diff --git "a/cross_validation.ipynb" "b/cross_validation.ipynb"
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+ "source": [
+ "# Import packages\n",
+ "import plotly.graph_objects as go\n",
+ "import pandas as pd\n",
+ "from sklearn.model_selection import KFold\n",
+ "\n",
+ "\n",
+ "def plot_cross_val(n_splits: int,\n",
+ " splitter_func,\n",
+ " df: pd.DataFrame,\n",
+ " title_text: str) -> None:\n",
+ " \n",
+ " \"\"\"Function to plot the cross validation of various\n",
+ " sklearn splitter objects.\"\"\"\n",
+ "\n",
+ " split = 1\n",
+ " plot_data = []\n",
+ "\n",
+ " for train_index, valid_index in splitter_func(n_splits=n_splits).split(df):\n",
+ " plot_data.append([train_index, 'Train', f'{split}'])\n",
+ " plot_data.append([valid_index, 'Test', f'{split}'])\n",
+ " split += 1\n",
+ "\n",
+ " plot_df = pd.DataFrame(plot_data,\n",
+ " columns=['Index', 'Dataset', 'Split'])\\\n",
+ " .explode('Index')\n",
+ "\n",
+ " fig = go.Figure()\n",
+ " for _, group in plot_df.groupby('Split'):\n",
+ " fig.add_trace(go.Scatter(x=group['Index'].loc[group['Dataset'] == 'Train'],\n",
+ " y=group['Split'].loc[group['Dataset'] == 'Train'],\n",
+ " name='Train',\n",
+ " line=dict(color=\"blue\", width=10)\n",
+ " ))\n",
+ " fig.add_trace(go.Scatter(x=group['Index'].loc[group['Dataset'] == 'Test'],\n",
+ " y=group['Split'].loc[group['Dataset'] == 'Test'],\n",
+ " name='Test',\n",
+ " line=dict(color=\"goldenrod\", width=10)\n",
+ " ))\n",
+ "\n",
+ " fig.update_layout(template=\"simple_white\", font=dict(size=20),\n",
+ " title_text=title_text, title_x=0.5, width=850,\n",
+ " height=450, xaxis_title='Index', yaxis_title='Split')\n",
+ "\n",
+ " legend_names = set()\n",
+ " fig.for_each_trace(\n",
+ " lambda trace:\n",
+ " trace.update(showlegend=False)\n",
+ " if (trace.name in legend_names) else legend_names.add(trace.name))\n",
+ "\n",
+ " return fig.show()\n",
+ " \n",
+ " \n",
+ "if __name__ == \"__main__\":\n",
+ "\n",
+ " # Read in the data\n",
+ " data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
+ " data['Month'] = pd.to_datetime(data['Month'])\n",
+ " \n",
+ " # Plot the cross validation\n",
+ " plot_cross_val(n_splits=5,\n",
+ " splitter_func=KFold,\n",
+ " df=data,\n",
+ " title_text='Cross-Validation')"
+ ]
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+ "# Import packages\n",
+ "from sklearn.model_selection import TimeSeriesSplit\n",
+ "\n",
+ "# Plot the time series cross validation splits\n",
+ "plot_cross_val(n_splits=5,\n",
+ " splitter_func=TimeSeriesSplit,\n",
+ " df=data,\n",
+ " title_text='Time Series Cross-Validation')"
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+ "x": 0.5
+ },
+ "width": 650,
+ "xaxis": {
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+ }
+ }
+ }
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+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ ],
+ "source": [
+ "# Import packages\n",
+ "import plotly.express as px\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "def plot_time_series(df: pd.DataFrame) -> None:\n",
+ " \"\"\"General function to plot the passenger data.\"\"\"\n",
+ " \n",
+ " fig = px.line(df, x='Month', y='#Passengers',\n",
+ " labels={'Month': 'Date', '#Passengers': 'Passengers'})\n",
+ " \n",
+ " fig.update_layout(template=\"simple_white\", font=dict(size=18),\n",
+ " title_text='Airline Passengers', width=650,\n",
+ " title_x=0.5, height=400)\n",
+ "\n",
+ " return fig.show()\n",
+ " \n",
+ " \n",
+ "if __name__ == \"__main__\":\n",
+ "\n",
+ " # Read in the data\n",
+ " data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
+ " data['Month'] = pd.to_datetime(data['Month'])\n",
+ " \n",
+ " # Plot the time series\n",
+ " plot_time_series(df=data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import packages\n",
+ "import plotly.graph_objects as go\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from sklearn.metrics import mean_absolute_percentage_error\n",
+ "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
+ "\n",
+ "\n",
+ "def hyperparameter_tuning_season_cv(n_splits: int,\n",
+ " gammas: list[float],\n",
+ " df: pd.DataFrame) -> pd.DataFrame: \n",
+ " \"\"\"Function to carry out cross-validation hyperparameter tuning\n",
+ " for the seasonal parameter in a Holt Winters' model. \"\"\"\n",
+ "\n",
+ " tscv = TimeSeriesSplit(n_splits=n_splits)\n",
+ " error_list = []\n",
+ "\n",
+ " for gamma in gammas:\n",
+ " \n",
+ " errors = []\n",
+ " \n",
+ " for train_index, valid_index in tscv.split(df):\n",
+ " train, valid = df.iloc[train_index], df.iloc[valid_index]\n",
+ " \n",
+ " model = ExponentialSmoothing(train['#Passengers'], trend='mul',\n",
+ " seasonal='mul', seasonal_periods=12) \\\n",
+ " .fit(smoothing_seasonal=gamma)\n",
+ " \n",
+ " forecasts = model.forecast(len(valid))\n",
+ " errors.append(mean_absolute_percentage_error(valid['#Passengers'], forecasts))\n",
