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Created October 8, 2022 19:42
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pycaret_ts_3p0p0rc4.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyMg/0VqofpJskCaWUYmyT1K",
"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/ngupta23/3be6b8872740519ebeefda1cb57170b0/pycaret_ts_3p0p0rc4.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": "6R3mxlssFygF",
"outputId": "ebf7b8e7-f2df-4fab-b980-b0a0926988b0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: pycaret in /usr/local/lib/python3.7/dist-packages (3.0.0rc4)\n",
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]
}
],
"source": [
"!pip install --pre pycaret"
]
},
{
"cell_type": "code",
"source": [
"from pycaret import show_versions\n",
"show_versions()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fLQrnUroHLhA",
"outputId": "770fa179-29af-448e-c64a-2912fdf4b4f9"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"System:\n",
" python: 3.7.14 (default, Sep 8 2022, 00:06:44) [GCC 7.5.0]\n",
"executable: /usr/bin/python3\n",
" machine: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\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: 1.0.5\n",
" imblearn: 0.8.1\n",
" category_encoders: 2.5.1.post0\n",
" lightgbm: 3.3.2\n",
" numba: 0.55.2\n",
" requests: 2.28.1\n",
" matplotlib: 3.5.3\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: 5.9.2\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: 3.4.1\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"
]
}
]
},
{
"cell_type": "code",
"source": [
"from pycaret.datasets import get_data\n",
"# from pycaret.time_series import *\n",
"from pycaret.time_series import TSForecastingExperiment"
],
"metadata": {
"id": "ecMl9OJTF2a5"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"df = get_data(\"airline\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
},
"id": "-OuCe9EcF_Fa",
"outputId": "dbcc2439-195c-410f-fa15-62d2382be54f"
},
"execution_count": 3,
"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",
"source": [
"exp = TSForecastingExperiment()\n",
"exp.setup(df, fh=7, fold=3, session_id=123)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "8KnN1-4CGAz6",
"outputId": "b3d6e290-3e85-495f-fc7e-cbfb85a70868"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3dff0d1d90>"
],
"text/html": [
"<style type=\"text/css\">\n",
"#T_aa1f8_row13_col1 {\n",
" background-color: lightgreen;\n",
"}\n",
"</style>\n",
"<table id=\"T_aa1f8_\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" >&nbsp;</th>\n",
" <th class=\"col_heading level0 col0\" >Description</th>\n",
" <th class=\"col_heading level0 col1\" >Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_aa1f8_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
" <td id=\"T_aa1f8_row0_col1\" class=\"data row0 col1\" >123</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_aa1f8_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
" <td id=\"T_aa1f8_row1_col1\" class=\"data row1 col1\" >Number of airline passengers</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_aa1f8_row2_col0\" class=\"data row2 col0\" >Approach</td>\n",
" <td id=\"T_aa1f8_row2_col1\" class=\"data row2 col1\" >Univariate</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_aa1f8_row3_col0\" class=\"data row3 col0\" >Exogenous Variables</td>\n",
" <td id=\"T_aa1f8_row3_col1\" class=\"data row3 col1\" >Not Present</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_aa1f8_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
" <td id=\"T_aa1f8_row4_col1\" class=\"data row4 col1\" >(144, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
" <td id=\"T_aa1f8_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
" <td id=\"T_aa1f8_row5_col1\" class=\"data row5 col1\" >(144, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
" <td id=\"T_aa1f8_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
" <td id=\"T_aa1f8_row6_col1\" class=\"data row6 col1\" >(137, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
" <td id=\"T_aa1f8_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
" <td id=\"T_aa1f8_row7_col1\" class=\"data row7 col1\" >(7, 1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
" <td id=\"T_aa1f8_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n",
" <td id=\"T_aa1f8_row8_col1\" class=\"data row8 col1\" >0.0%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
" <td id=\"T_aa1f8_row9_col0\" class=\"data row9 col0\" >Fold Generator</td>\n",
" <td id=\"T_aa1f8_row9_col1\" class=\"data row9 col1\" >ExpandingWindowSplitter</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
" <td id=\"T_aa1f8_row10_col0\" class=\"data row10 col0\" >Fold Number</td>\n",
" <td id=\"T_aa1f8_row10_col1\" class=\"data row10 col1\" >3</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
" <td id=\"T_aa1f8_row11_col0\" class=\"data row11 col0\" >Enforce Prediction Interval</td>\n",
