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107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
"""
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Linear regression is the most basic type of regression commonly used for
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predictive analysis. The idea is pretty simple: we have a dataset and we have
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features associated with it. Features should be chosen very cautiously
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as they determine how much our model will be able to make future predictions.
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We try to set the weight of these features, over many iterations, so that they best
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fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
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Rating). We try to best fit a line through dataset and estimate the parameters.
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"""
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import numpy as np
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import requests
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def collect_dataset():
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""" Collect dataset of CSGO
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The dataset contains ADR vs Rating of a Player
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:return : dataset obtained from the link, as matrix
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"""
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response = requests.get(
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"https://raw.githubusercontent.com/yashLadha/"
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+ "The_Math_of_Intelligence/master/Week1/ADRvs"
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+ "Rating.csv"
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)
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lines = response.text.splitlines()
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data = []
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for item in lines:
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item = item.split(",")
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data.append(item)
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data.pop(0) # This is for removing the labels from the list
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dataset = np.matrix(data)
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return dataset
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def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
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""" Run steep gradient descent and updates the Feature vector accordingly_
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:param data_x : contains the dataset
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:param data_y : contains the output associated with each data-entry
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:param len_data : length of the data_
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:param alpha : Learning rate of the model
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:param theta : Feature vector (weight's for our model)
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;param return : Updated Feature's, using
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curr_features - alpha_ * gradient(w.r.t. feature)
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"""
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n = len_data
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prod = np.dot(theta, data_x.transpose())
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prod -= data_y.transpose()
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sum_grad = np.dot(prod, data_x)
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theta = theta - (alpha / n) * sum_grad
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return theta
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def sum_of_square_error(data_x, data_y, len_data, theta):
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""" Return sum of square error for error calculation
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:param data_x : contains our dataset
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:param data_y : contains the output (result vector)
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:param len_data : len of the dataset
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:param theta : contains the feature vector
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:return : sum of square error computed from given feature's
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"""
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prod = np.dot(theta, data_x.transpose())
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prod -= data_y.transpose()
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sum_elem = np.sum(np.square(prod))
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error = sum_elem / (2 * len_data)
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return error
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def run_linear_regression(data_x, data_y):
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""" Implement Linear regression over the dataset
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:param data_x : contains our dataset
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:param data_y : contains the output (result vector)
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:return : feature for line of best fit (Feature vector)
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"""
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iterations = 100000
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alpha = 0.0001550
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no_features = data_x.shape[1]
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len_data = data_x.shape[0] - 1
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theta = np.zeros((1, no_features))
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for i in range(0, iterations):
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theta = run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta)
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error = sum_of_square_error(data_x, data_y, len_data, theta)
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print("At Iteration %d - Error is %.5f " % (i + 1, error))
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return theta
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def main():
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""" Driver function """
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data = collect_dataset()
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len_data = data.shape[0]
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data_x = np.c_[np.ones(len_data), data[:, :-1]].astype(float)
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data_y = data[:, -1].astype(float)
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theta = run_linear_regression(data_x, data_y)
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len_result = theta.shape[1]
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print("Resultant Feature vector : ")
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for i in range(0, len_result):
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print("%.5f" % (theta[0, i]))
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if __name__ == "__main__":
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main()
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