Compare commits
28 Commits
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@ -23,8 +23,4 @@ RUN chmod +x /load_data.sh
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RUN /load_data.sh
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RUN /load_data.sh
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RUN chmod +x /grab_avocado.py
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RUN chmod +x /grab_avocado.py
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RUN python3 /grab_avocado.py
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RUN python3 /grab_avocado.py
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# Run the model and train it
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RUN chmod +x /model.py
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RUN python3 /model.py
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12
MLProject
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12
MLProject
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name: s478841 regression model
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docker_env:
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image: s478841-image:latest
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entry_points:
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main:
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parameters:
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epochs: { type: string, default: "140" }
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steps: { type: string, default: "10" }
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save_model: { type: string, default: "--save" }
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command: "python3 scripts/mlflow_train.py -e {epochs} -s {steps} {save_model}"
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51
jenkins/evaluate.Jenkinsfile
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51
jenkins/evaluate.Jenkinsfile
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pipeline {
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agent {
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docker { image 's478841-image:latest' }
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}
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|
parameters {
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gitParameter branchFilter: 'origin/(.*)', defaultValue: 'develop', name: 'BRANCH_NAME', type:'PT_BRANCH'
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buildSelector(
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defaultSelector: upstream(),
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description: 'Build used for artifacts copying',
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name:'BUILD_SELECTOR')
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|
}
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|
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stages {
|
||||||
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stage('Copy artifacts') {
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|
steps {
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|
git branch: "${params.BRANCH_NAME}", url: 'https://git.wmi.amu.edu.pl/s478841/ium_478841.git'
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|
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copyArtifacts filter: 'data/*test*', fingerprintArtifacts: true, projectName: 's478841-create-dataset', selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts filter: 'data/*model*', fingerprintArtifacts: true, projectName: "s478841-training/${BRANCH_NAME}/", selector: buildParameter('BUILD_SELECTOR')
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copyArtifacts filter: 'evaluation_results.csv', projectName: "s478841-evaluation/${BRANCH_NAME}/", optional: true
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|
}
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|
}
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|
stage('Evaluate model') {
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|
steps {
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|
// sh 'chmod +x -R ${env.WORKSPACE}'
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|
sh 'python3 scripts/evaluate.py'
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|
}
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||||||
|
}
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|
stage('Archive artifacts') {
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|
steps {
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||||||
|
archiveArtifacts artifacts: '*data/evaluation_results.csv', onlyIfSuccessful: true
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|
archiveArtifacts artifacts: '*data/plots.png', onlyIfSuccessful: true
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|
}
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||||||
|
}
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||||||
|
}
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||||||
|
|
||||||
|
post {
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||||||
|
success {
|
||||||
|
emailext body: 'SUCCESS', subject: 's478841-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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|
}
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||||||
|
failure {
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||||||
|
emailext body: 'FAILURE', subject: 's478841-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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}
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|
unstable {
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emailext body: 'UNSTABLE', subject: 's478841-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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|
}
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||||||
|
changed {
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|
emailext body: 'CHANGED', subject: 's478841-evaluation', to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
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|
}
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||||||
|
}
|
||||||
|
}
|
75
jenkins/training.Jenkinsfile
Normal file
75
jenkins/training.Jenkinsfile