" <td id=\"T_aa1f8_row11_col1\" class=\"data row11 col1\" >False</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
" <td id=\"T_aa1f8_row12_col0\" class=\"data row12 col0\" >Seasonal Period(s) Tested</td>\n",
" <td id=\"T_aa1f8_row12_col1\" class=\"data row12 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
" <td id=\"T_aa1f8_row13_col0\" class=\"data row13 col0\" >Seasonality Present</td>\n",
" <td id=\"T_aa1f8_row13_col1\" class=\"data row13 col1\" >True</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
" <td id=\"T_aa1f8_row14_col0\" class=\"data row14 col0\" >Seasonalities Detected</td>\n",
" <td id=\"T_aa1f8_row14_col1\" class=\"data row14 col1\" >[12]</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
" <td id=\"T_aa1f8_row15_col0\" class=\"data row15 col0\" >Primary Seasonality</td>\n",
" <td id=\"T_aa1f8_row15_col1\" class=\"data row15 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
" <td id=\"T_aa1f8_row16_col0\" class=\"data row16 col0\" >Target Strictly Positive</td>\n",
" <td id=\"T_aa1f8_row16_col1\" class=\"data row16 col1\" >True</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
" <td id=\"T_aa1f8_row17_col0\" class=\"data row17 col0\" >Target White Noise</td>\n",
" <td id=\"T_aa1f8_row17_col1\" class=\"data row17 col1\" >No</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
" <td id=\"T_aa1f8_row18_col0\" class=\"data row18 col0\" >Recommended d</td>\n",
" <td id=\"T_aa1f8_row18_col1\" class=\"data row18 col1\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
" <td id=\"T_aa1f8_row19_col0\" class=\"data row19 col0\" >Recommended Seasonal D</td>\n",
" <td id=\"T_aa1f8_row19_col1\" class=\"data row19 col1\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
" <td id=\"T_aa1f8_row20_col0\" class=\"data row20 col0\" >Preprocess</td>\n",
" <td id=\"T_aa1f8_row20_col1\" class=\"data row20 col1\" >False</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
" <td id=\"T_aa1f8_row21_col0\" class=\"data row21 col0\" >CPU Jobs</td>\n",
" <td id=\"T_aa1f8_row21_col1\" class=\"data row21 col1\" >-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
" <td id=\"T_aa1f8_row22_col0\" class=\"data row22 col0\" >Use GPU</td>\n",
" <td id=\"T_aa1f8_row22_col1\" class=\"data row22 col1\" >False</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
" <td id=\"T_aa1f8_row23_col0\" class=\"data row23 col0\" >Log Experiment</td>\n",
" <td id=\"T_aa1f8_row23_col1\" class=\"data row23 col1\" >False</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
" <td id=\"T_aa1f8_row24_col0\" class=\"data row24 col0\" >Experiment Name</td>\n",
" <td id=\"T_aa1f8_row24_col1\" class=\"data row24 col1\" >ts-default-name</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_aa1f8_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
" <td id=\"T_aa1f8_row25_col0\" class=\"data row25 col0\" >USI</td>\n",
" <td id=\"T_aa1f8_row25_col1\" class=\"data row25 col1\" >9ca3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {}
},
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-a33f8302d46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mexp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTSForecastingExperiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m123\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/time_series/forecasting/oop.py\u001b[0m in \u001b[0;36msetup\u001b[0;34m(self, data, data_func, target, index, ignore_features, numeric_imputation_target, numeric_imputation_exogenous, transform_target, transform_exogenous, scale_target, scale_exogenous, fold_strategy, fold, fh, seasonal_period, point_alpha, coverage, enforce_exogenous, n_jobs, use_gpu, custom_pipeline, html, session_id, system_log, log_experiment, experiment_name, experiment_custom_tags, log_plots, log_profile, log_data, engine, verbose, profile, profile_kwargs, fig_kwargs)\u001b[0m\n\u001b[1;32m 1661\u001b[0m ._set_exp_model_engines(\n\u001b[1;32m 1662\u001b[0m \u001b[0mcontainer_default_engines\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_container_default_engines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1663\u001b[0;31m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1664\u001b[0m )\n\u001b[1;32m 1665\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_set_all_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36m_set_exp_model_engines\u001b[0;34m(self, container_default_engines, engine)\u001b[0m\n\u001b[1;32m 2927\u001b[0m \u001b[0;31m# If provided by user, then use that, else get from the defaults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2928\u001b[0m \u001b[0meng\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer_default_engines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2929\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meng\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseverity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2931\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36m_set_engine\u001b[0;34m(self, estimator, engine, severity)\u001b[0m\n\u001b[1;32m 2893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2894\u001b[0m \u001b[0;31m# Need to do this, else internal class variables are not reset with new engine.