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5_jenkins_predict

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This commit is contained in:
Wojciech Lidwin 2023-05-12 01:16:44 +02:00
parent 0735fbc4ef
commit ee9dacba25
5 changed files with 91 additions and 8 deletions
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1
Dockerfile
View File

@ -14,6 +14,7 @@ RUN pip3 install keras
RUN pip3 install tensorflow
RUN pip3 install scikit-learn
RUN pip3 install argparse
RUN pip3 install matplotlib
WORKDIR /app

36
Jenkinsfile_evaluation Normal file
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@ -0,0 +1,36 @@
node {
stage('Preparation') {
properties([
parameters([
string(
defaultValue: '1',
description: 'Build number',
name: 'build'),
gitParameter(
branchFilter: 'origin/(.*)',
defaultValue: 'master',
name: 'BRANCH',
type: 'PT_BRANCH'
),
])
])
}
stage('checkout: Check out from version control') {
checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's487197', url: 'https://git.wmi.amu.edu.pl/s487197/ium_487197']]])
}
stage('Dockerfile'){
def testImage = docker.image('s487197/ium:37')
testImage.inside{
copyArtifacts filter: 'baltimore_train.csv', projectName: 's487197-create-dataset'
copyArtifacts filter: 'baltimore_test.csv', projectName: 's487197-create-dataset'
copyArtifacts filter: 'baltimore_model.h5', fingerprintArtifacts: true, projectName: "s487197-training/$BRANCH", selector: buildParameter(lastSuccessful())
copyArtifacts filter: 'metrics.csv', projectName: "s487197-evaluation.eg/$BRANCH", optional: true
sh "python3 ium_predict.py -build $BUILD"
archiveArtifacts artifacts: 'metrics.csv, listfile.txt, metrics_img.png', onlyIfSuccessful: true
}
}
}

8
Jenkinsfile_train
View File

@ -25,12 +25,14 @@ node {
checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[credentialsId: 's487197', url: 'https://git.wmi.amu.edu.pl/s487197/ium_487197']]])
}
stage('Dockerfile'){
def testImage = docker.image('s487197/ium:36')
def testImage = docker.image('s487197/ium:37')
testImage.inside{
copyArtifacts filter: 'baltimore_train.csv', projectName: 's487197-create-dataset'
sh "python3 ium_train.py -epochs $EPOCHS -lr $LR -validation_split $VALIDATION_SPLIT"
archiveArtifacts artifacts: 'baltimore_model.h5'
archiveArtifacts artifacts: 'baltimore.zip'
stage('Buil'){
build job: 's449312-evaluation.eg/master', parameters: [string(name: 'build', value: "${currentBuild.number}")], wait:false
}
}

50
ium_predict.py
View File

@ -1,10 +1,13 @@
from keras.models import Sequential
from keras.models import load_model
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn import metrics
import math
import numpy as np
import os.path
import argparse
import matplotlib.pyplot as plt
import shutil
def write_list(names):
with open('listfile.txt', 'w') as fp:
@ -26,7 +29,11 @@ def get_x_y(data):
def predict():
model = load_model('baltimore_model.h5')
parser = argparse.ArgumentParser(description='Pred')
parser.add_argument('-build', type=int, default=1)
args = parser.parse_args()
shutil.unpack_archive('baltimore.zip', 'baltimore_model', 'zip')
model = load_model('baltimore_model')
train = pd.read_csv('baltimore_train.csv')
baltimore_data_test = pd.read_csv('baltimore_test.csv')
@ -36,11 +43,46 @@ def predict():
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
y_predicted = model.predict(x_test)
y_predicted = np.argmax(y_predicted,axis=1)
y_predicted = np.argmax(y_predicted, axis=1)
test_results = {}
test_results['Weapon'] = model.evaluate(
x_test,
y_test, verbose=0)
write_list(y_predicted)
print('Accuracy : ', scores[1] * 100)
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_predicted))
print('Root Mean Squared Error : ', math.sqrt(metrics.mean_squared_error(y_test, y_predicted)))
if os.path.exists("metrics.csv"):
df = pd.read_csv('metrics.csv')
data = {
'build': [args.build],
'mse': metrics.mean_squared_error(y_test, y_predicted),
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
'accuracy': scores[1] * 100
}
row = pd.DataFrame([data])
if df['build'].isin([int(args.build)]).any():
df[df['build'] == args.build] = row.iloc[0]
else:
df = pd.concat([df, row])
df['build'] = df['build'].astype('int')
df.to_csv('metrics.csv', index=False)
else:
data = {
'build': [args.build],
'mse': metrics.mean_squared_error(y_test, y_predicted),
'rmse': math.sqrt(metrics.mean_squared_error(y_test, y_predicted)),
'accuracy': scores[1] * 100
}
df = pd.DataFrame(data)
df['build'] = df['build'].astype('int')
df.to_csv('metrics.csv', index=False)
plt.plot(df['build'], df['mse'], label="mse")
plt.plot(df['build'], df['rmse'], label="rmse")
plt.plot(df['build'], df['accuracy'], label="accuracy")
plt.legend()
plt.show()
plt.savefig('metrics_img.png')
predict()

4
ium_train.py
View File

@ -10,6 +10,7 @@ import tensorflow as tf
import numpy as np
from sklearn.preprocessing import LabelEncoder
import argparse
import shutil
def get_x_y(data):
@ -57,7 +58,8 @@ def train_model():
validation_split=args.validation_split)
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
model.save('baltimore_model.h5')
model.save('baltimore_model')
shutil.make_archive('baltimore', 'zip', 'baltimore_model')
train_model()