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|||||||
|
pipeline {
|
||||||
|
agent {
|
||||||
|
docker {
|
||||||
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image 's478841-image:latest'
|
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|
args '-v /mlruns:/mlruns'
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|
}
|
||||||
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}
|
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|
parameters {
|
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string(
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defaultValue: '140',
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|
description: 'epochs number',
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name: 'epochs'
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)
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string(
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defaultValue: '10',
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description: 'Number of training steps between loss values logging',
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name: 'step'
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)
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string (
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defaultValue: '--save',
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description: 'save model after training',
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name: 'save_model'
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)
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}
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stages {
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stage('Checkout') {
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steps {
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checkout([$class: 'GitSCM', branches: [[name: '*/develop']], extensions: [], userRemoteConfigs: [
|
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[url: 'https://git.wmi.amu.edu.pl/s478841/ium_478841.git']]])
|
||||||
|
}
|
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}
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stage('Copy Artifacts') {
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|
steps {
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|
copyArtifacts filter: 'data/avocado.data*', fingerprintArtifacts: true, projectName: 's478841-create-dataset', selector: lastSuccessful()
|
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|
}
|
||||||
|
}
|
||||||
|
stage('Model training') {
|
||||||
|
steps {
|
||||||
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sh "chmod +x -R ${env.WORKSPACE}"
|
||||||
|
sh 'python3 scripts/sacred_train.py -e $epochs -s $step $save_model'
|
||||||
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sh 'python3 scripts/mlflow_train.py -e $epochs -s $step $save_model'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
stage('Archive artifacts') {
|
||||||
|
steps {
|
||||||
|
archiveArtifacts artifacts: 'mlruns/**', onlyIfSuccessful: true
|
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|
archiveArtifacts artifacts: '*data/predictions.csv', onlyIfSuccessful: true
|
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|
archiveArtifacts artifacts: '*data/model_scripted*', onlyIfSuccessful: true
|
||||||
|
dir('data/training_runs') {
|
||||||
|
archiveArtifacts artifacts: '**/**', onlyIfSuccessful: true
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
post {
|
||||||
|
success {
|
||||||
|
emailext body: 'SUCCESS', subject: "${env.JOB_NAME}", to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
|
||||||
|
build job: 's478841-evaluation/develop'
|
||||||
|
}
|
||||||
|
|
||||||
|
failure {
|
||||||
|
emailext body: 'FAILURE', subject: "${env.JOB_NAME}", to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
|
||||||
|
}
|
||||||
|
|
||||||
|
unstable {
|
||||||
|
emailext body: 'UNSTABLE', subject: "${env.JOB_NAME}", to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
|
||||||
|
}
|
||||||
|
|
||||||
|
changed {
|
||||||
|
emailext body: 'CHANGED', subject: "${env.JOB_NAME}", to: 'e19191c5.uam.onmicrosoft.com@emea.teams.ms'
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
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42
scripts/evaluate.py
Normal file
42
scripts/evaluate.py
Normal file
@ -0,0 +1,42 @@
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from model import AvocadoDataset, evaluate_model
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from torch.utils.data import DataLoader
|
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from torch.jit import load as load_model
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|
import pandas as pd
|
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import matplotlib.pyplot as plt
|
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|
import matplotlib
|
||||||
|
matplotlib.style.use('ggplot')
|
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|
|
||||||
|
|
||||||
|
# * Load the test data
|
||||||
|
test_data = DataLoader(AvocadoDataset(
|
||||||
|
'./data/avocado.data.test'), batch_size=1, shuffle=False)
|
||||||
|
|
||||||
|
# * Load the model
|
||||||
|
model = load_model('./data/model_scripted.pt')
|
||||||
|
model.eval()
|
||||||
|
|
||||||
|
# * Append new inference data
|
||||||
|
with open('./data/evaluation_results.csv', 'a+') as f:
|
||||||
|
f.write("{0},{1},{2}\n".format(*evaluate_model(test_data, model)))
|
||||||
|
|
||||||
|
# * Load all inference data gathered (till the current one)
|
||||||
|
results = pd.read_csv('./data/evaluation_results.csv',
|
||||||
|
names=['MSE', 'RMSE', 'MAE'])
|
||||||
|
|
||||||
|
# * Plot the results
|
||||||
|
plt.plot(range(1, len(results)+1), results['MSE'], color='green')
|
||||||
|
plt.scatter(range(1, len(results)+1),
|
||||||
|
results['MSE'], label='MSE', color='green', marker='.')
|
||||||
|
plt.plot(range(1, len(results)+1), results['RMSE'], color='darkred')
|
||||||
|
plt.scatter(range(1, len(results)+1),
|
||||||
|
results['RMSE'], label='RMSE', color='darkorange', marker='.')
|
||||||
|
plt.plot(range(1, len(results)+1), results['MAE'], color='blue')
|
||||||
|
plt.scatter(range(1, len(results)+1),
|
||||||
|
results['MAE'], label='MAE', color='blue', marker='.')