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_all_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2897\u001b[0m def _set_exp_model_engines(\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/internal/pycaret_experiment/tabular_experiment.py\u001b[0m in \u001b[0;36m_set_all_models\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2783\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mexperiment\u001b[0m \u001b[0mobject\u001b[0m \u001b[0mto\u001b[0m \u001b[0mallow\u001b[0m \u001b[0mchaining\u001b[0m \u001b[0mof\u001b[0m \u001b[0mmethods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2784\u001b[0m \"\"\"\n\u001b[0;32m-> 2785\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_all_models\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_all_models_internal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_models\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2786\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/time_series/forecasting/oop.py\u001b[0m in \u001b[0;36m_get_models\u001b[0;34m(self, raise_errors)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m for k, v in get_all_ts_model_containers(\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 242\u001b[0m ).items()\n\u001b[1;32m 243\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_special\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/containers/models/time_series.py\u001b[0m in \u001b[0;36mget_all_model_containers\u001b[0;34m(experiment, raise_errors)\u001b[0m\n\u001b[1;32m 2782\u001b[0m ) -> Dict[str, TimeSeriesContainer]:\n\u001b[1;32m 2783\u001b[0m return pycaret.containers.base_container.get_all_containers(\n\u001b[0;32m-> 2784\u001b[0;31m \u001b[0mglobals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexperiment\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimeSeriesContainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_errors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2785\u001b[0m )\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/containers/base_container.py\u001b[0m in \u001b[0;36mget_all_containers\u001b[0;34m(container_globals, experiment, type_var, raise_errors)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"active\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0minstance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiment\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0mmodel_containers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/containers/models/time_series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, experiment)\u001b[0m\n\u001b[1;32m 1327\u001b[0m )\n\u001b[1;32m 1328\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m \u001b[0mregressor_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_regressor_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# e.g. LinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1330\u001b[0m \u001b[0mregressor_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_regressor_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1331\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mregressor_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pycaret/containers/models/time_series.py\u001b[0m in \u001b[0;36mreturn_regressor_class\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2310\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgboost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1.1.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2311\u001b[0;31m self.logger.warning(\n\u001b[0m\u001b[1;32m 2312\u001b[0m \u001b[0;34mf\"Wrong xgboost version. Expected xgboost>=1.1.0, got xgboost=={xgboost.__version__}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2313\u001b[0m )\n",
"\u001b[0;31mAttributeError\u001b[0m: 'XGBCdsDtContainer' object has no attribute 'logger'"
]
}
]
},
{
"cell_type": "code",
"source": [
"best = exp.compare_models()"
],
"metadata": {
"id": "jgsst6U2GCEx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prediction = exp.predict_model(best, fh=90)\n",
"prediction.to_frame()\n",
"prediction"
],
"metadata": {
"id": "Ouf0Z798GDOZ"
},
"execution_count": null,
"outputs": []
}
]
}
@gabrielhribeiro
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Hello,

Im getting ths erro: AttributeError: 'XGBCdsDtContainer' object has no attribute 'logger'

Do you know what can I do to solve it?

Im using google colab.

@ngupta23
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Author

This will be fixed in the next version of the release. For now, can you update the xgboost version (through pip install in the notebook) to this

xgboost>=1.1.0

@gabrielhribeiro
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HI, Worked. Many thanks!!

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