ignore graph creation during time measurement

h = .02  # step size in the mesh

start_time = time()

is_measuring = True 

names = ["Nearest Neighbors", "Linear SVM", "Kernel SVM",

         "Decision Tree", "Random Forest", "Neural Net",

         "Naive Bayes"]

            linearly_separable

            ]

figure = plt.figure(figsize=(27, 11))

figure = plt.figure(figsize=(27, 11)) #if not is_measuring else None

i = 1

# iterate over datasets

for ds_cnt, ds in enumerate(datasets):

    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)

    if ds_cnt == 0:

        ax.set_title("Input data")

    if not is_measuring:

    # Plot the training points

        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,

                edgecolors='k')

        else:

            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

        if not is_measuring:

            # Put the result into a color plot

            Z = Z.reshape(xx.shape)

            ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

end_time = time()

time_elapsed = end_time-start_time

print(f"script ran for {time_elapsed} seconds")

if not is_measuring:

    plt.tight_layout()

    plt.show() # not needed in interactive Jupyter session

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