|
||||||
|
plt.xticks(range(1, len(results)+1))
|
||||||
|
plt.ylabel('Metric value')
|
||||||
|
plt.xlabel('Build number')
|
||||||
|
plt.legend()
|
||||||
|
|
||||||
|
# * Save figure
|
||||||
|
plt.savefig('data/plots.png')
|
212
scripts/mlflow_train.py
Normal file
212
scripts/mlflow_train.py
Normal file
@ -0,0 +1,212 @@
|
|||||||
|
from urllib.parse import urlparse
|
||||||
|
import mlflow
|
||||||
|
import mlflow.pytorch as model_logger
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from sklearn.metrics import mean_squared_error, mean_absolute_error
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
from torch.utils import data as t_u_data
|
||||||
|
|
||||||
|
|
||||||
|
# mlflow.set_tracking_uri("http://localhost:5000")
|
||||||
|
mlflow.set_tracking_uri("http://172.17.0.1:5000")
|
||||||
|
mlflow.set_experiment("s478841")
|
||||||
|
|
||||||
|
|
||||||
|
# * Customized Dataset class (base provided by PyTorch)
|
||||||
|
class AvocadoDataset(t_u_data.Dataset):
|
||||||
|
def __init__(self, path: str, target: str = 'AveragePrice'):
|
||||||
|
data = pd.read_csv(path)
|
||||||
|
y = data[target].values.astype('float32')
|
||||||
|
self.y = y.reshape((len(y), 1))
|
||||||
|
self.x_data = data.drop(
|
||||||
|
[target], axis=1).values.astype('float32')
|
||||||
|
self.x_shape = data.drop([target], axis=1).shape
|
||||||
|
# print("Data shape is: ", self.x_data.shape)
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.x_data)
|
||||||
|
|
||||||
|
def __getitem__(self, idx):
|
||||||
|
return [self.x_data[idx], self.y[idx]]
|
||||||
|
|
||||||
|
def get_shape(self):
|
||||||
|
return self.x_shape
|
||||||
|
|
||||||
|
def get_splits(self, n_test=0.33):
|
||||||
|
test_size = round(n_test * len(self.x_data))
|
||||||
|
train_size = len(self.x_data) - test_size
|
||||||
|
return t_u_data.random_split(self, [train_size, test_size])
|
||||||
|
|
||||||
|
|
||||||
|
class AvocadoRegressor(nn.Module):
|
||||||
|
def __init__(self, input_dim):
|
||||||
|
super(AvocadoRegressor, self).__init__()
|
||||||
|
self.hidden1 = nn.Linear(input_dim, 32)
|
||||||
|
nn.init.xavier_uniform_(self.hidden1.weight)
|
||||||
|
self.act1 = nn.ReLU()
|
||||||
|
self.hidden2 = nn.Linear(32, 8)
|
||||||
|
nn.init.xavier_uniform_(self.hidden2.weight)
|
||||||
|
self.act2 = nn.ReLU()
|
||||||
|
self.hidden3 = nn.Linear(8, 1)
|
||||||
|
nn.init.xavier_uniform_(self.hidden3.weight)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.hidden1(x)
|
||||||
|
x = self.act1(x)
|
||||||
|
x = self.hidden2(x)
|
||||||
|
x = self.act2(x)
|
||||||
|
x = self.hidden3(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def prepare_data(paths):
|
||||||
|
train_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[0]), batch_size=32, shuffle=True)
|
||||||
|
validate_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[1]), batch_size=128, shuffle=True)
|
||||||
|
test_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[2]), batch_size=1, shuffle=False)
|
||||||
|
return train_dl, validate_dl, test_dl
|
||||||
|
|
||||||
|
|
||||||
|
def train_model(train_dl, model, epochs, log_step):
|
||||||
|
criterion = nn.MSELoss()
|
||||||
