m@15: {
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m@15:   {
m@15:    "cell_type": "code",
m@60:    "execution_count": 1,
m@48:    "metadata": {},
m@15:    "outputs": [
m@15:     {
m@60:      "name": "stderr",
m@15:      "output_type": "stream",
m@15:      "text": [
m@60:       "/homes/mp305/anaconda/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
m@60:       "  warnings.warn('Could not import scikits.samplerate. '\n"
m@15:      ]
m@15:     }
m@15:    ],
m@15:    "source": [
m@15:     "import numpy as np\n",
m@42:     "import pandas as pd\n",
m@15:     "\n",
m@15:     "%matplotlib inline\n",
m@15:     "import matplotlib.pyplot as plt\n",
m@15:     "\n",
m@15:     "%load_ext autoreload\n",
m@15:     "%autoreload 2\n",
m@15:     "\n",
m@15:     "import sys\n",
m@15:     "sys.path.append('../')\n",
m@15:     "import scripts.load_dataset as load_dataset\n",
m@15:     "import scripts.map_and_average as mapper\n",
m@42:     "import scripts.classification as classification\n",
Maria@18:     "import scripts.outliers as outliers"
m@15:    ]
m@15:   },
m@15:   {
m@15:    "cell_type": "code",
m@55:    "execution_count": 2,
m@42:    "metadata": {
m@42:     "collapsed": true
m@42:    },
m@26:    "outputs": [],
m@15:    "source": [
m@17:     "OUTPUT_FILES = load_dataset.OUTPUT_FILES\n",
m@42:     "n_iters = 10"
m@15:    ]
m@15:   },
m@15:   {
m@15:    "cell_type": "code",
m@23:    "execution_count": 5,
m@48:    "metadata": {},
m@23:    "outputs": [
m@23:     {
m@23:      "data": {
m@23:       "text/plain": [
m@23:        "(8396, 108)"
m@23:       ]
m@23:      },
m@23:      "execution_count": 5,
m@23:      "metadata": {},
m@23:      "output_type": "execute_result"
m@23:     }
m@23:    ],
m@15:    "source": [
m@42:     "df = load_dataset.sample_dataset(csv_file=load_dataset.METADATA_FILE)\n",
m@21:     "df.shape"
m@21:    ]
m@21:   },
m@21:   {
m@21:    "cell_type": "code",
m@26:    "execution_count": 48,
m@48:    "metadata": {},
m@23:    "outputs": [
m@23:     {
m@23:      "name": "stdout",
m@23:      "output_type": "stream",
m@23:      "text": [
m@26:       "(45, 3600)\n",
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m@26:        "\n",
m@26:        "[5 rows x 3600 columns]"
m@26:       ]
m@26:      },
m@26:      "execution_count": 48,
m@26:      "metadata": {},
m@26:      "output_type": "execute_result"
m@23:     }
m@23:    ],
m@23:    "source": [
m@26:     "import pandas as pd\n",
m@26:     "import os\n",
m@26:     "pb_df = pd.read_csv(os.path.join('/import/c4dm-04/mariap/FeatureCsvs/PB-melodia/', '49483_SampleAudio.csv'), header=None)\n",
m@26:     "print pb_df.shape\n",
m@26:     "pbihist = np.loadtxt(os.path.join('/import/c4dm-04/mariap/FeatureCsvs/PB-melodia/', '49483_SampleAudio.csv'), delimiter=',')\n",
m@26:     "print pbihist.shape\n",
m@26:     "pb_df.head()"
m@26:    ]
m@26:   },
m@26:   {
m@26:    "cell_type": "code",
m@26:    "execution_count": 52,
m@48:    "metadata": {},
m@26:    "outputs": [
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MclT0mHfObSLvygA16CW3ScNyC96ncZtUD9fMW7lNdiDYXSnLnSnahgKmmnmL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\nxhzi/wP+tSnOiEfz1wAAAABJRU5ErkJggg==\n",
m@26:       "text/plain": [
m@26:        "<matplotlib.figure.Figure at 0x7f6a5fd9e610>"
m@26:       ]
m@26:      },
m@26:      "metadata": {},
m@26:      "output_type": "display_data"
m@26:     }
m@26:    ],
m@26:    "source": [
m@26:     "%matplotlib inline\n",
m@26:     "import matplotlib.pyplot as plt\n",
m@26:     "plt.imshow(pb_df, aspect='auto')\n",
m@26:     "print pb_df.iloc[:, 0]\n",
m@26:     "print pb_df.iloc[:, -1]\n",
m@26:     "print pb_df.iloc[:, 1]"
m@26:    ]
m@26:   },
m@26:   {
m@26:    "cell_type": "code",
m@26:    "execution_count": 56,
m@48:    "metadata": {},
m@26:    "outputs": [
m@26:     {
m@26:      "data": {
m@26:       "text/plain": [
m@26:        "array([ 59,   1,   1,   1,   1, 733, 733, 733, 733, 733, 733, 733, 733,\n",
m@26:        "       733, 733,  60,  60,  60, 123, 123, 123, 182,  60,   1,   1,   1,\n",
m@26:        "         1,   1,   1,   1,   1,   1,  60, 792, 792,  59,  59,  59,  59,\n",
m@26:        "        59,  59,  59,  59,  59,  59])"
m@26:       ]
m@26:      },
m@26:      "execution_count": 56,
m@26:      "metadata": {},
m@26:      "output_type": "execute_result"
m@26:     }
m@26:    ],
m@26:    "source": [
m@26:     "np.argmax(pb_df.get_values(), axis=1)"
m@23:    ]
m@23:   },
m@23:   {
m@23:    "cell_type": "code",
m@23:    "execution_count": 8,
m@48:    "metadata": {},
m@23:    "outputs": [
m@23:     {
m@23:      "data": {
m@23:       "text/html": [
m@23:        "<div>\n",
m@23:        "<table border=\"1\" class=\"dataframe\">\n",
m@23:        "  <thead>\n",
m@23:        "    <tr style=\"text-align: right;\">\n",
m@23:        "      <th></th>\n",
m@23:        "      <th>AlbumTitle</th>\n",
m@23:        "      <th>Artist</th>\n",
m@23:        "      <th>Artist_Album</th>\n",
m@23:        "      <th>BuyLinkTrackDownload</th>\n",
m@23:        "      <th>CatalogNumber</th>\n",
m@23:        "      <th>CatalogNumber_Album</th>\n",
m@23:        "      <th>Collection</th>\n",
m@23:        "      <th>ContentType</th>\n",
m@23:        "      <th>ContentType_Album</th>\n",
m@23:        "      <th>Country</th>\n",
m@23:        "      <th>...</th>\n",
m@23:        "      <th>Language_iso3</th>\n",
m@23:        "      <th>Language_iso1</th>\n",
m@23:        "      <th>Region</th>\n",
m@23:        "      <th>Latitude</th>\n",
m@23:        "      <th>Longitude</th>\n",
m@23:        "      <th>Audio</th>\n",
m@23:        "      <th>Speech</th>\n",
m@23:        "      <th>Melspec</th>\n",
m@23:        "      <th>Chroma</th>\n",
m@23:        "      <th>Melodia</th>\n",
m@23:        "    </tr>\n",
m@23:        "  </thead>\n",
m@23:        "  <tbody>\n",
m@23:        "    <tr>\n",
m@23:        "      <th>29182</th>\n",
m@23:        "      <td>Music of Afghanistan</td>\n",
m@23:        "      <td>Afghan National Orchestra</td>\n",
m@23:        "      <td>Various Artists</td>\n",
m@23:        "      <td>26138</td>\n",
m@23:        "      <td>FW04361_201</td>\n",
m@23:        "      <td>FW04361</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>Track</td>\n",
m@23:        "      <td>Album</td>\n",
m@23:        "      <td>Afghanistan</td>\n",
m@23:        "      <td>...</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>South Asia</td>\n",
m@23:        "      <td>33.93911</td>\n",
m@23:        "      <td>67.709953</td>\n",
m@23:        "      <td>/import/c4dm-scratch/matthiasm/data/smithsonia...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/SpeechMusic/2613...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/MelSpec/26138_Sa...</td>\n",
m@23:        "      <td>/import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/Melodia/26138_Sa...</td>\n",
m@23:        "    </tr>\n",
m@23:        "    <tr>\n",
m@23:        "      <th>29183</th>\n",
m@23:        "      <td>Music of Afghanistan</td>\n",
m@23:        "      <td>Yaqub Kasimi</td>\n",
m@23:        "      <td>Various Artists</td>\n",
m@23:        "      <td>26142</td>\n",
m@23:        "      <td>FW04361_205</td>\n",
m@23:        "      <td>FW04361</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>Track</td>\n",
m@23:        "      <td>Album</td>\n",
m@23:        "      <td>Afghanistan</td>\n",
m@23:        "      <td>...</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>South Asia</td>\n",
m@23:        "      <td>33.93911</td>\n",
m@23:        "      <td>67.709953</td>\n",
m@23:        "      <td>/import/c4dm-scratch/matthiasm/data/smithsonia...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/SpeechMusic/2614...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/MelSpec/26142_Sa...</td>\n",
m@23:        "      <td>/import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/Melodia/26142_Sa...</td>\n",
m@23:        "    </tr>\n",
m@23:        "    <tr>\n",
m@23:        "      <th>29184</th>\n",
m@23:        "      <td>Ustad Mohammad Omar: Virtuoso from Afghanistan</td>\n",
m@23:        "      <td>Ustad Mohammad Omar|Zakir Hussain</td>\n",
m@23:        "      <td>Ustad Mohammad Omar</td>\n",
m@23:        "      <td>51931</td>\n",
m@23:        "      <td>SFW40439_102</td>\n",
m@23:        "      <td>SFW40439</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>Track</td>\n",
m@23:        "      <td>Album</td>\n",
