"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
From c860a556c8b4bc6ba483dc06734f5553ce540cca Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Tue, 7 Jul 2020 16:45:55 +0700
Subject: [PATCH 02/44] Add files via upload
---
streamlit/basketball_app.py | 68 +++++++++++++++++++++++++++++++++++++
1 file changed, 68 insertions(+)
create mode 100644 streamlit/basketball_app.py
diff --git a/streamlit/basketball_app.py b/streamlit/basketball_app.py
new file mode 100644
index 0000000..9dc7283
--- /dev/null
+++ b/streamlit/basketball_app.py
@@ -0,0 +1,68 @@
+import streamlit as st
+import pandas as pd
+import base64
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+st.title('NBA Player Stats Explorer')
+
+st.markdown("""
+This app performs simple webscraping of NBA player stats data!
+* **Python libraries:** base64, pandas, streamlit
+* **Data source:** [Basketball-reference.com](https://www.basketball-reference.com/).
+""")
+
+st.sidebar.header('User Input Features')
+selected_year = st.sidebar.selectbox('Year', list(reversed(range(1950,2020))))
+
+# Web scraping of NBA player stats
+@st.cache
+def load_data(year):
+ url = "https://www.basketball-reference.com/leagues/NBA_" + str(year) + "_per_game.html"
+ html = pd.read_html(url, header = 0)
+ df = html[0]
+ raw = df.drop(df[df.Age == 'Age'].index) # Deletes repeating headers in content
+ raw = raw.fillna(0)
+ playerstats = raw.drop(['Rk'], axis=1)
+ return playerstats
+playerstats = load_data(selected_year)
+
+# Sidebar - Team selection
+sorted_unique_team = sorted(playerstats.Tm.unique())
+selected_team = st.sidebar.multiselect('Team', sorted_unique_team, sorted_unique_team)
+
+# Sidebar - Position selection
+unique_pos = ['C','PF','SF','PG','SG']
+selected_pos = st.sidebar.multiselect('Position', unique_pos, unique_pos)
+
+# Filtering data
+df_selected_team = playerstats[(playerstats.Tm.isin(selected_team)) & (playerstats.Pos.isin(selected_pos))]
+
+st.header('Display Player Stats of Selected Team(s)')
+st.write('Data Dimension: ' + str(df_selected_team.shape[0]) + ' rows and ' + str(df_selected_team.shape[1]) + ' columns.')
+st.dataframe(df_selected_team)
+
+# Download NBA player stats data
+# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806
+def filedownload(df):
+ csv = df.to_csv(index=False)
+ b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions
+ href = f'Download CSV File'
+ return href
+
+st.markdown(filedownload(df_selected_team), unsafe_allow_html=True)
+
+# Heatmap
+if st.button('Intercorrelation Heatmap'):
+ st.header('Intercorrelation Matrix Heatmap')
+ df_selected_team.to_csv('output.csv',index=False)
+ df = pd.read_csv('output.csv')
+
+ corr = df.corr()
+ mask = np.zeros_like(corr)
+ mask[np.triu_indices_from(mask)] = True
+ with sns.axes_style("white"):
+ f, ax = plt.subplots(figsize=(7, 5))
+ ax = sns.heatmap(corr, mask=mask, vmax=1, square=True)
+ st.pyplot()
From 87f2698fa3fa236a060f6e48ec5ce0a5dc3bb5ba Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Tue, 7 Jul 2020 16:46:37 +0700
Subject: [PATCH 03/44] Rename streamlit/basketball_app.py to
streamlit/part-5/basketball_app.py
---
streamlit/{ => part-5}/basketball_app.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename streamlit/{ => part-5}/basketball_app.py (100%)
diff --git a/streamlit/basketball_app.py b/streamlit/part-5/basketball_app.py
similarity index 100%
rename from streamlit/basketball_app.py
rename to streamlit/part-5/basketball_app.py
From c100977a8a15255b9654e12a2f81b5eca8ad410d Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Tue, 7 Jul 2020 16:46:58 +0700
Subject: [PATCH 04/44] Rename streamlit/part-5/basketball_app.py to
streamlit/part5/basketball_app.py
---
streamlit/{part-5 => part5}/basketball_app.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename streamlit/{part-5 => part5}/basketball_app.py (100%)
diff --git a/streamlit/part-5/basketball_app.py b/streamlit/part5/basketball_app.py
similarity index 100%
rename from streamlit/part-5/basketball_app.py
rename to streamlit/part5/basketball_app.py
From 13876b6ae82a0c4daa908436be0d107b9757434e Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Sun, 9 Aug 2020 17:00:33 +0700
Subject: [PATCH 05/44] Add files via upload
---
...cs_predicting_solubility_2_1_PyCaret.ipynb | 1306 +++++
...cs_predicting_solubility_2_2_PyCaret.ipynb | 4747 +++++++++++++++++
2 files changed, 6053 insertions(+)
create mode 100644 python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb
create mode 100644 python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb
diff --git a/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb b/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb
new file mode 100644
index 0000000..c01bb58
--- /dev/null
+++ b/python/cheminformatics_predicting_solubility_2_1_PyCaret.ipynb
@@ -0,0 +1,1306 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "2.1-Cheminformatics-PyCaret-predicting-solubility.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OQi3X7TNUl5Y",
+ "colab_type": "text"
+ },
+ "source": [
+ "# **Cheminformatics in Python [PART 2.1] Predicting Solubility of Molecules using PyCaret | End-to-End Data Science Project** \n",
+ "\n",
+ "Chanin Nantasenamat\n",
+ "\n",
+ "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n",
+ "\n",
+ "In this Jupyter notebook, we will continue our journey into the world of Cheminformatics (i.e. lies at the interface of Informatics and Chemistry) by simplifying this notebook via the use of the low-code machine learning library PyCaret.\n",
+ "\n",
+ "\n",
+ "**Information from the previous notebook:**\n",
+ "\n",
+ "We will be reproducing a research article (by John S. Delaney$^1$) by applying Linear Regression to predict the solubility of molecules (i.e. solubility of drugs is an important physicochemical property in Drug discovery, design and development).\n",
+ "\n",
+ "This idea for this notebook was inspired by the excellent blog post by Pat Walters$^2$ where he reproduced the linear regression model with similar degree of performance as that of Delaney. This example is also briefly described in the book ***Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More***.$^3$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AQW_Ts66R4Ms",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **1. Install conda and libraries**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-jNwdYoBR8ea",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "844c8e71-85af-48ea-99ea-1b5b01216feb"
+ },
+ "source": [
+ "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
+ "! chmod +x Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
+ "! bash ./Miniconda3-py37_4.8.2-Linux-x86_64.sh -b -f -p /usr/local\n",
+ "! conda install -c rdkit rdkit -y\n",
+ "import sys\n",
+ "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "--2020-07-28 02:18:57-- https://repo.anaconda.com/miniconda/Miniconda3-py37_4.8.2-Linux-x86_64.sh\n",
+ "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...\n",
+ "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 85055499 (81M) [application/x-sh]\n",
+ "Saving to: ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’\n",
+ "\n",
+ "\r Miniconda 0%[ ] 0 --.-KB/s \r Miniconda3 16%[==> ] 13.65M 68.3MB/s \r Miniconda3- 80%[===============> ] 65.22M 163MB/s \rMiniconda3-py37_4.8 100%[===================>] 81.12M 181MB/s in 0.4s \n",
+ "\n",
+ "2020-07-28 02:18:58 (181 MB/s) - ‘Miniconda3-py37_4.8.2-Linux-x86_64.sh’ saved [85055499/85055499]\n",
+ "\n",
+ "PREFIX=/usr/local\n",
+ "Unpacking payload ...\n",
+ "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n",
+ "Solving environment: - \b\b\\ \b\bdone\n",
+ "\n",
+ "## Package Plan ##\n",
+ "\n",
+ " environment location: /usr/local\n",
+ "\n",
+ " added / updated specs:\n",
+ " - _libgcc_mutex==0.1=main\n",
+ " - asn1crypto==1.3.0=py37_0\n",
+ " - ca-certificates==2020.1.1=0\n",
+ " - certifi==2019.11.28=py37_0\n",
+ " - cffi==1.14.0=py37h2e261b9_0\n",
+ " - chardet==3.0.4=py37_1003\n",
+ " - conda-package-handling==1.6.0=py37h7b6447c_0\n",
+ " - conda==4.8.2=py37_0\n",
+ " - cryptography==2.8=py37h1ba5d50_0\n",
+ " - idna==2.8=py37_0\n",
+ " - ld_impl_linux-64==2.33.1=h53a641e_7\n",
+ " - libedit==3.1.20181209=hc058e9b_0\n",
+ " - libffi==3.2.1=hd88cf55_4\n",
+ " - libgcc-ng==9.1.0=hdf63c60_0\n",
+ " - libstdcxx-ng==9.1.0=hdf63c60_0\n",
+ " - ncurses==6.2=he6710b0_0\n",
+ " - openssl==1.1.1d=h7b6447c_4\n",
+ " - pip==20.0.2=py37_1\n",
+ " - pycosat==0.6.3=py37h7b6447c_0\n",
+ " - pycparser==2.19=py37_0\n",
+ " - pyopenssl==19.1.0=py37_0\n",
+ " - pysocks==1.7.1=py37_0\n",
+ " - python==3.7.6=h0371630_2\n",
+ " - readline==7.0=h7b6447c_5\n",
+ " - requests==2.22.0=py37_1\n",
+ " - ruamel_yaml==0.15.87=py37h7b6447c_0\n",
+ " - setuptools==45.2.0=py37_0\n",
+ " - six==1.14.0=py37_0\n",
+ " - sqlite==3.31.1=h7b6447c_0\n",
+ " - tk==8.6.8=hbc83047_0\n",
+ " - tqdm==4.42.1=py_0\n",
+ " - urllib3==1.25.8=py37_0\n",
+ " - wheel==0.34.2=py37_0\n",
+ " - xz==5.2.4=h14c3975_4\n",
+ " - yaml==0.1.7=had09818_2\n",
+ " - zlib==1.2.11=h7b6447c_3\n",
+ "\n",
+ "\n",
+ "The following NEW packages will be INSTALLED:\n",
+ "\n",
+ " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
+ " asn1crypto pkgs/main/linux-64::asn1crypto-1.3.0-py37_0\n",
+ " ca-certificates pkgs/main/linux-64::ca-certificates-2020.1.1-0\n",
+ " certifi pkgs/main/linux-64::certifi-2019.11.28-py37_0\n",
+ " cffi pkgs/main/linux-64::cffi-1.14.0-py37h2e261b9_0\n",
+ " chardet pkgs/main/linux-64::chardet-3.0.4-py37_1003\n",
+ " conda pkgs/main/linux-64::conda-4.8.2-py37_0\n",
+ " conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0\n",
+ " cryptography pkgs/main/linux-64::cryptography-2.8-py37h1ba5d50_0\n",
+ " idna pkgs/main/linux-64::idna-2.8-py37_0\n",
+ " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7\n",
+ " libedit pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0\n",
+ " libffi pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4\n",
+ " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0\n",
+ " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0\n",
+ " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_0\n",
+ " openssl pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_4\n",
+ " pip pkgs/main/linux-64::pip-20.0.2-py37_1\n",
+ " pycosat pkgs/main/linux-64::pycosat-0.6.3-py37h7b6447c_0\n",
+ " pycparser pkgs/main/linux-64::pycparser-2.19-py37_0\n",
+ " pyopenssl pkgs/main/linux-64::pyopenssl-19.1.0-py37_0\n",
+ " pysocks pkgs/main/linux-64::pysocks-1.7.1-py37_0\n",
+ " python pkgs/main/linux-64::python-3.7.6-h0371630_2\n",
+ " readline pkgs/main/linux-64::readline-7.0-h7b6447c_5\n",
+ " requests pkgs/main/linux-64::requests-2.22.0-py37_1\n",
+ " ruamel_yaml pkgs/main/linux-64::ruamel_yaml-0.15.87-py37h7b6447c_0\n",
+ " setuptools pkgs/main/linux-64::setuptools-45.2.0-py37_0\n",
+ " six pkgs/main/linux-64::six-1.14.0-py37_0\n",
+ " sqlite pkgs/main/linux-64::sqlite-3.31.1-h7b6447c_0\n",
+ " tk pkgs/main/linux-64::tk-8.6.8-hbc83047_0\n",
+ " tqdm pkgs/main/noarch::tqdm-4.42.1-py_0\n",
+ " urllib3 pkgs/main/linux-64::urllib3-1.25.8-py37_0\n",
+ " wheel pkgs/main/linux-64::wheel-0.34.2-py37_0\n",
+ " xz pkgs/main/linux-64::xz-5.2.4-h14c3975_4\n",
+ " yaml pkgs/main/linux-64::yaml-0.1.7-had09818_2\n",
+ " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n",
+ "\n",
+ "\n",
+ "Preparing transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n",
+ "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
+ "installation finished.\n",
+ "WARNING:\n",
+ " You currently have a PYTHONPATH environment variable set. This may cause\n",
+ " unexpected behavior when running the Python interpreter in Miniconda3.\n",
+ " For best results, please verify that your PYTHONPATH only points to\n",
+ " directories of packages that are compatible with the Python interpreter\n",
+ " in Miniconda3: /usr/local\n",
+ "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
+ "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bfailed with initial frozen solve. Retrying with flexible solve.\n",
+ "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bfailed with repodata from current_repodata.json, will retry with next repodata source.\n",
+ "Collecting package metadata (repodata.json): \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
+ "Solving environment: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
+ "\n",
+ "## Package Plan ##\n",
+ "\n",
+ " environment location: /usr/local\n",
+ "\n",
+ " added / updated specs:\n",
+ " - rdkit\n",
+ "\n",
+ "\n",
+ "The following packages will be downloaded:\n",
+ "\n",
+ " package | build\n",
+ " ---------------------------|-----------------\n",
+ " blas-1.0 | mkl 6 KB\n",
+ " bzip2-1.0.8 | h7b6447c_0 78 KB\n",
+ " ca-certificates-2020.6.24 | 0 125 KB\n",
+ " cairo-1.14.12 | h8948797_3 906 KB\n",
+ " certifi-2020.6.20 | py37_0 156 KB\n",
+ " conda-4.8.3 | py37_0 2.8 MB\n",
+ " fontconfig-2.13.0 | h9420a91_0 227 KB\n",
+ " freetype-2.10.2 | h5ab3b9f_0 608 KB\n",
+ " glib-2.63.1 | h5a9c865_0 2.9 MB\n",
+ " icu-58.2 | he6710b0_3 10.5 MB\n",
+ " intel-openmp-2020.1 | 217 780 KB\n",
+ " jpeg-9b | h024ee3a_2 214 KB\n",
+ " libboost-1.67.0 | h46d08c1_4 13.0 MB\n",
+ " libgfortran-ng-7.3.0 | hdf63c60_0 1006 KB\n",
+ " libpng-1.6.37 | hbc83047_0 278 KB\n",
+ " libtiff-4.1.0 | h2733197_0 447 KB\n",
+ " libuuid-1.0.3 | h1bed415_2 15 KB\n",
+ " libxcb-1.14 | h7b6447c_0 505 KB\n",
+ " libxml2-2.9.9 | hea5a465_1 1.6 MB\n",
+ " mkl-2020.1 | 217 129.0 MB\n",
+ " mkl-service-2.3.0 | py37he904b0f_0 218 KB\n",
+ " mkl_fft-1.1.0 | py37h23d657b_0 143 KB\n",
+ " mkl_random-1.1.1 | py37h0573a6f_0 322 KB\n",
+ " numpy-1.18.5 | py37ha1c710e_0 5 KB\n",
+ " numpy-base-1.18.5 | py37hde5b4d6_0 4.1 MB\n",
+ " olefile-0.46 | py37_0 50 KB\n",
+ " openssl-1.1.1g | h7b6447c_0 2.5 MB\n",
+ " pandas-1.0.5 | py37h0573a6f_0 7.8 MB\n",
+ " pcre-8.44 | he6710b0_0 212 KB\n",
+ " pillow-7.1.2 | py37hb39fc2d_0 603 KB\n",
+ " pixman-0.40.0 | h7b6447c_0 370 KB\n",
