Compare commits
9 Commits
main
...
evaluation
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3
.dvc/.gitignore
vendored
3
.dvc/.gitignore
vendored
@ -1,3 +0,0 @@
|
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/config.local
|
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/tmp
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/cache
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@ -1,4 +0,0 @@
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[core]
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remote = ium_ssh_remote
|
||||
['remote "ium_ssh_remote"']
|
||||
url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl
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@ -1,3 +0,0 @@
|
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# Add patterns of files dvc should ignore, which could improve
|
||||
# the performance. Learn more at
|
||||
# https://dvc.org/doc/user-guide/dvcignore
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||||
77
Jenkinsfile
vendored
77
Jenkinsfile
vendored
@ -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") {
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||||
steps {
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||||
sh "chmod +x ./predict.py"
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sh "python3 ./predict.py"
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||||
archiveArtifacts artifacts: 'powerlifting_test_predictions.csv', onlyIfSuccessful: true
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||||
}
|
||||
}
|
||||
stage('Run metrics') {
|
||||
steps {
|
||||
sh 'chmod +x ./metrics.py'
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sh "python3 ./metrics.py ${currentBuild.number}"
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}
|
||||
}
|
||||
|
||||
stage('Download and preprocess') {
|
||||
environment {
|
||||
KAGGLE_USERNAME = "szymonbartanowicz"
|
||||
KAGGLE_KEY = "4692239eb65f20ec79f9a59ef30e67eb"
|
||||
}
|
||||
steps {
|
||||
withEnv([
|
||||
"KAGGLE_USERNAME=${env.KAGGLE_USERNAME}",
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||||
"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
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
5
dvc.yaml
5
dvc.yaml
@ -1,5 +0,0 @@
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||||
stages:
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||||
train:
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||||
cmd: python train.py --epochs=10
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||||
predict:
|
||||
cmd: python predict.py
|
||||
192
environment.yml
192
environment.yml
@ -1,192 +0,0 @@
|
||||
name: ium
|
||||
channels:
|
||||
- anaconda
|
||||
- conda-forge