m@23:        "      <td>Afghanistan</td>\n",
m@23:        "      <td>...</td>\n",
m@23:        "      <td>pbt</td>\n",
m@23:        "      <td>Indo-European</td>\n",
m@23:        "      <td>South Asia</td>\n",
m@23:        "      <td>33.93911</td>\n",
m@23:        "      <td>67.709953</td>\n",
m@23:        "      <td>/import/c4dm-scratch/matthiasm/data/smithsonia...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/SpeechMusic/5193...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/MelSpec/51931_Sa...</td>\n",
m@23:        "      <td>/import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/Melodia/51931_Sa...</td>\n",
m@23:        "    </tr>\n",
m@23:        "    <tr>\n",
m@23:        "      <th>29185</th>\n",
m@23:        "      <td>Music of Central Asia Vol. 3: Homayun Sakhi: T...</td>\n",
m@23:        "      <td>Homayun Sakhi</td>\n",
m@23:        "      <td>Homayun Sakhi</td>\n",
m@23:        "      <td>53229</td>\n",
m@23:        "      <td>SFW40522_101</td>\n",
m@23:        "      <td>SFW40522</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>Track</td>\n",
m@23:        "      <td>Album</td>\n",
m@23:        "      <td>Afghanistan</td>\n",
m@23:        "      <td>...</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>South Asia</td>\n",
m@23:        "      <td>33.93911</td>\n",
m@23:        "      <td>67.709953</td>\n",
m@23:        "      <td>/import/c4dm-scratch/matthiasm/data/smithsonia...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/SpeechMusic/5322...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/MelSpec/53229_Sa...</td>\n",
m@23:        "      <td>/import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/Melodia/53229_Sa...</td>\n",
m@23:        "    </tr>\n",
m@23:        "    <tr>\n",
m@23:        "      <th>29186</th>\n",
m@23:        "      <td>Music of Afghanistan</td>\n",
m@23:        "      <td>Rebab and Dhol Duet</td>\n",
m@23:        "      <td>Various Artists</td>\n",
m@23:        "      <td>26140</td>\n",
m@23:        "      <td>FW04361_203</td>\n",
m@23:        "      <td>FW04361</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>Track</td>\n",
m@23:        "      <td>Album</td>\n",
m@23:        "      <td>Afghanistan</td>\n",
m@23:        "      <td>...</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>NaN</td>\n",
m@23:        "      <td>South Asia</td>\n",
m@23:        "      <td>33.93911</td>\n",
m@23:        "      <td>67.709953</td>\n",
m@23:        "      <td>/import/c4dm-scratch/matthiasm/data/smithsonia...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/SpeechMusic/2614...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/MelSpec/26140_Sa...</td>\n",
m@23:        "      <td>/import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...</td>\n",
m@23:        "      <td>/import/c4dm-02/people/mariap/Melodia/26140_Sa...</td>\n",
m@23:        "    </tr>\n",
m@23:        "  </tbody>\n",
m@23:        "</table>\n",
m@23:        "<p>5 rows × 108 columns</p>\n",
m@23:        "</div>"
m@23:       ],
m@23:       "text/plain": [
m@23:        "                                              AlbumTitle  \\\n",
m@23:        "29182                               Music of Afghanistan   \n",
m@23:        "29183                               Music of Afghanistan   \n",
m@23:        "29184     Ustad Mohammad Omar: Virtuoso from Afghanistan   \n",
m@23:        "29185  Music of Central Asia Vol. 3: Homayun Sakhi: T...   \n",
m@23:        "29186                               Music of Afghanistan   \n",
m@23:        "\n",
m@23:        "                                  Artist         Artist_Album  \\\n",
m@23:        "29182          Afghan National Orchestra      Various Artists   \n",
m@23:        "29183                       Yaqub Kasimi      Various Artists   \n",
m@23:        "29184  Ustad Mohammad Omar|Zakir Hussain  Ustad Mohammad Omar   \n",
m@23:        "29185                      Homayun Sakhi        Homayun Sakhi   \n",
m@23:        "29186                Rebab and Dhol Duet      Various Artists   \n",
m@23:        "\n",
m@23:        "       BuyLinkTrackDownload CatalogNumber CatalogNumber_Album Collection  \\\n",
m@23:        "29182                 26138   FW04361_201             FW04361        NaN   \n",
m@23:        "29183                 26142   FW04361_205             FW04361        NaN   \n",
m@23:        "29184                 51931  SFW40439_102            SFW40439        NaN   \n",
m@23:        "29185                 53229  SFW40522_101            SFW40522        NaN   \n",
m@23:        "29186                 26140   FW04361_203             FW04361        NaN   \n",
m@23:        "\n",
m@23:        "      ContentType ContentType_Album      Country  \\\n",
m@23:        "29182       Track             Album  Afghanistan   \n",
m@23:        "29183       Track             Album  Afghanistan   \n",
m@23:        "29184       Track             Album  Afghanistan   \n",
m@23:        "29185       Track             Album  Afghanistan   \n",
m@23:        "29186       Track             Album  Afghanistan   \n",
m@23:        "\n",
m@23:        "                             ...                         Language_iso3  \\\n",
m@23:        "29182                        ...                                   NaN   \n",
m@23:        "29183                        ...                                   NaN   \n",
m@23:        "29184                        ...                                   pbt   \n",
m@23:        "29185                        ...                                   NaN   \n",
m@23:        "29186                        ...                                   NaN   \n",
m@23:        "\n",
m@23:        "       Language_iso1      Region  Latitude  Longitude  \\\n",
m@23:        "29182            NaN  South Asia  33.93911  67.709953   \n",
m@23:        "29183            NaN  South Asia  33.93911  67.709953   \n",
m@23:        "29184  Indo-European  South Asia  33.93911  67.709953   \n",
m@23:        "29185            NaN  South Asia  33.93911  67.709953   \n",
m@23:        "29186            NaN  South Asia  33.93911  67.709953   \n",
m@23:        "\n",
m@23:        "                                                   Audio  \\\n",
m@23:        "29182  /import/c4dm-scratch/matthiasm/data/smithsonia...   \n",
m@23:        "29183  /import/c4dm-scratch/matthiasm/data/smithsonia...   \n",
m@23:        "29184  /import/c4dm-scratch/matthiasm/data/smithsonia...   \n",
m@23:        "29185  /import/c4dm-scratch/matthiasm/data/smithsonia...   \n",
m@23:        "29186  /import/c4dm-scratch/matthiasm/data/smithsonia...   \n",
m@23:        "\n",
m@23:        "                                                  Speech  \\\n",
m@23:        "29182  /import/c4dm-02/people/mariap/SpeechMusic/2613...   \n",
m@23:        "29183  /import/c4dm-02/people/mariap/SpeechMusic/2614...   \n",
m@23:        "29184  /import/c4dm-02/people/mariap/SpeechMusic/5193...   \n",
m@23:        "29185  /import/c4dm-02/people/mariap/SpeechMusic/5322...   \n",
m@23:        "29186  /import/c4dm-02/people/mariap/SpeechMusic/2614...   \n",
m@23:        "\n",
m@23:        "                                                 Melspec  \\\n",
m@23:        "29182  /import/c4dm-02/people/mariap/MelSpec/26138_Sa...   \n",
m@23:        "29183  /import/c4dm-02/people/mariap/MelSpec/26142_Sa...   \n",
m@23:        "29184  /import/c4dm-02/people/mariap/MelSpec/51931_Sa...   \n",
m@23:        "29185  /import/c4dm-02/people/mariap/MelSpec/53229_Sa...   \n",
m@23:        "29186  /import/c4dm-02/people/mariap/MelSpec/26140_Sa...   \n",
m@23:        "\n",
m@23:        "                                                  Chroma  \\\n",
m@23:        "29182  /import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...   \n",
m@23:        "29183  /import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...   \n",
m@23:        "29184  /import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...   \n",
m@23:        "29185  /import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...   \n",
m@23:        "29186  /import/c4dm-04/mariap/FeatureCsvs/Smoothie-py...   \n",
m@23:        "\n",
m@23:        "                                                 Melodia  \n",
m@23:        "29182  /import/c4dm-02/people/mariap/Melodia/26138_Sa...  \n",
m@23:        "29183  /import/c4dm-02/people/mariap/Melodia/26142_Sa...  \n",
m@23:        "29184  /import/c4dm-02/people/mariap/Melodia/51931_Sa...  \n",