+ " py-boost-1.67.0 | py37h04863e7_4 278 KB\n",
+ " python-dateutil-2.8.1 | py_0 215 KB\n",
+ " pytz-2020.1 | py_0 184 KB\n",
+ " rdkit-2020.03.3.0 | py37hc20afe1_1 24.8 MB rdkit\n",
+ " zstd-1.3.7 | h0b5b093_0 401 KB\n",
+ " ------------------------------------------------------------\n",
+ " Total: 207.1 MB\n",
+ "\n",
+ "The following NEW packages will be INSTALLED:\n",
+ "\n",
+ " blas pkgs/main/linux-64::blas-1.0-mkl\n",
+ " bzip2 pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0\n",
+ " cairo pkgs/main/linux-64::cairo-1.14.12-h8948797_3\n",
+ " fontconfig pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0\n",
+ " freetype pkgs/main/linux-64::freetype-2.10.2-h5ab3b9f_0\n",
+ " glib pkgs/main/linux-64::glib-2.63.1-h5a9c865_0\n",
+ " icu pkgs/main/linux-64::icu-58.2-he6710b0_3\n",
+ " intel-openmp pkgs/main/linux-64::intel-openmp-2020.1-217\n",
+ " jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2\n",
+ " libboost pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4\n",
+ " libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0\n",
+ " libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0\n",
+ " libtiff pkgs/main/linux-64::libtiff-4.1.0-h2733197_0\n",
+ " libuuid pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2\n",
+ " libxcb pkgs/main/linux-64::libxcb-1.14-h7b6447c_0\n",
+ " libxml2 pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1\n",
+ " mkl pkgs/main/linux-64::mkl-2020.1-217\n",
+ " mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0\n",
+ " mkl_fft pkgs/main/linux-64::mkl_fft-1.1.0-py37h23d657b_0\n",
+ " mkl_random pkgs/main/linux-64::mkl_random-1.1.1-py37h0573a6f_0\n",
+ " numpy pkgs/main/linux-64::numpy-1.18.5-py37ha1c710e_0\n",
+ " numpy-base pkgs/main/linux-64::numpy-base-1.18.5-py37hde5b4d6_0\n",
+ " olefile pkgs/main/linux-64::olefile-0.46-py37_0\n",
+ " pandas pkgs/main/linux-64::pandas-1.0.5-py37h0573a6f_0\n",
+ " pcre pkgs/main/linux-64::pcre-8.44-he6710b0_0\n",
+ " pillow pkgs/main/linux-64::pillow-7.1.2-py37hb39fc2d_0\n",
+ " pixman pkgs/main/linux-64::pixman-0.40.0-h7b6447c_0\n",
+ " py-boost pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4\n",
+ " python-dateutil pkgs/main/noarch::python-dateutil-2.8.1-py_0\n",
+ " pytz pkgs/main/noarch::pytz-2020.1-py_0\n",
+ " rdkit rdkit/linux-64::rdkit-2020.03.3.0-py37hc20afe1_1\n",
+ " zstd pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0\n",
+ "\n",
+ "The following packages will be UPDATED:\n",
+ "\n",
+ " ca-certificates 2020.1.1-0 --> 2020.6.24-0\n",
+ " certifi 2019.11.28-py37_0 --> 2020.6.20-py37_0\n",
+ " conda 4.8.2-py37_0 --> 4.8.3-py37_0\n",
+ " openssl 1.1.1d-h7b6447c_4 --> 1.1.1g-h7b6447c_0\n",
+ "\n",
+ "\n",
+ "\n",
+ "Downloading and Extracting Packages\n",
+ "bzip2-1.0.8 | 78 KB | : 100% 1.0/1 [00:00<00:00, 10.01it/s]\n",
+ "pandas-1.0.5 | 7.8 MB | : 100% 1.0/1 [00:00<00:00, 2.22it/s] \n",
+ "mkl-service-2.3.0 | 218 KB | : 100% 1.0/1 [00:00<00:00, 19.17it/s]\n",
+ "conda-4.8.3 | 2.8 MB | : 100% 1.0/1 [00:00<00:00, 18.58s/it] \n",
+ "mkl_random-1.1.1 | 322 KB | : 100% 1.0/1 [00:00<00:00, 15.12it/s]\n",
+ "pcre-8.44 | 212 KB | : 100% 1.0/1 [00:00<00:00, 17.59it/s]\n",
+ "fontconfig-2.13.0 | 227 KB | : 100% 1.0/1 [00:00<00:00, 16.10it/s]\n",
+ "libxml2-2.9.9 | 1.6 MB | : 100% 1.0/1 [00:00<00:00, 5.63it/s]\n",
+ "pillow-7.1.2 | 603 KB | : 100% 1.0/1 [00:00<00:00, 10.25it/s]\n",
+ "libgfortran-ng-7.3.0 | 1006 KB | : 100% 1.0/1 [00:00<00:00, 11.04it/s]\n",
+ "py-boost-1.67.0 | 278 KB | : 100% 1.0/1 [00:00<00:00, 12.08it/s]\n",
+ "python-dateutil-2.8. | 215 KB | : 100% 1.0/1 [00:00<00:00, 16.41it/s]\n",
+ "libpng-1.6.37 | 278 KB | : 100% 1.0/1 [00:00<00:00, 16.63it/s]\n",
+ "numpy-base-1.18.5 | 4.1 MB | : 100% 1.0/1 [00:00<00:00, 3.82it/s]\n",
+ "intel-openmp-2020.1 | 780 KB | : 100% 1.0/1 [00:00<00:00, 13.02it/s]\n",
+ "freetype-2.10.2 | 608 KB | : 100% 1.0/1 [00:00<00:00, 11.86it/s]\n",
+ "pytz-2020.1 | 184 KB | : 100% 1.0/1 [00:00<00:00, 10.15it/s]\n",
+ "certifi-2020.6.20 | 156 KB | : 100% 1.0/1 [00:00<00:00, 17.12it/s]\n",
+ "libboost-1.67.0 | 13.0 MB | : 100% 1.0/1 [00:01<00:00, 1.08s/it] \n",
+ "libuuid-1.0.3 | 15 KB | : 100% 1.0/1 [00:00<00:00, 18.28it/s]\n",
+ "pixman-0.40.0 | 370 KB | : 100% 1.0/1 [00:00<00:00, 15.05it/s]\n",
+ "openssl-1.1.1g | 2.5 MB | : 100% 1.0/1 [00:00<00:00, 7.32it/s]\n",
+ "cairo-1.14.12 | 906 KB | : 100% 1.0/1 [00:00<00:00, 12.05it/s]\n",
+ "zstd-1.3.7 | 401 KB | : 100% 1.0/1 [00:00<00:00, 11.99it/s]\n",
+ "rdkit-2020.03.3.0 | 24.8 MB | : 100% 1.0/1 [00:04<00:00, 4.63s/it]\n",
+ "libtiff-4.1.0 | 447 KB | : 100% 1.0/1 [00:00<00:00, 11.43it/s]\n",
+ "olefile-0.46 | 50 KB | : 100% 1.0/1 [00:00<00:00, 16.29it/s]\n",
+ "blas-1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 19.66it/s]\n",
+ "mkl_fft-1.1.0 | 143 KB | : 100% 1.0/1 [00:00<00:00, 16.08it/s]\n",
+ "jpeg-9b | 214 KB | : 100% 1.0/1 [00:00<00:00, 14.71it/s]\n",
+ "ca-certificates-2020 | 125 KB | : 100% 1.0/1 [00:00<00:00, 19.14it/s]\n",
+ "icu-58.2 | 10.5 MB | : 100% 1.0/1 [00:00<00:00, 2.42it/s] \n",
+ "numpy-1.18.5 | 5 KB | : 100% 1.0/1 [00:00<00:00, 18.39it/s]\n",
+ "libxcb-1.14 | 505 KB | : 100% 1.0/1 [00:00<00:00, 10.61it/s]\n",
+ "glib-2.63.1 | 2.9 MB | : 100% 1.0/1 [00:00<00:00, 5.92it/s]\n",
+ "mkl-2020.1 | 129.0 MB | : 100% 1.0/1 [00:04<00:00, 4.79s/it] \n",
+ "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
+ "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
+ "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H661uGwCNFMC",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **2. Delaney's solubility dataset**\n",
+ "\n",
+ "The original [Delaney's dataset](https://pubs.acs.org/doi/10.1021/ci034243x) available as a [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x)$^4$. The full paper is entitled [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x).$^1$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s6o9QzQnNRVx",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **2.1. Download the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6KKvV74LM1it",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "# ! wget https://pubs.acs.org/doi/suppl/10.1021/ci034243x/suppl_file/ci034243xsi20040112_053635.txt"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PJGp_xenNYKy",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **2.2. Read in the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0ufiOpEbNooH",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nLS6bwiRNtuV",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 415
+ },
+ "outputId": "cfe35c3f-dabf-4629-fe89-0634a7e693ad"
+ },
+ "source": [
+ "delaney_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv'\n",
+ "sol = pd.read_csv(delaney_url)\n",
+ "sol"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ ",\n",
+ " ,\n",
+ " ,\n",
+ " ,\n",
+ " ]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 11
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "olyPX1TjQMvr",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.2. Calculate molecular descriptors**\n",
+ "\n",
+ "To predict **LogS** (log of the aqueous solubility), the study by Delaney makes use of 4 molecular descriptors:\n",
+ "1. **cLogP** *(Octanol-water partition coefficient)*\n",
+ "2. **MW** *(Molecular weight)*\n",
+ "3. **RB** *(Number of rotatable bonds)*\n",
+ "4. **AP** *(Aromatic proportion = number of aromatic atoms / total number of heavy atoms)*\n",
+ "\n",
+ "Unfortunately, rdkit readily computes the first 3. As for the AP descriptor, we will calculate this by manually computing the ratio of the *number of aromatic atoms* to the *total number of heavy atoms* which rdkit can compute."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iS4w5r5ocxT8",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import numpy as np\n",
+ "from rdkit.Chem import Descriptors"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nIF7IrIlcGPD",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "def AromaticProportion(m):\n",
+ " aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]\n",
+ " aa_count = []\n",
+ " for i in aromatic_atoms:\n",
+ " if i==True:\n",
+ " aa_count.append(1)\n",
+ " AromaticAtom = sum(aa_count)\n",
+ " HeavyAtom = Descriptors.HeavyAtomCount(m)\n",
+ " AR = AromaticAtom/HeavyAtom\n",
+ " return AR"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WkNMPVu_giw8",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "# Inspired by: https://codeocean.com/explore/capsules?query=tag:data-curation\n",
+ "\n",
+ "def generate(smiles, verbose=False):\n",
+ "\n",
+ " moldata= []\n",
+ " for elem in smiles:\n",
+ " mol=Chem.MolFromSmiles(elem) \n",
+ " moldata.append(mol)\n",
+ " \n",
+ " baseData= np.arange(1,1)\n",
+ " i=0 \n",
+ " for mol in moldata: \n",
+ " \n",
+ " desc_MolLogP = Descriptors.MolLogP(mol)\n",
+ " desc_MolWt = Descriptors.MolWt(mol)\n",
+ " desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol)\n",
+ " desc_AromaticProportion = AromaticProportion(mol)\n",
+ " \n",
+ " row = np.array([desc_MolLogP,\n",
+ " desc_MolWt,\n",
+ " desc_NumRotatableBonds,\n",
+ " desc_AromaticProportion]) \n",
+ " \n",
+ " if(i==0):\n",
+ " baseData=row\n",
+ " else:\n",
+ " baseData=np.vstack([baseData, row])\n",
+ " i=i+1 \n",
+ " \n",
+ " columnNames=[\"MolLogP\",\"MolWt\",\"NumRotatableBonds\",\"AromaticProportion\"] \n",
+ " descriptors = pd.DataFrame(data=baseData,columns=columnNames)\n",
+ " \n",
+ " return descriptors"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MzMulCVvcf59",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "X = generate(sol.SMILES)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JfVfpGa9wGaD",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **4. Preparing the X and Y Data Matrices**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3ZKZKPOuCVTY",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **4.1. X matrix (the computed descriptors)**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6VFAdZwbCg0T",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 415
+ },
+ "outputId": "738ceff2-b929-48c8-8f42-a412e5c960b7"
+ },
+ "source": [
+ "X"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Compound ID ... SMILES\n",
+ "0 1,1,1,2-Tetrachloroethane ... ClCC(Cl)(Cl)Cl\n",
+ "1 1,1,1-Trichloroethane ... CC(Cl)(Cl)Cl\n",
+ "2 1,1,2,2-Tetrachloroethane ... ClC(Cl)C(Cl)Cl\n",
+ "3 1,1,2-Trichloroethane ... ClCC(Cl)Cl\n",
+ "4 1,1,2-Trichlorotrifluoroethane ... FC(F)(Cl)C(F)(Cl)Cl\n",
+ "... ... ... ...\n",
+ "1139 vamidothion ... CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n",
+ "1140 Vinclozolin ... CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n",
+ "1141 Warfarin ... CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n",
+ "1142 Xipamide ... Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n",
+ "1143 XMC ... CNC(=O)Oc1cc(C)cc(C)c1\n",
+ "\n",
+ "[1144 rows x 4 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uqQLXGKQQAvX",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **3. Calculate molecular descriptors in rdkit**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iD_6apg8kYDy",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.1. Convert list of molecules to rdkit object**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bQjMv-wLOlmg",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "from rdkit import Chem"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AaHAVM2yFm3J",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "mol_list = [Chem.MolFromSmiles(element) for element in sol.SMILES]"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Fw5BCeh7F2c9",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "15c6d713-80e4-4d22-f7da-7887392def9c"
+ },
+ "source": [
+ "len(mol_list)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "1144"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uASSo7ZMF5iv",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 104
+ },
+ "outputId": "4c6a6f8a-5eef-4cf7-f4a5-dd5f6b0e7729"
+ },
+ "source": [
+ "mol_list[:5]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[| \n", + " | Compound ID | \n", + "measured log(solubility:mol/L) | \n", + "ESOL predicted log(solubility:mol/L) | \n", + "SMILES | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1,1,1,2-Tetrachloroethane | \n", + "-2.180 | \n", + "-2.794 | \n", + "ClCC(Cl)(Cl)Cl | \n", + "
| 1 | \n", + "1,1,1-Trichloroethane | \n", + "-2.000 | \n", + "-2.232 | \n", + "CC(Cl)(Cl)Cl | \n", + "
| 2 | \n", + "1,1,2,2-Tetrachloroethane | \n", + "-1.740 | \n", + "-2.549 | \n", + "ClC(Cl)C(Cl)Cl | \n", + "
| 3 | \n", + "1,1,2-Trichloroethane | \n", + "-1.480 | \n", + "-1.961 | \n", + "ClCC(Cl)Cl | \n", + "
| 4 | \n", + "1,1,2-Trichlorotrifluoroethane | \n", + "-3.040 | \n", + "-3.077 | \n", + "FC(F)(Cl)C(F)(Cl)Cl | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "vamidothion | \n", + "1.144 | \n", + "-1.446 | \n", + "CNC(=O)C(C)SCCSP(=O)(OC)(OC) | \n", + "
| 1140 | \n", + "Vinclozolin | \n", + "-4.925 | \n", + "-4.377 | \n", + "CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C | \n", + "
| 1141 | \n", + "Warfarin | \n", + "-3.893 | \n", + "-3.913 | \n", + "CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O | \n", + "
| 1142 | \n", + "Xipamide | \n", + "-3.790 | \n", + "-3.642 | \n", + "Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O | \n", + "
| 1143 | \n", + "XMC | \n", + "-2.581 | \n", + "-2.688 | \n", + "CNC(=O)Oc1cc(C)cc(C)c1 | \n", + "
1144 rows × 4 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion\n",
+ "0 2.59540 167.850 0.0 0.000000\n",
+ "1 2.37650 133.405 0.0 0.000000\n",
+ "2 2.59380 167.850 1.0 0.000000\n",
+ "3 2.02890 133.405 1.0 0.000000\n",
+ "4 2.91890 187.375 1.0 0.000000\n",
+ "... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000\n",
+ "1140 3.42130 286.114 2.0 0.333333\n",
+ "1141 3.60960 308.333 4.0 0.695652\n",
+ "1142 2.56214 354.815 3.0 0.521739\n",
+ "1143 2.02164 179.219 1.0 0.461538\n",
+ "\n",