|
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
|
||||||
|
to_compare = None
|
||||||
|
metrics = None
|
||||||
|
for epoch in range(1, epochs+1):
|
||||||
|
for _, (inputs, targets) in enumerate(train_dl):
|
||||||
|
optimizer.zero_grad()
|
||||||
|
yhat = model(inputs)
|
||||||
|
# * For loss value inspection
|
||||||
|
to_compare = (yhat, targets)
|
||||||
|
loss = criterion(yhat, targets)
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
if epoch == 1 or (epoch) % log_step == 0:
|
||||||
|
result, target = to_compare[0].detach(
|
||||||
|
).numpy(), to_compare[1].detach().numpy()
|
||||||
|
metrics = {'train.mse': mean_squared_error(target, result),
|
||||||
|
'train.mae': mean_absolute_error(target, result),
|
||||||
|
'train.rmse': mean_squared_error(target, result, squared=False)}
|
||||||
|
# _run.log_scalar("training.RMSE", np.sqrt(mse), epoch)
|
||||||
|
# _run.log_scalar("training.MAE", mae, epoch)
|
||||||
|
# _run.log_scalar('training.MSE', mse, epoch)
|
||||||
|
print(
|
||||||
|
f"Epoch {epoch}\t→\tMSE: {metrics['train.mse']},\tRMSE: {metrics['train.rmse']},\tMAE: {metrics['train.mae']}")
|
||||||
|
return metrics
|
||||||
|
|
||||||
|
|
||||||
|
def evaluate_model(test_dl, model):
|
||||||
|
predictions, actuals = list(), list()
|
||||||
|
for _, (inputs, targets) in enumerate(test_dl):
|
||||||
|
yhat = model(inputs)
|
||||||
|
# * retrieve numpy array
|
||||||
|
yhat = yhat.detach().numpy()
|
||||||
|
actual = targets.numpy()
|
||||||
|
actual = actual.reshape((len(actual), 1))
|
||||||
|
# * store predictions
|
||||||
|
predictions.append(yhat)
|
||||||
|
actuals.append(actual)
|
||||||
|
predictions, actuals = np.vstack(predictions), np.vstack(actuals)
|
||||||
|
# * return MSE value
|
||||||
|
mse = mean_squared_error(actuals, predictions)
|
||||||
|
rmse = mean_squared_error(actuals, predictions, squared=False)
|
||||||
|
mae = mean_absolute_error(actuals, predictions)
|
||||||
|
return mse, rmse, mae
|
||||||
|
|
||||||
|
|
||||||
|
def predict(row, model):
|
||||||
|
row = row[0].flatten()
|
||||||
|
yhat = model(row)
|
||||||
|
yhat = yhat.detach().numpy()
|
||||||
|
return yhat
|
||||||
|
|
||||||
|
|
||||||
|
def main(epochs, save_model, log_step):
|
||||||
|
print(
|
||||||
|
f"Your model will be trained for {epochs} epochs, logging every {log_step} steps. Trained model will {'not ' if save_model else ''}be saved.")
|
||||||
|
|
||||||
|
# * Paths to data
|
||||||
|
avocado_data = ['./data/avocado.data.train',
|
||||||
|
'./data/avocado.data.valid',
|
||||||
|
'./data/avocado.data.test']
|
||||||
|
|
||||||
|
# * Data preparation
|
||||||
|
train_dl, validate_dl, test_dl = prepare_data(paths=avocado_data)
|
||||||
|
print(f"""
|
||||||
|
Train set size: {len(train_dl.dataset)},
|
||||||
|
Validate set size: {len(validate_dl.dataset)}
|
||||||
|
Test set size: {len(test_dl.dataset)}
|
||||||
|
""")
|
||||||
|
|
||||||
|
# * Model definition
|
||||||
|
# ! 66 - in case only regions and type are used (among all the categorical vals)
|
||||||
|
model = AvocadoRegressor(235)
|
||||||
|
|
||||||
|
# * Train model
|
||||||
|
print("Let's start the training, mate!")
