2546 lines
614 KiB
Text
2546 lines
614 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bayesian Optimisation of starting Gaussian Process hyperparameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "Aovwtky_5Cao"
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},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"from shutil import copyfile\n",
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"import pickle"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "a517af1c-4204-45c9-aae4-865a2cb259e9"
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},
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"source": [
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"Data manipulation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "62628e60-28c6-4a9a-8a81-22e5bfd74722"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "acb33a41-06b9-4a1d-9ea7-6a2d87b1f4fb"
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},
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"source": [
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"Plotting / Visualisation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "bVyvgbND5642"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "E9mmvHyH57RO"
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"plt.rcParams[\"figure.figsize\"] = (15, 6)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "90fdac33-eed4-4ab4-b2b1-de0f1f27727b"
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},
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"source": [
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"Gaussian Process Regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "3Z6cHHaD6EkP"
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},
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"outputs": [],
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"source": [
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"import gpflow\n",
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[name: \"/device:CPU:0\"\n",
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" device_type: \"CPU\"\n",
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" memory_limit: 268435456\n",
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" locality {\n",
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" }\n",
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" incarnation: 5890044039288017903]"
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]
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},
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"execution_count": 47,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from tensorflow.python.client import device_lib\n",
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"from gpflow.ci_utils import ci_niter\n",
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"device_lib.list_local_devices()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"id": "-fqvYTly6E9D"
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},
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"outputs": [],
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"source": [
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"from gpflow.utilities import print_summary"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"id": "VpKUUEvC6F7i"
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},
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"outputs": [],
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"source": [
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"gpflow.config.set_default_summary_fmt(\"notebook\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm.contrib.itertools import product"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Input scaler:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import MinMaxScaler, RobustScaler\n",
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"from sklearn.exceptions import NotFittedError"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"from helpers import ScalerHelper"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "0aba0df5-b0e3-4738-bb61-1dad869d1ea3"
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},
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"source": [
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"## Load previously exported data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"dfs_train = []\n",
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"dfs_test = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_exps = ['Exp1', 'Exp3', 'Exp5', 'Exp6']\n",
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"test_exps = ['Exp2', 'Exp4', 'Exp7']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"for exp in train_exps:\n",
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" dfs_train.append(pd.read_csv(f\"../Data/Good_CARNOT/{exp}_table.csv\").rename(columns = {'Power': 'SimulatedHeat'}))\n",
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" \n",
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"for exp in test_exps:\n",
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" dfs_test.append(pd.read_csv(f\"../Data/Good_CARNOT/{exp}_table.csv\").rename(columns = {'Power': 'SimulatedHeat'}))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"#t_cols = ['time_h', 'time_m']\n",
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"t_cols = []\n",
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"w_cols = ['SolRad', 'OutsideTemp']\n",
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"u_cols = ['SimulatedHeat']\n",
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"y_cols = ['SimulatedTemp']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"t_lags = 0\n",
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"w_lags = 1\n",
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"u_lags = 2\n",
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"y_lags = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"dict_cols = {\n",
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" 't': (t_lags, t_cols),\n",
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" 'w': (w_lags, w_cols),\n",
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" 'u': (u_lags, u_cols),\n",
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" 'y': (y_lags, y_cols)\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Create the scaler and set up input data scaling:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "7uZWtjPo6XhD",
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"outputId": "e0c4a8be-881e-4adc-a344-0b7e4ee9bc75"
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},
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"outputs": [],
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"source": [
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"scaler = MinMaxScaler(feature_range = (-1, 1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_scaled_df(df, dict_cols, scaler):\n",
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" \n",
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" t_list = dict_cols['t'][1]\n",
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" w_list = dict_cols['w'][1]\n",
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" u_list = dict_cols['u'][1]\n",
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" y_list = dict_cols['y'][1]\n",
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" \n",
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" df_local = df[t_list + w_list + u_list + y_list]\n",
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" df_scaled = df_local.to_numpy()\n",
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" \n",