+ "[1144 rows x 4 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zZI9k4h6FsPF",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **4.2. Y matrix**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6m4Akv3rHG3E",
+ "colab_type": "text"
+ },
+ "source": [
+ "Assigning the second column (index 1) to the Y matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fcvXs7R7FrbC",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 225
+ },
+ "outputId": "fc95e058-bfdb-412c-d3e5-383f2db9e535"
+ },
+ "source": [
+ "Y = sol.iloc[:,1]\n",
+ "Y = Y.rename(\"logS\")\n",
+ "Y"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0 -2.180\n",
+ "1 -2.000\n",
+ "2 -1.740\n",
+ "3 -1.480\n",
+ "4 -3.040\n",
+ " ... \n",
+ "1139 1.144\n",
+ "1140 -4.925\n",
+ "1141 -3.893\n",
+ "1142 -3.790\n",
+ "1143 -2.581\n",
+ "Name: logS, Length: 1144, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "a7Y7XBrAnGHs",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 225
+ },
+ "outputId": "b45437e5-83eb-4302-edbe-ab171680b856"
+ },
+ "source": [
+ "Y"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0 -2.180\n",
+ "1 -2.000\n",
+ "2 -1.740\n",
+ "3 -1.480\n",
+ "4 -3.040\n",
+ " ... \n",
+ "1139 1.144\n",
+ "1140 -4.925\n",
+ "1141 -3.893\n",
+ "1142 -3.790\n",
+ "1143 -2.581\n",
+ "Name: logS, Length: 1144, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Eabg-PCIuCoY",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 285
+ },
+ "outputId": "f9890ad3-c5d3-4b84-a976-39a4c70aaef1"
+ },
+ "source": [
+ "Y.hist()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "
1144 rows × 4 columns
\n", + ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
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+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NKYJb_93k8s5",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **4.3. Combine X and Y into same dataframe**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "fozh-KEJlDe1",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 415
+ },
+ "outputId": "0b11dc7f-ec6b-40df-e4cb-5ad0790ef2be"
+ },
+ "source": [
+ "dataset = pd.concat([X,Y], axis=1)\n",
+ "dataset"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n",
+ "0 2.59540 167.850 0.0 0.000000 -2.180\n",
+ "1 2.37650 133.405 0.0 0.000000 -2.000\n",
+ "2 2.59380 167.850 1.0 0.000000 -1.740\n",
+ "3 2.02890 133.405 1.0 0.000000 -1.480\n",
+ "4 2.91890 187.375 1.0 0.000000 -3.040\n",
+ "... ... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000 1.144\n",
+ "1140 3.42130 286.114 2.0 0.333333 -4.925\n",
+ "1141 3.60960 308.333 4.0 0.695652 -3.893\n",
+ "1142 2.56214 354.815 3.0 0.521739 -3.790\n",
+ "1143 2.02164 179.219 1.0 0.461538 -2.581\n",
+ "\n",
+ "[1144 rows x 5 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AZ0setezmBbD",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "dataset.to_csv('delaney_solubility_with_descriptors.csv', index=False)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ARiv3f1iC565",
+ "colab_type": "text"
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EICKN2Hn5X_L",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jwM1QHeLbxJl",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **Reference**\n",
+ "\n",
+ "1. John S. Delaney. [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n",
+ "\n",
+ "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n",
+ "\n",
+ "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n",
+ "\n",
+ "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL: Estimating Aqueous Solubility Directly from Molecular Structure.\n",
+ "\n",
+ "5. Scott M. Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), A Unified Approach to Interpreting Model Predictions, ***Advances in Neural Information Processing Systems 30 (NIPS 2017)***, 2017."
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb b/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb
new file mode 100644
index 0000000..2c7bbb8
--- /dev/null
+++ b/python/cheminformatics_predicting_solubility_2_2_PyCaret.ipynb
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+ "source": [
+ "# **Cheminformatics in Python [PART 2.2] Predicting Solubility of Molecules using PyCaret | End-to-End Data Science Project** \n",
+ "\n",
+ "Chanin Nantasenamat\n",
+ "\n",
+ "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n",
+ "\n",
+ "In this Jupyter notebook, we will continue our journey into the world of Cheminformatics (i.e. lies at the interface of Informatics and Chemistry) by simplifying this notebook via the use of the low-code machine learning library PyCaret.\n",
+ "\n",
+ "\n",
+ "**Information from the previous notebook:**\n",
+ "\n",
+ "We will be reproducing a research article (by John S. Delaney$^1$) by applying Linear Regression to predict the solubility of molecules (i.e. solubility of drugs is an important physicochemical property in Drug discovery, design and development).\n",
+ "\n",
+ "This idea for this notebook was inspired by the excellent blog post by Pat Walters$^2$ where he reproduced the linear regression model with similar degree of performance as that of Delaney. This example is also briefly described in the book ***Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More***.$^3$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xWbQX5dWh5Il",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **1. Install PyCaret**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NoN9ejGKh896",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "968276bc-e9e0-4288-bd7d-f33cf9d35a9a"
+ },
+ "source": [
+ "! pip install pycaret"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Collecting pycaret\n",
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+ "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from pycaret) (0.16.0)\n",
+ "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from pycaret) (3.2.2)\n",
+ "Collecting scikit-learn==0.22\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/2e/d0/860c4f6a7027e00acff373d9f5327f4ae3ed5872234b3cbdd7bcb52e5eff/scikit_learn-0.22-cp36-cp36m-manylinux1_x86_64.whl (7.0MB)\n",
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+ "Collecting shap==0.32.1\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/57/43/08f152a59a1d60f0328b476bdd58c791498989981ab9c6d595ec5448a86a/shap-0.32.1.tar.gz (259kB)\n",
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+ " Downloading https://files.pythonhosted.org/packages/16/2b/af8efaee30c0ba4238cb4d0645a07100d33d11d20a8783c443ed8b813eb9/datefinder-0.7.0-py2.py3-none-any.whl\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pycaret) (1.18.5)\n",
+ "Collecting cufflinks==0.17.0\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/e3/79/1b8673b2723e02919307d558896dbcedcb46807c4e29acd25cfe43a36c8b/cufflinks-0.17.0.tar.gz (81kB)\n",
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+ "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (1.4.2)\n",
+ "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (0.5.1)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets->pycaret) (20.4)\n",
+ "Building wheels for collected packages: shap, cufflinks, pyLDAvis, pandas-profiling, pyod, funcy, htmlmin, combo, suod\n",
+ " Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for shap: filename=shap-0.32.1-cp36-cp36m-linux_x86_64.whl size=376809 sha256=f17f06e9725b8dd94d5c29f8c288d8058f25e82b811e2301e7ed2c678557ae77\n",
+ " Stored in directory: /root/.cache/pip/wheels/8e/b2/50/8fadb5a59789cb5bdeb01b800223be540651ae92915172050b\n",
+ " Building wheel for cufflinks (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for cufflinks: filename=cufflinks-0.17.0-cp36-none-any.whl size=67744 sha256=9ae622483a47091ce78f4f9e332a02d4b49d50e5108fe148245aded18ca68470\n",
+ " Stored in directory: /root/.cache/pip/wheels/44/d7/dc/e830ab00bc2dd3b2731295103baa070f8cbdda8891f71a7a8d\n",
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+ " Stored in directory: /root/.cache/pip/wheels/98/71/24/513a99e58bb6b8465bae4d2d5e9dba8f0bef8179e3051ac414\n",
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+ " Created wheel for pandas-profiling: filename=pandas_profiling-2.3.0-py2.py3-none-any.whl size=145035 sha256=0f4df88af247c98a2b95a0fe6267a0678ad4e461be9eded1bb5e77ae52776e83\n",
+ " Stored in directory: /root/.cache/pip/wheels/ce/c7/f1/dbfef4848ebb048cb1d4a22d1ed0c62d8ff2523747235e19fe\n",
+ " Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for pyod: filename=pyod-0.8.1-cp36-none-any.whl size=105653 sha256=cafc7f9fa6b8d305defedf9bb171e377ccf4dda34efb8af5a8166ad38aefe420\n",
+ " Stored in directory: /root/.cache/pip/wheels/2e/ca/18/727e9d98a41f5f4385a97d5b429f3a9c8fbee13f9780c18642\n",
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+ " Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n",
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+ " Stored in directory: /root/.cache/pip/wheels/43/07/ac/7c5a9d708d65247ac1f94066cf1db075540b85716c30255459\n",
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+ " Stored in directory: /root/.cache/pip/wheels/55/ec/e5/a2331372c676c467e70c6646e646edf6997d5c4905b8c0f5e6\n",
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+ " Stored in directory: /root/.cache/pip/wheels/57/55/e5/a4fca65bba231f6d0115059b589148774b41faea25b3f2aa27\n",
+ "Successfully built shap cufflinks pyLDAvis pandas-profiling pyod funcy htmlmin combo suod\n",
+ "Installing collected packages: scikit-learn, shap, datefinder, chart-studio, cufflinks, rsa, colorama, botocore, awscli, yellowbrick, zope.interface, DateTime, kmodes, catboost, funcy, pyLDAvis, lightgbm, htmlmin, phik, confuse, pandas-profiling, combo, suod, pyod, pycaret\n",
+ " Found existing installation: scikit-learn 0.22.2.post1\n",
+ " Uninstalling scikit-learn-0.22.2.post1:\n",
+ " Successfully uninstalled scikit-learn-0.22.2.post1\n",
+ " Found existing installation: cufflinks 0.17.3\n",
+ " Uninstalling cufflinks-0.17.3:\n",
+ " Successfully uninstalled cufflinks-0.17.3\n",
+ " Found existing installation: rsa 4.6\n",
+ " Uninstalling rsa-4.6:\n",
+ " Successfully uninstalled rsa-4.6\n",
+ " Found existing installation: botocore 1.17.24\n",
+ " Uninstalling botocore-1.17.24:\n",
+ " Successfully uninstalled botocore-1.17.24\n",
+ " Found existing installation: yellowbrick 0.9.1\n",
+ " Uninstalling yellowbrick-0.9.1:\n",
+ " Successfully uninstalled yellowbrick-0.9.1\n",
+ " Found existing installation: lightgbm 2.2.3\n",
+ " Uninstalling lightgbm-2.2.3:\n",
+ " Successfully uninstalled lightgbm-2.2.3\n",
+ " Found existing installation: pandas-profiling 1.4.1\n",
+ " Uninstalling pandas-profiling-1.4.1:\n",
+ " Successfully uninstalled pandas-profiling-1.4.1\n",
+ "Successfully installed DateTime-4.3 awscli-1.18.106 botocore-1.17.29 catboost-0.20.2 chart-studio-1.1.0 colorama-0.4.3 combo-0.1.1 confuse-1.3.0 cufflinks-0.17.0 datefinder-0.7.0 funcy-1.14 htmlmin-0.1.12 kmodes-0.10.1 lightgbm-2.3.1 pandas-profiling-2.3.0 phik-0.10.0 pyLDAvis-2.1.2 pycaret-1.0.0 pyod-0.8.1 rsa-4.5 scikit-learn-0.22 shap-0.32.1 suod-0.0.4 yellowbrick-1.0.1 zope.interface-5.1.0\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.colab-display-data+json": {
+ "pip_warning": {
+ "packages": [
+ "rsa"
+ ]
+ }
+ }
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LMkrqvALhtcu",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **2. Read in dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BWzXZuPMhbvs",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yfBVayaJhgGg",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "delaney_with_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n",
+ "dataset = pd.read_csv(delaney_with_descriptors_url)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DSLOSdoFh-3k",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 415
+ },
+ "outputId": "702e8120-df0f-4e0d-fe83-f3cca19ae045"
+ },
+ "source": [
+ "dataset"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kIk0leY4r3fu",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.2. Model comparison**\n",
+ "\n",
+ "Subsequent blocks of codes here will be using the ``training set`` (the 80% subset) for model building."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4HwZ8IODp4Ew",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 495,
+ "referenced_widgets": [
+ "57f2053ff696471ea22ecc3cbfa85355",
+ "eca493ab5b0d40e4a8669d593421adfa",
+ "175957f384414f328f27e4c5a6626db7"
+ ]
+ },
+ "outputId": "02689474-3270-4631-f532-f42e5f5d5c6f"
+ },
+ "source": [
+ "compare_models()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "X73jXIg0rmlc",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.3. Model Creation**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hNtwkPitkpQM",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 417,
+ "referenced_widgets": [
+ "c7e52175a25e42aa81cea9c946cea881",
+ "9fed28513370419f9074fc5b22bfb314",
+ "94dc7fd2ad49439aad65251c47fa4108"
+ ]
+ },
+ "outputId": "a0cd6a39-4a7e-4d96-85a2-eaf4bc45cfac"
+ },
+ "source": [
+ "et = create_model('et')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n",
+ "0 2.59540 167.850 0.0 0.000000 -2.180\n",
+ "1 2.37650 133.405 0.0 0.000000 -2.000\n",
+ "2 2.59380 167.850 1.0 0.000000 -1.740\n",
+ "3 2.02890 133.405 1.0 0.000000 -1.480\n",
+ "4 2.91890 187.375 1.0 0.000000 -3.040\n",
+ "... ... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000 1.144\n",
+ "1140 3.42130 286.114 2.0 0.333333 -4.925\n",
+ "1141 3.60960 308.333 4.0 0.695652 -3.893\n",
+ "1142 2.56214 354.815 3.0 0.521739 -3.790\n",
+ "1143 2.02164 179.219 1.0 0.461538 -2.581\n",
+ "\n",
+ "[1144 rows x 5 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Bay6QrUHkCyx",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **3. Model Building**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Un3wYDLOivW3",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.1. Model Setup**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0urRTofi6RSY",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "from pycaret.regression import *"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uxYbQPaDp4Ai",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 943,
