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last commit fixes

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This commit is contained in:
Kacper 2022-05-12 22:57:13 +02:00
parent 2aac0952eb
commit ea9514ed5e
13 changed files with 33 additions and 130562 deletions
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37
evaluate.py
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@ -1,37 +0,0 @@
import tensorflow as tf
from tensorflow import keras
from matplotlib import pyplot as plt
from matplotlib.ticker import MaxNLocator
import numpy as np
import pandas as pd
# Załadowanie modelu z pliku
model = keras.models.load_model('lego_reg_model')
# Załadowanie zbioru testowego
data_test = pd.read_csv('lego_sets_clean_test.csv')
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Prosta ewaluacja (mean absolute error)
test_results = model.evaluate(
test_piece_counts,
test_prices, verbose=0)
# Zapis wartości liczbowej metryki do pliku
with open('eval_results.txt', 'a+') as f:
f.write(str(test_results) + '\n')
# Wygenerowanie i zapisanie do pliku wykresu
with open('eval_results.txt') as f:
scores = [float(line) for line in f if line]
builds = list(range(1, len(scores) + 1))
plot = plt.plot(builds, scores)
plt.xlabel('Build number')
plt.xticks(range(1, len(scores) + 1))
plt.ylabel('Mean absolute error')
plt.title('Model error by build')
plt.savefig('error_plot.jpg')
plt.show()

9
lab5/Jenkinsfile vendored
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@ -12,14 +12,15 @@ pipeline {
stages {
stage('Stage 1') {
steps {
sh 'chmod u+x ./lab5/process_dataset.py ./lab5/simple_regression.py'
sh 'cd lab5/'
sh 'chmod u+x ./process_dataset.py ./simple_regression.py'
echo 'Processing dataset...'
sh 'python3 lab5/process_dataset.py'
sh 'python3 process_dataset.py'
echo 'Dataset processed'
echo 'Conducting simple regression model test'
sh 'python3 lab5/simple_regression.py $EPOCHS_NUM'
sh 'python3 simple_regression.py $EPOCHS_NUM'
echo 'Model predictions saved'
sh 'head lab5/lego_reg_results.csv'
sh 'head lego_reg_results.csv'
}
}
}

7
lab5/Jenkinsfile1
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@ -5,12 +5,13 @@ pipeline {
stages {
stage('Stage 1') {
steps {
sh 'chmod u+x ./lab5/process_dataset.py ./lab5/simple_regression.py'
sh 'cd lab5/'
sh 'chmod u+x ./process_dataset.py ./simple_regression.py'
echo 'Processing dataset...'
sh 'python3 lab5/process_dataset.py'
sh 'python3 process_dataset.py'
echo 'Dataset processed'
echo 'Conducting simple regression model test'
sh 'python3 lab5/simple_regression.py'
sh 'python3 simple_regression.py'
echo 'Model predictions saved'
}
}

7
lab6/Jenkinsfile_dataset_for_LAB6
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@ -5,12 +5,13 @@ pipeline {
stages {
stage('Stage 1') {
steps {
sh 'chmod u+x ./lab6/process_dataset.py'
sh 'cd lab6/'
sh 'chmod u+x ./process_dataset.py'
echo 'Processing dataset...'
sh 'python3 lab6/process_dataset.py'
sh 'python3 process_dataset.py'
echo 'Dataset processed'
echo 'Archiving clean train and test datasets...'
archiveArtifacts artifacts: 'lab6/lego_sets_clean_train.csv, lab6/lego_sets_clean_test.csv'
archiveArtifacts artifacts: 'lego_sets_clean_train.csv, lego_sets_clean_test.csv'
echo 'Datasets archived'
echo 'Launching the s449288-training job...'
build job: 's449288-training/master/'