|
||||||
|
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))
|
||||||
|
metrics = train_model(train_dl=train_dl, model=model,
|
||||||
|
epochs=epochs, log_step=log_step)
|
||||||
|
mlflow.log_param('epochs', epochs)
|
||||||
|
mlflow.log_metrics(metrics)
|
||||||
|
|
||||||
|
# * Evaluate model
|
||||||
|
val_metrics = {key: val for key, val in zip(
|
||||||
|
['validate.mse', 'validate.rmse', 'validate.mae'], evaluate_model(validate_dl, model))}
|
||||||
|
print(
|
||||||
|
f"\nEvaluation on VALIDATION set\t→\tMSE: {val_metrics['validate.mse']}, RMSE: {val_metrics['validate.rmse']}, MAE: {val_metrics['validate.mae']}")
|
||||||
|
mlflow.log_metrics(val_metrics)
|
||||||
|
|
||||||
|
test_loss = {key: val for key, val in zip(
|
||||||
|
['test.mse', 'test.rmse', 'test.mae'], evaluate_model(test_dl, model))}
|
||||||
|
print(
|
||||||
|
f"\nEvaluation on TEST set\t→\tMSE: {test_loss['test.mse']}, RMSE: {test_loss['test.rmse']}, MAE: {test_loss['test.mae']}")
|
||||||
|
mlflow.log_metrics(test_loss)
|
||||||
|
|
||||||
|
# tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
|
||||||
|
|
||||||
|
# if tracking_url_type_store != 'file':
|
||||||
|
# print('First option')
|
||||||
|
# model_logger.log_model(
|
||||||
|
# model, "avocados-model", registered_model_name="AvocadoModel_478841")
|
||||||
|
# else:
|
||||||
|
# print('Second option')
|
||||||
|
# model_logger.log_model(model, "model")
|
||||||
|
|
||||||
|
|
||||||
|
# * Save the trained model
|
||||||
|
if save_model:
|
||||||
|
print("Your model has been saved - have a nice day!")
|
||||||
|
scripted_model = torch.jit.script(model)
|
||||||
|
scripted_model.save('./data/model_scripted.pt')
|
||||||
|
# ex.add_artifact('./data/model_scripted.pt')
|
||||||
|
|
||||||
|
|
||||||
|
# ex.run()
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# * Model parameters
|
||||||
|
parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument(
|
||||||
|
"-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
|
||||||
|
parser.add_argument(
|
||||||
|
"-s", "--step", default=10, help="Number of steps to repeat logging loss values on")
|
||||||
|
parser.add_argument("--save", action="store_true",
|
||||||
|
help="Save trained model to file 'trained_model.h5'")
|
||||||
|
|
||||||
|
args = vars(parser.parse_args())
|
||||||
|
|
||||||
|
epochs = int(args['epochs'])
|
||||||
|
save_model = args['save']
|
||||||
|
log_step = int(args['step'])
|
||||||
|
|
||||||
|
main(epochs, save_model, log_step)
|
@ -1,6 +1,8 @@
|
|||||||
|
import argparse
|
||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from sklearn.metrics import mean_squared_error
|
from sklearn.metrics import mean_squared_error, mean_absolute_error
|
||||||
|
|
||||||
import torch
|
import torch
|
||||||
from torch import nn
|
from torch import nn
|
||||||
@ -99,7 +101,10 @@ def evaluate_model(test_dl, model):
|
|||||||
actuals.append(actual)
|
actuals.append(actual)
|
||||||
predictions, actuals = np.vstack(predictions), np.vstack(actuals)
|
predictions, actuals = np.vstack(predictions), np.vstack(actuals)
|
||||||
# * return MSE value
|
# * return MSE value
|
||||||
return mean_squared_error(actuals, predictions)
|
mse = mean_squared_error(actuals, predictions)
|
||||||
|
rmse = mean_squared_error(actuals, predictions, squared=False)
|
||||||
|
mae = mean_absolute_error(actuals, predictions)
|
||||||
|
return mse, rmse, mae
|
||||||
|
|
||||||
|
|
||||||
def predict(row, model):
|
def predict(row, model):
|
||||||
@ -111,6 +116,21 @@ def predict(row, model):
|
|||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
|
|
||||||
|
# * Model parameters
|
||||||
|
parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument(
|
||||||
|
"-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
|
||||||
|
parser.add_argument("--save", action="store_true",
|
||||||
|
help="Save trained model to file 'trained_model.h5'")
|
||||||
|
|
||||||
|
args = vars(parser.parse_args())
|
||||||
|
|
||||||
|
epochs = args['epochs']
|
||||||
|
save_model = args['save']
|
||||||
|
print(
|
||||||
|
f"Your model will be trained for {epochs} epochs. Trained model will {'not ' if save_model else ''}be saved.")
