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"# 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')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
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"# Import packages\n",
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"\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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},
{
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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": [
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"\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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"\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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"\n",
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"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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"\n",
"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"\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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"\n",
"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"\n",
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"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
"\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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"/home/ibnu/miniconda3/envs/py311-kfp240-airflow251/lib/python3.11/site-packages/statsmodels/tsa/holtwinters/model.py:83: RuntimeWarning:\n",
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}
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" n_splits=4,\n",
" gammas=list(np.arange(0, 1.1, 0.1)))"
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"pie": [
{
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"type": "pie"
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{
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"fillmode": "overlay",
"size": 10,
"solidity": 0.2
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"type": "scatter"
}
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"scatter3d": [
{
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"ticks": "outside"
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},
"marker": {
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}
},
"type": "scatter3d"
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"type": "scattercarpet"
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"scattergeo": [
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"ticks": "outside"
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"type": "scattergeo"
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"ticks": "outside"
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"type": "scattergl"
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"tickcolor": "rgb(36,36,36)",
"ticks": "outside"
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"type": "scattermapbox"
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"tickcolor": "rgb(36,36,36)",
"ticks": "outside"
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"type": "scatterpolar"
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"tickcolor": "rgb(36,36,36)",
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},
"type": "scatterpolargl"
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"tickcolor": "rgb(36,36,36)",
"ticks": "outside"
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"type": "scatterternary"
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0.8,
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"landcolor": "white",
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"showland": true,
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"hovermode": "closest",
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"polar": {
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"showgrid": false,
"showline": true,
"ticks": "outside"
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"bgcolor": "white",
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"showgrid": false,
"showline": true,
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},
"scene": {
"xaxis": {
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"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)"
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"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)"
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"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"
},
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"name": "python",
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