{"cells":[{"metadata":{"_uuid":"fbe52034bfdb8de5f330a55ff6d8f2a7d4bf8c01"},"cell_type":"markdown","source":"# CV splitting by earthquake ID\n\nThis kernels implements a Cross Validation (CV) strategy that separates the segments in the training data by Earthquake ID.\n\nFor this, we first identify the earthquakes in the training dataset and we assign an unique ID to every earthquake (the earthquake #). Then we use the \"Leave One Group Out cross-validator\" to test a simple model. \n\nThis kernel follows many of the suggestions in [this discussion](https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/78809) started by [Elliot](https://www.kaggle.com/tclf90). "},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom tqdm import tqdm\nimport os\nimport sys\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport pywt\nfrom sklearn.preprocessing import StandardScaler","execution_count":1,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Global parameters\n\nTo save time and memory, this notebooks save the features, time_to_failure, and the earthquake ids in hdf files.\nHence, once the data was computed, it can be loaded from these files quickly.\nThe files are saved in the **alternate_input** directory."},{"metadata":{"trusted":true},"cell_type":"code","source":"compute_features = False \n# The computed features are saved in an hdf file along with the time_to_failure to \n# save the time spend reading the training data and the feature computation\n\nif 'KAGGLE_URL_BASE' in os.environ:\n # If we are in a kaggle kernel, read from the csv file.\n train_data_format = 'csv'\nelse:\n # This only work in my local setup. \n # Loading the training dataset from the feather file is much faster than CSV\n train_data_format = 'feather' \n","execution_count":2,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Load the data"},{"metadata":{"_uuid":"5671142620326fe69d802ee9eb021ea0eadfa25a","trusted":true},"cell_type":"code","source":"def load_train_data(file_format):\n \"\"\"Load the training dataset.\"\"\"\n print(f\"Loading data from {file_format} file:\", end=\"\")\n if file_format.lower() == 'feather':\n train_file_name = '../input/train_4mhz.feather'\n train_df = pd.read_feather(train_file_name)\n else:\n train_df = pd.read_csv('../input/train.csv', \n dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32})\n print(\"Done\")\n return train_df","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Feature generator"},{"metadata":{"_uuid":"396d1f54d7e90f91f4f097dc8a0c29397f509b11","trusted":true},"cell_type":"code","source":"sampling_frequency = 4e6 #4mhz\nsampling_period = 1./sampling_frequency\n\ndef generate_features(segment_signal):\n \n segment_signal = segment_signal - segment_signal.mean()\n \n features = pd.Series()\n \n windows = 10\n signal_roll_std = segment_signal.rolling(windows).std().dropna().values\n features['std_roll_std_10'] = signal_roll_std.std()\n features['mean_roll_std_10'] = signal_roll_std.mean()\n features['q05_roll_std_10'] = np.quantile(signal_roll_std, 0.05)\n features['min_roll_std_10'] = signal_roll_std.min()\n features['q95_roll_std_10'] = np.quantile(signal_roll_std, 0.95) \n \n return features","execution_count":4,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Generate features\n\nHere we will generate the features and also identify each segment with the corresponding earthquake that should be predicted by it. Each earthquake will be identified by an unique ID (earthquake #).\n\nIf **compute_features** is set to False, the features, time_to_failure, and the earthquake ids are loaded from the hdf files. Otherwise, they are computed and saved in hdf format for future use."},{"metadata":{"_uuid":"9be335cb5e5de2716c32271a722a5f47fd7058a5","trusted":true},"cell_type":"code","source":"saved_files_present = ( os.path.isfile('../alternate_input/x_train.hdf') and \n os.path.isfile('../alternate_input/y_train.hdf') and \n os.path.isfile('../alternate_input/earthquakes_id.hdf') )\n\nif (not compute_features) and saved_files_present:\n print(\"Reading hdf files:\", end=\"\")\n x_train = pd.read_hdf('../alternate_input/x_train.hdf','data')\n y_train = pd.read_hdf('../alternate_input/y_train.hdf','data')\n earthquakes_id = pd.read_hdf('../alternate_input/earthquakes_id.hdf','data') \n print(\"Done\")\n \nelse: \n \n x_train = pd.DataFrame()\n earthquakes_id = pd.Series()\n y_train = pd.Series()\n \n train_df = load_train_data(train_data_format) \n chunksize = 150_000\n\n segments = int(np.floor(train_df.shape[0] / chunksize))\n\n current_quake_id = 0\n last_time_to_failure = train_df.iloc[0]['time_to_failure'] \n \n print(\"Computing features:\", end=\"\") \n sys.stdout.flush()\n for segment_number in tqdm(range(segments)):\n\n segment_df = train_df.iloc[segment_number*chunksize:\n segment_number*chunksize+chunksize]\n\n times_to_failure = segment_df['time_to_failure'].values\n\n # Ignore segments with an earthquake in it.\n if np.abs(times_to_failure[0]-times_to_failure[-1])>1:\n continue\n\n y_train.loc[segment_number] = times_to_failure[-1]\n\n features = generate_features(segment_df['acoustic_data'])\n x_train=x_train.append(features, ignore_index=True) \n\n if np.abs(times_to_failure[-1]-last_time_to_failure)>1:\n current_quake_id += 1\n\n earthquakes_id.loc[segment_number] = current_quake_id\n last_time_to_failure = times_to_failure[-1]\n print(\"Done\")\n \n print(\"Saving features, earthquake ids, and time_to_failure to hdf files:\", end=\"\") \n if not os.path.isdir(\"../alternate_input\"):\n os.makedirs(\"../alternate_input\")\n \n x_train.to_hdf(f'../alternate_input/x_train.hdf','data')\n y_train.to_hdf('../alternate_input/y_train.hdf','data')\n earthquakes_id.to_hdf('../alternate_input/earthquakes_id.hdf','data')\n print(\"Done\")\n \n del train_df","execution_count":14,"outputs":[{"output_type":"stream","text":"Reading hdf files:Done\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(earthquakes_id)","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16])"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Lets see how many earthquakes we have in the training