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MIC_CMN_EXP.py
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MIC_CMN_EXP.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 17:25:33 2021
@author: darwin
"""
# libs
import pandas as pd
import numpy as np
import os
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
import warnings
warnings.filterwarnings('ignore')
# dataset
mic_data = pd.read_csv("./withoutCMN-12X53mic.csv")
mic_data_cmn = pd.read_csv("./withCMN-12X53mic.csv")
# generate folds - #7
def fold(sound_dataframe, sets_loc):
big = []
for j in os.listdir(sets_loc):
file = open(sets_loc+j, 'r')
Lines = file.readlines()
count = 0
try:
set_dataframe = pd.DataFrame()
for i, line in enumerate(Lines):
A = sound_dataframe[(sound_dataframe['73'] == (Lines[i].strip()+".json"))]
set_dataframe = pd.DataFrame.append(set_dataframe,A)
except:
set_dataframe = pd.DataFrame()
for i, line in enumerate(Lines):
A = sound_dataframe[(sound_dataframe[73] == (Lines[i].strip()+".json"))]
set_dataframe = pd.DataFrame.append(set_dataframe,A)
big.append(set_dataframe)
fold_1 = shuffle(pd.concat(big[:6]))
test_1 = shuffle(big[6])
fold_2 = shuffle(pd.concat(big[1:7]))
test_2 = shuffle(big[0])
fold_3 = shuffle(pd.concat([big[0],big[2],big[3],big[4], big[5], big[6]]))
test_3 = shuffle(big[1])
fold_4 = shuffle(pd.concat([big[0],big[1],big[3],big[4], big[5], big[6]]))
test_4 = shuffle(big[2])
fold_5 = shuffle(pd.concat([big[0],big[2],big[1],big[4], big[5], big[6]]))
test_5 = shuffle(big[3])
fold_6 = shuffle(pd.concat([big[0],big[2],big[1],big[3], big[5], big[6]]))
test_6 = shuffle(big[4])
fold_7 = shuffle(pd.concat([big[0],big[2],big[1],big[4], big[3], big[6]]))
test_7 = shuffle(big[5])
return [fold_1,test_1,fold_2,test_2,fold_3,test_3,fold_4,test_4,fold_5,test_5, fold_6, test_6, fold_7,test_7]
sets_path = './Sets/'
mic_fold_list = fold(mic_data, sets_path)
mic_cmn_fold_list = fold(mic_data_cmn, sets_path)
# fitting xgboost
from xgboost import XGBClassifier
def xgb_classifier(train_test_folds) :
xgb_accuracy_test = []
xgb_accuracy_train = []
for i in range(0,len(train_test_folds), 2):
fold_train = train_test_folds[i]
fold_test = train_test_folds[i + 1]
X = fold_train.iloc[:, :-2]
y = fold_train.iloc[:, -2]
X_test = fold_test.iloc[:, :-2]
y_test = fold_test.iloc[:, -2]
classifier_xgb = XGBClassifier()
classifier_xgb.fit(X, y)
y_pred = classifier_xgb.predict(X_test)
a_test = accuracy_score(y_test, y_pred)
xgb_accuracy_test.append(a_test)
a_train = accuracy_score(y,classifier_xgb.predict(X))
xgb_accuracy_train.append(a_train)
mean_accuracy_xgb = {"test_mean_accuracy": np.mean(xgb_accuracy_test),
"train_mean_accuracy": np.mean(xgb_accuracy_train)}
return mean_accuracy_xgb
#fitting svm
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
def svm_classifier(train_test_folds):
svm_accuracy_test = []
svm_accuracy_train = []
for i in range(0,len(train_test_folds),2):
fold_train = train_test_folds[i]
fold_test = train_test_folds[i + 1]
X = fold_train.iloc[:, :-2]
y = fold_train.iloc[:, -2]
X_test = fold_test.iloc[:, :-2]
y_test = fold_test.iloc[:, -2]
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_test = scaler.transform(X_test)
X = pd.DataFrame(X)
X_test = pd.DataFrame(X_test)
classifier_svm = SVC()
classifier_svm.fit(X, y)
y_pred = classifier_svm.predict(X_test)
a_test = accuracy_score(y_test, y_pred)
svm_accuracy_test.append(a_test)
a_train = accuracy_score(y, classifier_svm.predict(X))
svm_accuracy_train.append(a_train)
mean_accuracy_svm = {"test_mean_accuracy": np.mean(svm_accuracy_test),
"train_mean_accuracy": np.mean(svm_accuracy_train)}
return mean_accuracy_svm
def evaluate():
res = {"mic": {"xgb": xgb_classifier(mic_fold_list), "svm": svm_classifier(mic_fold_list)},
"mic_cmn": {"xgb": xgb_classifier(mic_cmn_fold_list), "svm": svm_classifier(mic_cmn_fold_list)}}
return res