lightFM_retail_recom_sys_MODEL_CREATION.ipynb

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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy.sparse as sparse\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import spsolve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>23166</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>16008</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12347</td>\n",
       "      <td>17021</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347</td>\n",
       "      <td>20665</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12347</td>\n",
       "      <td>20719</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   CustomerID StockCode  Quantity\n",
       "0       12346     23166         1\n",
       "1       12347     16008        24\n",
       "2       12347     17021        36\n",
       "3       12347     20665         6\n",
       "4       12347     20719        40"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_purchased = pd.read_csv('cvs_data_file/online-retail.csv', header = 0)\n",
    "grouped_purchased.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\Office\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  \"\"\"\n",
      "C:\\Users\\Office\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: specifying 'categories' or 'ordered' in .astype() is deprecated; pass a CategoricalDtype instead\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "customers = list(np.sort(grouped_purchased.CustomerID.unique())) # Get our unique customers\n",
    "products = list(grouped_purchased.StockCode.unique()) # Get our unique products that were purchased\n",
    "quantity = list(grouped_purchased.Quantity) # All of our purchases\n",
    "\n",
    "rows = grouped_purchased.CustomerID.astype('category', categories = customers).cat.codes \n",
    "# Get the associated row indices\n",
    "cols = grouped_purchased.StockCode.astype('category', categories = products).cat.codes \n",
    "# Get the associated column indices\n",
    "purchases_sparse = sparse.csr_matrix((quantity, (rows, cols)), shape=(len(customers), len(products)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "execution_count": 4,
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   ],
   "source": [
    "matrix_size = purchases_sparse.shape[0]*purchases_sparse.shape[1] # Number of possible interactions in the matrix\n",
    "num_purchases = len(purchases_sparse.nonzero()[0]) # Number of items interacted with\n",
    "sparsity = 100*(1 - (num_purchases/matrix_size))\n",
    "sparsity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train(ratings, pct_test = 0.2):\n",
    "    test_set = ratings.copy() # Make a copy of the original set to be the test set. \n",
    "    test_set[test_set != 0] = 1 # Store the test set as a binary preference matrix\n",
    "    training_set = ratings.copy() # Make a copy of the original data we can alter as our training set. \n",
    "    nonzero_inds = training_set.nonzero() # Find the indices in the ratings data where an interaction exists\n",
    "    nonzero_pairs = list(zip(nonzero_inds[0], nonzero_inds[1])) # Zip these pairs together of user,item index into list\n",
    "    random.seed(0) # Set the random seed to zero for reproducibility\n",
    "    num_samples = int(np.ceil(pct_test*len(nonzero_pairs))) # Round the number of samples needed to the nearest integer\n",
    "    samples = random.sample(nonzero_pairs, num_samples) # Sample a random number of user-item pairs without replacement\n",
    "    user_inds = [index[0] for index in samples] # Get the user row indices\n",
    "    item_inds = [index[1] for index in samples] # Get the item column indices\n",
    "    training_set[user_inds, item_inds] = 0 # Assign all of the randomly chosen user-item pairs to zero\n",
    "    training_set.eliminate_zeros() # Get rid of zeros in sparse array storage after update to save space\n",
    "    return training_set, test_set, list(set(user_inds)) # Output the unique list of user rows that were altered  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "product_train, product_test, product_users_altered = make_train(purchases_sparse, pct_test = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  StockCode                          Description\n",
       "0    85123A   WHITE HANGING HEART T-LIGHT HOLDER\n",
       "1     71053                  WHITE METAL LANTERN\n",
       "2    84406B       CREAM CUPID HEARTS COAT HANGER\n",
       "3    84029G  KNITTED UNION FLAG HOT WATER BOTTLE\n",
       "4    84029E       RED WOOLLY HOTTIE WHITE HEART."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_lookup = pd.read_csv('cvs_data_file/item_lookup.csv', header = 0)\n",
    "item_lookup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Office\\Anaconda3\\lib\\site-packages\\lightfm\\_lightfm_fast.py:9: UserWarning: LightFM was compiled without OpenMP support. Only a single thread will be used.\n",
      "  warnings.warn('LightFM was compiled without OpenMP support. '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: train 0.91, test 0.91.\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from lightfm import LightFM\n",
    "from lightfm.evaluation import precision_at_k,auc_score\n",
    "\n",
    "\n",
    "# Instantiate and train the model\n",
    "model = LightFM(loss='warp')\n",
    "model.fit_partial(product_train, epochs=40, num_threads=2)\n",
    "\n",
    "with open('saved_model','wb') as f:\n",
    "            saved_model={'model':model}\n",
    "            pickle.dump(saved_model, f)\n",
    "\n",
    "\n",
    "# Evaluate the trained model\n",
    "train_auc = auc_score(model, product_train).mean()\n",
    "test_auc = auc_score(model, product_test).mean()\n",
    "\n",
    "print('AUC: train %.2f, test %.2f.' % (train_auc, test_auc))\n"
   ]
  }
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