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" try:\n",
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" df_scaled = scaler.transform(df_scaled)\n",
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" except NotFittedError:\n",
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" df_scaled = scaler.fit_transform(df_scaled)\n",
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" \n",
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" df_scaled = pd.DataFrame(df_scaled, index = df_local.index, columns = df_local.columns)\n",
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" \n",
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" return df_scaled"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
|
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>SolRad</th>\n",
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" <th>OutsideTemp</th>\n",
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" <th>SimulatedHeat</th>\n",
|
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" <th>SimulatedTemp</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
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" <th>0</th>\n",
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" <td>57.936582</td>\n",
|
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" <td>22.0</td>\n",
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" <td>-31500</td>\n",
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" <td>23.000000</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>1</th>\n",
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" <td>54.914443</td>\n",
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" <td>22.0</td>\n",
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" <td>-31500</td>\n",
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" <td>20.585367</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>73.944706</td>\n",
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" <td>22.0</td>\n",
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" <td>-31500</td>\n",
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" <td>20.300922</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
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" <th>3</th>\n",
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" <td>76.206334</td>\n",
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" <td>22.0</td>\n",
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" <td>-31500</td>\n",
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" <td>20.034647</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
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" <th>4</th>\n",
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" <td>65.120057</td>\n",
|
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" <td>22.0</td>\n",
|
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" <td>-31500</td>\n",
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" <td>19.786064</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
|
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"</div>"
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],
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"text/plain": [
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" SolRad OutsideTemp SimulatedHeat SimulatedTemp\n",
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"0 57.936582 22.0 -31500 23.000000\n",
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"1 54.914443 22.0 -31500 20.585367\n",
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"2 73.944706 22.0 -31500 20.300922\n",
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"3 76.206334 22.0 -31500 20.034647\n",
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"4 65.120057 22.0 -31500 19.786064"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df_train = pd.concat(dfs_train)\n",
|
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"df_train = df_train[t_cols + w_cols + u_cols + y_cols]\n",
|
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"df_train.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Fit the scaler and scale the data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_train_sc = get_scaled_df(df_train, dict_cols, scaler)\n",
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"#pickle.dump(scaler, open(Path(\"scaler.pkl\"), 'wb'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"scaler_helper = ScalerHelper(scaler)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"Scale the data for each experiment individually. Used for validation graphs and errors computation:"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"dfs_train_sc = []\n",
|
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"dfs_test_sc = []\n",
|
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"for df in dfs_train:\n",
|
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" df_sc = get_scaled_df(df, dict_cols, scaler)\n",
|
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" dfs_train_sc.append(df_sc)\n",
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" \n",
|
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"for df in dfs_test:\n",
|
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" df_sc = get_scaled_df(df, dict_cols, scaler)\n",
|
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" dfs_test_sc.append(df_sc)"
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]
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},
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{
|
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"cell_type": "markdown",
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"metadata": {},
|
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"source": [
|
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"Set up the function which generated the GPR input matrix from the experimental data (including all autoregressive inputs, etc.):"
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 25,
|
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"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
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"def data_to_gpr(df, dict_cols):\n",
|
|
" \n",
|
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" t_list = dict_cols['t'][1]\n",
|
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" w_list = dict_cols['w'][1]\n",
|
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" u_list = dict_cols['u'][1]\n",
|
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" y_list = dict_cols['y'][1]\n",
|
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" \n",
|
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" df_gpr = df[t_list + w_list + u_list + y_list].copy()\n",
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" \n",
|
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" for lags, names in dict_cols.values():\n",
|
|
" for name in names:\n",
|
|
" col_idx = df_gpr.columns.get_loc(name)\n",
|
|
" for lag in range(1, lags + 1):\n",
|
|
" df_gpr.insert(col_idx + lag, f\"{name}_{lag}\", df_gpr.loc[:, name].shift(lag))\n",
|
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"\n",
|
|
" df_gpr.dropna(inplace = True)\n",
|
|
" \n",
|
|
" return df_gpr"
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]
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},
|
|
{
|
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"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
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"data": {
|
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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|
" }\n",
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|
"\n",
