1769 lines
48 KiB
Text
1769 lines
48 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "135c08ed-bb56-402b-8f70-6753236fb3fd",
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"metadata": {},
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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": "code",
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"execution_count": 2,
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"id": "544a9dae-f975-4b9b-8ce8-6acb0cebe67f",
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"metadata": {},
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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": 3,
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"id": "4e4ea353-bcaa-4f11-8482-fc4a1350e6e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import casadi as cs"
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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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"id": "6ed9f89d-5004-46d6-bee0-912fda25edfd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import MinMaxScaler\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": "markdown",
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"id": "fdde6962-b16a-4986-b872-ecc692b3e7d9",
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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": 5,
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"id": "8e31e7be-e669-4c2a-a44d-79c2d61278c6",
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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": 6,
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"id": "2ea88fb9-7fb0-47fe-be7f-ff4401d12f14",
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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": 7,
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"id": "573381ca-2162-4a90-8e68-af4f5c61a1c9",
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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": 8,
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"id": "6c6d2eb9-7052-4161-82af-812d1cdae872",
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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": 9,
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"id": "4f0fa4b8-3aa3-479c-b6ee-5268896cb4e5",
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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": 10,
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"id": "56c6ceb7-b448-419f-9502-1fd96f674711",
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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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"id": "ad177bfe-dbbd-4808-9f52-8cb5118dd928",
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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": 11,
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"id": "4cd8297e-19c3-4615-a493-577a585f23d2",
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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": 12,
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"id": "025a3183-0cd9-45fe-98ca-d12ae5bc6307",
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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": 13,
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"id": "17ccf631-9789-4787-bcb4-83f45a36dddc",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_train = pd.concat(dfs_train)"
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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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"id": "d62d612e-9b00-4d7a-9c3f-12c4d418dc9c",
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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",
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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",
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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": 14,
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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 = 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",
|
|
"id": "0fbc4b0d-a91a-4a00-bce5-da119fe2c271",
|
|
"metadata": {},
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"source": [
|
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"Condition number of the raw input 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": 15,
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"id": "e9eace0f-3f1e-4746-b396-ad1af97690f0",
|
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"metadata": {},
|
|
"outputs": [
|
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{
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"data": {
|
|
"text/plain": [
|
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"16199.169760599052"
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|
]
|
|
},
|
|
"execution_count": 15,
|
|
"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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"np.linalg.cond(df_train.to_numpy())"
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]
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},
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{
|
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"cell_type": "markdown",
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"id": "3d5ccf02-919d-4781-a194-19f3599202d7",
|
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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": 16,
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"id": "913c6705-e8e7-4b40-9aed-6ec5e1d1b010",
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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)"
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]
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},
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{
|
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"cell_type": "markdown",
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"id": "5728348e-2aac-4975-b308-0de3c3347933",
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"metadata": {},
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"source": [
|
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"Check the condition number of the input 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": 17,
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"id": "847a66b9-a7b8-47b5-974e-7ca79369088a",
|
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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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"4.946636675064373"
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]
|
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},
|
|
"execution_count": 17,
|
|
"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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"np.linalg.cond(df_train_sc.to_numpy())"
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]
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},
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{
|
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"cell_type": "markdown",
|
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"id": "51ab5f01-60be-4ed4-9b2a-d9038b086236",
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"metadata": {},
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"source": [
|
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"NOTE: Condition number of scaled data is much smaller. This makes sense."
