minor change in comments notebook
This commit is contained in:
parent
6629a163c6
commit
de6ddfa29d
@ -957,7 +957,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"id": "7f54afc1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -998,6 +998,7 @@
|
||||
"\n",
|
||||
" return np.array(data), np.array(labels).reshape(-1, 1)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Reshape to have all features and timesteps ina single row\n",
|
||||
"def reshaping(X):\n",
|
||||
" reshaped_x = X.reshape(-1, X.shape[1] * X.shape[2])\n",
|
||||
@ -1209,7 +1210,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": null,
|
||||
"id": "cc4975ce",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -1245,7 +1246,6 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"df_energy.index.freq = \"h\"\n",
|
||||
"y_train = df_energy[\"price actual\"].iloc[:train_cutoff]\n",
|
||||
"y_test = df_energy[\"price actual\"].iloc[train_cutoff:]\n",
|
||||
@ -1387,21 +1387,39 @@
|
||||
],
|
||||
"source": [
|
||||
"import warnings\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"warnings.filterwarnings(\"ignore\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# history size (number of hours)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"hist_size = 24\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Generate training & test datasets\n",
|
||||
"X_train, y_train, X_test, y_test, train_cutoff, scaler_y, scaler_X = get_train_test(hist_size=hist_size)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"X_train, y_train, X_test, y_test, train_cutoff, scaler_y, scaler_X = get_train_test(\n",
|
||||
" hist_size=hist_size\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"X_train_arima = reshaping(X_train)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"X_test_arima = reshaping(X_test)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Fit AutoARIMA model (with exogenous variables)\n",
|
||||
"autoarima_model = auto_arima(\n",
|
||||
" y_train,\n",
|
||||
"\n",
|
||||
" # X=X_train_arima, # with extra features this takes a loooong time\n",
|
||||
" start_p=0,\n",
|
||||
" start_q=0,\n",
|
||||
@ -1419,43 +1437,82 @@
|
||||
" D=None,\n",
|
||||
" trace=True,\n",
|
||||
" error_action=\"ignore\",\n",
|
||||
"\n",
|
||||
" suppress_warnings=True,\n",
|
||||
" stepwise=True,\n",
|
||||
" n_jobs=-1,\n",
|
||||
" method=\"nm\",\n",
|
||||
" maxiter=100\n",
|
||||
" maxiter=100,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# TODO : Try out different values for m (seasonality) -- can influence comp. cost\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# TODO : Perform feature engineering, e.g. rolling means\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# TODO : Try out different history sizes\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Forecast\n",
|
||||
"y_pred = autoarima_model.predict(n_periods=len(y_test))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Inverse transform the predictions and actual values\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"y_pred_actual = y_pred.reshape(-1, 1)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"y_test_inv = y_test.reshape(-1, 1)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Calculate MAE\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mae = mean_absolute_error(y_pred_actual, y_test_inv)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Plot the results\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fig, axes = plt.subplots(figsize=(14, 6))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"axes.plot(\n",
|
||||
" df_energy[\"price actual\"].iloc[train_cutoff:].index[: len(y_test_inv)],\n",
|
||||
" y_test_inv,\n",
|
||||
" \"k.-\",\n",
|
||||
" label=\"Actual\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"axes.plot(\n",
|
||||
" df_energy[\"price actual\"].iloc[train_cutoff:].index[: len(y_pred_actual)],\n",
|
||||
" y_pred_actual,\n",
|
||||
"\n",
|
||||
" \"b.-\",\n",
|
||||
" label=\"Predicted\",\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"axes.set(xlabel=\"Date\", ylabel=\"Price actual\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"axes.set_title(f\"SARIMAX Predictions vs Actual Prices (MAE={mae:.3f})\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"axes.legend()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
