Amzn_dress_recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import indicoio\n",
    "import os\n",
    "import tqdm\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from indicoio.custom import Collection\n",
    "import re\n",
    "import operator\n",
    "from IPython.display import Image, display\n",
    "from random import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indicoio.config.api_key = 'b7a7565f3467db2830811e9a17715178'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_paths():\n",
    "    file_paths = []\n",
    "    for root,_, files in  os.walk(\"amazon-fashion-women-new\"):\n",
    "        for image in files:\n",
    "            file_paths.append(os.path.join(root,image))\n",
    "    return file_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filePaths = get_paths()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['amazon-fashion-women-new\\\\pic (1).jpg',\n",
       " 'amazon-fashion-women-new\\\\pic (10).jpg',\n",
       " 'amazon-fashion-women-new\\\\pic (100).jpg',\n",
       " 'amazon-fashion-women-new\\\\pic (101).jpg',\n",
       " 'amazon-fashion-women-new\\\\pic (102).jpg']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filePaths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FilePaths_labels = []\n",
    "FilePaths_labels_dict = {}\n",
    "for fileP in filePaths:\n",
    "    label = re.search(r\"\\(([0-9]+)\", fileP).groups()[0]\n",
    "    FilePaths_labels.extend([(fileP,label)])\n",
    "    FilePaths_labels_dict[int(label)] = fileP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'amazon-fashion-women-new\\\\pic (1).jpg'),\n",
       " (2, 'amazon-fashion-women-new\\\\pic (2).jpg'),\n",
       " (3, 'amazon-fashion-women-new\\\\pic (3).jpg'),\n",
       " (4, 'amazon-fashion-women-new\\\\pic (4).jpg'),\n",
       " (5, 'amazon-fashion-women-new\\\\pic (5).jpg')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For display purposes when predicting results\n",
    "FilePaths_labels_dict.items()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Randomly split the data set into Train and test.\n",
    "train,test = train_test_split(FilePaths_labels,test_size=0.05,random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "252 14\n"
     ]
    }
   ],
   "source": [
    "print len(train), len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'domain': None,\n",
       " u'input_type': u'image',\n",
       " u'model_type': u'classification',\n",
       " u'number_of_examples': 252,\n",
       " u'permissions': {u'read': [], u'write': []},\n",
       " u'public': False,\n",
       " u'registered': False,\n",
       " u'status': u'training'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For training purposes:\n",
    "collection = Collection(\"recommend_clothes\")\n",
    "\n",
    "# Add and Train the data\n",
    "collection.add_data(train,batch=True)\n",
    "collection.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'domain': None,\n",
       " u'input_type': u'image',\n",
       " u'model_type': u'classification',\n",
       " u'number_of_examples': 252,\n",
       " u'permissions': {u'read': [], u'write': []},\n",
       " u'public': False,\n",
       " u'registered': False,\n",
       " u'status': u'ready'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def showpic(file_ind):\n",
    "    img_label = int(file_ind)\n",
    "    pic_path = FilePaths_labels_dict[img_label]\n",
    "    display(Image(filename=pic_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Predict the test data and display the images\n",
    "\n",
    "def showPredictedImg(img):\n",
    "    predicted = collection.predict(img[0])\n",
    "    print 'Test Image:'\n",
    "    showpic(img[1])\n",