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
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" }\n",
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|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>SolRad</th>\n",
|
|
" <th>SolRad_1</th>\n",
|
|
" <th>OutsideTemp</th>\n",
|
|
" <th>OutsideTemp_1</th>\n",
|
|
" <th>SimulatedHeat</th>\n",
|
|
" <th>SimulatedHeat_1</th>\n",
|
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" <th>SimulatedHeat_2</th>\n",
|
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" <th>SimulatedTemp</th>\n",
|
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" <th>SimulatedTemp_1</th>\n",
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" <th>SimulatedTemp_2</th>\n",
|
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" <th>SimulatedTemp_3</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>-0.855164</td>\n",
|
|
" <td>-0.859463</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" <td>-0.244215</td>\n",
|
|
" <td>-0.020567</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>-0.876235</td>\n",
|
|
" <td>-0.855164</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" <td>-0.244215</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>-0.911207</td>\n",
|
|
" <td>-0.876235</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.340062</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>-0.933425</td>\n",
|
|
" <td>-0.911207</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.361066</td>\n",
|
|
" <td>-0.340062</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>-0.952322</td>\n",
|
|
" <td>-0.933425</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>0.051533</td>\n",
|
|
" <td>-0.361066</td>\n",
|
|
" <td>-0.340062</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" SolRad SolRad_1 OutsideTemp OutsideTemp_1 SimulatedHeat \\\n",
|
|
"3 -0.855164 -0.859463 0.058824 0.058824 -1.0 \n",
|
|
"4 -0.876235 -0.855164 0.058824 0.058824 -1.0 \n",
|
|
"5 -0.911207 -0.876235 0.058824 0.058824 -1.0 \n",
|
|
"6 -0.933425 -0.911207 0.058824 0.058824 1.0 \n",
|
|
"7 -0.952322 -0.933425 0.058824 0.058824 -1.0 \n",
|
|
"\n",
|
|
" SimulatedHeat_1 SimulatedHeat_2 SimulatedTemp SimulatedTemp_1 \\\n",
|
|
"3 -1.0 -1.0 -0.295224 -0.270561 \n",
|
|
"4 -1.0 -1.0 -0.318248 -0.295224 \n",
|
|
"5 -1.0 -1.0 -0.340062 -0.318248 \n",
|
|
"6 -1.0 -1.0 -0.361066 -0.340062 \n",
|
|
"7 1.0 -1.0 0.051533 -0.361066 \n",
|
|
"\n",
|
|
" SimulatedTemp_2 SimulatedTemp_3 \n",
|
|
"3 -0.244215 -0.020567 \n",
|
|
"4 -0.270561 -0.244215 \n",
|
|
"5 -0.295224 -0.270561 \n",
|
|
"6 -0.318248 -0.295224 \n",
|
|
"7 -0.340062 -0.318248 "
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"dfs_gpr_train = []\n",
|
|
"for df_sc in dfs_train_sc:\n",
|
|
" dfs_gpr_train.append(data_to_gpr(df_sc, dict_cols))\n",
|
|
"df_gpr_train = pd.concat(dfs_gpr_train)\n",
|
|
"df_gpr_train.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#df_gpr_train = df_gpr_train.sample(n = 500)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"dfs_gpr_test = []\n",
|
|
"for df_sc in dfs_test_sc:\n",
|
|
" dfs_gpr_test.append(data_to_gpr(df_sc, dict_cols))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"id": "eZAetwUd6YuE"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_input_train = df_gpr_train.drop(columns = dict_cols['w'][1] + dict_cols['u'][1] + dict_cols['y'][1])\n",
|
|
"df_output_train = df_gpr_train[dict_cols['y'][1]]\n",
|
|
"\n",
|
|
"np_input_train = df_input_train.to_numpy()\n",
|
|
"np_output_train = df_output_train.to_numpy().reshape(-1, 1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data_train = (np_input_train, np_output_train)\n",
|
|
"#pickle.dump(data_train, open(Path(\"data_train.pkl\"), 'wb'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>SolRad_1</th>\n",
|
|
" <th>OutsideTemp_1</th>\n",
|
|
" <th>SimulatedHeat_1</th>\n",
|
|
" <th>SimulatedHeat_2</th>\n",
|
|
" <th>SimulatedTemp_1</th>\n",
|
|
" <th>SimulatedTemp_2</th>\n",
|
|
" <th>SimulatedTemp_3</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>-0.859463</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" <td>-0.244215</td>\n",
|
|
" <td>-0.020567</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>-0.855164</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" <td>-0.244215</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>-0.876235</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" <td>-0.270561</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>-0.911207</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.340062</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" <td>-0.295224</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>-0.933425</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.361066</td>\n",
|
|
" <td>-0.340062</td>\n",
|
|
" <td>-0.318248</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" SolRad_1 OutsideTemp_1 SimulatedHeat_1 SimulatedHeat_2 SimulatedTemp_1 \\\n",
|
|
"3 -0.859463 0.058824 -1.0 -1.0 -0.270561 \n",
|
|
"4 -0.855164 0.058824 -1.0 -1.0 -0.295224 \n",
|
|
"5 -0.876235 0.058824 -1.0 -1.0 -0.318248 \n",
|
|
"6 -0.911207 0.058824 -1.0 -1.0 -0.340062 \n",
|
|
"7 -0.933425 0.058824 1.0 -1.0 -0.361066 \n",
|
|
"\n",
|
|
" SimulatedTemp_2 SimulatedTemp_3 \n",
|
|
"3 -0.244215 -0.020567 \n",
|
|
"4 -0.270561 -0.244215 \n",
|
|
"5 -0.295224 -0.270561 \n",
|
|
"6 -0.318248 -0.295224 \n",
|
|
"7 -0.340062 -0.318248 "
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_input_train.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {
|
|
"id": "l_VzOWL66aD3"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"## Define Kernel"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"id": "oBHgoYNf6b6t"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"nb_dims = np_input_train.shape[1]\n",
|
|
"rational_dims = np.arange(0, (dict_cols['t'][0] + 1) * len(dict_cols['t'][1]), 1)\n",
|
|
"nb_rational_dims = len(rational_dims)\n",
|
|
"squared_dims = np.arange(nb_rational_dims, nb_dims, 1)\n",
|
|
"nb_squared_dims = len(squared_dims)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "_WagEJum8uUG",
|
|
"outputId": "c65ec503-b964-49f6-fe3a-51c57a175f9b"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"rational: 0\n",
|
|
"squared: 7\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"rational: {nb_rational_dims}\")\n",
|
|
"print(f\"squared: {nb_squared_dims}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"id": "kTIQlLIP6dJz"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"squared_l = np.linspace(1, 1, nb_squared_dims)\n",
|
|
"rational_l = np.linspace(1, 1, nb_rational_dims)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {
|
|
"id": "MEGkQJvY_izQ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"variance = tf.math.reduce_variance(np_input_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {
|
|
"id": "WZfssVHG6edn"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"k0 = gpflow.kernels.SquaredExponential(lengthscales = squared_l, active_dims = squared_dims, variance = variance)\n",
|
|
"k1 = gpflow.kernels.Constant(variance = variance)\n",
|
|
"k2 = gpflow.kernels.RationalQuadratic(lengthscales = rational_l, active_dims = rational_dims, variance = variance)\n",
|
|
"k3 = gpflow.kernels.Periodic(k2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 169
|
|
},
|
|
"id": "vo8rcdBm6fuc",
|
|
"outputId": "75485dcd-961c-40d9-cf1f-d10516e2b80f"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table>\n",
|
|
"<thead>\n",
|
|
"<tr><th>name </th><th>class </th><th>transform </th><th>prior </th><th>trainable </th><th>shape </th><th>dtype </th><th>value </th></tr>\n",
|
|
"</thead>\n",
|
|
"<tbody>\n",
|
|
