DeepConvLSTM.ipynb

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 "cells": [
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 2,
   "source": [
    "Introduction"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This notebook presents the architecture of DeepConvLSTM: a deep framework for wearable activity recognition based on convolutional and LSTM recurrent units. To obtain a detailed description of the architecture consult the paper \"Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition\"."
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 2,
   "source": [
    "The data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "One of the benchmarks dataset employed to evaluate DeepConvLSTM is the 'OPPORTUNITY Activity Recognition Data Set'. OPPORTUNITY is a dataset devised to benchmark human activity recognition algorithms. It comprises the readings of motion sensors recorded while users executed typical daily activities and includes several annotations of gestures and modes of locomotion (visit https://archive.ics.uci.edu/ml/datasets/OPPORTUNITY+Activity+Recognition for further info). In this example DeepConvLSTM will perform recognition of sporadic gestures. This task concerns recognition of the different right-arm gestures. This is a 18 class segmentation and classification problem.\n",
    "\n",
    "The dataset must be be preprocessed prior to be feed to the neural network, in order to fill in missing values using linear interpolation and to do a per channel normalization to interval [0,1]. A Python script is provided to automatically preprocess the data, download the original OPPORTUNITY dataset if required and segment sensor data into train and test.\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We would recommend to download the OPPORTUNITY zip file from the UCI repository and then use the script to generate the data file."
   ]
  },
  {
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      "--2017-02-17 17:31:17--  https://archive.ics.uci.edu/ml/machine-learning-databases/00226/OpportunityUCIDataset.zip\r\nResolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249\r\nConnecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... "
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     "text": [
      "connected.\r\n"
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     "text": [
      "HTTP request sent, awaiting response... "
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      "\runityUCIDataset.zip  20%[===>                ]  58.70M  2.54MB/s    eta 1m 41s "
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      "\rnityUCIDataset.zip   20%[===>                ]  59.13M  2.59MB/s    eta 1m 41s "
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      "\rCIDataset.zip        21%[===>                ]  61.52M  2.47MB/s    eta 1m 40s "
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     "text": [
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     "text": [
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     "text": [
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     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
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     "text": [
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     "name": "stdout",
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     "text": [
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     "text": [
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     "text": [
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     "text": [
      "\r   OpportunityUCIDa  42%[=======>            ] 125.02M  2.35MB/s    eta 70s    "
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     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  42%[=======>            ] 125.32M  2.26MB/s    eta 70s    "
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    },
    {
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     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  42%[=======>            ] 125.66M  2.17MB/s    eta 70s    "
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    },
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     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  43%[=======>            ] 126.01M  2.18MB/s    eta 70s    "
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    },
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     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  43%[=======>            ] 126.37M  2.10MB/s    eta 69s    "
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    },
    {
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     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  43%[=======>            ] 126.70M  1.98MB/s    eta 69s    "
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    {
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     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  43%[=======>            ] 127.07M  1.89MB/s    eta 69s    "
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     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  43%[=======>            ] 127.40M  1.76MB/s    eta 69s    "
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    {
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     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  43%[=======>            ] 127.73M  1.77MB/s    eta 69s    "
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    {
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     "output_type": "stream",
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      "\runityUCIDataset.zip  43%[=======>            ] 128.07M  1.70MB/s    eta 69s    "
