Linear Regression for data read from a csv file

Linear Regression for a dataset read from a csv file

Author: azevedolab

linear_regression_01.py

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+#!/usr/bin/env python3
+#
+# Linear Regression for a dataset read from a csv file. We applied closed form
+# equations to determine theta1 and theta0 (James et al., 2021). We have two
+# datasets: CASF-2016 Ki (de Azevedo et al., 2024) and Amsterdam Apartments
+# dataset (Wolf, 2022).
+# This code is for linear models as expressed by the following equation:
+# y = theta0 + theta1*X, where theta0 is the fit intercept and theta1 the
+# coeficient, theta0 and theta1 are the model’s parameters (Géron, 2023).
+#
+# References
+# de Azevedo WF Jr, Quiroga R, Villarreal MA, da Silveira NJF,
+# Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M,
+# Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of
+# machine-learning models to explore the scoring function space. J Comput Chem.
+# 2024 Jun 20. doi: 10.1002/jcc.27449. Epub ahead of print. PMID: 38900052.
+#
+# Géron, Aurélien. 2023. Hands-on Machine Learning with Scikit-Learn, Keras,
+# and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.
+# 3rd ed. CA 95472: O’Reilly.
+#
+# James, G., Witten, D., Hastie, T., & Tibshirani, R. 2021. An introduction to
+# statistical learning: With applications in R (2nd ed.). Springer.
+#
+# Wolf, Andrew. The Machine Learning Simplified: A Gentle Introduction to
+# Supervised Learning. Andrew Wolf. Kindle Edition.
+#
+################################################################################
+# Dr. Walter F. de Azevedo, Jr.                                                #
+# https://github.com/azevedolab                                                #
+# July 20, 2024                                                                #
+################################################################################
+#
+# Import section
+import numpy as np
+import reg2D_model
+import csv
+from scipy.stats import pearsonr, spearmanr
+
+# Read csv file
+# CASF-2016 dataset
+# de Azevedo WF Jr, Quiroga R, Villarreal MA, da Silveira NJF,
+# Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M,
+# Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of
+# machine-learning models to explore the scoring function space. J Comput Chem.
+# 2024 Jun 20. doi: 10.1002/jcc.27449. Epub ahead of print. PMID: 38900052.
+file_in = "CASF-2016_Ki_training.csv"
+
+# Amsterdam Apartments dataset (Amsterdam)
+# Wolf, Andrew. The Machine Learning Simplified: A Gentle Introduction to
+# Supervised Learning. Andrew Wolf. Kindle Edition.
+#file_in = "Amsterdam_apartments_training_set.csv"
+
+# Get header from a csv file
+fo = open(file_in,"r")
+csv_in = csv.reader(fo)
+for line in csv_in:
+    i_y = line.index("pKi")
+    i_X = line.index("Gauss 2")
+    #i_y = line.index("Price")
+    #i_X = line.index("Area")
+    break
+fo.close()
+
+# Get numerical data
+data = np.genfromtxt(file_in, delimiter=',', skip_header = 1)
+X = data[:,i_X]
+y = data[:,i_y]
+
+# Calculate parameters (theta0,theta1) as defined elsewhere
+# James, G., Witten, D., Hastie, T., & Tibshirani, R. 2021. An introduction to
+# statistical learning: With applications in R (2nd ed.). Springer.
+X_bar = X.mean()
+y_bar = y.mean()
+theta1 = np.sum( (X - X_bar)*(y - y_bar) )/np.sum( (X - X_bar)**2 )
+theta0 = y_bar - theta1*X_bar
+
+# Make predictions
+X_new = np.array([[X.min()],[X.max()]])
+y_predict = theta0 + theta1*X_new
+
+# Plotting with reg2D_model() function
+theta0_str = "{:.8f}".format(theta0)
+theta1_str = "{:.8f}".format(theta1)
+#title_str = "CASF-2016 K$_i$ (Training Set)"
+title_str = "Amsterdan Apartments (Training Set)"
+label_str = "Predictions ( y = "+theta0_str+" + "+theta1_str+" x )"
+reg2D_model.plotting(title_str,
+                #X,y,"b.",[0,120,0,120],
+                #X_new,y_predict,"r-",label_str,"Area(m$^2$)","Price(10K*Euros)",
+                    X,y,"b.",[-200,3200,0,15],
+                    X_new,y_predict,"r-",label_str,"Gauss 2","pK$_i$",
+                    "upper left",True,file_in.replace(".csv",".png"),600)
+
+# Show predictive performance
+msg_out = "\nPredictive performance"
+print(msg_out)
+y_full_predictions = theta0 + theta1*X
+rss = np.sum( np.square(y_full_predictions - y)   )
+p_corr = pearsonr(y,y_full_predictions)
+s_corr = spearmanr(y,y_full_predictions)
+print("RSS = {:.5f}".format(rss))
+print("r = {:.5f}".format(p_corr[0]),"\tp-value = {:.5e}".format(p_corr[1]))
+print("rho = {:.5f}".format(s_corr[0]),"\tp-value = {:.5e}".format(s_corr[1]))