|
||||||
|
|
||||||
# * Paths to data
|
# * Paths to data
|
||||||
avocado_train = './data/avocado.data.train'
|
avocado_train = './data/avocado.data.train'
|
||||||
avocado_valid = './data/avocado.data.valid'
|
avocado_valid = './data/avocado.data.valid'
|
||||||
@ -135,14 +155,21 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
# * Train model
|
# * Train model
|
||||||
print("Let's start the training, mate!")
|
print("Let's start the training, mate!")
|
||||||
train_model(train_dl, model)
|
train_model(train_dl, model, int(epochs))
|
||||||
|
|
||||||
# * Evaluate model
|
# * Evaluate model
|
||||||
mse = evaluate_model(validate_dl, model)
|
mse, rmse, mae = evaluate_model(validate_dl, model)
|
||||||
print(f"\nEvaluation\t→\tMSE: {mse}, RMSE: {np.sqrt(mse)}")
|
print(f"\nEvaluation\t→\tMSE: {mse}, RMSE: {rmse}, MAE: {mae}")
|
||||||
|
|
||||||
# * Prediction
|
# * Prediction
|
||||||
predictions = [(predict(row, model)[0], row[1].item()) for row in test_dl]
|
predictions = [(predict(row, model)[0], row[1].item()) for row in test_dl]
|
||||||
preds_df = pd.DataFrame(predictions, columns=["Prediction", "Target"])
|
preds_df = pd.DataFrame(predictions, columns=["Prediction", "Target"])
|
||||||
print("\nNow predictions - hey ho, let's go!\n", preds_df.head())
|
print("\nNow predictions - hey ho, let's go!\n",
|
||||||
|
preds_df.head(), "\n\n...let's save them\ndum...\ndum...\ndum dum dum...\n\tDUM\n")
|
||||||
preds_df.to_csv("./data/predictions.csv", index=False)
|
preds_df.to_csv("./data/predictions.csv", index=False)
|
||||||
|
|
||||||
|
# * Save the trained model
|
||||||
|
if save_model:
|
||||||
|
print("Your model has been saved - have a nice day!")
|
||||||
|
scripted_model = torch.jit.script(model)
|
||||||
|
scripted_model.save('./data/model_scripted.pt')
|
||||||
|
206
scripts/sacred_train.py
Normal file
206
scripts/sacred_train.py
Normal file
@ -0,0 +1,206 @@
|
|||||||
|
from sacred import Experiment
|
||||||
|
from sacred.observers import FileStorageObserver, MongoObserver
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
from sklearn.metrics import mean_squared_error, mean_absolute_error
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
from torch.utils import data as t_u_data
|
||||||
|
|
||||||
|
|
||||||
|
ex = Experiment("478841 sacred_scopes", interactive=True, save_git_info=False)
|
||||||
|
ex.observers.append(MongoObserver(
|
||||||
|
url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||||
|
ex.observers.append(FileStorageObserver('./data/training_runs'))
|
||||||
|
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def my_config():
|
||||||
|
parser = argparse.ArgumentParser(description="Script performing logistic regression model training",
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument(
|
||||||
|
"-e", "--epochs", default=100, help="Number of epochs the model will be trained for")
|
||||||
|
parser.add_argument(
|
||||||
|
"-s", "--step", default=10, help="Number of steps to repeat logging loss values on")
|
||||||
|
parser.add_argument("--save", action="store_true",
|
||||||
|
help="Save trained model to file 'trained_model.h5'")
|
||||||
|
|
||||||
|
args = vars(parser.parse_args())
|
||||||
|
|
||||||
|
epochs = int(args['epochs'])
|
||||||
|
save_model = args['save']
|
||||||
|
log_step = int(args['step'])
|
||||||
|
|
||||||
|
|
||||||
|
# * Customized Dataset class (base provided by PyTorch)
|
||||||
|
class AvocadoDataset(t_u_data.Dataset):
|
||||||
|
def __init__(self, path: str, target: str = 'AveragePrice'):
|
||||||
|
data = pd.read_csv(path)
|
||||||
|
y = data[target].values.astype('float32')
|
||||||
|
self.y = y.reshape((len(y), 1))
|
||||||
|
self.x_data = data.drop(
|
||||||
|
[target], axis=1).values.astype('float32')
|
||||||
|
self.x_shape = data.drop([target], axis=1).shape
|
||||||
|
# print("Data shape is: ", self.x_data.shape)
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.x_data)
|
