test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "rational-snake",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#    _____ _  _____ __  __          \n",
    "#   / ____(_)/ ____|  \\/  |   /\\    \n",
    "#  | (___  _| |  __| \\  / |  /  \\   \n",
    "#   \\___ \\| | | |_ | |\\/| | / /\\ \\  \n",
    "#   ____) | | |__| | |  | |/ ____ \\ \n",
    "#  |_____/|_|\\_____|_|  |_/_/    \\_\\ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "automatic-river",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Read data :  5038\n",
      "The generation of 3d conformers: 100%|███████████████▉| 5037/5038 [31:45<00:00,  2.64it/s]\n",
      "## 3D coordinates generated successfully \n",
      "## All element types :  ['As', 'Br', 'C', 'Cl', 'F', 'I', 'N', 'O', 'P', 'S', 'Se']\n",
      "## All adduct types :  ['[M+H]+', '[M+Na]+', '[M-H]-']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\ecc\\lib\\site-packages\\numpy\\core\\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Build graph data successfully. Dataset: MyDataset(n_graphs=5038)\n",
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      "360/360 [==============================] - 19s 52ms/step - loss: 18.6474\n",
      "360/360 [==============================] - 19s 52ms/step - loss: 17.8082\n",
      "360/360 [==============================] - 19s 52ms/step - loss: 18.3387\n",
      "360/360 [==============================] - 19s 52ms/step - loss: 18.5459\n",
      "360/360 [==============================] - 19s 52ms/step - loss: 20.0587\n",
      "360/360 [==============================] - 19s 52ms/step - loss: 19.6527\n"
     ]
    }
   ],
   "source": [
    "from sigma.sigma import *\n",
    "ifile = 'data/TrainData.csv'\n",
    "ParameterPath = 'parameter/parameter.pkl'\n",
    "ofile = 'model/model_new.h5'\n",
    "ECC_Model = Model_train(ifile = ifile, \n",
    "                        ParameterPath = ParameterPath, \n",
    "                        ofile = ofile, \n",
    "                        EPOCHS = 300, \n",
    "                        BATCHS = 14, \n",
    "                        Vis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "medium-youth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Read data :  559\n",
      "## All Atoms  :  ['As', 'Br', 'C', 'Cl', 'F', 'I', 'N', 'O', 'P', 'S', 'Se']\n",
      "## All Adduct :  ['[M+H]+', '[M+Na]+', '[M-H]-']\n",
      "The generation of 3d conformers: 100%|█████████████████▉| 558/559 [03:28<00:00,  2.68it/s]\n",
      "## 3D coordinates generated successfully \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\ecc\\lib\\site-packages\\numpy\\core\\_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## Graph & Adduct dataset completed\n",
      "## Model loading completed\n",
      "Predictions: 100%|█████████████████████████████████████▉| 558/559 [00:08<00:00, 66.40it/s]\n",
      "## CCS predicted completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9937652188667493, 1.1864654787760114)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sigma.sigma import *\n",
    "ifile = 'data/TestData.csv'\n",
    "ParameterPath = 'parameter/parameter.pkl'\n",
    "mfileh5 = 'model/model_new.h5'\n",
    "ofile = 'data/TestData-pred.csv'\n",
    "\n",
    "Model_prediction(ifile,ParameterPath,mfileh5,ofile,Isevaluate=True)"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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