+ "referenced_widgets": [
+ "eced8f77156e439aa934f67a011e84b6",
+ "a14f8fbf57a24a748e69c6fd90a33520",
+ "2b2bc1ae050c4dc69d1acb9cc20df29a"
+ ]
+ },
+ "outputId": "e40b1807-a2cf-46c2-afe2-55ae0a6238a1"
+ },
+ "source": [
+ "model = setup(data = dataset, target = 'logS', train_size=0.8, silent=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " \n",
+ "Setup Succesfully Completed!\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "logS | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.180 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.740 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.480 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "-3.040 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "1.144 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "-4.925 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "-3.893 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "-3.790 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "-2.581 | \n", + "
1144 rows × 5 columns
\n", + "| Description | Value | |
|---|---|---|
| 0 | \n", + "session_id | \n", + "7903 | \n", + "
| 1 | \n", + "Transform Target | \n", + "False | \n", + "
| 2 | \n", + "Transform Target Method | \n", + "None | \n", + "
| 3 | \n", + "Original Data | \n", + "(1144, 5) | \n", + "
| 4 | \n", + "Missing Values | \n", + "False | \n", + "
| 5 | \n", + "Numeric Features | \n", + "4 | \n", + "
| 6 | \n", + "Categorical Features | \n", + "0 | \n", + "
| 7 | \n", + "Ordinal Features | \n", + "False | \n", + "
| 8 | \n", + "High Cardinality Features | \n", + "False | \n", + "
| 9 | \n", + "High Cardinality Method | \n", + "None | \n", + "
| 10 | \n", + "Sampled Data | \n", + "(1144, 5) | \n", + "
| 11 | \n", + "Transformed Train Set | \n", + "(915, 4) | \n", + "
| 12 | \n", + "Transformed Test Set | \n", + "(229, 4) | \n", + "
| 13 | \n", + "Numeric Imputer | \n", + "mean | \n", + "
| 14 | \n", + "Categorical Imputer | \n", + "constant | \n", + "
| 15 | \n", + "Normalize | \n", + "False | \n", + "
| 16 | \n", + "Normalize Method | \n", + "None | \n", + "
| 17 | \n", + "Transformation | \n", + "False | \n", + "
| 18 | \n", + "Transformation Method | \n", + "None | \n", + "
| 19 | \n", + "PCA | \n", + "False | \n", + "
| 20 | \n", + "PCA Method | \n", + "None | \n", + "
| 21 | \n", + "PCA Components | \n", + "None | \n", + "
| 22 | \n", + "Ignore Low Variance | \n", + "False | \n", + "
| 23 | \n", + "Combine Rare Levels | \n", + "False | \n", + "
| 24 | \n", + "Rare Level Threshold | \n", + "None | \n", + "
| 25 | \n", + "Numeric Binning | \n", + "False | \n", + "
| 26 | \n", + "Remove Outliers | \n", + "False | \n", + "
| 27 | \n", + "Outliers Threshold | \n", + "None | \n", + "
| 28 | \n", + "Remove Multicollinearity | \n", + "False | \n", + "
| 29 | \n", + "Multicollinearity Threshold | \n", + "None | \n", + "
| 30 | \n", + "Clustering | \n", + "False | \n", + "
| 31 | \n", + "Clustering Iteration | \n", + "None | \n", + "
| 32 | \n", + "Polynomial Features | \n", + "False | \n", + "
| 33 | \n", + "Polynomial Degree | \n", + "None | \n", + "
| 34 | \n", + "Trignometry Features | \n", + "False | \n", + "
| 35 | \n", + "Polynomial Threshold | \n", + "None | \n", + "
| 36 | \n", + "Group Features | \n", + "False | \n", + "
| 37 | \n", + "Feature Selection | \n", + "False | \n", + "
| 38 | \n", + "Features Selection Threshold | \n", + "None | \n", + "
| 39 | \n", + "Feature Interaction | \n", + "False | \n", + "
| 40 | \n", + "Feature Ratio | \n", + "False | \n", + "
| 41 | \n", + "Interaction Threshold | \n", + "None | \n", + "
| Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE | |
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Extra Trees Regressor | \n", + "0.518200 | \n", + "0.531300 | \n", + "0.725300 | \n", + "0.879300 | \n", + "0.206800 | \n", + "0.130500 | \n", + "
| 1 | \n", + "Random Forest | \n", + "0.526800 | \n", + "0.542400 | \n", + "0.731800 | \n", + "0.876800 | \n", + "0.206900 | \n", + "0.119400 | \n", + "
| 2 | \n", + "CatBoost Regressor | \n", + "0.543500 | \n", + "0.547200 | \n", + "0.735700 | \n", + "0.875900 | \n", + "0.210300 | \n", + "0.117100 | \n", + "
| 3 | \n", + "Light Gradient Boosting Machine | \n", + "0.563500 | \n", + "0.588700 | \n", + "0.762000 | \n", + "0.866900 | \n", + "0.218400 | \n", + "0.079700 | \n", + "
| 4 | \n", + "Gradient Boosting Regressor | \n", + "0.595800 | \n", + "0.617400 | \n", + "0.781700 | \n", + "0.859400 | \n", + "0.228300 | \n", + "0.059900 | \n", + "
| 5 | \n", + "Extreme Gradient Boosting | \n", + "0.597800 | \n", + "0.619900 | \n", + "0.783000 | \n", + "0.858800 | \n", + "0.228300 | \n", + "0.063700 | \n", + "
| 6 | \n", + "AdaBoost Regressor | \n", + "0.674300 | \n", + "0.764500 | \n", + "0.869500 | \n", + "0.826500 | \n", + "0.240100 | \n", + "0.137500 | \n", + "
| 7 | \n", + "Decision Tree | \n", + "0.648500 | \n", + "0.887700 | \n", + "0.938500 | \n", + "0.795800 | \n", + "0.251700 | \n", + "-0.054400 | \n", + "
| 8 | \n", + "Linear Regression | \n", + "0.764700 | \n", + "1.013900 | \n", + "1.001600 | \n", + "0.767000 | \n", + "0.289000 | \n", + "-0.212100 | \n", + "
| 9 | \n", + "Ridge Regression | \n", + "0.764800 | \n", + "1.013900 | \n", + "1.001600 | \n", + "0.767000 | \n", + "0.288900 | \n", + "-0.212700 | \n", + "
| 10 | \n", + "Least Angle Regression | \n", + "0.764700 | \n", + "1.013900 | \n", + "1.001600 | \n", + "0.767000 | \n", + "0.289000 | \n", + "-0.212100 | \n", + "
| 11 | \n", + "Bayesian Ridge | \n", + "0.765300 | \n", + "1.013800 | \n", + "1.001500 | \n", + "0.767000 | \n", + "0.288800 | \n", + "-0.214800 | \n", + "
| 12 | \n", + "Huber Regressor | \n", + "0.760100 | \n", + "1.016800 | \n", + "1.002600 | \n", + "0.766200 | \n", + "0.286900 | \n", + "-0.190400 | \n", + "
| 13 | \n", + "Random Sample Consensus | \n", + "0.762600 | \n", + "1.027300 | \n", + "1.007300 | \n", + "0.764100 | \n", + "0.287100 | \n", + "-0.198000 | \n", + "
| 14 | \n", + "TheilSen Regressor | \n", + "0.782200 | \n", + "1.106600 | \n", + "1.048200 | \n", + "0.745400 | \n", + "0.303000 | \n", + "-0.269000 | \n", + "
| 15 | \n", + "Elastic Net | \n", + "0.861800 | \n", + "1.258400 | \n", + "1.113000 | \n", + "0.713200 | \n", + "0.300000 | \n", + "-0.189100 | \n", + "
| 16 | \n", + "Orthogonal Matching Pursuit | \n", + "0.891200 | \n", + "1.334400 | \n", + "1.153800 | \n", + "0.692400 | \n", + "0.347300 | \n", + "-0.451400 | \n", + "
| 17 | \n", + "Lasso Regression | \n", + "0.915400 | \n", + "1.427500 | \n", + "1.185800 | \n", + "0.675300 | \n", + "0.307000 | \n", + "-0.164900 | \n", + "
| 18 | \n", + "K Neighbors Regressor | \n", + "0.928600 | \n", + "1.648000 | \n", + "1.275300 | \n", + "0.625000 | \n", + "0.325400 | \n", + "-0.053200 | \n", + "
| 19 | \n", + "Support Vector Machine | \n", + "1.191700 | \n", + "2.559600 | \n", + "1.592500 | \n", + "0.416700 | \n", + "0.387900 | \n", + "-0.040700 | \n", + "
| 20 | \n", + "Lasso Least Angle Regression | \n", + "1.667700 | \n", + "4.483200 | \n", + "2.109000 | \n", + "-0.013800 | \n", + "0.527000 | \n", + "-0.372900 | \n", + "
| 21 | \n", + "Passive Aggressive Regressor | \n", + "1.535700 | \n", + "4.240300 | \n", + "1.823000 | \n", + "-0.082500 | \n", + "0.443400 | \n", + "-0.486800 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MAE MSE RMSE R2 RMSLE MAPE\n",
+ "0 0.5665 0.6118 0.7822 0.8764 0.1978 -0.1375\n",
+ "1 0.5183 0.5134 0.7165 0.8819 0.2300 -0.0383\n",
+ "2 0.4812 0.4912 0.7009 0.8895 0.2159 -0.1843\n",
+ "3 0.5150 0.4697 0.6853 0.9106 0.1997 -0.1160\n",
+ "4 0.3844 0.3017 0.5493 0.8974 0.1836 -0.1667\n",
+ "5 0.5429 0.6408 0.8005 0.8629 0.2002 0.7580\n",
+ "6 0.5052 0.4760 0.6899 0.8615 0.1939 -0.2687\n",
+ "7 0.5837 0.6279 0.7924 0.8807 0.2078 1.8027\n",
+ "8 0.5431 0.5835 0.7639 0.8752 0.2025 -0.0889\n",
+ "9 0.5418 0.5965 0.7723 0.8569 0.2368 -0.2551\n",
+ "Mean 0.5182 0.5313 0.7253 0.8793 0.2068 0.1305\n",
+ "SD 0.0528 0.0981 0.0718 0.0159 0.0156 0.6241"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GWtYt-kcriye",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **3.4. Model Tuning**\n",
+ "\n",
+ "The learning parameters are subjected to optimization at this phase. Here, 50 iterations is used for the optimization process and the fitness function is the Mean Absolute Error (MAE) which is the performance metric used to judge at which learning parameter settings are optimal. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aXbz_Nfyk6pw",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 417,
+ "referenced_widgets": [
+ "6bc793b5047d421fa198c5365243b141",
+ "d35e09ab77c04c5694ceb707d88ba915",
+ "4d4291ef170e4a1caf00559c3604c2b8"
+ ]
+ },
+ "outputId": "8e7d30a1-ef80-4c7f-ae76-562170d56dff"
+ },
+ "source": [
+ "tuned_et = tune_model('et', n_iter = 50, optimize = 'mae')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "| \n", + " | MAE | \n", + "MSE | \n", + "RMSE | \n", + "R2 | \n", + "RMSLE | \n", + "MAPE | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "0.5665 | \n", + "0.6118 | \n", + "0.7822 | \n", + "0.8764 | \n", + "0.1978 | \n", + "-0.1375 | \n", + "
| 1 | \n", + "0.5183 | \n", + "0.5134 | \n", + "0.7165 | \n", + "0.8819 | \n", + "0.2300 | \n", + "-0.0383 | \n", + "
| 2 | \n", + "0.4812 | \n", + "0.4912 | \n", + "0.7009 | \n", + "0.8895 | \n", + "0.2159 | \n", + "-0.1843 | \n", + "
| 3 | \n", + "0.5150 | \n", + "0.4697 | \n", + "0.6853 | \n", + "0.9106 | \n", + "0.1997 | \n", + "-0.1160 | \n", + "
| 4 | \n", + "0.3844 | \n", + "0.3017 | \n", + "0.5493 | \n", + "0.8974 | \n", + "0.1836 | \n", + "-0.1667 | \n", + "
| 5 | \n", + "0.5429 | \n", + "0.6408 | \n", + "0.8005 | \n", + "0.8629 | \n", + "0.2002 | \n", + "0.7580 | \n", + "
| 6 | \n", + "0.5052 | \n", + "0.4760 | \n", + "0.6899 | \n", + "0.8615 | \n", + "0.1939 | \n", + "-0.2687 | \n", + "
| 7 | \n", + "0.5837 | \n", + "0.6279 | \n", + "0.7924 | \n", + "0.8807 | \n", + "0.2078 | \n", + "1.8027 | \n", + "
| 8 | \n", + "0.5431 | \n", + "0.5835 | \n", + "0.7639 | \n", + "0.8752 | \n", + "0.2025 | \n", + "-0.0889 | \n", + "
| 9 | \n", + "0.5418 | \n", + "0.5965 | \n", + "0.7723 | \n", + "0.8569 | \n", + "0.2368 | \n", + "-0.2551 | \n", + "
| Mean | \n", + "0.5182 | \n", + "0.5313 | \n", + "0.7253 | \n", + "0.8793 | \n", + "0.2068 | \n", + "0.1305 | \n", + "
| SD | \n", + "0.0528 | \n", + "0.0981 | \n", + "0.0718 | \n", + "0.0159 | \n", + "0.0156 | \n", + "0.6241 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MAE MSE RMSE R2 RMSLE MAPE\n",
+ "0 0.5543 0.5441 0.7376 0.8901 0.1807 -0.1316\n",
+ "1 0.5161 0.4614 0.6793 0.8939 0.2226 -0.1244\n",
+ "2 0.4941 0.5241 0.7239 0.8821 0.2313 -0.1848\n",
+ "3 0.4917 0.4173 0.6460 0.9206 0.1973 -0.0913\n",
+ "4 0.3921 0.2722 0.5217 0.9074 0.1681 -0.1948\n",
+ "5 0.5624 0.6388 0.7992 0.8633 0.1934 0.6988\n",
+ "6 0.4951 0.4216 0.6493 0.8773 0.1902 -0.2450\n",
+ "7 0.5966 0.6575 0.8108 0.8751 0.2082 1.8479\n",
+ "8 0.5320 0.5408 0.7354 0.8843 0.1966 -0.0753\n",
+ "9 0.5333 0.5837 0.7640 0.8599 0.2327 -0.2082\n",
+ "Mean 0.5168 0.5061 0.7067 0.8854 0.2021 0.1291\n",
+ "SD 0.0525 0.1101 0.0817 0.0177 0.0203 0.6292"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "V5c1tF3rVn64",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 139
+ },
+ "outputId": "85d21102-2134-44f8-c15c-097e17ba9b1c"
+ },
+ "source": [
+ "print(tuned_et)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "ExtraTreesRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mae',\n",
+ " max_depth=40, max_features='auto', max_leaf_nodes=None,\n",
+ " max_samples=None, min_impurity_decrease=0.0,\n",
+ " min_impurity_split=None, min_samples_leaf=1,\n",
+ " min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+ " n_estimators=280, n_jobs=None, oob_score=False,\n",
+ " random_state=7903, verbose=0, warm_start=False)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "if_60MOUkOtU",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **4. Model Analysis**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Dj0npLNfsR45",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### **4.1. Plot Models**\n",
+ "In this tutorial, we are performing regression and so further details of the regression plots are available at https://pycaret.org/plot-model/."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eKfsk-Pdt6S7",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Residuals Plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8HE-M4truI7S",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "85381a580de0430d89e671b451b555d9"
+ ]
+ },
+ "outputId": "f9ff095f-adcb-4d5d-cc57-8cd3a5da06d7"
+ },
+ "source": [
+ "plot_model(et, 'residuals')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | MAE | \n", + "MSE | \n", + "RMSE | \n", + "R2 | \n", + "RMSLE | \n", + "MAPE | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "0.5543 | \n", + "0.5441 | \n", + "0.7376 | \n", + "0.8901 | \n", + "0.1807 | \n", + "-0.1316 | \n", + "
| 1 | \n", + "0.5161 | \n", + "0.4614 | \n", + "0.6793 | \n", + "0.8939 | \n", + "0.2226 | \n", + "-0.1244 | \n", + "
| 2 | \n", + "0.4941 | \n", + "0.5241 | \n", + "0.7239 | \n", + "0.8821 | \n", + "0.2313 | \n", + "-0.1848 | \n", + "
| 3 | \n", + "0.4917 | \n", + "0.4173 | \n", + "0.6460 | \n", + "0.9206 | \n", + "0.1973 | \n", + "-0.0913 | \n", + "
| 4 | \n", + "0.3921 | \n", + "0.2722 | \n", + "0.5217 | \n", + "0.9074 | \n", + "0.1681 | \n", + "-0.1948 | \n", + "
| 5 | \n", + "0.5624 | \n", + "0.6388 | \n", + "0.7992 | \n", + "0.8633 | \n", + "0.1934 | \n", + "0.6988 | \n", + "
| 6 | \n", + "0.4951 | \n", + "0.4216 | \n", + "0.6493 | \n", + "0.8773 | \n", + "0.1902 | \n", + "-0.2450 | \n", + "
| 7 | \n", + "0.5966 | \n", + "0.6575 | \n", + "0.8108 | \n", + "0.8751 | \n", + "0.2082 | \n", + "1.8479 | \n", + "
| 8 | \n", + "0.5320 | \n", + "0.5408 | \n", + "0.7354 | \n", + "0.8843 | \n", + "0.1966 | \n", + "-0.0753 | \n", + "