m@23:        "29185  /import/c4dm-02/people/mariap/Melodia/53229_Sa...  \n",
m@23:        "29186  /import/c4dm-02/people/mariap/Melodia/26140_Sa...  \n",
m@23:        "\n",
m@23:        "[5 rows x 108 columns]"
m@23:       ]
m@23:      },
m@23:      "execution_count": 8,
m@23:      "metadata": {},
m@23:      "output_type": "execute_result"
m@23:     }
m@23:    ],
m@23:    "source": [
m@23:     "df.head()"
m@23:    ]
m@23:   },
m@23:   {
m@26:    "cell_type": "markdown",
m@26:    "metadata": {},
m@26:    "source": [
m@26:     "## test on a few samples"
m@26:    ]
m@26:   },
m@26:   {
m@26:    "cell_type": "code",
m@26:    "execution_count": 47,
m@48:    "metadata": {},
m@26:    "outputs": [
m@26:     {
m@26:      "name": "stdout",
m@26:      "output_type": "stream",
m@26:      "text": [
m@26:       "file 0 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 1 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 2 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 3 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 4 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 5 of 6\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "6 6 6 6 6 6\n",
m@26:       "(264, 400) (264, 240) (264, 80) (264, 120)\n",
m@26:       "file 0 of 2\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 1 of 2\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "2 2 2 2 2 2\n",
m@26:       "(88, 400) (88, 240) (88, 80) (88, 120)\n",
m@26:       "file 0 of 2\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "file 1 of 2\n",
m@26:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@26:       "2 2 2 2 2 2\n",
m@26:       "(85, 400) (85, 240) (85, 80) (85, 120)\n"
m@26:      ]
m@26:     }
m@26:    ],
m@26:    "source": [
m@26:     "train, val, test = load_dataset.features_for_train_test_sets(df.iloc[:10, :], write_output=False)"
m@26:    ]
m@26:   },
m@26:   {
m@26:    "cell_type": "markdown",
m@26:    "metadata": {},
m@26:    "source": [
m@26:     "## now run for the whole dataset"
m@26:    ]
m@26:   },
m@26:   {
m@23:    "cell_type": "code",
m@27:    "execution_count": 59,
m@48:    "metadata": {},
m@21:    "outputs": [
m@21:     {
m@21:      "name": "stdout",
m@21:      "output_type": "stream",
m@21:      "text": [
m@21:       "iteration 0\n",
m@21:       "file 0 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 1 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 2 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 3 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 4 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 5 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 6 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 7 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 8 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 9 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 10 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 11 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 12 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 13 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 14 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 15 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 16 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@26:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 17 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 18 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 19 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 20 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@26:       "(60, 240)\n",
m@26:       "60 44 44 44 60\n",
m@21:       "file 21 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@21:       "extracting onset patterns and mfccs...\n",
m@21:       "extracting chroma...\n",
m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@26:       "(45, 240)\n",
m@26:       "60 44 44 44 45\n",
m@21:       "file 22 of 5037\n",
m@21:       "loading speech/music segments...\n",
m@26:       "extracting onset patterns and mfccs...\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@47:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@47:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@26:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
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m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 499 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 500 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 501 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 502 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@47:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 503 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 504 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 505 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 506 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 507 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 508 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 509 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 510 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 511 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 512 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 513 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 514 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 515 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 516 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 517 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 518 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 519 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 520 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 521 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 522 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 523 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs_BL_old/PB-melodia/\n",
m@27:       "(60, 240)\n",
m@27:       "60 44 44 44 60\n",
m@27:       "file 524 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n",
m@27:       "extracting chroma...\n",
m@27:       "load precomputed pitch bihist /import/c4dm-04/mariap/FeatureCsvs/PB-melodia/\n",
m@27:       "(45, 240)\n",
m@27:       "60 44 44 44 45\n",
m@27:       "file 525 of 5037\n",
m@27:       "loading speech/music segments...\n",
m@27:       "extracting onset patterns and mfccs...\n"
m@27:      ]
m@27:     },
m@27:     {
m@27:      "ename": "ValueError",
m@27:      "evalue": "No columns to parse from file",
m@27:      "output_type": "error",
m@27:      "traceback": [
m@27:       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
m@27:       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
m@27:       "\u001b[0;32m<ipython-input-59-2ab5c77f448e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     load_dataset.OUTPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n\u001b[1;32m      4\u001b[0m                                  output_file in OUTPUT_FILES]\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mload_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeatures_for_train_test_sets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrite_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