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]
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},
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{
|
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"cell_type": "markdown",
|
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"id": "d76e19ec-5c52-43f5-9043-a8e25e730bdd",
|
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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:"
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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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"id": "fbb2019d-c5d1-463e-bca6-a57adcb3373a",
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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",
|
|
"id": "3ce1f200-136f-4ea2-aa51-dadf4b2a0cf3",
|
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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": 19,
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"id": "adc3cd49-b4f6-43a6-ad24-76e8fbce1746",
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"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
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"def data_to_gpr(df, dict_cols):\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_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",
|
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" for name in names:\n",
|
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" col_idx = df_gpr.columns.get_loc(name)\n",
|
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" for lag in range(1, lags + 1):\n",
|
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" df_gpr.insert(col_idx + lag, f\"{name}_{lag}\", df_gpr.loc[:, name].shift(lag))\n",
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"\n",
|
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" df_gpr.dropna(inplace = True)\n",
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" \n",
|
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" return df_gpr"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "ce6e9294-7a9d-440f-b017-85467719f2dd",
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
|
"<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",
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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",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <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>SolRad_1</th>\n",
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" <th>OutsideTemp</th>\n",
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" <th>OutsideTemp_1</th>\n",
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" <th>SimulatedHeat</th>\n",
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" <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",
|
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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>3</th>\n",
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" <td>-0.855164</td>\n",
|
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" <td>-0.859463</td>\n",
|
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" <td>0.058824</td>\n",
|
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" <td>0.058824</td>\n",
|
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" <td>-1.0</td>\n",
|
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" <td>-1.0</td>\n",
|
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" <td>-1.0</td>\n",
|
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" <td>-0.295224</td>\n",
|
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" <td>-0.270561</td>\n",
|
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" <td>-0.244215</td>\n",
|
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" <td>-0.020567</td>\n",
|
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" </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": 20,
|
|
"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": 21,
|
|
"id": "395b093c-04f8-437f-a2b4-4350ce851226",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_gpr_train = df_gpr_train.sample(n = 500)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "7bcc9265-9ff0-4630-abdf-321d75180da7",
|
|
"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": 23,
|
|
"id": "609b6a99-d122-4af9-a5e6-84005f899f2b",