| 9 | \n", + "0.5333 | \n", + "0.5837 | \n", + "0.7640 | \n", + "0.8599 | \n", + "0.2327 | \n", + "-0.2082 | \n", + "
| Mean | \n", + "0.5168 | \n", + "0.5061 | \n", + "0.7067 | \n", + "0.8854 | \n", + "0.2021 | \n", + "0.1291 | \n", + "
| SD | \n", + "0.0525 | \n", + "0.1101 | \n", + "0.0817 | \n", + "0.0177 | \n", + "0.0203 | \n", + "0.6292 | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8javpZPZuSy8",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Prediction Error Plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oyhrNvO-uMRv",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 378,
+ "referenced_widgets": [
+ "ec8757728c8b47ababbaf2d6e0071e16"
+ ]
+ },
+ "outputId": "8d56ec87-5210-4087-91ab-2d43db3ed004"
+ },
+ "source": [
+ "plot_model(et, 'error')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fOa-JJrllKtc",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Cooks Distance Plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_7sk8zbGlMf2",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "c6d7c0f6f3714e87bb9024dd5bb1973b",
+ "876f19457f9b49f79dea142c9fd7105d",
+ "623c5c5170194f17b2c8cc5c2b123c00"
+ ]
+ },
+ "outputId": "0a8367d0-823e-4800-a96e-c748f3a655af"
+ },
+ "source": [
+ "plot_model(et, 'cooks')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nORGjvKZlSOK",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Recursive Feature Selection**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Hsa_nFXylsw-",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "6f8d02f37c924d4eb32654731f580a60",
+ "4990778aa64048858fe8db08cc11bed6",
+ "a88c27792c534325a3354369979b4940"
+ ]
+ },
+ "outputId": "36032c8f-6741-4ad1-d303-207322ee7f3d"
+ },
+ "source": [
+ "plot_model(et, 'rfe')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QqvN2xuKuX_h",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Learning Curve**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "1MC1X6DMoXMT",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "d7b5d821471746d293e9606454fd18b4",
+ "aa95da81351a436491474f5a8cd23fde",
+ "cbc5e7dc9e4f40e2b4a177dd15b7d2cd"
+ ]
+ },
+ "outputId": "17db1484-d815-4e9d-d9db-3c1e54bd4ec7"
+ },
+ "source": [
+ "plot_model(et, 'learning')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9GvXUsgIlw38",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Validation Curve**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BseQKGRcl4au",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "50daf7d35c644cbabb12da1560737a1f",
+ "c427b7049a754b69ab57d6578167975f",
+ "c9c1d86e8830433db810ae603076cc11"
+ ]
+ },
+ "outputId": "ffcbc037-4dbc-4e28-eed6-cbb72604dc0c"
+ },
+ "source": [
+ "plot_model(et, 'vc')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hKlKtptHl7b1",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Manifold Learning**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vYHE3vOll-fX",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 366,
+ "referenced_widgets": [
+ "fd9243228f514bcba2f30cea03e09170",
+ "0a331f9d1d64417280748759aacedfde",
+ "3bb8a00901f44698b082d0424bb1939f"
+ ]
+ },
+ "outputId": "0462a521-b946-4ab4-e3df-a71290c158a5"
+ },
+ "source": [
+ "plot_model(et, 'manifold')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IN2Vn528mBnJ",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Feature Importance**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_qyBjC8fmEsA",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 349,
+ "referenced_widgets": [
+ "320f58c3baf14519856c70aa9ec3a3d0",
+ "89b99d193b61429e9837d1ef802244d8",
+ "07ebb71a54a146d2bf634c54cb31ece1"
+ ]
+ },
+ "outputId": "081a7990-ffa3-4ff9-c9f1-c3e2e382d813"
+ },
+ "source": [
+ "plot_model(et, 'feature')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7CVq2BBqmHNC",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Model Hyperparameter**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zDblppAzlOeV",
+ "colab_type": "text"
+ },
+ "source": [
+ "The hyperparameter of the learning model is displayed using the ``parameter`` argument in inside the ``plot_model()`` function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "PInxfiozmKkG",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 601
+ },
+ "outputId": "bc2815aa-666c-48eb-8f90-dbb785f706ce"
+ },
+ "source": [
+ "plot_model(et, 'parameter')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Parameters\n",
+ "bootstrap False\n",
+ "ccp_alpha 0\n",
+ "criterion mse\n",
+ "max_depth None\n",
+ "max_features auto\n",
+ "max_leaf_nodes None\n",
+ "max_samples None\n",
+ "min_impurity_decrease 0\n",
+ "min_impurity_split None\n",
+ "min_samples_leaf 1\n",
+ "min_samples_split 2\n",
+ "min_weight_fraction_leaf 0\n",
+ "n_estimators 100\n",
+ "n_jobs None\n",
+ "oob_score False\n",
+ "random_state 7903\n",
+ "verbose 0\n",
+ "warm_start False"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lsMAelymlq4O",
+ "colab_type": "text"
+ },
+ "source": [
+ "Here, the hyperparameter of the tuned model is displayed below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6rxAiyGclIHZ",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 601
+ },
+ "outputId": "3c703199-852c-4ebb-ff55-02ad1ec26b6a"
+ },
+ "source": [
+ "plot_model(tuned_et, 'parameter')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "| \n", + " | Parameters | \n", + "
|---|---|
| bootstrap | \n", + "False | \n", + "
| ccp_alpha | \n", + "0 | \n", + "
| criterion | \n", + "mse | \n", + "
| max_depth | \n", + "None | \n", + "
| max_features | \n", + "auto | \n", + "
| max_leaf_nodes | \n", + "None | \n", + "
| max_samples | \n", + "None | \n", + "
| min_impurity_decrease | \n", + "0 | \n", + "
| min_impurity_split | \n", + "None | \n", + "
| min_samples_leaf | \n", + "1 | \n", + "
| min_samples_split | \n", + "2 | \n", + "
| min_weight_fraction_leaf | \n", + "0 | \n", + "
| n_estimators | \n", + "100 | \n", + "
| n_jobs | \n", + "None | \n", + "
| oob_score | \n", + "False | \n", + "
| random_state | \n", + "7903 | \n", + "
| verbose | \n", + "0 | \n", + "
| warm_start | \n", + "False | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Parameters\n",
+ "bootstrap True\n",
+ "ccp_alpha 0\n",
+ "criterion mae\n",
+ "max_depth 40\n",
+ "max_features auto\n",
+ "max_leaf_nodes None\n",
+ "max_samples None\n",
+ "min_impurity_decrease 0\n",
+ "min_impurity_split None\n",
+ "min_samples_leaf 1\n",
+ "min_samples_split 2\n",
+ "min_weight_fraction_leaf 0\n",
+ "n_estimators 280\n",
+ "n_jobs None\n",
+ "oob_score False\n",
+ "random_state 7903\n",
+ "verbose 0\n",
+ "warm_start False"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c_AUpuWCYdAR",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Show all plots**\n",
+ "\n",
+ "The ``evaluate_model()`` displays all available plots here."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LaY-68C2XZfD",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 436,
+ "referenced_widgets": [
+ "af0e4a8ae3d84f119be17835e7a27fcd",
+ "b8552ed1dcd34ec7bbcc827a406c24a0",
+ "e8e45aba8b454323925fe2f0dfc9474f",
+ "c5766cd6a91a4b9e900ffbffcba90ad6",
+ "cb179ca7dcb14cb8b3f80f35571c7a30",
+ "e6524751856d4c43b59fc04819581985",
+ "f42ba058176e45f6bc6d704c5a7a91b7",
+ "9b121cd8373c48619e0c3b31310a2d9a",
+ "785e9061103e49aeabd9a6faa250bd15",
+ "1e01cf803d4b4ba3894819a410c35ca0",
+ "31771bd45bc64bb1b32efa89744cc306",
+ "2c3119e4da3649b3a540b1a1fd70d8f8",
+ "6fde7b807dcf4d96ac68569b38e96e5e",
+ "1c00fdd68f3941d6b4fdf939e32fdafc",
+ "d1e8902478194513a3a4bfe922f5a534",
+ "dc8befe0a84d49c897b6a3afeaab2553",
+ "123a5da00d644a0ab4cbb298732a7467",
+ "976eec94a1ec4c63b6463130589f7190",
+ "b25f61af8f114e609d2743b28bd391a8",
+ "03688f0ab5a14002a66b734636b20a1d",
+ "49bbf31fdaf24aa7bc7f2188ed538a4c",
+ "162cd7de94f74e748108c9814453c8d2"
+ ]
+ },
+ "outputId": "0d52ad57-2d57-4d6c-cedb-7be17039edb4"
+ },
+ "source": [
+ "evaluate_model(tuned_et)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "af0e4a8ae3d84f119be17835e7a27fcd",
+ "version_minor": 0,
+ "version_major": 2
+ },
+ "text/plain": [
+ "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Y99TDL3wnEPp",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### **4.2. Model Interpretaion**\n",
+ "\n",
+ "The ``interpret_model()`` function of PyCaret leverages the use of the SHAP library to produce stunning plots for depicting the **SHAP (SHapley Additive exPlanations)** values that was originally proposed by Lundberg and Lee in 2016.$^5$ In a nutshell, SHAP plots adds interpretability to constructed models so that the contribution of each features to the prediction can be elucidated."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OZjQ6zhcnLb1",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Summary Plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J9lw_MnonNkb",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 236
+ },
+ "outputId": "ac4b2e57-7a57-4131-ed0d-c6d06d894e91"
+ },
+ "source": [
+ "interpret_model(et)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | Parameters | \n", + "
|---|---|
| bootstrap | \n", + "True | \n", + "
| ccp_alpha | \n", + "0 | \n", + "
| criterion | \n", + "mae | \n", + "
| max_depth | \n", + "40 | \n", + "
| max_features | \n", + "auto | \n", + "
| max_leaf_nodes | \n", + "None | \n", + "
| max_samples | \n", + "None | \n", + "
| min_impurity_decrease | \n", + "0 | \n", + "
| min_impurity_split | \n", + "None | \n", + "
| min_samples_leaf | \n", + "1 | \n", + "
| min_samples_split | \n", + "2 | \n", + "
| min_weight_fraction_leaf | \n", + "0 | \n", + "
| n_estimators | \n", + "280 | \n", + "
| n_jobs | \n", + "None | \n", + "
| oob_score | \n", + "False | \n", + "
| random_state | \n", + "7903 | \n", + "
| verbose | \n", + "0 | \n", + "
| warm_start | \n", + "False | \n", + "
"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "v9vySBURT0st",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Correlation Plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B8CpjG_dT3lc",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "outputId": "fccf4bd0-56ba-4be5-e288-fe1da359dd6a"
+ },
+ "source": [
+ "interpret_model(et, plot = 'correlation')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
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+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x4oB5ywjT7Ah",
+ "colab_type": "text"
+ },
+ "source": [
+ "**Reason Plot at Observation Level**\n",
+ "\n",
+ "The *Reason Plot at Observation Level* as called by PyCaret is better known as the **force plot** and this plot essentially describes the ***push and pull effect*** that each individual features has on the **base value** that eventually leads to the predicted **output value**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ITozirCmT-U5",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 193
+ },
+ "outputId": "3c3f7f2d-9087-4361-e441-bc75b56989b3"
+ },
+ "source": [
+ "interpret_model(et, plot = 'reason', observation = 10)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nc0fvCpiW0W6",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **6.6. External Testing**\n",
+ "\n",
+ "We will now apply the trained model (built with 80% subset) to evaluate on the so-called **\"hold-out\"** testing set (the 20% subset) that serves as the unseen data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "B35BZl9WXKTS",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 79
+ },
+ "outputId": "2e1b4761-cd38-4bd3-d01f-3eae6c65ca07"
+ },
+ "source": [
+ "prediction_holdout = predict_model(et)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "
\n",
+ "
\n",
+ " "
+ ],
+ "text/plain": [
+ "\n",
+ " Visualization omitted, Javascript library not loaded!
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n", + " Have you run `initjs()` in this notebook? If this notebook was from another\n", + " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", + " this notebook on github the Javascript has been stripped for security. If you are using\n", + " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Model MAE MSE RMSE R2 RMSLE MAPE\n",
+ "0 Extra Trees Regressor 0.5274 0.5405 0.7352 0.8671 0.1973 -0.2672"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nzDFz104-3OT",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 202
+ },
+ "outputId": "2b27f9dd-6eaf-4125-ecde-1927de5cd75b"
+ },
+ "source": [
+ "prediction_holdout.head()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | Model | \n", + "MAE | \n", + "MSE | \n", + "RMSE | \n", + "R2 | \n", + "RMSLE | \n", + "MAPE | \n", + "
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Extra Trees Regressor | \n", + "0.5274 | \n", + "0.5405 | \n", + "0.7352 | \n", + "0.8671 | \n", + "0.1973 | \n", + "-0.2672 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion logS Label\n",
+ "0 4.87188 293.414 4.0 0.545455 -5.47 -5.0870\n",
+ "1 2.59540 167.850 0.0 0.000000 -2.18 -1.9772\n",
+ "2 3.53712 162.276 0.0 0.500000 -5.23 -4.1647\n",
+ "3 1.34960 116.160 3.0 0.000000 -1.36 -1.3400\n",
+ "4 3.51410 215.362 3.0 0.000000 -3.40 -3.7009"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ARiv3f1iC565",
+ "colab_type": "text"
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vxYjQZf7VgYz",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jwM1QHeLbxJl",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **Reference**\n",
+ "\n",
+ "1. John S. Delaney. [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n",
+ "\n",
+ "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n",
+ "\n",
+ "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n",
+ "\n",
+ "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL: Estimating Aqueous Solubility Directly from Molecular Structure.\n",
+ "\n",
+ "5. Scott M. Lundberg and Su-In Lee. [A Unified Approach to Interpreting Model Predictions](https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions), A Unified Approach to Interpreting Model Predictions, ***Advances in Neural Information Processing Systems 30 (NIPS 2017)***, 2017."
