playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "from PIL import Image\n",
    "import io\n",
    "\n",
    "import pystan\n",
    "import numpy as np\n",
    "from scipy import sparse\n",
    "from scipy import stats as sts\n",
    "\n",
    "import h5py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as img\n",
    "\n",
    "from chembl_webresource_client.new_client import new_client\n",
    "\n",
    "\n",
    "root = './dataset'\n",
    "hf = h5py.File(os.path.join(root, \"cdk2.h5\"), \"r\")\n",
    "ids = hf[\"chembl_id\"][()] # the name of each molecules\n",
    "ap = sparse.csr_matrix((hf[\"ap\"][\"data\"], hf[\"ap\"][\"indices\"], hf[\"ap\"][\"indptr\"]), shape=[len(hf[\"ap\"][\"indptr\"]) - 1, 2039])\n",
    "mg = sparse.csr_matrix((hf[\"mg\"][\"data\"], hf[\"mg\"][\"indices\"], hf[\"mg\"][\"indptr\"]), shape=[len(hf[\"mg\"][\"indptr\"]) - 1, 2039])\n",
    "tt = sparse.csr_matrix((hf[\"tt\"][\"data\"], hf[\"tt\"][\"indices\"], hf[\"tt\"][\"indptr\"]), shape=[len(hf[\"tt\"][\"indptr\"]) - 1, 2039])\n",
    "\n",
    "features = sparse.hstack([ap, mg, tt]).toarray() # the samples' features, each row is a sample, and each sample has 3*2039 features\n",
    "labels = hf[\"label\"][()] # the label of each molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chembl_webresource_client.new_client import new_client\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "root = './dataset'\n",
    "\n",
    "molecule = new_client.molecule\n",
    "res = molecule.search('CHEMBL28')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:chembl_webresource_client.url_query:headers:\n",
      "INFO:chembl_webresource_client.url_query:{'Accept': 'image/png'}\n",
      "INFO:chembl_webresource_client.url_query:https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL28\n",
      "INFO:chembl_webresource_client.url_query:From cache: True\n",
      "INFO:chembl_webresource_client.url_query:headers:\n",
      "INFO:chembl_webresource_client.url_query:{'Accept': 'image/png'}\n",
      "INFO:chembl_webresource_client.url_query:https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL29\n",
      "INFO:chembl_webresource_client.url_query:From cache: False\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from chembl_webresource_client.new_client import new_client\n",
    "from IPython.display import Image\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "Image(new_client.image.get('CHEMBL28'))\n",
    "Image(new_client.image.get('CHEMBL29'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:chembl_webresource_client.url_query:headers:\n",
      "INFO:chembl_webresource_client.url_query:{'Accept': 'image/png'}\n",
      "INFO:chembl_webresource_client.url_query:https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL28\n",
      "INFO:chembl_webresource_client.url_query:From cache: True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.5, 499.5, 499.5, -0.5)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_bytes = new_client.image.get('CHEMBL28')\n",
    "image = np.array(Image.open(io.BytesIO(image_bytes)))\n",
    "\n",
    "plt.imshow(image)\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [1, 0, 0, ..., 0, 1, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int16)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>...</th>\n",
       "      <th>0.8096</th>\n",
       "      <th>0.8097</th>\n",
       "      <th>0.8098</th>\n",
       "      <th>0.8099</th>\n",
       "      <th>0.8100</th>\n",
       "      <th>0.8101</th>\n",
       "      <th>0.8102</th>\n",
       "      <th>0.8103</th>\n",
       "      <th>0.8104</th>\n",
       "      <th>0.8105</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8193 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  ...  0.8096  0.8097  0.8098  \\\n",
       "0  1  0    0    0    0    0    0    0    0    0  ...       0       0       0   \n",
       "1  0  0    0    0    0    0    0    0    0    0  ...       0       0       0   \n",
       "2  1  0    0    0    0    0    0    0    0    0  ...       0       0       0   \n",
       "3  0  0    0    0    0    0    0    0    0    0  ...       0       0       0   \n",
       "4  1  0    0    0    0    0    0    0    0    0  ...       0       0       0   \n",
       "\n",
       "   0.8099  0.8100  0.8101  0.8102  0.8103  0.8104  0.8105  \n",
       "0       0       0       0       0       0       0       0  \n",
       "1       0       0       0       0       0       0       0  \n",
       "2       0       0       0       0       0       0       0  \n",
       "3       0       0       0       0       0       0       0  \n",
       "4       0       0       0       0       0       0       0  \n",
       "\n",
       "[5 rows x 8193 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.read_csv(os.path.join(root, \"cdk2_train.csv\"))\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1557"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import corex as ce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Data matrix values should be consecutive integers starting with 0,1,...\n",
      "[0 0 1]\n",
      "[0 0 1]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[0,0,0,1,0], # A matrix with rows as samples and columns as variables.\n",
    "              [0,0,1,1,23],\n",
    "              [1,1,1,0,0],\n",
    "              [1,1,1,1,1]], dtype=int)\n",
    "\n",
    "layer1 = ce.Corex(n_hidden=3)  # Define the number of hidden factors to use.\n",
    "layer1.fit(X)\n",
    "\n",
    "print(layer1.labels[0])  # Labels for each sample for Y_0\n",
    "\n",
    "print(layer1.labels[1])  # Labels for each sample for Y_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "distr = sts.poisson.rvs([4, 6, 2, 7, 8, 9, 2, 4, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5, 2, 4, 6, 8, 2, 4, 2])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}