{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import datetime\n", "import logging\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", "import IPython\n", "import IPython.display\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import tensorflow as tf\n", "import datetime as dt\n", "from sklearn.preprocessing import MinMaxScaler\n", "mpl.rcParams['figure.figsize'] = (8, 6)\n", "mpl.rcParams['axes.grid'] = False\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "from tensorflow.python.client import device_lib " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n", "Num GPUs Available: 1\n", "[name: \"/device:CPU:0\"\n", "device_type: \"CPU\"\n", "memory_limit: 268435456\n", "locality {\n", "}\n", "incarnation: 3269440932313652370\n", ", name: \"/device:XLA_CPU:0\"\n", "device_type: \"XLA_CPU\"\n", "memory_limit: 17179869184\n", "locality {\n", "}\n", "incarnation: 12201329347612944943\n", "physical_device_desc: \"device: XLA_CPU device\"\n", ", name: \"/device:GPU:0\"\n", "device_type: \"GPU\"\n", "memory_limit: 4951408640\n", "locality {\n", " bus_id: 1\n", " links {\n", " }\n", "}\n", "incarnation: 1527461956020098487\n", "physical_device_desc: \"device: 0, name: GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\"\n", ", name: \"/device:XLA_GPU:0\"\n", "device_type: \"XLA_GPU\"\n", "memory_limit: 17179869184\n", "locality {\n", "}\n", "incarnation: 2047935970097758025\n", "physical_device_desc: \"device: XLA_GPU device\"\n", "]\n" ] } ], "source": [ "#Some settings\n", "strategy = tf.distribute.MirroredStrategy()\n", "print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n", "print(device_lib.list_local_devices())\n", "tf.keras.backend.set_floatx('float64')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " device_id device_name property value \\\n", "0 0x7 hardcoded-test voltage-p3 243.614600 \n", "1 0x7 hardcoded-test current-p3 -0.076694 \n", "2 0x7 hardcoded-test current-total -0.233661 \n", "3 0x7 hardcoded-test power-p1 -0.271782 \n", "4 0x7 hardcoded-test current-total -0.229458 \n", "... ... ... ... ... \n", "999995 0x7 hardcoded-test current-p2 -0.074192 \n", "999996 0x7 hardcoded-test current-p3 -0.071615 \n", "999997 0x7 hardcoded-test current-total -0.225104 \n", "999998 0x7 hardcoded-test power-p2 -3.300210 \n", "999999 0x7 hardcoded-test power-p3 0.077652 \n", "\n", " timestamp \n", "0 2020-06-26 10:58:05.812186+00 \n", "1 2020-06-26 10:58:05.812186+00 \n", "2 2020-06-26 10:58:05.812186+00 \n", "3 2020-06-26 10:58:05.812186+00 \n", "4 2020-06-26 10:58:11.855156+00 \n", "... ... \n", "999995 2020-07-02 10:04:37.690632+00 \n", "999996 2020-07-02 10:04:37.690632+00 \n", "999997 2020-07-02 10:04:37.690632+00 \n", "999998 2020-07-02 10:04:37.690632+00 \n", "999999 2020-07-02 10:04:37.690632+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "1000000 0x7 hardcoded-test voltage-p3 244.430300 \n", "1000001 0x7 hardcoded-test power-p1 1.999539 \n", "1000002 0x7 hardcoded-test power-total -1.223019 \n", "1000003 0x7 hardcoded-test voltage-p1 237.486500 \n", "1000004 0x7 hardcoded-test current-p2 -0.071665 \n", "... ... ... ... ... \n", "1999995 0x7 hardcoded-test current-total -0.224328 \n", "1999996 0x7 hardcoded-test voltage-p3 243.254900 \n", "1999997 0x7 hardcoded-test current-p3 -0.075368 \n", "1999998 0x7 hardcoded-test power-p1 1.067715 \n", "1999999 0x7 hardcoded-test voltage-p2 244.512800 \n", "\n", " timestamp \n", "1000000 2020-07-02 10:04:37.690632+00 \n", "1000001 2020-07-02 10:04:37.690632+00 \n", "1000002 2020-07-02 10:04:37.690632+00 \n", "1000003 2020-07-02 10:04:37.690632+00 \n", "1000004 2020-07-02 10:04:43.814006+00 \n", "... ... \n", "1999995 2020-07-08 09:41:04.907076+00 \n", "1999996 2020-07-08 09:41:04.907076+00 \n", "1999997 2020-07-08 09:41:04.907076+00 \n", "1999998 2020-07-08 09:41:04.907076+00 \n", "1999999 2020-07-08 09:41:04.907076+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "2000000 0x7 hardcoded-test power-p3 -0.252369 \n", "2000001 0x7 hardcoded-test power-total -1.397736 \n", "2000002 0x7 hardcoded-test voltage-p2 244.964800 \n", "2000003 0x7 hardcoded-test voltage-p3 244.378100 \n", "2000004 0x7 hardcoded-test current-p1 -0.074092 \n", "... ... ... ... ... \n", "2999995 0x7 hardcoded-test power-total 1.320084 \n", "2999996 0x7 hardcoded-test voltage-p1 237.256400 \n", "2999997 0x7 hardcoded-test voltage-p3 244.210400 \n", "2999998 0x7 hardcoded-test current-total -0.223527 \n", "2999999 0x7 hardcoded-test power-p1 1.805409 \n", "\n", " timestamp \n", "2000000 2020-07-08 09:41:09.729805+00 \n", "2000001 2020-07-08 09:41:09.729805+00 \n", "2000002 2020-07-08 09:41:09.729805+00 \n", "2000003 2020-07-08 09:41:09.729805+00 \n", "2000004 2020-07-08 09:41:09.729805+00 \n", "... ... \n", "2999995 2020-07-15 02:58:00.938198+00 \n", "2999996 2020-07-15 02:58:00.938198+00 \n", "2999997 2020-07-15 02:58:00.938198+00 \n", "2999998 2020-07-15 02:58:06.023135+00 \n", "2999999 2020-07-15 02:58:06.023135+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "3000000 0x7 hardcoded-test voltage-p3 244.226400 \n", "3000001 0x7 hardcoded-test power-total -1.514214 \n", "3000002 0x7 hardcoded-test voltage-p1 237.152800 \n", "3000003 0x7 hardcoded-test voltage-p2 244.940300 \n", "3000004 0x7 hardcoded-test current-p1 -0.074868 \n", "... ... ... ... ... \n", "3999995 0x7 hardcoded-test current-p2 -0.073416 \n", "3999996 0x7 hardcoded-test current-p3 -0.076444 \n", "3999997 0x7 hardcoded-test power-p1 0.174717 \n", "3999998 0x7 hardcoded-test power-p3 -1.339497 \n", "3999999 0x7 hardcoded-test voltage-p1 236.709200 \n", "\n", " timestamp \n", "3000000 2020-07-15 02:58:06.023135+00 \n", "3000001 2020-07-15 02:58:06.023135+00 \n", "3000002 2020-07-15 02:58:06.023135+00 \n", "3000003 2020-07-15 02:58:06.023135+00 \n", "3000004 2020-07-15 02:58:06.023135+00 \n", "... ... \n", "3999995 2020-07-21 13:38:04.993814+00 \n", "3999996 2020-07-21 13:38:04.993814+00 \n", "3999997 2020-07-21 13:38:04.993814+00 \n", "3999998 2020-07-21 13:38:04.993814+00 \n", "3999999 2020-07-21 13:38:04.993814+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "4000000 0x7 hardcoded-test voltage-p2 244.529100 \n", "4000001 0x7 hardcoded-test voltage-p3 243.720800 \n", "4000002 0x7 hardcoded-test current-p1 -0.074617 \n", "4000003 0x7 hardcoded-test current-total -0.224478 \n", "4000004 0xc35e hardcoded-test cnt 481.000000 \n", "... ... ... ... ... \n", "4999995 0x7 hardcoded-test power-p3 0.349434 \n", "4999996 0x7 hardcoded-test voltage-p2 247.317200 \n", "4999997 0x7 hardcoded-test voltage-p3 246.105400 \n", "4999998 0x7 hardcoded-test current-p2 -0.078371 \n", "4999999 0x7 hardcoded-test current-total -0.226580 \n", "\n", " timestamp \n", "4000000 2020-07-21 13:38:04.993814+00 \n", "4000001 2020-07-21 13:38:04.993814+00 \n", "4000002 2020-07-21 