1101 lines
318 KiB
Plaintext
1101 lines
318 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "25d3944e",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'seaborn'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.model_selection import (\n",
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" train_test_split,\n",
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" cross_val_score,\n",
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" KFold,\n",
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" ShuffleSplit,\n",
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" learning_curve,\n",
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" LeaveOneOut,\n",
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")\n",
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"from sklearn.metrics import accuracy_score, mean_absolute_error\n",
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"from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression\n",
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"from sklearn.svm import LinearSVC\n",
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"from sklearn.tree import (\n",
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" DecisionTreeClassifier,\n",
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" ExtraTreeClassifier,\n",
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" DecisionTreeRegressor,\n",
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")\n",
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"from sklearn.ensemble import (\n",
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" RandomForestClassifier,\n",
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" AdaBoostClassifier,\n",
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" BaggingClassifier,\n",
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" ExtraTreesClassifier,\n",
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" AdaBoostRegressor,\n",
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")\n",
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"from sklearn.decomposition import PCA, KernelPCA\n",
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"from sklearn.cluster import KMeans, AgglomerativeClustering\n",
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"from scipy.cluster.hierarchy import dendrogram, linkage\n",
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"from sklearn.feature_selection import SelectKBest, SelectFromModel, f_classif\n",
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"# from mlxtend.plotting import plot_decision_regions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d42d4f12",
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"metadata": {},
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"source": [
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"# DataSet"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a0db7a27",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(33, 32)\n"
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]
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}
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],
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"source": [
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"df = pd.DataFrame(\n",
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" pd.read_csv(\n",
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" \"../top_Gads_updated.dat\",\n",
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" header=None,\n",
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" comment=\"#\",\n",
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" sep=\"\\s+\",\n",
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" squeeze=True,\n",
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" names=[\"material\", \"adsorbate\", \"DF_lower\", \"DF_upper\", \"DF\"],\n",
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" )\n",
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")\n",
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"\n",
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"\n",
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"df = df[[\"material\", \"adsorbate\", \"DF\"]]\n",
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"aminoacid = df[\"adsorbate\"].unique()\n",
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"\n",
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"n = {}\n",
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"for i in aminoacid:\n",
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" n[i] = list(df[df.adsorbate == i][\"DF\"])\n",
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"X = pd.DataFrame(n)\n",
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"\n",
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"X.head(5)\n",
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"print(X.shape)\n",
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"aminoacids_label = list(df[\"adsorbate\"].unique())\n",
