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classifier.py
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175 lines (152 loc) · 5.94 KB
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#!/home/hackpython/anaconda3/bin/python
'''
Implementation of KNN, Centroid, Linear Regression and SVM classifiers using Sklearn.
Author: Abhishek Sharma
Last Modified: June 2 2017
'''
from sklearn import cross_validation, neighbors,svm
from sklearn.neighbors.nearest_centroid import NearestCentroid
from sklearn.linear_model import LinearRegression
#from sklearn.model_selection import KFold, cross_val_score
from sklearn.cross_validation import KFold
from sklearn.cluster import KMeans
import sklearn.metrics as sm
import numpy as np
import pandas as pd
import sys
class classifier(object):
def __init__(self,algorithm,dataset):
self.iris_dataset = "dataset/irisdata150.txt"
self.atntface_dataset = "dataset/ATNTFaceImages400.txt"
self.atntletter_dataset = "dataset/HandWrittenLetters.txt"
self.nba_dataset = "dataset/nba.xlsx"
self.algorithm = algorithm
self.dataset = dataset
self.execute()
def iris_dataset_preprocessing(self):
df = pd.read_csv(self.iris_dataset,header=None)
data = np.array(df.iloc[:,:4])
label = np.array(df.iloc[:,4:])
return data,label
def atntface_dataset_preprocessing(self):
df = pd.read_csv(self.atntface_dataset,header=None)
df_train = df.drop(0,axis=0)
train_list = []
class_list = []
for column in df_train.columns:
nlist =[]
clist = []
nlist.append(df_train[column])
train_list.append(nlist)
X=np.array(train_list)
dataset_size = len(X)
data = X.reshape(dataset_size,-1)
label = np.array(df.iloc[0])
return data,label
def atntletter_dataset_preprocessing(self):
df = pd.read_csv(self.atntletter_dataset,header=None)
df_train = df.drop(0,axis=0)
train_list = []
class_list = []
for column in df_train.columns:
nlist =[]
clist = []
nlist.append(df_train[column])
train_list.append(nlist)
X=np.array(train_list)
dataset_size = len(X)
data = X.reshape(dataset_size,-1)
label =np.array(df.iloc[0])
return data,label
def nba_dataset_preprocessing(self):
x1 = pd.ExcelFile(self.nba_dataset,header=None)
df = x1.parse('Sheet1')
label = np.array(df.iloc[:401,2:3])
df = df.drop('Pos', 1)
df = df.drop('Rk',1)
df = df.drop('Player',1)
df = df.drop('Tm',1)
data = np.array(df.iloc[:401,0:10])
test_data = np.array(df.iloc[402:476,0:10])
return data, label, test_data
def dataset_preprocessing(self):
if self.dataset == "irisdataset":
data,label = self.iris_dataset_preprocessing()
return data,label
elif self.dataset == "atntface":
data,label = self.atntface_dataset_preprocessing()
return data,label
elif self.dataset == "atntletter":
data,label = self.atntletter_dataset_preprocessing()
return data,label
elif self.dataset == "nba":
data,label,test_data = self.nba_dataset_preprocessing()
return data,label,test_data
def knnclassifier(self):
data, label = self.dataset_preprocessing()
kf = KFold(n=len(data),n_folds=5,shuffle=True)
for train_index, test_index in kf:
X_train, X_test = data[train_index], data[test_index]
y_train, y_test = label[train_index], label[test_index]
clf = neighbors.KNeighborsClassifier(n_neighbors=3)
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)
def centriodclassifier(self):
data,label = self.dataset_preprocessing()
kf = KFold(n=len(data),n_folds=5,shuffle=True)
for train_index,test_index in kf:
X_train, X_test = data[train_index], data[test_index]
y_train, y_test = label[train_index], label[test_index]
clf = NearestCentroid()
clf.fit(X_train,y_train)
accuracy=clf.score(X_test,y_test)
print(accuracy)
def linearclassifier(self):
data,label = self.dataset_preprocessing()
kf = KFold(n=len(data),n_folds=5,shuffle=True)
for train_index,test_index in kf:
X_train, X_test = data[train_index], data[test_index]
y_train, y_test = label[train_index], label[test_index]
clf = LinearRegression(fit_intercept=True)
clf.fit(X_train,y_train)
accuracy = clf.score(X_test,y_test)
print(accuracy)
def svmclassifier(self):
data,label = self.dataset_preprocessing()
kf = KFold(n=len(data),n_folds=5,shuffle=True)
for train_index,test_index in kf:
X_train, X_test = data[train_index], data[test_index]
y_train, y_test = label[train_index], label[test_index]
clf = svm.SVC(kernel='linear',gamma=2)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
print(accuracy)
def execute(self):
if self.algorithm == "knn":
self.knnclassifier()
elif self.algorithm == "centroid":
self.centriodclassifier()
elif self.algorithm == "linear":
self.linearclassifier()
elif self.algorithm == "svm":
self.svmclassifier()
elif self.algorithm == "kmeans":
self.kmeansclustering()
else:
sys.exit("algorithm not found")
if __name__ == '__main__':
# Algorithms:
# 1. K Nearest Neighbour (knn)
# 2. Centroid Classifier (centroid)
# 3. Linear Regression (linear)
# 4. Support Vector Machine (svm)
# Dataset
# 1. irisdataset
# 2. atntface
# 3. atntletter
# 4. nba
classifier("knn","atntletter")
classifier("centroid","atntletter")
classifier("linear","atntletter")
classifier("svm","atntletter")