13:38:04.993814+00 \n", "4000003 2020-07-21 13:38:04.993814+00 \n", "4000004 2020-07-21 13:38:09.073343+00 \n", "... ... \n", "4999995 2020-07-27 17:02:08.092502+00 \n", "4999996 2020-07-27 17:02:08.092502+00 \n", "4999997 2020-07-27 17:02:08.092502+00 \n", "4999998 2020-07-27 17:02:08.092502+00 \n", "4999999 2020-07-27 17:02:08.092502+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "5000000 0x7 hardcoded-test power-total 3.630231 \n", "5000001 0x7 hardcoded-test voltage-p1 239.088600 \n", "5000002 0x7 hardcoded-test power-total 2.407212 \n", "5000003 0x7 hardcoded-test voltage-p1 239.093800 \n", "5000004 0x7 hardcoded-test voltage-p2 247.440300 \n", "... ... ... ... ... \n", "5999995 0x7 hardcoded-test current-p2 -0.076569 \n", "5999996 0x7 hardcoded-test power-total -1.727757 \n", "5999997 0x7 hardcoded-test voltage-p1 239.030600 \n", "5999998 0x7 hardcoded-test voltage-p2 246.517300 \n", "5999999 0x7 hardcoded-test current-p2 -0.077270 \n", "\n", " timestamp \n", "5000000 2020-07-27 17:02:08.092502+00 \n", "5000001 2020-07-27 17:02:08.092502+00 \n", "5000002 2020-07-27 17:02:13.915291+00 \n", "5000003 2020-07-27 17:02:13.915291+00 \n", "5000004 2020-07-27 17:02:13.915291+00 \n", "... ... \n", "5999995 2020-08-04 02:17:04.114064+00 \n", "5999996 2020-08-04 02:17:09.269824+00 \n", "5999997 2020-08-04 02:17:09.269824+00 \n", "5999998 2020-08-04 02:17:09.269824+00 \n", "5999999 2020-08-04 02:17:09.269824+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "6000000 0x7 hardcoded-test current-p3 -0.072616 \n", "6000001 0x7 hardcoded-test current-total -0.226405 \n", "6000002 0x7 hardcoded-test power-p3 0.232956 \n", "6000003 0x7 hardcoded-test voltage-p3 245.938000 \n", "6000004 0x7 hardcoded-test current-p1 -0.076519 \n", "... ... ... ... ... \n", "6999995 0x7 hardcoded-test power-p1 1.261845 \n", "6999996 0x7 hardcoded-test power-total -0.640629 \n", "6999997 0x7 hardcoded-test voltage-p1 238.169500 \n", "6999998 0x7 hardcoded-test voltage-p3 245.621000 \n", "6999999 0x7 hardcoded-test current-p1 -0.074893 \n", "\n", " timestamp \n", "6000000 2020-08-04 02:17:09.269824+00 \n", "6000001 2020-08-04 02:17:09.269824+00 \n", "6000002 2020-08-04 02:17:09.269824+00 \n", "6000003 2020-08-04 02:17:09.269824+00 \n", "6000004 2020-08-04 02:17:09.269824+00 \n", "... ... \n", "6999995 2020-05-24 20:56:44.938798+00 \n", "6999996 2020-05-24 20:56:44.938798+00 \n", "6999997 2020-05-24 20:56:44.938798+00 \n", "6999998 2020-05-24 20:56:44.938798+00 \n", "6999999 2020-05-24 20:56:44.938798+00 \n", "\n", "[1000000 rows x 5 columns]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " device_id device_name property value \\\n", "7000000 0x7 hardcoded-test current-p2 -0.076069 \n", "7000001 0x7 hardcoded-test current-total -0.223802 \n", "7000002 0x7 hardcoded-test power-p2 -0.562977 \n", "7000003 0x7 hardcoded-test power-p3 -1.339497 \n", "7000004 0x7 hardcoded-test voltage-p2 246.099400 \n", "... ... ... ... ... \n", "7999995 0x7 hardcoded-test voltage-p1 238.087500 \n", "7999996 0x7 hardcoded-test voltage-p2 245.880100 \n", "7999997 0x7 hardcoded-test voltage-p3 244.976200 \n", "7999998 0x7 hardcoded-test power-p1 1.223019 \n", "7999999 0x7 hardcoded-test power-p2 0.485325 \n", "\n", " timestamp \n", "7000000 2020-05-24 20:56:44.938798+00 \n", "7000001 2020-05-24 20:56:44.938798+00 \n", "7000002 2020-05-24 20:56:44.938798+00 \n", "7000003 2020-05-24 