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"material_label = list(df[\"material\"].unique())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef75347d",
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"metadata": {},
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"source": [
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"# Data Preparation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "69941d4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"X.rename(columns={\"C3H6\": \"PRP\", \"C4H6\": \"BUT-2\", \"C4H8\": \"BUT-1\"}, inplace=True)\n",
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"X.rename(\n",
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" index={\n",
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" \"C_amorph-1\": \"C-AM-1\",\n",
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" \"C_amorph-2\": \"C-AM-2\",\n",
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" \"C_amorph-3\": \"C-AM-3\",\n",
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" \"CNT15-COO--10\": \"CNT-COO$^{-}$-high\",\n",
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" \"CNT15-COO--3\": \"CNT-COO$^{-}$-low\",\n",
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" \"CNT15-COOH-30\": \"CNT-COOH-high\",\n",
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" \"CNT15-COOH-3\": \"CNT-COOH-low\",\n",
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" \"CNT15-NH2-14\": \"CNT-NH$_{2}$-high\",\n",
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" \"CNT15-NH2-2\": \"CNT-NH$_{2}$-low\",\n",
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" \"CNT15-NH3+-4\": \"CNT-NH$_{3}^{+}$-high\",\n",
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" \"CNT15-NH3+-2\": \"CNT-NH$^{+}_{3}$-low\",\n",
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" \"CNT15-OH-14\": \"CNT-OH-high\",\n",
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" \"CNT15-OH-4\": \"CNT-OH-low\",\n",
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" \"CNT15\": \"CNT\",\n",
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" \"Fe2O3-001O\": \"Fe$_{2}$O$_{3}$(001)\",\n",
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" \"graphene\": \"GR\",\n",
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" \"bi-graphene\": \"bi-GR\",\n",
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" \"tri-graphene\": \"tri-GR\",\n",
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" \"grapheneoxide\": \"GO\",\n",
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" \"redgrapheneoxide\": \"rGO\",\n",
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" \"SiO2-Q2\": \"SiO$_{2}$-Q2\",\n",
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" \"SiO2-Q4\": \"SiO$_{2}$-Q4\",\n",
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" \"TiO2-rut-110\": \"TiO$_{2}$-rut(110)\",\n",
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" \"TiO2-ana-101\": \"TiO$_{2}$-ana(101)\",\n",
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" \"TiO2-rut-100\": \"TiO$_{2}$-rut(100)\",\n",
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" \"TiO2-ana-100\": \"TiO$_{2}$-ana(100)\",\n",
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" \"TiO2-ana-101-NB\": \"TiO$_{2}$-ana(101)-NB\",\n",
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" \"ZnO-1010\": \"ZnO(10$\\overline{1}}$0)\",\n",
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" \"ZnO-1210\": \"ZnO(1$\\overline{2}}$10)\",\n",
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" \"ZnS-110\": \"ZnS(110)\",\n",
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" \"ZnS-110-coated\": \"ZnS(110)-coated\",\n",
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" },\n",
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" inplace=True,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "796ddb87",
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"metadata": {},
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"outputs": [],
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"source": [
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"main = [\"ASP\", \"VAL\", \"PRO\"]\n",
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"aminacid_order = [\n",
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" \"ALA\",\n",
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" \"ARG\",\n",
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" \"ASN\",\n",
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" \"ASP\",\n",
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" \"CYS\",\n",
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" \"CYM\",\n",
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" \"GLN\",\n",
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" \"GAN\",\n",
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" \"GLU\",\n",
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" \"HID\",\n",
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" \"HIE\",\n",
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" \"HIP\",\n",
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" \"ILE\",\n",
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" \"LEU\",\n",
