add subfolders lab5

lab5/create/Dockerfile

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+FROM ubuntu:latest
+
+RUN apt update >>/dev/null
+RUN apt install -y apt-utils >>/dev/null
+RUN apt install -y python3.8 >>/dev/null
+RUN apt install -y python3-pip >>/dev/null
+RUN apt install -y unzip >>/dev/null
+
+WORKDIR /app
+
+COPY ./test_eval.py ./
+COPY ./script.sh ./
+RUN chmod +x script.sh
+
+COPY ./requirements.txt ./
+
+RUN pip3 install -r requirements.txt >>/dev/null
+
+CMD ./script.sh

lab5/create/requirements.txt

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+kaggle
+pandas
+sklearn

lab5/create/script.sh

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+#!/bin/bash
+
+kaggle datasets download -d 'pcbreviglieri/smart-grid-stability'
+unzip smart-grid-stability.zip >>/dev/null 2>&1
+
+python3 create_dataset.py

lab5/eval/Dockerfile

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+FROM ubuntu:latest
+
+RUN apt update >>/dev/null
+RUN apt install -y apt-utils >>/dev/null
+RUN apt install -y python3.8 >>/dev/null
+RUN apt install -y python3-pip >>/dev/null
+RUN apt install -y unzip >>/dev/null
+
+WORKDIR /app
+
+COPY ./test_eval.py ./
+COPY ./script.sh ./
+RUN chmod +x script.sh
+
+COPY ./requirements.txt ./
+
+RUN pip3 install -r requirements.txt >>/dev/null
+
+CMD ./script.sh

lab5/eval/requirements.txt

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+numpy~=1.19.2
+pandas
+tensorflow

lab5/eval/script.sh

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+#!/bin/bash
+
+python3 eval.py

lab5/test_eval.py

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-import numpy as np
-import pandas as pd
-import tensorflow as tf
-
-from sklearn import preprocessing
-from sklearn.model_selection import train_test_split
-from tensorflow.keras import layers
-
-
-def onezero(label):
-  return 0 if label == 'unstable' else 1
-
-
-df = pd.read_csv('smart_grid_stability_augmented.csv')
-
-scaler = preprocessing.StandardScaler().fit(df.iloc[:, 0:-1])
-df_norm_array = scaler.transform(df.iloc[:, 0:-1])
-df_norm = pd.DataFrame(data=df_norm_array,
-                       columns=df.columns[:-1])
-df_norm['stabf'] = df['stabf']
-
-df_norm_data = df_norm.copy()
-df_norm_data = df_norm_data.drop('stab', axis=1)
-df_norm_labels = df_norm_data.pop('stabf')
-
-X_train, X_testAndValid, Y_train, Y_testAndValid = train_test_split(
-    df_norm_data,
-    df_norm_labels,
-    test_size=0.2,
-    random_state=42)
-
-X_test, X_valid, Y_test, Y_valid = train_test_split(
-    X_testAndValid,
-    Y_testAndValid,
-    test_size=0.5,
-    random_state=42)
-
-model = tf.keras.Sequential([
-            layers.Input(shape=(12,)),
-            layers.Dense(32),
-            layers.Dense(16),
-            layers.Dense(2, activation='softmax')
-        ])
-
-model.compile(
-    loss=tf.losses.BinaryCrossentropy(),
-    optimizer=tf.optimizers.Adam(),
-    metrics=[tf.keras.metrics.BinaryAccuracy()])
-
-Y_train_one_zero = [onezero(x) for x in Y_train]
-Y_train_onehot = np.eye(2)[Y_train_one_zero]
-
-Y_test_one_zero = [onezero(x) for x in Y_test]
-Y_test_onehot = np.eye(2)[Y_test_one_zero]
-
-history = model.fit(tf.convert_to_tensor(X_train, np.float32), Y_train_onehot, epochs=5)
-
-results = model.evaluate(X_test, Y_test_onehot, batch_size=64)
-f = open('model_eval.txt', 'w')
-f.write('test loss: ' + str(results[0]) + '\n' + 'test acc: ' + str(results[1]))
-f.close()

lab5/train/Dockerfile

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+FROM ubuntu:latest
+
+RUN apt update >>/dev/null
+RUN apt install -y apt-utils >>/dev/null
+RUN apt install -y python3.8 >>/dev/null
+RUN apt install -y python3-pip >>/dev/null
+RUN apt install -y unzip >>/dev/null
+
+WORKDIR /app
+
+COPY ./test_eval.py ./
+COPY ./script.sh ./
+RUN chmod +x script.sh
+
+COPY ./requirements.txt ./
+
+RUN pip3 install -r requirements.txt >>/dev/null
+
+CMD ./script.sh

lab5/train/requirements.txt

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+numpy~=1.19.2
+pandas
+tensorflow

lab5/train/script.sh

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+#!/bin/bash
+
+python3 train.py