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      "\rnityUCIDataset.zip   43%[=======>            ] 128.40M  1.65MB/s    eta 69s    "
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      "\rityUCIDataset.zip    44%[=======>            ] 128.79M  1.63MB/s    eta 69s    "
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      "\rtyUCIDataset.zip     44%[=======>            ] 129.15M  1.54MB/s    eta 69s    "
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      "\ryUCIDataset.zip      44%[=======>            ] 129.54M  1.60MB/s    eta 69s    "
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     "output_type": "stream",
     "text": [
      "\r       OpportunityU  47%[========>           ] 139.49M  1.76MB/s    eta 67s    "
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     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  47%[========>           ] 139.90M  1.76MB/s    eta 67s    "
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     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  47%[========>           ] 140.35M  1.78MB/s    eta 67s    "
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    },
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     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  48%[========>           ] 140.84M  1.82MB/s    eta 67s    "
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     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  48%[========>           ] 141.32M  1.86MB/s    eta 66s    "
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  48%[========>           ] 141.79M  1.93MB/s    eta 66s    "
     ]
    },
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     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  48%[========>           ] 142.26M  1.92MB/s    eta 66s    "
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    },
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     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  48%[========>           ] 142.74M  1.93MB/s    eta 66s    "
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    },
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     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  48%[========>           ] 142.79M  1.78MB/s    eta 66s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  49%[========>           ] 143.30M  1.81MB/s    eta 66s    "
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    {
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     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  49%[========>           ] 143.72M  1.49MB/s    eta 66s    "
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     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  49%[========>           ] 143.73M  1.37MB/s    eta 66s    "
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     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  49%[========>           ] 143.77M  1.29MB/s    eta 66s    "
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    {
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      "\runityUCIDataset.zip  49%[========>           ] 143.84M  1010KB/s    eta 68s    "
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      "\rnityUCIDataset.zip   49%[========>           ] 143.93M   904KB/s    eta 68s    "
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      "\rityUCIDataset.zip    49%[========>           ] 144.10M   846KB/s    eta 69s    "
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      "\rtyUCIDataset.zip     49%[========>           ] 144.29M   822KB/s    eta 69s    "
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      "\ryUCIDataset.zip      49%[========>           ] 144.37M   778KB/s    eta 69s    "
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     "text": [
      "\r       OpportunityU  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 45s "
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     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 45s "
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     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 47s "
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     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 47s "
     ]
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     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 49s "
     ]
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     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 49s "
     ]
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     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 51s "
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     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 51s "
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     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 52s "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 52s "
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     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  51%[=========>          ] 151.03M  --.-KB/s    eta 1m 54s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r       OpportunityU  62%[===========>        ] 183.55M  8.00KB/s    eta 2m 20s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  62%[===========>        ] 183.70M   125KB/s    eta 2m 20s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  62%[===========>        ] 183.95M   289KB/s    eta 2m 20s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  62%[===========>        ] 184.03M   195KB/s    eta 2m 20s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  62%[===========>        ] 184.03M   142KB/s    eta 2m 20s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  62%[===========>        ] 184.03M   111KB/s    eta 2m 22s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  62%[===========>        ] 184.03M  91.8KB/s    eta 2m 22s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  62%[===========>        ] 184.03M  78.1KB/s    eta 2m 23s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  62%[===========>        ] 184.03M  --.