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Fix R warning with extra regressor; disallow constant extra regressors.

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bl 2017-11-04 16:40:20 -07:00
parent d1bf4a1d06
commit feb7be397b
6 changed files with 21 additions and 8 deletions
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9
R/R/prophet.R
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@ -395,9 +395,13 @@ initialize_scales_fn <- function(m, initialize_scales, df) {
m$start <- min(df$ds)
m$t.scale <- time_diff(max(df$ds), m$start, "secs")
for (name in names(m$extra_regressors)) {
n.vals <- length(unique(df[[name]]))
if (n.vals < 2) {
stop('Regressor ', name, ' is constant.')
}
standardize <- m$extra_regressors[[name]]$standardize
if (standardize == 'auto') {
if (all(sort(unique(df[[name]])) == c(0, 1))) {
if (n.vals == 2 && all(sort(unique(df[[name]])) == c(0, 1))) {
# Don't standardize binary variables
standardize <- FALSE
} else {
@ -407,9 +411,6 @@ initialize_scales_fn <- function(m, initialize_scales, df) {
if (standardize) {
mu <- mean(df[[name]])
std <- stats::sd(df[[name]])
if (std == 0) {
std <- mu
}
m$extra_regressors[[name]]$mu <- mu
m$extra_regressors[[name]]$std <- std
}

5
R/tests/testthat/test_prophet.R
View file

@ -511,6 +511,11 @@ test_that("added_regressors", {
expect_equal(fcst$seasonal[1],
fcst$seasonalities[1] + fcst$extra_regressors[1])
expect_equal(fcst$yhat[1], fcst$trend[1] + fcst$seasonal[1])
# Check fails if constant extra regressor
df$constant_feature <- 5
m <- prophet()
m <- add_regressor(m, 'constant_feature')
expect_error(fit.prophet(m, df))
})
test_that("copy", {

2
docs/_docs/seasonality_and_holiday_effects.md
View file

@ -362,6 +362,6 @@ m.plot_components(forecast);
![png](/prophet/static/seasonality_and_holiday_effects_files/seasonality_and_holiday_effects_26_0.png)
NFL Sundays could also have been handled using the "holidays" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known.
NFL Sundays could also have been handled using the "holidays" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known. The regressor cannot be constant in the training data; fitting will exit with an error if it is.
The `add_regressor` function has optional arguments for specifying the prior scale (holiday prior scale is used by default) and whether or not the regressor is standardized - see the docstring with `help(Prophet.add_regressor)` in Python and `?add_regressor` in R.

2
notebooks/seasonality_and_holiday_effects.ipynb
View file

@ -728,7 +728,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"NFL Sundays could also have been handled using the \"holidays\" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known.\n",
"NFL Sundays could also have been handled using the \"holidays\" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known. The regressor cannot be constant in the training data; fitting will exit with an error if it is.\n",
"\n",
"The `add_regressor` function has optional arguments for specifying the prior scale (holiday prior scale is used by default) and whether or not the regressor is standardized - see the docstring with `help(Prophet.add_regressor)` in Python and `?add_regressor` in R."
]

5
python/fbprophet/forecaster.py
View file

@ -278,6 +278,9 @@ class Prophet(object):
self.t_scale = df['ds'].max() - self.start
for name, props in self.extra_regressors.items():
standardize = props['standardize']
n_vals = len(df[name].unique())
if n_vals < 2:
raise ValueError('Regressor {} is constant.'.format(name))
if standardize == 'auto':
if set(df[name].unique()) == set([1, 0]):
# Don't standardize binary variables.
@ -287,8 +290,6 @@ class Prophet(object):
if standardize:
mu = df[name].mean()
std = df[name].std()
if std == 0:
std = mu
self.extra_regressors[name]['mu'] = mu
self.extra_regressors[name]['std'] = std

6
python/fbprophet/tests/test_prophet.py
View file

@ -548,6 +548,12 @@ class TestProphet(TestCase):
fcst['yhat'][0],
fcst['trend'][0] + fcst['seasonal'][0],
)
# Check fails if constant extra regressor
df['constant_feature'] = 5
m = Prophet()
m.add_regressor('constant_feature')
with self.assertRaises(ValueError):
m.fit(df.copy())
def test_copy(self):
# These values are created except for its default values