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" \"LYS\",\n",
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" \"MET\",\n",
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" \"PHE\",\n",
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" \"SER\",\n",
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" \"THR\",\n",
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" \"TRP\",\n",
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" \"TYR\",\n",
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" \"VAL\",\n",
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" \"GLY\",\n",
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" \"PRO\",\n",
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" \"CHL\",\n",
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" \"PHO\",\n",
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" \"ETA\",\n",
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" \"EST\",\n",
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" \"PRP\",\n",
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" \"BUT-1\",\n",
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" \"BUT-2\",\n",
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" \"DGL\",\n",
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"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e9e4d60",
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"metadata": {},
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"source": [
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"fig, ax = plt.subplots(len(G0), 2, figsize=(10, 25))\n",
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"fig.subplots_adjust(left=0.06, right=1, wspace=0.2)\n",
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"X_LR=X[main]\n",
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"def Mary_LR():\n",
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" for i in G0:\n",
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" Y=X[i]\n",
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" model= LinearRegression()\n",
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" model.fit(X_LR, Y)\n",
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" ymodel=model.predict(X_LR)\n",
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" print(np.round(cross_val_score(model,X_LR,Y,cv=3).mean(),2))\n",
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" ax[G0.index(i)][0].scatter(Y,ymodel,c='black',label=i)\n",
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" ax[G0.index(i)][0].plot(Y,Y,c='gray',label=np.round(model.score(X_LR,Y),2))\n",
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" ax[G0.index(i)][0].legend()\n",
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" ax[G0.index(i)][1].bar(main,model.coef_, color='b',width=0.5)\n",
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" plt.savefig(\"LR-G0.pdf\")\n",
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"Mary_LR()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5bd0e4b",
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"metadata": {},
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"source": [
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"# LinearRegression modelling using test_size=0.3 and 10 ShuffleSplit "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "3e6a9668",
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"metadata": {},
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"outputs": [],
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"source": [
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"def AminoAcid_LR(i):\n",
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" X_LR = X[main]\n",
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" Y = X[i]\n",
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" rs = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)\n",
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" train_score = []\n",
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" test_score = []\n",
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" train_MAE = []\n",
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" test_MAE = []\n",
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" for train_index, test_index in rs.split(X_LR):\n",
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" Xtrain = X_LR.iloc[list(train_index)]\n",
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" Ytrain = Y.iloc[list(train_index)]\n",
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" xtest = X_LR.iloc[list(test_index)]\n",
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" ytest = Y.iloc[list(test_index)]\n",
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" model = LinearRegression()\n",
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" model.fit(Xtrain, Ytrain)\n",
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" Ytrain_pred = model.predict(Xtrain)\n",
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" ytest_pred = model.predict(xtest)\n",
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" train_score.append(np.round(model.score(Xtrain, Ytrain), 2))\n",
