62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
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()
|