-KB/s    eta 2m 23s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  62%[===========>        ] 184.03M  --.-KB/s    eta 2m 24s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  62%[===========>        ] 184.03M  --.-KB/s    eta 2m 24s "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  62%[===========>        ] 184.03M  --.-KB/s    eta 2m 25s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  62%[===========>        ] 184.04M  8.00KB/s    eta 2m 25s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\runityUCIDataset.zip  62%[===========>        ] 184.12M  1.63KB/s    eta 2m 56s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rnityUCIDataset.zip   63%[===========>        ] 184.24M  3.73KB/s    eta 2m 58s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rityUCIDataset.zip    63%[===========>        ] 184.48M  7.85KB/s    eta 2m 58s "
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     "output_type": "stream",
     "text": [
      "\rtyUCIDataset.zip     63%[===========>        ] 184.74M  12.5KB/s    eta 2m 58s "
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     "output_type": "stream",
     "text": [
      "\ryUCIDataset.zip      63%[===========>        ] 184.97M  16.1KB/s    eta 2m 57s "
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     "text": [
      "\rUCIDataset.zip       63%[===========>        ] 185.18M  19.6KB/s    eta 2m 57s "
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     "text": [
      "\rCIDataset.zip        63%[===========>        ] 185.51M  25.2KB/s    eta 2m 57s "
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     "text": [
      "\rIDataset.zip         63%[===========>        ] 185.77M  27.4KB/s    eta 2m 58s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
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     "text": [
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     "name": "stdout",
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     "text": [
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     "name": "stdout",
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     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r          Opportuni  66%[============>       ] 195.19M  15.7KB/s    eta 7m 47s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r         Opportunit  66%[============>       ] 195.19M  15.7KB/s    eta 7m 47s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r        Opportunity  66%[============>       ] 195.19M  15.7KB/s    eta 7m 48s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r       OpportunityU  66%[============>       ] 195.19M  --.-KB/s    eta 7m 48s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  66%[============>       ] 195.19M  --.-KB/s    eta 7m 49s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  66%[============>       ] 195.19M  --.-KB/s    eta 7m 49s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  66%[============>       ] 195.19M  --.-KB/s    eta 7m 50s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  66%[============>       ] 195.19M  --.-KB/s    eta 7m 50s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  66%[============>       ] 195.19M  --.-KB/s    eta 7m 51s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  66%[============>       ] 195.19M  --.-KB/s    eta 7m 51s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  66%[============>       ] 195.19M  --.-KB/s    eta 7m 52s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  66%[============>       ] 195.19M  --.-KB/s    eta 7m 52s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  66%[============>       ] 195.19M  --.-KB/s    eta 7m 53s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  66%[============>       ] 195.19M  --.-KB/s    eta 7m 53s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  66%[============>       ] 195.19M  --.-KB/s    eta 7m 54s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  66%[============>       ] 195.19M  --.-KB/s    eta 7m 54s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\runityUCIDataset.zip  66%[============>       ] 195.19M  --.-KB/s    eta 7m 55s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rnityUCIDataset.zip   66%[============>       ] 195.19M  --.-KB/s    eta 7m 55s "
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rityUCIDataset.zip    66%[============>       ] 195.19M  --.-KB/s    eta 7m 55s "
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     "text": [
      "\rtyUCIDataset.zip     66%[============>       ] 195.19M  --.-KB/s    eta 7m 55s "
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     "name": "stdout",
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     "text": [
      "\ryUCIDataset.zip      66%[============>       ] 195.19M  --.-KB/s    eta 7m 56s "
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     "text": [
      "\rUCIDataset.zip       66%[============>       ] 195.19M  --.-KB/s    eta 7m 56s "
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     "text": [
      "\rCIDataset.zip        66%[============>       ] 195.19M  --.-KB/s    eta 7m 57s "
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      "\rIDataset.zip         66%[============>       ] 195.19M  --.-KB/s    eta 7m 57s "
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     "name": "stdout",
