Assignment3 - Single Layer Perceptron Leaky Relu

"""
@author: chrispellegrino
Class: CS 767
Assignment: 3
Using single layer Perceptron neural network which is connected to “Leaky ReLU”
activation function with (a=0.05) to predict salary of baseball players using the data set
“Assignment_3_Hitters.csv”.
Use batch gradient descent to adjust the weights and predict salary
with L2 regularization and Lasso with lambda = 0.01 and 10.
(i) Input data is Assignment_3_Hitters.csv data, which is available in Blackboard.
(ii)    Write a code and build a single layer Perceptron with Leaky ReLU activation function as follows.
(iii)   Use all the features to predict Salary.
(iv)    Assume anything that is needed to solve the problem. Make sure to state your assumptions.
a. (30 points) Choose a learning rate. Show details of your work and all the steps that you take to choose a suitable learning rate.
b. (20 points) Plot total cost (MSE - Mean Square Error + regularization cost) as a function of iterations for both regularizations.
c. (50 points) Create a table of the weights and show the final weights of the solution
i. Without regularization
ii. With L2 regularization and two different lambdas
iii. With Lasso regularization and two different lambdas
"""

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

hitters = pd.read_csv('/Users/chrispellegrino/Desktop/CS 767 Machine Learning/Assignments/Assignment 3/Assignment_3_Hitters.csv')
hitters = hitters.dropna()
print(hitters)
print(hitters.dtypes)

#converts column to category so python knows each unique value is category
hitters['League'] = hitters.League.astype('category').cat.codes
hitters['NewLeague'] = hitters.NewLeague.astype('category').cat.codes
hitters['Division'] = hitters.Division.astype('category').cat.codes

#iloc means accessing by number, and loc is by name
x = hitters.loc[:, ['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years',
       'CAtBat', 'CHits', 'CHmRun', 'CRuns', 'CRBI', 'CWalks', 'League',
       'Division', 'PutOuts', 'Assists', 'Errors', 'NewLeague']]

at_bat = hitters.loc[:, ['AtBat']]
hits = hitters.loc[:, ['Hits']]
home_runs = hitters.loc[:, ['HmRun']]
runs = hitters.loc[:, ['Runs']]
rbi = hitters.loc[:, ['RBI']]
walks = hitters.loc[:, ['Walks']]
years = hitters.loc[:, ['Years']]
catbat = hitters.loc[:, ['CAtBat']]
chits = hitters.loc[:, ['CHits']]
chmrun = hitters.loc[:, ['CHmRun']]
cruns = hitters.loc[:, ['CRuns']]
crbi = hitters.loc[:, ['CRBI']]
cwalks = hitters.loc[:, ['CWalks']]
league = hitters.loc[:, ['League']]
division = hitters.loc[:, ['League']]
putouts = hitters.loc[:, ['PutOuts']]
assists = hitters.loc[:, ['Assists']]
errors = hitters.loc[:, ['Errors']]
new_league = hitters.loc[:, ['NewLeague']]
y = hitters.loc[:, ['Salary']]
x = x.to_numpy()
y = y.to_numpy()


#convert every row into a numpy array
x = [
     np.array(at_bat).reshape(1, 263),
     np.array(hits).reshape(1, 263),
     np.array(home_runs).reshape(1, 263),
     np.array(rbi).reshape(1, 263),
     np.array(walks).reshape(1, 263),
     np.array(years).reshape(1, 263),
     np.array(catbat).reshape(1, 263),
     np.array(chits).reshape(1, 263),
     np.array(chmrun).reshape(1, 263),
     np.array(cruns).reshape(1, 263),
     np.array(crbi).reshape(1, 263),
     np.array(cwalks).reshape(1, 263),
     np.array(league).reshape(1, 263),
     np.array(division).reshape(1, 263),
     np.array(putouts).reshape(1, 263),
     np.array(assists).reshape(1, 263),
     np.array(errors).reshape(1, 263),
     np.array(new_league).reshape(1, 263)
     ]



'''
def generate_weight(x, y):
    list1 =[]
    for i in range(x * y):
        list1.append(np.random.randn())
    return(np.array(list1).reshape(x, y)) #reshape keeps dimensions of weight (20, 5)

#20 features and 5 selected arbitrarily
weight1 = generate_weight(20, 5)
weight2 = generate_weight(5, 1) #1 because we are just trying to predict one output


#leaky relu
def leaky_relu(x):
    x = np.where(x > 0, x, x * 0.01)
    return x

#feed foward, x is the feature
def feed_forward(x, weight1, weight2):
    # input layer to hidden layer
    input_layer_input = x.dot(weight1)# input from layer 1
    input_layer_output = leaky_relu(input_layer_input)# output of layer 2 (output is the hidden layer)

    # hidden layer to output layer
    hidden_layer_input = input_layer_output.dot(weight2)# input of 1st output layer
    hidden_layer_output = leaky_relu(hidden_layer_input)# output of out layer
    return(hidden_layer_output)


# for loss we will be using mean square error(MSE)
def loss(out, Y): #out is the output from feed forward, and Y is actualy salary
    s =(np.square(out-Y))
    s = np.sum(s)/len(Y)
    return(s)

# Back propagation of error
def back_prop(x, y, weight1, weight2, learning_rate):

    # output layer back to hidden layer
    output_layer_input = x.dot(weight1)# input from layer 1
    output_layer_output = leaky_relu(output_layer_input)# output of layer 2

    # hidden layer back to input layer
    hidden_layer_input2 = output_layer_output.dot(weight2)# input of out layer
    hidden_layer_output2 = leaky_relu(hidden_layer_input2)# output of out layer
    # error in output layer
    d2 =(hidden_layer_output2-y)
    d1 = np.multiply((weight2.dot((d2.transpose()))).transpose(),
                                   (np.multiply(output_layer_output, 1-output_layer_output)))

    # Gradient for weight1 and weight2
    weight1_adj = x.transpose().dot(d1)
    weight2_adj = output_layer_output.transpose().dot(d2)

    # Updating parameters
    weight1 = weight1-(learning_rate*(weight1_adj))
    weight2 = weight2-(learning_rate*(weight2_adj))

    return(weight1, weight2)


#train is method to measure accuracy of model
def train(x, Y, weight1, weight2, learning_rate = 0.01, epoch = 10):
    accuracy_training_set =[]
    test_set =[]
    for j in range(epoch):
        list2 =[]
        for i in range(len(x)): #looping through all features
            out = feed_forward(x[i], weight1, weight2) #running through feed forward
            list2.append((test_set(out, Y[i]))) #adding output to the list
            weight1, weight2 = back_prop(x[i], Y[i], weight1, weight2, learning_rate) #back propogate to get new weights and update network
        accuracy_training_set.append((1-(sum(list2)/len(x)))*100)
        test_set.append(sum(list2)/len(x))
    return(accuracy_training_set, test_set, weight1, weight2)


def predict_salary(x, weight1, weight2):
    Out = feed_forward(x, weight1, weight2)
    for i in range(len(Out[0])):
        Out[0[i]]

# x is take all the features except salary, which is Y
accuracy_training_set, test_set, weight1, weight2 = train(x, y, weight1, weight2, 0.01, 100)

# ploting accuracy
plt.plot(accuracy_training_set)
plt.ylabel('Accuracy Training Set')
plt.xlabel('Epochs/Iterations:')
plt.show()

# plotting Loss
plt.plot(test_set)
plt.ylabel('Test Set')
plt.xlabel('Epochs/Iterations:')
plt.show()

#predict salary of players
predict_salary(x[i], weight1, weight2)

'''