prepare
This commit is contained in:
szymonj98
parent
ea5a76edbc
commit
27c2cb7956
12
Dockerfile
12
Dockerfile
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FROM ubuntu:latest
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WORKDIR /ium
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RUN apt update && apt install -y python3-pip
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RUN apt install unzip
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RUN pip3 install kaggle
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RUN mkdir /.kaggle && chmod o+w /.kaggle
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RUN pip3 install pandas
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RUN pip3 install numpy
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RUN pip3 install sklearn
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RUN pip3 install tensorflow
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COPY ./steam-200k.csv ./
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COPY ./biblioteki_dl.py ./
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41
Jenkinsfile
vendored
41
Jenkinsfile
vendored
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pipeline {
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parameters {
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string(
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defaultValue: 'szymonjadczak',
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description: 'Kaggle username',
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name: 'KAGGLE_USERNAME',
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trim: false
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)
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password(
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defaultValue: '',
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description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
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name: 'KAGGLE_KEY'
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)
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string(
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defaultValue: '',
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description: 'Value for head command',
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name: 'CUTOFF'
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)
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}
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environment {
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KAGGLE_USERNAME="$params.KAGGLE_USERNAME"
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KAGGLE_KEY="$params.KAGGLE_KEY"
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CUTOFF="$params.CUTOFF"
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}
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agent {
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dockerfile {
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additionalBuildArgs "-t ium"
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}
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}
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stages {
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stage('Stage 1') {
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steps {
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echo 'Hello world!!!'
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checkout([$class: 'GitSCM', branches: [[name: '*/master']], extensions: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s444386/ium_444386']]])
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sh "chmod u+x ./dataset_download.sh"
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sh "KAGGLE_USERNAME=${KAGGLE_USERNAME} KAGGLE_KEY=${KAGGLE_KEY} CUTOFF=${CUTOFF} ./dataset_download.sh"
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archiveArtifacts 'data.csv'
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}
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}
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}
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}
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21
Jenkinsfile2
21
Jenkinsfile2
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pipeline{
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agent {
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docker { image 'ium' }
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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 artifacts',
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name: 'BUILD_SELECTOR')
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}
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stages{
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stage('copy artefacts') {
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steps {
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copyArtifacts filter: 'data.csv', fingerprintArtifacts: true, projectName: 's444386-create-dataset', selector: lastSuccessful()
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sh 'chmod u+x ./kagle.py'
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sh 'python3 kagle.py'
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}
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}
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}
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}
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121
biblioteki_dl.py
121
biblioteki_dl.py
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import tensorflow as tf
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import os
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import pandas as pd
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import numpy as np
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import csv
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from sklearn.model_selection import train_test_split
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os.system("kaggle datasets download -d tamber/steam-video-games")
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os.system("unzip -o steam-video-games.zip")
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steam=pd.read_csv('steam-200k.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
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steam.isnull().values.any()
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steam['userId'] = steam.userId.astype(str)
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purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
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playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
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playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
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playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
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steam = steam[steam['behavior'] != 'purchase']
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steam = steam.groupby("game").filter(lambda x: len(x)>10)
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size=int(len(steam)/10)
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meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
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meanGame = meanGame.to_dict()
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meanGame = meanGame['hoursPlayed']
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purchaseCount = purchaseCount.to_dict()
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playCount = playCount.to_dict()
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playerPurchaseCount = playerPurchaseCount.to_dict()
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playerPlayCount = playerPlayCount.to_dict()
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steam['meanTime'] = 0;
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steam['purchaseCount'] = 0;
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steam['playCount'] = 0;
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steam['playerPurchaseCount'] =0;
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steam['playerPlayCount'] =0;
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steam['playPercent'] =0;
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for i in steam.index:
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steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
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steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
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steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
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steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
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steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
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steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
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steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
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steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
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print(steam)
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games = {}
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for i in steam['game']:
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games[i] = 0
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j=0
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for key,game in games.items():
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games[key]=j
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j=j+1
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for i in steam['game']:
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i = games[i]
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invGames = {v: k for k, v in games.items()}
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x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
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y_train = steam_train['game']
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x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
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y_test = steam_test['game']
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x_train = np.array(x_train)
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y_train = np.array(y_train)
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x_test = np.array(x_test)
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y_test = np.array(y_test)
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for i,j in enumerate(y_train):
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y_train[i] = games[j]