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     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "\r            Opportu  66%[============>       ] 195.19M  --.-KB/s    eta 8m 7s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r           Opportun  66%[============>       ] 195.19M  --.-KB/s    eta 8m 7s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r          Opportuni  66%[============>       ] 195.19M  --.-KB/s    eta 8m 8s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r         Opportunit  66%[============>       ] 195.19M  --.-KB/s    eta 8m 8s  "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r        Opportunity  66%[============>       ] 195.19M  --.-KB/s    eta 8m 9s  "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r       OpportunityU  66%[============>       ] 195.19M  --.-KB/s    eta 8m 9s  "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  66%[============>       ] 195.19M  --.-KB/s    eta 8m 10s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  66%[============>       ] 195.19M  --.-KB/s    eta 8m 10s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  66%[============>       ] 195.19M  --.-KB/s    eta 8m 11s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  66%[============>       ] 195.19M  --.-KB/s    eta 8m 11s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  66%[============>       ] 195.19M  --.-KB/s    eta 8m 12s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  66%[============>       ] 195.19M  --.-KB/s    eta 8m 12s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  66%[============>       ] 195.19M  --.-KB/s    eta 8m 13s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  66%[============>       ] 195.19M  --.-KB/s    eta 8m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  66%[============>       ] 195.19M  --.-KB/s    eta 8m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  66%[============>       ] 195.19M  --.-KB/s    eta 8m 13s "
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     "name": "stdout",
     "output_type": "stream",
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     "name": "stdout",
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     "text": [
      "\rtunityUCIDataset.zi  66%[============>       ] 195.19M  --.-KB/s    eta 8m 14s "
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     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     "output_type": "stream",
     "text": [
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     "text": [
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     "name": "stdout",
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     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  75%[==============>     ] 220.95M   112KB/s    eta 5m 57s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  75%[==============>     ] 220.95M  77.4KB/s    eta 5m 57s "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  75%[==============>     ] 220.95M  59.2KB/s    eta 5m 58s "
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  75%[==============>     ] 220.95M  48.0KB/s    eta 5m 58s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  75%[==============>     ] 220.95M  40.3KB/s    eta 5m 58s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  75%[==============>     ] 220.95M  --.-KB/s    eta 5m 58s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  75%[==============>     ] 220.96M  8.00KB/s    eta 5m 59s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  75%[==============>     ] 220.99M  6.32KB/s    eta 6m 0s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  75%[==============>     ] 221.12M  25.0KB/s    eta 6m 0s  "
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     "name": "stdout",
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     "text": [
      "\runityUCIDataset.zip  75%[==============>     ] 221.27M  47.4KB/s    eta 6m 0s  "
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      "\rnityUCIDataset.zip   75%[==============>     ] 221.40M  62.3KB/s    eta 5m 58s "
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     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  79%[==============>     ] 232.82M  78.9KB/s    eta 5m 25s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  79%[==============>     ] 233.73M  88.9KB/s    eta 5m 25s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  80%[===============>    ] 234.49M  98.2KB/s    eta 5m 0s  "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  80%[===============>    ] 235.80M   185KB/s    eta 5m 0s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  80%[===============>    ] 236.66M   551KB/s    eta 5m 0s  "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  81%[===============>    ] 237.68M   598KB/s    eta 4m 40s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  81%[===============>    ] 237.80M   303KB/s    eta 4m 44s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  81%[===============>    ] 238.84M   212KB/s    eta 4m 41s "
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     "output_type": "stream",
     "text": [
      "\runityUCIDataset.zip  81%[===============>    ] 239.30M   217KB/s    eta 4m 41s "
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  83%[===============>    ] 244.88M   371KB/s    eta 4m 15s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  83%[===============>    ] 244.88M   298KB/s    eta 4m 15s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  83%[===============>    ] 244.88M   249KB/s    eta 4m 15s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  83%[===============>    ] 244.88M   214KB/s    eta 4m 15s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  83%[===============>    ] 244.88M   187KB/s    eta 4m 16s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 16s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 16s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 16s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 16s "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 16s "