|
||||
- defaults
|
||||
dependencies:
|
||||
- _tflow_select=2.2.0=eigen
|
||||
- abseil-cpp=20211102.0=he9d5cce_0
|
||||
- absl-py=1.4.0=py311hecd8cb5_0
|
||||
- aiohttp=3.9.3=py311h6c40b1e_0
|
||||
- aiosignal=1.2.0=pyhd3eb1b0_0
|
||||
- alembic=1.8.1=py311hecd8cb5_0
|
||||
- aniso8601=9.0.1=pyhd3eb1b0_0
|
||||
- arrow-cpp=11.0.0=h9980dd5_2
|
||||
- astunparse=1.6.3=py_0
|
||||
- attrs=23.1.0=py311hecd8cb5_0
|
||||
- aws-c-common=0.4.57=hb1e8313_1
|
||||
- aws-c-event-stream=0.1.6=h23ab428_5
|
||||
- aws-checksums=0.1.9=hb1e8313_0
|
||||
- aws-sdk-cpp=1.8.185=he271ece_0
|
||||
- bcrypt=3.2.0=py311h6c40b1e_1
|
||||
- blas=1.0=mkl
|
||||
- blinker=1.6.2=py311hecd8cb5_0
|
||||
- boost-cpp=1.77.0=hff03dee_0
|
||||
- bottleneck=1.3.7=py311hb3a5e46_0
|
||||
- brotli=1.0.9=h6c40b1e_8
|
||||
- brotli-bin=1.0.9=h6c40b1e_8
|
||||
- brotli-python=1.0.9=py311hcec6c5f_7
|
||||
- bzip2=1.0.8=h6c40b1e_5
|
||||
- c-ares=1.19.1=h6c40b1e_0
|
||||
- ca-certificates=2024.3.11=hecd8cb5_0
|
||||
- cachetools=4.2.2=pyhd3eb1b0_0
|
||||
- certifi=2024.2.2=py311hecd8cb5_0
|
||||
- cffi=1.16.0=py311h6c40b1e_0
|
||||
- charset-normalizer=2.0.4=pyhd3eb1b0_0
|
||||
- click=8.1.7=py311hecd8cb5_0
|
||||
- cloudpickle=2.2.1=py311hecd8cb5_0
|
||||
- contourpy=1.2.0=py311ha357a0b_0
|
||||
- cryptography=41.0.3=py311ha2381d6_0
|
||||
- cycler=0.11.0=pyhd3eb1b0_0
|
||||
- docker-py=7.0.0=py311hecd8cb5_0
|
||||
- entrypoints=0.4=py311hecd8cb5_0
|
||||
- flask=2.2.5=py311hecd8cb5_0
|
||||
- flatbuffers=2.0.0=h23ab428_0
|
||||
- fonttools=4.51.0=py311h6c40b1e_0
|
||||
- freetype=2.12.1=hd8bbffd_0
|
||||
- frozenlist=1.4.0=py311h6c40b1e_0
|
||||
- gast=0.4.0=pyhd3eb1b0_0
|
||||
- gflags=2.2.2=hcec6c5f_1
|
||||
- giflib=5.2.1=h6c40b1e_3
|
||||
- gitdb=4.0.7=pyhd3eb1b0_0
|
||||
- gitpython=3.1.37=py311hecd8cb5_0
|
||||
- glog=0.5.0=hcec6c5f_1
|
||||
- google-auth=2.22.0=py311hecd8cb5_0
|
||||
- google-auth-oauthlib=0.5.2=py311hecd8cb5_0
|
||||
- google-pasta=0.2.0=pyhd3eb1b0_0
|
||||
- graphene=3.3=py311hecd8cb5_0
|
||||
- graphql-core=3.2.3=py311hecd8cb5_1
|
||||
- graphql-relay=3.2.0=py311hecd8cb5_0
|
||||
- greenlet=3.0.1=py311hcec6c5f_0
|
||||
- grpc-cpp=1.48.2=h3afe56f_0
|
||||
- grpcio=1.48.2=py311h3afe56f_0
|
||||
- gunicorn=21.2.0=py311hecd8cb5_0
|
||||
- h5py=3.9.0=py311hdb7e403_0
|
||||
- hdf5=1.12.1=h2b2ad87_2
|
||||
- icu=68.1=h23ab428_0
|
||||
- idna=3.4=py311hecd8cb5_0
|
||||
- importlib-metadata=7.0.1=py311hecd8cb5_0
|
||||
- intel-openmp=2021.4.0=hecd8cb5_3538
|
||||
- itsdangerous=2.0.1=pyhd3eb1b0_0
|
||||
- jinja2=3.1.3=py311hecd8cb5_0
|
||||
- joblib=1.2.0=py311hecd8cb5_0
|
||||
- jpeg=9e=h6c40b1e_1
|
||||
- keras=2.12.0=py311_0
|
||||
- keras-preprocessing=1.1.2=pyhd3eb1b0_0
|
||||