20:56:44.938798+00 \n", "7000004 2020-05-24 20:56:44.938798+00 \n", "... ... \n", "7999995 2020-05-30 20:28:20.92028+00 \n", "7999996 2020-05-30 20:28:20.92028+00 \n", "7999997 2020-05-30 20:28:20.92028+00 \n", "7999998 2020-05-30 20:28:20.92028+00 \n", "7999999 2020-05-30 20:28:20.92028+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "8000000 0x7 hardcoded-test current-p3 -0.074542 \n", "8000001 0x7 hardcoded-test current-total -0.223327 \n", "8000002 0x7 hardcoded-test power-p3 -0.116478 \n", "8000003 0x7 hardcoded-test power-total 1.591866 \n", "8000004 0x7 hardcoded-test current-p1 -0.076144 \n", "... ... ... ... ... \n", "8999995 0x7 hardcoded-test current-p2 -0.082725 \n", "8999996 0x7 hardcoded-test current-p3 -0.074542 \n", "8999997 0x7 hardcoded-test current-total -0.228732 \n", "8999998 0x7 hardcoded-test power-total 0.621216 \n", "8999999 0x7 hardcoded-test voltage-p1 238.647200 \n", "\n", " timestamp \n", "8000000 2020-05-30 20:28:20.92028+00 \n", "8000001 2020-05-30 20:28:20.92028+00 \n", "8000002 2020-05-30 20:28:20.92028+00 \n", "8000003 2020-05-30 20:28:20.92028+00 \n", "8000004 2020-05-30 20:28:20.92028+00 \n", "... ... \n", "8999995 2020-06-05 20:02:52.807251+00 \n", "8999996 2020-06-05 20:02:52.807251+00 \n", "8999997 2020-06-05 20:02:52.807251+00 \n", "8999998 2020-06-05 20:02:52.807251+00 \n", "8999999 2020-06-05 20:02:52.807251+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "9000000 0x7 hardcoded-test voltage-p2 246.420500 \n", "9000001 0x7 hardcoded-test current-p1 -0.071465 \n", "9000002 0x7 hardcoded-test power-p1 0.077652 \n", "9000003 0x7 hardcoded-test power-p2 0.640629 \n", "9000004 0x7 hardcoded-test power-p3 -0.097065 \n", "... ... ... ... ... \n", "9999995 0x7 hardcoded-test current-p2 -0.073291 \n", "9999996 0x7 hardcoded-test current-p3 -0.074117 \n", "9999997 0x7 hardcoded-test current-total -0.222326 \n", "9999998 0x7 hardcoded-test power-p1 1.125954 \n", "9999999 0x7 hardcoded-test power-p3 -2.193669 \n", "\n", " timestamp \n", "9000000 2020-06-05 20:02:52.807251+00 \n", "9000001 2020-06-05 20:02:52.807251+00 \n", "9000002 2020-06-05 20:02:52.807251+00 \n", "9000003 2020-06-05 20:02:52.807251+00 \n", "9000004 2020-06-05 20:02:52.807251+00 \n", "... ... \n", "9999995 2020-06-11 19:46:28.765859+00 \n", "9999996 2020-06-11 19:46:28.765859+00 \n", "9999997 2020-06-11 19:46:28.765859+00 \n", "9999998 2020-06-11 19:46:28.765859+00 \n", "9999999 2020-06-11 19:46:28.765859+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "10000000 0x7 hardcoded-test voltage-p2 244.257200 \n", "10000001 0x7 hardcoded-test power-p2 1.261845 \n", "10000002 0x7 hardcoded-test power-total 0.194130 \n", "10000003 0x7 hardcoded-test voltage-p1 237.131900 \n", "10000004 0x7 hardcoded-test current-p1 -0.071440 \n", "... ... ... ... ... \n", "10999995 0x7 hardcoded-test voltage-p2 244.966000 \n", "10999996 0x7 hardcoded-test voltage-p3 244.321500 \n", "10999997 0x7 hardcoded-test current-p3 -0.079797 \n", "10999998 0x7 hardcoded-test current-total -0.235638 \n", "10999999 0x7 hardcoded-test power-p2 -0.931824 \n", "\n", " timestamp \n", "10000000 2020-06-11 19:46:28.765859+00 \n", "10000001 2020-06-11 19:46:28.765859+00 \n", "10000002 2020-06-11 19:46:28.765859+00 \n", "10000003 2020-06-11 19:46:28.765859+00 \n", "10000004 2020-06-11 19:46:34.812225+00 \n", "... ... \n", "10999995 2020-06-17 19:17:54.957638+00 \n", "10999996 2020-06-17 19:17:54.957638+00 \n", "10999997 2020-06-17 19:17:54.957638+00 \n", "10999998 2020-06-17 19:17:54.957638+00 \n", "10999999 2020-06-17 19:17:54.957638+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "11000000 0x7 hardcoded-test current-p3 -0.077620 \n", "11000001 0x7 hardcoded-test power-p1 1.902474 \n", "11000002 0x7 hardcoded-test power-p2 -0.349434 \n", "11000003 0x7 hardcoded-test power-total 1.300671 \n", "11000004 0x7 hardcoded-test voltage-p1 237.224700 \n", "... ... ... ... ... \n", "11999995 0x7 hardcoded-test power-p2 -6.309225 \n", "11999996 0x7 hardcoded-test power-p3 0.621216 \n", "11999997 0x7 hardcoded-test current-p2 -0.076945 \n", "11999998 0x7 hardcoded-test power-p1 2.698407 \n", "11999999 0x7 hardcoded-test voltage-p2 247.504800 \n", "\n", " timestamp \n", "11000000 2020-06-17 19:18:00.860455+00 \n", "11000001 2020-06-17 19:18:00.860455+00 \n", "11000002 2020-06-17 19:18:00.860455+00 \n", "11000003 2020-06-17 19:18:00.860455+00 \n", "11000004 2020-06-17 19:18:00.860455+00 \n", "... ... \n", "11999995 2020-06-23 18:55:44.849905+00 \n", "11999996 2020-06-23 18:55:44.849905+00 \n", "11999997 2020-06-23 18:55:44.849905+00 \n", "11999998 2020-06-23 18:55:44.849905+00 \n", "11999999 2020-06-23 18:55:44.849905+00 \n", "\n", "[1000000 rows x 5 columns]\n", " device_id device_name property value \\\n", "12000000 0x7 hardcoded-test power-p3 1.106541 \n", "12000001 0x7 hardcoded-test voltage-p2 247.524900 \n", "12000002 0x7 hardcoded-test voltage-p3 246.454000 \n", "12000003 0x7 hardcoded-test current-p3 -0.076094 \n", "12000004 0x7 hardcoded-test current-total -0.223152 \n", "... ... ... ... ... \n", "12438733 0x7 hardcoded-test current-p2 -0.080373 \n", "12438734 0x7 hardcoded-test power-p2 -0.058239 \n", "12438735 0x7 hardcoded-test power-p3 -0.931824 \n", "12438736 0x7 hardcoded-test voltage-p1 236.461100 \n", "12438737 0x7 hardcoded-test voltage-p2 244.460900 \n", "\n", " timestamp \n", "12000000 2020-06-23 18:55:50.990868+00 \n", "12000001 2020-06-23 18:55:50.990868+00 \n", "12000002 2020-06-23 18:55:50.990868+00 \n", "12000003 2020-06-23 18:55:50.990868+00 \n", "12000004 2020-06-23 18:55:50.990868+00 \n", "... ... \n", "12438733 2020-06-26 10:58:05.812186+00 \n", "12438734 2020-06-26 10:58:05.812186+00 \n", "12438735 2020-06-26 10:58:05.812186+00 \n", "12438736 2020-06-26 10:58:05.812186+00 \n", "12438737 2020-06-26 10:58:05.812186+00 \n", "\n", "[438738 rows x 5 columns]\n" ] } ], "source": [ "for chunk in pd.read_csv(\"smartmeter.csv\", chunksize= 10**6):\n", " print(chunk)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data = pd.DataFrame(chunk)\n", "data = data.drop(['device_id', 'device_name', 'property'], axis = 1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Creating daytime input\n", "\n", "def time_d(x):\n", " k = datetime.datetime.strptime(x, \"%H:%M:%S\")\n", " y = k - datetime.datetime(1900, 1, 1)\n", " return y.total_seconds()\n", "\n", "daytime = data['timestamp'].str.slice(start = 11 ,stop=19)\n", "secondsperday = daytime.map(lambda i: time_d(i))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data['timestamp'] = data['timestamp'].str.slice(stop=19)\n", "data['timestamp'] = data['timestamp'].map(lambda i: dt.datetime.strptime(i, '%Y-%m-%d %H:%M:%S'))\n", "parse_dates = [data['timestamp']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Creating Weekday input\n", "\n", "wd_input = np.array(data['timestamp'].map(lambda i: int(i.weekday())))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Creating inputs sin\\cos\n", "\n", "seconds_in_day = 24*60*60\n", "data_seconds = np.array(data['timestamp'].map(lambda i: i.weekday()))\n", "\n", "input_sin = np.array(np.sin(2*np.pi*secondsperday/seconds_in_day))\n", "input_cos = np.array(np.cos(2*np.pi*secondsperday/seconds_in_day))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Putting inputs together in array\n", "\n", "df = pd.DataFrame(data = {'value':data['value'], 'input_sin':input_sin, 'input_cos':input_cos, 'input_wd': wd_input})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "column_indices = {name: i for i, name in enumerate(data.columns)}\n", "\n", "n = len(df)\n", "train_df = pd.DataFrame(df[0:int(n*0.7)])\n", "val_df = pd.DataFrame(df[int(n*0.7):int(n*0.9)])\n", "test_df = pd.DataFrame(df[int(n*0.9):])\n", "\n", "num_features = df.shape[1]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Standardization\n", "train_mean = train_df['value'].mean()\n", "train_std = train_df['value'].std()\n", "\n", "train_df['value'] = (train_df['value'] - train_mean) / train_std\n", "val_df['value'] = (val_df['value'] - train_mean) / train_std\n", "test_df['value'] = (test_df['value'] - train_mean) / train_std" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# 1st degree differencing\n", "train_df['value'] = train_df['value'] - train_df['value'].shift()\n", "\n", "# Handle negative values in 'value' for loging\n", "train_df['value'] = train_df['value'].map(lambda i: abs(i))\n", "train_df.loc[train_df.value <= 0, 'value'] = 0.000000001\n", "train_df['value'] = train_df['value'].map(lambda i: np.log(i))\n", "train_df = train_df.replace(np.nan, 0.000000001)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# 1st degree differencing\n", "val_df['value'] = val_df['value'] - val_df['value'].shift()\n", "\n", "# Handle negative values in 'value' for loging\n", "val_df['value'] = val_df['value'].map(lambda i: abs(i))\n", "val_df.loc[val_df.value <= 0, 'value'] = 0.000000001\n", "val_df['value'] = val_df['value'].map(lambda i: np.log(i))\n", "val_df = val_df.replace(np.nan, 0.000000001)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# 1st degree differencing\n", "test_df['value'] = test_df['value'] - test_df['value'].shift()\n", "\n", "# Handle negative values in 'value' for loging\n", "test_df['value'] = test_df['value'].map(lambda i: abs(i))\n", "test_df.loc[test_df.value <= 0, 'value'] = 0.000000001\n", "test_df['value'] = test_df['value'].map(lambda i: np.log(i))\n", "test_df = test_df.replace(np.nan, 0.000000001)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "class WindowGenerator():\n", " def __init__(self, input_width, label_width, shift,\n", " train_df=train_df, val_df=val_df, test_df=test_df,\n", " label_columns=None):\n", " # Store the raw data.\n", " self.train_df = train_df\n", " self.val_df = val_df\n", " self.test_df = test_df\n", "\n", " # Work out the label column indices.\n", " self.label_columns = label_columns\n", " if label_columns is not None:\n", " self.label_columns_indices = {name: i for i, name in\n", " enumerate(label_columns)}\n", " self.column_indices = {name: i for i, name in\n", " enumerate(train_df.columns)}\n", "\n", " # Work out the window parameters.