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analysis.py
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analysis.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Feb 16 14:57:02 2018
@author: velmurugan.m
"""
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.preprocessing import scale
import scipy
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import RFE
from sklearn.linear_model import LassoCV
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RandomizedLasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.feature_selection import VarianceThreshold
DATA_LOCATION = r'D:\ATT\newData\p1\hourly_oem_db_features.csv'
def pcaAnalysis(data, target):
column_names = list(data.columns.values)
#print type(target)
#data_std = StandardScaler().fit_transform(data)
pca = PCA(n_components=2)
data_r = pca.fit_transform(data)#.transform(data)
princiData = pd.DataFrame(data = data_r, columns = ['Prin Compo 1', 'Prin Compo 2'])#, 'Prin Compo 3', 'Prin Compo 4'])
finalDf = pd.concat([ princiData, target ], axis = 1)
# Percentage of variance explained for each components
print('explained variance ratio (first two components): %s' % str(pca.explained_variance_ratio_))
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title('2 component PCA', fontsize = 20)
targets = [True,False]
colors = ['r', 'g']
for target, color in zip(targets,colors):
indicesToKeep = finalDf.alert == target
ax.scatter(finalDf.loc[indicesToKeep, 'Prin Compo 1'], finalDf.loc[indicesToKeep, 'Prin Compo 2'], c = color, s = 50)
#plt.figure()
#colors = ['navy', 'orange']
#lw = 2
#for color, i, target_name in zip(colors, [0, 1], target_names):
# plt.scatter(data_r[target == i, 0], data_r[target == i, 1], color=color, alpha=.8, lw=lw, label=target_name)
#plt.legend(loc='best', shadow=False, scatterpoints=1)
#plt.title('PCA of ATT dataset')
#plt.show()
def ldaAnalysis(data, target):
lda = LinearDiscriminantAnalysis(n_components=2)
data_r2 = lda.fit(data, target).transform(data)
princiData = pd.DataFrame(data = data_r2, columns = ['LDA Compo 1'])#, 'LDA Compo 2'])
finalDf = pd.concat([ princiData, target ], axis = 1)
target_names = [True, False]
colors = ['navy', 'orange']
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('LDA Component 1', fontsize = 15)
ax.set_ylabel('LDA Component 2', fontsize = 15)
ax.set_title('2 component LDA', fontsize = 20)
targets = [True,False]
colors = ['r', 'g']
for target, color in zip(targets,colors):
indicesToKeep = finalDf.alert == target
ax.scatter(finalDf.loc[indicesToKeep, 'LDA Compo 1'], finalDf.loc[indicesToKeep, 'LDA Compo 2'], c = color, s = 50)
#plt.figure()
#for color, i, target_name in zip(colors, [1, 0], target_names):
# print data_r2
# plt.scatter(data_r2[target == i, 1], data_r2[target == i, 0], alpha=.8, color=color, label=target_name)
#plt.legend(loc='best', shadow=False, scatterpoints=1)
#plt.title('LDA of ATT dataset')
#plt.show()
def featureSelect(data, target):
# We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
clf = LassoCV()
# Set a minimum threshold of 0.25
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(data, target)
n_features = sfm.transform(data).shape[1]
# Reset the threshold till the number of features equals two.
# Note that the attribute can be set directly instead of repeatedly
# fitting the metatransformer.
while n_features > 2:
sfm.threshold += 0.1
X_transform = sfm.transform(data)
n_features = X_transform.shape[1]
# Plot the selected two features from X.
plt.title(
"Features selected from Boston using SelectFromModel with "
"threshold %0.3f." % sfm.threshold)
feature1 = X_transform[:, 0]
feature2 = X_transform[:, 1]
plt.plot(feature1, feature2, 'r.')
plt.xlabel("Feature number 1")
plt.ylabel("Feature number 2")
plt.ylim([np.min(feature2), np.max(feature2)])
plt.show()
def randomLassoFeatSelect(data, target):
column_names = list(data.columns.values)
rlasso = RandomizedLasso(alpha=0.1)
rlasso.fit(data, target)
print "Features sorted by their score:"
print sorted(zip(map(lambda x: round(x, 4), rlasso.scores_), column_names), reverse=True)
def recurseFeatEliminate(data, target):
column_names = list(data.columns.values)
#use linear regression as the model
lr = LinearRegression()
#rf = RandomForestRegressor()
#rank all features, i.e continue the elimination until the last one
rfe = RFE(lr, n_features_to_select=3)
rfe.fit(data,target)
#print "Features sorted by their rank: %s" % sorted(zip(map(lambda x: round(x, 4), rfe.ranking_), column_names))
sortedFeatSet = sorted(zip(map(lambda x: round(x, 4), rfe.ranking_), column_names))
selected_features = [ x[1] for x in sortedFeatSet ][:15]
return selected_features
def randForestRegressor(data, target):
column_names = list(data.columns.values)
rf = RandomForestRegressor()
rf.fit(data, target)
print "Features sorted by their score:"
sortedFeatSet = sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), column_names), reverse=True)
selected_features = [ x[1] for x in sortedFeatSet ][15:]
return selected_features
def dataPreProcessingNB(data, target):
column_names = list(data.columns.values)
scaled_features = {}
for col in column_names:
mean, std = data[col].mean(), data[col].std()
scaled_features[col] = [mean, std]
data.loc[:, col] = ( data[col] - mean)/std
return data
def NBClassifier(data, target):
#pData = dataPreProcessingNB(data, target)
train, test, train_labels, test_labels = train_test_split(data, target, test_size=0.2, random_state = 10)
gnb = GaussianNB()
model = gnb.fit(train, train_labels)
preds = gnb.predict(test)
NBConfMatrix = confusion_matrix(test_labels, preds)
#print(preds)
print(accuracy_score(test_labels, preds))
print NBConfMatrix
def RandForClassifier(data, target):
train, test, train_labels, test_labels = train_test_split(data, target, test_size=0.2, random_state = 10)
#clf = RandomForestClassifier(n_jobs=2, random_state=0)
clf = RandomForestClassifier(max_depth=10,n_estimators=10)
clf.fit(train, train_labels)
preds = clf.predict(test)
RFConfMatrix = confusion_matrix(test_labels, preds)
print(accuracy_score(test_labels, preds))
print RFConfMatrix
def featVarianceEval(data):
iColNames = (data.columns.values)
sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
sel.fit_transform(data)
oColNames = iColNames[ ~sel.get_support() ]
return oColNames
def dataProcessing():
rawData = pd.read_csv(DATA_LOCATION)
print rawData.columns.values[:6]
print rawData.columns.values[7:13]
print rawData.columns.values[14:20]
print rawData.columns.values[21:]
data = rawData.drop(['alert', 'collection_timestamp', 'target_guid'], axis=1)
#print data.isnull().sum()
target = (rawData['alert'])#.astype(int)
#data = rawData.loc[:, rawData.columns != ['alert', 'collection_timestamp'] ]
#pcaAnalysis(data, target)
#ldaAnalysis(data, target)
#featureSelect(data, target)
#randomLassoFeatSelect(data, target)
#recurseFeatEliminate(data, target)
#featVarianceEval(data)
#selFeatureList = randForestRegressor(data, target)
#selFeatureList = featVarianceEval(data)
#data = data.drop( selFeatureList, axis=1 )
# Gaussian Naive Bayes Classifier:
#NBClassifier(data, target)
# Gaussian Naive Bayes Classifier:
#RandForClassifier(data, target)
def main():
dataProcessing()
if __name__ == '__main__':
main()