010226</td>\n      <td>0.010723</td>\n      <td>0.010249</td>\n      <td>0.010736</td>\n      <td>0.010495</td>\n      <td>0.010190</td>\n      <td>0.010216</td>\n      <td>0.010239</td>\n      <td>0.023115</td>\n      <td>0.018187</td>\n      <td>0.036117</td>\n      <td>0.010165</td>\n      <td>0.010230</td>\n      <td>0.010218</td>\n      <td>0.010204</td>\n      <td>0.015536</td>\n      <td>0.022947</td>\n      <td>0.010395</td>\n      <td>0.015744</td>\n      <td>0.010199</td>\n      <td>0.012297</td>\n      <td>0.010794</td>\n      <td>0.011423</td>\n      <td>0.010233</td>\n      <td>0.010512</td>\n      <td>0.010328</td>\n      <td>0.010170</td>\n      <td>0.010312</td>\n      <td>0.010220</td>\n      <td>0.010818</td>\n      <td>0.010190</td>\n      <td>0.010188</td>\n      <td>0.010172</td>\n      <td>0.010375</td>\n      <td>0.010316</td>\n      <td>0.010167</td>\n      <td>0.010850</td>\n      <td>...</td>\n      <td>0.010204</td>\n      <td>0.010201</td>\n      <td>0.014631</td>\n      <td>0.010342</td>\n      <td>0.010583</td>\n      <td>0.010172</td>\n      <td>0.011242</td>\n      <td>0.010385</td>\n      <td>0.035212</td>\n      <td>0.011159</td>\n      <td>0.014607</td>\n      <td>0.016263</td>\n      <td>0.011424</td>\n      <td>0.030332</td>\n      <td>0.013748</td>\n      <td>0.010204</td>\n      <td>0.011556</td>\n      <td>0.010337</td>\n      <td>0.010177</td>\n      <td>0.010202</td>\n      <td>0.010201</td>\n      <td>0.010208</td>\n      <td>0.011273</td>\n      <td>0.010364</td>\n      <td>0.010226</td>\n      <td>0.011198</td>\n      <td>0.013807</td>\n      <td>0</td>\n      <td>0.011304</td>\n      <td>0.010457</td>\n      <td>0.010354</td>\n      <td>0.028564</td>\n      <td>0.010173</td>\n      <td>0.011770</td>\n      <td>0</td>\n      <td>0.021712</td>\n      <td>0.010418</td>\n      <td>0.011490</td>\n      <td>0.010175</td>\n      <td>0.010350</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0012633b.wav</td>\n      <td>0.012510</td>\n      <td>0.011381</td>\n      <td>0.011424</td>\n      <td>0.011470</td>\n      <td>0.011500</td>\n      <td>0.011981</td>\n      <td>0.011377</td>\n      <td>0.011788</td>\n      <td>0.011433</td>\n      <td>0.011458</td>\n      <td>0.017397</td>\n      <td>0.029406</td>\n      <td>0.025846</td>\n      <td>0.011375</td>\n      <td>0.011448</td>\n      <td>0.011435</td>\n      <td>0.011419</td>\n      <td>0.015980</td>\n      <td>0.013130</td>\n      <td>0.011511</td>\n      <td>0.011514</td>\n      <td>0.011746</td>\n      <td>0.012836</td>\n      <td>0.012104</td>\n      <td>0.013734</td>\n      <td>0.011797</td>\n      <td>0.011421</td>\n      <td>0.012586</td>\n      <td>0.011381</td>\n      <td>0.013318</td>\n      <td>0.011455</td>\n      <td>0.011401</td>\n      <td>0.011404</td>\n      <td>0.011420</td>\n      <td>0.011383</td>\n      <td>0.011611</td>\n      <td>0.011451</td>\n      <td>0.011378</td>\n      <td>0.018449</td>\n      <td>...