"<tr><td>SquaredExponential.variance </td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>() </td><td>float64</td><td>0.4682764543328539</td></tr>\n",
|
|
"<tr><td>SquaredExponential.lengthscales</td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>(7,) </td><td>float64</td><td>[1., 1., 1.... </td></tr>\n",
|
|
"</tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"k = (k0 + k1) * k2\n",
|
|
"k = k0\n",
|
|
"print_summary(k)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "4af25a43-15c9-4543-af73-3c313b5fc7af"
|
|
},
|
|
"source": [
|
|
"## Compile Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 190
|
|
},
|
|
"id": "PC4cbp926j29",
|
|
"outputId": "72c9441d-2657-4e0f-de70-11a197d07ad3"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table>\n",
|
|
"<thead>\n",
|
|
"<tr><th>name </th><th>class </th><th>transform </th><th>prior </th><th>trainable </th><th>shape </th><th>dtype </th><th>value </th></tr>\n",
|
|
"</thead>\n",
|
|
"<tbody>\n",
|
|
"<tr><td>GPR.kernel.variance </td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>() </td><td>float64</td><td>0.4682764543328539</td></tr>\n",
|
|
"<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>(7,) </td><td>float64</td><td>[1., 1., 1.... </td></tr>\n",
|
|
"<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td> </td><td>True </td><td>() </td><td>float64</td><td>1.0 </td></tr>\n",
|
|
"</tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"m = gpflow.models.GPR(\n",
|
|
" data = data_train, \n",
|
|
" kernel = k, \n",
|
|
" mean_function = None,\n",
|
|
" )\n",
|
|
"print_summary(m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#m.likelihood.variance.assign(0.5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#gpflow.set_trainable(m.likelihood.variance, False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "08f41235-12df-4e9c-bf63-e7a4390cf21a"
|
|
},
|
|
"source": [
|
|
"## Train Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"metadata": {
|
|
"id": "Pn5TwPPT6ogs"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"opt = gpflow.optimizers.Scipy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {
|
|
"id": "slQg9Ohv6oxR"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from datetime import datetime"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 212
|
|
},
|
|
"id": "GhsxZhc56p43",
|
|
"outputId": "778ec150-cfc3-44b7-9e21-e52bf69d494a",
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"WARNING:tensorflow:From /usr/lib/python3.9/site-packages/tensorflow/python/ops/array_ops.py:5044: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
|
|
"Instructions for updating:\n",
|
|
"The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
|
|
"Finished fitting in 0:01:42.284430\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<table>\n",
|
|
"<thead>\n",
|
|
"<tr><th>name </th><th>class </th><th>transform </th><th>prior </th><th>trainable </th><th>shape </th><th>dtype </th><th>value </th></tr>\n",
|
|
"</thead>\n",
|
|
"<tbody>\n",
|
|
"<tr><td>GPR.kernel.variance </td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>() </td><td>float64</td><td>3280.601468330353 </td></tr>\n",
|
|
"<tr><td>GPR.kernel.lengthscales</td><td>Parameter</td><td>Softplus </td><td> </td><td>True </td><td>(7,) </td><td>float64</td><td>[596.0924648, 27.43660916, 240.57126655...</td></tr>\n",
|
|
"<tr><td>GPR.likelihood.variance</td><td>Parameter</td><td>Softplus + Shift</td><td> </td><td>True </td><td>() </td><td>float64</td><td>1e-06 </td></tr>\n",
|
|
"</tbody>\n",
|
|
"</table>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"start_time = datetime.now()\n",
|
|
"opt.minimize(m.training_loss, m.trainable_variables)\n",
|
|
"print(f\"Finished fitting in {datetime.now() - start_time}\")\n",
|
|
"print_summary(m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Export model parameters:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pickle.dump(m, open(Path('gp_model.pkl'), 'wb'))\n",
|
|
"pickle.dump(dict_cols, open(Path('dict_cols.pkl'), 'wb'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Train SVGP model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "KeyboardInterrupt",
|
|
"evalue": "",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-48-134cc7444c59>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mmaxiter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mci_niter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0mlogf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_adam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;32m<ipython-input-48-134cc7444c59>\u001b[0m in \u001b[0;36mrun_adam\u001b[0;34m(model, iterations)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0moptimization_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0melbo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mtraining_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 915\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 916\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 918\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3021\u001b[0m (graph_function,\n\u001b[1;32m 3022\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3023\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 3024\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1958\u001b[0m and executing_eagerly):\n\u001b[1;32m 1959\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1960\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1961\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1962\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 589\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 592\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 593\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/lib/python3.9/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"N = data_train[0].shape[0]\n",
|
|
"M = 150 # Number of inducing locations\n",
|
|
"Z = data_train[0][:M, :].copy()\n",
|
|
"\n",
|
|
"m = gpflow.models.SVGP(k, gpflow.likelihoods.Gaussian(), Z, num_data = N)\n",
|
|
"\n",
|
|
"elbo = tf.function(m.elbo)\n",
|
|
"\n",
|
|
"###\n",
|
|
"# Training\n",
|
|
"###\n",
|
|
"\n",
|
|
"minibatch_size = 100\n",
|
|
"train_dataset = tf.data.Dataset.from_tensor_slices(data_train).repeat().shuffle(N)\n",
|
|
"\n",
|
|
"# Turn off training for inducing point locations\n",
|
|
"gpflow.set_trainable(m.inducing_variable, False)\n",
|
|
"\n",
|
|
"def run_adam(model, iterations):\n",
|
|
" \"\"\"\n",
|
|
" Utility function running the Adam optimizer\n",
|
|
"\n",
|
|
" :param model: GPflow model\n",
|
|
" :param interations: number of iterations\n",
|
|
" \"\"\"\n",
|
|
" # Create an Adam Optimizer action\n",
|
|
" logf = []\n",
|
|
" train_iter = iter(train_dataset.batch(minibatch_size))\n",
|
|
" training_loss = model.training_loss_closure(train_iter, compile=True)\n",
|
|
" optimizer = tf.optimizers.Adam()\n",
|
|
"\n",
|
|
" @tf.function\n",
|
|
" def optimization_step():\n",
|
|
" optimizer.minimize(training_loss, model.trainable_variables)\n",
|
|
"\n",
|
|
" for step in range(iterations):\n",
|
|
" optimization_step()\n",
|
|
" if step % 10 == 0:\n",
|
|
" elbo = -training_loss().numpy()\n",
|
|
" logf.append(elbo)\n",
|
|
" return logf\n",
|
|
"\n",
|
|
"\n",
|
|
"maxiter = ci_niter(10000)\n",
|
|
"logf = run_adam(m, maxiter)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "7dd49280-bb3f-4903-a339-b7225a56ae16"
|
|
},
|
|
"source": [
|
|
"## Evaluate performance on training data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1440x1440 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"nb_plts = len(dfs_train)\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(4, 1, figsize=(20, 20))\n",
|
|
"\n",
|
|
"for idx, df_iter in enumerate(dfs_gpr_train):\n",
|
|
