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ngupta23/blind_test.ipynb

Created May 18, 2023 19:15
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{
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"metadata": {
"id": "view-in-github",
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"source": [
"<a href=\"https://colab.research.google.com/gist/ngupta23/aab9598df11072f2d995973124100856/blind_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Q6p3iyub7VGt"
},
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"source": [
"# !pip install pycaret"
]
},
{
"cell_type": "code",
"source": [
"# Set the warning filter to \"ignore\"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
],
"metadata": {
"id": "-n76AyZL9cZL"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"from pycaret.datasets import get_data\n",
"from pycaret.time_series import TSForecastingExperiment"
],
"metadata": {
"id": "1oIpdltX7YfJ"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data = get_data(\"airline\", verbose=False)\n",
"data = data[-48:]\n",
"data.index = pd.period_range(end=\"2023-04\", periods = len(data), freq=\"M\")\n",
"data.name = \"Sales\""
],
"metadata": {
"id": "UXW6u1I477ka"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"exp = TSForecastingExperiment()\n",
"exp.setup(data=data, fh=6, fold=1, session_id=42, verbose=False)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Da0T0fID8Ic4",
"outputId": "141e4d14-f172-4aed-83c5-e5e393108c31"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.time_series.forecasting.oop.TSForecastingExperiment at 0x7fd3c557d900>"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"exp.plot_model(plot=\"train_test_split\")\n",
"exp.plot_model(plot=\"cv\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "DO3SYqjS8xrI",
"outputId": "67bb191e-3bdb-4490-e516-aa4cd75b15c5"
},
"execution_count": 8,
"outputs": [
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" <th id=\"T_0e035_level0_col2\" class=\"col_heading level0 col2\" >RMSSE</th>\n",
" <th id=\"T_0e035_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
" <th id=\"T_0e035_level0_col4\" class=\"col_heading level0 col4\" >RMSE</th>\n",
" <th id=\"T_0e035_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
" <th id=\"T_0e035_level0_col6\" class=\"col_heading level0 col6\" >SMAPE</th>\n",
" <th id=\"T_0e035_level0_col7\" class=\"col_heading level0 col7\" >R2</th>\n",
" <th id=\"T_0e035_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_0e035_level0_row0\" class=\"row_heading level0 row0\" >ets</th>\n",
" <td id=\"T_0e035_row0_col0\" class=\"data row0 col0\" >ETS</td>\n",
" <td id=\"T_0e035_row0_col1\" class=\"data row0 col1\" >0.3994</td>\n",
" <td id=\"T_0e035_row0_col2\" class=\"data row0 col2\" >0.4874</td>\n",
" <td id=\"T_0e035_row0_col3\" class=\"data row0 col3\" >11.9660</td>\n",
" <td id=\"T_0e035_row0_col4\" class=\"data row0 col4\" >17.9594</td>\n",
" <td id=\"T_0e035_row0_col5\" class=\"data row0 col5\" >0.0273</td>\n",
" <td id=\"T_0e035_row0_col6\" class=\"data row0 col6\" >0.0267</td>\n",
" <td id=\"T_0e035_row0_col7\" class=\"data row0 col7\" >0.8551</td>\n",
" <td id=\"T_0e035_row0_col8\" class=\"data row0 col8\" >11.8000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0e035_level0_row1\" class=\"row_heading level0 row1\" >stlf</th>\n",
" <td id=\"T_0e035_row1_col0\" class=\"data row1 col0\" >STLF</td>\n",
" <td id=\"T_0e035_row1_col1\" class=\"data row1 col1\" >0.4872</td>\n",
" <td id=\"T_0e035_row1_col2\" class=\"data row1 col2\" >0.5009</td>\n",
" <td id=\"T_0e035_row1_col3\" class=\"data row1 col3\" >14.5944</td>\n",
" <td id=\"T_0e035_row1_col4\" class=\"data row1 col4\" >18.4553</td>\n",
" <td id=\"T_0e035_row1_col5\" class=\"data row1 col5\" >0.0325</td>\n",
" <td id=\"T_0e035_row1_col6\" class=\"data row1 col6\" >0.0322</td>\n",
" <td id=\"T_0e035_row1_col7\" class=\"data row1 col7\" >0.8470</td>\n",
" <td id=\"T_0e035_row1_col8\" class=\"data row1 col8\" >0.2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_0e035_level0_row2\" class=\"row_heading level0 row2\" >arima</th>\n",
" <td id=\"T_0e035_row2_col0\" class=\"data row2 col0\" >ARIMA</td>\n",
" <td id=\"T_0e035_row2_col1\" class=\"data row2 col1\" >0.5175</td>\n",
" <td id=\"T_0e035_row2_col2\" class=\"data row2 col2\" >0.5291</td>\n",
" <td id=\"T_0e035_row2_col3\" class=\"data row2 col3\" >15.5036</td>\n",
" <td id=\"T_0e035_row2_col4\" class=\"data row2 col4\" >19.4968</td>\n",
" <td id=\"T_0e035_row2_col5\" class=\"data row2 col5\" >0.0346</td>\n",
" <td id=\"T_0e035_row2_col6\" class=\"data row2 col6\" >0.0341</td>\n",
" <td id=\"T_0e035_row2_col7\" class=\"data row2 col7\" >0.8292</td>\n",
" <td id=\"T_0e035_row2_col8\" class=\"data row2 col8\" >8.2000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
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"**Observations**\n",
"- ETS got selected as the best fit.\n",
"- Validation period metric (MASE) = 0.3994"
],
"metadata": {
"id": "smwzGXLVE28R"
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"# Blind Test Period (not used to select model, only used to test the unbiased assessment of the models of choice)\n",
"exp.predict_model(best)"
],
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" <th id=\"T_cf0d2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
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" <th id=\"T_cf0d2_level0_col3\" class=\"col_heading level0 col3\" >MAE</th>\n",
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" <td id=\"T_cf0d2_row0_col0\" class=\"data row0 col0\" >ETS</td>\n",
" <td id=\"T_cf0d2_row0_col1\" class=\"data row0 col1\" >0.2417</td>\n",
" <td id=\"T_cf0d2_row0_col2\" class=\"data row0 col2\" >0.2333</td>\n",
" <td id=\"T_cf0d2_row0_col3\" class=\"data row0 col3\" >8.2000</td>\n",
" <td id=\"T_cf0d2_row0_col4\" class=\"data row0 col4\" >9.4588</td>\n",
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" <td id=\"T_cf0d2_row0_col6\" class=\"data row0 col6\" >0.0156</td>\n",
" <td id=\"T_cf0d2_row0_col7\" class=\"data row0 col7\" >0.9879</td>\n",
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" y_pred\n",
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"**Observations**\n",
"\n",
"- So in this case, we can say that the ETS performance remains consistent between validation period and the blind test period.\n",
"- If the blind test period MASE were way more than validation period, then it would mean that the model is overfitting the validation period.\n",
"- In this case, we have to find an alternate model as our final choice."
],
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"source": [
"# Future predictions (repeat with best model into the future)\n",
"best_final = exp.finalize_model(best)"
],
"metadata": {
"id": "-FzB_T4BD33f"
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"execution_count": 11,
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