</td>\n      <td>0.011419</td>\n      <td>0.011416</td>\n      <td>0.011978</td>\n      <td>0.011455</td>\n      <td>0.012591</td>\n      <td>0.011384</td>\n      <td>0.012232</td>\n      <td>0.011545</td>\n      <td>0.011396</td>\n      <td>0.011925</td>\n      <td>0.015684</td>\n      <td>0.012376</td>\n      <td>0.012636</td>\n      <td>0.015937</td>\n      <td>0.013489</td>\n      <td>0.011419</td>\n      <td>0.013269</td>\n      <td>0.011683</td>\n      <td>0.011388</td>\n      <td>0.011417</td>\n      <td>0.012387</td>\n      <td>0.011424</td>\n      <td>0.012101</td>\n      <td>0.011871</td>\n      <td>0.011444</td>\n      <td>0.012035</td>\n      <td>0.014425</td>\n      <td>0</td>\n      <td>0.011749</td>\n      <td>0.011976</td>\n      <td>0.011446</td>\n      <td>0.023689</td>\n      <td>0.011385</td>\n      <td>0.017037</td>\n      <td>0</td>\n      <td>0.012090</td>\n      <td>0.012056</td>\n      <td>0.013421</td>\n      <td>0.011386</td>\n      <td>0.012359</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>001ed5f1.wav</td>\n      <td>0.011018</td>\n      <td>0.011465</td>\n      <td>0.011010</td>\n      <td>0.026148</td>\n      <td>0.011427</td>\n      <td>0.011799</td>\n      <td>0.011508</td>\n      <td>0.017791</td>\n      <td>0.011019</td>\n      <td>0.011809</td>\n      <td>0.012419</td>\n      <td>0.016346</td>\n      <td>0.011089</td>\n      <td>0.010964</td>\n      <td>0.011310</td>\n      <td>0.011283</td>\n      <td>0.012456</td>\n      <td>0.016562</td>\n      <td>0.012054</td>\n      <td>0.011175</td>\n      <td>0.011413</td>\n      <td>0.019380</td>\n      <td>0.011794</td>\n      <td>0.011115</td>\n      <td>0.010984</td>\n      <td>0.011724</td>\n      <td>0.016131</td>\n      <td>0.011156</td>\n      <td>0.011030</td>\n      <td>0.011396</td>\n      <td>0.011023</td>\n      <td>0.012626</td>\n      <td>0.015267</td>\n      <td>0.011949</td>\n      <td>0.010971</td>\n      <td>0.014878</td>\n      <td>0.010979</td>\n      <td>0.010966</td>\n      <td>0.017408</td>\n      <td>...</td>\n      <td>0.012299</td>\n      <td>0.027684</td>\n      <td>0.012654</td>\n      <td>0.011376</td>\n      <td>0.011123</td>\n      <td>0.011570</td>\n      <td>0.011277</td>\n      <td>0.017399</td>\n      <td>0.010984</td>\n      <td>0.014144</td>\n      <td>0.011052</td>\n      <td>0.011034</td>\n      <td>0.016410</td>\n      <td>0.011001</td>\n      <td>0.010998</td>\n      <td>0.019653</td>\n      <td>0.011078</td>\n      <td>0.019111</td>\n      <td>0.011999</td>\n      <td>0.011004</td>\n      <td>0.016580</td>\n      <td>0.011011</td>\n      <td>0.011161</td>\n      <td>0.012813</td>\n      <td>0.011030</td>\n      <td>0.011599</td>\n      <td>0.011066</td>\n      <td>0</td>\n      <td>0.011742</td>\n      <td>0.012469</td>\n      <td>0.011015</td>\n      <td>0.011170</td>\n      <td>0.011199</td>\n      <td>0.011655</td>\n      <td>0</td>\n      <td>0.011116</td>\n      <td>0.011197</td>\n      <td>0.012863</td>\n      <td>0.011158</td>\n      <td>0.011453</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>00294be0.wav</td>\n      <td>0.009614</td>\n      <td>0.009503</td>\n      <td>0.009496</td>\n      <td>0.010411</td>\n      <td>0.013096</td>\n      <td>0.011849</td>\n      <td>0.009391</td>\n      <td>0.009414</td>\n      <td>0.021746</td>\n      <td>0.009459</td>\n      <td>0.015398</td>\n      <td>0.010003</td>\n      <td>0.013421</td>\n      <td>0.009390</td>\n      <td>0.009583</td>\n      <td>0.009439</td>\n      <td>0.009429</td>\n      <td>0.010533</td>\n      <td>0.043291</td>\n      <td>0.009940</td>\n      <td>0.010523</td>\n      <td>0.010231</td>\n      <td>0.011296</td>\n      <td>0.014930</td>\n      <td>0.009408</td>\n      <td>0.009690</td>\n      <td>0.010100</td>\n      <td>0.009501</td>\n      <td>0.009395</td>\n      <td>0.009736</td>\n      <td>0.029091</td>\n      <td>0.011881</td>\n      <td>0.010234</td>\n      <td>0.009817</td>\n      <td>0.009397</td>\n      <td>0.009654</td>\n      <td>0.009403</td>\n      <td>0.009392</td>\n      <td>0.009478</td>\n      <td>...