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mikaleta 2023-04-20 19:48:17 +02:00
parent 485673f131
commit 52f3e10bb1
2 changed files with 127 additions and 43 deletions
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83
Jenkinsfile vendored
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pipeline { pipeline {
agent any agent any
//Definijuemy parametry, które będzie można podać podczas wywoływania zadania
parameters{
string(
defaultValue: 'mikaleta',
description: 'Kaggle username',
name: 'KAGGLE_USERNAME',
trim: false
)
password(
defaultValue: '',
description: 'Kaggle token taken from kaggle.json file, as described in https://github.com/Kaggle/kaggle-api#api-credentials',
name: 'KAGGLE_KEY'
)
string(
defaultValue: '500',
description: 'CUTOFF',
name: 'CUTOFF',
trim: false
)
}
stages { stages {
stage('Clone repository') { stage('clear_before') {
steps { steps {
checkout([$class: 'GitSCM', branches: [[name: '*/master']], sh 'rm -rf *'
doGenerateSubmoduleConfigurations: false, }
extensions: [], submoduleCfg: [], }
userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s486867/ium_s486867']]])
}
}
stage('Process data') {
steps {
sh './process_data.sh'
}
}
stage('Archive artifacts') { stage('Clone Git') {
steps { steps {
archiveArtifacts artifacts: 'results.txt', onlyIfSuccessful: true sh 'git clone https://git.wmi.amu.edu.pl/s486867/ium_z486867'
}
}
stage('Build') {
steps {
withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
"KAGGLE_KEY=${params.KAGGLE_KEY}" ]) {
sh 'kaggle datasets download -d dansbecker/powerlifting-database'
sh 'unzip video-game-sales-with-ratings.zip -d ./ium_z486867'
} }
} }
} }
} stage('Docker') {
agent {
dockerfile {
filename 'Dockerfile'
dir 'ium_z486867'
reuseNode true
}
}
steps {
sh 'python ./ium_z486867/create-dataset.py'
archiveArtifacts 'X_test.csv'
archiveArtifacts 'X_dev.csv'
archiveArtifacts 'X_train.csv'
}
}
stage('clear_after') {
steps {
sh 'rm -rf *'
}
}
}
}

87
main.py
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import os
from kaggle.api.kaggle_api_extended import KaggleApi from kaggle.api.kaggle_api_extended import KaggleApi
import zipfile import zipfile
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
import pandas as pd import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
pd.set_option('display.max_columns', 100) pd.set_option('display.max_columns', 100)
api = KaggleApi()
api.authenticate()
api.dataset_download_files('shivamb/netflix-shows', path='./data')
with zipfile.ZipFile('./data/netflix-shows.zip', 'r') as zip_ref:
zip_ref.extractall('./data')
netflix = pd.read_csv('./data/netflix_titles.csv') DATA_DIRECTORY = './data'
netflix.dropna(inplace=True) CSV_NAME = DATA_DIRECTORY + '/openpowerlifting.csv'
def download_data_from_kaggle():
api = KaggleApi()
api.authenticate()
api.dataset_download_files('dansbecker/powerlifting-database', path=DATA_DIRECTORY)
def extract_data_from_zip():
for file_name in os.listdir(DATA_DIRECTORY):
if file_name.endswith(".zip"):
file_path = os.path.join(DATA_DIRECTORY, file_name)
with zipfile.ZipFile(file_path, "r") as zip_ref:
zip_ref.extractall(DATA_DIRECTORY)
print(f"The file {file_name} has been unzipped.")
def process_data(csv_name):
# Read in the data and drop the specified columns
data = pd.read_csv(csv_name)
data.drop(columns=["Squat4Kg", "Bench4Kg", "Deadlift4Kg"], inplace=True)
data.dropna(inplace=True)
random_seed = 42 # Remove negative values
train_data, test_data = train_test_split(netflix, test_size=0.2, random_state=random_seed) numeric_cols = data.select_dtypes(include=np.number).columns
train_data, dev_data = train_test_split(train_data, test_size=0.25, random_state=random_seed) data[numeric_cols] = data[numeric_cols].apply(lambda x: x.clip(lower=0)).dropna()
train_stats = train_data.describe(include='all') # Split the data into train, dev, and test sets if not already done
print(f"\nTraining set statistics:\n{train_stats}") if "train" not in data.columns or "dev" not in data.columns or "test" not in data.columns:
dev_stats = dev_data.describe(include='all') data_train, data_devtest = train_test_split(data, test_size=0.2, random_state=42, stratify=data["Division"])
print(f"\nDevelopment set statistics:\n{dev_stats}") data_dev, data_test = train_test_split(data_devtest, test_size=0.5, random_state=42, stratify=data_devtest["Division"])
test_stats = test_data.describe(include='all') data_train["Set"] = "train"
print(f"\nTest set statistics:\n{test_stats}") data_dev["Set"] = "dev"
data_test["Set"] = "test"
data = pd.concat([data_train, data_dev, data_test], ignore_index=True)
train_class_dist = train_data["type"].value_counts() # Collect and print statistics for the data and its subsets
print(f"\nTraining set class distribution:\n{train_class_dist}") print("Data Set Statistics:")
dev_class_dist = dev_data["type"].value_counts() print("Size: {}".format(len(data)))
print(f"\nDevelopment set class distribution:\n{dev_class_dist}") print("Avg values:")
test_class_dist = test_data["type"].value_counts() print(data.mean())
print(f"\nTest set class distribution:\n{test_class_dist}") print("Min values:")
print(data.min())
print("Max values:")
print(data.max())
print("Standard deviations:")
print(data.std())
print("Median values:")
print(data.median())
# Compute the frequency distribution of examples for individual classes
print("\nFrequency distribution of examples for individual classes:")
print(data["Class"].value_counts())
# Normalize the data to the range of 0.0 - 1.0
scaler = MinMaxScaler()
data.iloc[:, :-2] = scaler.fit_transform(data.iloc[:, :-2])
# Clear the collection of artifacts (e.g. blank lines, examples with invalid values)
data.dropna(inplace=True)
# Clear the remaining columns from negative and empty values
data[data.columns[:-2]] = data[data.columns[:-2]].apply(lambda x: x.clip(lower=0))
return data
# download_data_from_kaggle()
# extract_data_from_zip()
process_data(CSV_NAME)