" plt.subplot(4, 1, idx + 1)\n",
|
|
" df_input_iter = df_iter.drop(columns = dict_cols['w'][1] + dict_cols['y'][1] + dict_cols['u'][1])\n",
|
|
" df_output_iter = df_iter[dict_cols['y'][1]]\n",
|
|
" np_input_iter = df_input_iter.to_numpy()\n",
|
|
" np_output_iter = df_output_iter.to_numpy().reshape(-1, 1)\n",
|
|
" \n",
|
|
" mean, var = m.predict_f(np_input_iter)\n",
|
|
" \n",
|
|
" mean = scaler_helper.inverse_scale_output(mean).reshape((-1, 1))\n",
|
|
" #var = scaler_helper.inverse_scale_output(var).reshape((-1, 1))\n",
|
|
" scaled_measures = scaler_helper.inverse_scale_output(np_output_iter[:, :])\n",
|
|
" \n",
|
|
" plt.plot(df_iter.index, scaled_measures, label = 'Measured data')\n",
|
|
" plt.plot(df_iter.index, mean, label = 'Gaussian Process Prediction')\n",
|
|
" plt.fill_between(\n",
|
|
" df_iter.index, \n",
|
|
" mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),\n",
|
|
" mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),\n",
|
|
" alpha = 0.2\n",
|
|
" )\n",
|
|
" plt.title(f\"Model Performance on training data: {train_exps[idx]}\")\n",
|
|
" plt.legend()\n",
|
|
"plt.savefig(f\"../Thesis/Plots/GP_training_performance.pdf\", bbox_inches='tight')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Evaluate performance on test data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def SMSE(measured, predicted):\n",
|
|
" N = measured.size\n",
|
|
" measured_var = np.var(measured)\n",
|
|
" SMSE = np.power(measured - predicted, 2).sum()/(N*measured_var)\n",
|
|
" return SMSE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 47,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def RMSE(measured, predicted):\n",
|
|
" N = measured.size\n",
|
|
" RMSE = np.sqrt(np.power(measured - predicted, 2).sum()/N)\n",
|
|
" return RMSE"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 48,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def LPD(measured, predicted_mean, predicted_var):\n",
|
|
" N = measured.size\n",
|
|
" sum_part = np.log(predicted_var) + np.power(measured - predicted_mean, 2)/predicted_var\n",
|
|
" LPD = 1/2*np.log(2*np.pi) + 1/(2*N)*sum_part.sum()\n",
|
|
" return LPD"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def MSLL(measured, predicted_mean, predicted_var):\n",
|
|
" measured_var = np.var(measured)\n",
|
|
" measured_mean = np.mean(measured)\n",
|
|
" return LPD(measured, predicted_mean, predicted_var) - LPD(measured, measured_mean, measured_var)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"nb_plts = len(dfs_test)\n",
|
|
"\n",
|
|
"test_smse = 0\n",
|
|
"test_rmse = 0\n",
|
|
"test_lpd = 0\n",
|
|
"test_msll = 0\n",
|
|
"\n",
|
|
"plt.figure(figsize = (20, 20))\n",
|
|
"\n",
|
|
"for idx, df_iter in enumerate(dfs_gpr_test):\n",
|
|
" plt.subplot(nb_plts, 1, idx + 1)\n",
|
|
" df_input_iter = df_iter.drop(columns = dict_cols['w'][1] + dict_cols['y'][1] + dict_cols['u'][1])\n",
|
|
" df_output_iter = df_iter[dict_cols['y'][1]]\n",
|
|
" np_input_iter = df_input_iter.to_numpy()\n",
|
|
" np_output_iter = df_output_iter.to_numpy().reshape(-1, 1)\n",
|
|
" \n",
|
|
" mean, var = m.predict_f(np_input_iter)\n",
|
|
" mean = mean.numpy()\n",
|
|
" var = var.numpy()\n",
|
|
" \n",
|
|
" mean = scaler_helper.inverse_scale_output(mean).reshape((-1, 1))\n",
|
|
" #var = scaler_helper.inverse_scale_output(var).reshape((-1, 1))\n",
|
|
" scaled_measures = scaler_helper.inverse_scale_output(np_output_iter[:, :])\n",
|
|
" \n",
|
|
" test_smse += SMSE(np_output_iter, mean)\n",
|
|
" test_rmse += RMSE(np_output_iter, mean)\n",
|
|
" test_lpd += LPD(np_output_iter, mean, var)\n",
|
|
" test_msll += MSLL(np_output_iter, mean, var)\n",
|
|
"\n",
|
|
" \n",
|
|
" plt.plot(df_iter.index, scaled_measures, label = 'Measured data')\n",
|
|
" plt.plot(df_iter.index, mean[:, :], label = 'Gaussian Process Prediction')\n",
|
|
" plt.fill_between(\n",
|
|
" df_iter.index, \n",
|
|
" mean[:, 0] - 1.96 * np.sqrt(var[:, 0]),\n",
|
|
" mean[:, 0] + 1.96 * np.sqrt(var[:, 0]),\n",
|
|
" alpha = 0.2\n",
|
|
" )\n",
|
|
" plt.title(f\"Model Performance on test data: {test_exps[idx]}\")\n",
|
|
" plt.legend()\n",
|
|
"plt.savefig(f\"Performance_test_exps.png\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"--- Lags ---\n",
|
|
"w_lags: 1, u_lags: 2, y_lags: 1\n",
|
|
"--- Test errors ---\n",
|
|
"RMSE: 76.26786776377332, SMSE: 17775.113622467106, MSLL: 9743413793.02701, LPD: 9743413793.98197\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(f\"--- Lags ---\")\n",
|
|
"print(f\"w_lags: {w_lags}, u_lags: {u_lags}, y_lags: {y_lags}\")\n",
|
|
"print(\"--- Test errors ---\")\n",
|
|
"print(f\"RMSE: {test_rmse}, SMSE: {test_smse}, MSLL: {test_msll}, LPD: {test_lpd}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Generate a table of errors and lengthscales"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 52,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#t_cols = ['time_h', 'time_m']\n",
|
|
"t_cols = []\n",
|
|
"w_cols = ['SolRad', 'OutsideTemp']\n",
|
|
"u_cols = ['SimulatedHeat']\n",
|
|
"y_cols = ['SimulatedTemp']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 53,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Max lags, used to generate columns index\n",
|
|
"t_lags = 0\n",
|
|
"w_lags = 5\n",
|
|
"u_lags = 5\n",
|
|
"y_lags = 5"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"dict_cols = {\n",
|
|
" 't': (t_lags, t_cols),\n",
|
|
" 'w': (w_lags, w_cols),\n",
|
|
" 'u': (u_lags, u_cols),\n",
|
|
" 'y': (y_lags, y_cols)\n",
|
|
"}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 55,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"lags_cols = ['w_lags', 'u_lags', 'y_lags']\n",
|
|
"err_cols = ['rmse', 'smse', 'msll', 'lpd'] + ['variance']\n",
|
|
"lscales_cols = data_to_gpr(df_sc, dict_cols).drop(columns = dict_cols['w'][1] + dict_cols['y'][1] + dict_cols['u'][1]).columns.to_list()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 56,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_perf_cols = lags_cols + err_cols + lscales_cols"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 57,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np_perf = np.empty((0, len(df_perf_cols)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"w_range = np.arange(1,6)\n",
|
|
"u_range = np.arange(1,6)\n",
|
|
"y_range = np.arange(1,6)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "e35fff50426243a69720a697e1a97aaf",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
" 0%| | 0/125 [00:00<?, ?it/s]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"np_perf = np.empty((0, len(df_perf_cols)))\n",
|
|
"for w_iter, u_iter, y_iter in product(w_range, u_range, y_range):\n",
|
|
" \n",
|
|
" # Define dict_cols\n",
|
|
" dict_cols = {\n",
|
|
" 't': (t_lags, t_cols),\n",
|
|
" 'w': (w_iter, w_cols),\n",
|
|
" 'u': (u_iter, u_cols),\n",
|
|
" 'y': (y_iter, y_cols)\n",
|
|
" }\n",
|
|
" \n",
|
|
" # Training data\n",
|
|
" dfs_gpr_train = []\n",
|
|
" for df_sc in dfs_train_sc:\n",
|
|
" dfs_gpr_train.append(data_to_gpr(df_sc, dict_cols))\n",
|
|
" df_gpr_train = pd.concat(dfs_gpr_train)\n",
|
|
" \n",
|
|
" dfs_gpr_test = []\n",
|
|
" for df_sc in dfs_test_sc:\n",
|
|
" dfs_gpr_test.append(data_to_gpr(df_sc, dict_cols))\n",
|
|
" \n",
|
|
" df_input_train = df_gpr_train.drop(columns = dict_cols['w'][1] + dict_cols['u'][1] + dict_cols['y'][1])\n",
|
|
" df_output_train = df_gpr_train[dict_cols['y'][1]]\n",
|
|
"\n",
|
|
" np_input_train = df_input_train.to_numpy()\n",
|
|
" np_output_train = df_output_train.to_numpy().reshape(-1, 1)\n",
|
|
"\n",
|
|
" data_train = (np_input_train, np_output_train)\n",
|
|
"\n",
|
|
" \n",
|
|
" # Kernel\n",
|
|
" nb_dims = np_input_train.shape[1]\n",
