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9 Commits

Author SHA1 Message Date
Szymon Bartanowicz
7c8fe37562 fix 2024-05-18 19:43:36 +02:00
Szymon Bartanowicz
4a7fe811f5 evaluation metrics plot 2024-05-15 00:57:08 +02:00
Szymon Bartanowicz
8d92919488 evaluation metrics plot 2024-05-15 00:53:08 +02:00
Szymon Bartanowicz
17be57bcd3 fix 2024-05-15 00:41:20 +02:00
Szymon Bartanowicz
adf3b77091 docker 2024-05-15 00:33:17 +02:00
Szymon Bartanowicz
cb364fee5f docker 2024-05-15 00:27:38 +02:00
Szymon Bartanowicz
dc3284677a docker 2024-05-15 00:21:21 +02:00
Szymon Bartanowicz
d5306f5b06 evaluation 2024-05-15 00:10:35 +02:00
Szymon Bartanowicz
a6f8a4fe78 evaluation 2024-05-15 00:07:51 +02:00
6 changed files with 95 additions and 28 deletions

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@ -2,7 +2,7 @@ FROM ubuntu:latest
RUN apt-get update && apt-get install -y python3-pip unzip coreutils
RUN pip install --user kaggle pandas scikit-learn tensorflow
RUN pip install --no-cache-dir wheel kaggle pandas scikit-learn tensorflow
WORKDIR /app

77
Jenkinsfile vendored
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@ -1,37 +1,62 @@
pipeline {
agent any
parameters {
string(name: 'CUTOFF', defaultValue: '100', description: 'Ilość wierszy do odcięcia')
string(name: 'KAGGLE_USERNAME', defaultValue: '', description: 'Kaggle username')
password(name: 'KAGGLE_KEY', defaultValue: '', description: 'Kaggle API key')
agent {
dockerfile true
}
triggers {
upstream(upstreamProjects: 's464937-training/training', threshold: hudson.model.Result.SUCCESS)
}
parameters {
buildSelector(defaultSelector: lastSuccessful(), description: 'Which build to use for copying artifacts', name: 'BUILD_SELECTOR')
gitParameter branchFilter: 'origin/(.*)', defaultValue: 'training', name: 'BRANCH', type: 'PT_BRANCH'
}
stages {
stage('Clone repo') {
stage('Clone Repository') {
steps {
git branch: "main", url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
git branch: 'evaluation', url: "https://git.wmi.amu.edu.pl/s464937/ium_464937"
}
}
stage('Copy Dataset Artifacts') {
steps {
copyArtifacts filter: 'data/dev.csv,data/test.csv,data/train.csv', projectName: 'z-s464937-create-dataset', selector: buildParameter('BUILD_SELECTOR')
}
}
stage('Copy Training Artifacts') {
steps {
copyArtifacts filter: 'powerlifting_model.h5', projectName: 's464937-training/' + params.BRANCH, selector: buildParameter('BUILD_SELECTOR')
}
}
stage('Copy Evaluation Artifacts') {
steps {
copyArtifacts filter: 'metrics.txt', projectName: 's464937-evaluation/evaluation', selector: buildParameter('BUILD_SELECTOR'), optional: true
}
}
stage("Run predictions") {
steps {
sh "chmod +x ./predict.py"
sh "python3 ./predict.py"
archiveArtifacts artifacts: 'powerlifting_test_predictions.csv', onlyIfSuccessful: true
}
}
stage('Run metrics') {
steps {
sh 'chmod +x ./metrics.py'
sh "python3 ./metrics.py ${currentBuild.number}"
}
}
stage('Download and preprocess') {
environment {
KAGGLE_USERNAME = "szymonbartanowicz"
KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
}
steps {
withEnv([
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
"KAGGLE_KEY=${env.KAGGLE_KEY}"
]) {
sh "bash ./script1.sh ${params.CUTOFF}"
}
}
stage('Run plot') {
steps {
sh 'chmod +x ./plot.py'
sh 'python3 ./plot.py'
}
}
stage('Archive') {
steps {
archiveArtifacts artifacts: 'data/*', onlyIfSuccessful: true
}
stage('Archive Artifacts') {
steps {
archiveArtifacts artifacts: '*', onlyIfSuccessful: true
}
}
}
}
}

14
metrics.py Normal file
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@ -0,0 +1,14 @@
import pandas as pd
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
from math import sqrt
import sys
data = pd.read_csv('powerlifting_test_predictions.csv')
y_pred = data['predicted_TotalKg']
y_test = data['actual_TotalKg']
build_number = sys.argv[1]
rmse = sqrt(mean_squared_error(y_test, y_pred))
with open(r"metrics.txt", "a") as f:
f.write(f"{build_number},{rmse}\n")

0
metrics.txt Normal file
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20
plot.py Normal file
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@ -0,0 +1,20 @@
import matplotlib.pyplot as plt
def main():
accuracy = []
build_numbers = []
with open("metrics.txt") as f:
for line in f:
accuracy.append(float(line.split(",")[0]))
build_numbers.append(int(line.split(",")[1]))
plt.plot(build_numbers, accuracy)
plt.xlabel("Build Number")
plt.ylabel("RMSE")
plt.title("RMSE of the model over time")
plt.xticks(range(min(build_numbers), max(build_numbers) + 1))
plt.savefig("plot.png")
if __name__ == "__main__":
main()

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@ -4,11 +4,18 @@ from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from keras.metrics import MeanSquaredError
loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
data = pd.read_csv('openpowerlifting.csv')
data = data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
data['Age'] = pd.to_numeric(data['Age'], errors='coerce')
data['BodyweightKg'] = pd.to_numeric(data['BodyweightKg'], errors='coerce')
data['TotalKg'] = pd.to_numeric(data['TotalKg'], errors='coerce')
features = data[['Sex', 'Age', 'BodyweightKg']]
target = data['TotalKg']
@ -20,8 +27,9 @@ preprocessor = ColumnTransformer(
('cat', OneHotEncoder(), ['Sex'])
]
)
X_test_transformed = preprocessor.fit_transform(X_test)
X_test_transformed = preprocessor.fit_transform(X_test)
predictions = loaded_model.predict(X_test_transformed)
predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
predictions_df['actual_TotalKg'] = y_test.reset_index(drop=True)
predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)