|
|
"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": 24,
|
|
"id": "f884ea7d-5dde-404b-8b99-94592d1aa2b9",
|
|
"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": 25,
|
|
"id": "c3a19716-82ba-4ef8-86bc-47da3a518d06",
|
|
"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>176</th>\n",
|
|
" <td>0.320390</td>\n",
|
|
" <td>-0.411765</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.022707</td>\n",
|
|
" <td>-0.000856</td>\n",
|
|
" <td>-0.024018</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>491</th>\n",
|
|
" <td>-0.242351</td>\n",
|
|
" <td>0.058824</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.360771</td>\n",
|
|
" <td>0.336727</td>\n",
|
|
" <td>0.312594</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>111</th>\n",
|
|
" <td>-0.994599</td>\n",
|
|
" <td>-0.647059</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.674620</td>\n",
|
|
" <td>-0.262339</td>\n",
|
|
" <td>-0.282320</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>187</th>\n",
|
|
" <td>-0.515202</td>\n",
|
|
" <td>-0.882353</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-1.0</td>\n",
|
|
" <td>-0.762459</td>\n",
|
|
" <td>-0.736413</td>\n",
|
|
" <td>-0.322186</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>335</th>\n",
|
|
" <td>-0.994964</td>\n",
|
|
" <td>-0.647059</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>-0.406616</td>\n",
|
|
" <td>-0.817147</td>\n",
|
|
" <td>-0.794250</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" SolRad_1 OutsideTemp_1 SimulatedHeat_1 SimulatedHeat_2 \\\n",
|
|
"176 0.320390 -0.411765 1.0 1.0 \n",
|
|
"491 -0.242351 0.058824 1.0 1.0 \n",
|
|
"111 -0.994599 -0.647059 1.0 -1.0 \n",
|
|
"187 -0.515202 -0.882353 1.0 -1.0 \n",
|
|
"335 -0.994964 -0.647059 1.0 1.0 \n",
|
|
"\n",
|
|
" SimulatedTemp_1 SimulatedTemp_2 SimulatedTemp_3 \n",
|
|
"176 0.022707 -0.000856 -0.024018 \n",
|
|
"491 0.360771 0.336727 0.312594 \n",
|
|
"111 -0.674620 -0.262339 -0.282320 \n",
|
|
"187 -0.762459 -0.736413 -0.322186 \n",
|
|
"335 -0.406616 -0.817147 -0.794250 "
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_input_train.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "46b1c9ca-557c-40f8-a8a9-187f1f6bbba6",
|
|
"metadata": {
|
|
"id": "l_VzOWL66aD3"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"## Define Kernel"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "8bfeac5e-22ae-408f-a928-03c66e4280ff",
|
|
"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": 28,
|
|
"id": "0672bd9e-f2e7-48f4-9d30-7f00171b7a9b",
|
|
"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": 29,
|
|
"id": "d2e67ab9-c903-4605-9167-76fe0b811e3a",
|
|
"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": 30,
|
|
"id": "ab407ff8-aac1-48c5-94aa-ee693a451a38",
|
|
"metadata": {
|
|
"id": "MEGkQJvY_izQ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"variance = tf.math.reduce_variance(np_input_train)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 303,
|
|
"id": "b28d8306-8d13-46e7-a6ec-c3e0549bf07b",
|
|
"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": 458,
|
|
"id": "cb2867d9-85ff-4c4e-84ae-182cec152dd6",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 169
|
|
},
|
|
"id": "vo8rcdBm6fuc",
|
|
"outputId": "75485dcd-961c-40d9-cf1f-d10516e2b80f"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"k = (k0 + k1) * k2\n",
|
|
"k = k0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bbde76b9-07ec-4292-b419-3b49663c008b",
|
|
"metadata": {
|
|
"id": "4af25a43-15c9-4543-af73-3c313b5fc7af"
|
|
},
|
|
"source": [
|
|
"## Compile Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 459,
|
|
"id": "fd569132-6a46-4029-9eb5-8d1878f172d7",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 190
|
|
},
|
|
"id": "PC4cbp926j29",
|
|
"outputId": "72c9441d-2657-4e0f-de70-11a197d07ad3"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"m = gpflow.models.GPR(\n",
|
|
" data = data_train, \n",
|
|
" kernel = k, \n",
|
|
" mean_function = None,\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fd24e71e-9c98-4577-82de-0aeffd966286",
|
|
"metadata": {
|
|
"id": "08f41235-12df-4e9c-bf63-e7a4390cf21a"
|
|
},
|
|
"source": [
|
|
"## Train Model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 460,
|
|
"id": "9c70c87e-22c0-456b-9a09-fc98f273cdbf",
|
|
"metadata": {
|
|