||||||
|
|
||||||
|
def __getitem__(self, idx):
|
||||||
|
return [self.x_data[idx], self.y[idx]]
|
||||||
|
|
||||||
|
def get_shape(self):
|
||||||
|
return self.x_shape
|
||||||
|
|
||||||
|
def get_splits(self, n_test=0.33):
|
||||||
|
test_size = round(n_test * len(self.x_data))
|
||||||
|
train_size = len(self.x_data) - test_size
|
||||||
|
return t_u_data.random_split(self, [train_size, test_size])
|
||||||
|
|
||||||
|
|
||||||
|
class AvocadoRegressor(nn.Module):
|
||||||
|
def __init__(self, input_dim):
|
||||||
|
super(AvocadoRegressor, self).__init__()
|
||||||
|
self.hidden1 = nn.Linear(input_dim, 32)
|
||||||
|
nn.init.xavier_uniform_(self.hidden1.weight)
|
||||||
|
self.act1 = nn.ReLU()
|
||||||
|
self.hidden2 = nn.Linear(32, 8)
|
||||||
|
nn.init.xavier_uniform_(self.hidden2.weight)
|
||||||
|
self.act2 = nn.ReLU()
|
||||||
|
self.hidden3 = nn.Linear(8, 1)
|
||||||
|
nn.init.xavier_uniform_(self.hidden3.weight)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.hidden1(x)
|
||||||
|
x = self.act1(x)
|
||||||
|
x = self.hidden2(x)
|
||||||
|
x = self.act2(x)
|
||||||
|
x = self.hidden3(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def prepare_data(paths):
|
||||||
|
train_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[0]), batch_size=32, shuffle=True)
|
||||||
|
validate_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[1]), batch_size=128, shuffle=True)
|
||||||
|
test_dl = t_u_data.DataLoader(AvocadoDataset(
|
||||||
|
paths[2]), batch_size=1, shuffle=False)
|
||||||
|
return train_dl, validate_dl, test_dl
|
||||||
|
|
||||||
|
|
||||||
|
@ex.capture
|
||||||
|
def train_model(train_dl, model, epochs, log_step, _run):
|
||||||
|
criterion = nn.MSELoss()
|
||||||
|
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
|
||||||
|
to_compare = None
|
||||||
|
|
||||||
|
for epoch in range(1, epochs+1):
|
||||||
|
for _, (inputs, targets) in enumerate(train_dl):
|
||||||
|
optimizer.zero_grad()
|
||||||
|
yhat = model(inputs)
|
||||||
|
# * For loss value inspection
|
||||||
|
to_compare = (yhat, targets)
|
||||||
|
loss = criterion(yhat, targets)
|
||||||
|
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
if epoch == 1 or (epoch) % log_step == 0:
|
||||||
|
result, target = to_compare[0].detach(
|
||||||
|
).numpy(), to_compare[1].detach().numpy()
|
||||||
|
mse = mean_squared_error(target, result)
|
||||||
|
mae = mean_absolute_error(target, result)
|
||||||
|
_run.log_scalar("training.RMSE", np.sqrt(mse), epoch)
|
||||||
|
_run.log_scalar("training.MAE", mae, epoch)
|
||||||
|
_run.log_scalar('training.MSE', mse, epoch)
|
||||||
|
|
||||||
|
print(
|
||||||
|
f"Epoch {epoch}\t→\tMSE: {mse},\tRMSE: {np.sqrt(mse)},\tMAE: {mae}")
|
||||||
|
|
||||||
|
|
||||||
|
def evaluate_model(test_dl, model):
|
||||||
|
predictions, actuals = list(), list()
|
||||||
|
for _, (inputs, targets) in enumerate(test_dl):
|
||||||
|
yhat = model(inputs)
|
||||||
|
# * retrieve numpy array
|
||||||
|
yhat = yhat.detach().numpy()
|
||||||
|
actual = targets.numpy()
|
||||||
|
actual = actual.reshape((len(actual), 1))
|
||||||
|
# * store predictions
|
||||||
|
predictions.append(yhat)
|
||||||
|
actuals.append(actual)
|
||||||
|
predictions, actuals = np.vstack(predictions), np.vstack(actuals)
|
||||||
|
# * return MSE value
|
||||||
|
mse = mean_squared_error(actuals, predictions)
|
||||||
|
rmse = mean_squared_error(actuals, predictions, squared=False)
|
||||||
|
mae = mean_absolute_error(actuals, predictions)
|
||||||
|
return mse, rmse, mae
|
||||||
|
|
||||||
|
|
||||||
|
def predict(row, model):
|
||||||
|
row = row[0].flatten()
|
||||||
|
yhat = model(row)
|
||||||
|
yhat = yhat.detach().numpy()
|
||||||
|
return yhat
|
||||||
|
|
||||||
|
|
||||||
|
@ex.main
|
||||||
|
def main(epochs, save_model, log_step, _run):
|
||||||
|
print(
|
||||||
|
f"Your model will be trained for {epochs} epochs. Trained model will {'not ' if save_model else ''}be saved.")