+ ]
+ }
+ ]
+}
\ No newline at end of file
From c3ba93c5c2edcdd80985c5e219398f71777aa08a Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Sun, 9 Aug 2020 22:43:08 +0700
Subject: [PATCH 06/44] Add files via upload
---
...le_linear_regression_model_in_python.ipynb | 1349 +++++++++++++++++
1 file changed, 1349 insertions(+)
create mode 100644 python/How_to_build_a_simple_linear_regression_model_in_python.ipynb
diff --git a/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb b/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb
new file mode 100644
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+++ b/python/How_to_build_a_simple_linear_regression_model_in_python.ipynb
@@ -0,0 +1,1349 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "How_to_build_a_simple_linear_regression_model_in_python.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OQi3X7TNUl5Y",
+ "colab_type": "text"
+ },
+ "source": [
+ "# **How to Build a Simple Linear Regression Model in Python** \n",
+ "\n",
+ "Chanin Nantasenamat\n",
+ "\n",
+ "[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor \n",
+ "\n",
+ "In this Jupyter notebook, we will building a simple linear regression model using the **Delaney Molecular Solubility** dataset."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H661uGwCNFMC",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **1. Retrieving the Dataset**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZkglmVcwpoXG",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **1.1. Original dataset**\n",
+ "\n",
+ "The original [Delaney's dataset](https://pubs.acs.org/doi/10.1021/ci034243x) available as a [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x)$^4$. The full paper is entitled [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x).$^1$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mupO58ZfpiqE",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5NSgd-6Mol_T",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 419
+ },
+ "outputId": "26d12350-2f85-4955-fdcf-c7ca8366f2b4"
+ },
+ "source": [
+ "delaney_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney.csv'\n",
+ "delaney_df = pd.read_csv(delaney_url)\n",
+ "delaney_df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "logS | \n", + "Label | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "4.87188 | \n", + "293.414 | \n", + "4.0 | \n", + "0.545455 | \n", + "-5.47 | \n", + "-5.0870 | \n", + "
| 1 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.18 | \n", + "-1.9772 | \n", + "
| 2 | \n", + "3.53712 | \n", + "162.276 | \n", + "0.0 | \n", + "0.500000 | \n", + "-5.23 | \n", + "-4.1647 | \n", + "
| 3 | \n", + "1.34960 | \n", + "116.160 | \n", + "3.0 | \n", + "0.000000 | \n", + "-1.36 | \n", + "-1.3400 | \n", + "
| 4 | \n", + "3.51410 | \n", + "215.362 | \n", + "3.0 | \n", + "0.000000 | \n", + "-3.40 | \n", + "-3.7009 | \n", + "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Compound ID ... SMILES\n",
+ "0 1,1,1,2-Tetrachloroethane ... ClCC(Cl)(Cl)Cl\n",
+ "1 1,1,1-Trichloroethane ... CC(Cl)(Cl)Cl\n",
+ "2 1,1,2,2-Tetrachloroethane ... ClC(Cl)C(Cl)Cl\n",
+ "3 1,1,2-Trichloroethane ... ClCC(Cl)Cl\n",
+ "4 1,1,2-Trichlorotrifluoroethane ... FC(F)(Cl)C(F)(Cl)Cl\n",
+ "... ... ... ...\n",
+ "1139 vamidothion ... CNC(=O)C(C)SCCSP(=O)(OC)(OC)\n",
+ "1140 Vinclozolin ... CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\n",
+ "1141 Warfarin ... CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O \n",
+ "1142 Xipamide ... Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\n",
+ "1143 XMC ... CNC(=O)Oc1cc(C)cc(C)c1\n",
+ "\n",
+ "[1144 rows x 4 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "biqQ78_hqdb6",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **1.2. Delaney dataset with computed molecular descriptors**\n",
+ "\n",
+ "As demonstrated in a previous YouTube video [Data Science for Computational Drug Discovery using Python](https://www.youtube.com/watch?v=VXFFHHoE1wk) on the Data Professor YouTube channel, SMILES notation from the Delaney dataset was used as *input* for molecular descriptor calculation using the **rdkit** Python library. This produced the 4 molecular descriptors as used by the authors in their published research article."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "olyPX1TjQMvr",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### **Definition of variables**\n",
+ "\n",
+ "The **Y** variable (response variable) is **LogS** (log of the aqueous solubility).\n",
+ "\n",
+ "The **X** variables are comprised of 4 molecular descriptors:\n",
+ "1. **cLogP** *(Octanol-water partition coefficient)*\n",
+ "2. **MW** *(Molecular weight)*\n",
+ "3. **RB** *(Number of rotatable bonds)*\n",
+ "4. **AP** *(Aromatic proportion = number of aromatic atoms / total number of heavy atoms)*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "q9kna0SWkamZ",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 419
+ },
+ "outputId": "3d948275-2329-4cc0-b465-0198efafb183"
+ },
+ "source": [
+ "delaney_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n",
+ "delaney_descriptors_df = pd.read_csv(delaney_descriptors_url)\n",
+ "delaney_descriptors_df"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | Compound ID | \n", + "measured log(solubility:mol/L) | \n", + "ESOL predicted log(solubility:mol/L) | \n", + "SMILES | \n", + "
|---|---|---|---|---|
| 0 | \n", + "1,1,1,2-Tetrachloroethane | \n", + "-2.180 | \n", + "-2.794 | \n", + "ClCC(Cl)(Cl)Cl | \n", + "
| 1 | \n", + "1,1,1-Trichloroethane | \n", + "-2.000 | \n", + "-2.232 | \n", + "CC(Cl)(Cl)Cl | \n", + "
| 2 | \n", + "1,1,2,2-Tetrachloroethane | \n", + "-1.740 | \n", + "-2.549 | \n", + "ClC(Cl)C(Cl)Cl | \n", + "
| 3 | \n", + "1,1,2-Trichloroethane | \n", + "-1.480 | \n", + "-1.961 | \n", + "ClCC(Cl)Cl | \n", + "
| 4 | \n", + "1,1,2-Trichlorotrifluoroethane | \n", + "-3.040 | \n", + "-3.077 | \n", + "FC(F)(Cl)C(F)(Cl)Cl | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "vamidothion | \n", + "1.144 | \n", + "-1.446 | \n", + "CNC(=O)C(C)SCCSP(=O)(OC)(OC) | \n", + "
| 1140 | \n", + "Vinclozolin | \n", + "-4.925 | \n", + "-4.377 | \n", + "CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C | \n", + "
| 1141 | \n", + "Warfarin | \n", + "-3.893 | \n", + "-3.913 | \n", + "CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O | \n", + "
| 1142 | \n", + "Xipamide | \n", + "-3.790 | \n", + "-3.642 | \n", + "Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O | \n", + "
| 1143 | \n", + "XMC | \n", + "-2.581 | \n", + "-2.688 | \n", + "CNC(=O)Oc1cc(C)cc(C)c1 | \n", + "
1144 rows × 4 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n",
+ "0 2.59540 167.850 0.0 0.000000 -2.180\n",
+ "1 2.37650 133.405 0.0 0.000000 -2.000\n",
+ "2 2.59380 167.850 1.0 0.000000 -1.740\n",
+ "3 2.02890 133.405 1.0 0.000000 -1.480\n",
+ "4 2.91890 187.375 1.0 0.000000 -3.040\n",
+ "... ... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000 1.144\n",
+ "1140 3.42130 286.114 2.0 0.333333 -4.925\n",
+ "1141 3.60960 308.333 4.0 0.695652 -3.893\n",
+ "1142 2.56214 354.815 3.0 0.521739 -3.790\n",
+ "1143 2.02164 179.219 1.0 0.461538 -2.581\n",
+ "\n",
+ "[1144 rows x 5 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qQYE-jCRSmCn",
+ "colab_type": "text"
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WRXEmm941h13",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **2. Create X and Y variables**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JeSbdwYA1l3K",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 419
+ },
+ "outputId": "5e139655-ff97-4f95-947a-c6ecbc9c8861"
+ },
+ "source": [
+ "X = delaney_descriptors_df.drop('logS', axis=1)\n",
+ "X"
+ ],
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "logS | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.180 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.740 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.480 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "-3.040 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "1.144 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "-4.925 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "-3.893 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "-3.790 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "-2.581 | \n", + "
1144 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion\n",
+ "0 2.59540 167.850 0.0 0.000000\n",
+ "1 2.37650 133.405 0.0 0.000000\n",
+ "2 2.59380 167.850 1.0 0.000000\n",
+ "3 2.02890 133.405 1.0 0.000000\n",
+ "4 2.91890 187.375 1.0 0.000000\n",
+ "... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000\n",
+ "1140 3.42130 286.114 2.0 0.333333\n",
+ "1141 3.60960 308.333 4.0 0.695652\n",
+ "1142 2.56214 354.815 3.0 0.521739\n",
+ "1143 2.02164 179.219 1.0 0.461538\n",
+ "\n",
+ "[1144 rows x 4 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VGwuoNKs2ReN",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ },
+ "outputId": "27773f04-d07f-4d6f-80b8-323944ddac7a"
+ },
+ "source": [
+ "Y = delaney_descriptors_df.logS\n",
+ "Y"
+ ],
+ "execution_count": 21,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0 -2.180\n",
+ "1 -2.000\n",
+ "2 -1.740\n",
+ "3 -1.480\n",
+ "4 -3.040\n",
+ " ... \n",
+ "1139 1.144\n",
+ "1140 -4.925\n",
+ "1141 -3.893\n",
+ "1142 -3.790\n",
+ "1143 -2.581\n",
+ "Name: logS, Length: 1144, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SzrfuUZNFg_X",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **3. Data split**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dMRn8EVjFlrT",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "from sklearn.model_selection import train_test_split"
+ ],
+ "execution_count": 22,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aOIAljc1FmXb",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
+ ],
+ "execution_count": 23,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "39nTAc3UFUMW",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **4. Linear Regression Model**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "K0MokzGBCimk",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "from sklearn import linear_model\n",
+ "from sklearn.metrics import mean_squared_error, r2_score"
+ ],
+ "execution_count": 24,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vkR1siPuFZ6X",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "e20472e7-0179-419f-8dc8-8710cfd008cc"
+ },
+ "source": [
+ "model = linear_model.LinearRegression()\n",
+ "model.fit(X_train, Y_train)"
+ ],
+ "execution_count": 25,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aG4DMzc5Rks9",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Predicts the X_train**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tZr9CBGvRp1F",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "Y_pred_train = model.predict(X_train)"
+ ],
+ "execution_count": 26,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0x3saPCyRtJP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "68235d8c-e66a-4acb-e4d3-83ef26e92629"
+ },
+ "source": [
+ "print('Coefficients:', model.coef_)\n",
+ "print('Intercept:', model.intercept_)\n",
+ "print('Mean squared error (MSE): %.2f'\n",
+ " % mean_squared_error(Y_train, Y_pred_train))\n",
+ "print('Coefficient of determination (R^2): %.2f'\n",
+ " % r2_score(Y_train, Y_pred_train))"
+ ],
+ "execution_count": 27,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [-0.76779153 -0.00668131 0.00654032 -0.36959403]\n",
+ "Intercept: 0.3108998121270652\n",
+ "Mean squared error (MSE): 1.01\n",
+ "Coefficient of determination (R^2): 0.77\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "M6evZTPNRecd",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Predicts the X_test**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I_eFbrlaHhPU",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "Y_pred_test = model.predict(X_test)"
+ ],
+ "execution_count": 28,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TQnDfyl5HkUr",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "32b22212-424a-458f-8f0f-57095f5903ec"
+ },
+ "source": [
+ "print('Coefficients:', model.coef_)\n",
+ "print('Intercept:', model.intercept_)\n",
+ "print('Mean squared error (MSE): %.2f'\n",
+ " % mean_squared_error(Y_test, Y_pred_test))\n",
+ "print('Coefficient of determination (R^2): %.2f'\n",
+ " % r2_score(Y_test, Y_pred_test))"
+ ],
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [-0.76779153 -0.00668131 0.00654032 -0.36959403]\n",
+ "Intercept: 0.3108998121270652\n",
+ "Mean squared error (MSE): 1.00\n",
+ "Coefficient of determination (R^2): 0.74\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nERFfdQBRFF5",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Linear Regression Equation**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "j3xLiGWHFiY1",
+ "colab_type": "text"
+ },
+ "source": [
+ "The work of Delaney$^1$ provided the following linear regression equation:\n",
+ "\n",
+ "> LogS = 0.16 - 0.63 cLogP - 0.0062 MW + 0.066 RB - 0.74 AP\n",
+ "\n",
+ "The reproduction by Pat Walters$^2$ provided the following:\n",
+ "\n",
+ "> LogS = 0.26 - 0.74 LogP - 0.0066 MW + 0.0034 RB - 0.42 AP\n",
+ "\n",
+ "This notebook's reproduction gave the following equation:\n",
+ "\n",
+ "* Based on the Train set\n",
+ "> LogS = 0.30 -0.75 LogP - .0066 MW -0.0041 RB - 0.36 AP\n",
+ "\n",
+ "* Based on the Full dataset\n",
+ "> LogS = 0.26 -0.74 LogP - 0.0066 + MW 0.0032 RB - 0.42 AP"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FaWyYnMbWtYu",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### **Our linear regression equation**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0TH6J9evHIIE",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "9c960b7b-d2cf-4309-d187-60fa783a9529"
+ },
+ "source": [
+ "print('LogS = %.2f %.2f LogP %.4f MW + %.4f RB %.2f AP' % (model.intercept_, model.coef_[0], model.coef_[1], model.coef_[2], model.coef_[3] ) )"
+ ],
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "LogS = 0.31 -0.77 LogP -0.0067 MW + 0.0065 RB -0.37 AP\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VcJyUzsLSz2A",
+ "colab_type": "text"
+ },
+ "source": [
+ "The same equation can also be produced with the following code (which breaks up the previous one-line code into several comprehensible lines."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "byUbJ9QqK5gA",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "yintercept = '%.2f' % model.intercept_\n",
+ "LogP = '%.2f LogP' % model.coef_[0]\n",
+ "MW = '%.4f MW' % model.coef_[1]\n",
+ "RB = '%.4f RB' % model.coef_[2]\n",
+ "AP = '%.2f AP' % model.coef_[3]"
+ ],
+ "execution_count": 31,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QY-9rh--S-6g",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "095cefae-a92c-466d-8fb2-67abe856494f"
+ },
+ "source": [
+ "print('LogS = ' + \n",
+ " ' ' + \n",
+ " yintercept + \n",
+ " ' ' + \n",
+ " LogP + \n",
+ " ' ' + \n",
+ " MW + \n",
+ " ' + ' + \n",
+ " RB + \n",
+ " ' ' + \n",
+ " AP)"
+ ],
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "LogS = 0.31 -0.77 LogP -0.0067 MW + 0.0065 RB -0.37 AP\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "R3lRkSOJRm1q",
+ "colab_type": "text"
+ },
+ "source": [
+ "#### **Use entire dataset for model training (For Comparison)**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QUye6SsIRl9T",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "d93a60d1-cfb8-4ddd-843c-b3bd42be4161"
+ },
+ "source": [
+ "full = linear_model.LinearRegression()\n",
+ "full.fit(X, Y)"
+ ],
+ "execution_count": 35,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 35
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6tMI8n0oR1b5",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "full_pred = model.predict(X)"
+ ],
+ "execution_count": 36,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7ZVD8Fg1R6zt",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "323a7815-5a7d-49c6-cb13-ad60c7f57e69"
+ },
+ "source": [
+ "print('Coefficients:', full.coef_)\n",