21
lab6/Jenkinsfile_eval
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@ -16,28 +16,29 @@ pipeline {
stage('Stage 1') {
steps {
git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s449288/ium_s449288.git'
sh 'chmod u+x ./lab6/evaluate.py'
sh 'cd lab6/'
sh 'chmod u+x ./evaluate.py'
echo 'Copying datasets from the create-dataset job...'
copyArtifacts filter: 'lab6/lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'
echo 'Datasets copied'
echo 'Copying model from the training job...'
copyArtifacts filter: 'lab6/lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR')
copyArtifacts filter: 'lego_reg_model.tar.gz', projectName: "s449288-training/${BRANCH}/", selector: buildParameter('BUILD_SELECTOR')
echo 'Model copied'
sh 'tar xvzf lab6/lego_reg_model.tar.gz'
sh 'tar xvzf lego_reg_model.tar.gz'
echo 'Optional copying of the metrics file from previous build...'
copyArtifacts filter: 'lab6/eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
copyArtifacts filter: 'eval_results.txt', projectName: 's449288-evaluation/master/', optional: true
echo 'Metrics file copied if it did not exist'
echo 'Evaluating model...'
sh 'python3 lab6/evaluate.py'
sh 'python3 evaluate.py'
echo 'Model evaluated. Metrics saved. Plot saved.'
sh 'head lab6/eval_results.txt'
sh 'file lab6/error_plot.jpg'
sh 'head eval_results.txt'
sh 'file error_plot.jpg'
echo 'Archiving metrics file...'
archiveArtifacts 'lab6/eval_results.txt'
archiveArtifacts 'eval_results.txt'
echo 'File archived'
script {
LAST_MAE = sh (
script: 'tail -1 lab6/eval_results.txt',
script: 'tail -1 eval_results.txt',
returnStdout: true
).trim()
}

13
lab6/Jenkinsfile_train
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@ -14,18 +14,19 @@ pipeline {
stages {
stage('Stage 1') {
steps {
sh 'chmod u+x ./lab6/simple_regression.py'
sh 'cd lab6/'
sh 'chmod u+x ./simple_regression.py'
echo 'Copying datasets from create-dataset...'
copyArtifacts filter: '*', projectName: 's449288-create-dataset'
echo 'Datasets copied'
echo 'Conducting simple regression model test'
sh 'python3 lab6/simple_regression.py $EPOCHS_NUM'
sh 'python3 simple_regression.py $EPOCHS_NUM'
echo 'Model and predictions saved'
sh 'head lab6/lego_reg_results.csv'
sh 'ls -lh lab6/lego_reg_model'
sh 'head lego_reg_results.csv'
sh 'ls -lh lego_reg_model'
echo 'Archiving model...'
sh 'tar -czf lab6/lego_reg_model.tar.gz lab6/lego_reg_model/'
archiveArtifacts 'lab6/lego_reg_model.tar.gz'
sh 'tar -czf lego_reg_model.tar.gz lego_reg_model/'
archiveArtifacts 'lego_reg_model.tar.gz'
echo 'Model archived'
echo 'Launching the s449288-evaluation job...'
build job: 's449288-evaluation/master/'

1
lab8/Jenkinsfile_eval
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@ -16,6 +16,7 @@ pipeline {
stage('Stage 1') {
steps {
git branch: "${params.BRANCH}", url: 'https://git.wmi.amu.edu.pl/s449288/ium_s449288.git'
cd 'lab8/'
sh 'chmod u+x ./evaluate.py'
echo 'Copying datasets from the create-dataset job...'
copyArtifacts filter: 'lego_sets_clean_test.csv', projectName: 's449288-create-dataset'

1
lab8/Jenkinsfile_train
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@ -7,6 +7,7 @@ pipeline {
stages {
stage('Stage 1') {
steps {
sh 'cd lab8/'
sh 'chmod u+x ./simple_regression_lab8.py'
echo 'Copying datasets from create-dataset...'
copyArtifacts filter: '*', projectName: 's449288-create-dataset'