m@27:       "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/load_dataset.py\u001b[0m in \u001b[0;36mfeatures_for_train_test_sets\u001b[0;34m(df, write_output)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0mX_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Country'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0mtrain_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_train_val_test_idx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_audio_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwin2sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWIN_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_audio_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mval_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwin2sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWIN_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_audio_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwin2sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWIN_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32m/homes/mp305/code/pythoncode/plosone_underreview/scripts/load_dataset.py\u001b[0m in \u001b[0;36mextract_features\u001b[0;34m(df, win2sec)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \"\"\"\n\u001b[1;32m    110\u001b[0m     \u001b[0mfeat_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_features\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFeatureLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwin2sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwin2sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m     \u001b[0mframes_rhy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mfcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_chroma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_audio_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeat_loader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0mframes_rhy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mfcc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_chroma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes_rhy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_mfcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes_chroma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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m@27:       "\u001b[0;32m/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)\u001b[0m\n\u001b[1;32m    489\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32m/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32m/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_options_with_defaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32m/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    722\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    725\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32m/homes/mp305/anaconda/lib/python2.7/site-packages/pandas/io/parsers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1091\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'allow_leading_cols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m         \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
m@27:       "\u001b[0;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:4979)\u001b[0;34m()\u001b[0m\n",
m@27:       "\u001b[0;31mValueError\u001b[0m: No columns to parse from file"
m@26:      ]
m@21:     }
m@21:    ],
m@21:    "source": [
m@15:     "for n in range(n_iters):\n",
m@15:     "    print \"iteration %d\" % n\n",
m@15:     "    load_dataset.OUTPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n",
m@15:     "                                 output_file in OUTPUT_FILES]\n",
m@15:     "    load_dataset.features_for_train_test_sets(df, write_output=True)"
m@15:    ]
m@15:   },
m@15:   {
m@48:    "cell_type": "markdown",
m@47:    "metadata": {
m@47:     "collapsed": true
m@47:    },
m@47:    "source": [
m@48:     "## Map frames and write output for the lda transformed frames"
m@47:    ]
m@47:   },
m@47:   {
m@47:    "cell_type": "code",
m@55:    "execution_count": null,
m@59:    "metadata": {
m@59:     "collapsed": true
m@59:    },
m@55:    "outputs": [],
m@15:    "source": [
m@48:     "MAPPER_OUTPUT_FILES = mapper.OUTPUT_FILES\n",
m@15:     "for n in range(n_iters):\n",
m@15:     "    print \"iteration %d\" % n\n",
m@15:     "    \n",
m@15:     "    print \"mapping...\"\n",
m@15:     "    mapper.INPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n",
m@15:     "                                 output_file in OUTPUT_FILES]\n",
m@47:     "    mapper.OUTPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n",
m@47:     "                                 output_file in MAPPER_OUTPUT_FILES]\n",
m@15:     "    _, _, ldadata_list, _, _, Y, Yaudio = mapper.lda_map_and_average_frames(min_variance=0.99)\n",
m@47:     "    mapper.write_output([], [], ldadata_list, [], [], Y, Yaudio)"
m@47:    ]
m@47:   },
m@47:   {
m@48:    "cell_type": "markdown",
m@48:    "metadata": {},
m@48:    "source": [
m@48:     "## Classification only - assuming mapper files are exported "
m@48:    ]
m@48:   },
m@48:   {
m@47:    "cell_type": "code",
m@59:    "execution_count": 21,
m@48:    "metadata": {},
m@48:    "outputs": [
m@48:     {
m@48:      "name": "stdout",
m@48:      "output_type": "stream",
m@48:      "text": [
m@55:       "iteration 0\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_0.pickle\n",
m@55:       "0.17294625462\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "136        Botswana  0.590909         88          52\n",
m@55:       "31      Ivory Coast  0.571429         14           8\n",
m@55:       "86           Gambia  0.541667         48          26\n",
m@55:       "42            Benin  0.538462         26          14\n",
m@55:       "102            Fiji  0.466667         15           7\n",
m@55:       "20         Pakistan  0.461538         91          42\n",
m@55:       "64           Uganda  0.437500         80          35\n",
m@55:       "14          Liberia  0.425000         40          17\n",
m@55:       "78      El Salvador  0.424242         33          14\n",
m@55:       "50   Western Sahara  0.421687         83          35\n",
m@55:       "least outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "1         Lithuania  0.000000         47           0\n",
m@55:       "30      Afghanistan  0.000000         24           0\n",
m@55:       "28       Tajikistan  0.000000         19           0\n",
m@55:       "27      South Korea  0.000000         11           0\n",
m@55:       "113         Iceland  0.000000         14           0\n",
m@55:       "119         Denmark  0.000000         16           0\n",
m@55:       "74   Czech Republic  0.000000         41           0\n",
m@55:       "15      Netherlands  0.014925         67           1\n",
m@55:       "121          Poland  0.040000        100           4\n",
m@55:       "134        Paraguay  0.043478         23           1\n",
m@55:       "writing file\n",
m@55:       "iteration 1\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_1.pickle\n",
m@55:       "0.149811300704\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "           Country  Outliers  N_Country  N_Outliers\n",
m@55:       "60            Chad  0.545455         11           6\n",
m@55:       "62            Fiji  0.533333         15           8\n",
m@55:       "86          Gambia  0.520833         48          25\n",
m@55:       "21        Pakistan  0.500000         88          44\n",
m@55:       "43           Benin  0.500000         26          13\n",
m@55:       "32     Ivory Coast  0.500000         14           7\n",
m@55:       "136       Botswana  0.488095         84          41\n",
m@55:       "78     El Salvador  0.484848         33          16\n",
m@55:       "106          Nepal  0.436782         87          38\n",
m@55:       "135  French Guiana  0.428571         28          12\n",
m@55:       "least outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "1         Lithuania  0.000000         47           0\n",
m@55:       "113         Iceland  0.000000         14           0\n",
m@55:       "119         Denmark  0.000000         16           0\n",
m@55:       "74   Czech Republic  0.000000         41           0\n",
m@55:       "28      South Korea  0.000000         11           0\n",
m@55:       "16      Netherlands  0.029851         67           2\n",
m@55:       "31      Afghanistan  0.041667         24           1\n",
m@55:       "134        Paraguay  0.043478         23           1\n",
m@55:       "105           Sudan  0.045455         66           3\n",
m@55:       "120      Kazakhstan  0.045455         88           4\n",
m@55:       "writing file\n",
m@55:       "iteration 2\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_2.pickle\n",
m@55:       "0.178052269426\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "136        Botswana  0.615385         78          48\n",
m@55:       "86           Gambia  0.520833         48          25\n",
m@55:       "72      Ivory Coast  0.500000         14           7\n",
m@55:       "62             Fiji  0.466667         15           7\n",
m@55:       "43            Benin  0.461538         26          12\n",
m@55:       "20         Pakistan  0.451613         93          42\n",
m@55:       "17    French Guiana  0.428571         28          12\n",
m@55:       "14          Liberia  0.425000         40          17\n",
m@55:       "78      El Salvador  0.424242         33          14\n",
m@55:       "51   Western Sahara  0.414634         82          34\n",
m@55:       "least outliers \n",
m@55:       "                 Country  Outliers  N_Country  N_Outliers\n",