+ "print('Intercept:', full.intercept_)\n",
+ "print('Mean squared error (MSE): %.2f'\n",
+ " % mean_squared_error(Y, full_pred))\n",
+ "print('Coefficient of determination (R^2): %.2f'\n",
+ " % r2_score(Y, full_pred))"
+ ],
+ "execution_count": 37,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [-0.74173609 -0.00659927 0.00320051 -0.42316387]\n",
+ "Intercept: 0.2565006830997194\n",
+ "Mean squared error (MSE): 1.01\n",
+ "Coefficient of determination (R^2): 0.77\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AFYYzcc1VqIo",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "full_yintercept = '%.2f' % full.intercept_\n",
+ "full_LogP = '%.2f LogP' % full.coef_[0]\n",
+ "full_MW = '%.4f MW' % full.coef_[1]\n",
+ "full_RB = '+ %.4f RB' % full.coef_[2]\n",
+ "full_AP = '%.2f AP' % full.coef_[3]"
+ ],
+ "execution_count": 38,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zwU4QJhhVsKb",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "7e3042f6-6447-497b-e6a8-6b58a57599e7"
+ },
+ "source": [
+ "print('LogS = ' + \n",
+ " ' ' + \n",
+ " full_yintercept + \n",
+ " ' ' + \n",
+ " full_LogP + \n",
+ " ' ' + \n",
+ " full_MW + \n",
+ " ' ' + \n",
+ " full_RB + \n",
+ " ' ' + \n",
+ " full_AP)"
+ ],
+ "execution_count": 39,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "LogS = 0.26 -0.74 LogP -0.0066 MW + 0.0032 RB -0.42 AP\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qp-hjUv4IWe-",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **Scatter plot of experimental vs. predicted LogS**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Q6bP41fKEY9O",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Quick check of the variable dimensions of Train and Test sets**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LA5dH5oiEUnP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "11d5f348-d3da-4bc3-ae52-96c6d607e14f"
+ },
+ "source": [
+ "Y_train.shape, Y_pred_train.shape"
+ ],
+ "execution_count": 41,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "((915,), (915,))"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 41
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HIu7YbbFP-7o",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "6460df10-4f85-4b2b-bf60-d555afb7dc41"
+ },
+ "source": [
+ "Y_test.shape, Y_pred_test.shape"
+ ],
+ "execution_count": 42,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "((229,), (229,))"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 42
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OHqv3TlYa5qF",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Vertical plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "shQPfrHIOmRD",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 660
+ },
+ "outputId": "1ddf9dac-2be1-482f-8451-3e4a978fffd0"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "plt.figure(figsize=(5,11))\n",
+ "\n",
+ "# 2 row, 1 column, plot 1\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n",
+ "\n",
+ "# Add trendline\n",
+ "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n",
+ "z = np.polyfit(Y_train, Y_pred_train, 1)\n",
+ "p = np.poly1d(z)\n",
+ "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
+ "\n",
+ "plt.ylabel('Predicted LogS')\n",
+ "\n",
+ "\n",
+ "# 2 row, 1 column, plot 2\n",
+ "plt.subplot(2, 1, 2)\n",
+ "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n",
+ "\n",
+ "z = np.polyfit(Y_test, Y_pred_test, 1)\n",
+ "p = np.poly1d(z)\n",
+ "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
+ "\n",
+ "plt.ylabel('Predicted LogS')\n",
+ "plt.xlabel('Experimental LogS')\n",
+ "\n",
+ "plt.savefig('plot_vertical_logS.png')\n",
+ "plt.savefig('plot_vertical_logS.pdf')\n",
+ "plt.show()"
+ ],
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "
1144 rows × 4 columns
\n", + ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PswCQ7Yra_CW",
+ "colab_type": "text"
+ },
+ "source": [
+ "### **Horizontal plot**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xG7NWEscT8QO",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 334
+ },
+ "outputId": "c22932ab-ba03-46d6-a83f-ae0ebe0d3dca"
+ },
+ "source": [
+ "plt.figure(figsize=(11,5))\n",
+ "\n",
+ "# 1 row, 2 column, plot 1\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.scatter(x=Y_train, y=Y_pred_train, c=\"#7CAE00\", alpha=0.3)\n",
+ "\n",
+ "z = np.polyfit(Y_train, Y_pred_train, 1)\n",
+ "p = np.poly1d(z)\n",
+ "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
+ "\n",
+ "plt.ylabel('Predicted LogS')\n",
+ "plt.xlabel('Experimental LogS')\n",
+ "\n",
+ "# 1 row, 2 column, plot 2\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.scatter(x=Y_test, y=Y_pred_test, c=\"#619CFF\", alpha=0.3)\n",
+ "\n",
+ "z = np.polyfit(Y_test, Y_pred_test, 1)\n",
+ "p = np.poly1d(z)\n",
+ "plt.plot(Y_test,p(Y_test),\"#F8766D\")\n",
+ "\n",
+ "plt.xlabel('Experimental LogS')\n",
+ "\n",
+ "plt.savefig('plot_horizontal_logS.png')\n",
+ "plt.savefig('plot_horizontal_logS.pdf')\n",
+ "plt.show()"
+ ],
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ARiv3f1iC565",
+ "colab_type": "text"
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jwM1QHeLbxJl",
+ "colab_type": "text"
+ },
+ "source": [
+ "## **Reference**\n",
+ "\n",
+ "1. John S. Delaney. [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.\n",
+ "\n",
+ "2. Pat Walters. [Predicting Aqueous Solubility - It's Harder Than It Looks](http://practicalcheminformatics.blogspot.com/2018/09/predicting-aqueous-solubility-its.html). ***Practical Cheminformatics Blog***\n",
+ "\n",
+ "3. Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande. [Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More](https://learning.oreilly.com/library/view/deep-learning-for/9781492039822/), O'Reilly, 2019.\n",
+ "\n",
+ "4. [Supplementary file](https://pubs.acs.org/doi/10.1021/ci034243x) from Delaney's ESOL: Estimating Aqueous Solubility Directly from Molecular Structure."
+ ]
+ }
+ ]
+}
\ No newline at end of file
From 5c8ed10f01a14bf4f4085035142c3734931460f9 Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Thu, 20 Aug 2020 13:14:28 +0700
Subject: [PATCH 07/44] Add files via upload
---
streamlit/boston-house-ml-app.py | 86 ++++++++++++++++++++++++++++++++
1 file changed, 86 insertions(+)
create mode 100644 streamlit/boston-house-ml-app.py
diff --git a/streamlit/boston-house-ml-app.py b/streamlit/boston-house-ml-app.py
new file mode 100644
index 0000000..87d9bcd
--- /dev/null
+++ b/streamlit/boston-house-ml-app.py
@@ -0,0 +1,86 @@
+import streamlit as st
+import pandas as pd
+import shap
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.ensemble import RandomForestRegressor
+
+st.write("""
+# Boston House Price Prediction App
+
+This app predicts the **Boston House Price**!
+""")
+st.write('---')
+
+# Loads the Boston House Price Dataset
+boston = datasets.load_boston()
+X = pd.DataFrame(boston.data, columns=boston.feature_names)
+Y = pd.DataFrame(boston.target, columns=["MEDV"])
+
+# Sidebar
+# Header of Specify Input Parameters
+st.sidebar.header('Specify Input Parameters')
+
+def user_input_features():
+ CRIM = st.sidebar.slider('CRIM', X.CRIM.min(), X.CRIM.max(), X.CRIM.mean())
+ ZN = st.sidebar.slider('ZN', X.ZN.min(), X.ZN.max(), X.ZN.mean())
+ INDUS = st.sidebar.slider('INDUS', X.INDUS.min(), X.INDUS.max(), X.INDUS.mean())
+ CHAS = st.sidebar.slider('CHAS', X.CHAS.min(), X.CHAS.max(), X.CHAS.mean())
+ NOX = st.sidebar.slider('NOX', X.NOX.min(), X.NOX.max(), X.NOX.mean())
+ RM = st.sidebar.slider('RM', X.RM.min(), X.RM.max(), X.RM.mean())
+ AGE = st.sidebar.slider('AGE', X.AGE.min(), X.AGE.max(), X.AGE.mean())
+ DIS = st.sidebar.slider('DIS', X.DIS.min(), X.DIS.max(), X.DIS.mean())
+ RAD = st.sidebar.slider('RAD', X.RAD.min(), X.RAD.max(), X.RAD.mean())
+ TAX = st.sidebar.slider('TAX', X.TAX.min(), X.TAX.max(), X.TAX.mean())
+ PTRATIO = st.sidebar.slider('PTRATIO', X.PTRATIO.min(), X.PTRATIO.max(), X.PTRATIO.mean())
+ B = st.sidebar.slider('B', X.B.min(), X.B.max(), X.B.mean())
+ LSTAT = st.sidebar.slider('LSTAT', X.LSTAT.min(), X.LSTAT.max(), X.LSTAT.mean())
+ data = {'CRIM': CRIM,
+ 'ZN': ZN,
+ 'INDUS': INDUS,
+ 'CHAS': CHAS,
+ 'NOX': NOX,
+ 'RM': RM,
+ 'AGE': AGE,
+ 'DIS': DIS,
+ 'RAD': RAD,
+ 'TAX': TAX,
+ 'PTRATIO': PTRATIO,
+ 'B': B,
+ 'LSTAT': LSTAT}
+ features = pd.DataFrame(data, index=[0])
+ return features
+
+df = user_input_features()
+
+# Main Panel
+
+# Print specified input parameters
+st.header('Specified Input parameters')
+st.write(df)
+st.write('---')
+
+# Build Regression Model
+model = RandomForestRegressor()
+model.fit(X, Y)
+# Apply Model to Make Prediction
+prediction = model.predict(df)
+
+st.header('Prediction of MEDV')
+st.write(prediction)
+st.write('---')
+
+# Explaining the model's predictions using SHAP values
+# https://github.com/slundberg/shap
+explainer = shap.TreeExplainer(model)
+shap_values = explainer.shap_values(X)
+
+st.header('Feature Importance')
+plt.title('Feature importance based on SHAP values')
+shap.summary_plot(shap_values, X)
+st.pyplot(bbox_inches='tight')
+st.write('---')
+
+plt.title('Feature importance based on SHAP values (Bar)')
+shap.summary_plot(shap_values, X, plot_type="bar")
+st.pyplot(bbox_inches='tight')
From 988c7337baa3d3874aaefae798f8785ea0430770 Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Thu, 20 Aug 2020 13:14:49 +0700
Subject: [PATCH 08/44] Rename streamlit/boston-house-ml-app.py to
streamlit/part6/boston-house-ml-app.py
---
streamlit/{ => part6}/boston-house-ml-app.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename streamlit/{ => part6}/boston-house-ml-app.py (100%)
diff --git a/streamlit/boston-house-ml-app.py b/streamlit/part6/boston-house-ml-app.py
similarity index 100%
rename from streamlit/boston-house-ml-app.py
rename to streamlit/part6/boston-house-ml-app.py
From 2d8dfdb2678b201e3187fa412cb24e65acd9a0dd Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Sun, 6 Sep 2020 23:00:35 +0700
Subject: [PATCH 09/44] Add files via upload
---
python/Hummingbird_ML.ipynb | 360 ++++++++++++++++++++++++++++++++++++
1 file changed, 360 insertions(+)
create mode 100644 python/Hummingbird_ML.ipynb
diff --git a/python/Hummingbird_ML.ipynb b/python/Hummingbird_ML.ipynb
new file mode 100644
index 0000000..2e7d19f
--- /dev/null
+++ b/python/Hummingbird_ML.ipynb
@@ -0,0 +1,360 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Hummingbird-ML.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IpOdlr3WAPHJ",
+ "colab_type": "text"
+ },
+ "source": [
+ "# **Hummingbird-ML**\n",
+ "\n",
+ "[How to Harness GPU to Speed Up Machine Learning with Hummingbird-ML](https://www.youtube.com/watch?v=qN8jcUmo8TI)\n",
+ "\n",
+ "Adapted from: https://github.com/microsoft/hummingbird"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ir3DZd5-_jiu",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Install Hummingbird-ML"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ra3JEgWN_bfp",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 408
+ },
+ "outputId": "4fae39de-26f0-4939-846d-039fb876725a"
+ },
+ "source": [
+ "! pip install hummingbird-ml[extra]"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Collecting hummingbird-ml[extra]\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/ed/3b/cf1b8c1e7531377adead8de29e29b00b5aed380544ad0def4c0188b50d80/hummingbird_ml-0.0.5-py2.py3-none-any.whl (60kB)\n",
+ "\r\u001b[K |█████▌ | 10kB 16.6MB/s eta 0:00:01\r\u001b[K |███████████ | 20kB 1.8MB/s eta 0:00:01\r\u001b[K |████████████████▍ | 30kB 2.2MB/s eta 0:00:01\r\u001b[K |█████████████████████▉ | 40kB 2.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████▎ | 51kB 2.0MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61kB 1.8MB/s \n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.18.5)\n",
+ "Requirement already satisfied: torch>=1.4.* in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.6.0+cu101)\n",
+ "Collecting onnxconverter-common>=1.6.0\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/fe/7a/7e30c643cd7d2ad87689188ef34ce93e657bd14da3605f87bcdbc19cd5b1/onnxconverter_common-1.7.0-py2.py3-none-any.whl (64kB)\n",
+ "\u001b[K |████████████████████████████████| 71kB 3.7MB/s \n",
+ "\u001b[?25hRequirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (0.22.2.post1)\n",
+ "Requirement already satisfied: xgboost==0.90; extra == \"extra\" in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (0.90)\n",
+ "Requirement already satisfied: lightgbm>=2.2; extra == \"extra\" in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (2.2.3)\n",
+ "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.*->hummingbird-ml[extra]) (0.16.0)\n",
+ "Collecting onnx\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/36/ee/bc7bc88fc8449266add978627e90c363069211584b937fd867b0ccc59f09/onnx-1.7.0-cp36-cp36m-manylinux1_x86_64.whl (7.4MB)\n",
+ "\u001b[K |████████████████████████████████| 7.4MB 16.0MB/s \n",
+ "\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (3.12.4)\n",
+ "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22.1->hummingbird-ml[extra]) (0.16.0)\n",
+ "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22.1->hummingbird-ml[extra]) (1.4.1)\n",
+ "Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.6/dist-packages (from onnx->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (3.7.4.3)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from onnx->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (1.15.0)\n",
+ "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (49.6.0)\n",
+ "Installing collected packages: onnx, onnxconverter-common, hummingbird-ml\n",
+ "Successfully installed hummingbird-ml-0.0.5 onnx-1.7.0 onnxconverter-common-1.7.0\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YnA-PmeA_q70",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Import libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lkIThThi_puf",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from hummingbird.ml import convert"
+ ],
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rFw_4cGa_-tF",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Create some random data for binary classification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "hGGngPPp__mx",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "num_classes = 2\n",
+ "X = np.random.rand(100000, 28)\n",
+ "y = np.random.randint(num_classes, size=100000)"
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WusxNKH4AHII",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Create and train a model (scikit-learn RandomForestClassifier)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GMRJRuBwAGeV",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "skl_model = RandomForestClassifier(n_estimators=10, max_depth=10)"
+ ],
+ "execution_count": 4,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "M_kGo80yAYTn",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "aa863652-02f8-4578-8fb7-e3b028685cd7"
+ },
+ "source": [
+ "%%timeit\n",
+ "skl_model.fit(X, y)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "1 loop, best of 3: 4.78 s per loop\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Hp4a8I0tAbBl",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "4e083fd5-981f-4238-9158-3f4500585560"
+ },
+ "source": [
+ "%%timeit\n",
+ "skl_model.predict(X)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "10 loops, best of 3: 85.6 ms per loop\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mNiBvy9BA7wR",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Use Hummingbird to convert the model to PyTorch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vcAOpuxxAzPc",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "model = convert(skl_model, 'pytorch')"
+ ],
+ "execution_count": 7,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dpt6_4l8BF7e",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Run predictions on CPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_BiU63hNBDu-",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "1bd8b158-a62b-4fe0-be09-ca382c817247"