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" test_score.append(np.round(model.score(xtest, ytest), 2))\n",
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" train_MAE.append(mean_absolute_error(Ytrain, Ytrain_pred))\n",
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" test_MAE.append(mean_absolute_error(ytest, ytest_pred))\n",
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" return (\n",
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" np.round(np.average(train_score), 2),\n",
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" np.round(np.std(train_score), 2),\n",
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" np.round(np.average(test_score), 2),\n",
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" np.round(np.std(test_score), 2),\n",
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" np.round(np.average(train_MAE), 2),\n",
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" np.round(np.std(train_MAE), 2),\n",
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" np.round(np.average(test_MAE), 2),\n",
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" np.round(np.std(test_MAE), 2),\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "4f090a3e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"\n",
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"warnings.simplefilter(\"ignore\")\n",
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"rest = [i for i in aminacid_order if i not in main]\n",
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"with open(\"LR_result.csv\", \"w\") as out_file:\n",
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" out_file.write(\n",
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" \"#AminoAcid, r2_avg_train, r2_std_train, r2_avg_test, r2_std_test, MAE_avg_train, MAE_std_train, MAE_avg_test, MAE_std_test\"\n",
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" + \"\\n\"\n",
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" )\n",
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" for i in rest:\n",
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" (\n",
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" r2_avg_train,\n",
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" r2_std_train,\n",
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" r2_avg_test,\n",
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" r2_std_test,\n",
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" MAE_avg_train,\n",
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" MAE_std_train,\n",
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" MAE_avg_test,\n",
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" MAE_std_test,\n",
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" ) = AminoAcid_LR(i)\n",
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" out_file.write(\n",
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" \"%s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f\\n\"\n",
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" % (\n",
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" i,\n",
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" r2_avg_train,\n",
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" r2_std_train,\n",
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" r2_avg_test,\n",
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" r2_std_test,\n",
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" MAE_avg_train,\n",
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" MAE_std_train,\n",
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" MAE_avg_test,\n",
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" MAE_std_test,\n",
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" )\n",
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" + \"\\n\"\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d0338ce2",
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"metadata": {},
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"source": [
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"# LinearRegression modelling performance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "11c227ff",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_27872/1770714819.py:4: FutureWarning: The squeeze argument has been deprecated and will be removed in a future version. Append .squeeze(\"columns\") to the call to squeeze.\n",
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"\n",
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"\n",
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" df=pd.DataFrame(pd.read_csv('LR_result.csv',header=None, comment=\"#\",sep='\\s+',\n",
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"/tmp/ipykernel_27872/1770714819.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
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" ax[0].set_yticklabels(df.AminoAcid, fontsize=12)\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1200x1000 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"flatui = [\n",
|
|
" \"black\",\n",
|
|
" \"grey\",\n",
