Implementing the Gradient Descent Algorithm¶

In this lab, we'll implement the basic functions of the Gradient Descent algorithm to find the boundary in a small dataset. First, we'll start with some functions that will help us plot and visualize the data.

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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

#Some helper functions for plotting and drawing lines

def plot_points(X, y):
    admitted = X[np.argwhere(y==1)]
    rejected = X[np.argwhere(y==0)]
    plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'blue', edgecolor = 'k')
    plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'red', edgecolor = 'k')

def display(m, b, color='g--'):
    plt.xlim(-0.05,1.05)
    plt.ylim(-0.05,1.05)
    x = np.arange(-10, 10, 0.1)
    plt.plot(x, m*x+b, color)

Reading and plotting the data¶

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data = pd.read_csv('data.csv', header=None)
X = np.array(data[[0,1]])
y = np.array(data[2])
plot_points(X,y)
plt.show()

TODO: Implementing the basic functions¶

Here is your turn to shine. Implement the following formulas, as explained in the text.

  • Sigmoid activation function

$$\sigma(x) = \frac{1}{1+e^{-x}}$$

  • Output (prediction) formula

$$\hat{y} = \sigma(w_1 x_1 + w_2 x_2 + b)$$

  • Error function

$$Error(y, \hat{y}) = - y \log(\hat{y}) - (1-y) \log(1-\hat{y})$$

  • The function that updates the weights

$$ w_i \longrightarrow w_i + \alpha (y - \hat{y}) x_i$$

$$ b \longrightarrow b + \alpha (y - \hat{y})$$

In [ ]:
# Activation (sigmoid) function
def sigmoid(x):
    pass

def output_formula(features, weights, bias):
    pass

def error_formula(y, output):
    pass

def update_weights(x, y, weights, bias, learnrate):
    pass

Training function¶

This function will help us iterate the gradient descent algorithm through all the data, for a number of epochs. It will also plot the data, and some of the boundary lines obtained as we run the algorithm.

In [ ]:
np.random.seed(44)

epochs = 100
learnrate = 0.01

def train(features, targets, epochs, learnrate, graph_lines=False):
    
    errors = []
    n_records, n_features = features.shape
    last_loss = None
    weights = np.random.normal(scale=1 / n_features**.5, size=n_features)
    bias = 0
    for e in range(epochs):
        del_w = np.zeros(weights.shape)
        for x, y in zip(features, targets):
            weights, bias = update_weights(x, y, weights, bias, learnrate)
        
        # Printing out the log-loss error on the training set
        out = output_formula(features, weights, bias)
        loss = np.mean(error_formula(targets, out))
        errors.append(loss)
        if e % (epochs / 10) == 0:
            print("\n========== Epoch", e,"==========")
            if last_loss and last_loss < loss:
                print("Train loss: ", loss, "  WARNING - Loss Increasing")
            else:
                print("Train loss: ", loss)
            last_loss = loss
            
            # Converting the output (float) to boolean as it is a binary classification
            # e.g. 0.95 --> True (= 1), 0.31 --> False (= 0)
            predictions = out > 0.5
            
            accuracy = np.mean(predictions == targets)
            print("Accuracy: ", accuracy)
        if graph_lines and e % (epochs / 100) == 0:
            display(-weights[0]/weights[1], -bias/weights[1])
            

    # Plotting the solution boundary
    plt.title("Solution boundary")
    display(-weights[0]/weights[1], -bias/weights[1], 'black')

    # Plotting the data
    plot_points(features, targets)
    plt.show()

    # Plotting the error
    plt.title("Error Plot")
    plt.xlabel('Number of epochs')
    plt.ylabel('Error')
    plt.plot(errors)
    plt.show()

Time to train the algorithm!¶

When we run the function, we'll obtain the following:

  • 10 updates with the current training loss and accuracy
  • A plot of the data and some of the boundary lines obtained. The final one is in black. Notice how the lines get closer and closer to the best fit, as we go through more epochs.
  • A plot of the error function. Notice how it decreases as we go through more epochs.
In [ ]:
train(X, y, epochs, learnrate, True)
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