130196
lego_sets.csv
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30
process_dataset.py
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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
# usuwamy przy okazji puste pola
lego = pd.read_csv('lego_sets.csv', encoding='utf-8').dropna()
# list_price moze byc do dwoch miejsc po przecinku
lego['list_price'] = lego['list_price'].round(2)
# num_reviews, piece_count i prod_id moga byc wartosciami calkowitymi
lego['num_reviews'] = lego['num_reviews'].apply(np.int64)
lego['piece_count'] = lego['piece_count'].apply(np.int64)
lego['prod_id'] = lego['prod_id'].apply(np.int64)
# wglad, statystyki
print(lego)
print(lego.describe(include='all'))
# pierwszy podzial, wydzielamy zbior treningowy
lego_train, lego_rem = train_test_split(lego, train_size=0.8)
# drugi podział, wydzielamy walidacyjny i testowy
lego_valid, lego_test = train_test_split(lego_rem, test_size=0.5)
# zapis
lego.to_csv('lego_sets_clean.csv', index=None, header=True)
lego_train.to_csv('lego_sets_clean_train.csv', index=None, header=True)
lego_valid.to_csv('lego_sets_clean_valid.csv', index=None, header=True)
lego_test.to_csv('lego_sets_clean_test.csv', index=None, header=True)

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simple_regression.py
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import tensorflow as tf
from keras import layers
from keras.models import save_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
# Pobranie przykładowego argumentu trenowania
EPOCHS_NUM = int(sys.argv[1])
# Wczytanie danych
data_train = pd.read_csv('lego_sets_clean_train.csv')
data_test = pd.read_csv('lego_sets_clean_test.csv')
# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
train_piece_counts = np.array(data_train['piece_count'])
train_prices = np.array(data_train['list_price'])
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Normalizacja
normalizer = layers.Normalization(input_shape=[1, ], axis=None)
normalizer.adapt(train_piece_counts)
# Inicjalizacja
model = tf.keras.Sequential([
normalizer,
layers.Dense(units=1)
])
# Kompilacja
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error'
)
# Trening
history = model.fit(
train_piece_counts,
train_prices,
epochs=EPOCHS_NUM,
verbose=0,
validation_split=0.2
)
# Wykonanie predykcji na danych ze zbioru testującego
y_pred = model.predict(test_piece_counts)
# Zapis predykcji do pliku
results = pd.DataFrame({'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
results.to_csv('lego_reg_results.csv', index=False, header=True)
# Zapis modelu do pliku
model.save('lego_reg_model')
# Opcjonalne statystyki, wykresy
'''
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
print(hist.tail())
plt.scatter(train_piece_counts, train_prices, label='Data')
plt.plot(x, y_pred, color='k', label='Predictions')
plt.xlabel('pieces')
plt.ylabel('price')
plt.legend()
plt.show()
'''

86
simple_regression_lab7.py
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@ -1,86 +0,0 @@
import tensorflow as tf
from keras import layers
from keras.models import save_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver
# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
ex = Experiment(save_git_info=False)
# Dodanie obserwatora FileObserver
ex.observers.append(FileStorageObserver('runs'))
#Dodanie obserwatora Mongo
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
@ex.config
def config():
epochs = 100
units = 1
learning_rate = 0.1
# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
@ex.capture
def train(epochs, units, learning_rate, _run):
# Wczytanie danych
data_train = pd.read_csv('lego_sets_clean_train.csv')
data_test = pd.read_csv('lego_sets_clean_test.csv')
# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
train_piece_counts = np.array(data_train['piece_count'])
train_prices = np.array(data_train['list_price'])
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Normalizacja
normalizer = layers.Normalization(input_shape=[1, ], axis=None)
normalizer.adapt(train_piece_counts)
# Inicjalizacja
model = tf.keras.Sequential([
normalizer,
layers.Dense(units=units)
])
# Kompilacja
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
loss='mean_absolute_error'
)
# Trening
history = model.fit(
train_piece_counts,
train_prices,
epochs=epochs,
verbose=0,
validation_split=0.2
)
# Wykonanie predykcji na danych ze zbioru testującego
y_pred = model.predict(test_piece_counts)
# Zapis predykcji do pliku
results = pd.DataFrame(
{'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
results.to_csv('lego_reg_results.csv', index=False, header=True)
# Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
model.save('lego_reg_model')
ex.add_artifact('lego_reg_model/saved_model.pb')
# Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
_run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
@ex.automain
def main(units, learning_rate):
train()