|
|
" \"rosybrown\",\n",
|
|
" \"darkred\",\n",
|
|
" \"indianred\",\n",
|
|
" \"salmon\",\n",
|
|
" \"red\",\n",
|
|
" \"coral\",\n",
|
|
" \"tan\",\n",
|
|
" \"gold\",\n",
|
|
" \"y\",\n",
|
|
" \"olive\",\n",
|
|
" \"yellow\",\n",
|
|
" \"greenyellow\",\n",
|
|
" \"darkgreen\",\n",
|
|
" \"lime\",\n",
|
|
" \"lightseagreen\",\n",
|
|
" \"aqua\",\n",
|
|
" \"lightsteelblue\",\n",
|
|
" \"deepskyblue\",\n",
|
|
" \"royalblue\",\n",
|
|
" \"slateblue\",\n",
|
|
" \"mediumpurple\",\n",
|
|
" \"darkviolet\",\n",
|
|
" \"violet\",\n",
|
|
" \"magenta\",\n",
|
|
" \"deeppink\",\n",
|
|
" \"pink\",\n",
|
|
" \"crimson\",\n",
|
|
"]\n",
|
|
"fig, ax = plt.subplots(1, 4, figsize=(12, 10), sharey=True)\n",
|
|
"fig.subplots_adjust(left=0.06, right=0.95, wspace=0.15)\n",
|
|
"df = pd.DataFrame(\n",
|
|
" pd.read_csv(\n",
|
|
" \"LR_result.csv\",\n",
|
|
" header=None,\n",
|
|
" comment=\"#\",\n",
|
|
" sep=\"\\s+\",\n",
|
|
" squeeze=True,\n",
|
|
" names=[\n",
|
|
" \"AminoAcid\",\n",
|
|
" \"r2_avg_train\",\n",
|
|
" \"r2_std_train\",\n",
|
|
" \"r2_avg_test\",\n",
|
|
" \"r2_std_test\",\n",
|
|
" \"MAE_avg_train\",\n",
|
|
" \"MAE_std_train\",\n",
|
|
" \"MAE_avg_test\",\n",
|
|
" \"MAE_std_test\",\n",
|
|
" ],\n",
|
|
" )\n",
|
|
")\n",
|
|
"ax[0].barh(df.AminoAcid, df.r2_avg_train, xerr=df.r2_std_train, color=flatui)\n",
|
|
"ax[0].set_xlabel(\"$R^2$ score Train\", fontsize=14)\n",
|
|
"ax[0].set_yticklabels(df.AminoAcid, fontsize=12)\n",
|
|
"ax[0].set_xlim(-1, 1)\n",
|
|
"\n",
|
|
"ax[0].set_xlim(0, 1)\n",
|
|
"ax[1].barh(df.AminoAcid, df.r2_avg_test, xerr=df.r2_std_test, color=flatui)\n",
|
|
"ax[1].set_xlabel(\"$R^2$ score Test\", fontsize=14)\n",
|
|
"ax[1].set_xlim(-1, 1)\n",
|
|
"\n",
|
|
"ax[2].barh(df.AminoAcid, df.MAE_avg_train, xerr=df.MAE_std_train, color=flatui)\n",
|
|
"ax[2].set_xlabel(\"MAE (kJ/mol) Train\", fontsize=14)\n",
|
|
"ax[2].set_xlim(0, 4)\n",
|
|
"\n",
|
|
"ax[3].barh(df.AminoAcid, df.MAE_avg_test, xerr=df.MAE_std_test, color=flatui)\n",
|
|
"ax[3].set_xlabel(\"MAE (kJ/mol) Test\", fontsize=14)\n",
|
|
"ax[3].set_xlim(0, 4)\n",
|
|
"\n",
|
|
"ax[0].set_ylabel(\"Biomolecules\", fontsize=14)\n",
|
|
"plt.savefig(\"LR_result.png\", format=\"png\", dpi=1000, bbox_inches=\"tight\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 59,
|
|
"id": "0d2033f1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# modify the main\n",
|
|
"main = [\"ASP\", \"VAL\", \"PRO\", \"ETA\", \"PHO\"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 60,
|
|
"id": "ee420e98",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def AminoAcid_LR(i):\n",
|
|
" X_LR = X[main]\n",
|
|
" Y = X[i]\n",
|
|
" rs = ShuffleSplit(n_splits=20, test_size=0.3, random_state=0)\n",
|
|
" train_score = []\n",
|
|
" test_score = []\n",
|
|
" train_MAE = []\n",
|
|
" test_MAE = []\n",
|
|
" for train_index, test_index in rs.split(X_LR):\n",
|
|
" Xtrain = X_LR.iloc[list(train_index)]\n",
|
|
" Ytrain = Y.iloc[list(train_index)]\n",
|
|
" xtest = X_LR.iloc[list(test_index)]\n",
|
|
" ytest = Y.iloc[list(test_index)]\n",
|
|
" model = LinearRegression()\n",
|
|
" model.fit(Xtrain, Ytrain)\n",
|
|
" Ytrain_pred = model.predict(Xtrain)\n",
|
|
" ytest_pred = model.predict(xtest)\n",
|
|
" train_score.append(np.round(model.score(Xtrain, Ytrain), 2))\n",
|
|
" test_score.append(np.round(model.score(xtest, ytest), 2))\n",
|
|
" train_MAE.append(mean_absolute_error(Ytrain, Ytrain_pred))\n",
|
|
" test_MAE.append(mean_absolute_error(ytest, ytest_pred))\n",
|
|
" return (\n",
|
|
" np.round(np.average(train_score), 2),\n",
|
|
" np.round(np.std(train_score), 2),\n",
|
|
" np.round(np.average(test_score), 2),\n",
|
|
" np.round(np.std(test_score), 2),\n",
|
|
" np.round(np.average(train_MAE), 2),\n",
|
|
" np.round(np.std(train_MAE), 2),\n",
|
|
" np.round(np.average(test_MAE), 2),\n",
|
|
" np.round(np.std(test_MAE), 2),\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"id": "0167b57e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import warnings\n",
|
|
"\n",
|
|
"warnings.simplefilter(\"ignore\")\n",
|
|
"rest = [i for i in aminacid_order if i not in main]\n",
|
|
"with open(\"LR_modify_result.csv\", \"w\") as out_file:\n",
|
|
" out_file.write(\n",
|
|
" \"#AminoAcid, r2_avg_train, r2_std_train, r2_avg_test, r2_std_test, MAE_avg_train, MAE_std_train, MAE_avg_test, MAE_std_test\"\n",
|
|
" + \"\\n\"\n",
|
|
" )\n",
|
|
" for i in rest:\n",
|
|
" (\n",
|
|
" r2_avg_train,\n",
|
|
" r2_std_train,\n",
|
|
" r2_avg_test,\n",
|
|
" r2_std_test,\n",
|
|
" MAE_avg_train,\n",
|
|
" MAE_std_train,\n",
|
|
" MAE_avg_test,\n",
|
|
" MAE_std_test,\n",
|
|
" ) = AminoAcid_LR(i)\n",
|
|
" out_file.write(\n",
|
|
" \"%s %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f %5.2f\\n\"\n",
|
|
" % (\n",
|
|
" i,\n",
|
|
" r2_avg_train,\n",
|
|
" r2_std_train,\n",
|
|
" r2_avg_test,\n",
|
|
" r2_std_test,\n",
|
|
" MAE_avg_train,\n",
|
|
" MAE_std_train,\n",
|
|
" MAE_avg_test,\n",
|
|
" MAE_std_test,\n",
|
|
" )\n",
|
|
