Create a Program to Implement Likelihood in Python Assignment Solution

Instructions

Objective

Write a python assignment program to implement likelihood.

Requirements and Specifications

Source Code

import numpy as np from numpy import linalg as lg import pandas as pd import math from cvxopt import matrix, solvers from sklearn import svm, metrics from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.preprocessing import StandardScaler ACCURACY_train=[] #train dataset train_file=open("/Users/Desktop/Assignment_2/park_train.data") dataset_train=np.loadtxt(train_file, dtype=np.dtype(float), delimiter=',') #test dataset test_file=open("/Users/Desktop/Assignment_2/park_test.data") dataset_test=np.loadtxt(test_file, dtype=np.dtype(float), delimiter=',') #validation dataset val_file=open("/Users/Desktop/Assignment_2/park_validation.data") dataset_val=np.loadtxt(val_file, dtype=np.dtype(float), delimiter=',') #validation test df_val=dataset_val df_val=np.delete(df_val,0,axis=1) y_val=dataset_val[:,0] #test set df_test=dataset_test df_test=np.delete(df_test,0,axis=1) y_test=dataset_test[:,0] df=dataset_train df=np.delete(df,0,axis=1) labels=dataset_train[:,0] for i in range(len(labels)): if labels[i]==0: labels[i]=-1 for i in range(len(y_val)): if y_val[i]==0: y_val[i]=-1 for i in range(len(y_test)): if y_test[i]==0: y_test[i]=-1 def sigmoid(z): return 1/float(1+np.exp(-z)) def calculate_logit(w, b, x): v=np.exp((np.multiply(w.T,x)+b)) p_1=float(v)/float(1+v) p_minus_1=1/float(1+v) return p_1, p_minus_1 def predict(p_1, p_minus_1): y_pred=[] for i in range(len(p_1)): if p_1[i]>=p_minus_1[i]: y_pred.append(1) else: y_pred.append(-1) return y_pred def find_accuracy(y_pred, y_true): count=0 for i in range(len(y_pred)): if y_pred[i]==y_true[i]: count+=1 acc= float(count)/float(len(y_true)) return acc def loss(h,y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() def predict_probs(X, theta): return sigmoid(np.dot(X, theta)) def predict(X, theta, threshold=0.5): return predict_probs(X, theta) >= threshold model = LogisticRegression(C=1e5, solver='lbfgs') model.fit(df, labels) y_pred=model.predict(df_test) acc=metrics.accuracy_score(y_pred, y_test) print "accuracy on test set is", acc y_pred_val=model.predict(df_val) acc1=metrics.accuracy_score(y_pred_val,y_val) print "accuracy on val set is", acc1 choice_of_c=dict() parameters=dict() C=[0.000001, 0.00001, 0.0001,0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] for c in C: model1 = LogisticRegression(C=c, penalty='l2', solver='sag', random_state=0) model1.fit(df, labels) y_pred1=model1.predict(df_val) acc11=metrics.accuracy_score(y_pred1, y_val) print "accuracy on validation set using l2 is", acc11 choice_of_c[c]=acc11 w=model1.coef_ #b=np.hstack((model.intercept_[:,None], model.coef_)) b=model1.intercept_[:,None] parameters[c]=[w, b] best_acc=np.max(list(choice_of_c.values())) print "best accuracy using l2 penalty is", best_acc chosen_c=0 for x in choice_of_c.keys(): if(choice_of_c[x]==best_acc): chosen_c=x break; print "chosen c using l2 penalty on validation set is", chosen_c modeli = LogisticRegression(C=chosen_c, penalty='l2', solver='sag', random_state=0) modeli.fit(df, labels) y_pred_test=modeli.predict(df_test) ac=metrics.accuracy_score(y_pred_test, y_test) print "accuracy on test set using chosen c is", ac # ****************************************************************** choice_of_c_t=dict() parameters_t=dict() C_t=[0.0000001,0.00001, 0.0001,0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] for c in C_t: model2 = LogisticRegression(C=c, penalty='l1',random_state=0) model2.fit(df, labels) y_pred1=model2.predict(df_val) acc11=metrics.accuracy_score(y_pred1, y_val) print "accuracy on validation set using l1 is", acc11 choice_of_c_t[c]=acc11 w=model2.coef_ #b=np.hstack((model.intercept_[:,None], model.coef_)) b=model2.intercept_[:,None] parameters_t[c]=[w, b] best_acc_t=np.max(list(choice_of_c_t.values())) print "best accuracy using l1 penalty is", best_acc_t chosen_c_t=0 for x in choice_of_c_t.keys(): if(choice_of_c_t[x]==best_acc_t): chosen_c_t=x break; print "chosen c using l1 penalty on validation set is", chosen_c_t modelit = LogisticRegression(C=chosen_c_t, penalty='l1', random_state=0) modelit.fit(df, labels) y_pred_test1=modelit.predict(df_test) ac1=metrics.accuracy_score(y_pred_test1, y_test) print "accuracy on test set using chosen c is", ac1