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zadanka

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s434695 2021-05-15 11:50:27 +02:00
parent 69db80f48a
commit d839f3d1a3
8 changed files with 329 additions and 2 deletions
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evaluate.py Normal file
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print('test')

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sacred1.py Normal file
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#! /usr/bin/python3
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import wget
import numpy as np
import requests
from sacred.observers import FileStorageObserver
from sacred import Experiment
from datetime import datetime
import os
ex = Experiment("ium_s434695", interactive=False)
ex.observers.append(FileStorageObserver('ium_s434695/my_runs'))
@ex.config
def my_config():
train_size_param = 0.8
test_size_param = 0.2
@ex.capture
def prepare_model(train_size_param, test_size_param, _run):
_run.info["prepare_model_ts"] = str(datetime.now())
url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
r = requests.get(url, allow_redirects=True)
open('vgsales.csv', 'wb').write(r.content)
df = pd.read_csv('vgsales.csv')
def regression_model():
model = Sequential()
model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
model.add(Dense(64,activation = "relu"))
model.add(Dense(1,activation = "relu"))
model.compile(optimizer = "adam", loss = "mean_squared_error")
return model
df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
df
y = df.Nintendo
df=((df-df.min())/(df.max()-df.min()))
x = df.drop(['Nintendo'],axis = 1)
x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)
y_pred = model.predict(x_test)
y_pred[:5]
y_pred = np.around(y_pred, decimals=0)
y_pred[:5]
return(classification_report(y_test,y_pred))
@ex.main
def my_main(train_size_param, test_size_param):
print(prepare_model())
r = ex.run()
ex.add_artifact("vgsales_model/saved_model/saved_model.pb")

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sacred2.py Normal file
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#! /usr/bin/python3
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import wget
import numpy as np
import requests
from sacred.observers import FileStorageObserver
from sacred import Experiment
from datetime import datetime
import os
from sacred.observers import MongoObserver
ex = Experiment("ium_s434695", interactive=False)
ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
db_name='sacred'))
@ex.config
def my_config():
train_size_param = 0.8
test_size_param = 0.2
@ex.capture
def prepare_model(train_size_param, test_size_param, _run):
_run.info["prepare_model_ts"] = str(datetime.now())
url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
r = requests.get(url, allow_redirects=True)
open('vgsales.csv', 'wb').write(r.content)
df = pd.read_csv('vgsales.csv')
def regression_model():
model = Sequential()
model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
model.add(Dense(64,activation = "relu"))
model.add(Dense(1,activation = "relu"))
model.compile(optimizer = "adam", loss = "mean_squared_error")
return model
df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
df
y = df.Nintendo
df=((df-df.min())/(df.max()-df.min()))
x = df.drop(['Nintendo'],axis = 1)
x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)
y_pred = model.predict(x_test)
y_pred[:5]
y_pred = np.around(y_pred, decimals=0)
y_pred[:5]
return(classification_report(y_test,y_pred))
@ex.main
def my_main(train_size_param, test_size_param):
print(prepare_model())
r = ex.run()
ex.add_artifact("vgsales_model/saved_model/saved_model.pb")

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train.py Normal file
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#! /usr/bin/python3
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import requests
url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
r = requests.get(url, allow_redirects=True)
open('vgsales.csv', 'wb').write(r.content)
df = pd.read_csv('vgsales.csv')
def regression_model():
model = Sequential()
model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
model.add(Dense(32,activation = "relu"))
model.add(Dense(1,activation = "relu"))
model.compile(optimizer = "adam", loss = "mean_squared_error")
return model
df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
df
y = df.Nintendo
df=((df-df.min())/(df.max()-df.min()))
x = df.drop(['Nintendo'],axis = 1)
x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)
y_pred = model.predict(x_test)
model.save('model1')

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# Nasz obraz będzie dzidziczył z obrazu Ubuntu w wersji latest
FROM ubuntu:latest
# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
RUN apt update && apt install -y figlet
RUN apt install -y git
RUN apt install -y python3-pip
RUN pip3 install --user kaggle
RUN pip3 install --user pandas
RUN pip3 install --user numpy
RUN pip3 install --user seaborn
RUN pip3 install --user sklearn
RUN pip3 install --user matplotlib
RUN pip3 install --user tensorflow
RUN pip3 install --user sacred
RUN pip3 install --user wget
WORKDIR /app
COPY ./../train.py ./
COPY ./../evaluate.py ./
COPY ./../sacred1.py ./
COPY ./../sacred2.py ./
COPY ./../skrypt.sh ./
COPY ./../zadanie2.py ./
COPY ./../zadanie5.py ./

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train_evaluate/Jenkinsfile_evaluate Normal file
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pipeline {
agent {
dockerfile true
}
parameters{
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying data artifacts',
name: 'WHICH_BUILD_DATA'
)
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying train artifacts',
name: 'WHICH_BUILD_TRAIN'
)
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying current project artifacts',
name: 'WHICH_BUILD_THIS'
)
}
stages {
stage('copy artifacts')
{
steps
{
copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD_DATA'))
copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training', selector: buildParameter('WHICH_BUILD_TRAIN'))
copyArtifacts(fingerprintArtifacts: true, optional: true, projectName: 's434695-evaluation', selector: buildParameter('WHICH_BUILD_THIS'))
}
}
stage('evaluate')
{
steps
{
catchError {
sh 'python3 evaluate.py'
}
}
}
stage('send email') {
steps {
emailext body: currentBuild.result ?: 'SUCCESS',
subject: 's434760 - evaluation',
to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
}
}
}
}

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pipeline {
agent {
dockerfile true
}
parameters{
buildSelector(
defaultSelector: lastSuccessful(),
description: 'Which build to use for copying artifacts',
name: 'WHICH_BUILD'
)
string(
defaultValue: '16',
description: 'batch size',
name: 'BATCH_SIZE'
)
string(
defaultValue: '15',
description: 'epochs',
name: 'EPOCHS'
)
}
stages {
stage('checkout') {
steps {
copyArtifacts fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD')
}
}
stage('Docker'){
steps{
sh 'python3 "./train.py"'
sh 'python3 "./sacred1.py"'
sh 'python3 "./sacred2.py"'
}
}
stage('archiveArtifacts') {
steps{
archiveArtifacts 'ium_s434695/**'
archiveArtifacts 'model1'
}
}
}
post {
success {
build job: 's434695-evaluation/master'
mail body: 'SUCCESS TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
}
failure {
mail body: 'FAILURE TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
}
}
}

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zadanie5.py
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def regression_model(): def regression_model():
model = Sequential() model = Sequential()
model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],))) model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
model.add(Dense(64,activation = "relu")) model.add(Dense(32,activation = "relu"))
model.add(Dense(1,activation = "relu")) model.add(Dense(1,activation = "relu"))
model.compile(optimizer = "adam", loss = "mean_squared_error") model.compile(optimizer = "adam", loss = "mean_squared_error")