How to make 2D Z values for matpotlib surface plot?

user_1044422 0
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I am trying to create a surface plot of the error function in linear regression. I do it like this:

class LinearRegression:
def __init__(self):
    self.data = pd.read_csv("data.csv")

def computeCost(self):
    j = 0.5 * (
        (self.data.hypothesis - self.data.y)**2).sum() / self.data.y.size
    return j

def regress(self, theta0, theta1):
    self.data["hypothesis"] = theta1 * self.data.x + theta0

def plotCostFunction3D(self):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')

    theta0_vals = np.linspace(-100, 100, 200)
    theta1_vals = np.linspace(-100, 100, 200)
    costs = []

    for theta0 in theta0_vals:
        for theta1 in theta1_vals:
            self.regress(theta0, theta1)
            costs.append(self.computeCost())

    ax.plot_surface(
        theta0_vals,
        theta1_vals,
        np.array(costs),
    )


if __name__ == "__main__":
    regression = LinearRegression()

    regression.plotCostFunction3D()

    plt.show()

I get the following error:

ValueError: Argument Z must be 2-dimensional.

I am aware that I need to use np.meshgrid for theta0_vals and theta1_vals, but I'm not sure how to compute the costs from those results. How would I go about it?

linear-regression machine-learning matplotlib
1 Answers

The error is caused by the method call ax.plot_surface(theta0_vals, theta1_vals, np.array(costs)), because Axes3D.plot_surface(X, Y, Z) expects its arguments to be two-dimensional arrays.

So as you note, np.meshgrid() should be used to compute the grid spanned by theta0_vals and theta1_vals. Regarding Z, you have already computed the cost at every point of the grid using the nested for loops, so you just need to turn the one-dimensional costs list into a two-dimensional array corresponding to the X-Y grid. This can be done with np.reshape().

    def plotCostFunction3D(self):
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')

        theta0_vals = np.linspace(-100, 100, 200)
        theta1_vals = np.linspace(-100, 100, 200)
        costs = []

        for theta0 in theta0_vals:
            for theta1 in theta1_vals:
                self.regress(theta0, theta1)
                costs.append(self.computeCost())
         
        X, Y = np.meshgrid(theta0_vals, theta1_vals)
        Z = np.reshape(costs, (200, 200))
        
        ax.plot_surface(X, Y, Z)

For better performance, it would be nice to avoid the nested for loops. You could store the X-Y grid points in a dataframe and then compute the Z column with df.apply().

user_130141720
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