add py scripts lab5

lab5/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_dataset.py

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+import pandas as pd
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+
+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)
+
+train = pd.concat([X_train, Y_train], axis=1)
+test = pd.concat([X_test, Y_test], axis=1)
+valid = pd.concat([X_valid, Y_valid], axis=1)
+
+train.to_csv('train.cs', index_col = False)
+test.to_csv('test.csv', index_col = False)
+valid.to_csv('valid.csv', index_col = False)

lab5/eval.py

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+import pandas as pd
+import numpy as np
+import tensorflow as tf
+
+def onezero(label):
+    return 0 if label == 'unstable' else 1
+
+
+X_test = pd.read_csv('test.csv')
+Y_test = X_test.pop('stabf')
+
+Y_test_one_zero = [onezero(x) for x in Y_test]
+Y_test_onehot = np.eye(2)[Y_test_one_zero]
+
+model = tf.keras.models.load_model('grid_stability.h5')
+
+results = model.evaluate(X_test, Y_test_onehot, batch_size=64)
+
+f = open('eval.csv', 'a+')
+
+f.write(results[0], ',')
+f.write(results[1], ',')
+

lab5/requirements.txt

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+kaggle
+numpy~=1.19.2
+pandas
+sklearn
+tensorflow

lab5/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 test_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.py

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+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras import layers
+
+def onezero(label):
+  return 0 if label == 'unstable' else 1
+
+
+X_train = pd.read_csv('train.csv')
+X_test = pd.read_csv('test.csv')
+
+Y_train = X_train.pop('stabf')
+Y_test = X_test.pop('stabf')
+
+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]
+
+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()])
+
+history = model.fit(tf.convert_to_tensor(X_train, np.float32),
+          Y_train_onehot, epochs=5)
+
+model.save('grid_stability.h5')