set"},{"metadata":{"trusted":true},"cell_type":"code","source":"fig, ax1 = plt.subplots(figsize=(9,6))\nax1.plot(np.arange(y_train.size), y_train, color='r', label=\"Time to failure\")\nax1.set_ylabel(\"Time to failure\")\nax1.set_xlabel(\"Segment #\")\n\nax2 = ax1.twinx() \nax2.plot(np.arange(y_train.size), earthquakes_id, color='b', label=\"Earthquake #\")\nax2.set_ylabel(\"Earthquake #\")\nax1.legend(loc='center left', bbox_to_anchor=(0.05,0.95))\nax2.legend(loc='center', bbox_to_anchor=(0.7,0.95))\nplt.show()\n","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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GrD84AVK4C9e233hGHcpLFRvS9ZYrcfFWALkl1YIDFMteF5KvTf8cmfYZjysECyCwskhqk2/FB/zqjNMJUJ5hFzDBZIdmGBxDDVBof6M0x4tm613YOSsECyCwskhqk2evdW4f4skBimMg6PExZIdmGBxDBpp9gSAYf6M0w4HB4nLJDswgKJYaqBwsK0fqi/w/4VDOMELJCYErBAYphqxPOAd98Ftmyx3ROGcRsWSE4ihPilEGKTEOLNIv/3TSGEFEIMouwDmUByYedSx/z5wA032O4FUw1w0VqGKU3QsurwGMmyQAJwK4DTCz8UQowGcCqAVdQdoMzReSuA6wHcHvzQ5M6ljrPOUu8XXmi3H0z6CQqk44+32xeGcZBVGI21/Y8EFu8Anm1zLmW1r+GyKpCklM8IIVqK/Ne1AL4D4PfUfSC7IlzYudSycyfQ3Gy7F0yaGTsW6NnT6adjhrGFlMDh+Ad2vdsxz37Qbn/K0aeP7R64gxDi4wDWSin/Lgr9LgkwKplN71xqWbQImDrVdi+YNFNbC0yYwAKJYYrQ1gbsQjM+P/Ev+P8Wfx/4/pXAjBm2u9WN2tqqNgDXCSEWBP6eK6WcW+rLQoheAP4dagXKCMYEUtSdE0LMATAHAOrr6wl75hATJqjyEO+8wwKJSY7nAW+9ZbsXDOMc+bx6P2RCHqctfhRoOhU4zT2BVOW0SSmnRfj+eACtAHwDyygAfxVCHCOl3EDRQZNRbMGdW4GDOzes2JellHOllNOklNPqHFsbJmPCBPXOT/2MDjxPVS1va7PdE4Zxiny7WsGo7d0ADBrEc24KkFK+IaUcIqVskVK2AFgD4GgqcQQYFEg2di519Oyp3nmwMjrwPODAAWD5cts9YRin8C1ItUKqccJ1C51DCHEXgBcAeEKINUKIC0z3gTLM3/rOpRYWSEwUSiWD5JpsDFOUTgtSjeSs844ipfyslHK4lLKHlHKUlPKWgv9vkVKSJnojE0gu7FxqWbQIaG+33QsmTRQLeuBcSAxTlE4LUk2HBWnDBmDHDrudYpyDM2m7yO7dwNq1tnvBpJ0BA9i/gmGK0E0gATxOmG6wQHKJ4FIJr4kzOvBrsjEM0wkLJCYMLJBcY/Bg9c6DldEBCySG6UYXgTR+vEo4xOOEKYAFkmuMGAH07s2DldGD5wEbNwLvvWe7JwzjDF2ctOvrgdZWnnOZbrBAcg0hOKqC0Ye/fMBLtgzTSRcLEsCWVqYoLJBchAcrowv2r2CYbhQVSIsXc/Qw0wUWSC7iecDKlcCePbZ7wqSdcePYv4JhCuiyxAYoq/2ePcDq1RZ7xbgGCyQX8TwV0bZkie2eMGmH/SsYphsHLUgdFiNeimaKwALJRXhZhNEJL9kyTBe6lBoBeM5lisICyUUmTlTvPFiZMJQqNeLD/hUM04VuS2zDhgHNzTznMl1ggeQS/o2ud29g5Eg29zLhKVZqxMfz2L+CYQLk29Rc2ymQhGBLK9ONOtsdYArwb3Q8WBldBJcPxo612xeGCcl//RewYAHNtt9dPwBAQCABapw8+yxNg0wqYYHkKp4H3HWXsiqVsw4wTCWCAunUU+32hWFCctVVQM+eypiunf01OA7P4cjR2w9+5nnAnXeqWpi9ehE0yqQNFkiu4nnA9u3A5s3AkCG2e8OkmaFDgT59eMmWSRX5PHDeecDVVxNsfPk6YNwJwIBbD37mP0gsXgwcdRRBo0zaYB8kV+GoCkYX7F/BpJB8XqXwMkYup955nDAdsEByFRZIjE5YIDEpw7hA4uhhpgAWSK4yZoxagOfByugglwNWrVL+FQyTAowLpKYmYPRonnOZTlgguUptrXqi4cHK6CDoX8EwjtPeruJTjAokQI0T9tVjOmCB5DK8LMLogpdsmRTRmenahkB6553KyVeZTMACySUKB2UuByxbBhw4YKc/ptmzBzj/fGDjRts9qT7Yv4JJEVYF0o4dPAcxAFgguUcw55HnAW1twPLl9vpjkkceAX71K+ArX7Hdk3QRJldWr17Kr40FEpMCyAVSKQsRW1qZACyQXCZrg3XECPWelf01DftXMCnBmAWp8MEia3MuUxYWSC6TtcHqz4ZZ2V/TsH8FkxKsLbGNHg00NPAcxABggeQ2/fsDgwdnb7D6syOjF/avYFKCNYFUU8PRw0wnLJBcJ6uRbG1ttntQfXCmYCYlWBNIQHbnXKYbLJBcJ6uDNSuO6SbJ2pItk1qsC6Tly4H9+y00zrgECyTX8Txg0yZVuDZL8E1cP6NHA42NfGwZ57EukPJ5YOlSC40zLsECyXWyuizC0Vb6Yf8KJiVYF0gAz0GWEUL8UgixSQjxZuCzq4UQbwshXhdC3C+E6EfZBzKB5MLOVQVZWhYJRldlYX9tkNUlWyZVOCGQeJzY5lYApxd89hiAI6SUkwAsAvBdyg7UEW77VgDXA7g98NljAL4rpWwTQvwEauf+jbAP6aJY+PW4cWqWyNrTDE9ONHgecN99yr+ivt52b5iUcuAA7QrUihXq3YpA6tsXGDqU5yDLSCmfEUK0FHz2aODPFwF8mrIPZALJhZ1LJYWJy+rrlUjK0mAdMiRb+5uUKHmNfP+KZcuAQw6h6xNT1Xzzm8B119G309REtOFKY4YtrWngfAB3UzZAaUGqBPnOVQ1ZG6yeBzz7rMrZ06eP7d6kg0qlRnyCywcskJiYbNoEDBsGXHstXRsNDcAZZ9BtH0DpceN5wP33EzeeeeqEEAsCf8+VUs4N80MhxH8AaANwJ0nPOrAikMLsnBBiDoA5AFCf9aUAzwMefxxob1eOttVOLqcE0jvvANOn2+5NdZFVp39GK/m8ymN7zjm2e0JELgds2QJs2wYMGGC7N9VKm5RyWtQfCSHOAzAbwIekpC0LYPxuG9i5fyq3c1LKuVLKaVLKaXV1Ng1dDuB5wN69wKpVtntiBnaSpIP9KxgN5POW/INMwXOQkwghTgfwHQBnSil3U7dnVCCZ3rmqIWuDdfx4ZSnLyv6aJmtLtox2WCAx1Agh7gLwAgBPCLFGCHEBVOBXM4DHhBCvCSFuoOwDmWmmY+dmARgkhFgD4HtQUWs9oXYOAF6UUl5I1YeqIbgsctppdvtigp49gdbW7EXumYL9K5iEVL1Aam0F6upYIFlESvnZIh/fYrIPlFFs1neuahg6VDkrV/tgDa64spWDDs9j/womEVUvkHr0UJZsfkjLNBnw+K0ChMiWYPD3d9Ei5ZjO6IWXD5iEVL1AArI15zJFYYHkEuUc8n3BkBU8D9izB1izxnZPqg8upcAkJDMCacmSg2m9q5l8Pnv1PkPAAsk1yuXlWL0aeP99s/2xBVs56GhtVUsIfGyZmGRGIO3bB6xcabsn9FxxhcrbwCKpCyyQglx4IXAnad6p+PiCYfFiu/0wBQskOurqlH8FH1smJqkXSGHS52RpDnrsMfX+xht2++EYLJCC3Hgj8LnP2e5FcbI0WAGVprd37+zsbxLi5ErL5fjYMrFJvUDyKZeBPktJVbN2fwkJC6S0MHGies/KBZw1x/SkhC014pMl/woqli0DbrrJdi+sUDUCqRyDBwP9+mVjDpowQb1nYV8jwAKpGC7eNBobgTFjsnUBs0CiI0v+FVR8+tPAnDmqMFnGyIRAytJDWkODes/CvkaABVIxVq+23YPiVPtgLVwq8jxVXmXPHjv9qWbYpJ4cvwRSBqMBMyGQgOxFD/N80AUWSMVw9SLxBRJtfT77+MtFWXNMNwkLpORk+BhmSiCtXQvs2mW7J2ZYtKj67y8RYIFUDFcnPM9TA3X9ets9MUOGb0DkDBqkwnqz9HSsm9ZW9Z7B6zNTAgnI1jhZt852D5yBBVKQvn3Vu60Jr5Jyz9pgzZpjukmy5F9Bhb/ElsFjmDmBVO3nOHjvqfZ9jQBZLbZUY/MCKReNFByss2YZ6Y5VmpqA0aN5wFLhecDjj9vuRfqxdH0uWgRcdRXQ1ma+7XXrgKOPNt+ucSZMUHNyluagd94BTj7Zdi+cgAVSMVwdDKNGqWg2V/tHAVs56PA84Lbb1LJt7962e5Ne/HQJhk0q993xPm65pQljxkTP8pCUAQNS/owW1s+moQFoacnOHJQ1MVgBFkjFWLNGlfRoarLdk67U1GQvwZ/nAb/+tZrQTN8Fqh0/Ed6iRRkxB2jGv8nm88CKFSo7uUHabroVwEVYsliiRz2PjViEmVOy9JCWtftLBdgHqZARI9S7q34+WbuAczlgxw5g40bbPXGXuFEnWfGvMIGFY5jfsBkAULuRnWpJyeWyE92VJTEYAhZIhbjuCO15wPLlKslfFuCbeDjiWNey6F9BhQ2BNHAIAKBmMZ8/UjxPrShkIbrL85Q1dO9e2z1xAhZIhUyc6PZNw/OA9nZg6VLbPdFPsSc01wVrmsmafwUVlspR5AcMRi3a+PxRk6WHNM9T8/CSJbZ74gQskApxvaRHFgZr0BoyZoy6kVfz/tqETerJsXQM8737ohZ5Pn/UZGHO9R9ODzlEvfMDKQAWSMVx+aaRhcEapKZGWfWysr+m8UspZMG/ggpbAknWsEAywciRKmAnC8fZD9zIwr6GgAVSEP8mYaukR5j2+vQBhg3LlsJ3WbCmnSz5V1DheSq7/Y4dRpvtFEhZmgtsIER2gmN691aCMAv7GgIWSIX4g8Hlkh5ZEwyeByxbBuzfb7sn1UfWLJI6CT5QAcaFSr5dKIG0YkV2gjZskaU5NytiMAQskIph86bBeTm643kq18yyZbZ7Un2wQEqOJb+NTgtSezs71VKTpeiurBRFDwELpGK4ftPI5YCtW9UrC7h+PtLMiBHZ8a+gwlK6hE6BBPD5i0rUm3+Wors8D3j3XWDLFts9sQ4LpGK4XtIja4KBHQfpyJJ/BRU9e1pJl5CXggVSUsLmDwtmna92snZ/KQMLpGL4JT1cHQzVegGXeqrr1w8YMsTd85F2srZkS4GFY5hvr0FtjVRWQD5/tGTpIa1a7y8xYIFUCpdvGq2tQI8e7vYvKcWe6lw+H7ZJ6iuQJf8KKvx0Ce3txprsXGLjsUFPc3N1C9HgHDJ2rLKKVuu+RoAFUilcLulRV6cKY2bpAuabQHmSFPL1/SuqMTu7KTwP2L0bWLvWWJPdBBI71dKShTlICKC2VvnVscWeTiAJIX4phNgkhHgz8NkAIcRjQojFHe/9qdqPRbBivOslPbIwWIN4HrB5s3IeZPTCJvXkWDiGygepnZ1qTZElIerA/cUFDUFpQboVwOkFn10K4M9SyokA/tzxt5u4ftPwPBVRkc/b7okZXD8faSZL/hU6Cd4obQik9hrUijzXKzRFloRoLqeMA21tNntxKyxriDqqDUspnxFCtBR8/HEAszr+fRuApwD8G1UfEmHjphHlycTzgAMHlO/I+PFkXXKG4A1o5ky7fak2OHtubLahP/ZvAFAzAug1DvjbemCDmbZ3t9WrJbbgXHXccWYazyLBOWjwYLt9oca/vyxfrko9WcAFDUEmkEowVErpp6feAGCo4fbD45f0MH3TCOtLEhysWRBIra3K94pv4jRwqH9kHlkyAR/BNmA4AAgAS4FboF5GOBJT614DWiYC9fV8/qgJzrnHH2+3L9QE99WSQCqBUQ1hWiB1IqWUQoiSJhMhxBwAcwCgvr7eWL+64MA6bEmCF/AZZ9jtiwl69MieY7pJPA+4++6ufnhMWVbv6AsA+OEPgf79Adxyi8r2/t//baYDd92FY16/Gaj9s3Kq5bFBS5aiu4L3l9mzqVqpE0IsCPw9V0o5N+yPK2kIHYQSSEKIsQAmSikfF0I0AqiTUu6M0d5GIcRwKeV6IcRwAJtKfbHjQM0FgKamJjtecZ4H3HuvlaYrMnCgmpWrabBWWmJ0WbCmnaB/RbUvH2gi366E5Be+oCLAsWE1cOWPgC9cphLNUvP6s8DCN9S/PQ9YuJC+zWohjqN1lqK7BgwABg2inm/bpJTTIv4mtIbQQUUnbSHElwDMA3Bjx0ejAMyP2d4DAL7Q8e8vAPh9zO2YwfPcLekhRPUKhlIWjFwuW47pJmEn+MjkpZo+a2s7PrBZjsLzXHCqTR9RraXVuhRdTDC6eX8xqiHCRLFdBOA4ADsAQEq5GMCQSj8SQtwF4AUAnhBijRDiAgA/BvBhIcT+X3iWAAAgAElEQVRiAKd0/O0OhReJ6zcNNy9gOjxP5aVatcp2T6oP1691B/EtSF0EEmDnGAadahk6ql2IBgWj5fuLDg0hhJiUpA9hBNI+KeX+QIN1ACraJ6WUn5VSDpdS9pBSjpJS3iKl3Cql/JCUcqKU8hQp5bYknSeh8AIB3L1peB6wfj2wY4ftnpjB9fORZrLkX6GJbgLJdOSr5TQDmSRLQtTzgI0bgffes9J8Eg0hhPCdt28NfPaTqH0II5CeFkL8O4BGIcSHAdwD4MGoDaWSlha3S3pkLf8J3wSKo8OxOkv+FZrotsRmI11CMLEtwGODmiwd5zPOAG6+OXCBp4pfCCFeBDBGCPEVIcRx6J5TqSJhBNKlADYDeAPAlwH8AcBlURtKJXV1bkeHZE0gDR6sCte6ej7STtaWbBPiW5DqgqEufk020wwYoAI3sjIX2CJLAumII4ALLlDCP2VIKT8ppZwJpV12ATgLQIsQ4ukolqSyUWxCiFoAt0sp/wnATUk6nFpM3jSiRlZMmADU1GRjsALV7ZjuAp4HPPig8q+os5YBJBkHDqhorkmJXA9C0c2CBKhjeNdddtIl8Nigx0x0F5MQIcRfACwG0AvAmwB+B+BkKL+lGWG3U9aCJKXMAxgrhLCUiMgB/MgpU055USbVnj3VMmCWBivfBOioBv+Kn/wEOOoo4K9/JW+qmw8SoI7h9u2qbqBpeGyYgY+z80gpjwXwg44/zwfwKIAJAK4BMCzsdsIssS0D8LwQ4j+FEJf4r6gdTi3+TWPlSts9KU41DdYwFjTPUxXTd+2i70/WqIaabP44feEF8qaKWpBsHkPPAzZsyE7Qhi1sLaNSUoUFeKWUSwBsk1JeLKU8EUrL/BrA2LDbCCOQlgJ4qOO7zYFXNnB9zdkfrO3ttntiBv98LF5stx/ViOvXehgM+uXl2wUE2rsafW2H+ttqO20kEQQmhegzz6h6m6aoviz6Hwj8+14p5QIp5U/D/riio4GU8vuxupVGivkNuF7Sw/OA3buVVWX0aNu90UO5QRo8H1OmmOlPVqgG/4qBA9W7gX3IyxpVLDb4nGkyXUK5vG3Tp9O3Xw3EEQRBKyH1cT7xRPVehRYeE0gp9wb+/YNy3y1GRYEkhHgSRfIeSSlPjtpYKhk0SN04XL1pBAdrtQikcowfryY1V89H2qmWJVsTAqlddAikHgc/9NMlmDqGwRv8+PGq/Wo4fy7DQjQzhAlV+Vbg3w0APgWgStOIlsDlm0ZwsJ5yit2+mKCxUT2lu3o+0o7nAQ8/bLsXyTGwLJGXvkAqwPOAN98kb78b9fVAayuPDWpsCFEuIm2Fij5IUspXA6/npZSXAJhF3zWHcFkgjRih8lRUm9NgOVw+H2nHcvbcxASXIoh9RPLtNaUF0rJlKrjDNDw26LEhRDeR1mRlShCmWO2AwGuQEOI0AH0N9M0dXC7pIUT1FlAshe+YzuvyCp3HoZqSjxKPibIWpLY2O+kScjkVwJCVoA1bmBaiWZrfNSKEyAkh/iyEeLPj70lCiNCJrsNEsb0KYEHH+wsAvgnggjidTS2mbhpxb3RZe2r0PBXmv26d7Z64gy7zezVFQlELpHIWJAPtF8XzgD17gDVrzLedJTzPrBCthvFoh5sAfBfAAQCQUr4O4JywPw4TxdYau2vVgu8IvWgRMG2a3b4Uw/OA3/5WTYyNjbZ7Q0/wBjRypN2+VBvjxqXa0ff9fXX4EX6AXegNXDdePdoR8ezqscUFUjBw4mMfo+tAMYJjY8wYs21niaAQpTzOAwcCW7fSj8fqtcb3klK+LLo+QIb2oS4pkIQQnyz3QynlfWEbST0mS3rEsQR4nrrAlywBjjxSf59MEXaQBm8CJ2cjmNIY9fVKJKVUIL2wdAj+G59HE3ah7m8CoNyNvX1xEv4E4BNdP7eZLiE4Nj78YfPtZwVTQrSm5mA7Jqg+R/AtQojx6IjEF0J8GsD6sD8uZ0Eq9+gjAVSfQCoVKeB6SY/gYE2zQPKpNEhHjgSamtw9H2knxUu2B/LqhvLncXMwo/kt4LXX6Bq77L+BH/0IKLXMZuKpv3CsDBsGNDen9vylBtNClM9nXC4CMBfAIUKItQCWA/hc2B+XFEhSyn9O3rcqwuWbRjWUiIiC75heDY7ELpLLAX/+s/KvqAnjpugO+Q6tUtsyGnhhvr198DzgoYfMt8sFncORdElp6FCgTx9zx3nZMmD/fmXhZaKwVkp5ihCiCUCNlHKnEGJA2B+XnDmEEJ/reL+k2EtDx9OFyyU9mpqAUaPoB+srrwA7d9K2EZasRe6ZxPevWL3adk8i01lAtmW0GWflUtZOz1Oh2du307Zfqm0eG+GIu6RkMnq4qUkp/2XL6NuqPu4TQtRJKd/vEEfDADwW9sflHq2aOt6bS7yyRbCkh4tQW1Ta24FjjgFOOomujSh4nkoGuG+f7Z5UHymOZOsUSK0dfiG29sFmugTPA1atUvMVFTfeCFx/Pd3204ApIZri8egA8wHcI4SoFUK0AHgUKqotFCUFkpTyxo737xd7Jex0+nD9IvUHK1U0gr/dV1+l2X5UPE+JtiVLbPek+nD9Wi9DXnYIpPEt6gPbAslG+/6SO2VB5wsvBC6+mG77acCEEJWyunKTGUZKeROAx6GE0oMALpRSPhr292ESRTYIIS4SQvyfEOKX/it+l1OK6zcNz1Pm/M2bbffEDK6fjzRj2r9CI/l2NaXVDh2kMszb2geb6RJM3lDbslV1qgv+caYUogDQvz8wZAjttVRlYf4F7kANAMYAeA3AzCguQmG8F38NYBiA0wA8DWAUAEccUQwyfLjdCbcSJgWDC35YWXNMN0mKHX07l9jqLO+DzXQJEyeqdxNt28gW7gom51xT13L1hPkH3YF6Q0XdL0FEF6EwxWonSCnPFkJ8XEp5mxDiNwCejdHhdFDqAjEROZVExQcH6wkn6OlPKVatUmkPdBNl/5ubVR26FN7EtUNRyNLzgGee0btNA3RGsdV0LE08/zxdY5WuV2on3lLnvakJGD2atu26OmU9euedg4Isa/j7bcJSl8sBDzxA306VoMsNKIwFya+4uF0IcQRUHbYhOhp3jkoTngkVH/dGN2aMytdkQjC4cgxSauVIBSb8Kwjo9EGqxcF92LPHTmdMl6MobJtybJhYxvvhD932MezVS827pixImzcD775L31YVIYQYLIS4WgjxByHEE/4r7O/DCKS5Qoj+AC4D8ACAtwD8JGZ/043nAStX2ptwy1Fbq55oqAZrUDy6IkqoHdOzjCn/Cs34Pkg1teJghnnKfSgn5j0P2LtXiTTTUI+NAR2pZKjmgh07gP/4D2DmTJrt64Ij2VznTgBvA2gF8H0AKwC8EvbH5fIgfb3jnwullO9KKZ+RUo6TUg7xI9wyR7Ckh4uYGqyuDFLPU09UW7fa7kn1kdIJudMHqUba91OzXbR2xw5g40badqj2zReero9tUw9pKR2PDjBQSnkLgANSyqellOcDCF2fqpwFyc+kfV2S3lUVrl+knqeSiR04UPm7SXBl/23fAKuZCRPUe8qObadAqoX968N2LiSAft9Tdn10okvQ5HK0QtT3Mxs3Tvl9pfV428O/Ga4XQnxUCDEFQPJM2gAWCiEWA/CEEK8HXm8IIV5P0uPUYnvCrYTnKcdJ6sgSV/bfdcGaZkz6V2iki0AylWG+FDbTJZiaqzZsUAKBEkofrqTBDabmoB49Ul1E2iL/JYToC+CbAL4F4GYA/xr2x+VqsX22Iy33nwCcmbSXVYHtCbcSwcHqT5C6aWhQ5Rvef18dD5u0tKhwalfPR9pJoRN8p5N2TYeFwOY+2EyXMGaMGqumltynT6fbPlXUrA6Cc+6JJ9K3ZcLHtIqQUvoFEd8DELkMRFknbSnlBinlUVLKlYWvOJ31EUL8qxDiH0KIN4UQdwkhGpJszygu3zQonxr9AXTIIerdBefd2lq1FOTq+Ug7KXSCz+cDFiTA/j5QzxelLCA1NSpog3J5b+hQ9U4531BtXxejRwONjeYctZcsOZjLggLH8iAl1QpCiF8FE1xHTXRtvMy1EGIkgK8BmCalPAJALYBzTPejKGHyyfhFa128afTvDwwebCa8l3pSDIvLgjXteJ4qTrxhg+2ehKbNz6Rd1zGOfWflTZv0Nxbmes3lVNHf99833z61xWHCBCXEsuzn5AtRUwJp3z47UZEW0KQVHgLwcMfrzwD6ANgV9sdhEkVSUAegUQhxAEAvAOss9SM6wZIeQzSng9IhuqgFw4QJSkS6Mml5HvDQQ8r3qs7W5VylBMXw8OHaN79/v373kn1tHQIpuMQGqH3wLR4mCaZLmDzZfNv3368OdH29/u337Am0tmZbIAHqOP/tb2baAdTxaG2lb88NEmkFKeW9wb+FEHcBeC7s70NbkIQQvYUQvSP0rShSyrUArgGwCsB6AO9FKR5nHWrnRx1Og5QTSmMjvfNulGPgeSpqj8Ixfd48Jb5ch8qaSRiF9Ze/KBe2xka9r+89MA01yB/UytROtGEszpTtV2o7n1eRrZRtUEfppUEgLV+uhKhugmPb9SAhzRBphYmIkOi64iO3EOJIALdDhcYJIcRmAF+QUr4Zp3cdSSc/DpW4aTuAe4QQn5NS3lHwvTkA5gBAPcXTT1xMlvSIg+cBt9yirFz9+tG14cogDZ4P3SUPzj5bvbu4nFoIhe/AqFFk/hXLlyuj3ze/CQwapHHDL7+M8fdfjbq6n6m/TWaYL4bJumiFBMeG7ztI0caTTypTYA2Rx4Yrc00pgkKU4jj7Y3vIEKBvX/ePR3jqhBALAn/PlVLO9f8IqxXKIYTYCUACEB3vGwD8W+gOhvjOjQAukVI+2dHgLABzARwbtpECTgGwXEq5uWN793Vsq8tOdxyouQDQ1NTkzh3K9oRbieCkOGMGXRu/+hVNDbCoBJ+qZs+macOF/bRBTQ1ZPTHfz/SrX1XRy9q46e/A/fMAdAgkkz4ixfDTJdjIhWTC4uB5qrLAmjVqP3UzYIA7UbOlCB5nKiEKpLqIdAnapJTTyvx/KK1QDill6MK0xQgj+Zt8cdTR4FMAklypqwDMFEL0EkIIAB8CsDDB9sxCXdIjKVQm/aAVxfOAXbuA9ev1thGHgQPVi/J8UDj4pgWiCTm/S5Xr6Yw200Uxa5/tm4qt9vv1U1YHyrapRFhh1KxuganTKmxyGdX2tWyWxFpBCHF0uVel34cRSMuEEP8phGjpeF0GIPaitpTyJQDzAPwVwBsdfZhb9keu4fJF6mdcpXpi9Z9iAHeOAbUfhCv7aQMi/4r8rb8GANRuIhLZQYufn2GewkckDDZTDVDNVbKIEzwF1NnIdViG+/ZVAQCmBNLateoBVScOuhFo0gr/B+DFjt/dBOAlAP8PwE+h/JvKEkYgnQ9gMID7ANwLYBAOliGJhZTye1LKQ6SUR0gpz5VS7kuyPa2EGTCmSnrEwUTGVRcFEkVffOcYV/bTBrmcWg9bulTrZtu2qwm+dqmBpScqZ+WwNxWqdAlh05JQOqgPHw707k3XhmtRs6UwXbTWZcGoEQ1aYR2AqVLKaVLKqQCmAFgrpTxJSlmxJlsYgXSKlPJrUsqjpZRTpZTfAPDhiJ1MB1EmvLY22uiQJBD5jXQycqTyraBcxouC59GUPPB9KlyfnCkhEsP5ISptQO1yA4WfbQt6m9FHngds2QJs20azfWq/GBNRszqgttQF2wHcPx7u4Ekp3/D/6AguOzTsj8MIpO+G/Cw72CxCGQbPU3lXqGoYmcjSGwWqScOPynFlP21AJZD6K+ucMQsSQHMew1qcAbuRbJTXMLX1xGWXBh9KIRq8xnyLWpbnpGi8LoS4WQgxq+N1E4DQtWRLCiQhxEeEENcBGCmE+N/A61YAbcn7nWKoHROT4nnA3r16M64We5JxJRcU9Q3I9cmZkj59gGHD9AskKO/s2mUGStaYcFYuh8lyFIWYEGeep+aaPXv0bbMwKMT1kjemRHBjIzB2bLbnpGj8M4B/APh6x+stRHARKmdBWgdgAYC9AF4NvB4AcFrMzlYHlCU9dKwBm5oUly9Xqe9tM368Coei2l9X/c1MQSCG8+0dNdN0W5BK3URtWiFsphpobVVBGyZ8EinqM/pLeK5EzZaCI9mcREq5V0p5rZTyrI7XtVLKvWF/X1IgSSn/LqW8DcAEKeVtgdd9Usp3tfQ+zbh8kVIOVl/AeZ5awtPsvBuL+nrakgcu+5uZgCBKMC87BNKqZbTFN32o/fIqYWu+6NFDPUBQp/0A6CPZXJ1vAaClhV6I+rhcD9QxhBDHCSEeE0IsEkIs819hf1/RB0lKmeFH5zK4LJBMZFx1bdKiuAFKqXIsAe7sZzGoJ0oC/4q8X1QWeWDFCm3b7aTQEut5qn7iu5ae7WxaXKnEoX+MqbOFuzbXFMMXoiZ8g3I5ZVFbp7GEafWKrVsA/A+A4wFMD7xCQZQbPgN4nkoguH277Z50x0TGVdfqAlE5pqdhcgZow3MJjkHnEhvyZsOjbZ1H3+Kq0xIZNsO75wFLltBZ6pqaVFkaqmNLFTWrG4o5t1TiU8BdFw+3eE9K+Ucp5SYp5Vb/FfbHUYrV9orXv5QR9gKxPeFWgjp5Yp8+KgeKK/vvlzxYvVrvdin9zdICgRjOy4AFKa0CKcpTt+1Itn37gJUradug2je/5I3O+YzCYkIlRItZQ4Fsz0kVCGTKflIIcbUQ4gNRMmj7VBRIQohjhRBvAXi74++jhBD/F7/rDpOWCS8MuZwSC++/r2d7LpZwCEIZzuzSftqgtVUtIWi2IAm0Q5iK7vIzzOtuKw0PVKaCNnRGmhVuh3qZUAcmhCiQHouaXX7a8ZoBYBqAHwY+q5hB2yeMBelaqKi1rYBy3gbwwYidrT6oJlxdUEaW+OietJJMrpQ3gawLpLo67Y6++XahrEe6j22pa8hEhvlyEKVLCAXFw0OxtB87dgAbN+prA+gaFOJK1GwpTIlgwiLS1UJHpuyTAFzg/zvw2RfDbifUEpuUsnDdwkDYiePYnnArQTVYC2tcbdsGbA29pBu9jbAMGwY0N9PcbG07+LqAZiGTl0QCqRzUy86VsHVTGzxY5YKingsAWkdtV6JmS8Gh/i4yr8hn94T9cRiBtFoIcSwAKYToIYT4FiJW1K1aXF4XnziRvoaRS8uMVI7pLhbntYFm/4p8e81BgbR+vapVppNiItt35DeRVqAYtm5qJoI2qDN2p2EMDhqkfBZNCaQVK9y2qFlECHGIEOJTAPoKIT4ZeJ0HoCHsdupCfOdCAD8HMBLAWgCPArgoRp+rD88DHn9cPdnUOBYQaKKGUXDSOvZYunbC4nnAs8/SbBdQk//Mmfq3n5C1u/vjl1u+gvwVhI28fQ6wvxG4ZIe6CSTk+ZUjDwokQB3bqVMTb7csvo/IqlXKr8o0nqesrVu3HkwfYbLtP/+ZbvtjxgANDXTzjWtRs8UQwpyV0PPUA/WSJcDhh9O3lz48ALMB9APwscDnOwF8KexGKgokKeUWAP8UtXeZIFjSo6VFzzZ1Ow1SDtaWFu3Ou4nI5YA77wR271ZOjLrQ7W/2yivAMccAL74IzJiReHO3rTgRl289B/i+hr6VZIp6/a+u7Y3ETLwAHHKI+vOdd+gFUvAma0sg+e3reqAIO1/kcsDtt6v8Ob1762k7CHW2cNeiZkvhPzTrolQqh+C1pEMgVVkeJCnl74UQDwH4NynlD+NuJ0wUW6sQ4n+EEPcJIR7wX3EbrCpcN/vqjCwpto26OlU80ZX9p3JM1+1v9sIL6v1Xv9KyuX3t6jmnvV2dJpLX5i2QEJDX/kzP9n70Y7yAY5Xzd01NOkP9o44rm+2nrWit61GzpfA8lcBR95JxIVQWtSrKgySlzAP4RJJthFkXmg9gBYDrcDBM7qdJGnWaKBdIGgQSdQ0jlyYtyvOh03Q+bpx613Szyssa1CBPO7cNHEjjX9Gzp7JEmhANJjLMl4MgXUJoTIizXE4lwty/X08bQHdHcFfmmlKYiB4GVEBKGixq9nleCHG9EOKEOHmQwvgg7ZVSajOsO03UJ0LbE24lgk8ZI0bo2WaxpGUPP6zqldWFuZwI0V3yoLDe1GOPKQff2tpk262vV++a+tnp8IyE/SoHpaOvqRsfxT5EUaV+ugQbkXQTJugP2ig2F+TzSiT5S6c6yeUORs2a9uEKS1CIHh36Phy/LVfvPe4wueP9ysBnEsDJYX4cxoL0cyHE9+JmoqxqTESHJMGUWf3AAT31tJIuBTY1AaNH09wEgg6+utBUSykva1ArNJdYKQalQFq0SG+ZmFLCxfZ4tdV+YyMwdmy6o1p1bp/K54ZCiJZCd3LOKqQwB1LHK5Q4AsIJpCOhvL5/jBiZKKse2xNuOUaNUhNj1mqyUd3EAf2TswZRkJe+BYkYKv8Kz1OO9WvX6t1uqbbWrlVLzzagrotWqW3qhyUgHQLJR/e6dEOD3iXjcnieys22ZQt9WylGCPFRIcR3hBCX+6+wvw0jkM4GME5KeWIcBVb1eB6wZo2+kh460ZlxtdRTCoWVKsmkRfVURTX5a7BIGbUgAXrOtX9+TOeZMuUjUq79/fv1WFzjtL1oEZ3FoV8/5Xag8/oI4lrUbCl0O6uXs4YC7h8PiwghbgDwGQAXAxBQemZs2N+HEUhvQuUSYIrhW1BsZugtB7WFa9AgYMAAdwap5ykLh+6SB1T+Zhq2Z9SCBOi/1m0IJFvXq26La9Sgkl27tC3tlmyDysLjWtRsKfwEwtRLXxQPLNXHsVLKzwN4V0r5fQAfAJAL++MwAqkfgLeFEH/iMP8iuL4urruGUamcHK5MWjpvQMHzQeVvpksgmbAgTZhAE5I/fLjKzWNiotfpIxJnvNqcL0y0bSJjtytzTSk8T60oUC8Zt7SogA/Xj4dd9nS87xZCjABwAMDwsD8OI5C+B+AsdK2Gy2H+Pn5JD11P1brXxE3UMHJp0tJtISgMM9Z9E9ewPWMWJN0h+T4mgx1MZJgvh8lyFIWYsF5R1y30fbja2mi2rwNTVsra2nRY1OzykBCiH4CrAfwVKmXRXWF/XFEgSSmfLvaK3d1qw/aEWwkTg9Wvp7VjB10bYaEseUDhb5YmCxKQ/lB/3W1FfaCxGfk6cqTKMJ/moA2dUbNUcNFaZ5BS/kBKuV1KeS+U79EhUsr/DPv7kgJJCPFcx/tOIcSOwGunEMKBOyEBcZe4XL5ITUSZmUgnEBbKkge691PT058xCxKgz7+i8PeeB6xcCezZU/z7USknXKidlStha77QGbRRCmpx4FrUbDFGjlQpR0z0MZdTqwMuW9QsIIT4TuDfZwOAlHKflPI9IUTo0iPlLEhNHRttllL2CbyapZR9Yve8GnE5H0WfPsCwYckHa7l90zUp6jp+VDcg3ZNzLgesXp3YImXcgqTTv8IXMrncweKb1JhwVq7Uvi2LK7U401W30GTUrG50Fq0tF8UGHLSoLV+evK3q4pzAv79b8H+nh91IOYHk4N3eUUyU9EgCdf4Tk/W0wuA7picteVA4Sfv+Zrr2U1PIeV4KcxYkKgtBliLZbN7kPU8tT+3dS7N93XULC8WBrqhZExFmJs6v7WvZXUSJfxf7uyTlBNIQIcQlpV6RulrtuH6RUvtc6HbeTeqoHix5oLMvuvzN/Mk5WMk+AcYtSID+a93k0oltK4RtgaTDUlfJopyWSDaqAobUQjTYDuDuvccessS/i/1dknICqRZAbwDNJV6xEUL0E0LME0K8LYRYKIT4QJLtWcf1dXHPU/WLtm6lbcMVszflpKFzctZUO075IBkSSCNGqJB83ce2qUllfjdhGTDhrFwOnekSot7gdYqzcgkMKbOFu+zz6WNqyXjgQPVy3aIWkwRa4SjfZxrApKAPNVR1kFCUqy66Xkp5ZZn/T8LPATwipfy0EKIeQC+idsxgoqRHEoKC4dhj6dp4+mmVUqAmTPYIQqgF0l/+Utk3IAy9emmxSCkLkqElNt+/IukNttiEbOrGp8tZOe5NRZfFNU77poI2/LqFra0027/1VuXD1cdRd9jgHHTEEfRtUQpeu8TSClJKLZW7y93JSI6WEKIvgA8CuAUApJT7pZTbKdqKRZyLxPaEWwlTof6m6mlVom9fYOhQOoGk08FXgygwakEC6JY4dEaXVRrH1NFclbDVfnOzSszpeiRbmKAQVyzWxTC9ZOzqw3kCXNAK5QTSh4jabAWwGcCvhBB/E0LcLIRoImorGkkmZl0XKYWK11HDqNKxcW0tnOoGpGE/t+2owyisRuOJ09H4xENofPVZNDZKNDYi1uvBdVPRQxzQtIMhoPKv8Dxg+3aVaJAafx+SZpiPO159MaihWHGstl0XSD6lklHq2j4Vzc1qOdqUQNqwwY08dHqxrhVKLrFJKbcRtnk0gIullC8JIX4O4FIAXZI3CSHmAJgDAPX19URd0YjnAfPmqQm3Z0/bvemKiRpGwUnrlFPo2gmL5wHz5yfbRrFltOB+nhyvZvO6rT2xFqNw5jHv4pCey4DHHwfOv0j59sTh9w/iQ7sfBHBzvN9HJehfoXP5IHhshwzRt91SbbW3q304/HDatkq171tcR4823/bvfpdsmbjcA9PgwTR1C31ci5othQ4hGuYcBcfN9OnJ2jNLnRBiQeDvuVLKucH/RwitQNpBUw0FWANgjZTypY6/50HtdBc6DtRcAGhqanLTgyxIsKTHYYfZ7k13dFlUSg1Wv54WpZUqCp4HbNkCbNumwoJ14Tv4JjDv59vVMfzCR7fgk1O2Ao9fCpw9A5g1K94Gl98JvPlm7P5Ehsq/IrjdE07Qt91KbdkSSH77NgTSu++q8TF4cPztlJoLqLOFU5W80Y3nAb/9rR5/xXIEl/PSJZDapJTTyvx/KK1AiXFvWinlBgCrhRAdMwQ+BOAt0/3QjutmX+oaRjqTo+mA6nxo8DfzBZD+JlQAACAASURBVFJtjXT/uimGDv+KYmJ49Gh18zMhsm0fd5vtm/JJpN6+yz5IwMEl4y1baNtJi0UtIi5oBVvhRhcDuFMI8TqAyVCFcNNNGkL9qWsYueSHRenImXA/OwVSLZQocDkCshi9eytLmu5zXVtLVyamEBPOyuXQlS4hzlgxJZDWrlUBDVTbt+XDFRZTIrhnTxUtmKY5JDxWtYIVgSSlfE1KOU1KOUlK+QkpJVHpZ4P06WN3wq1EUsEQ5snc81Ror656WklobdVT8qAYuVwiB18/PUxtjaStHUeJ60VrwwiHpFaIJEvCOiyucdtvaQHq6834JMbNEh8mKCRJ1KyJvD9pKlrraB4k21rBcsIaB0livXBpiakQU0+NpuppVaJHD2V6ppg0gg6+MehiQfK3l7SfpnOYUNUf9DyVAf2Agag82+HRtpaJamtV0AZl27rmm3LJKCm3r4OxY+mFqI/nKTGaxKLmZh4kq7BA0ontCbccumoYlcO2X0chOs4HQZhxFx8kf3vLlycPOTcJlX+F5yk/OR1lYsK0tW1bsn1IclPxPGDlSjsWV2qLw4QJeusWFuLaXFMMX4iaePjxPHUdrVkTvy2mGyyQgiR9Gk464ZoooEj5xOWaHxZVyYOE+1nUgtTebkYU6CLpDcqFau22r1ebFtdcTkXcJgnaKDcXNDYqCwrVsdURNWsCUw/NaRCMKYQFkk5cv0ipB6uuelq6yOWUVWblSr3bTehvlu+wgnexIAHuHLcw6BIX5fJMxSHKQ4bt425ToPlBG8uX07VB6XJAnUpAF56nhCj1krHta7lKYYGkEx0XKXW+jPXraTOuujRpOVq0tq2twIJk25IRh7Fjk4fkF6NfP5Uk0sSx0JFhPgm2BRJ12zpLxxTDZZ9PH3/JmFKIAsCwYSoy0/XjkTJYIOnEn3Bdzc+RZPki7CTnT1pxJkUKh1+AxhqRwMG22xIbZe04KnT4V5TClMg2kWG+HDrSJSQpdQLQC6S4dQvTFjVbClOWHdfy0FUJLJB0YnvCrYSpSfG994BNm+jaCMvgwcoiQXEDSuBv1s1J29+eq9dNKaj6rGOiDyscbB/3JO0neaAYOFC9XA/aKHceXYqaLYVJnzpb11IVwwJJN7Yn3HJMmECfcdWlZUbfT4EqWSQQaz8PWpCqQCDF9a+oZJ3btElFyVHj70McZ2UdNxWqdAlR2o5DWAsPkO1ItgEDVARxkuMcReyvWqXyQ8WBw/y7wQKpkKQXSS5HW9IjCX4NoySCIUrhRBegtHIA8QRSZ6LIwIeeB2zdql5pwfevSJKd3Xa1dhMZ5iu1b8vimnRsVJoLNNQtLEtafPdMR7LFTc7JdIMFkm5sT7iVoB6sY8YoIeaKHxZVyYMEDr7dfJAA94RlGKj6nGS7US0xSfch6QOV7ZpsGzcqgUaBhrqFZUkSNWvSYmfKNyiNc4jjsEAKostkDrh7kVLXMDJZTysMVI7pCfzNSi6xAbQO9LqheoIfN46uTEwhtserSR8VG23HfSCLUnTYheX8clALUZ+JE9W7Kw+nVQALJN3YnnArEbeGUdT8Mq7sP6WFIOZ+HnTSDnzY2pos5NyG/0BS/4pS9OihRJKJa8iEs3I5fItrNYf6r1gB7N0b7/dhlvRt+XCFxdQ9oalJFb92Ze6tAupsd6DqSFLSw8QgDz71jx5N04bnAb//vVpq7NGDpo2wJCh58M6+Fjyy+Azg5yW+sPN8YNGTwP/kC9bLyvPyXwcDAOqCFqS6uuS142xAWbSW0lcuiM3w6KQW1yTCeNw4+qCNXE5Zq5cuBQ4/nGb7vg/X0KH6t6+DoEA65hj6ttI2hzgMCyQKklyk1JaA4GA95ZTovw/Tv1zuYD0tv70wUAjEhobYJQ/+c9O/4J4dpwPfKPWNj6nXN6NueRSasQMD+hY48qcxj4nnAQ8/HP13/rkul0bhscfUzbWG2NDtecAjj9C2UY5cDnjzTfPt9uypLJfUS2CAaoNCIAW376pAGj9eCeG4xznKPcHzgF//2k4B6yqEl9gosFWlOwwjRtDXMHJtmTGmYN0re+KIPiuxbRuKv/74ErahP7b99k+lv1PsdecfsQlD0Le54CZDVTuOEir/Cs9TyzKrVundbqm2NmyInmFel6D3PPUwETVdgs40A3EI+7AEZDvUv74+vhCNSi6nruONG6P9zuUlSouwQCpEh+r2PPqSHnExkXHVdmRQITFLHuRlLepr2tC/P4q/pk9Af2xH/7X/KP2dYq/eB9CAfcX7uX+/uxGQxaC6QcW9scaZ6G3fZP10CTaKFXueCgunCtpIWLewIq5FzZbC1ENzkmuZLU7dYIFEgc3IlDDEeWqMcuPp319lsXblqc7zgPffj1zyIC9rUCvK7PfAgfH9zYph+0YdB6pr3XQupLht6Xqgitu+jrb37AFWr6Ztg2q+cS1qthTUQjTYDuD+8UgJLJAocP0iNVHDyCVnwZjnI48a1IoKE5rOyd/166YY48bF968ox5AhqkadiWMxfjy9s3I5bAsk6raTWE/CCFCX5ppSmBCigLKoNTS4fzxSAgukILrWYW1PuJUwUcPIJT+suAJJ1tIIpFIMGqSsb65eN8Wor48Xkl9prPllYkws0/rOyrauV9/imqZcSFHTfsSsWxh6+1F9uEz73JjK+l1Tkw6LWkpggUSBX9LD1YvUxJKCyXpalRg5UuUIiWFBqhMVHKbjOvgC3Y8lZe04SpL4tBHkmYqFbStE3PaTLvENGwY0N9PPBQCto3ZcHy5Tfjcml3HTOIc4CgskKmxPuOXwM6667Kitk5iO6aEtSIBePyQXjlkUqPwrPA9Ys0b5j1FDnWE+TPs2zntSS10YqOeCNNRkiytE4wYdLFumAj6YRLBAoiLOhGvK7Nu7t7KqUCeIA6K1Qbn/MW4CoX2QAH3HMpdTzuQ7d4b/je2cJ1T+FSaDHfx9WLMm/G90Xq+5XHSLq672qaNax46NniU+6hIe4LZAMiFEfTxPpQqJYlHjMP+isECiwp9wo5b0MGnypRQvVM67ccnlIpc8yMsa1NZUEEi+v1mUm3i5Y+l6BGQxqIvW6jq25bBthbDtqL1qlSpBREGCuoWh5kPXomZLYUogxb2WOcy/GyyQCtF1kbj+VENdwyiu8y5AM1B9x/SlS0P/JI8QS2xJshEX20/Xr5tixOlzmOsuQZmYyNg+7rYFEqCWSSnboF7Gc33MeJ6yslIJ0WA7gPvHIwWwQKLC9YvU8w7WMKJsw5X9j3E+QvkgAXqXKEyKAl0MHaoSAuruc2OjCls2cSyGD4+XYV6XmLdpcaUSuIVtLF2qnKkpSINjsgkhCgD9+qk0GWmaQxyFBRIVcSdcU8QVcFHrAi1e7EbpjBhm51A+SIBeB9+GBrcjIItBGX1nKrrLpI9IMZJYXJMSN2gj6lxw4ACwfHm0NqJs35Wo2VKYTn6apjnEUVggUWF7wq2EqQRx+/aZqadVieZmVYcukgUpgkCK6uBbaXuuXjeloOoz9VJwsbZsYav9piZg9GgzkWxUVh7XLfZAPCEaNwDD9rVcJbBAosTl6uxRaxglqXHliuk74qShfJBC7HfUybnSsYxZO84quRyNo6/nAbt2qdqG1FA7K4dpP2q6BJ0+k5TjVPcYSbp9G/TqRS9EfTxPJebcto2+rSrGmkASQtQKIf4mhHjIVh/IMVHSIy4mahi5NmlFtEZEsiAB+pYoPE/dpKNGQNokqn9F2Bsg9Y21WFuUGeYrtW/L4kptqRs4UL2olvFaW6P5cNl6+DAZ6g+483CaAJtawaYF6esAFlpsnx4/coraKS8u1IM1aj0t6knL84B33w1d8iBUFBug39/MNWEZhjh9DltnK+p242JSjLnWvuepbPAbN+rbZrE2qM5jXB8u06HtppaMbV/LerGmFawIJCHEKAAfBXCzjfbLonPARFXxNuoDRa1hFIWYGazJiDhphLYg6XZStp2TJw5U2dlHjlRLEyaOhYkM8+Wwed6jth1nrqKeC9Lgd+N5Kgnshg207bS2qvxTuh9YDGNbK9TZaBTAzwB8B0CzpfbN4PqNLljDyBcPlYhTF+ipp0J9dd9+gePwCtaeOQnoEa2ZUOQ/DGAdMLsv0Fj56xvbhqFOhHxw8Tzg+efDfbfSzSVq7TgXnv569aIJya+piXdjjTPZx3FW1nlToUqXEIbgw8OJJ4b7TZy54NZblaWqT59ovw27/cceU1GztbX6t6+D4HEePpyunR497EVF6sWqVjAukIQQswFsklK+KoSYVeZ7cwDMAYD6+npDvdNMUxMwapS7Kj44WMMKpDht3HGHqqfV1FT2q1t31uNVTMPxo3bgsBkE51zWALc8DLQcCcyYUfHr4u7f4YLxrwP4UOVt53LAXXcpf7PGEOoLKH2u41jeXHj6o3qCz+WAV1/Vv91Sbdm6qdiMfB0zRqWYMOWTOH06zfZ9H67WVv3b10FwVWHWrMrfT1JGyH2LWp0QYkHg77lSyrn+H2G1AiU2LEjHAThTCHEGgAYAfYQQd0gpPxf8UseBmgsATU1N9I/IVE/hLl+kUZac4h6f4IQwZUrZr+bb1UTwhY9sxhd/RPCEiRrg+WuBlonAjfMrf/3pK4CBk8NtOuhvNmlSol52bu/FF5NvxySeB9x2W7hJPWqtrXnz1M2vZ89kfQzT1h132KtvF8HiqpWaGrNBG5UEUtKoWVcF0ujR6gHKlE/do4+6bFFrk1JOK/P/obQCJcZ9kKSU35VSjpJStgA4B8ATJnfYOCbzuETFRA2jCCLMzydZW+NW0drQ2wX0OmqvXOlmBGQpfP+KsI6+YQWI56nQ9zBlYpKOMxPOypXaX7NGWVzDoNtnknIuiFO3MGoySsDdB1LAjBD18S1qK1fSt0WAC1qB8yBRY6KkRxKoJ8WJE0OXzvAtSLW1xAKJouSBbn+zGLXjrEN1g3I5ko2qfRvh2Z6nMl3v30+z/SR1C8MQNWrWFqaWcW1fy1WAVYEkpXxKSjnbZh/Icf0ipRZIEeppdQokyquSquRBFH+zMFYO16+bYlD12WSwg4ll5zDthxFIFGkG8nkVtEHVNuV841rUbCmohWiwHcD94xECW1qBLUjUpCGSbfNmlR8oDHHT3oeY8A8KJGILEkBzA4w6+Zc7lq5fN8UYNYrGv6JPHxXxY8Jp3YSzcjkiWFy1E/WGmmQu0FG3sNT2XR8zvhCltg4PHqwK19oQ21UCC6RCdDtm+iU9XB20Jkz6If2wOn2QqJfYAJqbgM4SIb17R64dZx1K/wpTNz5/H2xlII5gcdWOCVGey4WrW5jEQhXGh8umIDAhRP3fRRk3LkTCOgYLJGr8kh6uqviwgzVpCYcQ9bSMLLENGAAMGkR3E9fpb5aGp+FCwvY5apSYyWNh+6Zia5moXz/lx+NI0AaAeLmWgPAC11akImBmGTeNc4hDsEAygcsX6bhx0WoYxSHkk+lBJ226rgCgK8ypW2y6HAFZCir/ilxOFd6sVCZGx7HyPOWHQ+0jUq59W+edeq6i9otJg99N374qKaiJPuZyqqbjrl30bVUhLJB8KCcjf8INU9LD9BONiYyrISctI2H+AN0TOkUk2/btykcsLVD5V5iOZAvrrEzVfgiLKwD98wW1QNJdt7AQmz5cUaB6SCvWDlAVRWttwALJBMGSHi5CvaQQ0nnXqAVpwwaV76YcUUVzVH+zSsfSxPKnbqhD/U1M9LYd5G1aQTxPWem2bSv/vbjXHHW2cJs+XFEwtaqQBouaw7BAMoHrF6nnqQzQvglHNyHraRmJYgOinY8ogtD3N9NpQQL095MSqmu9pUVZO034BtkerzaFsYlrjtp6Yso6k4SwQjQpEyakw6LmKCyQTGB7wq1EsIZRKQw4CzopkOJsW9d2x451OwKyGH36AMOGVb5BRXXSrqtTk72JYxHWWZnKcudbXG0liwTol9xXrSqfJT5pUIjrvntRrJRJxH5jo5pH0mSFdggWSCYwER2SBFOT4ooVSoiVwNgS2/jxdI7pYRx8w05GtbVKFLj+NFwIZTkXFyPZdBPS4kpCa6sSo9QWHr9uYSXi5loK68NlC5NWwrDXsitWaIdggVQI1UXicoZXUwKpvR1YsqTkV4w5adfX05U88B18w2TqDnOtuXzdlIJSIC1ZUr5MjK4n4bDLNFTzhS2B1qOHeoBwIGgjNrZ9yMLgC1FTfki68rNlDBZIpnB5XdxEDaMQTrbGltj8/lA8veme/P3acWEiIF0hl6Pxr/DLxKxYoXe7pdqKkmGeov3ly8taXEnbprRsTJyo3m2G+tsWCyaEqI/nqcSZ69bRt1VlsEAyheepBILbt9vuSXf8yBLKJ+YQT3XGltiAg47plUoeRN1filD/tjb9teMo4aK1ycnl1LVZKV0CVaLKJUsqB23EbTtK3cI4RCl5Y3NZiSPZnKfOdgecgfqJIniRzphhpw/l8DzgySfLfmUBpuIfL3pA7zgN9AH6fg14eBAwvPg3XvvbQAAGBZJf8mDMGH3b7d9f1UDS9fQavG588eU6wT5/4APFvxPnWg+Kz49+NF7fwhLch5kzaduq1P5hh5lve98+YOVKlSONqg0qC49NH64oeB7wyCNKiFJOesFxc/LJdO1UISyQTBFGIAH2nmg8D/j1r5Uptqmp+/9LiU9jHlbe0QLcEbeRnwPPQb2K0ooa5DG4n4HlpOD50CmQ/G1ThPp/7GN6tklNa2u4kPyo1/qgQapUjAnLgEkfkWLYzoXkt00pkO64o3I0Y5JUAgsWxPutKTxPBXNUEqJJr+WRI4FevdwXjA7CAskU48bZnXAr4T9lLFoETJlS9Cu70Qv/NP0d/OBuL14b//7vwB//CPztb8X//6mn0Hz+pzFo+MPxth+F4FPVhz+sd9ueBzzwQOXvhZn4KGvHUVFXR+dfYWpZIkyGeUqLr58uwUb7QYH0kY/QtbFjB7Bxo9pPiu3Pm6csYT176t++DsIIUR3nOC0WNQdhgWQKEyU9khAcrCUEUh616N+4D62tMduYPgj47WtA8xZ10y/k7T0AtsbceESGDQOam+lu4r6Db//+erbn6nVTCspItj/9qfT/6xQNto+7rfYHD1apSUwFbVAJJD9q9vDD9W9fByaEaLCtl18u/f+2ndYdhZ20C6Fc4rI94ZYjRA2jPGqTRZi55CwYpuRB1GSGPhSRbBT9pMQPydednd3zVH6bSmVidLVFsQ9R2rcR+RomaENH4liAPpLN1chhQD0kUgtRHz8P3d69pb/j2hziACyQTEJd0iMJIWoYtaGOViCZfoqhtHIA+vbT5QjIUvj+FaVC8uOea5M3vqCzcimoH6i2blUv04QZG0n2fcwYoKEh27mQqOvSBfGTc5bJQ8d0hwWSSfwJd/Vq2z0pToXBmtiCFLWeFjV+yYPdu/Vu1/c3q3QTD3uDScPTcCFh+uyCdS5MW7aOe5h9pUxUuXatykhNQU1N+bqFSR+W+vQBhg93Z64phelQ/zTNIQ7AAsnHhPXC9aeachlXpUwukEzW0wqDP2mEKXkQBd3+Zi4tTYaFqs/jx6ubK6V1w8f2cbfZfjBog7INyvOYBsdkz1MJHHfupG3H9XuPo7BAMontCbcSfg2jEhlXlUCqkFgxTBsmbm5h+wLQnA+dk/O4cXS146iIEpIfhZ496crEFGLCWbkcNi2upsoPVapbmHT7riznl6KSZUeXf2FzMzBiRLrmEAdggWSSSiU9bA/aMpOilEA7alErNDhnLl1avp6WKfySB+Ump7jo9DejrB1HCdUTvCnLQCUfEerxWildAmX7EyZUDNpIjF+3cNkyuu1v26bK3pTCtmOyyaWvNFjUHIMFkknCOOXZTn0PFB2sfkWOxHXSTNbTqkSvXhUd0xMlqtu3T/k4FRK3xlvaJjcqceEvBRcrE6NbNNg+7rbab2wExo6lFWemItlcHjcmhKiPfy3ZfhBPESyQTGN7wi1HmYyrviFEi0AC3DkGtiLZgGjiK2ztOJeo5F+RRHz6ZWKoyeVonZUr4acasGFxpX6YY4GkIvlaWswJpHffLW5RY9FUFBZIhVBbcDxPTezvv0/bThzKZFzNt6kBlFggueYs6O8vheUB0OuovXdvcYuUq1AtH7gUyWZivrBlcS0XtKGDfv2U20GxY6ujTdeiZkthOpKtVFu2lxsdhAWSaVwPtywlkNrV4EkskKicd+MSLHmgk0r+ZlFJw9NwIVR9pkohUK4t25FsNuYLz1MPciWCNrS1QWWlci1qthS5HK0Q9bF9LacQFkimcf0i9TOu7tvX5eODS2walnhKTYo2zLzlzkeS/pTLRhzXBwlw57iFYcKEcCH5URk+HOjd28wY8n1EXM2FRJ2oslzbutpI8/Z14AvRtWuL/7+uc9zSogI+XD8eDsECycfUTcb2hFuJYA2jAJ0CKWkUm9+GK4OU8gakcz+HDlXJ72wt9cShZ8/S/hU6xKeJa6iSszI1Ni2upgSSX7eQavuuRM2WwtTDT22tuv+4eu9xEOMCSQgxWgjxpBDiLSHEP4QQXzfdB6uEKOlhlRKDtVMg1WoSSBs2lK6nZfJmP3q0OidUjtrl/M2i7KdJUaCTcjW9KMQnxYOO7eNuKzx7xIiSQRvajrMJR21XomZLYdqnLiVziAtawYYFqQ3AN6WUhwGYCeAiIcRhFvphD5cv0lICyfdB0mVBKtKGFSqVPEiC7mzELl83paBy9M3llMP6nj16t1uMUvtgyupsa0m6TNAGAD0PMqWCNtIiwHQwciTQ1GROILluUTuIda1gXCBJKddLKf/a8e+dABYCGGm6H1YplY/CBV+S5uaiNYy0RbEB7k1aNkP9o5DLqTp+LkZAlqKSf0WS7Uqpv0xMqbbKZJg30v769aUtrtRtU45Tv24hlQijFmA6EOKgozY1vkVt+fKun7t0PDpwQSvUmWysECFEC4ApAF6y2Y8umFje8Sfc9euVGbuDeVtPwuVvXgh5KH0XyvLeC8DvZJezcmDvQACaBJJfT8uVtXDPA+67T5U8qK/Xt92JE4sngUtayX7xYmDy5GR9M0XwBjVqlL7tBsXnpEn6tluprZEWnuWCkWzTpplv+3e/UykmGhr0b1933cJCKvlwueK753nASwZug8Hx6FcS8HHlWBTBllawJpCEEL0B3AvgG1LKbo9GQog5AOYAQL3Om5YLBCfcgEB6Ysc0LN09HJ8gnu8rsn8zsGY1MKnl4Gd7DuDYFb/BKYdoMDqarKcVhmDJg0MOOfh50jpIlfzNom47eN2kRSAF+/yhD+nbbqV8Wjon+2BbJ59M104pgsfQhkCSUgVtHHEEXRuUD0tpWJr2PODuu4sLUZ3XWPBamj1b33bjUSeEWBD4e66Ucm7hlyppBUqsCCQhRA+oHb5TSnlfse90HKi5ANDU1OSe/S8JwYv0pJM6P86jBgN67MLddw+w1LEOrn0WuOQS4P9tVk9gALB+O/DgF4CBv9DThkuTVvB8BAWSrm3r2k//ic+V4xaGESOKh+QnFZ9NTcoiZeJYlMkwbwQ/XUIxEWEiUSWg2g4KJJ1LMp4HPPqoekiprdW33eD2H3lE/3Z1UkqI6l76GjhQvdyYQ9qklGUVfxitQImNKDYB4BYAC6WU/2O6fScoMeHmZS1qhAOlJMr5zuiakP0192DpDFvr4JTZvXU6KYepHecavn8F1bE14VtSyVmZmnLpEqgpNzZ0JuMsVbdQ1/bLRc26gMkKAy49nJbBBa1gI4rtOADnAjhZCPFax+sMC/3oismbc4kJNy9rUOuCQDIxWE3W06pE374qzxDVTdz3N/NJWqg1BZNbFyid4E1kIA62ZQtb5725WVkBqecCoGsbui1Uhdt3DdMCyRX/z/JY1wo2otiek1IKKeUkKeXkjtcfTPfDOkUu0jwcEUjFahiZrFVmw1kwLZFspSIgXcbzgJUr9Yfkex7w3nvApk16t1uqrcIM8ybPgT9f2LC4UlvPqOeCNAgkE0LUJw0WNbihFTiTti08T4VaBibcvKxFLRwQSCZqGLk2aZVarqGsWB5n26UsUg5HoHTxr9C9XcDcTaVIhnlj5HLA7t360yWEgVqUDx6sCtdSnUfXomZL4UrRWqYTFki2yOXUhLt0aedHziyxAfRmWJP1tMLgecCWLcC2bXq3O3Kk3kzdrhc7/v/bu/sYO6rzjuPfh10vtrEN2IAx2MQ29l4CxcEGApQGykt5C6qjikqmSUMoFShqKtIXtRCkAkrf0kohTRWQKIXQCEKaUGFKScElTpOoCW/lncjYYIgTKG/BjjEEG/v0jzNjj8f37q7tOS/j+/tIq907e++cuXPmzjz3zHPO6abXLZRQY9xA8wFj6otKyvI7HT8VyBtvhFl/6FHic+s120uvQDTGsdymFumIFCDVxfom3uUg9QHS5jjlj6bT8d+WQ424OtJkrimEugA1neCb+kK9K8red03X9WGH+YtfjH3RKxhLeL6IJsYFNcZtvNwDgk4H1q4NF4iWDj/c9xZMdSy3iAKkVLqccLPJQYLecxg1PSZHLhf6kBegJk/OM2eGmzsulEmTfEta09s8MLDjNDGhLnwxc0S66TVcQuxxmEKV3en424dvv+0fhwjA6jlckFdQECsIHhpqR4taBvo7QFq/Ptws0qOZMmWHKT02u4G8AiTYtn2hJgGNNZ/WaObM8YnpIVq0uiX4wq6dnEPOHRdSyCT4WPsiZVf/kMMljGb2bH9RDT2YI+w4dUyTQwnk0mu2F01am53+DZA2boT994cvJhyKqXaQZpeDBOGbvavzaaVs9h4c9E3PIVojmk7wbePJLVSib6fjR0DftKnZ9fYqK2UPwlS3pAcG2t9pow23pj/wAR+IxrplvHLlji1qsp3+DZCGhvw3o/KEk+KkVw+QcrrFFmPE1Zhjf4xFt8Ajx27GXXpAZq/Mr3j9df+4yeCznCYmtDJZ+c03jEbRkgAAEZhJREFUw5fVq/zqcAmxhxkI+TmdN6/7vIVNaUOAFCMQLZUtamvWhC+rxfo3QIL038Q7Hd9rqkjKy6abfyl0k36OAdKqVf6C26TyfTYVjJctUpUekNnrdoEKGXyGyC2Jcdt5tPJDDJcw1rKffz5cp40JE3wLSqhzQW69ZnsJ9SWtWzmQTyeZTClA6pa4F7N82PqByKoFCcIHkL3m00qVODk87FtlXnqp2fV2yTfbLW34NlwXaptj523EKiu38oeH/W3M1av941A5iaHeW269ZnspA9HQt4xTB/st0d8B0vDwjol7MS/O9QAppxwk6D7iaogxOXI5acXsyQa7vi9TX6h3Ragu+fvtBwcdFOdEXyYrp9rvKScrDtUCWC+jnDqmbQFYUzod30q3enXYY/ngg33PzJD1uQfo7wAp9YWmNqWH78WWyThIsH0zbKgPa+rE1/q2QJiLbZMn5ylT/AmuTd/+unXJb0qs3l3dckRiXlS6DZewJ43DVI4S//LL25Y1PZRALr1me4l1TQo9OOceQgESpDtIyhNu0YKS5S02CH9SjDWf1mgOOMD3bAxxARoe3i7fbLfVW97a8O2vekLONfjMqaycyo/RaSNGT7Zqr9kcxcwNSn0st8Bg6g1Iqpa4t5rZPPnsfFgacRum/C488jNYCm9s2o8p4zMIFErlHEYrVsCpp4YpI3WQWhXyW1X1fTYRHHQ6cOedu7+emIaHYenSbfkVTY5x8/rrccY063TgnnvCJSuPpfzbb8+i123jqp0ZZs1qfv3Vz2CI9Tdh6lT/RS1WTt1tt/k5/qSr/g6QqhdE51jCHTz0tRPgazE34kr/62MAh3H81Ix6JsWYw6h60po+PVw5Y9XpwLJlYdYL/n0ONvCx63R8d/NUXc53RTW/oun1Qpxv3fVk5dhStrh2OvDtb4db/6GHwsSJ/jNyxhnNr7+aw1UGSDm2vNYD0VDb2GtwTtmqvwMk8AfJD34AwHomc+a81fzdv86JV/7SpXDtNXDXUvjc5+jM3ACcFq/80YT+1jhr1rbk3VwCpFtv3TblQVOq+WZHHeWX7c6JL7chEsYiZk+20BeVkHl5uZbf6cAtt/hOGyHKbnrewrpqr9kzzwxTRhOGh+Hee8OXk1PrfaYUIJVN1u+8w2YGmDbxXRYujFj+uwfCtY/D4FOwz3MwcEDEwseg04Hly7cNhdD0haeavHvKKc2ue1eEao0YHNyW4FsGSLujjSe3UNs8d67fvytW+BaIkFLv95Tl18sONT7Pww+H7RSSS6/ZXspAdO3asOWEmkR6D9LfSdqwXTPjZgYYsATfyiDfC123oRCallPCcT1XqOleNE2djMq543I9brrZf3848MDmey2OG+eDpBj7Ikay8khCDZcwFjFaLTud7ectDDGUQC69ZnuJdU2YONG34Oe+PxJSgFQ5GDczwMBekQ+U1Cfc0cTqyRZrPq3RzJu3LTG9aeVI3U0k+HabO64Nqrdsmw4+U/Rkix3MhxwuYTTVThuhlKPEhxotPKdes73EHvw01bHcAgqQKol7SQIkyLu7ZawA6f3348ynNZq99/b5QqECpGqC7+6ekHI+bnoJ2Utw5crmp4npVVbK/V7N04l5UYvdaaON62/C4Yf7QDhmgKQWpK4UIJWJe889V9xiSzAOUeoT7khizGGU20krVKJo0++zyRapWDod/+296fyKTsfflvnJT5pdb6+y6iPMx5SyxTVWV/9+DpCGhsIHoqVOxx/Hr74avqwWUoAEWz/0yVqQhofTnnBHUh8bKMYkoKlVpzxoer3QbIC0aZPP2WiLUBfA+oTAIVtWUh+vKVtcQ7fUTZ4MhxwSbt8edhiMH5/PuaaXWLe+Uh/LmVOABFs/9D5AStSCBPnOzl62VIRSn08rtU4HNmzw36qaPDlNm+YHgmsqUbt63LQlfyBmV/9QYo67NFL5IT+TI5VddtoIOZRCqOBgr73S5XDtjNDn3Go5kP/+SEQBEviD5Je/TNOLrSwf8r1VUrZUhDQ87EdDzkHI+iiOtcbWBXkkt4/V3Lk