|
|
" rational_dims = np.arange(0, (dict_cols['t'][0] + 1) * len(dict_cols['t'][1]), 1)\n",
|
|
" nb_rational_dims = len(rational_dims)\n",
|
|
" squared_dims = np.arange(nb_rational_dims, nb_dims, 1)\n",
|
|
" nb_squared_dims = len(squared_dims)\n",
|
|
" \n",
|
|
" squared_l = np.linspace(1, 1, nb_squared_dims)\n",
|
|
" rational_l = np.linspace(1, 1, nb_rational_dims)\n",
|
|
" \n",
|
|
" k0 = gpflow.kernels.SquaredExponential(lengthscales = squared_l, active_dims = squared_dims, variance = variance)\n",
|
|
" \n",
|
|
" k = k0\n",
|
|
" \n",
|
|
" nb_tries = 0\n",
|
|
" train_success = False\n",
|
|
" while True:\n",
|
|
" try:\n",
|
|
" if nb_tries > 2:\n",
|
|
" break\n",
|
|
" k0 = gpflow.kernels.SquaredExponential(lengthscales = squared_l, active_dims = squared_dims, variance = variance)\n",
|
|
" k = k0\n",
|
|
"\n",
|
|
" # Model definition and training\n",
|
|
" m = gpflow.models.GPR(\n",
|
|
" data = data_train, \n",
|
|
" kernel = k, \n",
|
|
" mean_function = None,\n",
|
|
" )\n",
|
|
"\n",
|
|
" opt = gpflow.optimizers.Scipy()\n",
|
|
" opt.minimize(m.training_loss, m.trainable_variables)\n",
|
|
" train_success = True\n",
|
|
" break\n",
|
|
" except:\n",
|
|
" nb_tries += 1\n",
|
|
" \n",
|
|
" if not train_success:\n",
|
|
" continue\n",
|
|
" \n",
|
|
" nb_plts = len(dfs_test)\n",
|
|
"\n",
|
|
" test_smse = 0\n",
|
|
" test_rmse = 0\n",
|
|
" test_lpd = 0\n",
|
|
" test_msll = 0\n",
|
|
"\n",
|
|
" for idx, df_iter in enumerate(dfs_gpr_test):\n",
|
|
" df_input_iter = df_iter.drop(columns = dict_cols['w'][1] + dict_cols['y'][1] + dict_cols['u'][1])\n",
|
|
" df_output_iter = df_iter[dict_cols['y'][1]]\n",
|
|
" np_input_iter = df_input_iter.to_numpy()\n",
|
|
" np_output_iter = df_output_iter.to_numpy().reshape(-1, 1)\n",
|
|
"\n",
|
|
" mean, var = m.predict_f(np_input_iter)\n",
|
|
"\n",
|
|
" test_smse += SMSE(np_output_iter, mean.numpy())\n",
|
|
" test_rmse += RMSE(np_output_iter, mean.numpy())\n",
|
|
" test_lpd += LPD(np_output_iter, mean.numpy(), var.numpy())\n",
|
|
" test_msll += MSLL(np_output_iter, mean.numpy(), var.numpy())\n",
|
|
" \n",
|
|
" # Compute the current row in df_perf\n",
|
|
" \n",
|
|
" iter_lagcols = df_input_train.columns.tolist()\n",
|
|
" \n",
|
|
" np_perf_iter = np.nan * np.ones((1, len(df_perf_cols)))\n",
|
|
" np_perf_iter[0,0] = w_iter\n",
|
|
" np_perf_iter[0,1] = u_iter\n",
|
|
" np_perf_iter[0,2] = y_iter\n",
|
|
" np_perf_iter[0,3] = test_rmse\n",
|
|
" np_perf_iter[0,4] = test_smse\n",
|
|
" np_perf_iter[0,5] = test_msll\n",
|
|
" np_perf_iter[0,6] = test_lpd\n",
|
|
" np_perf_iter[0,7] = gpflow.utilities.parameter_dict(m)['.kernel.variance'].numpy()\n",
|
|
" \n",
|
|
" for iter_lag in iter_lagcols:\n",
|
|
" iter_lag_idx = df_input_train.columns.to_list().index(iter_lag)\n",
|
|
" perf_lag_idx = df_perf_cols.index(iter_lag)\n",
|
|
" np_perf_iter[0,perf_lag_idx] = gpflow.utilities.parameter_dict(m)['.kernel.lengthscales'].numpy()[iter_lag_idx]\n",
|
|
" \n",
|
|
"\n",
|
|
" np_perf = np.vstack([np_perf, np_perf_iter])\n",
|
|
" \n",
|
|
" # Save the output for this iteration\n",
|
|
" df_perf_iter = pd.DataFrame(np_perf, columns = df_perf_cols).to_csv(f\"df_perf_GP_{w_iter}w_{u_iter}u_{y_iter}y.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np_perf = np.empty((0, len(df_perf_cols)))\n",
|
|
"for w_iter, u_iter, y_iter in product(w_range, u_range, y_range):\n",
|
|
"\n",
|
|
" # Define dict_cols\n",
|
|
" dict_cols = {\n",
|
|
" 't': (t_lags, t_cols),\n",
|
|
" 'w': (w_iter, w_cols),\n",
|
|
" 'u': (u_iter, u_cols),\n",
|
|
" 'y': (y_iter, y_cols)\n",
|
|
" }\n",
|
|
"\n",
|
|
" # Training data\n",
|
|
" dfs_gpr_train = []\n",
|
|
" for df_sc in dfs_train_sc:\n",
|
|
" dfs_gpr_train.append(data_to_gpr(df_sc, dict_cols))\n",
|
|
" df_gpr_train = pd.concat(dfs_gpr_train)\n",
|
|
"\n",
|
|
" dfs_gpr_test = []\n",
|
|
" for df_sc in dfs_test_sc:\n",
|
|
" dfs_gpr_test.append(data_to_gpr(df_sc, dict_cols))\n",
|
|
"\n",
|
|
" df_input_train = df_gpr_train.drop(columns = dict_cols['w'][1] + dict_cols['u'][1] + dict_cols['y'][1])\n",
|
|
" df_output_train = df_gpr_train[dict_cols['y'][1]]\n",
|
|
"\n",
|
|
" np_input_train = df_input_train.to_numpy()\n",
|
|
" np_output_train = df_output_train.to_numpy().reshape(-1, 1)\n",
|
|
"\n",
|
|
" data_train = (np_input_train, np_output_train)\n",
|
|
"\n",
|
|
"\n",
|
|
" # Kernel\n",
|
|
" nb_dims = np_input_train.shape[1]\n",
|
|
" rational_dims = np.arange(0, (dict_cols['t'][0] + 1) * len(dict_cols['t'][1]), 1)\n",
|
|
" nb_rational_dims = len(rational_dims)\n",
|
|
" squared_dims = np.arange(nb_rational_dims, nb_dims, 1)\n",
|
|
" nb_squared_dims = len(squared_dims)\n",
|
|
"\n",
|
|
" squared_l = np.linspace(1, 1, nb_squared_dims)\n",
|
|
" rational_l = np.linspace(1, 1, nb_rational_dims)\n",
|
|
"\n",
|
|
" nb_tries = 0\n",
|
|
" train_success = False\n",
|
|
" while True:\n",
|
|
" try:\n",
|
|
" if nb_tries > 2:\n",
|
|
" break\n",
|
|
" k0 = gpflow.kernels.SquaredExponential(lengthscales = squared_l, active_dims = squared_dims, variance = variance)\n",
|
|
" k = k0 \n",
|
|
"\n",
|
|
" N = data_train[0].shape[0]\n",
|
|
" M = 150 # Number of inducing locations\n",
|
|
" Z = data_train[0][:M, :].copy()\n",
|
|
"\n",
|
|
" m = gpflow.models.SVGP(k, gpflow.likelihoods.Gaussian(), Z, num_data = N)\n",
|
|
"\n",
|
|
" elbo = tf.function(m.elbo)\n",
|
|
"\n",
|
|
" ###\n",
|
|
" # Training\n",
|
|
" ###\n",
|
|
"\n",
|
|
" minibatch_size = 100\n",
|
|
" train_dataset = tf.data.Dataset.from_tensor_slices(data_train).repeat().shuffle(N)\n",
|
|
"\n",
|
|
" # Turn off training for inducing point locations\n",
|
|
" gpflow.set_trainable(m.inducing_variable, False)\n",
|
|
"\n",
|
|
" def run_adam(model, iterations):\n",
|
|
" \"\"\"\n",
|
|
" Utility function running the Adam optimizer\n",
|
|
"\n",
|
|
" :param model: GPflow model\n",
|
|
" :param interations: number of iterations\n",
|
|
" \"\"\"\n",
|
|
" # Create an Adam Optimizer action\n",
|
|
" logf = []\n",
|
|
" train_iter = iter(train_dataset.batch(minibatch_size))\n",
|
|
" training_loss = model.training_loss_closure(train_iter, compile=True)\n",
|
|
" optimizer = tf.optimizers.Adam()\n",
|
|
"\n",
|
|
" @tf.function\n",
|
|
" def optimization_step():\n",
|
|
" optimizer.minimize(training_loss, model.trainable_variables)\n",
|
|
"\n",
|
|
" for step in range(iterations):\n",
|
|
" optimization_step()\n",
|
|
" if step % 10 == 0:\n",
|
|
" elbo = -training_loss().numpy()\n",
|
|
" logf.append(elbo)\n",
|
|
" return logf\n",
|
|
"\n",
|
|
"\n",
|
|
" maxiter = ci_niter(10000)\n",
|
|
" logf = run_adam(m, maxiter)\n",
|
|
"\n",
|
|
" train_success = True\n",
|
|
" break\n",
|
|
" except:\n",
|
|
" nb_tries += 1\n",
|
|
"\n",
|
|
" if not train_success:\n",
|
|
" continue\n",
|
|
"\n",
|
|
" nb_plts = len(dfs_test)\n",
|
|
"\n",
|
|
" test_smse = 0\n",
|
|
" test_rmse = 0\n",
|
|
" test_lpd = 0\n",
|
|
" test_msll = 0\n",
|
|
"\n",
|
|
" for idx, df_iter in enumerate(dfs_gpr_test):\n",
|
|
" df_input_iter = df_iter.drop(columns = dict_cols['w'][1] + dict_cols['y'][1] + dict_cols['u'][1])\n",
|
|
" df_output_iter = df_iter[dict_cols['y'][1]]\n",
|
|
" np_input_iter = df_input_iter.to_numpy()\n",
|
|
" np_output_iter = df_output_iter.to_numpy().reshape(-1, 1)\n",
|
|
"\n",
|
|
" mean, var = m.predict_f(np_input_iter)\n",
|
|
"\n",
|
|
" test_smse += SMSE(np_output_iter, mean.numpy())\n",
|
|
" test_rmse += RMSE(np_output_iter, mean.numpy())\n",
|
|
" test_lpd += LPD(np_output_iter, mean.numpy(), var.numpy())\n",
|
|
" test_msll += MSLL(np_output_iter, mean.numpy(), var.numpy())\n",
|
|
"\n",
|
|
" # Compute the current row in df_perf\n",
|
|
"\n",
|
|
" iter_lagcols = df_input_train.columns.tolist()\n",