m@55:       "119              Denmark  0.000000         16           0\n",
m@55:       "113              Iceland  0.000000         14           0\n",
m@55:       "27           South Korea  0.000000         11           0\n",
m@55:       "1              Lithuania  0.000000         47           0\n",
m@55:       "31        Czech Republic  0.024390         41           1\n",
m@55:       "15           Netherlands  0.029851         67           2\n",
m@55:       "30           Afghanistan  0.041667         24           1\n",
m@55:       "105                Sudan  0.045455         66           3\n",
m@55:       "120           Kazakhstan  0.045455         88           4\n",
m@55:       "100  Antigua and Barbuda  0.047619         42           2\n",
m@55:       "writing file\n",
m@55:       "iteration 3\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_3.pickle\n",
m@55:       "0.177243715126\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "136        Botswana  0.617284         81          50\n",
m@55:       "31      Ivory Coast  0.571429         14           8\n",
m@55:       "86           Gambia  0.541667         48          26\n",
m@55:       "43            Benin  0.538462         26          14\n",
m@55:       "62             Fiji  0.533333         15           8\n",
m@55:       "20         Pakistan  0.468750         96          45\n",
m@55:       "51   Western Sahara  0.439024         82          36\n",
m@55:       "14          Liberia  0.425000         40          17\n",
m@55:       "78      El Salvador  0.424242         33          14\n",
m@55:       "106           Nepal  0.416667         96          40\n",
m@55:       "least outliers \n",
m@55:       "                 Country  Outliers  N_Country  N_Outliers\n",
m@55:       "113              Iceland  0.000000         14           0\n",
m@55:       "30           Afghanistan  0.000000         24           0\n",
m@55:       "119              Denmark  0.000000         16           0\n",
m@55:       "134             Paraguay  0.000000         23           0\n",
m@55:       "27           South Korea  0.000000         11           0\n",
m@55:       "1              Lithuania  0.000000         47           0\n",
m@55:       "100  Antigua and Barbuda  0.023810         42           1\n",
m@55:       "74        Czech Republic  0.024390         41           1\n",
m@55:       "15           Netherlands  0.029851         67           2\n",
m@55:       "105                Sudan  0.045455         66           3\n",
m@55:       "writing file\n",
m@55:       "iteration 4\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_4.pickle\n",
m@55:       "0.186733308352\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "60             Chad  0.727273         11           8\n",
m@55:       "136        Botswana  0.630952         84          53\n",
m@55:       "72      Ivory Coast  0.571429         14           8\n",
m@55:       "62             Fiji  0.533333         15           8\n",
m@55:       "86           Gambia  0.520833         48          25\n",
m@55:       "43            Benin  0.500000         26          13\n",
m@55:       "20         Pakistan  0.468085         94          44\n",
m@55:       "135   French Guiana  0.464286         28          13\n",
m@55:       "64       Mozambique  0.441176         34          15\n",
m@55:       "51   Western Sahara  0.439024         82          36\n",
m@55:       "least outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "1         Lithuania  0.000000         47           0\n",
m@55:       "27      South Korea  0.000000         11           0\n",
m@55:       "113         Iceland  0.000000         14           0\n",
m@55:       "119         Denmark  0.000000         16           0\n",
m@55:       "15      Netherlands  0.014925         67           1\n",
m@55:       "31   Czech Republic  0.024390         41           1\n",
m@55:       "112          Israel  0.030000        100           3\n",
m@55:       "30      Afghanistan  0.041667         24           1\n",
m@55:       "134        Paraguay  0.043478         23           1\n",
m@55:       "105           Sudan  0.045455         66           3\n",
m@55:       "writing file\n",
m@55:       "iteration 5\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_5.pickle\n",
m@55:       "0.163125082162\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "         Country  Outliers  N_Country  N_Outliers\n",
m@55:       "136     Botswana  0.607143         84          51\n",
m@55:       "72   Ivory Coast  0.571429         14           8\n",
m@55:       "21      Pakistan  0.553191         94          52\n",
m@55:       "95          Chad  0.545455         11           6\n",
m@55:       "63          Fiji  0.533333         15           8\n",
m@55:       "86        Gambia  0.520833         48          25\n",
m@55:       "44         Benin  0.500000         26          13\n",
m@55:       "78   El Salvador  0.454545         33          15\n",
m@55:       "117     Zimbabwe  0.428571         14           6\n",
m@55:       "66        Uganda  0.418605         86          36\n",
m@55:       "least outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "119         Denmark  0.000000         16           0\n",
m@55:       "1         Lithuania  0.000000         47           0\n",
m@55:       "28      South Korea  0.000000         11           0\n",
m@55:       "113         Iceland  0.000000         14           0\n",
m@55:       "32   Czech Republic  0.024390         41           1\n",
m@55:       "16      Netherlands  0.029851         67           2\n",
m@55:       "31      Afghanistan  0.041667         24           1\n",
m@55:       "134        Paraguay  0.043478         23           1\n",
m@55:       "120      Kazakhstan  0.045455         88           4\n",
m@55:       "105           Sudan  0.045455         66           3\n",
m@55:       "writing file\n",
m@55:       "iteration 6\n",
m@55:       "classifying...\n",
m@55:       "/import/c4dm-04/mariap/train_data_melodia_8_6.pickle\n",
m@55:       "0.179816192246\n",
m@55:       "detecting outliers...\n",
m@55:       "most outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "136        Botswana  0.574468         94          54\n",
m@55:       "32      Ivory Coast  0.571429         14           8\n",
m@55:       "86           Gambia  0.520833         48          25\n",
m@55:       "21         Pakistan  0.516854         89          46\n",
m@55:       "62             Fiji  0.466667         15           7\n",
m@55:       "43            Benin  0.461538         26          12\n",
m@55:       "95             Chad  0.454545         11           5\n",
m@55:       "78      El Salvador  0.454545         33          15\n",
m@55:       "51   Western Sahara  0.439024         82          36\n",
m@55:       "63          Senegal  0.405405         37          15\n",
m@55:       "least outliers \n",
m@55:       "            Country  Outliers  N_Country  N_Outliers\n",
m@55:       "1         Lithuania  0.000000         47           0\n",
m@55:       "119         Denmark  0.000000         16           0\n",
m@55:       "28      South Korea  0.000000         11           0\n",
m@55:       "113         Iceland  0.000000         14           0\n",
m@55:       "16      Netherlands  0.014925         67           1\n",
m@55:       "74   Czech Republic  0.024390         41           1\n",
m@55:       "13          Germany  0.040000        100           4\n",
m@55:       "31      Afghanistan  0.041667         24           1\n",
m@55:       "105           Sudan  0.045455         66           3\n",
m@55:       "120      Kazakhstan  0.045455         88           4\n",
m@59:       "writing file\n",
m@59:       "iteration 7\n"
m@59:      ]
m@59:     },
m@59:     {
m@59:      "name": "stdout",
m@59:      "output_type": "stream",
m@59:      "text": [
m@59:       "classifying...\n",
m@59:       "/import/c4dm-04/mariap/train_data_melodia_8_7.pickle\n",
m@59:       "0.179777654473\n",
m@59:       "detecting outliers...\n",
m@59:       "most outliers \n",
m@59:       "         Country  Outliers  N_Country  N_Outliers\n",
m@59:       "136     Botswana  0.636364         88          56\n",
m@59:       "95          Chad  0.636364         11           7\n",
m@59:       "86        Gambia  0.511111         45          23\n",
m@59:       "42         Benin  0.500000         26          13\n",
m@59:       "14       Liberia  0.500000         40          20\n",
