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ngupta23/pycaret_sktime_sp_detection.ipynb

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pycaret_sktime_sp_detection.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNf6RX9tnoyNO8MPQs/zyCN",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ngupta23/9193058fbdab4b88dd9486cc3b2bfd78/pycaret_sktime_sp_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2RmqbFUsUM8t",
"outputId": "fed8bfd4-4719-4dc3-834a-9b5feebacdbf"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"System:\n",
" python: 3.8.15 (default, Oct 12 2022, 19:14:39) [GCC 7.5.0]\n",
"executable: /usr/bin/python3\n",
" machine: Linux-5.10.133+-x86_64-with-glibc2.27\n",
"\n",
"PyCaret required dependencies:\n",
" pip: 21.1.3\n",
" setuptools: 57.4.0\n",
" pycaret: 3.0.0rc4\n",
" IPython: 7.9.0\n",
" ipywidgets: 7.7.1\n",
" tqdm: 4.64.1\n",
" numpy: 1.21.6\n",
" pandas: 1.3.5\n",
" jinja2: 2.11.3\n",
" scipy: 1.7.3\n",
" joblib: 1.2.0\n",
" sklearn: 1.0.2\n",
" pyod: 1.0.6\n",
" imblearn: 0.8.1\n",
" category_encoders: 2.5.1.post0\n",
" lightgbm: 3.3.3\n",
" numba: 0.55.2\n",
" requests: 2.28.1\n",
" matplotlib: 3.6.2\n",
" scikitplot: 0.3.7\n",
" yellowbrick: 1.5\n",
" plotly: 5.5.0\n",
" kaleido: 0.2.1\n",
" statsmodels: 0.12.2\n",
" sktime: 0.13.4\n",
" tbats: 1.1.1\n",
" pmdarima: 1.8.5\n",
" psutil: 5.9.4\n",
"\n",
"PyCaret optional dependencies:\n",
" shap: Not installed\n",
" interpret: Not installed\n",
" umap: Not installed\n",
" pandas_profiling: 1.4.1\n",
" explainerdashboard: Not installed\n",
" autoviz: Not installed\n",
" fairlearn: Not installed\n",
" xgboost: 0.90\n",
" catboost: Not installed\n",
" kmodes: Not installed\n",
" mlxtend: 0.14.0\n",
" statsforecast: Not installed\n",
" tune_sklearn: Not installed\n",
" ray: Not installed\n",
" hyperopt: 0.1.2\n",
" optuna: Not installed\n",
" skopt: Not installed\n",
" mlflow: Not installed\n",
" gradio: Not installed\n",
" fastapi: Not installed\n",
" uvicorn: Not installed\n",
" m2cgen: Not installed\n",
" evidently: Not installed\n",
" nltk: 3.7\n",
" pyLDAvis: Not installed\n",
" gensim: 3.6.0\n",
" spacy: 3.4.3\n",
" wordcloud: 1.8.2.2\n",
" textblob: 0.15.3\n",
" fugue: Not installed\n",
" streamlit: Not installed\n",
" prophet: 1.1.1\n"
]
}
],
"source": [
"try:\n",
" from pycaret import show_versions\n",
"except ModuleNotFoundError:\n",
" !pip install --pre pycaret\n",
" from pycaret import show_versions\n",
"\n",
"show_versions()"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"from pmdarima.arima.utils import ndiffs, nsdiffs\n",
"\n",
"from sktime.utils.plotting import plot_series\n",
"from sktime.datasets import load_airline\n",
"from sktime.param_est.seasonality import SeasonalityACF\n",
"from sktime.transformations.series.difference import Differencer\n",
"\n",
"from pycaret.datasets import get_data"
],
"metadata": {
"id": "0auy-2F5UaAW"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"properties = get_data(\"index\", folder = \"time_series/seasonal\", verbose=False)\n",
"properties[[\"index\", \"s\"]]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 830
},
"id": "hpoeElzeaZKi",
"outputId": "99634c8b-1baf-466e-fb9f-bb60cb4b558d"
},
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"outputs": [
{
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"data": {
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"519 0.343249502380434 -0.146881709557787 0.1961214... \n",
"\n",
" theta \n",
"0 0.631477142440397 -0.163116680352825 0.6648996... \n",
"1 -0.0851445749108851 -0.544165855934772 -1.5055... \n",
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"</table>\n",
"<p>520 rows × 2 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-828a2c25-7891-4ef6-ac75-067876a2c6d3')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-828a2c25-7891-4ef6-ac75-067876a2c6d3 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-828a2c25-7891-4ef6-ac75-067876a2c6d3');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"def detect_sp(X, verbose=False, plot=False):\n",
" Xt = X.copy()\n",
" for i in np.arange(ndiffs(X)):\n",
" if verbose: \n",
" print(f\"Differencing: {i+1}\")\n",
" differencer = Differencer()\n",