+ },
+ "source": [
+ "%%timeit\n",
+ "model.predict(X)"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "1 loop, best of 3: 174 ms per loop\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F10tJEMKBPZG",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Run predictions on GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "l2PUbqoHBJBX",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "model.to('cuda')"
+ ],
+ "execution_count": 9,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-AB23_VTBRMP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "b9efea7d-913c-4326-c14a-6b6ca0e9c063"
+ },
+ "source": [
+ "%%timeit\n",
+ "model.predict(X)"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "The slowest run took 5.22 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+ "100 loops, best of 3: 14.8 ms per loop\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dbkQU69JDt7T",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Calculation Time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hr1R_9nwDwpc",
+ "colab_type": "text"
+ },
+ "source": [
+ "Methods | Timing | Performance\n",
+ "--|--|--\n",
+ "scikit-learn | 85.6 ms | -\n",
+ "PyTorch (CPU) | 174 ms | 2 X slower than scikit-learn\n",
+ "PyTorch (GPU) | 14.8 ms | Almost 6 X faster than scikit-learn; Almost 12 X faster than PyTorch (CPU)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9lmR3LHoEzhl",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From 4cff6de4938580932383557042a8f72334e2cbe3 Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Sun, 6 Sep 2020 23:01:55 +0700
Subject: [PATCH 10/44] Add files via upload
From 09d4a36e564f5d41701f675c8968def2f948a66e Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Wed, 16 Sep 2020 00:27:33 +0700
Subject: [PATCH 11/44] Add files via upload
---
streamlit/solubility-web-app.ipynb | 714 +++++++++++++++++++++++++++++
1 file changed, 714 insertions(+)
create mode 100644 streamlit/solubility-web-app.ipynb
diff --git a/streamlit/solubility-web-app.ipynb b/streamlit/solubility-web-app.ipynb
new file mode 100644
index 0000000..d050266
--- /dev/null
+++ b/streamlit/solubility-web-app.ipynb
@@ -0,0 +1,714 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Streamlit-Solubility.ipynb",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QQHZHevuXdEy",
+ "colab_type": "text"
+ },
+ "source": [
+ "# **Model Building for Solubility Dataset**\n",
+ "\n",
+ "Chanin Nantasenamat\n",
+ "\n",
+ "*Data Professor YouTube channel, http://youtube.com/dataprofessor*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "g1qtHa0zXfWM",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Read in data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9MdfbvFKXtXq",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import pandas as pd"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nerGP0fCXfgP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 419
+ },
+ "outputId": "2bb155a6-2710-4461-accb-df64045ba70d"
+ },
+ "source": [
+ "delaney_with_descriptors_url = 'https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv'\n",
+ "dataset = pd.read_csv(delaney_with_descriptors_url)\n",
+ "dataset"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YzKTmvZrbFVI",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Save Model as Pickle Object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DzjpPyVyb8XO",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import pickle"
+ ],
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "b2K9ajBaaYUk",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "pickle.dump(model, open('solubility_model.pkl', 'wb'))"
+ ],
+ "execution_count": 19,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ef4fyvrEb-NC",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
From 495a750f862ea990179e1e77c430192a3632c108 Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Wed, 16 Sep 2020 00:28:13 +0700
Subject: [PATCH 12/44] Update and rename streamlit/solubility-web-app.ipynb to
streamlit/part7/solubility-web-app.ipynb
---
streamlit/{ => part7}/solubility-web-app.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
rename streamlit/{ => part7}/solubility-web-app.ipynb (99%)
diff --git a/streamlit/solubility-web-app.ipynb b/streamlit/part7/solubility-web-app.ipynb
similarity index 99%
rename from streamlit/solubility-web-app.ipynb
rename to streamlit/part7/solubility-web-app.ipynb
index d050266..8a480c5 100644
--- a/streamlit/solubility-web-app.ipynb
+++ b/streamlit/part7/solubility-web-app.ipynb
@@ -3,7 +3,7 @@
"nbformat_minor": 0,
"metadata": {
"colab": {
- "name": "Streamlit-Solubility.ipynb",
+ "name": "solubility-web-app.ipynb",
"provenance": [],
"toc_visible": true
},
@@ -711,4 +711,4 @@
"outputs": []
}
]
-}
\ No newline at end of file
+}
From 48b3f462eb05074107d1ec39e1e86a5812276510 Mon Sep 17 00:00:00 2001
From: Chanin Nantasenamat <51851491+dataprofessor@users.noreply.github.com>
Date: Wed, 16 Sep 2020 00:29:01 +0700
Subject: [PATCH 13/44] Add files via upload
---
streamlit/part7/solubility-app.py | 110 +++++++++++++++++++++++++++
streamlit/part7/solubility-logo.jpg | Bin 0 -> 218579 bytes
streamlit/part7/solubility_model.pkl | Bin 0 -> 569 bytes
3 files changed, 110 insertions(+)
create mode 100644 streamlit/part7/solubility-app.py
create mode 100644 streamlit/part7/solubility-logo.jpg
create mode 100644 streamlit/part7/solubility_model.pkl
diff --git a/streamlit/part7/solubility-app.py b/streamlit/part7/solubility-app.py
new file mode 100644
index 0000000..596c595
--- /dev/null
+++ b/streamlit/part7/solubility-app.py
@@ -0,0 +1,110 @@
+######################
+# Import libraries
+######################
+import numpy as np
+import pandas as pd
+import streamlit as st
+import pickle
+from PIL import Image
+from rdkit import Chem
+from rdkit.Chem import Descriptors
+
+######################
+# Custom function
+######################
+## Calculate molecular descriptors
+def AromaticProportion(m):
+ aromatic_atoms = [m.GetAtomWithIdx(i).GetIsAromatic() for i in range(m.GetNumAtoms())]
+ aa_count = []
+ for i in aromatic_atoms:
+ if i==True:
+ aa_count.append(1)
+ AromaticAtom = sum(aa_count)
+ HeavyAtom = Descriptors.HeavyAtomCount(m)
+ AR = AromaticAtom/HeavyAtom
+ return AR
+
+def generate(smiles, verbose=False):
+
+ moldata= []
+ for elem in smiles:
+ mol=Chem.MolFromSmiles(elem)
+ moldata.append(mol)
+
+ baseData= np.arange(1,1)
+ i=0
+ for mol in moldata:
+
+ desc_MolLogP = Descriptors.MolLogP(mol)
+ desc_MolWt = Descriptors.MolWt(mol)
+ desc_NumRotatableBonds = Descriptors.NumRotatableBonds(mol)
+ desc_AromaticProportion = AromaticProportion(mol)
+
+ row = np.array([desc_MolLogP,
+ desc_MolWt,
+ desc_NumRotatableBonds,
+ desc_AromaticProportion])
+
+ if(i==0):
+ baseData=row
+ else:
+ baseData=np.vstack([baseData, row])
+ i=i+1
+
+ columnNames=["MolLogP","MolWt","NumRotatableBonds","AromaticProportion"]
+ descriptors = pd.DataFrame(data=baseData,columns=columnNames)
+
+ return descriptors
+
+######################
+# Page Title
+######################
+
+image = Image.open('solubility-logo.jpg')
+
+st.image(image, use_column_width=True)
+
+st.write("""
+# Molecular Solubility Prediction Web App
+
+This app predicts the **Solubility (LogS)** values of molecules!
+
+Data obtained from the John S. Delaney. [ESOL: Estimating Aqueous Solubility Directly from Molecular Structure](https://pubs.acs.org/doi/10.1021/ci034243x). ***J. Chem. Inf. Comput. Sci.*** 2004, 44, 3, 1000-1005.
+***
+""")
+
+
+######################
+# Input molecules (Side Panel)
+######################
+
+st.sidebar.header('User Input Features')
+
+## Read SMILES input
+SMILES_input = "NCCCC\nCCC\nCN"
+
+SMILES = st.sidebar.text_area("SMILES input", SMILES_input)
+SMILES = "C\n" + SMILES #Adds C as a dummy, first item
+SMILES = SMILES.split('\n')
+
+st.header('Input SMILES')
+SMILES[1:] # Skips the dummy first item
+
+## Calculate molecular descriptors
+st.header('Computed molecular descriptors')
+X = generate(SMILES)
+X[1:] # Skips the dummy first item
+
+######################
+# Pre-built model
+######################
+
+# Reads in saved model
+load_model = pickle.load(open('solubility_model.pkl', 'rb'))
+
+# Apply model to make predictions
+prediction = load_model.predict(X)
+#prediction_proba = load_model.predict_proba(X)
+
+st.header('Predicted LogS values')
+prediction[1:] # Skips the dummy first item
diff --git a/streamlit/part7/solubility-logo.jpg b/streamlit/part7/solubility-logo.jpg
new file mode 100644
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\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion logS\n",
+ "0 2.59540 167.850 0.0 0.000000 -2.180\n",
+ "1 2.37650 133.405 0.0 0.000000 -2.000\n",
+ "2 2.59380 167.850 1.0 0.000000 -1.740\n",
+ "3 2.02890 133.405 1.0 0.000000 -1.480\n",
+ "4 2.91890 187.375 1.0 0.000000 -3.040\n",
+ "... ... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000 1.144\n",
+ "1140 3.42130 286.114 2.0 0.333333 -4.925\n",
+ "1141 3.60960 308.333 4.0 0.695652 -3.893\n",
+ "1142 2.56214 354.815 3.0 0.521739 -3.790\n",
+ "1143 2.02164 179.219 1.0 0.461538 -2.581\n",
+ "\n",
+ "[1144 rows x 5 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 2
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "tgFxx8m_YEUy",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 419
+ },
+ "outputId": "fd6feedd-253b-4189-d400-a8d3f5bf1f25"
+ },
+ "source": [
+ "X = dataset.drop(['logS'], axis=1)\n",
+ "X"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "logS | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.180 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "-2.000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.740 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "-1.480 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "-3.040 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "1.144 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "-4.925 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "-3.893 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "-3.790 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "-2.581 | \n", + "
1144 rows × 5 columns
\n", + "\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " MolLogP MolWt NumRotatableBonds AromaticProportion\n",
+ "0 2.59540 167.850 0.0 0.000000\n",
+ "1 2.37650 133.405 0.0 0.000000\n",
+ "2 2.59380 167.850 1.0 0.000000\n",
+ "3 2.02890 133.405 1.0 0.000000\n",
+ "4 2.91890 187.375 1.0 0.000000\n",
+ "... ... ... ... ...\n",
+ "1139 1.98820 287.343 8.0 0.000000\n",
+ "1140 3.42130 286.114 2.0 0.333333\n",
+ "1141 3.60960 308.333 4.0 0.695652\n",
+ "1142 2.56214 354.815 3.0 0.521739\n",
+ "1143 2.02164 179.219 1.0 0.461538\n",
+ "\n",
+ "[1144 rows x 4 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 5
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "JDwxgKHqYmD4",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ },
+ "outputId": "a725d7b7-baad-4a99-9686-4dfe1d852c22"
+ },
+ "source": [
+ "Y = dataset.iloc[:,-1]\n",
+ "Y"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0 -2.180\n",
+ "1 -2.000\n",
+ "2 -1.740\n",
+ "3 -1.480\n",
+ "4 -3.040\n",
+ " ... \n",
+ "1139 1.144\n",
+ "1140 -4.925\n",
+ "1141 -3.893\n",
+ "1142 -3.790\n",
+ "1143 -2.581\n",
+ "Name: logS, Length: 1144, dtype: float64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LNohCdqQY5VZ",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Linear Regression Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EanoyG2eX9cV",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "from sklearn import linear_model\n",
+ "from sklearn.metrics import mean_squared_error, r2_score"
+ ],
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mLQJ2KLLY_9a",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "6349fa74-f087-4d81-916e-294789c6455c"
+ },
+ "source": [
+ "model = linear_model.LinearRegression()\n",
+ "model.fit(X, Y)"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F5f8KGWjZRSc",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Model Prediction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MI3c8LB2ZCYW",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 51
+ },
+ "outputId": "19b50c6a-7d1c-4bfd-8789-d5884b42d594"
+ },
+ "source": [
+ "Y_pred = model.predict(X)\n",
+ "Y_pred"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([-2.77628837, -2.38661054, -2.77190108, ..., -4.73721496,\n",
+ " -4.19663007, -2.61784284])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fXv7bcolZqa-",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Model Performance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6f13gYleZVKy",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 85
+ },
+ "outputId": "99894d58-83b4-4b64-f54c-848e8430cd5e"
+ },
+ "source": [
+ "print('Coefficients:', model.coef_)\n",
+ "print('Intercept:', model.intercept_)\n",
+ "print('Mean squared error (MSE): %.2f'\n",
+ " % mean_squared_error(Y, Y_pred))\n",
+ "print('Coefficient of determination (R^2): %.2f'\n",
+ " % r2_score(Y, Y_pred))"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Coefficients: [-0.74173609 -0.00659927 0.00320051 -0.42316387]\n",
+ "Intercept: 0.2565006830997194\n",
+ "Mean squared error (MSE): 1.01\n",
+ "Coefficient of determination (R^2): 0.77\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Yhuc402dZsk3",
+ "colab_type": "text"
+ },
+ "source": [
+ "## Model Equation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QnoUESmXZcMo",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "c2e3b76f-4d9a-425c-99e4-5793dc6c1620"
+ },
+ "source": [
+ "print('LogS = %.2f %.2f LogP %.4f MW + %.4f RB %.2f AP' % (model.intercept_, model.coef_[0], model.coef_[1], model.coef_[2], model.coef_[3] ) )"
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "LogS = 0.26 -0.74 LogP -0.0066 MW + 0.0032 RB -0.42 AP\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uWvxj1iSaL3n",
+ "colab_type": "text"
+ },
+ "source": [
+ "# Data Visualization (Experimental vs Predicted LogS for Training Data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iPcFF0MjZlh8",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np"
+ ],
+ "execution_count": 11,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QRNyIlGAaQQI",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 351
+ },
+ "outputId": "949bd284-5952-496f-a57e-47a1333fd50b"
+ },
+ "source": [
+ "plt.figure(figsize=(5,5))\n",
+ "plt.scatter(x=Y, y=Y_pred, c=\"#7CAE00\", alpha=0.3)\n",
+ "\n",
+ "# Add trendline\n",
+ "# https://stackoverflow.com/questions/26447191/how-to-add-trendline-in-python-matplotlib-dot-scatter-graphs\n",
+ "z = np.polyfit(Y, Y_pred, 1)\n",
+ "p = np.poly1d(z)\n",
+ "\n",
+ "plt.plot(Y,p(Y),\"#F8766D\")\n",
+ "plt.ylabel('Predicted LogS')\n",
+ "plt.xlabel('Experimental LogS')"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Experimental LogS')"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "| \n", + " | MolLogP | \n", + "MolWt | \n", + "NumRotatableBonds | \n", + "AromaticProportion | \n", + "
|---|---|---|---|---|
| 0 | \n", + "2.59540 | \n", + "167.850 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 1 | \n", + "2.37650 | \n", + "133.405 | \n", + "0.0 | \n", + "0.000000 | \n", + "
| 2 | \n", + "2.59380 | \n", + "167.850 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 3 | \n", + "2.02890 | \n", + "133.405 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| 4 | \n", + "2.91890 | \n", + "187.375 | \n", + "1.0 | \n", + "0.000000 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 1139 | \n", + "1.98820 | \n", + "287.343 | \n", + "8.0 | \n", + "0.000000 | \n", + "
| 1140 | \n", + "3.42130 | \n", + "286.114 | \n", + "2.0 | \n", + "0.333333 | \n", + "
| 1141 | \n", + "3.60960 | \n", + "308.333 | \n", + "4.0 | \n", + "0.695652 | \n", + "
| 1142 | \n", + "2.56214 | \n", + "354.815 | \n", + "3.0 | \n", + "0.521739 | \n", + "
| 1143 | \n", + "2.02164 | \n", + "179.219 | \n", + "1.0 | \n", + "0.461538 | \n", + "
1144 rows × 4 columns
\n", + "