+v+LNN5u9+B10EOy7b5zk27lz/YW26bGyxqqs97KnV4qyN24MV0Y5LU8onU5zU/6EMjwcp35nzYIJEzSadg8KkGDrhz7ZLbaydwjk2RJQnhTbXsZYhdyW6rp3t64POMC3SLXJ0JAPMJpW3gqOoUxWTqUcLiGFPeFc0ORnMJRYx3LZoiZdKUCC7QOkFEnaqU+4o4l9Ukx90jrkEJ+8H0LT+7LMvWmTUMdTzCA7dUCfqvyDD/Z5QiHFDJBy1U/HcsYUIMHWxL3NDDAwkKi7Y84HaYxvGDm9f7NwgUfT7zOn/TZWe1KAlCqYT1V+yM9GKXQ9tuFLRYxAtNTGW/WRKECCrc2MyXKQIO8LXTkUAoSf4yoXbbmI57bfxiLGvg0dOKTe7ynLDx2czZ4dZr2l1HU3FtVbxqGP5TJgfPfdsOW0kAKk0vBwul5skP+HNvT2lfNp5SLUyanpfLPcj5tuQu3bfrotkUOAFCqxd2AgbE5m2Ws2d7HqOPWxnLEkAZKZnWNmK8xslZldkWIb6txwhy1qQeot9PaV82nlItT7bTrfLPfjpptQtzjmzYt3yyn1fs8hQFq5MnwZbV1/ExQgJY8VogdIZjYAfAU4FzgSuNDMjoy9HXVbho8ASNOLDbI+SIE9o/fKzgiZp9Dk+yznjmuT6dNhypTm1zthgs8njCFmjkg3KVtcY00/BOGGPsnpXNNLrG3cd9845eykHGKFFGfWDwOrnHMvOOc2AncAixNsx3Y2z/MH42CqW2wHH+yn9MhVjA9rTsmTbQmQyrnj2iRkl/xYF5WYwwp0k7LFtey0ETKpt9y35byFodafszZsY1jJY4UUX0EOBdZUHv8UOCHBdnDKKds+41s2LQJgYMK4FJviT7gnnpjvvfEFC/wYNtOmhSsjpxPC5Mlw1FE+cG3aggW+vidMaGZ9xx3X/NxmoZXTuYRY7/33N7/ebj74wbRzWC1aBC+/HL/ccgiMkIH50Uf73+vWhVl/TueaXubPh4kT47TwjB/f3AC2zUkeK2SUFbs9M7sUuBRgaGgoSBlTplS/BA1y3ilvc/afLwxS1pjcdZdPUMzRjBnw0kthA7jFi+Hhh/2FJwcPPuhbaJr2iU/4C0BT+/Kmm2BLopbPXXX55XDyyc2v9+KLfUtsjNtPn/88rFkz+vNCueGGsCNaj+TFF8PeYlyyxO/byy4Ls/7TToNPfxpOSPLdfGwmToSnn/bn3tCefz5ca11vg2b2SOXxjc65G2NvxEjMNT0h52gFmp0EXOOcO7t4fCWAc+5ver1mn332cRs2bIi0hSIiIhKSmb3jnOs5Iu+uxApNS5GD9DAw38zmmNkQsAS4O8F2iIiISJ6SxwrRb7E55943s88A9wEDwM3OuWdib4eIiIjkKYdYIfottl2hW2wiIiJ7jtFuseWgZQOoiIiIiISnAElERESkRgGSiIiISI0CJBEREZEaBUgiIiIiNQqQRERERGoUIImIiIjUKEASERERqVGAJCIiIlKjAElERESkphVTjZjZFuDdQKsfBN4PtG4JQ3XWPqqzdlK9tU9b6myCcy7rRppWBEghmdkjzrnjUm+HjJ3qrH1UZ+2kemsf1Vlzso7eRERERFJQgCQiIiJSowAJbky9AbLTVGftozprJ9Vb+6jOGtL3OUgiIiIidWpBEhEREanp2wDJzM4xsxVmtsrMrki9Pf3OzG42s9fM7OnKsqlmtszMVha/9y+Wm5l9uai7J81sUeU1FxXPX2lmF6V4L/3CzGaZ2XIze9bMnjGzy4vlqrdMmdl4M3vIzJ4o6uzaYvkcM3uwqJtvmNlQsXzv4vGq4v+zK+u6sli+wszOTvOO+oeZDZjZY2Z2T/FYdRZYXwZIZjYAfAU4FzgSuNDMjky7VX3vq8A5tWVXAA845+YDDxSPwdfb/OLnUuAG8Bdm4GrgBODDwNXlxVmCeB/4E+fckcCJwB8UnyPVW77eA053zn0IOAY4x8xOBL4AXOecmwe8BVxSPP8S4K1i+XXF8yjqeQlwFP5ze31xXpVwLgd+XHmsOgusLwMk/El4lXPuBefcRuAOYHHibeprzrnvAT+vLV4M3Fr8fSvwscryf3Hej4D9zGwGcDawzDn3c+fcW8Aydgy6pCHOuVecc/9b/L0ef/I+FNVbtop9/3bxcFzx44DTgW8Vy+t1Vtblt4AzzMyK5Xc4595zzq0GVuHPqxKAmc0EPgrcVDw2VGfB9WuAdCiwpvL4p8Uyyct059wrxd//B0wv/u5Vf6rXRIpm/IXAg6jeslbcqnkceA0fjD4PrHXOlaMvV/f/1rop/r8OmIbqLLYvAX8GbCkeT0N1Fly/BkjSMs53t1SXywyZ2STgTuCzzrlfVP+nesuPc26zc+4YYCa+BeGIxJskIzCz84HXnHOPpt6WftOvAdLPgFmVxzOLZZKXV4tbMBS/XyuW96o/1WtkZjYOHxzd5pz7t2Kx6q0FnHNrgeXASfjbnYPFv6r7f2vdFP/fF3gT1VlMJwO/aWYv4tNBTgf+AdVZcP0aID0MzC96AQzhE9fuTrxNsqO7gbJH00XA0sryTxa9ok4E1hW3dO4DzjKz/Ysk37OKZRJAkdfwz8CPnXNfrPxL9ZYpMzvQzPYr/p4A/AY+d2w5cEHxtHqdlXV5AfCdolXwbmBJ0WNqDj7x/qE476K/OOeudM7NdM7Nxl+rvuOc+ziqs/Ccc335A5wHPIe//35V6u3p9x/g68ArwCb8vfFL8PfNHwBWAv8FTC2ea/heiM8DTwHHVdbze/jkw1XAxanf1578A/wa/vbZk8Djxc95qrd8f4AFwGNFnT0N/EWxfC7+YrkK+Cawd7F8fPF4VfH/uZV1XVXU5Qrg3NTvrR9+gF8H7lGdxfnRSNoiIiIiNf16i01ERESkJwVIIiIiIjUKkERERERqFCCJiIiI1ChAEhEREalRgCQiIzKzq4qZ3580s8fN7ITU21RlZrPN7HfG8LyvF2OffdbMLoyxbSLSXgqQRKQnMzsJOB9Y5JxbAJzJ9vM55WA2MGqABMx2fpLOU4HvBd0iEWk9BUgiMpIZwBvOufcAnHNvOOdeBjCzY83sv83sUTO7rzK9yPGV1qa/N7Oni+WfMrO7zGyZmb1oZp8xsz82s8fM7EdmNrV43uFm9p/Fer9vZkcUy79qZl82s/8xsxfMrBxF+G+BjxTl/VH9DZjZbWb2LHBEMUnrWcB/mNnvh911ItJmCpBEZCT3A7PM7Dkzu97MToWtc7D9I3CBc+5Y4Gbgr4rX3AJc5vyEqJtr6/sV4LeA44vnv+OcWwj8EPhk8ZwbgT8s1vunwPWV18/Aj+B9Pj4wArgC+L5z7hjn3HX1N+D8tAxXA38JLAbuLZ570y7tERHpC4OjP0VE+pVz7m0zOxb4CHAa8A0zuwJ4BB/sLPNTsjEAvFLM8zXZOffDYhW344OZ0nLn3HpgvZmtA/69WP4UsMDMJgG/CnyzWC/A3pXX3+Wc2wI8a2bTd+KtLMJPf7IAeGInXicifUoBkoiMyDm3Gfgu8F0zewo/EeajwDPOuZOqzy0nQh3Be5W/t1Qeb8Gfj/YC1hatT6O93no8p7o95wF/DczBB2oHAhvM7Azn3GmjvV5E+pdusYlIT2bWMbP5lUXHAC/hJ7s8sEjixszGmdlRzrm1+Nahsqfbkp0pzzn3C2C1mf12sV4zsw+N8rL1wOQe67sXOBZ42jl3NPAMsFDBkYiMRgGSiIxkEnCrmT1rZk8CRwLXOOc2AhcAXzCzJ4DH8bfGAC4B/qlIiN4HWLeTZX4cuKRY7zP4vKGRPAlsNrMnuiVpAwuBJ8xsCBhXBGEiIiMy51zqbRCRPYiZTXLOvV38fQUwwzl3eeLNEhHZKcpBEpGmfdTMrsSfX14CPpV2c0REdp5akERERERqlIMkIiIiUqMASURERKRGAZKIiIhIjQIkERERkRoFSCIiIiI1CpBEREREav4f54oHav+qymsAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"In the previous plot, we see that the Earthquake 0 and 16 only contain a small number of segments. Hence, during the the CV we will only use those segment for training but not for validation."},{"metadata":{},"cell_type":"markdown","source":"## Features correlations with Time_to_failure\n\nLets see how well the features are correlated with the time to failure."},{"metadata":{"trusted":true},"cell_type":"code","source":"correlation_coefficients = np.abs(x_train.corrwith(y_train)).sort_values(ascending=False)\n\nimport seaborn as sns\nsns.barplot(x=correlation_coefficients.values, y=correlation_coefficients.index)\nplt.title('Features correlation with quaketime')\nplt.tight_layout()","execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"The q05, q95 quantiles have the best correlation with the time to failure, suggesting that they are very important features."},{"metadata":{"_uuid":"6ad68bf144d3d9d950d7736b92df5bf615923ab8"},"cell_type":"markdown","source":"## Test model performance using a KFold validation strategy using the earthquakes IDs"},{"metadata":{},"cell_type":"markdown","source":"### Define model"},{"metadata":{"_uuid":"73c42260ca514df8eb3ef1f375fe2284ccad72eb","trusted":true},"cell_type":"code","source":"from catboost import CatBoostRegressor\n\n# Fit the scaler for the data\nscaler = StandardScaler()\nscaler.fit(x_train)\n\ndef get_model():\n model = CatBoostRegressor(iterations=2000, \n loss_function='MAE', \n boosting_type='Plain')\n fit_kwargs = dict(silent=True) \n return model, fit_kwargs ","execution_count":9,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### CV validation strategy\n\n\nFollowing the discussions in https://www.kaggle.com/c/LANL-Earthquake-Prediction/discussion/78809 we will use the LeaveOneGroupOut validation strategy.