" Xt = differencer.fit_transform(Xt)\n",
"\n",
" if plot:\n",
" _ = plot_series(X, Xt, labels=[\"original\", \"differenced\"])\n",
"\n",
" nobs = len(Xt)\n",
" # Increasing lags otherwise high sp values are not detected since they are\n",
" # limited by internal nlags calculation in SeasonalityACF\n",
" # lags_to_use = min(10 * np.log10(nobs), nobs - 1)\n",
" # lags_to_use = max(lags_to_use, nobs/3)\n",
" lags_to_use = nobs - 1\n",
" lags_to_use = int(lags_to_use)\n",
" \n",
" sp_est = SeasonalityACF(nlags=lags_to_use)\n",
" sp_est.fit(Xt)\n",
"\n",
" primary_sp = sp_est.get_fitted_params().get('sp')\n",
" significant_sps = sp_est.get_fitted_params().get('sp_significant')\n",
" if verbose:\n",
" print(f\"\\tLags used for seasonal detection: {lags_to_use}\")\n",
" print(f\"\\tDetected Significant SP: {significant_sps}\")\n",
" print(f\"\\tDetected Primary SP: {primary_sp}\")\n",
"\n",
" return primary_sp, significant_sps, lags_to_use"
],
"metadata": {
"id": "--jBDQN_yR2G"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sktime.utils.seasonality import autocorrelation_seasonality_test\n",
"detected_sp = []\n",
"detected_sp_extra_test = []\n",
"nlags = []\n",
"for _, (index, primary_sp) in enumerate(properties[[\"index\", \"s\"]].values):\n",
" print(f\"Index: {index}, Actual SP: {primary_sp}\")\n",
" X = get_data(index, folder = \"time_series/seasonal\", verbose=False)\n",
" primary_sp, significant_sps, lags_used = detect_sp(X=X, verbose=False)\n",
"\n",
" seasonality_test_results = [\n",
" autocorrelation_seasonality_test(X, sp)\n",
" for sp in significant_sps\n",
" ]\n",
" sp_values_and_test_result = zip(significant_sps, seasonality_test_results)\n",
"\n",
" # What seasonal period should be used for modeling?\n",
" all_sp_values = [\n",
" sp\n",
" for sp, seasonality_present in sp_values_and_test_result\n",
" if seasonality_present\n",
" ] or [1]\n",
"\n",
" nlags.append(lags_used)\n",
" detected_sp.append(primary_sp) \n",
" detected_sp_extra_test.append(all_sp_values[0]) "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "n_zw3Ef5bRQH",
"outputId": "78f00d5d-f388-46e4-89cb-b03b9e9c76c9"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index: 1, Actual SP: 1\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": []
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"metadata": {}
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Index: 2, Actual SP: 1\n"
]
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{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
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"metadata": {}
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{
"output_type": "stream",
"name": "stdout",
"text": [
"Index: 3, Actual SP: 1\n"
]
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{
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"<IPython.core.display.HTML object>"
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"text": [
"Index: 4, Actual SP: 1\n"
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"<IPython.core.display.HTML object>"
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"Index: 5, Actual SP: 1\n"
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"<IPython.core.display.HTML object>"
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"properties[\"nlags\"] = nlags\n",
"\n",
"properties[\"detected_sp\"] = detected_sp\n",
"properties[\"equal\"] = properties[\"s\"] == properties[\"detected_sp\"]\n",
"properties[\"multiple\"] = properties[\"detected_sp\"] % properties[\"s\"] == 0\n",
"\n",
"properties[\"detected_sp_extra_test\"] = detected_sp_extra_test\n",
"properties[\"equal_extra_test\"] = properties[\"s\"] == properties[\"detected_sp_extra_test\"]\n",
"properties[\"multiple_extra_test\"] = properties[\"detected_sp_extra_test\"] % properties[\"s\"] == 0\n",
"\n",
"properties[[\"index\", \"n\", \"nlags\", \"s\", \"detected_sp\", \"equal\", \"multiple\", \"equal_extra_test\", \"multiple_extra_test\"]]"
],
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"base_uri": "https://localhost:8080/",
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"id": "GGL8yS6XcTDW",
"outputId": "e20f6d64-5da5-495b-bd65-25663e926eb6"
},
"execution_count": 6,
