{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "018b8bb1", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import threading\n", "import joblib\n", "import random\n", "from sklearn import metrics\n", "from sklearn.utils import shuffle\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import mean_squared_error, r2_score, make_scorer\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.svm import SVR\n", "import seaborn as sn\n", "import matplotlib.pyplot as plt\n", "import pickle" ] }, { "cell_type": "markdown", "id": "9c7a2f64", "metadata": {}, "source": [ "# load_data" ] }, { "cell_type": "code", "execution_count": 3, "id": "00b1ba5b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"execute_result" } ], "source": [ "df = pd.read_csv('anti-HBV-S_ALL_feature_filter.txt', sep='\\t')\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "9845f21d", "metadata": {}, "outputs": [], "source": [ "def set_to_one(x):\n", " if x > 0:\n", " return 1\n", " else:\n", " return x" ] }, { "cell_type": "code", "execution_count": 5, "id": "ce0b0672", "metadata": {}, "outputs": [], "source": [ "X = df.drop(columns=['Bench_code', 'activity', 'cell_toxicity'])\n", "X = X.applymap(set_to_one)\n", "y = df.activity" ] }, { "cell_type": "markdown", "id": "c4961ad4", "metadata": {}, "source": [ "# Feature_filter" ] }, { "cell_type": "code", "execution_count": 6, "id": "73b86a63", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flist = [0]*1184\n", "\n", "rf = RandomForestRegressor(n_estimators=1000, n_jobs=5, random_state=2023)\n", "for i in range(20):\n", " rf.fit(X,y)\n", " flist = [x + y for x, y in zip(flist, rf.feature_importances_)]\n", "flist = [x / 20 for x in flist]\n", "\n", "importances = pd.Series(flist, index = X.columns)\n", "sorted_importances = importances.sort_values()\n", "sorted_importances.plot(kind = 'barh', color = '#00F5FF')\n", "plt.tick_params(axis='y', labelsize=2)\n", "plt.show()\n", "\n", "sorted_importances = pd.DataFrame({'Importance': flist, 'CAS': X.columns})\n", "sorted_importances.to_csv('oil_importance_features_sAg_reg.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 8, "id": "8bd3ecdb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "59" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "###threshold selection\n", "df_f = sorted_importances[sorted_importances[\"Importance\"] > 0.0038]\n", "important_features = df_f['CAS'].to_list()\n", "len(important_features)" ] }, { "cell_type": "code", "execution_count": 9, "id": "f6b587e4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"execution_count": 10, "id": "b33882d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameters: {'C': 40, 'gamma': 0.01, 'kernel': 'rbf'}\n", "Best score: 0.21540177443675507\n" ] } ], "source": [ "r2_scorer = make_scorer(r2_score)\n", "\n", "param_grid = {\"C\": [0.1, 1, 10, 20, 40, 60, 80, 100, 1000],\n", " \"kernel\": [\"poly\", \"rbf\"],\n", " \"gamma\": [1e-7, 1e-4, 1e-3, 1e-2, 0.1]\n", " }\n", "\n", "regressor = SVR()\n", "grid_search = GridSearchCV(regressor, param_grid, cv=10, scoring=r2_scorer)\n", "grid_search.fit(X, y)\n", "\n", "print(\"Best parameters: \", grid_search.best_params_)\n", "print(\"Best score: \", grid_search.best_score_)" ] }, { "cell_type": "code", "execution_count": 11, "id": "79219a56", "metadata": {}, "outputs": [], "source": [ "def run_SVR_thread(x_train,x_test,y_train,y_test,cv):\n", " clf = SVR(C=40, kernel='rbf', gamma=0.01)\n", " SVR_model=clf.fit(x_train, y_train)\n", " y_pred=SVR_model.predict(x_test)\n", " r2 = r2_score(y_test, y_pred)\n", " mse = mean_squared_error(y_test, y_pred)\n", " return r2, mse" ] }, { "cell_type": "code", "execution_count": 12, "id": "f4d2c0eb", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.19688691239478273,0.13392218432057348\n", "0.3317207214192971,0.08784886651748307\n", "0.3887436977492912,0.10924284690425187\n", "0.22413502764045357,0.1980674366346922\n", "-0.11433256560506622,0.13352166469796467\n", "0.32900921317259346,0.17944218544584564\n", "0.3106455090698024,0.13303829810927487\n", "0.5848436304310343,0.091111297119424\n", "0.24401524846242173,0.18129700322887204\n", "0.41501751957562993,0.11397782158648609\n", "0.713933353465162,0.04134507466482099\n", "0.31943135811453593,0.15588840375378563\n", "0.20251347738451098,0.14155802170173276\n", "0.3125510016748394,0.10936739287816466\n", "0.2781915247294272,0.09872754448875844\n", 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y_test=train_test_split(X,y,test_size=0.1, random_state=cv)\n", " r2,mse = run_SVR_thread(x_train, x_test, y_train, y_test,cv)\n", " print(str(r2) +','+ str(mse))\n", " f1.write(str(r2) +','+ str(mse) +'\\n')\n", " \n", "f1.close()" ] }, { "cell_type": "markdown", "id": "26c25276", "metadata": {}, "source": [ "# Save_model" ] }, { "cell_type": "code", "execution_count": 13, "id": "a09d43b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['sAg_reg_SVR_seed96.pkl.z']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_train, x_test, y_train, y_test=train_test_split(X,y,test_size=0.1, random_state=96)\n", "model = SVR(C=40, kernel='rbf', gamma=0.01)\n", "model.fit(x_train, y_train)\n", "joblib.dump(model, 'sAg_reg_SVR_seed96.pkl.z')" ] }, { "cell_type": "markdown", "id": "276a4743", "metadata": {}, "source": [ "# Prediction" ] }, { "cell_type": "code", "execution_count": 14, "id": "767fff51", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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