mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-09-05 20:30:32 +00:00
254 lines
175 KiB
Text
254 lines
175 KiB
Text
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"metadata": {
|
||
|
|
"block_hidden": true,
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"%load_ext rpy2.ipython\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"from fbprophet import Prophet\n",
|
||
|
|
"import pandas as pd"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {
|
||
|
|
"block_hidden": true,
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/usr/lib/python2.7/dist-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: Loading required package: Rcpp\n",
|
||
|
|
"\n",
|
||
|
|
" warnings.warn(x, RRuntimeWarning)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%%R\n",
|
||
|
|
"library(prophet)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"By default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast should saturate at this point.\n",
|
||
|
|
"\n",
|
||
|
|
"Prophet allows you to make forecasts using a [logistic growth](https://en.wikipedia.org/wiki/Logistic_function) trend model, with a specified carrying capacity. We illustrate this with the log number of page visits to the [R (programming language)](https://en.wikipedia.org/wiki/R_%28programming_language%29) page on Wikipedia:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df = pd.read_csv('../examples/example_wp_R.csv')\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"df['y'] = np.log(df['y'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"%%R\n",
|
||
|
|
"df <- read.csv('../examples/example_wp_R.csv')\n",
|
||
|
|
"df$y <- log(df$y)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We must specify the carrying capacity in a column `cap`. Here we will assume a particular value, but this would usually be set using data or expertise about the market size."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df['cap'] = 8.5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"%%R\n",
|
||
|
|
"df$cap <- 8.5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"The important things to note are that `cap` must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then `cap` can be an increasing sequence.\n",
|
||
|
|
"\n",
|
||
|
|
"We then fit the model as before, except pass in an additional argument to specify logistic growth:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"output_hidden": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<fbprophet.forecaster.Prophet at 0x7f15cdd81bd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"m = Prophet(growth='logistic')\n",
|
||
|
|
"m.fit(df)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"output_hidden": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"STAN OPTIMIZATION COMMAND (LBFGS)\n",
|
||
|
|
"init = user\n",
|
||
|
|
"save_iterations = 1\n",
|
||
|
|
"init_alpha = 0.001\n",
|
||
|
|
"tol_obj = 1e-12\n",
|
||
|
|
"tol_grad = 1e-08\n",
|
||
|
|
"tol_param = 1e-08\n",
|
||
|
|
"tol_rel_obj = 10000\n",
|
||
|
|
"tol_rel_grad = 1e+07\n",
|
||
|
|
"history_size = 5\n",
|
||
|
|
"seed = 625631580\n",
|
||
|
|
"initial log joint probability = -67.9808\n",
|
||
|
|
"Optimization terminated normally: \n",
|
||
|
|
" Convergence detected: relative gradient magnitude is below tolerance\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%%R\n",
|
||
|
|
"m <- prophet(df, growth = 'logistic')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We make a dataframe for future predictions as before, except we must also specify the capacity in the future. Here we keep capacity constant at the same value as in the history, and forecast 3 years into the future:"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"output_hidden": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAGpCAYAAAB/KasqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEX4z6mv9Z/nILSobw9j
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f15ce230d10>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"future = m.make_future_dataframe(periods=1826)\n",
|
||
|
|
"future['cap'] = 8.5\n",
|
||
|
|
"fcst = m.predict(future)\n",
|
||
|
|
"m.plot(fcst);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\ng8GgK3kVFRVarZbcDgw
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%%R -w 10 -h 6 -u in\n",
|
||
|
|
"future <- make_future_dataframe(m, periods = 1826)\n",
|
||
|
|
"future$cap <- 8.5\n",
|
||
|
|
"fcst <- predict(m, future)\n",
|
||
|
|
"plot(m, fcst);"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 2",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python2"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 2
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython2",
|
||
|
|
"version": "2.7.13"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|