porzadki
This commit is contained in:
parent
a51ee6d1f9
commit
1055cde3af
76
sacred1.py
76
sacred1.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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import requests
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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ex = Experiment("ium_s434695", interactive=False)
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ex.observers.append(FileStorageObserver('ium_s434695/my_runs'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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y_pred[:5]
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y_pred = np.around(y_pred, decimals=0)
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y_pred[:5]
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return(classification_report(y_test,y_pred))
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@ex.main
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def my_main(train_size_param, test_size_param):
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print(prepare_model())
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r = ex.run()
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ex.add_artifact("vgsales_model/saved_model/saved_model.pb")
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78
sacred2.py
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sacred2.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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import requests
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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from sacred.observers import MongoObserver
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ex = Experiment("ium_s434695", interactive=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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y_pred[:5]
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y_pred = np.around(y_pred, decimals=0)
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y_pred[:5]
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return(classification_report(y_test,y_pred))
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@ex.main
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def my_main(train_size_param, test_size_param):
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print(prepare_model())
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r = ex.run()
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ex.add_artifact("vgsales_model/saved_model/saved_model.pb")
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42
train.py
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train.py
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import numpy as np
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import requests
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(32,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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model.save('model1')
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# Nasz obraz będzie dzidziczył z obrazu Ubuntu w wersji latest
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FROM ubuntu:latest
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# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
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RUN apt update && apt install -y figlet
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RUN apt install -y git
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RUN apt install -y python3-pip
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RUN pip3 install setuptools
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RUN pip3 install kaggle
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RUN pip3 install pandas
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RUN pip3 install numpy
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RUN pip3 install seaborn
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RUN pip3 install sklearn
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RUN pip3 install matplotlib
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RUN pip3 install tensorflow
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RUN pip3 install sacred
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RUN pip3 install wget
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RUN pip3 install keras
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RUN pip3 install GitPython
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RUN pip3 install pymongo
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RUN pip3 install mlflow
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pipeline {
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agent {
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dockerfile true
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}
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parameters{
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying data artifacts',
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name: 'WHICH_BUILD_DATA'
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)
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying train artifacts',
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name: 'WHICH_BUILD_TRAIN'
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)
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying current project artifacts',
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name: 'WHICH_BUILD_THIS'
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)
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}
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stages {
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stage('copy artifacts')
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{
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steps
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{
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copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD_DATA'))
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copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training', selector: buildParameter('WHICH_BUILD_TRAIN'))
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copyArtifacts(fingerprintArtifacts: true, optional: true, projectName: 's434695-evaluation', selector: buildParameter('WHICH_BUILD_THIS'))
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}
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}
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stage('evaluate')
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{
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steps
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{
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catchError {
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sh 'python3 evaluate.py'
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}
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}
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}
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stage('send email') {
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steps {
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emailext body: currentBuild.result ?: 'SUCCESS',
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subject: 's434695 - evaluation',
