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{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ngupta23/89a47f5ed75021b6f103c551eba9e5c9/pycaret_ts_prophet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BBjOhmMQpshK",
"outputId": "0280e770-e0fd-44d0-95b0-96db49a4684f"
},
"source": [
"!pip install prophet"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting prophet\n",
" Downloading prophet-1.0.1.tar.gz (65 kB)\n",
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"Collecting cmdstanpy==0.9.68\n",
" Downloading cmdstanpy-0.9.68-py3-none-any.whl (49 kB)\n",
"\u001b[K |████████████████████████████████| 49 kB 3.9 MB/s \n",
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"Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from prophet) (4.62.3)\n",
"Collecting ujson\n",
" Downloading ujson-4.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (214 kB)\n",
"\u001b[K |████████████████████████████████| 214 kB 11.9 MB/s \n",
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"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (0.10.0)\n",
"Building wheels for collected packages: prophet\n",
" Building wheel for prophet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for prophet: filename=prophet-1.0.1-py3-none-any.whl size=6641132 sha256=299945fa6dac0840ed3239f70e2dc4e37bf698911c836c9f64c027f5feb5b267\n",
" Stored in directory: /root/.cache/pip/wheels/4e/a0/1a/02c9ec9e3e9de6bdbb3d769d11992a6926889d71567d6b9b67\n",
"Successfully built prophet\n",
"Installing collected packages: ujson, cmdstanpy, prophet\n",
" Attempting uninstall: cmdstanpy\n",
" Found existing installation: cmdstanpy 0.9.5\n",
" Uninstalling cmdstanpy-0.9.5:\n",
" Successfully uninstalled cmdstanpy-0.9.5\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"fbprophet 0.7.1 requires cmdstanpy==0.9.5, but you have cmdstanpy 0.9.68 which is incompatible.\u001b[0m\n",
"Successfully installed cmdstanpy-0.9.68 prophet-1.0.1 ujson-4.2.0\n"
]
}
]
},
{
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_fI3uxUcpyKJ",
"outputId": "d53f2778-3a65-4d30-d36a-dc61e026f3ad"
},
"source": [
"!pip install --pre pycaret"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting pycaret-ts-alpha\n",
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"\u001b[?25hRequirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from pycaret-ts-alpha) (0.11.2)\n",
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"Successfully built htmlmin imagehash alembic databricks-cli pyLDAvis pyod umap-learn pynndescent\n",
"Installing collected packages: threadpoolctl, tangled-up-in-unicode, smmap, scipy, multimethod, llvmlite, websocket-client, visions, tenacity, statsmodels, scikit-learn, requests, python-editor, numba, Mako, imagehash, gitdb, querystring-parser, pyyaml, pynndescent, pydantic, prometheus-flask-exporter, pmdarima, plotly, phik, htmlmin, gunicorn, gitpython, funcy, docker, databricks-cli, alembic, yellowbrick, umap-learn, tbats, sktime, scikit-plot, pyod, pyLDAvis, pandas-profiling, mlxtend, mlflow, lightgbm, kmodes, imbalanced-learn, Boruta, pycaret-ts-alpha\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.4.1\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Successfully installed Boruta-0.3 Mako-1.1.5 alembic-1.4.1 databricks-cli-0.16.2 docker-5.0.3 funcy-1.16 gitdb-4.0.9 gitpython-3.1.24 gunicorn-20.1.0 htmlmin-0.1.12 imagehash-4.2.1 imbalanced-learn-0.7.0 kmodes-0.11.1 lightgbm-3.3.1 llvmlite-0.37.0 mlflow-1.21.0 mlxtend-0.19.0 multimethod-1.6 numba-0.54.1 pandas-profiling-3.1.0 phik-0.12.0 plotly-5.3.1 pmdarima-1.8.3 prometheus-flask-exporter-0.18.4 pyLDAvis-3.2.2 pycaret-ts-alpha-3.0.0.dev1634867798 pydantic-1.8.2 pynndescent-0.5.5 pyod-0.9.5 python-editor-1.0.4 pyyaml-5.4.1 querystring-parser-1.2.4 requests-2.26.0 scikit-learn-0.24.2 scikit-plot-0.3.7 scipy-1.5.4 sktime-0.8.0 smmap-5.0.0 statsmodels-0.12.2 tangled-up-in-unicode-0.1.0 tbats-1.1.0 tenacity-8.0.1 threadpoolctl-3.0.0 umap-learn-0.5.1 visions-0.7.4 websocket-client-1.2.1 yellowbrick-1.3.post1\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 146