"id": "Pn5TwPPT6ogs"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"opt = gpflow.optimizers.Scipy()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 461,
|
|
"id": "405d1197-23df-4d00-8fef-9fa26b63e236",
|
|
"metadata": {
|
|
"id": "slQg9Ohv6oxR"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from datetime import datetime"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 462,
|
|
"id": "81cff1a8-dd95-4cf1-96b2-c92af0843068",
|
|
"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": [
|
|
"Finished fitting in 0:00:01.293315\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"start_time = datetime.now()\n",
|
|
"opt.minimize(m.training_loss, m.trainable_variables)\n",
|
|
"print(f\"Finished fitting in {datetime.now() - start_time}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8bf5c4b8-1c6e-4f39-8665-c3518f54c207",
|
|
"metadata": {},
|
|
"source": [
|
|
"## CasADi Optimization part"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 350,
|
|
"id": "cefb26f7-0c9a-4b8a-b658-ee6eb6f16ea0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_tf_evaluator(model):\n",
|
|
" @tf.function\n",
|
|
" def tensor_model_eval(tf_input):\n",
|
|
" preds = model.predict_f(tf_input)\n",
|
|
" return preds\n",
|
|
" return tensor_model_eval"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 364,
|
|
"id": "706a8865-9a1a-42b4-bcfe-7802256cad0f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"tensor_model_eval = get_tf_evaluator(m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 399,
|
|
"id": "69aa5a0d-53ff-41a3-a22f-08d74a102407",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(<tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.03053204]])>,\n",
|
|
" <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.15445209e-06]])>)"
|
|
]
|
|
},
|
|
"execution_count": 399,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"m.predict_f(tf_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 400,
|
|
"id": "2493b2c2-ef9a-4370-9887-31eb43e181e6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(<tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.03053322]])>,\n",
|
|
" <tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.15737976e-06]])>)"
|
|
]
|
|
},
|
|
"execution_count": 400,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"tensor_model_eval(tf_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 401,
|
|
"id": "efefc120-53e4-4ab4-9899-7d79f62ac7b7",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(-7.03164e-195)"
|
|
]
|
|
},
|
|
"execution_count": 401,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model(np_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 405,
|
|
"id": "67c4057a-5b73-408b-be06-10828a10da4f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cs_model.update_model(m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 407,
|
|
"id": "c40e3957-19cf-43fe-accf-aacb58f7587e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(1.03053)"
|
|
]
|
|
},
|
|
"execution_count": 407,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model(np_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 516,
|
|
"id": "1864beb3-7896-42cd-8553-1bacf1bf5aaf",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Package the resulting regression model in a CasADi callback\n",
|
|
"class GPR(cs.Callback):\n",
|
|
" def __init__(self, name, model, opts={}):\n",
|
|
" cs.Callback.__init__(self)\n",
|
|
"\n",
|
|
" print(\"Instance of GPR callback\")\n",
|
|
" self.model = model\n",
|
|
" self.tf_evaluator = get_tf_evaluator(model)\n",
|
|
" self.n_in = model.data[0].shape[1]\n",
|
|
" self.tf_var = tf.Variable(np.ones((1, self.n_in)), dtype = tf.float64)\n",
|
|
" # Create a variable to keep all the gradient callback references\n",
|
|
" self.refs = []\n",
|
|
"\n",
|
|
" self.construct(name, opts)\n",
|
|
" \n",
|
|
" # Update tf_evaluator\n",
|
|
" def update_model(self, model):\n",
|
|
" self.model = model\n",
|
|
" self.tf_evaluator = get_tf_evaluator(model)\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",
|
|
" self.tf_var.assign(arg[0])\n",
|
|
" [mean, _] = self.tf_evaluator(self.tf_var)\n",
|
|
" #[mean, _] = self.model.predict_f(arg[0])\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.n_in, self.tf_evaluator, self.tf_var)\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)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 517,