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for i,j in enumerate(y_test):
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y_test[i] = games[j]
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model = tf.keras.models.Sequential([
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tf.keras.layers.Flatten(input_shape=(5,1)),
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tf.keras.layers.Dense(256, activation='relu'),
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tf.keras.layers.Dropout(0.01),
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tf.keras.layers.Dense(1000, activation='softmax')
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])
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model.compile(optimizer='adam',
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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y_train = np.array(y_train).astype(np.float32)
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y_test = np.array(y_test).astype(np.float32)
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model.fit(x_train, y_train, epochs=100)
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model.evaluate(x_test, y_test)
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prediction = model.predict(x_test)
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classes_x=np.argmax(prediction,axis=1)
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rows = []
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for j,i in enumerate(classes_x):
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row = [invGames[i],invGames[y_test[j]]]
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rows.append(row)
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with open('results.csv','w',encoding='UTF-8',newline='') as f:
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writer = csv.writer(f)
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writer.writerow(["predicted", "expected"])
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for row in rows:
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writer.writerow(row)
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23
data.txt
23
data.txt
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151603712,"The Elder Scrolls V Skyrim",purchase,1.0,0
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151603712,"The Elder Scrolls V Skyrim",play,273.0,0
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151603712,"Fallout 4",purchase,1.0,0
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151603712,"Fallout 4",play,87.0,0
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151603712,"Spore",purchase,1.0,0
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151603712,"Spore",play,14.9,0
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151603712,"Fallout New Vegas",purchase,1.0,0
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151603712,"Fallout New Vegas",play,12.1,0
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151603712,"Left 4 Dead 2",purchase,1.0,0
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151603712,"Left 4 Dead 2",play,8.9,0
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151603712,"HuniePop",purchase,1.0,0
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151603712,"HuniePop",play,8.5,0
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151603712,"Path of Exile",purchase,1.0,0
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151603712,"Path of Exile",play,8.1,0
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151603712,"Poly Bridge",purchase,1.0,0
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151603712,"Poly Bridge",play,7.5,0
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151603712,"Left 4 Dead",purchase,1.0,0
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151603712,"Left 4 Dead",play,3.3,0
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151603712,"Team Fortress 2",purchase,1.0,0
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151603712,"Team Fortress 2",play,2.8,0
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151603712,"Tomb Raider",purchase,1.0,0
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151603712,"Tomb Raider",play,2.5,0
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151603712,"The Banner Saga",purchase,1.0,0
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kaggle datasets download -d tamber/steam-video-games
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unzip -o steam-video-games.zip
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> data.csv
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head -n $CUTOFF steam-200k.csv >> data.csv
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wc -l data.csv >> number_of_lines.txt
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79
kagle.py
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kagle.py
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import os
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import pandas as pd
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from sklearn.model_selection import train_test_split
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#os.system("kaggle datasets download -d tamber/steam-video-games")
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#os.system("unzip -o steam-video-games.zip")
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steam=pd.read_csv('data.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
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steam.isnull().values.any()
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steam['userId'] = steam.userId.astype(str)
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print("Zbior danych:")
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print(steam)
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print("Describe:")
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print(steam.describe(include='all'),"\n\n")
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print("Gracze z najwieksza aktywnoscia:")
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print(steam["userId"].value_counts(),"\n\n")
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print("Gracze z najwieksza liczba kupionych gier:")
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print(steam[steam["behavior"] != "play"]["userId"].value_counts())
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print("Mediana:")
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print(steam[steam["behavior"] != "play"]["userId"].value_counts().median(),"\n\n")
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print("Gracze ktorzy zagrali w najwieksza liczbe gier:")
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print(steam[steam["behavior"] != "purchase"]["userId"].value_counts())
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print("Mediana:")
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print(steam[steam["behavior"] != "purchase"]["userId"].value_counts().median(),"\n\n")
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print("Gry:")
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print(steam["game"].value_counts(),"\n\n")
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print("Sredni czas grania w grania w dana gre")
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print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False))
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print("Mediana:")
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print(steam[steam["behavior"] != "purchase"].groupby("game").mean().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n")
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print("Najczesciej kupowana gra")
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print(steam[steam["behavior"] != "play"]["game"].value_counts())
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print("Mediana:")
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print(steam[steam["behavior"] != "play"]["game"].value_counts().median(),"\n\n")
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print("Gra w ktora zagralo najwiecej graczy")
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print(steam[steam["behavior"] != "purchase"]["game"].value_counts())
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print("Mediana:")
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print(steam[steam["behavior"] != "purchase"]["game"].value_counts().median(),"\n\n")
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print("Liczba kupionych gier i liczba gier w ktore gracze zagrali")
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print(steam["behavior"].value_counts(),"\n\n")
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print("Gra z najwieksza liczba godzin dla jednego gracza")
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print(steam[steam["behavior"] != "purchase"][["userId","hoursPlayed","game"]].sort_values(by="hoursPlayed",ascending=False))
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print("Mediana:")
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print(steam[steam["behavior"] != "purchase"]["hoursPlayed"].sort_values(ascending=False).median(),"\n\n")
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print("Suma rozegranych godzin dla danej gry")
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print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False))
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print("Mediana:")
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print(steam[steam["behavior"] != "purchase"].groupby("game").sum().sort_values(by="hoursPlayed",ascending=False).median(),"\n\n")
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#odrzucenie gier dla których jest mniej niż 10 wierszy
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steam = steam.groupby("game").filter(lambda x: len(x)>10)
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#rozmiar zbioru testowego i dev proporcje 8:1:1
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size=int(len(steam)/10)
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steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
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steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
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print("Zbior trenujacy")
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print(steam_train["game"].value_counts(),"\n")
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print("Zbior testujacy")
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print(steam_test["game"].value_counts(),"\n")
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print("Zbior dev")
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print(steam_dev["game"].value_counts(),"\n")
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200000
steam-200k.csv
200000
steam-200k.csv
File diff suppressed because it is too large
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