+ "\n",
+ " error_list.append([gamma, sum(errors) / len(errors)])\n",
+ "\n",
+ " return pd.DataFrame(error_list, columns=['Gamma', 'MAPE'])\n",
+ " \n",
+ " \n",
+ "def plot_error_cv(df: pd.DataFrame,\n",
+ " title: str) -> None: \n",
+ " \"\"\"Bar chart to plot the errors from the different\n",
+ " hyperparameters.\"\"\"\n",
+ "\n",
+ " fig = px.bar(df, x='Gamma', y='MAPE')\n",
+ " fig.update_layout(template=\"simple_white\", font=dict(size=18), title_text=title,\n",
+ " width=800, title_x=0.5, height=400)\n",
+ "\n",
+ " return fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in the data\n",
+ "data = pd.read_csv('../coal-price-data/AirPassengers.csv')\n",
+ "data['Month'] = pd.to_datetime(data['Month'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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+ "overflow encountered in matmul\n",
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+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
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+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
+ "\n",
+ "/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
+ "\n",
+ "overflow encountered in matmul\n",
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+ }
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+ "source": [
+ "# Carry out cv for hyperparameter tuning for the seasonal parameter\n",
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+ 0.1111111111111111,
+ "#482878"
+ ],
+ [
+ 0.2222222222222222,
+ "#3e4989"
+ ],
+ [
+ 0.3333333333333333,
+ "#31688e"
+ ],
+ [
+ 0.4444444444444444,
+ "#26828e"
+ ],
+ [
+ 0.5555555555555556,
+ "#1f9e89"
+ ],
+ [
+ 0.6666666666666666,
+ "#35b779"
+ ],
+ [
+ 0.7777777777777778,
+ "#6ece58"
+ ],
+ [
+ 0.8888888888888888,
+ "#b5de2b"
+ ],
+ [
+ 1,
+ "#fde725"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#440154"
+ ],
+ [
+ 0.1111111111111111,
+ "#482878"
+ ],
+ [
+ 0.2222222222222222,
+ "#3e4989"
+ ],
+ [
+ 0.3333333333333333,
+ "#31688e"
+ ],
+ [
+ 0.4444444444444444,
+ "#26828e"
+ ],
+ [
+ 0.5555555555555556,
+ "#1f9e89"
+ ],
+ [
+ 0.6666666666666666,
+ "#35b779"
+ ],
+ [
+ 0.7777777777777778,
+ "#6ece58"
+ ],
+ [
+ 0.8888888888888888,
+ "#b5de2b"
+ ],
+ [
+ 1,
+ "#fde725"
+ ]
+ ]
+ },
+ "colorway": [
+ "#1F77B4",
+ "#FF7F0E",
+ "#2CA02C",
+ "#D62728",
+ "#9467BD",
+ "#8C564B",
+ "#E377C2",
+ "#7F7F7F",
+ "#BCBD22",
+ "#17BECF"
+ ],
+ "font": {
+ "color": "rgb(36,36,36)"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "white",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "white",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside"
+ },
+ "bgcolor": "white",
+ "radialaxis": {
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside"
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "white",
+ "gridcolor": "rgb(232,232,232)",
+ "gridwidth": 2,
+ "linecolor": "rgb(36,36,36)",
+ "showbackground": true,
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside",
+ "zeroline": false,
+ "zerolinecolor": "rgb(36,36,36)"
+ },
+ "yaxis": {
+ "backgroundcolor": "white",
+ "gridcolor": "rgb(232,232,232)",
+ "gridwidth": 2,
+ "linecolor": "rgb(36,36,36)",
+ "showbackground": true,
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside",
+ "zeroline": false,
+ "zerolinecolor": "rgb(36,36,36)"
+ },
+ "zaxis": {
+ "backgroundcolor": "white",
+ "gridcolor": "rgb(232,232,232)",
+ "gridwidth": 2,
+ "linecolor": "rgb(36,36,36)",
+ "showbackground": true,
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside",
+ "zeroline": false,
+ "zerolinecolor": "rgb(36,36,36)"
+ }
+ },
+ "shapedefaults": {
+ "fillcolor": "black",
+ "line": {
+ "width": 0
+ },
+ "opacity": 0.3
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside"
+ },
+ "baxis": {
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside"
+ },
+ "bgcolor": "white",
+ "caxis": {
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside"
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside",
+ "title": {
+ "standoff": 15
+ },
+ "zeroline": false,
+ "zerolinecolor": "rgb(36,36,36)"
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "rgb(232,232,232)",
+ "linecolor": "rgb(36,36,36)",
+ "showgrid": false,
+ "showline": true,
+ "ticks": "outside",
+ "title": {
+ "standoff": 15
+ },
+ "zeroline": false,
+ "zerolinecolor": "rgb(36,36,36)"
+ }
+ }
+ },
+ "title": {
+ "text": "Hyperparameter Results",
+ "x": 0.5
+ },
+ "width": 800,
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "Gamma"
+ }
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "title": {
+ "text": "MAPE"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Plot the tuning results\n",
+ "plot_error_cv(df=error_df, title='Hyperparameter Results')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "py311-kfp240-airflow251",
+ "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": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}