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ngupta23/sktime_prophet_insample.ipynb

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sktime_prophet_insample.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [],
"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/ngupta23/501b7459f2f73502a766a60814b6fbf8/sktime_prophet_insample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oPZFmtlA8yl9",
"outputId": "c7f2810f-4254-472c-dee5-392796f2340c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"System:\n",
" python: 3.7.15 (default, Oct 12 2022, 19:14:55) [GCC 7.5.0]\n",
"executable: /usr/bin/python3\n",
" machine: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\n",
"\n",
"Python dependencies:\n",
" pip: 21.1.3\n",
" setuptools: 57.4.0\n",
" sklearn: 1.0.2\n",
" sktime: 0.13.4\n",
" statsmodels: 0.12.2\n",
" numpy: 1.21.6\n",
" scipy: 1.7.3\n",
" pandas: 1.3.5\n",
" matplotlib: 3.2.2\n",
" joblib: 1.2.0\n",
" numba: 0.56.3\n",
" pmdarima: None\n",
" tsfresh: None\n"
]
}
],
"source": [
"try:\n",
" from sktime import show_versions\n",
"except ModuleNotFoundError:\n",
" !pip install sktime\n",
"\n",
"try:\n",
" import prophet\n",
"except ModuleNotFoundError:\n",
" !pip install prophet\n",
"\n",
"show_versions()"
]
},
{
"cell_type": "code",
"source": [
"from sktime.datasets import load_longley\n",
"\n",
"original_data = load_longley()[1].merge(\n",
" load_longley()[0].rename(\"TOTEMP\"),\n",
" left_index=True,\n",
" right_index=True\n",
")\n",
"# Prophet requires to have data with a pandas.DatetimeIndex\n",
"original_data_p = original_data.to_timestamp(freq=\"Y\")"
],
"metadata": {
"id": "AI5eN6qW9Cns"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"y = original_data_p[\"TOTEMP\"]\n",
"X = original_data_p.drop(columns=[\"TOTEMP\"])\n",
"original_data_p.shape, X.shape, y.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "P3EcbjZk2Gra",
"outputId": "e7e78ddd-6300-44e9-ede5-1e3ff8515004"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((16, 6), (16, 5), (16,))"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"from sktime.forecasting.fbprophet import Prophet\n",
"\n",
"forecaster = Prophet()\n",
"forecaster.fit(y=y, X=X, fh=[1, 2, 3])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mWtWCW8K9MlP",
"outputId": "043f24ba-9968-4f42-dae2-0463764bff73"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
"INFO:prophet:n_changepoints greater than number of observations. Using 11.\n",
"DEBUG:cmdstanpy:input tempfile: /tmp/tmpmyzh2i0u/ujkr3zgp.json\n",
"DEBUG:cmdstanpy:input tempfile: /tmp/tmpmyzh2i0u/ogt09je1.json\n",
"DEBUG:cmdstanpy:idx 0\n",
"DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
"DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.7/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=18197', 'data', 'file=/tmp/tmpmyzh2i0u/ujkr3zgp.json', 'init=/tmp/tmpmyzh2i0u/ogt09je1.json', 'output', 'file=/tmp/tmpmyzh2i0u/prophet_model9kgh1iso/prophet_model-20221103141617.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
"14:16:17 - cmdstanpy - INFO - Chain [1] start processing\n",
"INFO:cmdstanpy:Chain [1] start processing\n",
"14:16:17 - cmdstanpy - INFO - Chain [1] done processing\n",
"INFO:cmdstanpy:Chain [1] done processing\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Prophet()"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"# Insample\n",
"y_pred = forecaster.predict(X=X)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"id": "vsthg9z19R92",
"outputId": "4f7735a7-d168-40f8-db8b-f62f9e2eebd9"
},
"execution_count": 8,
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-b99a87e0cd4c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Insample\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[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforecaster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\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/sktime/forecasting/base/_base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, fh, X)\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;31m# we call the ordinary _predict if no looping/vectorization needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_vectorized\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_inner\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 359\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0;31m# otherwise we call the vectorized version of predict\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/sktime/forecasting/base/adapters/_fbprophet.py\u001b[0m in \u001b[0;36m_predict\u001b[0;34m(self, fh, X)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"floor\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrowth_floor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forecaster\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\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 166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/prophet/forecaster.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, df, vectorized)\u001b[0m\n\u001b[1;32m 1209\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1210\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1211\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Dataframe has no rows.'\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 1212\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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[1;32m 1213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Dataframe has no rows."
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "W9Ojxhsx9TaO"
},
"execution_count": null,
"outputs": []
}
]
}
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