From 18ba40c7500c75942f65ea9353e9b5bf95be4370 Mon Sep 17 00:00:00 2001 From: Susan Li Date: Mon, 7 Sep 2020 22:32:27 -0400 Subject: [PATCH] Add notebook --- ...detection using LSTM Autoencoder JNJ.ipynb | 31801 ++++++++++++++++ 1 file changed, 31801 insertions(+) create mode 100644 Timeseries anomaly detection using LSTM Autoencoder JNJ.ipynb diff --git a/Timeseries anomaly detection using LSTM Autoencoder JNJ.ipynb b/Timeseries anomaly detection using LSTM Autoencoder JNJ.ipynb new file mode 100644 index 0000000..a214747 --- /dev/null +++ b/Timeseries anomaly detection using LSTM Autoencoder JNJ.ipynb @@ -0,0 +1,31801 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensorflow version: 2.3.0-dev20200605\n" + ] + } + ], + "source": [ + "from tensorflow import keras\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "import plotly.graph_objects as go\n", + "\n", + "np.random.seed(1)\n", + "tf.random.set_seed(1)\n", + "\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, LSTM, Dropout, RepeatVector, TimeDistributed\n", + "\n", + "print('Tensorflow version: ', tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('data/JNJ.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Date Close\n", + "7059 2013-09-04 86.900002\n", + "7060 2013-09-05 87.040001\n", + "7061 2013-09-06 87.160004\n", + "7062 2013-09-09 87.559998\n", + "7063 2013-09-10 88.529999" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((7059, 2), (1764, 2))" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.shape,test.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + } + ], + "source": [ + "scaler = StandardScaler()\n", + "scaler = scaler.fit(train[['Close']])\n", + "\n", + "train['Close'] = scaler.transform(train[['Close']])\n", + "test['Close'] = scaler.transform(test[['Close']])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "TIME_STEPS=30\n", + "\n", + "def create_sequences(X, y, time_steps=TIME_STEPS):\n", + " Xs, ys = [], []\n", + " for i in range(len(X)-time_steps):\n", + " Xs.append(X.iloc[i:(i+time_steps)].values)\n", + " ys.append(y.iloc[i+time_steps])\n", + " \n", + " return np.array(Xs), np.array(ys)\n", + "\n", + "X_train, y_train = create_sequences(train[['Close']], train['Close'])\n", + "X_test, y_test = create_sequences(test[['Close']], test['Close'])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training shape: (7029, 30, 1)\n", + "Testing shape: (1734, 30, 1)\n" + ] + } + ], + "source": [ + "print(f'Training shape: {X_train.shape}')\n", + "\n", + "print(f'Testing shape: {X_test.shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "lstm (LSTM) (None, 128) 66560 \n", + "_________________________________________________________________\n", + "dropout (Dropout) (None, 128) 0 \n", + "_________________________________________________________________\n", + "repeat_vector (RepeatVector) (None, 30, 128) 0 \n", + "_________________________________________________________________\n", + "lstm_1 (LSTM) (None, 30, 128) 131584 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 30, 128) 0 \n", + "_________________________________________________________________\n", + "time_distributed (TimeDistri (None, 30, 1) 129 \n", + "=================================================================\n", + "Total params: 198,273\n", + "Trainable params: 198,273\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(LSTM(128, input_shape=(X_train.shape[1], X_train.shape[2])))\n", + "model.add(Dropout(rate=0.2))\n", + "model.add(RepeatVector(X_train.shape[1]))\n", + "model.add(LSTM(128, return_sequences=True))\n", + "model.add(Dropout(rate=0.2))\n", + "model.add(TimeDistributed(Dense(X_train.shape[2])))\n", + "model.compile(optimizer='adam', loss='mae')\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "198/198 [==============================] - 11s 53ms/step - loss: 0.1356 - val_loss: 0.1838\n", + "Epoch 2/100\n", + "198/198 [==============================] - 10s 53ms/step - loss: 0.1009 - val_loss: 0.1244\n", + "Epoch 3/100\n", + "198/198 [==============================] - 10s 51ms/step - loss: 0.0715 - val_loss: 0.0356\n", + "Epoch 4/100\n", + "198/198 [==============================] - 10s 51ms/step - loss: 0.0627 - val_loss: 0.0249\n", + "Epoch 5/100\n", + "198/198 [==============================] - 10s 52ms/step - loss: 0.0649 - val_loss: 0.0385\n", + "Epoch 6/100\n", + "198/198 [==============================] - 10s 51ms/step - loss: 0.0701 - val_loss: 0.0398\n", + "Epoch 7/100\n", + "198/198 [==============================] - 10s 50ms/step - loss: 0.0736 - val_loss: 0.0458\n" + ] + } + ], + "source": [ + "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.1,\n", + " callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=3, mode='min')], shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(history.history['loss'], label='Training loss')\n", + "plt.plot(history.history['val_loss'], label='Validation loss')\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "55/55 [==============================] - 1s 19ms/step - loss: 0.1920\n" + ] + }, + { + "data": { + "text/plain": [ + "0.19198592007160187" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(X_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstruction error threshold: 0.5889649267144524\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "X_train_pred = model.predict(X_train, verbose=0)\n", + "train_mae_loss = np.mean(np.abs(X_train_pred - X_train), axis=1)\n", + "\n", + "plt.hist(train_mae_loss, bins=50)\n", + "plt.xlabel('Train MAE loss')\n", + "plt.ylabel('Number of Samples');\n", + "\n", + "threshold = np.max(train_mae_loss)\n", + "print(f'Reconstruction error threshold: {threshold}')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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DateCloselossthresholdanomaly
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DateCloselossthresholdanomaly
81022017-10-244.2054760.5934510.588965True
87082020-03-243.2971930.7748110.588965True
87092020-03-253.3060890.7491070.588965True
87102020-03-263.5960440.6784340.588965True
87202020-04-094.1888950.5996840.588965True
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" + ], + "text/plain": [ + " Date Close loss threshold anomaly\n", + "8102 2017-10-24 4.205476 0.593451 0.588965 True\n", + "8708 2020-03-24 3.297193 0.774811 0.588965 True\n", + "8709 2020-03-25 3.306089 0.749107 0.588965 True\n", + "8710 2020-03-26 3.596044 0.678434 0.588965 True\n", + "8720 2020-04-09 4.188895 0.599684 0.588965 True" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anomalies = test_score_df.loc[test_score_df['anomaly'] == True]\n", + "anomalies.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(22, 5)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anomalies.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": 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