m@59:       "63    Mozambique  0.500000         34          17\n",
m@59:       "78   El Salvador  0.424242         33          14\n",
m@59:       "62       Senegal  0.416667         36          15\n",
m@59:       "20      Pakistan  0.415730         89          37\n",
m@59:       "106        Nepal  0.402174         92          37\n",
m@59:       "least outliers \n",
m@59:       "         Country  Outliers  N_Country  N_Outliers\n",
m@59:       "1      Lithuania  0.000000         47           0\n",
m@59:       "119      Denmark  0.000000         16           0\n",
m@59:       "113      Iceland  0.000000         14           0\n",
m@59:       "27   South Korea  0.000000         11           0\n",
m@59:       "15   Netherlands  0.015152         66           1\n",
m@59:       "120   Kazakhstan  0.034884         86           3\n",
m@59:       "30   Afghanistan  0.041667         24           1\n",
m@59:       "102    Nicaragua  0.050000         20           1\n",
m@59:       "112       Israel  0.050000        100           5\n",
m@59:       "28    Tajikistan  0.052632         19           1\n",
m@59:       "writing file\n",
m@59:       "iteration 8\n",
m@59:       "classifying...\n",
m@59:       "/import/c4dm-04/mariap/train_data_melodia_8_8.pickle\n",
m@59:       "0.165005035342\n",
m@59:       "detecting outliers...\n",
m@59:       "most outliers \n",
m@59:       "         Country  Outliers  N_Country  N_Outliers\n",
m@59:       "95          Chad  0.636364         11           7\n",
m@59:       "43         Benin  0.576923         26          15\n",
m@59:       "136     Botswana  0.571429         77          44\n",
m@59:       "14       Liberia  0.525000         40          21\n",
m@59:       "86        Gambia  0.488889         45          22\n",
m@59:       "78   El Salvador  0.484848         33          16\n",
m@59:       "64    Mozambique  0.470588         34          16\n",
m@59:       "62          Fiji  0.466667         15           7\n",
m@59:       "20      Pakistan  0.436782         87          38\n",
m@59:       "63       Senegal  0.416667         36          15\n",
m@59:       "least outliers \n",
m@59:       "         Country  Outliers  N_Country  N_Outliers\n",
m@59:       "1      Lithuania  0.000000         47           0\n",
m@59:       "119      Denmark  0.000000         16           0\n",
m@59:       "113      Iceland  0.000000         14           0\n",
m@59:       "27   South Korea  0.000000         11           0\n",
m@59:       "102    Nicaragua  0.000000         20           0\n",
m@59:       "28    Tajikistan  0.000000         19           0\n",
m@59:       "15   Netherlands  0.015152         66           1\n",
m@59:       "89       Croatia  0.032258         31           1\n",
m@59:       "120   Kazakhstan  0.034884         86           3\n",
m@59:       "30   Afghanistan  0.041667         24           1\n",
m@59:       "writing file\n",
m@59:       "iteration 9\n",
m@59:       "classifying...\n",
m@59:       "/import/c4dm-04/mariap/train_data_melodia_8_9.pickle\n",
m@59:       "0.168630986212\n",
m@59:       "detecting outliers...\n",
m@59:       "most outliers \n",
m@59:       "           Country  Outliers  N_Country  N_Outliers\n",
m@59:       "43           Benin  0.576923         26          15\n",
m@59:       "136       Botswana  0.567901         81          46\n",
m@59:       "60            Chad  0.545455         11           6\n",
m@59:       "86          Gambia  0.533333         45          24\n",
m@59:       "14         Liberia  0.525000         40          21\n",
m@59:       "65          Uganda  0.482759         87          42\n",
m@59:       "64      Mozambique  0.470588         34          16\n",
m@59:       "20        Pakistan  0.465909         88          41\n",
m@59:       "135  French Guiana  0.464286         28          13\n",
m@59:       "67          Brazil  0.460000        100          46\n",
m@59:       "least outliers \n",
m@59:       "              Country  Outliers  N_Country  N_Outliers\n",
m@59:       "1           Lithuania  0.000000         47           0\n",
m@59:       "90   French Polynesia  0.000000         15           0\n",
m@59:       "102         Nicaragua  0.000000         20           0\n",
m@59:       "113           Iceland  0.000000         14           0\n",
m@59:       "119           Denmark  0.000000         16           0\n",
m@59:       "15        Netherlands  0.015152         66           1\n",
m@59:       "18        New Zealand  0.029412         34           1\n",
m@59:       "120        Kazakhstan  0.034884         86           3\n",
m@59:       "31     Czech Republic  0.048780         41           2\n",
m@59:       "28         Tajikistan  0.052632         19           1\n",
m@55:       "writing file\n"
m@48:      ]
m@48:     }
m@48:    ],
m@47:    "source": [
m@59:     "n_iters = 10\n",
m@55:     "OUTPUT_FILES = load_dataset.OUTPUT_FILES\n",
m@55:     "MAPPER_OUTPUT_FILES = mapper.OUTPUT_FILES\n",
m@47:     "for n in range(n_iters):\n",
m@47:     "    print \"iteration %d\" % n\n",
m@48:     "    CLASS_INPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n",
m@55:     "                                 output_file in MAPPER_OUTPUT_FILES]\n",
m@55:     "    mapper.INPUT_FILES = [output_file.split('.pickle')[0]+'_'+str(n)+'.pickle' for \n",
m@55:     "                                 output_file in OUTPUT_FILES]\n",
m@55:     "    X, Y, Yaudio = classification.load_data_from_pickle(CLASS_INPUT_FILES[0])\n",
m@55:     "    #X = np.concatenate(ldadata_list, axis=1)\n",
m@15:     "    # classification and confusion\n",
m@15:     "    print \"classifying...\"\n",
m@47:     "    traininds, testinds = classification.get_train_test_indices(Yaudio)\n",
Maria@18:     "    X_train, Y_train, X_test, Y_test = classification.get_train_test_sets(X, Y, traininds, testinds)\n",
Maria@18:     "    accuracy, _ = classification.confusion_matrix(X_train, Y_train, X_test, Y_test, saveCF=False, plots=False)\n",
m@15:     "    print accuracy\n",
m@15:     "    \n",
m@15:     "    # outliers\n",
m@15:     "    print \"detecting outliers...\"\n",
m@42:     "    #ddf = outliers.load_metadata(Yaudio, metadata_file=load_dataset.METADATA_FILE)\n",
m@42:     "    df_global, threshold, MD = outliers.get_outliers_df(X, Y, chi2thr=0.999)\n",
m@42:     "    outliers.print_most_least_outliers_topN(df_global, N=10)\n",
m@15:     "    \n",
m@15:     "    # write output\n",
m@15:     "    print \"writing file\"\n",
m@15:     "    df_global.to_csv('../data/outliers_'+str(n)+'.csv', index=False)"
m@15:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "markdown",
m@42:    "metadata": {},
m@42:    "source": [
m@42:     "## correlation of outlier results"
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "markdown",
m@42:    "metadata": {},
m@42:    "source": [
m@42:     "Let's use Kendal correlation to compare the ranked list of countries sorted by most to least outliers.\n",
m@42:     "<br> First load the ranked list of outlier countries.\n",
m@42:     "<br> Sort by outlier percentage in descending order."
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "code",
m@59:    "execution_count": 45,
m@60:    "metadata": {
m@60:     "collapsed": true
m@60:    },
m@42:    "outputs": [],
m@42:    "source": [
m@42:     "ranked_countries = pd.DataFrame()\n",
m@42:     "ranked_outliers = pd.DataFrame()\n",
m@42:     "for n in range(n_iters):\n",
m@42:     "    df_global = pd.read_csv('../data/outliers_'+str(n)+'.csv')\n",
m@59:     "    df_global = df_global.sort_values('Outliers', axis=0, ascending=False).reset_index()\n",
m@42:     "    ranked_countries = pd.concat([ranked_countries, df_global['Country']], axis=1)\n",
m@42:     "    ranked_outliers = pd.concat([ranked_outliers, df_global['Outliers']], axis=1)"
m@42:    ]
m@42:   },
m@42:   {
m@59:    "cell_type": "code",
m@59:    "execution_count": 49,
m@59:    "metadata": {},
m@59:    "outputs": [
m@59:     {
m@59:      "data": {
m@59:       "text/plain": [
m@59:        "(133, 10)"
m@59:       ]
m@59:      },
m@59:      "execution_count": 49,
m@59:      "metadata": {},
m@59:      "output_type": "execute_result"
m@59:     }
m@59:    ],
m@59:    "source": [
m@59:     "ranked_outliers.shape"
m@59:    ]
m@59:   },
m@59:   {
m@42:    "cell_type": "markdown",
m@42:    "metadata": {},
m@42:    "source": [
m@42:     "Remove countries with 0% outliers as these are in random (probably alphabetical) order."
