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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",
" n_splits: int, splitter_func, df: pd.DataFrame, title_text: str\n",
") -> None:\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(\n",
" plot_data, columns=[\"Index\", \"Dataset\", \"Split\"]\n",
" ).explode(\"Index\")\n",
"\n",
" fig = go.Figure()\n",
" for _, group in plot_df.groupby(\"Split\"):\n",
" fig.add_trace(\n",
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" 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",
" )\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" 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",
"\n",
" fig.update_layout(\n",
" template=\"simple_white\",\n",
" font=dict(size=20),\n",
" title_text=title_text,\n",
" title_x=0.5,\n",
" width=850,\n",
" height=450,\n",
" xaxis_title=\"Index\",\n",
" yaxis_title=\"Split\",\n",
" )\n",
"\n",
" legend_names = set()\n",
" fig.for_each_trace(\n",
" lambda trace: trace.update(showlegend=False)\n",
" if (trace.name in legend_names)\n",
" else legend_names.add(trace.name)\n",
" )\n",
"\n",
" return fig.show()\n",
"\n",
"\n",
"if __name__ == \"__main__\":\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",
" n_splits=5, splitter_func=KFold, df=data, title_text=\"Cross-Validation\"\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"source": [
"# Import packages\n",
"from sklearn.model_selection import TimeSeriesSplit\n",
"\n",
"# Plot the time series cross validation splits\n",
"plot_cross_val(\n",
" n_splits=5,\n",
" splitter_func=TimeSeriesSplit,\n",
" df=data,\n",
" title_text=\"Time Series Cross-Validation\",\n",
")"
]
},
{
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"execution_count": 4,
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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(\n",
" df,\n",
" x=\"Month\",\n",
" y=\"#Passengers\",\n",
" labels={\"Month\": \"Date\", \"#Passengers\": \"Passengers\"},\n",
" )\n",
"\n",
" fig.update_layout(\n",
" template=\"simple_white\",\n",
" font=dict(size=18),\n",
" title_text=\"Airline Passengers\",\n",
" width=650,\n",
" title_x=0.5,\n",
" height=400,\n",
" )\n",
"\n",
" return fig.show()\n",
"\n",
"\n",
"if __name__ == \"__main__\":\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",
" n_splits: int, gammas: list[float], df: pd.DataFrame\n",
") -> 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",
" 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(\n",
" train[\"#Passengers\"],\n",
" trend=\"mul\",\n",
" seasonal=\"mul\",\n",
" seasonal_periods=12,\n",
" ).fit(smoothing_seasonal=gamma)\n",
"\n",
" forecasts = model.forecast(len(valid))\n",
" errors.append(\n",
" mean_absolute_percentage_error(valid[\"#Passengers\"], forecasts)\n",
" )\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, 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(\n",
" template=\"simple_white\",\n",
" font=dict(size=18),\n",
" title_text=title,\n",
" width=800,\n",
" title_x=0.5,\n",
" height=400,\n",
" )\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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"/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",
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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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"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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"\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",
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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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"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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"\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",
"\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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]
}
],
"source": [
"# Carry out cv for hyperparameter tuning for the seasonal parameter\n",
"error_df = hyperparameter_tuning_season_cv(\n",
" df=data, n_splits=4, gammas=list(np.arange(0, 1.1, 0.1))\n",
")"
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"showgrid": false,
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"gridwidth": 2,
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"showgrid": false,
"showline": true,
"ticks": "outside",
"zeroline": false,
"zerolinecolor": "rgb(36,36,36)"
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},
"shapedefaults": {
"fillcolor": "black",
"line": {
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"opacity": 0.3
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"ternary": {
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"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
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"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)"
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}
},
"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"
}
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}
},
"metadata": {},
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],
"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": {
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"language": "python",
"name": "python3"
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