tutorial4.ipynb

{
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  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## Part4 Regression models with Random Forest\n",
    "\n",
    "* https://github.com/dataprofessor/bioinformatics_freecodecamp/blob/main/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb\n",
    "* https://youtu.be/jBlTQjcKuaY?t=3719"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/06_bioactivity_data_3class_pubchem_fp.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing features\n",
    "#### Input features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "     PubchemFP0  PubchemFP1  PubchemFP2  PubchemFP3  PubchemFP4  PubchemFP5  \\\n",
       "0             1           1           0           0           0           0   \n",
       "1             1           1           0           0           0           0   \n",
       "2             1           1           0           0           0           0   \n",
       "3             1           1           1           0           0           0   \n",
       "4             1           1           0           0           0           0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
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       "116           1           1           1           0           0           0   \n",
       "117           1           1           0           0           0           0   \n",
       "\n",
       "     PubchemFP6  PubchemFP7  PubchemFP8  PubchemFP9  ...  PubchemFP871  \\\n",
       "0             0           0           0           1  ...             0   \n",
       "1             0           0           0           1  ...             0   \n",
       "2             0           0           0           1  ...             0   \n",
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       "..          ...         ...         ...         ...  ...           ...   \n",
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       "117           0           0           0           1  ...             0   \n",
       "\n",
       "     PubchemFP872  PubchemFP873  PubchemFP874  PubchemFP875  PubchemFP876  \\\n",
       "0               0             0             0             0             0   \n",
       "1               0             0             0             0             0   \n",
       "2               0             0             0             0             0   \n",
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       "..            ...           ...           ...           ...           ...   \n",
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       "\n",
       "     PubchemFP877  PubchemFP878  PubchemFP879  PubchemFP880  \n",
       "0               0             0             0             0  \n",
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       "..            ...           ...           ...           ...  \n",
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       "\n",
       "[118 rows x 881 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop('pIC50', axis=1)\n",
    "X"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      4.869666\n",
       "1      4.882397\n",
       "2      6.008774\n",
       "3      6.022276\n",
       "4      4.950782\n",
       "         ...   \n",
       "113    4.974694\n",
       "114    4.995679\n",
       "115    4.939302\n",
       "116    4.970616\n",
       "117    4.102923\n",
       "Name: pIC50, Length: 118, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.pIC50\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118, 881)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove low variance feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))\n",
    "X = selection.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118, 197)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data split (80/20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((94, 197), (94,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24, 197), (24,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, Y_test.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Regression model using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.015844391313310613"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=100)\n",
    "model.fit(X_train, Y_train)\n",
    "r2 = model.score(X_test, Y_test)\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plot of experimental vs predicted pIC50 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "sns.set_style(\"white\")\n",
    "\n",
    "ax = sns.regplot(x= Y_test, y= Y_pred, scatter_kws={'alpha':0.4})\n",
    "ax.set_xlabel('Experimental pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
    "ax.set_xlim(0, 12)\n",
    "ax.set_ylim(0, 12)\n",
    "ax.figure.set_size_inches(5, 5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drug-discovery",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}