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 17s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\runityUCIDataset.zip  83%[===============>    ] 244.88M  --.-KB/s    eta 4m 17s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rnityUCIDataset.zip   83%[===============>    ] 244.88M  --.-KB/s    eta 4m 17s "
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     "text": [
      "\rityUCIDataset.zip    83%[===============>    ] 244.88M  --.-KB/s    eta 4m 17s "
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      "\rUCIDataset.zip       83%[===============>    ] 244.88M  --.-KB/s    eta 4m 18s "
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      "\rCIDataset.zip        83%[===============>    ] 244.88M  --.-KB/s    eta 4m 18s "
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     "output_type": "stream",
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      "\r        Opportunity  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 10s "
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     "output_type": "stream",
     "text": [
      "\r       OpportunityU  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 10s "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  84%[===============>    ] 246.75M  --.-KB/s    eta 4m 11s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  84%[===============>    ] 246.76M  8.00KB/s    eta 4m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  84%[===============>    ] 246.79M  10.1KB/s    eta 4m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  84%[===============>    ] 246.80M  12.5KB/s    eta 4m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  84%[===============>    ] 246.88M  27.8KB/s    eta 4m 13s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  84%[===============>    ] 246.99M  47.0KB/s    eta 4m 12s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  84%[===============>    ] 247.15M  71.9KB/s    eta 4m 12s "
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     "name": "stdout",
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      "\runityUCIDataset.zip  84%[===============>    ] 247.34M   102KB/s    eta 4m 12s "
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rnityUCIDataset.zip   84%[===============>    ] 247.49M   124KB/s    eta 4m 12s "
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     "text": [
      "\rityUCIDataset.zip    84%[===============>    ] 247.65M   142KB/s    eta 4m 8s  "
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      "\rtyUCIDataset.zip     84%[===============>    ] 247.91M   179KB/s    eta 4m 8s  "
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      "\r        Opportunity  96%[==================> ] 281.44M  --.-KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r       OpportunityU  96%[==================> ] 281.44M  --.-KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  96%[==================> ] 281.44M  --.-KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  96%[==================> ] 281.44M  --.-KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  96%[==================> ] 281.44M  --.-KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rportunityUCIDataset  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rortunityUCIDataset.  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rrtunityUCIDataset.z  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtunityUCIDataset.zi  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\runityUCIDataset.zip  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rnityUCIDataset.zip   96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rityUCIDataset.zip    96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtyUCIDataset.zip     96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ryUCIDataset.zip      96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rUCIDataset.zip       96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rCIDataset.zip        96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rIDataset.zip         96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rDataset.zip          96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rataset.zip           96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rtaset.zip            96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\raset.zip             96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rset.zip              96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\ret.zip               96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rt.zip                96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r.zip                 96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rzip                  96%[==================> ] 281.44M  --.-KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rip                   96%[==================> ] 281.44M  --.