From b105fbf4f36dc7510627a2cc5aa22512e0ccce05 Mon Sep 17 00:00:00 2001
From: Maike Vahl <m.vahl@tu-braunschweig.de>
Date: Tue, 3 Oct 2023 08:44:02 +0000
Subject: [PATCH] Delete QSRR_MLR_notebook.ipynb
---
QSRR_MLR_notebook.ipynb | 348 ----------------------------------------
1 file changed, 348 deletions(-)
delete mode 100644 QSRR_MLR_notebook.ipynb
diff --git a/QSRR_MLR_notebook.ipynb b/QSRR_MLR_notebook.ipynb
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "5d630471",
- "metadata": {},
- "source": [
- "# Notebook to perform multivariate linear regression (MLR)\n",
- "\n",
- "A regression class is provided. By initializing, two settings need to be specified:\n",
- "1) Standardization True/False: Whether the data is standardized before the regression task\\\n",
- "2) Choice for linear model: linBay=True -> BayesianRidge module, linBay=False -> LinearRegression module"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e4d6a6e5",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import pickle as pkl\n",
- "\n",
- "from sklearn.linear_model import LinearRegression, BayesianRidge\n",
- "from sklearn.preprocessing import PolynomialFeatures, StandardScaler"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6f9e97a4",
- "metadata": {},
- "source": [
- "### Data preparation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "42aaafae",
- "metadata": {},
- "outputs": [],
- "source": [
- "# read in pandas dataframe\n",
- "df = pd.read_pickle('QC_and_descriptor_dataframe.pkl') # M=3570 molecules\n",
- "df_ref = pd.read_pickle('QC_and_descriptor_dataframe_ref.pkl') # K=27 molecules\n",
- "\n",
- "df_noRef = df.drop(index=df_ref.index.values).copy() # M-K=L=3543 molecules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8378b6c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "# get descriptor values from dataframes, exemplary for C_FG\n",
- "X_raw = np.stack(df_noRef.C_FG.values) # L=3543\n",
- "X_ref_raw = np.stack(df_ref.C_FG.values) # K=27"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a50f3bbe",
- "metadata": {},
- "outputs": [],
- "source": [
- "# get HOMO and LUMO energies from dataframes\n",
- "y_LU = df_noRef.ELUMO.values # L=3545\n",
- "y_HO = df_noRef.EHOMO.values # L=3545\n",
- "\n",
- "y_ref_LU = df_ref.ELUMO.values # K=27\n",
- "y_ref_HO = df_ref.EHOMO.values # K=27"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ab5e62b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# get experimental E parameters for K=27 reference molecules\n",
- "E = df_ref.E2012.values.astype(float)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "addc2d78",
- "metadata": {},
- "outputs": [],
- "source": [
- "# data preparation\n",
- "X = PolynomialFeatures(1).fit_transform(StandardScaler().fit_transform(X_raw))\n",
- "X_ref = PolynomialFeatures(1).fit_transform(StandardScaler().fit_transform(X_ref_raw))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f8515261",
- "metadata": {},
- "source": [
- "### Regression class"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f0032879",
- "metadata": {},
- "outputs": [],
- "source": [
- "class regression:\n",
- " \n",
- " def __init__(self, HO_ref, LU_ref, std=True, linBay=False):\n",
- " \n",
- " self.LU_ref = LU_ref\n",
- " self.HO_ref = HO_ref\n",
- " \n",
- " self.std = std\n",
- " self.linBay = linBay\n",
- " \n",
- " # prepare for eq 6 (main text)\n",
- " set_scaler = np.append(self.HO_ref.reshape(-1,1), self.LU_ref.reshape(-1,1), axis=1)\n",
- " set_scaler = PolynomialFeatures(2).fit_transform(set_scaler)\n",
- " set_scaler = np.append(set_scaler, (1 / (self.LU_ref - self.HO_ref)).reshape(-1,1), axis=1)\n",
- " \n",
- " self.CDFT_scaler = StandardScaler().fit(set_scaler[:,1:])\n",
- "\n",
- " #-----------------------------------------------------------------------------#\n",
- " \n",
- " def RMSE(self, f, y):\n",
- " \n",
- " '''Calculating the root-mean-square error (RMSE).'''\n",
- " \n",
- " return np.sqrt(np.mean((y.flatten() - f.flatten())**2))\n",
- " \n",
- " #-----------------------------------------------------------------------------#\n",
- " \n",
- " def r2(self, f, y):\n",
- " \n",
- " '''Calculating the squared correlation coefficient.'''\n",
- " \n",
- " return np.corrcoef(f.flatten(), y.flatten())[0,1]**2\n",
- " \n",
- " #-----------------------------------------------------------------------------#\n",
- " \n",
- " def R2(self, f, y):\n",
- " \n",
- " '''Calculating coefficient of determination R^2 values.'''\n",