|
|
"id": "6084bd09-4a0f-43bc-8090-fec07556556f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class GPR_grad(cs.Callback):\n",
|
|
" def __init__(self, name, n_in, tf_evaluator, tf_var, opts={}):\n",
|
|
" cs.Callback.__init__(self)\n",
|
|
" self.tf_evaluator = tf_evaluator\n",
|
|
" self.n_in = n_in\n",
|
|
" self.tf_var = tf_var\n",
|
|
"\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",
|
|
" self.tf_var.assign(arg[0])\n",
|
|
" with tf.GradientTape() as tape:\n",
|
|
" preds = self.tf_evaluator(self.tf_var)\n",
|
|
"\n",
|
|
" grads = tape.gradient(preds, self.tf_var)\n",
|
|
" return [grads.numpy()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 519,
|
|
"id": "eb5224ae-09b0-4cb5-92f1-8476cfa32469",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Instance of GPR callback\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model = GPR(\"gpr\", m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 419,
|
|
"id": "9294cdc6-e2de-4230-9214-440712df5bb3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np_test = np.ones((1, 7))\n",
|
|
"tf_test = tf.Variable(np_test, dtype = tf.float64)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 422,
|
|
"id": "4545f9d9-8488-4e75-9143-b63671395143",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"5.13 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%timeit\n",
|
|
"tf_test.assign(2 * np_test)\n",
|
|
"tensor_model_eval(tf_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 421,
|
|
"id": "779dc6b2-b6b3-478f-ace2-edb9443233cf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"13.9 ms ± 533 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%timeit\n",
|
|
"casual_model_eval(np_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 273,
|
|
"id": "4529796e-879c-4984-bce7-e17051546b5e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"41.8 ms ± 381 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%timeit\n",
|
|
"tf_iter = tf.Variable(np_test, dtype = tf.float64)\n",
|
|
"with tf.GradientTape() as tape:\n",
|
|
" preds = m.predict_f(tf_iter)\n",
|
|
"grads = tape.gradient(preds, tf_iter)\n",
|
|
"grads"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 117,
|
|
"id": "088c73cf-5512-443e-b9db-4f8ed2311414",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"22.7 ms ± 733 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%timeit\n",
|
|
"with tf.GradientTape() as tape:\n",
|
|
" preds = tensor_model_eval(tf_test)\n",
|
|
"grads = tape.gradient(preds, tf_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "074b2c86-9a01-4963-8dda-57f442261a4b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Multiple shooting problem formulation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 520,
|
|
"id": "debd1d82-f50b-4ddf-aa34-bdd808c5e6d0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"N_horizon = 8\n",
|
|
"n_states = 7"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 521,
|
|
"id": "259d01bf-abad-43a7-8a14-0c5725b3aef9",
|
|
"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": 522,
|
|
"id": "fdda49b3-7751-43a5-a4ff-858631ebf73a",
|
|
"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": 523,
|
|
"id": "1e2ae7b4-a311-4080-8c22-cf0d10693fcc",
|
|
"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": 524,
|
|
"id": "373aee32-112d-4d55-b553-a6d360e98748",
|
|
"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": 525,
|
|
"id": "c0ba0f93-f284-41a4-b70e-61ee04c8591a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"p = cs.vertcat(cs.vec(W), cs.vec(x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 526,
|
|
"id": "0e33b5fa-9f3e-47f5-8369-c4f62ae09bcc",
|
|
"metadata": {},
|
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"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-4, \"tol\": 1e-4,\n",
|
|
" \"linear_solver\": \"ma27\",\n",
|
|
" #\"warm_start_init_point\": \"yes\"\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": 527,
|
|
"id": "92802031-6a68-45b9-b894-f9ea44fbb2b5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
|
"array([[0.19784954, 0.75380109, 0.20258375, 0.48384381, 0.26975897,\n",
|
|
" 0.59621339, 0.89703682]])"
|
|
]
|
|
},
|
|
"execution_count": 527,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"real_x0 = np.random.rand(*x0.shape)\n",
|
|
"real_x0"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 528,