-KB/s    eta 61s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rp                    96%[==================> ] 281.45M  8.00KB/s    eta 61s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r                     96%[==================> ] 281.57M  21.9KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r                  O  96%[==================> ] 281.73M  40.5KB/s    eta 60s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r                 Op  96%[==================> ] 281.91M  43.5KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r                Opp  96%[==================> ] 282.12M  57.8KB/s    eta 59s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r               Oppo  96%[==================> ] 282.45M  84.3KB/s    eta 56s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r              Oppor  96%[==================> ] 282.84M   115KB/s    eta 56s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r             Opport  96%[==================> ] 283.21M   141KB/s    eta 56s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r            Opportu  96%[==================> ] 283.54M   160KB/s    eta 49s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r           Opportun  97%[==================> ] 283.90M  39.2KB/s    eta 49s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r          Opportuni  97%[==================> ] 284.45M  47.8KB/s    eta 49s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r         Opportunit  97%[==================> ] 285.04M  57.0KB/s    eta 49s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r        Opportunity  97%[==================> ] 285.68M  67.0KB/s    eta 49s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r       OpportunityU  97%[==================> ] 285.90M  67.5KB/s    eta 37s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r      OpportunityUC  98%[==================> ] 286.63M  78.4KB/s    eta 37s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r     OpportunityUCI  98%[==================> ] 287.45M  90.4KB/s    eta 37s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r    OpportunityUCID  98%[==================> ] 288.29M   103KB/s    eta 37s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r   OpportunityUCIDa  98%[==================> ] 288.71M   108KB/s    eta 21s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r  OpportunityUCIDat  99%[==================> ] 289.54M   116KB/s    eta 16s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r OpportunityUCIData  99%[==================> ] 290.38M   126KB/s    eta 12s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rOpportunityUCIDatas  99%[==================> ] 291.38M   149KB/s    eta 12s    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\rpportunityUCIDatase  99%[==================> ] 292.39M   164KB/s    eta 12s    \rOpportunityUCIDatas 100%[===================>] 292.43M   164KB/s    in 27m 16s \r\n\r\n2017-02-17 17:58:34 (183 KB/s) - ‘OpportunityUCIDataset.zip’ saved [306636009/306636009]\r\n\r\n"
     ]
    }
   ],
   "source": [
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00226/OpportunityUCIDataset.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: preprocess_data.py [-h] -i INPUT -o OUTPUT [-t {gestures,locomotion}]\r\n\r\nPreprocess OPPORTUNITY dataset\r\n\r\noptional arguments:\r\n  -h, --help            show this help message and exit\r\n  -i INPUT, --input INPUT\r\n                        OPPORTUNITY zip file\r\n  -o OUTPUT, --output OUTPUT\r\n                        Processed data file\r\n  -t {gestures,locomotion}, --task {gestures,locomotion}\r\n                        Type of activities to be recognized\r\n"
     ]
    }
   ],
   "source": [
    "!python preprocess_data.py -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking dataset data/OpportunityUCIDataset.zip\r\n... dataset path data/OpportunityUCIDataset.zip not found\r\n... creating directory data\r\n... downloading data from https://archive.ics.uci.edu/ml/machine-learning-databases/00226/OpportunityUCIDataset.zip\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset files ...\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-Drill.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-ADL1.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-ADL2.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-ADL3.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-ADL4.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S1-ADL5.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-Drill.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-ADL1.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-ADL2.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-ADL3.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-Drill.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-ADL1.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-ADL2.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-ADL3.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-ADL4.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S2-ADL5.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-ADL4.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... file OpportunityUCIDataset/dataset/S3-ADL5.dat\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final datasets with size: | train (557963, 113) | test (118750, 113) | \r\n"
     ]
    }
   ],
   "source": [
    "!python preprocess_data.py -i data/OpportunityUCIDataset.zip -o oppChallenge_gestures.data"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 2,
   "source": [
    "Running DeepConvLSTM"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "DeepConvLSTM is defined as a neural netowrk which combines convolutional and recurrent layers. The convolutional\n",
    "layers act as feature extractors and provide abstract representations of the input sensor data in feature\n",
    "maps. The recurrent layers model the temporal dynamics of the activation of the feature maps."
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 3,
   "source": [
    "Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named lasagne",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mImportError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-259e46ed0da6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mlasagne\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named lasagne"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "import lasagne\n",
    "import theano\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import cPickle as cp\n",
    "import theano.tensor as T\n",
