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ngupta23/online_learning_pycaret_sktime.ipynb

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"text": [
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]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140
},
"id": "cymVKSj-d9qt",
"outputId": "179e2ab3-a61e-4f16-f687-2a95fb347252"
},
"source": [
"#### Load data ----\n",
"from pycaret.datasets import get_data\n",
"y = get_data(\"airline\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Period\n",
"1949-01 112.0\n",
"1949-02 118.0\n",
"1949-03 132.0\n",
"1949-04 129.0\n",
"1949-05 121.0\n",
"Freq: M, Name: Number of airline passengers, dtype: float64"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VrspY2JffioC",
"outputId": "97d9c413-ff34-44c2-e591-b31e407a43aa"
},
"source": [
"y_for_later = y[-1:]\n",
"y_for_later"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Period\n",
"1960-12 432.0\n",
"Freq: M, Name: Number of airline passengers, dtype: float64"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "khMNVbUPfqc5",
"outputId": "6e8a2850-e0d8-44b9-cd5b-ccd748b4ae5d"
},
"source": [
"print(len(y))\n",
"y = y[:-1]\n",
"print(len(y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"144\n",
"143\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 682,
"referenced_widgets": [
"20043a60d1a245c59fbe33cccdd388f3",
"91e8b9af2b7d45c9a51aaa28dd4fc50f",
"04bd25ae32584d6a91c08ff263df97d3"
]
},
"id": "ceQL1hkafYBz",
"outputId": "adc17940-aeb6-48be-be43-35961986c8c0"
},
"source": [
"#### Setup experiment ----\n",
"from pycaret.internal.pycaret_experiment.time_series_experiment import TimeSeriesExperiment\n",
"exp = TimeSeriesExperiment()\n",
"exp.setup(data=y, fh=12, session_id=42)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Description</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>session_id</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Original Data</td>\n",
" <td>(143, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Missing Values</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Transformed Train Set</td>\n",
" <td>(131,)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Transformed Test Set</td>\n",
" <td>(12,)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Fold Generator</td>\n",
" <td>ExpandingWindowSplitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Fold Number</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Enforce Prediction Interval</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Seasonal Period Tested</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Seasonality Detected</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Target Strictly Positive</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Target White Noise</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Recommended d</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Recommended Seasonal D</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>CPU Jobs</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Use GPU</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Log Experiment</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Experiment Name</td>\n",
" <td>ts-default-name</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>USI</td>\n",
" <td>681e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Imputation Type</td>\n",
" <td>simple</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Description Value\n",
"0 session_id 42\n",
"1 Original Data (143, 1)\n",
"2 Missing Values False\n",
"3 Transformed Train Set (131,)\n",
"4 Transformed Test Set (12,)\n",
"5 Fold Generator ExpandingWindowSplitter\n",
"6 Fold Number 3\n",
"7 Enforce Prediction Interval False\n",
"8 Seasonal Period Tested 12\n",
"9 Seasonality Detected True\n",
"10 Target Strictly Positive True\n",
"11 Target White Noise No\n",
"12 Recommended d 1\n",
"13 Recommended Seasonal D 1\n",
"14 CPU Jobs -1\n",
"15 Use GPU False\n",
"16 Log Experiment False\n",
"17 Experiment Name ts-default-name\n",
"18 USI 681e\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7fd85adb8ad0>"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 728,