|
||||||
|
|
||||||
|
# * Paths to data
|
||||||
|
avocado_data = ['./data/avocado.data.train',
|
||||||
|
'./data/avocado.data.valid',
|
||||||
|
'./data/avocado.data.test']
|
||||||
|
|
||||||
|
# * Data preparation
|
||||||
|
train_dl, validate_dl, test_dl = prepare_data(paths=avocado_data)
|
||||||
|
print(f"""
|
||||||
|
Train set size: {len(train_dl.dataset)},
|
||||||
|
Validate set size: {len(validate_dl.dataset)}
|
||||||
|
Test set size: {len(test_dl.dataset)}
|
||||||
|
""")
|
||||||
|
|
||||||
|
# * Model definition
|
||||||
|
# ! 66 - in case only regions and type are used (among all the categorical vals)
|
||||||
|
model = AvocadoRegressor(235)
|
||||||
|
|
||||||
|
# * Train model
|
||||||
|
print("Let's start the training, mate!")
|
||||||
|
train_model(train_dl=train_dl, model=model,
|
||||||
|
epochs=epochs, log_step=log_step)
|
||||||
|
|
||||||
|
# * Evaluate model
|
||||||
|
mse, rmse, mae = evaluate_model(validate_dl, model)
|
||||||
|
print(
|
||||||
|
f"\nEvaluation on validation set\t→\tMSE: {mse}, RMSE: {rmse}, MAE: {mae}")
|
||||||
|
|
||||||
|
_run.log_scalar("validation.RMSE", rmse, epochs+1)
|
||||||
|
_run.log_scalar("validation.MAE", mae, epochs+1)
|
||||||
|
_run.log_scalar('validation.MSE', mse, epochs+1)
|
||||||
|
|
||||||
|
# * Prediction
|
||||||
|
predictions = [(predict(row, model)[0], row[1].item()) for row in test_dl]
|
||||||
|
preds_df = pd.DataFrame(predictions, columns=["Prediction", "Target"])
|
||||||
|
test_loss = evaluate_model(test_dl, model)
|
||||||
|
|
||||||
|
print("\nNow predictions - hey ho, let's go!\n", preds_df.head(),
|
||||||
|
f"\nLoss values for test data: \t→\tMSE: {test_loss[0]}, RMSE: {test_loss[1]}, MAE: {test_loss[2]}")
|
||||||
|
print("\n...let's save them\ndum...\ndum...\ndum dum dum...\n\tDUM\n")
|
||||||
|
|
||||||
|
preds_df.to_csv("./data/predictions.csv", index=False)
|
||||||
|
|
||||||
|
# * Save the trained model
|
||||||
|
if save_model:
|
||||||
|
print("Your model has been saved - have a nice day!")
|
||||||
|
scripted_model = torch.jit.script(model)
|
||||||
|
scripted_model.save('./data/model_scripted.pt')
|
||||||
|
ex.add_artifact('./data/model_scripted.pt')
|
||||||
|
|
||||||
|
|
||||||
|
ex.run()
|
Loading…
Reference in New Issue
Block a user