" + \"\\n\"\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "5224a9cf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/tmp/ipykernel_27872/3337223957.py:8: FutureWarning: The squeeze argument has been deprecated and will be removed in a future version. Append .squeeze(\"columns\") to the call to squeeze.\n",
|
|
"\n",
|
|
"\n",
|
|
" df=pd.DataFrame(pd.read_csv('LR_modify_result.csv',header=None, comment=\"#\",sep='\\s+',\n",
|
|
"/tmp/ipykernel_27872/3337223957.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
|
|
" ax[0].set_yticklabels(df.AminoAcid, fontsize=12)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
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\n",
|
|
"text/plain": [
|
|
"<Figure size 1200x1000 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"flatui = [\n",
|
|
" \"black\",\n",
|
|
" \"grey\",\n",
|
|
" \"rosybrown\",\n",
|
|
" \"darkred\",\n",
|
|
" \"indianred\",\n",
|
|
" \"salmon\",\n",
|
|
" \"red\",\n",
|
|
" \"coral\",\n",
|
|
" \"tan\",\n",
|
|
" \"gold\",\n",
|
|
" \"y\",\n",
|
|
" \"olive\",\n",
|
|
" \"yellow\",\n",
|
|
" \"greenyellow\",\n",
|
|
" \"darkgreen\",\n",
|
|
" \"lime\",\n",
|
|
" \"lightseagreen\",\n",
|
|
" \"aqua\",\n",
|
|
" \"lightsteelblue\",\n",
|
|
" \"deepskyblue\",\n",
|
|
" \"royalblue\",\n",
|
|
" \"slateblue\",\n",
|
|
" \"violet\",\n",
|
|
" \"magenta\",\n",
|
|
" \"deeppink\",\n",
|
|
" \"pink\",\n",
|
|
" \"crimson\",\n",
|
|
"]\n",
|
|
"\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(1, 4, figsize=(12, 10), sharey=True)\n",
|
|
"fig.subplots_adjust(left=0.06, right=0.95, wspace=0.15)\n",
|
|
"df = pd.DataFrame(\n",
|
|
" pd.read_csv(\n",
|
|
" \"LR_modify_result.csv\",\n",
|
|
" header=None,\n",
|
|
" comment=\"#\",\n",
|
|
" sep=\"\\s+\",\n",
|
|
" squeeze=True,\n",
|
|
" names=[\n",
|
|
" \"AminoAcid\",\n",
|
|
" \"r2_avg_train\",\n",
|
|
" \"r2_std_train\",\n",
|
|
" \"r2_avg_test\",\n",
|
|
" \"r2_std_test\",\n",
|
|
" \"MAE_avg_train\",\n",
|
|
" \"MAE_std_train\",\n",
|
|
" \"MAE_avg_test\",\n",
|
|
" \"MAE_std_test\",\n",
|
|
" ],\n",
|
|
" )\n",
|
|
")\n",
|
|
"ax[0].barh(df.AminoAcid, df.r2_avg_train, xerr=df.r2_std_train, color=flatui)\n",
|
|
"ax[0].set_xlabel(\"$R^2$ score Train\", fontsize=14)\n",
|
|
"ax[0].set_xlim(0, 1)\n",
|
|
"ax[0].set_yticklabels(df.AminoAcid, fontsize=12)\n",
|
|
"\n",
|
|
"ax[1].barh(df.AminoAcid, df.r2_avg_test, xerr=df.r2_std_test, color=flatui)\n",
|
|
"ax[1].set_xlabel(\"$R^2$ score Test\", fontsize=14)\n",
|
|
"ax[1].set_xlim(-1, 1)\n",
|
|
"\n",
|
|
"ax[2].barh(df.AminoAcid, df.MAE_avg_train, xerr=df.MAE_std_train, color=flatui)\n",
|
|
"ax[2].set_xlabel(\"MAE (kJ/mol) Train\", fontsize=14)\n",
|
|
"ax[2].set_xlim(0, 4)\n",
|
|
"\n",
|
|
"ax[3].barh(df.AminoAcid, df.MAE_avg_test, xerr=df.MAE_std_test, color=flatui)\n",
|
|
"ax[3].set_xlabel(\"MAE (kJ/mol) Test\", fontsize=14)\n",
|
|
"ax[3].set_xlim(0, 4)\n",
|
|
"\n",
|
|
"ax[0].set_ylabel(\"Biomolecules\", fontsize=14)\n",
|
|
"plt.savefig(\"LR_modify_result.png\", format=\"png\", dpi=1000, bbox_inches=\"tight\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "31539f92",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Predicted vs Real vales for testing data set (one of 10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "d7f2159d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def Mary_pred(i):\n",
|
|
" Y = X[i]\n",
|
|
" Xtrain, xtest, Ytrain, ytest = train_test_split(\n",
|
|
" X_LR, Y, test_size=0.3, random_state=99\n",
|
|
" )\n",
|
|
" model = LinearRegression()\n",
|
|
" model.fit(Xtrain, Ytrain)\n",
|
|
" ytest_pred = model.predict(xtest)\n",
|
|
" test_score = np.round(model.score(xtest, ytest), 2)\n",
|
|
" d = {\n",
|
|
" \"Aminoacid\": i,\n",
|
|
" \"Predict\": list(ytest_pred),\n",
|
|
" \"Real\": list(ytest),\n",
|
|
" \"score\": test_score,\n",
|
|
" \"Method\": \"LR\",\n",
|
|
" }\n",
|
|
" return d"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "28b7aae8",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"main = [\"ASP\", \"VAL\", \"PRO\", \"ETA\", \"PHO\"]\n",
|
|
"rest = [i for i in aminacid_order if i not in main]\n",
|
|
"X_LR = X[main]\n",
|
|
"df_list = []\n",
|
|
"for i in rest:\n",
|
|
" df = pd.DataFrame.from_dict(Mary_pred(i))\n",
|
|
" df_list.append(df)\n",
|
|
"result = pd.concat(df_list, ignore_index=True)\n",
|
|
"result.to_csv(\"LR_ypredict-yreal.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "cbe04bac",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Combing results of other modelling and make a figure of predicted vs real modelling "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "c2d82d6c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"profile1 = pd.DataFrame(\n",
|
|
" pd.read_csv(\"LR_ypredict-yreal.csv\"),\n",
|
|
" columns=[\"Aminoacid\", \"Predict\", \"Real\", \"score\", \"Method\"],\n",
|
|
")\n",
|
|
"profile2 = pd.DataFrame(\n",
|
|
" pd.read_csv(\"AdaBoost_ypredict-yreal.csv\"),\n",
|
|
" columns=[\"Aminoacid\", \"Predict\", \"Real\", \"score\", \"Method\"],\n",
|
|
")\n",
|
|
"profile3 = pd.DataFrame(\n",
|
|
" pd.read_csv(\"NN_ypredict-yreal.csv\"),\n",
|
|
" columns=[\"Aminoacid\", \"Predict\", \"Real\", \"score\", \"Method\"],\n",
|
|
")\n",
|
|