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "code",
m@59:    "execution_count": 48,
m@59:    "metadata": {},
m@59:    "outputs": [
m@59:     {
m@59:      "name": "stdout",
m@59:      "output_type": "stream",
m@59:      "text": [
m@59:       "       Country   Country      Country      Country      Country      Country  \\\n",
m@59:       "0     Botswana      Chad     Botswana     Botswana         Chad     Botswana   \n",
m@59:       "1  Ivory Coast      Fiji       Gambia  Ivory Coast     Botswana  Ivory Coast   \n",
m@59:       "2       Gambia    Gambia  Ivory Coast       Gambia  Ivory Coast     Pakistan   \n",
m@59:       "3        Benin     Benin         Fiji        Benin         Fiji         Chad   \n",
m@59:       "4         Fiji  Pakistan        Benin         Fiji       Gambia         Fiji   \n",
m@59:       "\n",
m@59:       "       Country     Country   Country   Country  \n",
m@59:       "0     Botswana    Botswana      Chad     Benin  \n",
m@59:       "1  Ivory Coast        Chad     Benin  Botswana  \n",
m@59:       "2       Gambia      Gambia  Botswana      Chad  \n",
m@59:       "3     Pakistan  Mozambique   Liberia    Gambia  \n",
m@59:       "4         Fiji       Benin    Gambia   Liberia  \n",
m@59:       "   Outliers  Outliers  Outliers  Outliers  Outliers  Outliers  Outliers  \\\n",
m@59:       "0  0.590909  0.545455  0.615385  0.617284  0.727273  0.607143  0.574468   \n",
m@59:       "1  0.571429  0.533333  0.520833  0.571429  0.630952  0.571429  0.571429   \n",
m@59:       "2  0.541667  0.520833  0.500000  0.541667  0.571429  0.553191  0.520833   \n",
m@59:       "3  0.538462  0.500000  0.466667  0.538462  0.533333  0.545455  0.516854   \n",
m@59:       "4  0.466667  0.500000  0.461538  0.533333  0.520833  0.533333  0.466667   \n",
m@59:       "\n",
m@59:       "   Outliers  Outliers  Outliers  \n",
m@59:       "0  0.636364  0.636364  0.576923  \n",
m@59:       "1  0.636364  0.576923  0.567901  \n",
m@59:       "2  0.511111  0.571429  0.545455  \n",
m@59:       "3  0.500000  0.525000  0.533333  \n",
m@59:       "4  0.500000  0.488889  0.525000  \n"
m@59:      ]
m@59:     }
m@59:    ],
m@42:    "source": [
m@42:     "zero_idx = np.where(np.sum(ranked_outliers, axis=1)==0)[0]\n",
m@42:     "first_zero_idx = np.min(zero_idx)\n",
m@42:     "ranked_countries = ranked_countries.iloc[:first_zero_idx, :]\n",
m@42:     "ranked_outliers = ranked_outliers.iloc[:first_zero_idx, :]\n",
m@42:     "\n",
m@42:     "print ranked_countries.head()\n",
m@42:     "print ranked_outliers.head()"
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "markdown",
m@42:    "metadata": {},
m@42:    "source": [
m@42:     "And now kendalltau correlation"
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "code",
m@59:    "execution_count": 54,
m@48:    "metadata": {},
m@42:    "outputs": [
m@42:     {
m@42:      "name": "stdout",
m@42:      "output_type": "stream",
m@42:      "text": [
m@59:       "KendalltauResult(correlation=0.11870585554796083, pvalue=0.042684955693776824)\n",
m@59:       "KendalltauResult(correlation=0.061289587605377081, pvalue=0.29535042403787393)\n",
m@59:       "KendalltauResult(correlation=0.14057871952608797, pvalue=0.016384498702657929)\n",
m@59:       "KendalltauResult(correlation=0.043062200956937809, pvalue=0.46219181347134564)\n",
m@59:       "KendalltauResult(correlation=0.038049669628617007, pvalue=0.51591269004232343)\n",
m@59:       "KendalltauResult(correlation=0.15516062884483939, pvalue=0.0080680863973824919)\n",
m@59:       "KendalltauResult(correlation=0.097972203235361141, pvalue=0.094371801845320874)\n",
m@59:       "KendalltauResult(correlation=0.070403280929596718, pvalue=0.22933906132681292)\n",
m@59:       "KendalltauResult(correlation=0.087263613579403057, pvalue=0.13624109595088119)\n",
m@59:       "KendalltauResult(correlation=0.026657552973342449, pvalue=0.64900123852931668)\n",
m@59:       "KendalltauResult(correlation=0.012531328320802006, pvalue=0.83057867073317604)\n",
m@59:       "KendalltauResult(correlation=0.15698336750968331, pvalue=0.0073549938316186895)\n",
m@59:       "KendalltauResult(correlation=0.072226019594440652, pvalue=0.21750692637496993)\n",
m@59:       "KendalltauResult(correlation=0.064479380268853956, pvalue=0.27093205134080134)\n",
m@59:       "KendalltauResult(correlation=0.07518796992481204, pvalue=0.19922707586147026)\n",
m@59:       "KendalltauResult(correlation=0.017088174982911826, pvalue=0.77046791234681555)\n",
m@59:       "KendalltauResult(correlation=0.098200045568466648, pvalue=0.093608177106345392)\n",
m@59:       "KendalltauResult(correlation=0.11004784688995217, pvalue=0.060250899787989511)\n",
m@59:       "KendalltauResult(correlation=0.051720209614946465, pvalue=0.37719896672100306)\n",
m@59:       "KendalltauResult(correlation=0.099567099567099596, pvalue=0.089129953079656793)\n",
m@59:       "KendalltauResult(correlation=-0.081795397584871282, pvalue=0.16254238954046385)\n",
m@59:       "KendalltauResult(correlation=0.089769879243563472, pvalue=0.12534294310051713)\n",
m@59:       "KendalltauResult(correlation=0.10047846889952156, pvalue=0.086241531926005505)\n",
m@59:       "KendalltauResult(correlation=0.014809751651856917, pvalue=0.80037548797424396)\n",
m@59:       "KendalltauResult(correlation=0.021189336978810668, pvalue=0.71751195692767422)\n",
m@59:       "KendalltauResult(correlation=0.020733652312599684, pvalue=0.7233346465763022)\n",
m@59:       "KendalltauResult(correlation=-0.057644110275689227, pvalue=0.32501053989276085)\n",
m@59:       "KendalltauResult(correlation=0.04647983595352017, pvalue=0.42743119135699703)\n",
m@59:       "KendalltauResult(correlation=-0.02939166097060834, pvalue=0.6157855679677966)\n",
m@59:       "KendalltauResult(correlation=-0.01754385964912281, pvalue=0.76452558103925983)\n",
m@59:       "KendalltauResult(correlation=-0.00022784233310549102, pvalue=0.99689609964041026)\n",
m@59:       "KendalltauResult(correlation=0.053087263613579412, pvalue=0.36471883993264553)\n",
m@59:       "KendalltauResult(correlation=0.11027568922305765, pvalue=0.059721613251292195)\n",