- kiwisolver=1.4.4=py311hcec6c5f_0
|
||||
- krb5=1.20.1=hdba6334_1
|
||||
- lcms2=2.12=hf1fd2bf_0
|
||||
- lerc=3.0=he9d5cce_0
|
||||
- libbrotlicommon=1.0.9=h6c40b1e_8
|
||||
- libbrotlidec=1.0.9=h6c40b1e_8
|
||||
- libbrotlienc=1.0.9=h6c40b1e_8
|
||||
- libcurl=8.2.1=ha585b31_0
|
||||
- libcxx=14.0.6=h9765a3e_0
|
||||
- libdeflate=1.17=hb664fd8_1
|
||||
- libedit=3.1.20230828=h6c40b1e_0
|
||||
- libev=4.33=h9ed2024_1
|
||||
- libevent=2.1.12=h0a4fc7d_0
|
||||
- libffi=3.4.4=hecd8cb5_0
|
||||
- libgfortran=5.0.0=11_3_0_hecd8cb5_28
|
||||
- libgfortran5=11.3.0=h9dfd629_28
|
||||
- libnghttp2=1.52.0=h1c88b7d_1
|
||||
- libpng=1.6.39=h6c40b1e_0
|
||||
- libprotobuf=3.20.3=hfff2838_0
|
||||
- libsodium=1.0.18=h1de35cc_0
|
||||
- libssh2=1.10.0=hdb2fb19_2
|
||||
- libthrift=0.15.0=h48f73ad_2
|
||||
- libtiff=4.5.1=hcec6c5f_0
|
||||
- libwebp-base=1.3.2=h6c40b1e_0
|
||||
- llvm-openmp=14.0.6=h0dcd299_0
|
||||
- lz4-c=1.9.4=hcec6c5f_1
|
||||
- mako=1.2.3=py311hecd8cb5_0
|
||||
- markdown=3.4.1=py311hecd8cb5_0
|
||||
- markupsafe=2.1.3=py311h6c40b1e_0
|
||||
- matplotlib=3.8.4=py311hecd8cb5_0
|
||||
- matplotlib-base=3.8.4=py311h41a4f6b_0
|
||||
- mkl=2021.4.0=hecd8cb5_637
|
||||
- mkl-service=2.4.0=py311h6c40b1e_0
|
||||
- mkl_fft=1.3.1=py311hbc8bb1e_0
|
||||
- mkl_random=1.2.2=py311hc5848a5_0
|
||||
- mlflow=2.12.2=h6eed73b_0
|
||||
- mlflow-skinny=2.12.2=py311h6eed73b_0
|
||||
- mlflow-ui=2.12.2=py311h6eed73b_0
|
||||
- multidict=6.0.4=py311h6c40b1e_0
|
||||
- ncurses=6.4=hcec6c5f_0
|
||||
- numexpr=2.8.4=py311h72c71eb_0
|
||||
- numpy=1.23.5=py311h72c71eb_0
|
||||
- numpy-base=1.23.5=py311h0e1ec55_0
|
||||
- oauthlib=3.2.2=py311hecd8cb5_0
|
||||
- openjpeg=2.4.0=h66ea3da_0
|
||||
- openssl=1.1.1w=hca72f7f_0
|
||||
- opt_einsum=3.3.0=pyhd3eb1b0_1
|
||||
- orc=1.7.4=h995b336_1
|
||||
- packaging=23.2=py311hecd8cb5_0
|
||||
- pandas=2.2.1=py311hdb55bb0_0
|
||||
- paramiko=2.8.1=pyhd3eb1b0_0
|
||||
- pillow=10.3.0=py311h6c40b1e_0
|
||||
- pip=23.3.1=py311hecd8cb5_0
|
||||
- platformdirs=3.10.0=py311hecd8cb5_0
|
||||
- pooch=1.7.0=py311hecd8cb5_0
|
||||
- prometheus_client=0.14.1=py311hecd8cb5_0
|
||||
- prometheus_flask_exporter=0.22.4=py311hecd8cb5_0
|
||||
- protobuf=3.20.3=py311hcec6c5f_0
|
||||
- pyarrow=11.0.0=py311hf41f4e6_1
|
||||
- pyasn1=0.4.8=pyhd3eb1b0_0
|
||||
- pyasn1-modules=0.2.8=py_0
|
||||
- pycparser=2.21=pyhd3eb1b0_0
|
||||
- pyjwt=2.4.0=py311hecd8cb5_0
|
||||
- pynacl=1.5.0=py311h6c40b1e_0
|
||||
- pyopenssl=23.2.0=py311hecd8cb5_0
|
||||
- pyparsing=3.0.9=py311hecd8cb5_0
|
||||
- pysocks=1.7.1=py311hecd8cb5_0
|
||||
- python=3.11.5=h1fd4e5f_0
|
||||
- python-dateutil=2.8.2=pyhd3eb1b0_0
|
||||
- python-flatbuffers=2.0=pyhd3eb1b0_0
|
||||