"profiletot = pd.concat([profile1, profile2, profile3])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "48e50d66",
|
|
"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>Predict</th>\n",
|
|
" <th>Real</th>\n",
|
|
" <th>score</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Method</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>AdaBoost</th>\n",
|
|
" <td>-3.208497</td>\n",
|
|
" <td>-3.446067</td>\n",
|
|
" <td>0.851852</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>LR</th>\n",
|
|
" <td>-4.175035</td>\n",
|
|
" <td>-4.255783</td>\n",
|
|
" <td>0.891111</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NN</th>\n",
|
|
" <td>-2.823465</td>\n",
|
|
" <td>-2.907650</td>\n",
|
|
" <td>0.772963</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Predict Real score\n",
|
|
"Method \n",
|
|
"AdaBoost -3.208497 -3.446067 0.851852\n",
|
|
"LR -4.175035 -4.255783 0.891111\n",
|
|
"NN -2.823465 -2.907650 0.772963"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"profiletot.groupby(\"Method\").mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 77,
|
|
"id": "ef0f0d0a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1050x700 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"flatui = [\n",
|
|
" \"black\",\n",
|
|
" \"grey\",\n",
|
|
" \"rosybrown\",\n",
|
|
" \"darkred\",\n",
|
|
" \"indianred\",\n",
|
|
" \"salmon\",\n",
|
|
" \"red\",\n",
|
|
" \"coral\",\n",
|
|
" \"tan\",\n",
|
|
" \"gold\",\n",
|
|
" \"y\",\n",
|
|
" \"olive\",\n",
|
|
" \"yellow\",\n",
|
|
" \"greenyellow\",\n",
|
|
" \"darkgreen\",\n",
|
|
" \"lime\",\n",
|
|
" \"lightseagreen\",\n",
|
|
" \"aqua\",\n",
|
|
" \"lightsteelblue\",\n",
|
|
" \"deepskyblue\",\n",
|
|
" \"royalblue\",\n",
|
|
" \"slateblue\",\n",
|
|
" \"violet\",\n",
|
|
" \"magenta\",\n",
|
|
" \"deeppink\",\n",
|
|
" \"pink\",\n",
|
|
" \"crimson\",\n",
|
|
"]\n",
|
|
"\n",
|
|
"with sns.plotting_context(\"notebook\", font_scale=1.0):\n",
|
|
" g = sns.FacetGrid(\n",
|
|
" profiletot,\n",
|
|
" hue=\"Aminoacid\",\n",
|
|
" col=\"Method\",\n",
|
|
" col_wrap=2,\n",
|
|
" margin_titles=False,\n",
|
|
" palette=sns.color_palette(flatui),\n",
|
|
" xlim=(-28, 6.6),\n",
|
|
" ylim=(-28, 6.6),\n",
|
|
" aspect=1.5,\n",
|
|
" height=3.5,\n",
|
|
" sharex=False,\n",
|
|
" sharey=True,\n",
|
|
" )\n",
|
|
"g.map(plt.scatter, \"Predict\", \"Real\", marker=\".\", s=200)\n",
|
|
"\n",
|
|
"\n",
|
|
"M = [0.89, 0.85, 0.77]\n",
|
|
"col_n = 0\n",
|
|
"for ax in g.fig.axes:\n",
|
|
" ax.plot(list(np.arange(-28, 7, 1)), list(np.arange(-28, 7, 1)), color=\"black\")\n",
|
|
" ax.text(-5, -20, \"$R^2$ score=\", fontsize=11)\n",
|
|
" ax.text(2.15, -20, M[col_n], fontsize=11)\n",
|
|
" col_n += 1\n",
|
|
"\n",
|
|
"\n",
|
|
"g.set_axis_labels(\n",
|
|
" r\"Predicted binding free energy (kJ/mol)\",\n",
|
|
" r\"MetaD binding free energy (kJ/mol)\",\n",
|
|
" fontsize=11,\n",
|
|
")\n",
|
|
"g.set_titles(r\"Method={col_name}\", loc=\"center\", y=0.90, size=11)\n",
|
|
"g.fig.tight_layout(pad=5, h_pad=1, w_pad=2)\n",
|
|
"plt.legend(title=\"Biomolecules\", ncol=4, loc=\"upper right\", bbox_to_anchor=(2.1, 0.97))\n",
|
|
"g.savefig(\"tot_ypredict-yreal-pic.pdf\", dpi=1000)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6c4ea2c1",
|
|
"metadata": {},
|
|
"source": [
|
|
"# LinearRegression modelling performance for all biomolecules"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "08a11795",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
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"/tmp/ipykernel_14960/4048209594.py:1: FutureWarning: The squeeze argument has been deprecated and will be removed in a future version. Append .squeeze(\"columns\") to the call to squeeze.\n",
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"\n",
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"\n",
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" df=pd.DataFrame(pd.read_csv('LR_result.csv',header=None, comment=\"#\",sep='\\s+',\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>r2_avg_train</th>\n",
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" <th>r2_std_train</th>\n",
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" <th>r2_avg_test</th>\n",
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" <th>r2_std_test</th>\n",
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" <th>MAE_avg_train</th>\n",
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" <th>MAE_std_train</th>\n",
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" <th>MAE_avg_test</th>\n",
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" <th>MAE_std_test</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" <td>29.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>0.906207</td>\n",
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" <td>0.021034</td>\n",
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" <td>0.794828</td>\n",
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" <td>0.152414</td>\n",
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" <td>0.877241</td>\n",
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" <td>0.121379</td>\n",
