Fashion-MNIST.ipynb

{
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   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/rishianand/devanagari-character-set\n",
    "from springyknn.springyknn import *\n",
    "\n",
    "%matplotlib notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"fashion-mnist/fashion-mnist_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      2       0       0       0       0       0       0       0       0   \n",
       "1      9       0       0       0       0       0       0       0       0   \n",
       "2      6       0       0       0       0       0       0       0       5   \n",
       "3      0       0       0       0       1       2       0       0       0   \n",
       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9    ...     pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0    ...            0         0         0         0         0   \n",
       "1       0    ...            0         0         0         0         0   \n",
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       "3       0    ...            3         0         0         0         0   \n",
       "4       0    ...            0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0         0         0         0         0         0  \n",
       "1         0         0         0         0         0  \n",
       "2         0         0         0         0         0  \n",
       "3         1         0         0         0         0  \n",
       "4         0         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x_train = data.iloc[:,1:785].as_matrix()/255.0\n",
    "y_train = np.asarray(data[\"label\"]).reshape(-1,1)\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "#print(x_train[0])\n",
    "#print(y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained total variation: , 0.9281115846798532\n",
      "(60000, 128)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=128)\n",
    "pca.fit(x_train[:20000])\n",
    "cx_train = pca.transform(x_train)\n",
    "cy_train = y_train\n",
    "print('Explained total variation: ,', format(sum(pca.explained_variance_ratio_)))\n",
    "print(cx_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started kNN of data on pivots...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1154/60000 [00:00<00:05, 11512.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished kNN of data on pivots in  11.658495903015137  secs\n",
      "Started spring strength computation between pivots...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60000/60000 [00:02<00:00, 20347.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished spring strength computation in  2.9508039951324463  secs\n",
      "Started kNN graph computation from lil_matrix...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5000/5000 [00:00<00:00, 40847.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished kNN graph of pivots in  0.36719703674316406  secs\n",
      "Preparing networkx graph...\n",
      "Finished preparing networkx graph in  0.10038208961486816  secs\n",
      "Saved gexf file\n"
     ]
    }
   ],
   "source": [
    "springyknn = SpringyKNN(cx_train, cy_train, randomize=True, num_pivots=5000)\n",
    "G = springyknn.get_springy_knn_graph(k1=5, k2=5)\n",
    "springyknn.write_gexf(G, filename='fashion_springyknn_graph.gexf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing networkx visualization...\n",
      "Computing circular layout positions...\n",
      "Finished computing circular layout positions in  0.0018281936645507812  secs\n",
      "Computing spring layout positions...\n"
     ]
    }
   ],
   "source": [
    "springyknn.viz_spring_graph(G, dim=2, iterations=150, scale=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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