@ -1565,9 +1622,12 @@
|
||||
" verbose=False,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# perform predictions\n",
|
||||
"y_pred = xgb_model.predict(X_test_xgb)\n",
|
||||
"y_pred_actual = scaler_y.inverse_transform(y_pred.reshape(-1, 1))\n",
|
||||
"y_test_inv = scaler_y.inverse_transform(y_test)\n",
|
||||
"\n",
|
||||
"# Calculate MAE\n",
|
||||
"mae = mean_absolute_error(y_pred_actual, y_test_inv)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@ -1662,7 +1722,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"execution_count": null,
|
||||
"id": "f531a91a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -38792,7 +38852,6 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\n",
|
||||
"# Initialize LIME explainer\n",
|
||||
"lime_explainer = lime.lime_tabular.LimeTabularExplainer(\n",
|
||||
" training_data=np.array(X_train_xgb),\n",
|
||||
@ -38810,7 +38869,9 @@
|
||||
" predict_fn=xgb_model.predict,\n",
|
||||
" num_features=len(X_train_xgb.columns),\n",
|
||||
" )\n",
|
||||
" display(HTML(explanation.as_html()))"
|
||||
" display(HTML(explanation.as_html()))\n",
|
||||
" # Or, you can save the explanation to an HTML file:\n",
|
||||
" # explanation.save_to_file(f\"lime_explanation_{i}.html\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -39033,7 +39094,7 @@
|
||||
"n_features = X_train.shape[2] # Number of features in the input\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Build LSTM model\n",
|
||||
"# Build a simple LSTM model\n",
|
||||
"model = Sequential()\n",
|
||||
"model.add(Input(shape=(hist_size, n_features)))\n",
|
||||
"model.add(LSTM(50, activation=\"relu\"))\n",
|
||||
@ -39046,7 +39107,7 @@
|
||||
"# fit the model\n",
|
||||
"model.fit(X_train, y_train, epochs=50)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Predict on the test set\n",
|
||||
"y_pred = model.predict(X_test)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@ -39141,7 +39202,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"execution_count": null,
|
||||
"id": "7a3da2a1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -40179,7 +40240,7 @@
|
||||
"source": [
|
||||
"from ShapTime import ShapleyValues, TimeImportance, TimeHeatmap\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# calculate ShapTime values -- beware this can take a long time\n",
|
||||
"Tn = 10 # number of time steps\n",
|
||||
"shap_values = ShapleyValues(model, X_train, Tn)\n",
|
||||
"time_columns = [f\"Time Step {i+1}\" for i in range(Tn)]\n",
|
||||
@ -40188,7 +40249,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"execution_count": null,
|
||||
"id": "a1c887c3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -40215,8 +40276,6 @@
|
||||
],
|
||||
"source": [
|
||||
"TimeImportance(Tn, shap_values, time_columns)\n",
|
||||
"# ax = TimeHeatmap(Tn, shap_values, time_columns)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Normalize SHAP values for color mapping\n",
|
||||
"norm = plt.Normalize(shap_values.min(), shap_values.max())\n",
|
||||
@ -40435,7 +40494,9 @@
|
||||
"# Prepare training data\n",
|
||||
"df_train = df_energy.iloc[:train_cutoff].copy()\n",
|
||||
"df_train = df_train.rename(columns={\"price actual\": \"y\"}) # Prophet requires 'y'\n",
|
||||
"df_train[\"ds\"] = df_train.index.to_series().dt.tz_localize(None) # Prophet requires 'ds' without timezone\n",
|
||||
"df_train[\"ds\"] = df_train.index.to_series().dt.tz_localize(\n",
|
||||
" None\n",
|
||||
") # Prophet requires 'ds' without timezone\n",
|
||||
"\n",
|
||||
"# Initialize and fit the Prophet model\n",
|
||||
"prophet_model = prophet.Prophet()\n",
|
||||
@ -40445,7 +40506,9 @@
|
||||
"\n",
|
||||
"# Create future dataframe for prediction\n",
|
||||
"df_future = df_energy.iloc[train_cutoff:].copy()\n",
|
||||
"df_future[\"ds\"] = df_future.index.to_series().dt.tz_localize(None) # Prophet requires 'ds' without timezone\n",
|
||||
"df_future[\"ds\"] = df_future.index.to_series().dt.tz_localize(\n",
|
||||
" None\n",
|
||||
") # Prophet requires 'ds' without timezone\n",
|
||||
"\n",
|
||||
"# Make forecasts\n",
|
||||
"df_forecast = prophet_model.predict(df_future)\n",
|
||||
@ -40490,7 +40553,7 @@
|
||||
"axes[1].legend()\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()\n"
|
||||
"plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user