    "from sliding_window import sliding_window\n",
    "\n",
    "# Hardcoded number of sensor channels employed in the OPPORTUNITY challenge\n",
    "NB_SENSOR_CHANNELS = 113\n",
    "\n",
    "# Hardcoded number of classes in the gesture recognition problem\n",
    "NUM_CLASSES = 18\n",
    "\n",
    "# Hardcoded length of the sliding window mechanism employed to segment the data\n",
    "SLIDING_WINDOW_LENGTH = 24\n",
    "\n",
    "# Length of the input sequence after convolutional operations\n",
    "FINAL_SEQUENCE_LENGTH = 8\n",
    "\n",
    "# Hardcoded step of the sliding window mechanism employed to segment the data\n",
    "SLIDING_WINDOW_STEP = 12\n",
    "\n",
    "# Batch Size\n",
    "BATCH_SIZE = 100\n",
    "\n",
    "# Number filters convolutional layers\n",
    "NUM_FILTERS = 64\n",
    "\n",
    "# Size filters convolutional layers\n",
    "FILTER_SIZE = 5\n",
    "\n",
    "# Number of unit in the long short-term recurrent layers\n",
    "NUM_UNITS_LSTM = 128"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 3,
   "source": [
    "Load the sensor data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Load the OPPORTUNITY processed dataset. Sensor data is segmented using a sliding window of fixed length. The class associated with each segment corresponds to the gesture which has been observed during that interval. Given a sliding window of length T, we choose the class of the sequence as the label at t=T, or in other words, the label of last sample in the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data...\n",
      " ..from file data/oppChallenge_gestures.data"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ..reading instances: train (557963, 113), test (118750, 113)\n",
      " ..after sliding window (testing): inputs (9894, 24, 113), targets (9894,)"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def load_dataset(filename):\n",
    "\n",
    "    f = file(filename, 'rb')\n",
    "    data = cp.load(f)\n",
    "    f.close()\n",
    "\n",
    "    X_train, y_train = data[0]\n",
    "    X_test, y_test = data[1]\n",
    "\n",
    "    print(\" ..from file {}\".format(filename))\n",
    "    print(\" ..reading instances: train {0}, test {1}\".format(X_train.shape, X_test.shape))\n",
    "\n",
    "    X_train = X_train.astype(np.float32)\n",
    "    X_test = X_test.astype(np.float32)\n",
    "\n",
    "    # The targets are casted to int8 for GPU compatibility.\n",
    "    y_train = y_train.astype(np.uint8)\n",
    "    y_test = y_test.astype(np.uint8)\n",
    "\n",
    "    return X_train, y_train, X_test, y_test\n",
    "\n",
    "print(\"Loading data...\")\n",
    "X_train, y_train, X_test, y_test = load_dataset('data/oppChallenge_gestures.data')\n",
    "\n",
    "assert NB_SENSOR_CHANNELS == X_train.shape[1]\n",
    "def opp_sliding_window(data_x, data_y, ws, ss):\n",
    "    data_x = sliding_window(data_x,(ws,data_x.shape[1]),(ss,1))\n",
    "    data_y = np.asarray([[i[-1]] for i in sliding_window(data_y,ws,ss)])\n",
    "    return data_x.astype(np.float32), data_y.reshape(len(data_y)).astype(np.uint8)\n",
    "\n",
    "# Sensor data is segmented using a sliding window mechanism\n",
    "X_test, y_test = opp_sliding_window(X_test, y_test, SLIDING_WINDOW_LENGTH, SLIDING_WINDOW_STEP)\n",
    "print(\" ..after sliding window (testing): inputs {0}, targets {1}\".format(X_test.shape, y_test.shape))\n",
    "\n",
    "# Data is reshaped since the input of the network is a 4 dimension tensor\n",
    "X_test = X_test.reshape((-1, 1, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS))"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 3,
   "source": [
    "Define the Lasagne network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def iterate_minibatches(inputs, targets, batchsize, shuffle=False):\n",
    "    assert len(inputs) == len(targets)\n",
    "    if shuffle:\n",
    "        indices = np.arange(len(inputs))\n",
    "        np.random.shuffle(indices)\n",
    "    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):\n",
    "        if shuffle:\n",
    "            excerpt = indices[start_idx:start_idx + batchsize]\n",
    "        else:\n",
    "            excerpt = slice(start_idx, start_idx + batchsize)\n",
    "        yield inputs[excerpt], targets[excerpt]\n",
    "        \n",
    "# Classification of the testing data\n",
    "print(\"Processing {0} instances in mini-batches of {1}\".format(X_test.shape[0],BATCH_SIZE))\n",
    "test_pred = np.empty((0))\n",
    "test_true = np.empty((0))\n",
    "start_time = time.time()\n",
    "for batch in iterate_minibatches(X_test, y_test, BATCH_SIZE):\n",
    "    inputs, targets = batch\n",
    "    y_pred, = test_fn(inputs)\n",
    "    test_pred = np.append(test_pred, y_pred, axis=0)\n",
    "    test_true = np.append(test_true, targets, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'lasagne' is not defined",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-ee72facb07ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mnet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlasagne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInputLayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSLIDING_WINDOW_LENGTH\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNB_SENSOR_CHANNELS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'conv1/5x1'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlasagne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConv2DLayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNUM_FILTERS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mFILTER_SIZE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'conv2/5x1'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlasagne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConv2DLayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'conv1/5x1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNUM_FILTERS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mFILTER_SIZE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'conv3/5x1'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlasagne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConv2DLayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'conv2/5x1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNUM_FILTERS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mFILTER_SIZE\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'lasagne' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "net = {}\n",