</td>\n      <td>0.009426</td>\n      <td>0.009424</td>\n      <td>0.009829</td>\n      <td>0.010210</td>\n      <td>0.009607</td>\n      <td>0.009609</td>\n      <td>0.010649</td>\n      <td>0.009960</td>\n      <td>0.012601</td>\n      <td>0.015342</td>\n      <td>0.012294</td>\n      <td>0.018364</td>\n      <td>0.015223</td>\n      <td>0.009713</td>\n      <td>0.023959</td>\n      <td>0.016469</td>\n      <td>0.016988</td>\n      <td>0.010394</td>\n      <td>0.009693</td>\n      <td>0.010400</td>\n      <td>0.009424</td>\n      <td>0.009471</td>\n      <td>0.016598</td>\n      <td>0.012780</td>\n      <td>0.011751</td>\n      <td>0.022758</td>\n      <td>0.013596</td>\n      <td>0</td>\n      <td>0.034094</td>\n      <td>0.021119</td>\n      <td>0.016110</td>\n      <td>0.032756</td>\n      <td>0.009398</td>\n      <td>0.010624</td>\n      <td>0</td>\n      <td>0.010081</td>\n      <td>0.009882</td>\n      <td>0.011531</td>\n      <td>0.009399</td>\n      <td>0.010675</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>003fde7a.wav</td>\n      <td>0.010799</td>\n      <td>0.010389</td>\n      <td>0.010446</td>\n      <td>0.010431</td>\n      <td>0.010469</td>\n      <td>0.010423</td>\n      <td>0.010385</td>\n      <td>0.017657</td>\n      <td>0.015148</td>\n      <td>0.010460</td>\n      <td>0.010495</td>\n      <td>0.010633</td>\n      <td>0.010402</td>\n      <td>0.010384</td>\n      <td>0.012037</td>\n      <td>0.010438</td>\n      <td>0.010427</td>\n      <td>0.011146</td>\n      <td>0.010507</td>\n      <td>0.010827</td>\n      <td>0.017628</td>\n      <td>0.012613</td>\n      <td>0.011215</td>\n      <td>0.010391</td>\n      <td>0.011725</td>\n      <td>0.010834</td>\n      <td>0.010449</td>\n      <td>0.011422</td>\n      <td>0.010729</td>\n      <td>0.010457</td>\n      <td>0.010440</td>\n      <td>0.010407</td>\n      <td>0.010410</td>\n      <td>0.012596</td>\n      <td>0.012328</td>\n      <td>0.011754</td>\n      <td>0.010412</td>\n      <td>0.010386</td>\n      <td>0.010481</td>\n      <td>...</td>\n      <td>0.010423</td>\n      <td>0.010544</td>\n      <td>0.010827</td>\n      <td>0.011448</td>\n      <td>0.010703</td>\n      <td>0.010392</td>\n      <td>0.011052</td>\n      <td>0.016431</td>\n      <td>0.010915</td>\n      <td>0.010496</td>\n      <td>0.010905</td>\n      <td>0.010619</td>\n      <td>0.013822</td>\n      <td>0.010765</td>\n      <td>0.010394</td>\n      <td>0.017585</td>\n      <td>0.017893</td>\n      <td>0.017747</td>\n      <td>0.010396</td>\n      <td>0.010463</td>\n      <td>0.010491</td>\n      <td>0.011187</td>\n      <td>0.010416</td>\n      <td>0.010473</td>\n      <td>0.011075</td>\n      <td>0.011167</td>\n      <td>0.010596</td>\n      <td>0</td>\n      <td>0.014217</td>\n      <td>0.021739</td>\n      <td>0.011667</td>\n      <td>0.010396</td>\n      <td>0.010393</td>\n      <td>0.012563</td>\n      <td>0</td>\n      <td>0.011052</td>\n      <td>0.010605</td>\n      <td>0.020161</td>\n      <td>0.010394</td>\n      <td>0.095865</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv('submission.csv', index = False)","execution_count":34,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls","execution_count":35,"outputs":[{"output_type":"stream","text":"__notebook_source__.ipynb  submission.csv\r\n","name":"stdout"}]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}