|
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"id": "f7d9ee2b-7e75-4dbd-899b-1a002bcd3d3f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
|
"array([[0.870293 , 0.08461537],\n",
|
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" [0.65722421, 0.34585863],\n",
|
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" [0.70636811, 0.12671317],\n",
|
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" [0.78048239, 0.64362432],\n",
|
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" [0.50636252, 0.66469496],\n",
|
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" [0.77969876, 0.09749162],\n",
|
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" [0.25604673, 0.25789882],\n",
|
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" [0.1594542 , 0.46832943]])"
|
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]
|
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},
|
|
"execution_count": 528,
|
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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": [
|
|
"real_W = np.random.rand(*W.shape)\n",
|
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"real_W"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 529,
|
|
"id": "4eccd416-9939-43aa-b81f-4ce981d7e353",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"real_p = cs.vertcat(cs.vec(real_W), cs.vec(real_x0))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 530,
|
|
"id": "f6e595f5-3126-445a-877e-b54168f8d7de",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(1.03058)"
|
|
]
|
|
},
|
|
"execution_count": 530,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model(np_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 512,
|
|
"id": "80b023cf-6563-4517-9d83-d39bba9dd6da",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cs_model.update_model(m)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 513,
|
|
"id": "cf3fd768-4b47-4a69-bc3f-96e7343608ae",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(1.03058)"
|
|
]
|
|
},
|
|
"execution_count": 513,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model(np_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 536,
|
|
"id": "1e94b993-7e81-46be-9e20-94b964c27347",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"This is Ipopt version 3.13.4, running with linear solver ma27.\n",
|
|
"\n",
|
|
"Number of nonzeros in equality constraint Jacobian...: 135\n",
|
|
"Number of nonzeros in inequality constraint Jacobian.: 0\n",
|
|
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
|
|
"\n",
|
|
"Total number of variables............................: 63\n",
|
|
" variables with only lower bounds: 0\n",
|
|
" variables with lower and upper bounds: 8\n",
|
|
" variables with only upper bounds: 0\n",
|
|
"Total number of equality constraints.................: 55\n",
|
|
"Total number of inequality constraints...............: 0\n",
|
|
" inequality constraints with only lower bounds: 0\n",
|
|
" inequality constraints with lower and upper bounds: 0\n",
|
|
" inequality constraints with only upper bounds: 0\n",
|
|
"\n",
|
|
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
|
|
" 0 0.0000000e+00 8.97e-01 0.00e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
|
|
" 1 5.7575353e-02 2.49e-03 8.97e-01 -6.0 8.97e-01 - 9.43e-01 1.00e+00h 1\n",
|
|
" 2 6.3726414e-02 9.47e-07 3.51e-01 -1.9 1.05e-01 - 9.95e-01 1.00e+00h 1\n",
|
|
" 3 4.6035747e-02 6.50e-07 4.49e-03 -3.1 5.84e-02 - 1.00e+00 1.00e+00f 1\n",
|
|
" 4 4.6032694e-02 1.12e-08 1.98e-05 -5.3 8.15e-04 - 1.00e+00 1.00e+00h 1\n",
|
|
"\n",
|
|
"Number of Iterations....: 4\n",
|
|
"\n",
|
|
" (scaled) (unscaled)\n",
|
|
"Objective...............: 4.6032694495482543e-02 4.6032694495482543e-02\n",
|
|
"Dual infeasibility......: 1.9751167684148774e-05 1.9751167684148774e-05\n",
|
|
"Constraint violation....: 1.1249818632208530e-08 1.1249818632208530e-08\n",
|
|
"Complementarity.........: 5.7908803598244531e-06 5.7908803598244531e-06\n",
|
|
"Overall NLP error.......: 1.9751167684148774e-05 1.9751167684148774e-05\n",
|
|
"\n",
|
|
"\n",
|
|
"Number of objective function evaluations = 5\n",
|
|
"Number of objective gradient evaluations = 5\n",
|
|
"Number of equality constraint evaluations = 5\n",
|
|
"Number of inequality constraint evaluations = 0\n",
|
|
"Number of equality constraint Jacobian evaluations = 5\n",