\n", " self.input_width = input_width\n", " self.label_width = label_width\n", " self.shift = shift\n", "\n", " self.total_window_size = input_width + shift\n", "\n", " self.input_slice = slice(0, input_width)\n", " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", "\n", " self.label_start = self.total_window_size - self.label_width\n", " self.labels_slice = slice(self.label_start, None)\n", " self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n", "\n", " def __repr__(self):\n", " return '\\n'.join([\n", " f'Total window size: {self.total_window_size}',\n", " f'Input indices: {self.input_indices}',\n", " f'Label indices: {self.label_indices}',\n", " f'Label column name(s): {self.label_columns}'])\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def split_window(self, features):\n", " inputs = features[:, self.input_slice, :]\n", " labels = features[:, self.labels_slice, :]\n", " if self.label_columns is not None:\n", " labels = tf.stack(\n", " [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n", " axis=-1)\n", "\n", " # Slicing doesn't preserve static shape information, so set the shapes\n", " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", " inputs.set_shape([None, self.input_width, None])\n", " labels.set_shape([None, self.label_width, None])\n", "\n", " return inputs, labels\n", "\n", "WindowGenerator.split_window = split_window" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def plot(self, model=None, plot_col='value', max_subplots=3):\n", " inputs, labels = self.example\n", " plt.figure(figsize=(12, 8))\n", " plot_col_index = self.column_indices[plot_col]\n", " max_n = min(max_subplots, len(inputs))\n", " for n in range(max_n):\n", " plt.subplot(3, 1, n+1)\n", " plt.ylabel(f'{plot_col} [normed]')\n", " plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n", " label='Inputs', marker='.', zorder=-10)\n", "\n", " if self.label_columns:\n", " label_col_index = self.label_columns_indices.get(plot_col, None)\n", " else:\n", " label_col_index = plot_col_index\n", "\n", " if label_col_index is None:\n", " continue\n", "\n", " plt.scatter(self.label_indices, labels[n, :, label_col_index],\n", " edgecolors='k', label='Labels', c='#2ca02c', s=64)\n", " if model is not None:\n", " predictions = model(inputs)\n", " plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n", " marker='X', edgecolors='k', label='Predictions',\n", " c='#ff7f0e', s=64)\n", "\n", " if n == 0:\n", " plt.legend()\n", "\n", " plt.xlabel('Time [h]')\n", "\n", "WindowGenerator.plot = plot" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def make_dataset(self, data):\n", " data = np.array(data, dtype=np.float64)\n", " ds = tf.keras.preprocessing.timeseries_dataset_from_array(\n", " data=data,\n", " targets=None,\n", " sequence_length=self.total_window_size,\n", " sequence_stride=1,\n", " shuffle=True,\n", " batch_size=32,)\n", "\n", " ds = ds.map(self.split_window)\n", "\n", " return ds\n", "\n", "WindowGenerator.make_dataset = make_dataset\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "@property\n", "def train(self):\n", " return self.make_dataset(self.train_df)\n", "\n", "@property\n", "def val(self):\n", " return self.make_dataset(self.val_df)\n", "\n", "@property\n", "def test(self):\n", " return self.make_dataset(self.test_df)\n", "\n", "@property\n", "def example(self):\n", " \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n", " result = getattr(self, '_example', None)\n", " if result is None:\n", " # No example batch was found, so get one from the `.train` dataset\n", " result = next(iter(self.train))\n", " # And cache it for next time\n", " self._example = result\n", " return result\n", "\n", "WindowGenerator.train = train\n", "WindowGenerator.val = val\n", "WindowGenerator.test = test\n", "WindowGenerator.example = example\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "class Baseline(tf.keras.Model):\n", " def __init__(self, label_index=None):\n", " super().