Update utils.py. Removed the disp=False argument in sm.Logit() and up…

…dated the fit() method

msdbook/utils.py

@@ -3,30 +3,30 @@
 import numpy as np
 import statsmodels.api as sm
 
-
 def fit_logit(dta, predictors):
     """Logistic regression"""
 
-    # concatenate intercept column of 1s
+    # Add intercept column of 1s
     dta["Intercept"] = np.ones(np.shape(dta)[0])
-    # get columns of predictors
+
+    # Get columns of predictors
     cols = dta.columns.tolist()[-1:] + predictors + ["Interaction"]
-    # fit logistic regression
-    logit = sm.Logit(dta["Success"], dta[cols], disp=False)
-    result = logit.fit()
-
+
+    # Fit logistic regression without the deprecated 'disp' argument
+    logit = sm.Logit(dta["Success"], dta[cols])
+    result = logit.fit(method='bfgs')  # Use method='bfgs' or another supported method
+
     return result
 
-
 def plot_contour_map(
     ax, result, dta, contour_cmap, dot_cmap, levels, xgrid, ygrid, xvar, yvar, base
 ):
     """Plot the contour map"""
 
-    # TODO:  see why this warning is being raised about the tight layout
+    # Ignore tight layout warnings
     warnings.filterwarnings("ignore")
 
-    # find probability of success for x=xgrid, y=ygrid
+    # Generate probability of success for x=xgrid, y=ygrid
     X, Y = np.meshgrid(xgrid, ygrid)
     x = X.flatten()
     y = Y.flatten()
@@ -36,9 +36,12 @@ def plot_contour_map(
     Z = np.reshape(z, np.shape(X))
 
     contourset = ax.contourf(X, Y, Z, levels, cmap=contour_cmap, aspect="auto")
+
+    # Plot scatter points based on the data
     xpoints = np.mean(dta[xvar].values.reshape(-1, 10), axis=1)
     ypoints = np.mean(dta[yvar].values.reshape(-1, 10), axis=1)
     colors = np.round(np.mean(dta["Success"].values.reshape(-1, 10), axis=1), 0)
+
     ax.scatter(xpoints, ypoints, s=10, c=colors, edgecolor="none", cmap=dot_cmap)
     ax.set_xlim(np.min(xgrid), np.max(xgrid))
     ax.set_ylim(np.min(ygrid), np.max(ygrid))