|
|
"\n",
|
|
" np_perf_iter = np.nan * np.ones((1, len(df_perf_cols)))\n",
|
|
" np_perf_iter[0,0] = w_iter\n",
|
|
" np_perf_iter[0,1] = u_iter\n",
|
|
" np_perf_iter[0,2] = y_iter\n",
|
|
" np_perf_iter[0,3] = test_rmse\n",
|
|
" np_perf_iter[0,4] = test_smse\n",
|
|
" np_perf_iter[0,5] = test_msll\n",
|
|
" np_perf_iter[0,6] = test_lpd\n",
|
|
" np_perf_iter[0,7] = gpflow.utilities.parameter_dict(m)['.kernel.variance'].numpy()\n",
|
|
"\n",
|
|
" for iter_lag in iter_lagcols:\n",
|
|
" iter_lag_idx = df_input_train.columns.to_list().index(iter_lag)\n",
|
|
" perf_lag_idx = df_perf_cols.index(iter_lag)\n",
|
|
" np_perf_iter[0,perf_lag_idx] = gpflow.utilities.parameter_dict(m)['.kernel.lengthscales'].numpy()[iter_lag_idx]\n",
|
|
"\n",
|
|
"\n",
|
|
" np_perf = np.vstack([np_perf, np_perf_iter])\n",
|
|
"\n",
|
|
" # Save the output for this iteration\n",
|
|
" df_perf_iter = pd.DataFrame(np_perf, columns = df_perf_cols).to_csv(f\"df_perf_SVGP_{w_iter}w_{u_iter}u_{y_iter}y.csv\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"gpflow.utilities.parameter_dict(m)['.kernel.lengthscales'].numpy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Multistep prediction"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_input = dfs_gpr_train[0].drop(columns = dict_cols['w'][1] + dict_cols['u'][1] + dict_cols['y'][1])\n",
|
|
"df_output = dfs_gpr_train[0][dict_cols['y'][1]]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"start_idx = 25\n",
|
|
"nb_predictions = 25\n",
|
|
"N_pred = 20"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"plt.plot(df_output.iloc[start_idx:start_idx + nb_predictions + N_pred], 'o-', label = 'measured', color = 'darkblue')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_iter"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure()\n",
|
|
"\n",
|
|
"y_name = dict_cols['y'][1][0]\n",
|
|
"for idx in range(start_idx, start_idx + nb_predictions):\n",
|
|
" df_iter = df_input.iloc[idx:(idx + N_pred)].copy()\n",
|
|
" for idxx in range(N_pred - 1):\n",
|
|
" idx_old = df_iter.index[idxx]\n",
|
|
" idx_new = df_iter.index[idxx+1]\n",
|
|
" mean, var = m.predict_f(df_iter.loc[idx_old, :].to_numpy().reshape(1, -1))\n",
|
|
" df_iter.loc[idx_new, f'{y_name}_1'] = mean.numpy().flatten()\n",
|
|
" for lag in range(2, dict_cols['y'][0] + 1):\n",
|
|
" df_iter.loc[idx_new, f\"{y_name}_{lag}\"] = df_iter.loc[idx_old, f\"{y_name}_{lag-1}\"]\n",
|
|
" \n",
|
|
" mean_iter, var_iter = m.predict_f(df_iter.to_numpy())\n",
|
|
" plt.plot(df_iter.index, mean_iter.numpy(), '.-', label = 'predicted', color = 'orange')\n",
|
|
"plt.plot(df_output.iloc[start_idx:start_idx + nb_predictions + N_pred], 'o-', label = 'measured', color = 'darkblue')\n",
|
|
"plt.title(f\"Prediction over {N_pred} steps\")\n",
|
|
"plt.savefig(f\"prediction_{N_pred}_steps.png\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Test CasADi problem"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import casadi as cs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class GPR(cs.Callback):\n",
|
|
" def __init__(self, name, model, opts={}):\n",
|
|
" cs.Callback.__init__(self)\n",
|
|
"\n",
|
|
" self.model = model\n",
|
|
" self.n_in = model.data[0].shape[1]\n",
|
|
" # Create a variable to keep all the gradient callback references\n",
|
|
" self.refs = []\n",
|
|
"\n",
|
|
" self.construct(name, opts)\n",
|
|
" \n",
|
|
" # Number of inputs/outputs\n",
|
|
" def get_n_in(self): return 1\n",
|
|
" def get_n_out(self): return 1\n",
|
|
" \n",
|
|
"\n",
|
|
" # Sparsity of the input/output\n",
|
|
" def get_sparsity_in(self,i):\n",
|
|
" return cs.Sparsity.dense(1,self.n_in)\n",
|
|
" def get_sparsity_out(self,i):\n",
|
|
" return cs.Sparsity.dense(1,1)\n",
|
|
"\n",
|
|
"\n",
|
|
" def eval(self, arg):\n",
|
|
" inp = np.array(arg[0])\n",
|
|
" inp = tf.Variable(inp, dtype=tf.float64)\n",
|
|
" [mean, _] = self.model.predict_f(inp)\n",
|
|
" return [mean.numpy()]\n",
|
|
" \n",
|
|
" def has_reverse(self, nadj): return nadj==1\n",
|
|
" def get_reverse(self, nadj, name, inames, onames, opts):\n",
|
|
" grad_callback = GPR_grad(name, self.model)\n",
|
|
" self.refs.append(grad_callback)\n",
|
|
" \n",
|
|
" nominal_in = self.mx_in()\n",
|
|
" nominal_out = self.mx_out()\n",
|
|
" adj_seed = self.mx_out()\n",
|
|
" return cs.Function(name, nominal_in+nominal_out+adj_seed, grad_callback.call(nominal_in), inames, onames)\n",
|
|
" \n",
|
|
"class GPR_grad(cs.Callback):\n",
|
|
" def __init__(self, name, model, opts={}):\n",
|
|
" cs.Callback.__init__(self) \n",
|
|
" self.model = model\n",
|
|
" self.n_in = model.data[0].shape[1]\n",
|
|
"\n",
|
|
" self.construct(name, opts)\n",
|
|
"\n",
|
|
" \n",
|
|
" def get_n_in(self): return 1\n",
|
|
" def get_n_out(self): return 1\n",
|
|
" \n",
|
|
" def get_sparsity_in(self,i):\n",
|
|
" return cs.Sparsity.dense(1,self.n_in)\n",
|
|
" def get_sparsity_out(self,i):\n",
|
|
" return cs.Sparsity.dense(1,self.n_in)\n",
|
|
"\n",
|
|
"\n",
|
|
" def eval(self, arg):\n",
|
|
" inp = np.array(arg[0])\n",
|
|
" inp = tf.Variable(inp, dtype=tf.float64)\n",
|
|
" \n",
|
|
" with tf.GradientTape() as tape:\n",
|
|
" preds = self.model.predict_f(inp)\n",
|
|
"\n",
|
|
" grads = tape.gradient(preds, inp)\n",
|
|
" return [grads.numpy()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cs_model = GPR(\"gpr\", m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"N_horizon = 5;\n",
|
|
"\n",
|
|
"T_set = 23;\n",
|
|
"T_set_sc = scaler.transform(np.array([0, 0, 0, T_set]).reshape(1, -1))[0, 3]\n",
|
|
"n_states = m.data[0].shape[1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"total_cols = 0\n",
|
|
"for lags, cols in dict_cols.values():\n",
|
|
" total_cols += lags*(len(cols)+1)\n",
|
|
"total_cols = total_cols -2\n",
|
|
"print(total_cols)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"W = cs.MX.sym(\"W\", N_horizon, 2)\n",
|
|
"U = cs.MX.sym(\"U\", N_horizon, 1)\n",
|
|
"x0 = cs.MX.sym(\"x0\", 1, n_states)\n",
|
|
"Xk = cs.MX.sym(\"Xk\", 0, n_states)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"yhats = cs.MX.sym(\"yhats\", 0, 1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"yhat = cs_model(x0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"yhats = cs.vertcat(yhats, yhat)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### First step"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"xk = x0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### All the other steps"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for row_idx in range(N_horizon):\n",
|
|
" # w\n",
|
|
" base_idx = 0\n",
|
|
" nb_cols = len(dict_cols['w'][1])*(dict_cols['w'][0])\n",
|
|
" wk_1 = xk[base_idx:base_idx + nb_cols]\n",
|
|
" \n",
|
|
" wk = cs.MX.sym(\"wk\", 1, 0)\n",
|
|
" nb_lags = dict_cols['w'][0]\n",
|
|
"\n",
|
|
" for idx in range(W.shape[1]):\n",
|
|
" base_col = idx * (nb_lags - 1)\n",
|
|
" wk = cs.horzcat(wk, W[row_idx, idx], wk_1[base_col:base_col + nb_lags - 1])\n",
|
|
" \n",
|
|
" # u\n",
|
|
" base_idx += nb_cols\n",
|
|
" nb_cols = len(dict_cols['u'][1])*dict_cols['u'][0]\n",
|
|
" uk_1 = xk[base_idx:base_idx + nb_cols]\n",
|
|
"\n",
|
|
" \n",
|
|
" nb_lags = dict_cols['u'][0] - 1\n",
|
|
" uk = cs.horzcat(U[row_idx], uk_1[:nb_lags])\n",
|
|
" \n",
|
|
" # y\n",
|
|
" base_idx += nb_cols\n",
|
|
" nb_cols = len(dict_cols['y'][1])*dict_cols['y'][0]\n",
|
|
" yk_1 = xk[base_idx: base_idx + nb_cols]\n",
|
|
" \n",
|
|
" nb_lags = dict_cols['y'][0] - 1\n",
|
|
" yk = cs.horzcat(yhat, yk_1[:nb_lags])\n",
|
|
" \n",
|
|
" xk = cs.horzcat(wk, uk, yk)\n",
|
|
" Xk = cs.vertcat(Xk, xk)\n",
|
|
" yhat = cs_model(xk)\n",
|
|