    "net['input'] = lasagne.layers.InputLayer((BATCH_SIZE, 1, SLIDING_WINDOW_LENGTH, NB_SENSOR_CHANNELS))\n",
    "net['conv1/5x1'] = lasagne.layers.Conv2DLayer(net['input'], NUM_FILTERS, (FILTER_SIZE, 1))\n",
    "net['conv2/5x1'] = lasagne.layers.Conv2DLayer(net['conv1/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))\n",
    "net['conv3/5x1'] = lasagne.layers.Conv2DLayer(net['conv2/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))\n",
    "net['conv4/5x1'] = lasagne.layers.Conv2DLayer(net['conv3/5x1'], NUM_FILTERS, (FILTER_SIZE, 1))\n",
    "net['shuff'] = lasagne.layers.DimshuffleLayer(net['conv4/5x1'], (0, 2, 1, 3))\n",
    "net['lstm1'] = lasagne.layers.LSTMLayer(net['shuff'], NUM_UNITS_LSTM)\n",
    "net['lstm2'] = lasagne.layers.LSTMLayer(net['lstm1'], NUM_UNITS_LSTM)\n",
    "# In order to connect a recurrent layer to a dense layer, it is necessary to flatten the first two dimensions\n",
    "# to cause each time step of each sequence to be processed independently (see Lasagne docs for further information)\n",
    "net['shp1'] = lasagne.layers.ReshapeLayer(net['lstm2'], (-1, NUM_UNITS_LSTM))\n",
    "net['prob'] = lasagne.layers.DenseLayer(net['shp1'],NUM_CLASSES, nonlinearity=lasagne.nonlinearities.softmax)\n",
    "# Tensors reshaped back to the original shape\n",
    "net['shp2'] = lasagne.layers.ReshapeLayer(net['prob'], (BATCH_SIZE, FINAL_SEQUENCE_LENGTH, NUM_CLASSES))\n",
    "# Last sample in the sequence is considered\n",
    "net['output'] = lasagne.layers.SliceLayer(net['shp2'], -1, 1)"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 3,
   "source": [
    "Load the model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The model is populated with the weights of the pretrained network\n",
    "all_params_values = cp.load(open('weights/DeepConvLSTM_oppChallenge_gestures.pkl'))\n",
    "lasagne.layers.set_all_param_values(net['output'], all_params_values)"
   ]
  },
  {
   "cell_type": "heading",
   "metadata": {},
   "level": 3,
   "source": [
    "Run the model"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Compile the Theano function required to classify the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan.py:1019: Warning: In the strict mode, all neccessary shared variables must be passed as a part of non_sequences\n",
      "  'must be passed as a part of non_sequences', Warning)\n",
      "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_perform_ext.py:135: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility\n",
      "  from scan_perform.scan_perform import *\n"
     ]
    }
   ],
   "source": [
    "# Compilation of theano functions\n",
    "# Obtaining the probability distribution over classes\n",
    "test_prediction = lasagne.layers.get_output(net['output'], deterministic=True)\n",
    "# Returning the predicted output for the given minibatch\n",
    "test_fn =  theano.function([ net['input'].input_var], [T.argmax(test_prediction, axis=1)])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Testing data are segmented in minibatches and classified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "def iterate_minibatches(inputs, targets, batchsize, shuffle=False):\n",
    "    assert len(inputs) == len(targets)\n",
    "    if shuffle:\n",
    "        indices = np.arange(len(inputs))\n",
    "        np.random.shuffle(indices)\n",
    "    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):\n",
    "        if shuffle:\n",
    "            excerpt = indices[start_idx:start_idx + batchsize]\n",
    "        else:\n",
    "            excerpt = slice(start_idx, start_idx + batchsize)\n",
    "        yield inputs[excerpt], targets[excerpt]\n",
    "        \n",
    "# Classification of the testing data\n",
    "print(\"Processing {0} instances in mini-batches of {1}\".format(X_test.shape[0],BATCH_SIZE))\n",
    "test_pred = np.empty((0))\n",
    "test_true = np.empty((0))\n",
    "start_time = time.time()\n",
    "for batch in iterate_minibatches(X_test, y_test, BATCH_SIZE):\n",
    "    inputs, targets = batch\n",
    "    y_pred, = test_fn(inputs)\n",
    "    test_pred = np.append(test_pred, y_pred, axis=0)\n",
    "    test_true = np.append(test_true, targets, axis=0)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Models is evaluated using the F-Measure, a measure that considers the correct classification of each class equally important. Class imbalance is countered by weighting classes according to their sample proportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "||Results||\n",
      "\tTook 35.687s.\n",
      "\tTest fscore:\t0.9157 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Results presentation\n",
    "print(\"||Results||\")\n",
    "print(\"\\tTook {:.3f}s.\".format( time.time() - start_time))\n",
    "import sklearn.metrics as metrics\n",
    "print(\"\\tTest fscore:\\t{:.4f} \".format(metrics.f1_score(test_true, test_pred, average='weighted')))"
   ]
  }
 ],
 "metadata": {
  "name": ""
 },
 "nbformat": 4,
 "nbformat_minor": 0
}