Poprawki do zajęć 3,4 i 5
This commit is contained in:
Dominik Strzako
parent
33d62c375c
commit
b40e075716
@ -1,2 +1,2 @@
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#Uruchomienie skryptu i wyświetlenie 10 pierwszych wierszy wyjściowej tabeli
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#Uruchomienie skryptu i wyświetlenie 10 pierwszych wierszy wyjściowej tabeli
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python3 Zadanie_5_Docker.py
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python3 Zadanie_05_Docker.py
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@ -20,9 +20,8 @@ RUN pip3 install --user sacred
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WORKDIR /app
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WORKDIR /app
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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COPY ./test.sh ./
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COPY ./Docker_todo.sh ./
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COPY ./Python_file.py ./
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COPY ./Zadanie_05_Docker.py ./
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COPY ./Zadanie_5_Docker.py ./
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# Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
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# Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
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CMD ./test.sh
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CMD ./Docker_todo.sh
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@ -1,31 +0,0 @@
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import wget
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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='winequality-red.csv', bar=None)
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import pandas as pd
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wine=pd.read_csv('winequality-red.csv')
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wine
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from sklearn.model_selection import train_test_split
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wine_train, wine_test = train_test_split(wine, test_size=360,train_size=959, random_state=1)
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wine_test["quality"].value_counts()
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wine_train["quality"].value_counts()
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wine
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wine["quality"].value_counts()
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wine.describe(include='all')
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wine["quality"]=((wine["quality"]-wine["quality"].min())/(wine["quality"].max()-wine["quality"].min()))*20
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wine["quality"].value_counts()
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wine.isnull().sum()
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wine.dropna(inplace=True)
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print(wine)
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from tensorflow.keras.models import Sequential, load_model
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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 tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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import pandas as pd
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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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import wget
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import wget
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import numpy as np
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import numpy as np
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import os
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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='winequality-red.csv', bar=None)
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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='winequality-red.csv', bar=None)
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wine=pd.read_csv('winequality-red.csv')
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wine
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wine=pd.read_csv('winequality-red.csv')
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wine
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y = wine.quality
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y.head()
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y = wine.quality
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y.head()
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x = wine.drop(['quality'], axis= 1)
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x.head()
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x = wine.drop(['quality'], axis= 1)
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x.head()
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x=((x-x.min())/(x.max()-x.min())) #Normalizacja
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x=((x-x.min())/(x.max()-x.min())) #Normalizacja
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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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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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def regression_model():
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model = Sequential()
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def regression_model():
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model = Sequential()
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model.add(Dense(64,activation = "relu"))
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model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(1,activation = "relu"))
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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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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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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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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 = 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 = np.around(y_pred, decimals=0)
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y_pred[:5]
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dirpath = os.getcwd()
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print("dirpath = ", dirpath, "\n")
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pd.DataFrame(y_pred).to_csv("preds.csv")
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output_path = os.path.join(dirpath,'output.csv')
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print(accuracy_score(y_test, y_pred))
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print(output_path,"\n")
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print(classification_report(y_test,y_pred))
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pd.DataFrame(y_pred).to_csv(output_path)
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print(accuracy_score(y_test, y_pred))
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print(classification_report(y_test,y_pred))
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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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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='winequality-red.csv', bar=None)
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wine=pd.read_csv('winequality-red.csv')
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wine
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y = wine.quality
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y.head()
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x = wine.drop(['quality'], axis= 1)
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x.head()
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x=((x-x.min())/(x.max()-x.min())) #Normalizacja
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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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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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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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print(accuracy_score(y_test, y_pred))
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print(classification_report(y_test,y_pred))
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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 git
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RUN apt install -y python3-pip
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RUN apt install -y curl
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RUN pip3 install --user wget
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RUN pip3 install --user kaggle
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RUN pip3 install --user seaborn
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RUN pip3 install --user sklearn
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RUN pip3 install --user pandas
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RUN pip3 install --user numpy
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RUN pip3 install --user matplotlib
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RUN pip3 install --user tensorflow
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RUN pip3 install --user sacred
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# 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)
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WORKDIR /app
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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COPY ./test.sh ./
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COPY ./Python_file.py ./
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COPY ./Zadanie_5_Docker.py ./
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# Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
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CMD ./test.sh
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@ -1,31 +0,0 @@
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import wget
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url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
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wget.download(url, out='winequality-red.csv', bar=None)
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import pandas as pd
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wine=pd.read_csv('winequality-red.csv')
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wine
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from sklearn.model_selection import train_test_split
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wine_train, wine_test = train_test_split(wine, test_size=360,train_size=959, random_state=1)
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wine_test["quality"].value_counts()
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wine_train["quality"].value_counts()
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wine
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wine["quality"].value_counts()
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wine.describe(include='all')
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wine["quality"]=((wine["quality"]-wine["quality"].min())/(wine["quality"].max()-wine["quality"].min()))*20
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wine["quality"].value_counts()
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wine.isnull().sum()
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wine.dropna(inplace=True)
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print(wine)
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