},
"id": "ULvKWTsap2jV",
"outputId": "42c5ae98-53cc-4b85-d9ad-d9303ecf4299"
},
"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/",
"height": 695,
"referenced_widgets": [
"47d1a37709e84be99bbe00b05b2a3c0a",
"520af8dd60d74bce91a23db77f3bae34",
"3fc5ff5d7ff2415ea1aef4f5fa8c067b"
]
},
"id": "fG7ECaOnqjlm",
"outputId": "f4a2f161-b70c-4750-e9a4-79a2192ebb1a"
},
"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)"
],
"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>(144, 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>(132,)</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>de1d</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 (144, 1)\n",
"2 Missing Values False\n",
"3 Transformed Train Set (132,)\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 de1d\n",
"19 Imputation Type simple"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pycaret.internal.pycaret_experiment.time_series_experiment.TimeSeriesExperiment at 0x7f2d7640ba90>"
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731,
"referenced_widgets": [
"e53634c27b154ddfb8e4a285b509943f",
"cd60c1d7ffb043b692eb8dcacd0ce328",
"5183098a891d49a3af28a983bcd9e44f"
]
},
"id": "zzKgiPmSqmiP",
"outputId": "2df3d9e2-6372-4702-8f6c-cee6c0dc3f9c"
},
"source": [
"#### Initial Training ----\n",
"model = exp.create_model(\"prophet\")\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>1956-12-31</td>\n",
" <td>23.4076</td>\n",
" <td>28.9635</td>\n",
" <td>0.0612</td>\n",
" <td>0.0618</td>\n",
" <td>0.7269</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1957-12-31</td>\n",
" <td>40.6538</td>\n",
" <td>43.8188</td>\n",
" <td>0.1131</td>\n",
" <td>0.1067</td>\n",
" <td>0.4970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1958-12-31</td>\n",
" <td>26.6558</td>\n",
" <td>34.2205</td>\n",
" <td>0.0603</td>\n",
" <td>0.0606</td>\n",
" <td>0.7380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>NaT</td>\n",
" <td>30.2390</td>\n",
" <td>35.6676</td>\n",
" <td>0.0782</td>\n",
" <td>0.0764</td>\n",
" <td>0.6540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>NaT</td>\n",
" <td>7.4828</td>\n",
" <td>6.1504</td>\n",
" <td>0.0247</td>\n",
" <td>0.0215</td>\n",
" <td>0.1111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12-31 23.4076 28.9635 0.0612 0.0618 0.7269\n",
"1 1957-12-31 40.6538 43.8188 0.1131 0.1067 0.4970\n",
"2 1958-12-31 26.6558 34.2205 0.0603 0.0606 0.7380\n",
"Mean NaT 30.2390 35.6676 0.0782 0.0764 0.6540\n",
"SD NaT 7.4828 6.1504 0.0247 0.0215 0.1111"
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"source": [
"#### Tune Training ----\n",
"tuned_model = exp.tune_model(model)\n",
"exp.plot_model(tuned_model)"
],
"execution_count": null,
"outputs": [
{
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" <th>0</th>\n",
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" cutoff MAE RMSE MAPE SMAPE R2\n",
"0 1956-12-31 27.3440 31.1103 0.0705 0.0736 0.6850\n",
"1 1957-12-31 13.2361 16.7122 0.0333 0.0332 0.9268\n",
"2 1958-12-31 18.7115 22.9716 0.0416 0.0427 0.8819\n",
"Mean NaT 19.7639 23.5980 0.0485 0.0498 0.8312\n",
"SD NaT 5.8074 5.8947 0.0160 0.0172 0.1051"
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