"referenced_widgets": [
"c43b93e7edaa4bf1b540e358f9b591e6",
"e67f868128a149c3855180d58affd554",
"efdb2113202d476f831edfd70ae79ec0"
]
},
"id": "7kIAeLcpffRE",
"outputId": "dd8d9ba8-687a-4987-8028-8f4bc8d3b7b5"
},
"source": [
"model = exp.create_model(\"ets\")\n",
"exp.plot_model(model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cutoff</th>\n",
" <th>MAE</th>\n",
" <th>RMSE</th>\n",
" <th>MAPE</th>\n",
" <th>SMAPE</th>\n",
" <th>R2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1956-11</td>\n",
" <td>13.1645</td>\n",
" <td>17.4295</td>\n",
" <td>0.0330</td>\n",
" <td>0.0339</td>\n",
" <td>0.9080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-11</td>\n",
" <td>20.8489</td>\n",
" <td>23.4345</td>\n",
" <td>0.0575</td>\n",
" <td>0.0555</td>\n",
" <td>0.8564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-11</td>\n",
" <td>15.1164</td>\n",
" <td>16.8565</td>\n",
" <td>0.0359</td>\n",
" <td>0.0367</td>\n",
" <td>0.9441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaN</td>\n",
" <td>16.3766</td>\n",
" <td>19.2402</td>\n",
" <td>0.0422</td>\n",
" <td>0.0420</td>\n",
" <td>0.9029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaN</td>\n",
" <td>3.2612</td>\n",
" <td>2.9750</td>\n",
" <td>0.0109</td>\n",
" <td>0.0096</td>\n",
" <td>0.0360</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-11 13.1645 17.4295 0.0330 0.0339 0.9080\n",
"1 1957-11 20.8489 23.4345 0.0575 0.0555 0.8564\n",
"2 1958-11 15.1164 16.8565 0.0359 0.0367 0.9441\n",
"Mean NaN 16.3766 19.2402 0.0422 0.0420 0.9029\n",
"SD NaN 3.2612 2.9750 0.0109 0.0096 0.0360"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"exp.plot_model(final_model)"
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},
{
"cell_type": "markdown",
"metadata": {
"id": "Kyg3hlzhgohY"
},
"source": [
"## Now let's do further processing in `sktime`\n",
"\n",
"We are currently in Nov=1960 and making a forecast for the next 12 months (i.e. Dec-1960 to Nov-1961)."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "JIfpI1JmgGsw",
"outputId": "d6ad64af-9469-418e-88e8-4a3bbc2e6acc"
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"source": [
"final_model.predict()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1960-12 432.316342\n",
"1961-01 445.528105\n",
"1961-02 418.490486\n",
"1961-03 464.811500\n",
"1961-04 494.695446\n",
"1961-05 505.635115\n",
"1961-06 573.510234\n",
"1961-07 663.810912\n",
"1961-08 654.956678\n",
"1961-09 546.827872\n",
"1961-10 488.390757\n",
"1961-11 415.835671\n",
"Freq: M, dtype: float64"
]
},
"metadata": {},
"execution_count": 8
}
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},
{
"cell_type": "markdown",
"metadata": {
"id": "QJNBK_wcg0K7"
},
"source": [
"Now, lets say a month passes by and we get the actual value for Dec-1960. In our example, we had saved this value in `y_for_later`.\n",
"\n",
"Using the powerful `sktime` library, we can now \"retrain\" the existing model incrementally using this single additinal point and make a forecast for the next 12 months (which is now Jan-1961 - Dec 1962)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "K1M8UacRgKcI"
},
"source": [
"updated_model = final_model.update(y=y_for_later, update_params=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
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},
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"source": [
"updated_model.predict()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1961-01 445.423134\n",
"1961-02 418.392568\n",
"1961-03 464.704180\n",
"1961-04 494.583079\n",
"1961-05 505.519132\n",
"1961-06 573.379318\n",
"1961-07 663.660667\n",
"1961-08 654.808734\n",
"1961-09 546.704527\n",
"1961-10 488.279402\n",
"1961-11 415.739767\n",
"1961-12 460.150678\n",
"Freq: M, dtype: float64"
]
},
"metadata": {},
"execution_count": 10
}
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},
{
"cell_type": "markdown",
"metadata": {
"id": "4oQVi9gIhQNw"
},
"source": [
"That's how easy it is. Happy forecasting!"
]
}
]
}
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