m@59:       "KendalltauResult(correlation=0.1319207108680793, pvalue=0.024296399889465414)\n",
m@59:       "KendalltauResult(correlation=0.11050353155616316, pvalue=0.059196189350124301)\n",
m@59:       "KendalltauResult(correlation=0.081339712918660295, pvalue=0.16489618845189757)\n",
m@59:       "KendalltauResult(correlation=0.091136933242196419, pvalue=0.11969173188443738)\n",
m@59:       "KendalltauResult(correlation=0.010252904989747097, pvalue=0.86103426355600943)\n",
m@59:       "KendalltauResult(correlation=0.026201868307131469, pvalue=0.6546080905364744)\n",
m@59:       "KendalltauResult(correlation=0.056049213943950793, pvalue=0.33857618122131272)\n",
m@59:       "KendalltauResult(correlation=0.075415812257917533, pvalue=0.19786889281527764)\n",
m@59:       "KendalltauResult(correlation=0.026657552973342449, pvalue=0.64900123852931668)\n",
m@59:       "KendalltauResult(correlation=0.091136933242196419, pvalue=0.11969173188443738)\n",
m@59:       "KendalltauResult(correlation=0.1964000911369333, pvalue=0.00079845943724486494)\n",
m@59:       "KendalltauResult(correlation=0.049441786283891551, pvalue=0.39857590952666144)\n"
m@42:      ]
m@42:     }
m@42:    ],
m@42:    "source": [
m@42:     "from scipy.stats import kendalltau\n",
m@59:     "for i in range(n_iters-1):\n",
m@59:     "    for j in range(i+1, n_iters):\n",
m@42:     "        print kendalltau(ranked_countries.iloc[:, i], ranked_countries.iloc[:, j])"
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "code",
m@59:    "execution_count": 53,
m@48:    "metadata": {},
m@42:    "outputs": [
m@42:     {
m@42:      "data": {
m@42:       "text/plain": [
m@59:        "133"
m@42:       ]
m@42:      },
m@59:      "execution_count": 53,
m@42:      "metadata": {},
m@42:      "output_type": "execute_result"
m@42:     }
m@42:    ],
m@42:    "source": [
m@59:     "len(ranked_countries)"
m@59:    ]
m@59:   },
m@59:   {
m@59:    "cell_type": "code",
m@59:    "execution_count": 56,
m@60:    "metadata": {
m@60:     "collapsed": true
m@60:    },
m@59:    "outputs": [],
m@59:    "source": [
m@42:     "from scipy.stats import spearmanr\n",
m@59:     "r, p = spearmanr(ranked_countries)"
m@59:    ]
m@59:   },
m@59:   {
m@59:    "cell_type": "code",
m@59:    "execution_count": 58,
m@59:    "metadata": {},
m@59:    "outputs": [
m@59:     {
m@59:      "data": {
m@59:       "text/plain": [
m@59:        "array([[  1.00000000e+00,   1.74432009e-01,   8.97001663e-02,\n",
m@59:        "          1.99727609e-01,   6.82200753e-02,   5.39272197e-02,\n",
m@59:        "          2.21325022e-01,   1.33629528e-01,   1.08109487e-01,\n",
m@59:        "          1.31114761e-01],\n",
m@59:        "       [  1.74432009e-01,   1.00000000e+00,   4.20573142e-02,\n",
m@59:        "          2.07251507e-02,   2.28481652e-01,   1.01916936e-01,\n",
m@59:        "          1.01442548e-01,   1.12532008e-01,   1.89806266e-02,\n",
m@59:        "          1.48213138e-01],\n",
m@59:        "       [  8.97001663e-02,   4.20573142e-02,   1.00000000e+00,\n",
m@59:        "          1.53308985e-01,   7.91412044e-02,   1.41734934e-01,\n",
m@59:        "         -1.14419359e-01,   1.23519450e-01,   1.50641189e-01,\n",
m@59:        "          3.17074913e-02],\n",
m@59:        "       [  1.99727609e-01,   2.07251507e-02,   1.53308985e-01,\n",
m@59:        "          1.00000000e+00,   3.04934657e-02,   3.27786903e-02,\n",
m@59:        "         -7.58255884e-02,   6.98727824e-02,  -4.16900460e-02,\n",
m@59:        "         -2.15208986e-02],\n",
m@59:        "       [  6.82200753e-02,   2.28481652e-01,   7.91412044e-02,\n",
m@59:        "          3.04934657e-02,   1.00000000e+00,  -8.00848798e-04,\n",
m@59:        "          8.02532110e-02,   1.65796105e-01,   1.91678314e-01,\n",
m@59:        "          1.62863060e-01],\n",
m@59:        "       [  5.39272197e-02,   1.01916936e-01,   1.41734934e-01,\n",
m@59:        "          3.27786903e-02,  -8.00848798e-04,   1.00000000e+00,\n",
m@59:        "          1.17969619e-01,   1.31221881e-01,   2.06996460e-02,\n",
m@59:        "          3.92160863e-02],\n",
m@59:        "       [  2.21325022e-01,   1.01442548e-01,  -1.14419359e-01,\n",
m@59:        "         -7.58255884e-02,   8.02532110e-02,   1.17969619e-01,\n",
m@59:        "          1.00000000e+00,   8.75832730e-02,   1.10578345e-01,\n",
m@59:        "          4.28326583e-02],\n",
m@59:        "       [  1.33629528e-01,   1.12532008e-01,   1.23519450e-01,\n",
m@59:        "          6.98727824e-02,   1.65796105e-01,   1.31221881e-01,\n",
m@59:        "          8.75832730e-02,   1.00000000e+00,   1.31374909e-01,\n",
m@59:        "          2.78868814e-01],\n",
m@59:        "       [  1.08109487e-01,   1.89806266e-02,   1.50641189e-01,\n",
m@59:        "         -4.16900460e-02,   1.91678314e-01,   2.06996460e-02,\n",
m@59:        "          1.10578345e-01,   1.31374909e-01,   1.00000000e+00,\n",
m@59:        "          7.53103927e-02],\n",
m@59:        "       [  1.31114761e-01,   1.48213138e-01,   3.17074913e-02,\n",
m@59:        "         -2.15208986e-02,   1.62863060e-01,   3.92160863e-02,\n",
m@59:        "          4.28326583e-02,   2.78868814e-01,   7.53103927e-02,\n",
m@59:        "          1.00000000e+00]])"
m@59:       ]
m@59:      },
m@59:      "execution_count": 58,
m@59:      "metadata": {},
m@59:      "output_type": "execute_result"
m@59:     }
m@59:    ],
m@59:    "source": [
m@59:     "r"
m@42:    ]
m@42:   },
m@42:   {
m@42:    "cell_type": "code",
m@42:    "execution_count": null,
m@42:    "metadata": {
m@42:     "collapsed": true
m@42:    },
m@42:    "outputs": [],
m@42:    "source": []
m@15:   }
m@15:  ],
m@15:  "metadata": {
m@15:   "kernelspec": {
m@15:    "display_name": "Python 2",
m@15:    "language": "python",
m@15:    "name": "python2"
m@19:   },
m@19:   "language_info": {
m@19:    "codemirror_mode": {
m@19:     "name": "ipython",
m@19:     "version": 2
m@19:    },
m@19:    "file_extension": ".py",
m@19:    "mimetype": "text/x-python",
m@19:    "name": "python",
m@19:    "nbconvert_exporter": "python",
m@19:    "pygments_lexer": "ipython2",
m@19:    "version": "2.7.12"
m@15:   }
m@15:  },
m@15:  "nbformat": 4,
m@17:  "nbformat_minor": 1
m@15: }