Updated Notebook comments

This commit is contained in:
Radu C. Martin 2021-07-31 12:57:47 +02:00
parent 12c879f016
commit 633c4d12d3
5 changed files with 216 additions and 2413 deletions

View file

@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian Optimisation of starting Gaussian Process hyperparameters"
"# Gaussian Process Model Training and Performance Evaluation"
]
},
{
@ -213,7 +213,7 @@
"id": "0aba0df5-b0e3-4738-bb61-1dad869d1ea3"
},
"source": [
"## Load previously exported data"
"## Load previously exported CARNOT 'experimental' data"
]
},
{
@ -226,6 +226,13 @@
"dfs_test = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separate into training and testing data sets:"
]
},
{
"cell_type": "code",
"execution_count": 16,
@ -249,6 +256,13 @@
" dfs_test.append(pd.read_csv(f\"../Data/Good_CARNOT/{exp}_table.csv\").rename(columns = {'Power': 'SimulatedHeat'}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separate columns into exogenous inputs, controlled inputs and outputs:"
]
},
{
"cell_type": "code",
"execution_count": 18,
@ -262,6 +276,13 @@
"y_cols = ['SimulatedTemp']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Impose the autoregressive lags for each input group:"
]
},
{
"cell_type": "code",
"execution_count": 19,
@ -511,6 +532,13 @@
" return df_gpr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Merge all the training dataframes:"
]
},
{
"cell_type": "code",
"execution_count": 28,
@ -667,13 +695,19 @@
"df_gpr_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select all points in the training dataset:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"train_dataset_size = 15 * 96\n",
"train_dataset_size = -1"
]
},
@ -1401,6 +1435,13 @@
"y_range = np.arange(1,6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Iterate over all combination of lags and compute for each the RMSE, SMSE, LPD and MSLL errors:"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -1545,6 +1586,13 @@
"## Multistep prediction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select the dataset which will be used for multistep prediction:"
]
},
{
"cell_type": "code",
"execution_count": 54,
@ -1556,6 +1604,13 @@
"df_output = dfs_gpr_test[test_dataset_idx][dict_cols['y'][1]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select the starting index in the test dataset and the number of consecutive points to simulate:"
]
},
{
"cell_type": "code",
"execution_count": 55,
@ -1617,26 +1672,6 @@
"plt.title(f\"Multi step prediction over {N_pred} steps for Test dataset {test_dataset_idx}\")\n",
"plt.savefig(f\"../Thesis/Plots/GP_{w_lags}{u_lags}{y_lags}_{train_dataset_size}pts_test_prediction_{N_pred}_steps.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"TensorShape([2612, 7])"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.data[0].shape"
]
}
],
"metadata": {