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to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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}
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pipeline {
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agent any;
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parameters{
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buildSelector(
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defaultSelector: lastSuccessful(),
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description: 'Which build to use for copying artifacts',
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name: 'WHICH_BUILD'
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)
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string(
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defaultValue: '16',
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description: 'batch size',
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name: 'BATCH_SIZE'
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)
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string(
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defaultValue: '15',
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description: 'epochs',
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name: 'EPOCHS'
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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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copyArtifacts fingerprintArtifacts: true, projectName: 's434695-create-dataset', selector: buildParameter('WHICH_BUILD')
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}
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}
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stage('docker-training') {
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steps {
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script {
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def img = docker.build('shroomy/ium2:1')
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img.inside {
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sh "python3 train.py"
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sh "python3 sacred1.py"
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sh "python3 sacred2.py"
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}
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}
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}
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}
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stage('archiveArtifacts') {
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steps{
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archiveArtifacts 'ium_s434695/**'
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archiveArtifacts 'model1'
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}
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}
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}
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post {
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success {
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build job: 's434695-evaluation/master'
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mail body: 'SUCCESS TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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failure {
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mail body: 'FAILURE TRAINING', subject: 's434695', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
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}
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}
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}
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print('test')
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import wget
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import numpy as np
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import requests
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from sacred.observers import FileStorageObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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ex = Experiment("ium_s434695", interactive=False)
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ex.observers.append(FileStorageObserver('ium_s434695/my_runs'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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@ex.capture
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def prepare_model(train_size_param, test_size_param, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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|
||||||
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")
|
|
||||||
@ -1,78 +0,0 @@
|
|||||||
#! /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")
|
|
||||||
@ -1,19 +0,0 @@
|
|||||||
#Pobranie pliku .csv
|
|
||||||
curl -OL https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/branch/master/vgsales.csv
|
|
||||||
|
|
||||||
|
|
||||||
#Podzielenie pliku csv na test/dev/train
|
|
||||||
head -n 1 vgsales.csv > header.csv
|
|
||||||
tail -n +2 vgsales.csv | shuf > data.shuffled
|
|
||||||
|
|
||||||
head -n 3320 data.shuffled > games.data.test
|
|
||||||
head -n 6640 data.shuffled | tail -n 3320 > games.data.dev
|
|
||||||
tail -n +6641 data.shuffled > games.data.train
|
|
||||||
|
|
||||||
cat header.csv games.data.test > test.csv
|
|
||||||
cat header.csv games.data.dev > dev.csv
|
|
||||||
cat header.csv games.data.train > train.csv
|
|
||||||
|
|
||||||
#Obcinanie danych
|
|
||||||
head -n $1 data.shuffled > obcietedane.data
|
|
||||||
cat header.csv obcietedane.data > obcietedane.csv
|
|
||||||
@ -1,42 +0,0 @@
|
|||||||
#! /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')
|
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1,34 +0,0 @@
|
|||||||
#! /usr/bin/python3
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
import pandas as pd
|
|
||||||
vgsales = pd.read_csv('vgsales.csv')
|
|
||||||
vgsales
|
|
||||||
|
|
||||||
vgsales.describe(include='all')
|
|
||||||
|
|
||||||
vgsales["Publisher"].value_counts()
|
|
||||||
|
|
||||||
vgsales["Platform"].value_counts()
|
|
||||||
|
|
||||||
vgsales["Platform"].value_counts().plot(kind="bar")
|
|
||||||
|
|
||||||
vgsales[["Platform","JP_Sales"]].groupby("Platform").mean().plot(kind="bar")
|
|
||||||
|
|
||||||
import seaborn as sns
|
|
||||||
sns.set_theme()
|
|
||||||
sns.relplot(data=vgsales, x="JP_Sales", y="NA_Sales", hue="Genre")
|
|
||||||
|
|
||||||
from sklearn.model_selection import train_test_split
|
|
||||||
vgsales_train, vgsales_test = train_test_split(vgsales, test_size = 0.6, random_state = 1)
|
|
||||||
vgsales_train["Platform"].value_counts()
|
|
||||||
|
|
||||||
vgsales_test["Platform"].value_counts()
|
|
||||||
|
|
||||||
print(vgsales_train["Platform"])
|
|
||||||
|
|
||||||
@ -1,53 +0,0 @@
|
|||||||
#! /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)
|
|
||||||
|
|
||||||
y_pred[:5]
|
|
||||||
|
|
||||||
y_pred = np.around(y_pred, decimals=0)
|
|
||||||
|
|
||||||
y_pred[:5]
|
|
||||||
|
|
||||||
print(accuracy_score(y_test, y_pred))
|
|
||||||
|
|
||||||
print(classification_report(y_test,y_pred))
|
|
||||||
|
|
||||||
pd.DataFrame(y_pred).to_csv("preds.csv")
|
|
||||||
Loading…
Reference in New Issue
Block a user