|
|
"Number of inequality constraint Jacobian evaluations = 0\n",
|
|
"Number of Lagrangian Hessian evaluations = 0\n",
|
|
"Total CPU secs in IPOPT (w/o function evaluations) = 1.002\n",
|
|
"Total CPU secs in NLP function evaluations = 5.199\n",
|
|
"\n",
|
|
"EXIT: Optimal Solution Found.\n",
|
|
" solver : t_proc (avg) t_wall (avg) n_eval\n",
|
|
" nlp_f | 31.00us ( 6.20us) 31.34us ( 6.27us) 5\n",
|
|
" nlp_g | 376.39ms ( 75.28ms) 177.69ms ( 35.54ms) 5\n",
|
|
" nlp_grad | 888.99ms (888.99ms) 217.22ms (217.22ms) 1\n",
|
|
" nlp_grad_f | 65.00us ( 10.83us) 63.12us ( 10.52us) 6\n",
|
|
" nlp_jac_g | 5.94 s (989.93ms) 1.60 s (266.53ms) 6\n",
|
|
" total | 7.21 s ( 7.21 s) 2.00 s ( 2.00 s) 1\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"res = solver(lbx = cs.vec(lbx), ubx = cs.vec(ubx), p = real_p, lbg = lbg, ubg = ubg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 445,
|
|
"id": "902f3a30-4fca-4e10-84a4-2fc380bdff81",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## Old result"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 443,
|
|
"id": "ca224e84-6c4a-4205-bf8d-006e67599f02",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(\n",
|
|
"[[0.761242, 0.36861, 0.556879, 0.104919, 0.160625, 0.774017, 0.547034], \n",
|
|
" [0.480629, 0.922048, -0.000639741, 0.556879, 0.2753, 0.160625, 0.774017], \n",
|
|
" [0.00685743, 0.247726, -0.000615418, -0.000639741, 0.187375, 0.2753, 0.160625], \n",
|
|
" [0.447852, 0.283534, -0.000597185, -0.000615418, 0.192971, 0.187375, 0.2753], \n",
|
|
" [0.624033, 0.160237, -0.000576846, -0.000597185, 0.200781, 0.192971, 0.187375], \n",
|
|
" [0.771545, 0.00394968, -0.000558971, -0.000576846, 0.205897, 0.200781, 0.192971], \n",
|
|
" [0.660621, 0.609428, -0.000568339, -0.000558971, 0.206814, 0.205897, 0.200781], \n",
|
|
" [0.914765, 0.539141, -0.000550041, -0.000568339, 0.221992, 0.206814, 0.205897], \n",
|
|
" [0.0561185, 0.209053, 0, -0.000550041, 0.236093, 0.221992, 0.206814]])"
|
|
]
|
|
},
|
|
"execution_count": 443,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"res['x'].reshape((N_horizon + 1, n_states))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 444,
|
|
"id": "6a3dd26b-64ca-4a88-be86-248f800f2f88",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(0.2753)"
|
|
]
|
|
},
|
|
"execution_count": 444,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model([0.761242, 0.36861, 0.556879, 0.104919, 0.160625, 0.774017, 0.547034])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 446,
|
|
"id": "865b7e52-1b57-4aee-a027-89639d12d0f3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## New result"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 466,
|
|
"id": "9f362d2f-5c14-40f7-989d-db691ebc3005",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(\n",
|
|
"[[0.761242, 0.36861, 0.556879, 0.104919, 0.160625, 0.774017, 0.547034], \n",
|
|
" [0.480629, 0.922048, -0.000640366, 0.556879, 0.275295, 0.160625, 0.774017], \n",
|
|
" [0.00685743, 0.247726, -0.000615877, -0.000640366, 0.187418, 0.275295, 0.160625], \n",
|
|
" [0.447852, 0.283534, -0.00059751, -0.000615877, 0.193019, 0.187418, 0.275295], \n",
|
|
" [0.624033, 0.160237, -0.000577033, -0.00059751, 0.200834, 0.193019, 0.187418], \n",
|
|
" [0.771545, 0.00394968, -0.000558939, -0.000577033, 0.205969, 0.200834, 0.193019], \n",
|
|
" [0.660621, 0.609428, -0.000568317, -0.000558939, 0.206929, 0.205969, 0.200834], \n",
|
|
" [0.914765, 0.539141, -0.000550168, -0.000568317, 0.22211, 0.206929, 0.205969], \n",
|
|
" [0.0561185, 0.209053, 0, -0.000550168, 0.236208, 0.22211, 0.206929]])"
|
|
]
|
|
},
|
|
"execution_count": 466,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"res['x'].reshape((N_horizon + 1, n_states))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 467,
|
|
"id": "1c5a8976-257f-4a47-bed9-4529077a78e2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"DM(0.275295)"
|
|
]
|
|
},
|
|
"execution_count": 467,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cs_model([0.761242, 0.36861, 0.556879, 0.104919, 0.160625, 0.774017, 0.547034])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c3dba702-e4cf-48de-b034-5485b5bfd7ac",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"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": "3.9.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|