"outputs": [
{
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" index n nlags s detected_sp equal multiple equal_extra_test \\\n",
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".. ... ... ... .. ... ... ... ... \n",
"515 516 582 581 52 52 True True True \n",
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"517 518 416 415 52 17 False False False \n",
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" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>467</th>\n",
" <td>468</td>\n",
" <td>722</td>\n",
" <td>721</td>\n",
" <td>47</td>\n",
" <td>15</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td>476</td>\n",
" <td>425</td>\n",
" <td>424</td>\n",
" <td>48</td>\n",
" <td>16</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>481</td>\n",
" <td>519</td>\n",
" <td>518</td>\n",
" <td>49</td>\n",
" <td>16</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>482</th>\n",
" <td>483</td>\n",
" <td>223</td>\n",
" <td>222</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>485</th>\n",
" <td>486</td>\n",
" <td>373</td>\n",
" <td>372</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>486</th>\n",
" <td>487</td>\n",
" <td>223</td>\n",
" <td>222</td>\n",
" <td>49</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>496</td>\n",
" <td>551</td>\n",
" <td>550</td>\n",
" <td>50</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503</th>\n",
" <td>504</td>\n",
" <td>299</td>\n",
" <td>298</td>\n",
" <td>51</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>517</th>\n",
" <td>518</td>\n",
" <td>416</td>\n",
" <td>415</td>\n",
" <td>52</td>\n",
" <td>17</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>518</th>\n",
" <td>519</td>\n",
" <td>266</td>\n",
" <td>265</td>\n",
" <td>52</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39282d3d-b9da-4929-91fa-4ac5347e74e8')\"\n",
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" \n",
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" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
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"\n",
" .colab-df-convert {\n",
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" border: none;\n",
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"\n",
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"\n",
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" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
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"\n",
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" const dataTable =\n",
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]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"# X = load_airline() \n",
"X = get_data(\"95\", folder = \"time_series/seasonal\", verbose=False)\n",
"primary_sp, significant_sps, lags_used = detect_sp(X=X, verbose=True, plot=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 317
},
"id": "Ljm4zvzEZTuz",
"outputId": "3ee7abee-2e6b-4c90-83ca-058a508e0191"
},
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": []
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\tLags used for seasonal detection: 47\n",
"\tDetected Significant SP: [ 3 10]\n",
"\tDetected Primary SP: 3\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"# With 1 test for sp detection----\n",
"len(properties.query(\"equal == True\")) / len(properties), len(properties.query(\"multiple == True\")) / len(properties)"
],
"metadata": {
"id": "dOUThzAy0Mqh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ec1f9863-8aab-4511-a11a-942eb0fcc270"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.9211538461538461, 0.9307692307692308)"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"# With additonal (2) test for sp detection ----\n",
"len(properties.query(\"equal_extra_test == True\")) / len(properties), len(properties.query(\"multiple_extra_test == True\")) / len(properties)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "akOJ1ZmcrTBF",
"outputId": "1b59a4ce-40a7-4807-9250-e39df2764af1"
},
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.8076923076923077, 0.8173076923076923)"
]
},
"metadata": {},
"execution_count": 12
}
]
}
]
}
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