Instacart+Market+Basket+Analysis_word2vec.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import gensim\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "train_orders = pd.read_csv(\"/Users/siero5335/Desktop/Instacart Market Basket Analysis/order_products__train.csv\")\n",
    "prior_orders = pd.read_csv(\"/Users/siero5335/Desktop/Instacart Market Basket Analysis/order_products__prior.csv\")\n",
    "products = pd.read_csv(\"/Users/siero5335/Desktop/Instacart Market Basket Analysis/products.csv\").set_index('product_id')"
   ]
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    "train_orders[\"product_id\"] = train_orders[\"product_id\"].astype(str)\n",
    "prior_orders[\"product_id\"] = prior_orders[\"product_id\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "train_products = train_orders.groupby(\"order_id\").apply(lambda order: order['product_id'].tolist())\n",
    "prior_products = prior_orders.groupby(\"order_id\").apply(lambda order: order['product_id'].tolist())"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "sentences = prior_products.append(train_products)\n",
    "longest = np.max(sentences.apply(len))\n",
    "sentences = sentences.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = gensim.models.Word2Vec(sentences, size=100, window=longest)"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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    "vocab = list(model.wv.vocab.keys())\n",
    "vocab = pd.Series(vocab)\n",
    "vocab.to_csv('vocab.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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   "source": [
    "model.wv.syn0\n",
    "np.savetxt(\"model.wv.syn0.csv\", model.wv.syn0, delimiter=\",\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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