- python-tzdata=2023.3=pyhd3eb1b0_0
|
||||
- python_abi=3.11=2_cp311
|
||||
- pytz=2023.3.post1=py311hecd8cb5_0
|
||||
- pyyaml=6.0.1=py311h6c40b1e_0
|
||||
- querystring_parser=1.2.4=py311hecd8cb5_0
|
||||
- re2=2022.04.01=he9d5cce_0
|
||||
- readline=8.2=hca72f7f_0
|
||||
- requests=2.31.0=py311hecd8cb5_1
|
||||
- requests-oauthlib=1.3.0=py_0
|
||||
- rsa=4.7.2=pyhd3eb1b0_1
|
||||
- scikit-learn=1.2.2=py311hcec6c5f_1
|
||||
- scipy=1.10.1=py311hb23b6d4_0
|
||||
- setuptools=68.2.2=py311hecd8cb5_0
|
||||
- six=1.16.0=pyhd3eb1b0_1
|
||||
- smmap=4.0.0=pyhd3eb1b0_0
|
||||
- snappy=1.1.10=hcec6c5f_1
|
||||
- sqlalchemy=2.0.25=py311h6c40b1e_0
|
||||
- sqlite=3.41.2=h6c40b1e_0
|
||||
- sqlparse=0.4.4=py311hecd8cb5_0
|
||||
- tbb=2021.8.0=ha357a0b_0
|
||||
- tensorboard=2.12.1=py311_0
|
||||
- tensorboard-data-server=0.7.0=py311h7242b5c_0
|
||||
- tensorboard-plugin-wit=1.6.0=py_0
|
||||
- tensorflow=2.12.0=eigen_py311h4c7017d_0
|
||||
- tensorflow-base=2.12.0=eigen_py311hbf87084_0
|
||||
- tensorflow-estimator=2.12.0=py311_0
|
||||
- termcolor=2.1.0=py311hecd8cb5_0
|
||||
- threadpoolctl=2.2.0=pyh0d69192_0
|
||||
- tk=8.6.12=h5d9f67b_0
|
||||
- tornado=6.3.3=py311h6c40b1e_0
|
||||
- typing-extensions=4.9.0=py311hecd8cb5_1
|
||||
- typing_extensions=4.9.0=py311hecd8cb5_1
|
||||
- tzdata=2024a=h04d1e81_0
|
||||
- unicodedata2=15.1.0=py311h6c40b1e_0
|
||||
- urllib3=1.26.18=py311hecd8cb5_0
|
||||
- utf8proc=2.6.1=h6c40b1e_1
|
||||
- websocket-client=1.8.0=py311hecd8cb5_0
|
||||
- werkzeug=2.3.8=py311hecd8cb5_0
|
||||
- wheel=0.35.1=pyhd3eb1b0_0
|
||||
- wrapt=1.14.1=py311h6c40b1e_0
|
||||
- xz=5.4.6=h6c40b1e_0
|
||||
- yaml=0.2.5=haf1e3a3_0
|
||||
- yarl=1.9.3=py311h6c40b1e_0
|
||||
- zipp=3.17.0=py311hecd8cb5_0
|
||||
- zlib=1.2.13=h4dc903c_0
|
||||
- zstd=1.5.5=hc035e20_2
|
||||
prefix: /opt/anaconda3/envs/ium
|
||||
14
metrics.py
Normal file
14
metrics.py
Normal file
@ -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
0
metrics.txt
Normal file
@ -1,7 +0,0 @@
|
||||
name: MLflow_s464937
|
||||
conda_env: conda.yaml
|
||||
entry_points:
|
||||
main:
|
||||
parameters:
|
||||
epochs: { type: int, default: 20 }
|
||||
command: 'python mlflow_model.py {epochs}'
|
||||
@ -1,12 +0,0 @@
|
||||
name: MLflow_s464937
|
||||
channels:
|
||||
- defaults
|
||||
dependencies:
|
||||
- python=3.11
|
||||
- pip
|
||||
- pip:
|
||||
- wheel
|
||||
- mlflow
|
||||
- tensorflow
|
||||
- pandas
|
||||
- scikit-learn
|
||||
@ -1,79 +0,0 @@
|
||||
import sys
|
||||
import pandas as pd
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.preprocessing import StandardScaler, OneHotEncoder
|
||||
from sklearn.compose import ColumnTransformer
|
||||
from sklearn.pipeline import Pipeline
|
||||