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" <td>1.026552</td>\n",
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" <td>0.265862</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>0.150176</td>\n",
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" <td>0.026772</td>\n",
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" <td>0.377128</td>\n",
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" <td>0.304955</td>\n",
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" <td>0.663227</td>\n",
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" <td>0.094858</td>\n",
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" <td>0.806072</td>\n",
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" <td>0.207149</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>0.420000</td>\n",
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" <td>0.000000</td>\n",
|
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" <td>-0.690000</td>\n",
|
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" <td>0.010000</td>\n",
|
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" <td>0.170000</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.190000</td>\n",
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" <td>0.030000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>0.930000</td>\n",
|
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" <td>0.010000</td>\n",
|
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" <td>0.900000</td>\n",
|
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" <td>0.020000</td>\n",
|
|
" <td>0.330000</td>\n",
|
|
" <td>0.040000</td>\n",
|
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" <td>0.360000</td>\n",
|
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" <td>0.090000</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>50%</th>\n",
|
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" <td>0.960000</td>\n",
|
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" <td>0.010000</td>\n",
|
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" <td>0.930000</td>\n",
|
|
" <td>0.050000</td>\n",
|
|
" <td>0.650000</td>\n",
|
|
" <td>0.100000</td>\n",
|
|
" <td>0.800000</td>\n",
|
|
" <td>0.220000</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>75%</th>\n",
|
|
" <td>0.980000</td>\n",
|
|
" <td>0.030000</td>\n",
|
|
" <td>0.970000</td>\n",
|
|
" <td>0.100000</td>\n",
|
|
" <td>1.270000</td>\n",
|
|
" <td>0.140000</td>\n",
|
|
" <td>1.310000</td>\n",
|
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" <td>0.340000</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>max</th>\n",
|
|
" <td>0.990000</td>\n",
|
|
" <td>0.120000</td>\n",
|
|
" <td>0.990000</td>\n",
|
|
" <td>1.560000</td>\n",
|
|
" <td>2.700000</td>\n",
|
|
" <td>0.400000</td>\n",
|
|
" <td>3.100000</td>\n",
|
|
" <td>0.790000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
|
" r2_avg_train r2_std_train r2_avg_test r2_std_test MAE_avg_train \\\n",
|
|
"count 29.000000 29.000000 29.000000 29.000000 29.000000 \n",
|
|
"mean 0.906207 0.021034 0.794828 0.152414 0.877241 \n",
|
|
"std 0.150176 0.026772 0.377128 0.304955 0.663227 \n",
|
|
"min 0.420000 0.000000 -0.690000 0.010000 0.170000 \n",
|
|
"25% 0.930000 0.010000 0.900000 0.020000 0.330000 \n",
|
|
"50% 0.960000 0.010000 0.930000 0.050000 0.650000 \n",
|
|
"75% 0.980000 0.030000 0.970000 0.100000 1.270000 \n",
|
|
"max 0.990000 0.120000 0.990000 1.560000 2.700000 \n",
|
|
"\n",
|
|
" MAE_std_train MAE_avg_test MAE_std_test \n",
|
|
"count 29.000000 29.000000 29.000000 \n",
|
|
"mean 0.121379 1.026552 0.265862 \n",
|
|
"std 0.094858 0.806072 0.207149 \n",
|
|
"min 0.020000 0.190000 0.030000 \n",
|
|
"25% 0.040000 0.360000 0.090000 \n",
|
|
"50% 0.100000 0.800000 0.220000 \n",
|
|
"75% 0.140000 1.310000 0.340000 \n",
|
|
"max 0.400000 3.100000 0.790000 "
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
|
],
|
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"source": [
|
|
"df = pd.DataFrame(\n",
|
|
" pd.read_csv(\n",
|
|
" \"LR_result.csv\",\n",
|
|
" header=None,\n",
|
|
" comment=\"#\",\n",
|
|
" sep=\"\\s+\",\n",
|
|
" squeeze=True,\n",
|
|
" names=[\n",
|
|
" \"AminoAcid\",\n",
|
|
" \"r2_avg_train\",\n",
|
|
" \"r2_std_train\",\n",
|
|
" \"r2_avg_test\",\n",
|
|
" \"r2_std_test\",\n",
|
|
" \"MAE_avg_train\",\n",
|
|
" \"MAE_std_train\",\n",
|
|
" \"MAE_avg_test\",\n",
|
|
" \"MAE_std_test\",\n",
|
|
" ],\n",
|
|
" )\n",
|
|
")\n",
|
|
"df.describe()"
|
|
]
|
|
}
|
|
],
|
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"metadata": {
|
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"kernelspec": {
|
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"display_name": "Python 3",
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"language": "python",
|
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"name": "python3"
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},
|
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"language_info": {
|
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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