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simple_regression_lab8.py
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import tensorflow as tf
from keras import layers
from keras.models import save_model
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sacred import Experiment
from sacred.observers import FileStorageObserver
from sacred.observers import MongoObserver
import mlflow
from urllib.parse import urlparse
# Konfiguracja serwera i nazwy eksperymentu MLflow
mlflow.set_tracking_uri('http://tzietkiewicz.vm.wmi.amu.edu.pl:5000/#/')
mlflow.set_experiment('s449288')
# Stworzenie obiektu klasy Experiment do śledzenia przebiegu regresji narzędziem Sacred
ex = Experiment(save_git_info=False)
# Dodanie obserwatora FileObserver
ex.observers.append(FileStorageObserver('runs'))
#Dodanie obserwatora Mongo
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
# Przykładowa modyfikowalna z Sacred konfiguracja wybranych parametrów treningu
@ex.config
def config():
epochs = 100
units = 1
learning_rate = 0.1
# Reszta kodu wrzucona do udekorowanej funkcji train do wywołania przez Sacred, żeby coś było capture'owane
@ex.capture
def train(epochs, units, learning_rate, _run):
# Podpięcie treningu do MLflow
with mlflow.start_run() as run:
print('MLflow run experiment_id: {0}'.format(run.info.experiment_id))
print('MLflow run artifact_uri: {0}'.format(run.info.artifact_uri))
# Wczytanie danych
data_train = pd.read_csv('lego_sets_clean_train.csv')
data_test = pd.read_csv('lego_sets_clean_test.csv')
# Wydzielenie zbiorów dla predykcji ceny zestawu na podstawie liczby klocków, którą zawiera
train_piece_counts = np.array(data_train['piece_count'])
train_prices = np.array(data_train['list_price'])
test_piece_counts = np.array(data_test['piece_count'])
test_prices = np.array(data_test['list_price'])
# Normalizacja
normalizer = layers.Normalization(input_shape=[1, ], axis=None)
normalizer.adapt(train_piece_counts)
# Inicjalizacja
model = tf.keras.Sequential([
normalizer,
layers.Dense(units=units)
])
# Kompilacja
model.compile(
optimizer=tf.optimizers.Adam(learning_rate=learning_rate),
loss='mean_absolute_error'
)
# Trening
history = model.fit(
train_piece_counts,
train_prices,
epochs=epochs,
verbose=0,
validation_split=0.2
)
# Wykonanie predykcji na danych ze zbioru testującego
y_pred = model.predict(test_piece_counts)
# Zapis predykcji do pliku
results = pd.DataFrame(
{'test_set_piece_count': test_piece_counts.tolist(), 'predicted_price': [round(a[0], 2) for a in y_pred.tolist()]})
results.to_csv('lego_reg_results.csv', index=False, header=True)
# Zapis modelu do pliku standardowo poprzez metodę kerasa i poprzez metodę obiektu Experiment z Sacred
model.save('lego_reg_model')
ex.add_artifact('lego_reg_model/saved_model.pb')
# Przykładowo zwracamy loss ostatniej epoki w charakterze wyników, żeby było widoczne w plikach zapisanych przez obserwator
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
_run.log_scalar('final.training.loss', hist['loss'].iloc[-1])
# Ewaluacja MAE na potrzeby MLflow (kopia z evaluate.py)
mae = model.evaluate(
test_piece_counts,
test_prices, verbose=0)
# Zapis parametrów i metryk dla MLflow
mlflow.log_param('epochs', epochs)
mlflow.log_param('units', units)
mlflow.log_param('learning_rate', learning_rate)
mlflow.log_metric("mae", mae)
# Logowanie i zapis modelu dla Mlflow
signature = mlflow.models.signature.infer_signature(train_piece_counts, model.predict(train_piece_counts))
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
if tracking_url_type_store != 'file':
mlflow.keras.log_model(model, 'lego-model', registered_model_name='TFLegoModel',
signature=signature)
else:
mlflow.keras.log_model(model, 'model', signature=signature, input_example=500)
@ex.automain
def main(epochs, units, learning_rate):
train()