from tensorflow.keras.models import Sequential
|
||||
from tensorflow.keras.layers import Dense
|
||||
import tensorflow as tf
|
||||
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
|
||||
from math import sqrt
|
||||
import mlflow
|
||||
|
||||
|
||||
mlflow.set_tracking_uri("http://localhost:5000")
|
||||
|
||||
|
||||
def main():
|
||||
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']
|
||||
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
|
||||
|
||||
with mlflow.start_run() as run:
|
||||
preprocessor = ColumnTransformer(
|
||||
transformers=[
|
||||
('num', StandardScaler(), ['Age', 'BodyweightKg']),
|
||||
('cat', OneHotEncoder(), ['Sex'])
|
||||
],
|
||||
)
|
||||
|
||||
model = Sequential([
|
||||
Dense(64, activation='relu', input_dim=5),
|
||||
Dense(64, activation='relu'),
|
||||
Dense(1)
|
||||
])
|
||||
|
||||
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
|
||||
pipeline = Pipeline(steps=[
|
||||
('preprocessor', preprocessor),
|
||||
('model', model)
|
||||
])
|
||||
|
||||
X_train_excluded = X_train.iloc[1:]
|
||||
y_train_excluded = y_train.iloc[1:]
|
||||
pipeline.fit(X_train_excluded, y_train_excluded, model__epochs=int(sys.argv[1]), model__validation_split=0.1)
|
||||
pipeline['model'].save('powerlifting_model.h5')
|
||||
loaded_model = tf.keras.models.load_model('powerlifting_model.h5')
|
||||
|
||||
test_data = pd.read_csv('openpowerlifting.csv')
|
||||
test_data = test_data[['Sex', 'Age', 'BodyweightKg', 'TotalKg']].dropna()
|
||||
test_data['Age'] = pd.to_numeric(test_data['Age'], errors='coerce')
|
||||
test_data['BodyweightKg'] = pd.to_numeric(test_data['BodyweightKg'], errors='coerce')
|
||||
test_data['TotalKg'] = pd.to_numeric(test_data['TotalKg'], errors='coerce')
|
||||
test_features = test_data[['Sex', 'Age', 'BodyweightKg']]
|
||||
test_target = test_data['TotalKg']
|
||||
|
||||
X_test_transformed = preprocessor.transform(test_features)
|
||||
predictions = loaded_model.predict(X_test_transformed)
|
||||
predictions_df = pd.DataFrame(predictions, columns=['predicted_TotalKg'])
|
||||
predictions_df['actual_TotalKg'] = test_target.reset_index(drop=True)
|
||||
predictions_df.to_csv('powerlifting_test_predictions.csv', index=False)
|
||||
|
||||
data = pd.read_csv('powerlifting_test_predictions.csv')
|
||||
y_pred = data['predicted_TotalKg']
|
||||
y_test = data['actual_TotalKg']
|
||||
rmse = sqrt(mean_squared_error(y_test, y_pred))
|
||||
|
||||
|
||||
mlflow.log_param("epochs", int(sys.argv[1]))
|
||||
mlflow.log_metric("rmse", rmse)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@ -1,6 +0,0 @@
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Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user