" yhats = cs.vertcat(yhats, yhat)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Helper functions to easily reproduce everything:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ff = cs.Function('ff', [W, U, x0], [yhats])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Ff = cs.Function('Ff', [W, U, x0], [Xk])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Compute the objective function:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"y_diff = yhats - T_set_sc\n",
|
|
"J = cs.dot(y_diff, y_diff)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Compute the parameters vector:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"p = cs.vertcat(cs.vec(W), cs.vec(x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"prob = {\"x\": cs.vec(U), \"f\": J, \"p\": p}\n",
|
|
"options = {\"ipopt\": {\"hessian_approximation\": \"limited-memory\", \"max_cpu_time\": 500,\n",
|
|
" \"acceptable_tol\": 1e-8, \"acceptable_obj_change_tol\": 1e-6}}"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"solver = cs.nlpsol(\"solver\",\"ipopt\",prob, options)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Check the functions on \"real\" values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_W = np.random.rand(*W.shape)\n",
|
|
"real_U = np.random.rand(*U.shape).reshape(-1, 1)\n",
|
|
"real_x0 = np.random.rand(*x0.shape).reshape(1, -1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"yhats_ff = ff(real_W, real_U, real_x0)\n",
|
|
"np.array(yhats_ff)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Xk_Ff = Ff(real_W, real_U, real_x0)\n",
|
|
"pd.DataFrame(np.array(Xk_Ff), columns = df_input_train.columns)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np_realxk = np.empty((N_horizon + 1, n_states))\n",
|
|
"np_realxk[0, :] = real_x0\n",
|
|
"np_realxk[1:, :2] = real_W\n",
|
|
"np_realxk[1:, 2] = real_U.ravel()\n",
|
|
"\n",
|
|
"for row_idx in range(N_horizon):\n",
|
|
" mean, _ = m.predict_f(np_realxk[row_idx, :].reshape(1, -1))\n",
|
|
" np_realxk[row_idx + 1, 3] = np_realxk[row_idx, 2]\n",
|
|
" np_realxk[row_idx + 1, 4] = mean\n",
|
|
" np_realxk[row_idx + 1, 5] = np_realxk[row_idx, 4]\n",
|
|
" np_realxk[row_idx + 1, 6] = np_realxk[row_idx, 5]\n",
|
|
"np_yhats, _ = m.predict_f(np_realxk)\n",
|
|
"np_yhats"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"yhats_ff - np_yhats"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"mean, _ = m.predict_f(np_realxk[2,:].reshape(1, -1))\n",
|
|
"mean"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Set up the problem and solve it"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"start_idx = 25\n",
|
|
"test_gpr = data_to_gpr(dfs_test_sc[0], dict_cols).drop(columns = u_cols + y_cols)\n",
|
|
"real_x0 = cs.DM(test_gpr.iloc[start_idx, :].to_numpy())\n",
|
|
"real_W0 = cs.DM(test_gpr.iloc[start_idx + 1: start_idx + N_horizon, :2].to_numpy())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_p = cs.vertcat(cs.vec(real_W0), cs.vec(real_x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"test_gpr.iloc[start_idx: start_idx + N_horizon + 1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"scrolled": true,
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"res = solver(lbx = -1, ubx = 1, p = real_p, x0 = -1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"res['x']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Ff(real_W, np.ones((N_horizon, 1)), real_x0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Ff(real_W, res['x'], real_x0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cs.norm_2(ff(real_W, np.ones((N_horizon, 1)), real_x0) - T_set_sc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cs.norm_2(ff(real_W, res['x'], real_x0) - T_set_sc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"T_set"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multiple shooting problem formulation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"N_horizon = 15"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"X = cs.MX.sym(\"X\", N_horizon + 1, n_states)\n",
|
|
"lbd = cs.MX.sym(\"lambda\")\n",
|
|
"x0 = cs.MX.sym(\"x0\", 1, n_states)\n",
|
|
"W = cs.MX.sym(\"W\", N_horizon, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"g = []\n",
|
|
"lbg = []\n",
|
|
"ubg = []\n",
|
|
"\n",
|
|
"lbx = -np.inf*np.ones(X.shape)\n",
|
|
"ubx = np.inf*np.ones(X.shape)\n",
|
|
"\n",
|
|
"T_set_sc = 2.5\n",
|
|
"##\n",
|
|
"# Set up the opjective function\n",
|
|
"##\n",
|
|
"\n",
|
|
"# stage cost\n",
|
|
"u_cost = cs.dot(X[:, 2], X[:, 2])\n",
|
|
"\n",
|
|
"# temperature constraint\n",
|
|
"y_cost = 0.01 * cs.dot(X[:, 4], X[:, 4])\n",
|
|
"\n",
|
|
"J = u_cost + y_cost"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Set up equality constraints for the first step\n",
|
|
"for idx in range(n_states):\n",
|
|
" g.append(X[0, idx] - x0[0, idx])\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Set up equality constraints for the following steps\n",
|
|
"for idx in range(1, N_horizon + 1):\n",
|
|
" base_col = 0\n",
|
|
" # w\n",
|
|
" nb_cols = dict_cols['w'][0]\n",
|
|
" for w_idx in range(W.shape[1]):\n",
|
|
" w_base_col = w_idx * nb_cols\n",
|
|
" g.append(X[idx, base_col + w_base_col] - W[idx - 1, w_idx])\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)\n",
|
|
" for w_lag_idx in range(1, nb_cols):\n",
|
|
" g.append(X[idx, base_col + w_base_col + w_lag_idx] - X[idx - 1, base_col + w_base_col + w_lag_idx - 1])\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)\n",
|
|
" \n",
|
|
" base_col += nb_cols * W.shape[1]\n",
|
|
" # u\n",
|
|
" nb_cols = dict_cols['u'][0]\n",
|
|
"\n",
|
|
" lbx[idx, base_col] = -1 #lower bound on input\n",
|
|
" ubx[idx, base_col] = 1 #upper bound on input\n",
|
|
" for u_lag_idx in range(1, nb_cols):\n",
|
|
" g.append(X[idx, base_col + u_lag_idx] - X[idx - 1, base_col + u_lag_idx - 1])\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)\n",
|
|
" \n",
|
|
" base_col += nb_cols\n",
|
|
" # y\n",
|
|
" nb_cols = dict_cols['y'][0]\n",
|
|
" g.append(X[idx, base_col] - cs_model(X[idx - 1, :]))\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)\n",
|
|
" for y_lag_idx in range(1, nb_cols):\n",
|
|
" g.append(X[idx, base_col + y_lag_idx] - X[idx - 1, base_col + y_lag_idx - 1])\n",
|
|
" lbg.append(0)\n",
|
|
" ubg.append(0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"p = cs.vertcat(cs.vec(W), cs.vec(x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"prob = {'f': J, 'x': cs.vec(X), 'g': cs.vertcat(*g), 'p': p}\n",
|
|
"options = {\"ipopt\": {\"hessian_approximation\": \"limited-memory\", \"max_iter\": 100,\n",
|
|
" #\"acceptable_tol\": 1e-6, \"tol\": 1e-6,\n",
|
|
" \"linear_solver\": \"ma97\",\n",
|
|
" #\"acceptable_obj_change_tol\": 1e-5, \n",
|
|
" #\"mu_strategy\": \"adaptive\",\n",
|
|
" }}\n",
|
|
"solver = cs.nlpsol(\"solver\",\"ipopt\",prob, options)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_x0 = np.random.rand(*x0.shape)\n",
|
|
"real_x0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_W = np.random.rand(*W.shape)\n",
|
|
"real_W"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_p = cs.vertcat(cs.vec(real_W), cs.vec(real_x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"res = solver(lbx = cs.vec(lbx), ubx = cs.vec(ubx), p = real_p, lbg = lbg, ubg = ubg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np_res = np.array(res['x'].reshape(X.shape))\n",
|
|
"pd.DataFrame(np_res, columns = df_input.columns)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"mean, var = m.predict_f(np_res)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"mean"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"accelerator": "GPU",
|
|
"colab": {
|
|
"name": "Untitled3.ipynb",
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|