    "    top5 = sorted(predicted.items(),key=operator.itemgetter(1),reverse=1)[:5]\n",
    "    print 'Top 5 Predicted Images:'\n",
    "    for top_label,_ in top5:\n",
    "        showpic(top_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Predict a random image from the test file.\n",
    "def predictRandomTestImage():\n",
    "    N = len(test)\n",
    "    index_test_file = randint(0,N-1) #index_test_file = {0...N-1} N: number of test files\n",
    "    test_this_file = test[index_test_file]\n",
    "    showPredictedImg(test_this_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predicted Images:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictRandomTestImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predicted Images:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictRandomTestImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predicted Images:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Zca3D0U2o4IOPAa1a9uGZBtWoIuRKhLS0vHXwkra/otP0FWXhnyJXou77H19T\nL0u7b1EI76LHuDTec7SU9y8keoC2hz/EKnqqU+zhrCHZtQwi0+lEONKWlQyEhMKYQtOSeMJcP/Qi\nrrCrTW7Mi1oUpSqEilKUApSlAKUpQClKUArpmSmIkdciS4ltpAypR6Cu1XSqde0ncu1mTcZtuuVt\njt2cvZZDCg4jag7grcVYSQMZyBirRjYJG7Y/aCs9iuBsVnkthK2CqTcMlSmc9EtoHJURnkkY9D5U\ni7S9Uvas1pLu3fPuMnEeIl45LcZGQgH0J5UR6qPWvbIt6Gojd9etsaVHdcwULuAcG/0cbQoLAz64\nHHnX5D0lAflGXN1bpWFEWcqWzJU6vjyS0lIOfngf0qz8pF1jdmOjSEMR25CHkPzUuFRSsFW5X4c+\nuM5+mK53NQZMKCQ66YyEqfAUCC4vKiR6HCueuSPLgV3sRbX+2HlR5CX4Mc7GnC2Wlv8AXxlPqBn0\n/DwOleS5L96u8uU4yGA4reEnjcemcnqo9ePyqv7R0p12ZtHZuhU68R2Yl7lWySlC1kNJDrTrQ5KN\ng5VngEE+fw42/s6YdZ0Zf3nGggFkqbV/CFoc4+pH5itG0g7Ltbsa5bnrdBlB9tE5iJ34QD4F8Dnk\nZTzjrmt/0bqHSyLzfGrtMYatUtlxhhsuFsq3KTsT0yMJZbzwT4ulW1Z0hdGW1K8q3xbDMUglqFNj\nPyMDJQhRwVfIbxn51HGtJcS8vXCVZtWOvMQHPDDfZDaSfF/oYOVgbT4iAeRmpK0Xqy06901Isdy9\n1gakkMPB1PdqDbuMBG3OQE/dG3OeCflDOpYr1uuibqzHCY0pSkKaA4adSAHGlfMeJKvMEHqDiHFE\nSk+trwxovTrenH7In3ZUd59Lz2WvtFLT90hXpyeny6V+abkpckPWtZ2MzW+7SVHhDmdzas+m4AH4\nE1kdBWD/ABLqmHblIjlncHHe+eSzubSoFSdyiEhSh4Rk+efn2a/0yvSev5tmuDfusTvu/YUzl1AY\nWNyChX4wnO3P8Jp+HHf8jrsjNtDyHb40tcIIU24htY3ISTghX7oCir4ZOcivoJ7N+tYOp+zyHCTP\n94n2oGG8F8OKQjhtZzyctlGT65qiI0qL1AYuekWTPljLM+G1IQ2Sr8K0pVyQocFOByK/NKXHXPZ9\nqph+PHvViktOBaGXHC0lWPI7gUqTg4IIIINEr0Rkg77UfUAUqHvZ57RL/rRMhnUTsNuQhvvWmURd\ninG8gb0uBe1eDwoBCcZSckEVMNVaadM5p2KUpUEilKUApSlAKUpQCtH1t2WaQ1dOen3aG+ZTwQHF\ntyFhKtvTKM7TwB5eVKVKbT0Q1ZHd89mfS82amZFNtDiCQEyYThBSfI928jOK1Wf7JEZ4lce9wGVk\n87YzoBHpy4rHz5pSrdm/SL6+GIa9lB6NJCXLhDmAqON8x1ofAEBskj6ishI9mS6XaCqKlzS9oZZy\nlAjNuPOvf7nVJBT88LJpSrMt3ktHmj+yddW3m0i72VhhIwEt98taj1JUpec5+ASPhXddPZpuTLKm\nURdLSkLAClyH30qV89rYxz6HilKhJE/LPyzhbfZXTFecU9qFUVS1csQHFobbH7oUsKUofE4r1TfZ\nZtr0BTTNyxIIOJLj7ilKPllJ8Ix8APOlKSSovjmzUoXs49qFqliPFvekyGzgOB+QgrA4BIDXXGMj\nOKyL3s1a8kvJVMuunAD97uJ0oJIPUbVNkfSlKgfLKqs8kb2R9UyStB1VaobSXCtPdF1wc/wlKcfP\nNZmD7K+toj6SrXlsuDQSEoTLZfIb9VAb+ePw5ApSps5OTuib+zXshsejJsa5+/S7lcY6FIbdcCWm\nkFQwpSWkAJyRkZOTg1JFKVRtv0JUKUpUEilKUB//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictRandomTestImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predicted Images:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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oZq6PP4Y/1K0zSvwTNafxIisFp/xkcFb3huDj8Cu9AAHVsORnAdj8kRa6nupe\nQh7SJWREUEiQCIiAIiIAiIgP/9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictRandomTestImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Image:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predicted Images:\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": 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jY42sY0Na0Ya0DAA+C/SIpICIiAIiIAiIgCIiA//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictRandomTestImage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}