__init__()\n", " self.label_index = label_index\n", "\n", " def call(self, inputs):\n", " if self.label_index is None:\n", " return inputs\n", " result = inputs[:, :, self.label_index]\n", " return result[:, :, tf.newaxis]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "single_step_window = WindowGenerator(\n", " input_width=1, label_width=1, shift=1,\n", " label_columns=['value'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2743/2743 [==============================] - 4s 2ms/step - loss: 25.7312 - mean_absolute_error: 3.8646\n" ] } ], "source": [ "baseline = Baseline(label_index=column_indices['value'])\n", "\n", "baseline.compile(loss=tf.losses.MeanSquaredError(),\n", " metrics=[tf.metrics.MeanAbsoluteError()])\n", "\n", "val_performance = {}\n", "performance = {}\n", "val_performance['Baseline'] = baseline.evaluate(single_step_window.val)\n", "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Total window size: 26\n", "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", " 24]\n", "Label indices: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n", " 25]\n", "Label column name(s): ['value']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wide_window = WindowGenerator(\n", " input_width=25, label_width=25, shift=1,\n", " label_columns=['value'])\n", "\n", "wide_window\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wide_window.plot(baseline)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "MAX_EPOCHS = 20\n", "\n", "def compile_and_fit(model, window, patience=2):\n", " early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n", " patience=patience,\n", " mode='min')\n", "\n", " model.compile(loss=tf.losses.MeanSquaredError(),\n", " optimizer=tf.optimizers.SGD(),\n", " metrics=[tf.metrics.MeanAbsoluteError()])\n", "\n", " history = model.fit(window.train, epochs=MAX_EPOCHS,\n", " validation_data=window.val,\n", " callbacks=[early_stopping])\n", " return history" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "### LSTM ###" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "lstm_model = tf.keras.models.Sequential([\n", " # Shape [batch, time, features] => [batch, time, lstm_units]\n", " tf.keras.layers.LSTM(32, return_sequences=True),\n", " # Shape => [batch, time, features]\n", " tf.keras.layers.Dense(units=1)\n", "])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "wide_window = WindowGenerator(\n", " input_width=50, label_width=50, shift=1,\n", " label_columns=['value'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2741/2741 [==============================] - 11s 4ms/step - loss: 12.5831 - mean_absolute_error: 2.9593\n" ] } ], "source": [ "history = compile_and_fit(lstm_model, wide_window)\n", "\n", "IPython.display.clear_output()\n", "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)\n", "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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