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IUM_2.py
7
IUM_2.py
@ -1,13 +1,7 @@
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import pandas as pd
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import pandas as pd
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import kaggle
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import StandardScaler
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def download_file():
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('nasa/meteorite-landings', path='.', unzip=True)
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def split(data):
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def split(data):
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meteorite_train, meteorite_test = train_test_split(data, test_size=0.2, random_state=1)
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meteorite_train, meteorite_test = train_test_split(data, test_size=0.2, random_state=1)
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meteorite_train, meteorite_val = train_test_split(meteorite_train, test_size=0.25, random_state=1)
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meteorite_train, meteorite_val = train_test_split(meteorite_train, test_size=0.25, random_state=1)
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@ -28,7 +22,6 @@ def preprocessing(data):
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data.loc[(data['mass'].isnull()) & (data['name'].str.startswith('Österplana')), 'mass'] = 0
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data.loc[(data['mass'].isnull()) & (data['name'].str.startswith('Österplana')), 'mass'] = 0
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return data
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return data
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download_file()
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data = pd.read_csv("meteorite-landings.csv")
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data = pd.read_csv("meteorite-landings.csv")
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meteorite_train, meteorite_test, meteorite_val = split(data)
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meteorite_train, meteorite_test, meteorite_val = split(data)
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10
Jenkinsfile
vendored
10
Jenkinsfile
vendored
@ -10,6 +10,16 @@ pipeline {
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steps {
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steps {
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checkout scm
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checkout scm
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}
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}
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}
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stage('Download dataset') {
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steps {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}", "KAGGLE_KEY=${params.KAGGLE_KEY}"]) {
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sh 'pip install kaggle'
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sh 'kaggle datasets download -d nasa/meteorite-landings'
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sh 'unzip -o meteorite-landings.zip'
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sh 'rm meteorite-landings.zip'
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}
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}
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}
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}
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stage('Build') {
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stage('Build') {
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steps {
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steps {
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