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regression.py
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regression.py
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import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
class Regression:
n = m = p = nabla = 0
X = y = theta = []
def __init__(self, input_dataset, output_dataset, number_of_iterations, learning_rate):
self.X = input_dataset
self.y = output_dataset
self.m = len(self.X)
# add fake feature
for i in range(self.m):
self.X[i].append(1)
self.n = len(self.X[0])
self.p = number_of_iterations
self.nabla = learning_rate
self.theta = [0]*self.n
self.gradiant_decent()
def h(self, idx):
res = 0
for i in range(self.n):
res += self.X[idx][i]*self.theta[i]
return res
def J(self):
res = 0
for i in range(self.m):
res += (self.h(self.X[i])-self.y[i])**2
res /= 2*self.m
return res
def gradiant(self):
gradiant_vector = [0]*self.n
for j in range(self.n):
for i in range(self.m):
gradiant_vector[j] += (self.h(i)-self.y[i])*self.X[i][j]
gradiant_vector[j] /= self.m
return gradiant_vector
def gradiant_decent(self):
for i in range(self.p):
for j in range(self.n):
self.theta[j] -= self.nabla*self.gradiant()[j]
def predict(self, x):
if len(x) < self.n:
x.append(1)
res = 0
for i in range(self.n):
res += self.theta[i]*x[i]
return res
# number_of_iterations = 1000
# learning_rate = 0.1
# input_dataset = [
# [1],
# [2],
# [3],
# [4],
# [5],
# [6]
# ]
# output_dataset = [1, 2, 3, 4, 5, 6]
# reg = Regression(input_dataset, output_dataset, number_of_iterations, learning_rate)
# test = []
# for i in range(6, 10):
# test.append(reg.h([i]))
# print(test)
df = pd.read_csv("/home/pouya/downloads/Flight_Price_Dataset_Q2.csv")
# lb = LabelEncoder()
# df["Mapping"] = lb.fit_transform(df["class"])
# df1 = pd.concat([df, departure_time_mapping], axis=1)
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
departure_time_mapping = {
"Early_Morning": 0,
"Morning": 1,
"Afternoon": 2,
"Night": 3,
"Late_Night": 4
}
stops_mapping = {
"zero": 0,
"one": 1,
"two_or_more": 2
}
class_mapping = {
"Economy": 0,
"Business": 1
}
df["departure_time"] = df["departure_time"].map(departure_time_mapping)
df["stops"] = df["stops"].map(stops_mapping)
df["arrival_time"] = df["arrival_time"].map(departure_time_mapping)
df["class"] = df["class"].map(class_mapping)
df = df.dropna()
df = df.reset_index(drop=True)
y = df["price"]
X = df.drop("price", axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
learning_rate = 0.1
number_of_iterations = 10
reg = Regression(X_train.values.tolist(), y_train.values.tolist(), number_of_iterations, learning_rate)
def test(X_test, y_test):
y_hat = [0]*len(X_test)
for i in range(len(X_test)):
y_hat[i] = reg.predict(X_test[i])
print(y_hat)
print(mean_absolute_error(y_hat, y_test))
print(mean_squared_error(y_hat, y_test))
print(r2_score(y_hat, y_test))
test(X_test.values.tolist(), y_test.values.tolist())