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Dominik 2021-04-08 17:01:17 +02:00
parent 86488cd92c
commit efcf66a3bc
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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 git
RUN apt install -y python3-pip
RUN apt install -y curl
RUN pip3 install --user kaggle
RUN pip3 install --user seaborn
RUN pip3 install --user sklearn
RUN pip3 install --user pandas
RUN pip3 install --user matplotlib
# Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
WORKDIR /app
# Skopiujmy nasz skrypt do katalogu /app w kontenerze
COPY ./test.sh ./
COPY ./IUM_1_434788.ipynb ./

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import wget
url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
filename = wget.download(url)
import pandas as pd
wine=pd.read_csv('winequality-red.csv')
wine
from sklearn.model_selection import train_test_split
wine_train, wine_test = train_test_split(wine, test_size=360,train_size=959, random_state=1)
wine_test["quality"].value_counts()
wine_train["quality"].value_counts()
wine
wine["quality"].value_counts()
wine.describe(include='all')
wine["quality"]=((wine["quality"]-wine["quality"].min())/(wine["quality"].max()-wine["quality"].min()))*20
wine["quality"].value_counts()
wine.isnull().sum()
wine.dropna(inplace=True)
wine

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# 5. Zapisanie zbioru danych [4 pkt] (Pobranie pliku .csv)
curl -OL https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv
# 5. Zapisanie zbioru danych [4 pkt] (Podzielenie pliku csv na test/dev/train)
head -n 1 winequality-red.csv > header.csv
tail -n +2 winequality-red.csv | shuf > data.shuffled
head -n 266 data.shuffled > wine.data.test
head -n 532 data.shuffled | tail -n 266 > wine.data.dev
tail -n +333 data.shuffled > wine.data.train
cat header.csv wine.data.test > test.csv
cat header.csv wine.data.dev > dev.csv
cat header.csv wine.data.train > train.csv
# 7. Parametry zadania [1 pkt]
head -n $1 data.shuffled > zadanie7.data
cat header.csv zadanie7.data > Final_file.csv

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