classifier.py

import argparse
import pandas as  pd
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.metrics import plot_confusion_matrix


def preprocess(file):
    data = pd.read_csv(file)

    data["Decision"]=data["Decision"].replace({"VERY_FRESH":0,"EARLY_SPOILED":1,"HALF_SPOILED":2,"FULL_SPOILED":3})
    X = np.array(data[["S1", "S2", "S3", "S4", "S5", "S6"]])
    Y = np.array(data["Decision"])
    
    return X,Y

def train(clf, X, y, name, log,csvFileName ):
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
    print("Traning the model")
    clf.fit(X_train, y_train)
    print("Trained model")
    score = clf.score(X_test, y_test)
    log.write(str(score)+","+name+",\n")
    print("Score ", score)
    plot_confusion_matrix(clf, X_test, y_test)
    if not os.path.isdir("images/confusion"):
        os.mkdir("images/confusion")
    plt.title(name)    
    plt.savefig(os.getcwd()+"/images/confusion/"+csvFileName+"/"+name.replace(" ","_")+".png")
    
def main():
    parser = argparse.ArgumentParser(description='Process some integers.')
    parser.add_argument('--file', help='location of csv file location')
    parser.add_argument('--log', help='log file',default="log.txt")
    args = parser.parse_args()
    if (not os.path.isdir("images/confusion/"+args.file)):
        os.mkdir("images/confusion/"+args.file)
    X, y = preprocess(args.file)
    classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="linear", C=0.025),
    SVC(gamma=2, C=1),
    GaussianProcessClassifier(1.0 * RBF(1.0)),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    MLPClassifier(alpha=1, max_iter=1000),
    AdaBoostClassifier(),
    GaussianNB(),
    QuadraticDiscriminantAnalysis()]
    names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process",
         "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",
         "Naive Bayes", "QDA"]
    log=open(str(args.log),"w")
    for i in range(len(classifiers)):
        print("Classifier", names[i])
        try:
            train(classifiers[i], X, y, names[i], log,args.file)
        except Exception as e:
            print("Error in ",names[i],e)

    log.close()    



if __name__ == "__main__":
    main()