- " \n",
- " return (1 - (self.RMSE(f, y)**2/(self.RMSE(np.mean(y), y)**2)))\n",
- " \n",
- " #-----------------------------------------------------------------------------# \n",
- " \n",
- " def cdft_features(self, HO, LU):\n",
- " \n",
- " '''Preparing frontier molecular orbital energies for eq 6 (main text).'''\n",
- " \n",
- " X_cdft_full = np.append(HO.reshape(-1,1), LU.reshape(-1,1), axis=1)\n",
- " X_cdft_full = PolynomialFeatures(2).fit_transform(X_cdft_full)\n",
- " X_cdft_full = np.append(X_cdft_full, (1 / (LU - HO)).reshape(-1,1), axis=1)\n",
- " \n",
- " if self.std == True:\n",
- " print('std=True')\n",
- " X_cdft_full = X_cdft_full[:,1:]\n",
- " \n",
- " X_cdft_full = self.CDFT_scaler.transform(X_cdft_full)\n",
- " X_cdft_full = StandardScaler().fit_transform(X_cdft_full)\n",
- " X_cdft_full = PolynomialFeatures(1).fit_transform(X_cdft_full)\n",
- " \n",
- " return X_cdft_full\n",
- " \n",
- " #-----------------------------------------------------------------------------#\n",
- " \n",
- " def regression(self, X, y):\n",
- " \n",
- " '''Performing the MLR task. Returning the model, the predictions, \n",
- " and values for coefficient of determination and RMSE.'''\n",
- " \n",
- " if self.linBay == True:\n",
- " model = BayesianRidge(fit_intercept=False, lambda_init=1e-3).fit(X, y) \n",
- " print(model)\n",
- " else:\n",
- " model = LinearRegression(fit_intercept=False).fit(X, y) \n",
- " print(model)\n",
- " \n",
- " y_pred = model.predict(X) \n",
- " R2 = self.R2(y_pred, y) \n",
- " RMSE = self.RMSE(y_pred, y) \n",
- " \n",
- " return model, y_pred, R2, RMSE\n",
- " \n",
- " #-----------------------------------------------------------------------------#"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "57e11442",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build class regression with specified input\n",
- "self = regression(y_ref_HO, y_ref_LU, std=True, linBay=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aa4eca68",
- "metadata": {},
- "source": [
- "## The second step (QMP to $E$) \n",
- "\n",
- "Reference MLR (rMLR)\\\n",
- "Final settings: std=True, linBay=False"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c3926ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "# rMLR model trained on K=27 molecules:\n",
- "X_rMLR = self.cdft_features(y_ref_HO, y_ref_LU)\n",
- "model_rMLR, y_pred_rMLR, R2_rMLR, RMSE_rMLR = self.regression(X_rMLR, E)\n",
- "rMLR_coefs = model_rMLR.coef_.copy()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e2337912",
- "metadata": {},
- "source": [
- "## The first step (structure to QMP) \n",
- "\n",
- "Path A and path B:"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b23ae7a2",
- "metadata": {},
- "source": [
- "### Path A"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6f93524f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# calculation of E for non-reference values\n",
- "# prepare QC values (for eq 6, main text)\n",
- "X_cdft_QC = self.cdft_features(y_HO, y_LU)\n",
- "E_ML_QC = model_rMLR.predict(X_cdft_QC)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4b30ce63",
- "metadata": {},
- "outputs": [],
- "source": [
- "# path A: set up MLR model\n",
- "model_A, y_pred_A, R2_A, RMSE_A = self.regression(X, E_ML_QC)\n",
- "\n",
- "# path A: predictions for training (L=3543) and test (K=27) data\n",
- "pred_A = model_A.predict(X)\n",
- "pred_ref_A = model_A.predict(X_ref)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8ac8ace1",
- "metadata": {},
- "source": [
- "### Path B"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "20056317",
- "metadata": {},
- "outputs": [],
- "source": [
- "# path B: set up MLR models\n",
- "model_LU, y_pred_LU, R2_LU, RMSE_LU = self.regression(X, y_LU)\n",
- "model_HO, y_pred_HO, R2_HO, RMSE_HO = self.regression(X, y_HO)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aa0e1a12",
- "metadata": {},
- "outputs": [],
- "source": [
- "# path B: predictions of LUMO and HOMO energies for K=27 molecules \n",
- "pred_ref_LU = model_LU.predict(X_ref)\n",
- "pred_ref_HO = model_HO.predict(X_ref)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b1e857e2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# path B: predictions of E with rMLR\n",
- "X_cdft_ML_B = self.cdft_features(pred_ref_HO, pred_ref_LU)\n",
- "E_ML_B = model_rMLR.predict(X_cdft_ML_B)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
--
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