\n- Use one earthquake for validation\n- Use the rest for training"},{"metadata":{"_uuid":"27dee3b0142a72795881d1ad9168cd9286b5a934","trusted":true},"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nfrom sklearn.model_selection import LeaveOneGroupOut\n\ngroup_kfold = LeaveOneGroupOut()\nfold_splitter = group_kfold.split(x_train, y_train, earthquakes_id)\n\ntrain_errors = list()\nvalidation_errors = list()\nfor group_out, (train_index, valid_index) in enumerate(fold_splitter):\n if group_out in [0,16]:\n continue\n \n print('Validation Earthquake:', group_out,end=\" \")\n \n train_signal, validation_signal = x_train.iloc[train_index], x_train.iloc[valid_index]\n train_quaketime, validation_quaketime = y_train.iloc[train_index], y_train.iloc[valid_index]\n \n train_signal = scaler.transform(train_signal)\n validation_signal = scaler.transform(validation_signal) \n \n selected_model , fit_kwargs = get_model()\n selected_model.fit(train_signal, train_quaketime, **fit_kwargs)\n \n train_error = mean_absolute_error(selected_model.predict(train_signal), train_quaketime)\n validation_error = mean_absolute_error(selected_model.predict(validation_signal), validation_quaketime)\n print(f'train_error: {train_error:.2f}',end=\" \")\n print(f'validation_error:{validation_error:.2f}', )\n \n train_errors.append(train_error)\n validation_errors.append(validation_error)\n \nmean_train_error = np.asarray(train_errors).mean()\nmean_validation_error = np.asarray(validation_errors).mean()\n\nprint(f\"\\nMean train_error: {mean_train_error:.2f}\")\nprint(f\"Mean validation_errors: {mean_validation_error:.2f}\")","execution_count":10,"outputs":[{"output_type":"stream","text":"Validation Earthquake: 1 train_error: 1.97 validation_error:1.70\nValidation Earthquake: 2 train_error: 1.86 validation_error:2.95\nValidation Earthquake: 3 train_error: 1.97 validation_error:1.36\nValidation Earthquake: 4 train_error: 1.93 validation_error:2.25\nValidation Earthquake: 5 train_error: 1.91 validation_error:2.33\nValidation Earthquake: 6 train_error: 1.92 validation_error:2.35\nValidation Earthquake: 7 train_error: 1.74 validation_error:3.94\nValidation Earthquake: 8 train_error: 1.90 validation_error:2.62\nValidation Earthquake: 9 train_error: 1.99 validation_error:1.08\nValidation Earthquake: 10 train_error: 1.98 validation_error:1.57\nValidation Earthquake: 11 train_error: 1.98 validation_error:1.43\nValidation Earthquake: 12 train_error: 1.96 validation_error:1.49\nValidation Earthquake: 13 train_error: 1.94 validation_error:1.75\nValidation Earthquake: 14 train_error: 1.79 validation_error:3.59\nValidation Earthquake: 15 train_error: 1.97 validation_error:1.38\n\nMean train_error: 1.92\nMean validation_errors: 2.12\n","name":"stdout"}]},{"metadata":{"_uuid":"9ca5ad287c9b24dae79cc5149dd9aa47f99b1e33"},"cell_type":"markdown","source":"## Train the model using all the available data"},{"metadata":{"_uuid":"984949271773f7f77ce23971d6c551f24d9fe3a4","trusted":true},"cell_type":"code","source":"# First we shuffle the data\nfrom sklearn.utils import shuffle\n\nshuffled_features = shuffle(x_train)\nshuffled_quaketime = y_train.iloc[shuffled_features.index]\n \ncatboost_reg_model = CatBoostRegressor(iterations=1300, loss_function='MAE', boosting_type='Plain')\ncatboost_reg_model.fit(shuffled_features, shuffled_quaketime, silent=True)","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":""},"metadata":{}}]},{"metadata":{"_uuid":"883595bee83cf120f0f7a8ab6e2c8b1631871f4e","trusted":true},"cell_type":"code","source":"from bokeh.plotting import figure, show\nfrom bokeh.io import output_notebook\noutput_notebook()\n\nquaketime_predict = catboost_reg_model.predict(x_train)\n\n# create a new plot with a title and axis labels\np = figure(title=\"Time to failure prediction\", x_axis_label='x', y_axis_label='y')\n\n# add a line renderer with legend and line thickness\np.line (np.arange(quaketime_predict.shape[0]), quaketime_predict, line_color='red')\n\np.line (np.arange(y_train.values.shape[0]), y_train, line_color='blue')\n\n# show the results\nshow(p)\n","execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/html":"\n
\n \n Loading BokehJS ...\n
"},"metadata":{}},{"output_type":"display_data","data":{"application/javascript":"\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n var JS_MIME_TYPE = 'application/javascript';\n var HTML_MIME_TYPE = 'text/html';\n var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n var CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n var script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n var cell = handle.cell;\n\n var id = cell.output_area._bokeh_element_id;\n var server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd, {\n iopub: {\n output: function(msg) {\n var id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));","application/vnd.bokehjs_load.v0+json":"\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1001\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"\n\n\n\n\n\n
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Application\",\"version\":\"1.0.4\"}};\n var render_items = [{\"docid\":\"4fd0b1e4-43ee-48de-8d99-a68cd60d32b5\",\"roots\":{\"1003\":\"da9bde75-307c-4b0c-a1f3-7b384d27bad1\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n var attempts = 0;\n var timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n embed_document(root);\n clearInterval(timer);\n }\n attempts++;\n if (attempts > 100) {\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n clearInterval(timer);\n }\n }, 10, root)\n }\n})(window);","application/vnd.bokehjs_exec.v0+json":""},"metadata":{"application/vnd.bokehjs_exec.v0+json":{"id":"1003"}}}]},{"metadata":{"_uuid":"e58795909f9eee6c458e2b32a36e1e3794cde0e1"},"cell_type":"markdown","source":"# Submit prediction"},{"metadata":{"_uuid":"31ab3e402656371289b8ffae3da90ef7a837c815","trusted":true},"cell_type":"code","source":"selected_model = catboost_reg_model\n\nsubmission = pd.read_csv('../input/sample_submission.csv', index_col='seg_id')\nX_test = pd.DataFrame()\n\nfor seg_id in submission.index:\n seg = pd.read_csv('../input/test/' + seg_id + '.csv')\n ch = generate_features(seg['acoustic_data'])\n X_test = X_test.append(ch, ignore_index=True)\n\nsubmission['time_to_failure'] = selected_model.predict(X_test).clip(0, 16)\nsubmission.to_csv('submission.csv') ","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"}},"nbformat":4,"nbformat_minor":1}