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Warn in cross validation if initial period is less than seasonality (#557), and fix bug that can produce error if period > initial

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This commit is contained in:
Ben Letham 2019-05-03 08:40:20 -07:00
parent 35d470cbff
commit c8f2e8f847
2 changed files with 47 additions and 14 deletions
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36
R/R/diagnostics.R
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@ -29,8 +29,11 @@ generate_cutoffs <- function(df, horizon, initial, period) {
# If data does not exist in data range (cutoff, cutoff + horizon]
if (!any((df$ds > cutoff) & (df$ds <= cutoff + horizon))) {
# Next cutoff point is 'closest date before cutoff in data - horizon'
closest.date <- max(df$ds[df$ds <= cutoff])
cutoff <- closest.date - horizon
if (cutoff > min(df$ds)) {
closest.date <- max(df$ds[df$ds <= cutoff])
cutoff <- closest.date - horizon
}
# else no data left, leave cutoff as is, it will be dropped.
}
result <- c(result, cutoff)
}
@ -73,19 +76,34 @@ generate_cutoffs <- function(df, horizon, initial, period) {
cross_validation <- function(
model, horizon, units, period = NULL, initial = NULL) {
df <- model$history
te <- max(df$ds)
ts <- min(df$ds)
horizon.dt <- as.difftime(horizon, units = units)
# Set period
if (is.null(period)) {
period <- 0.5 * horizon
}
if (is.null(initial)) {
initial <- 3 * horizon
}
horizon.dt <- as.difftime(horizon, units = units)
initial.dt <- as.difftime(initial, units = units)
period.dt <- as.difftime(period, units = units)
# Identify largest seasonality period
period.max <- 0
for (s in model$seasonalities) {
period.max <- max(period.max, s$period)
}
seasonality.dt <- as.difftime(period.max, units = 'days')
# Set initial
if (is.null(initial)) {
initial.dt <- max(
as.difftime(3 * horizon, units = units),
seasonality.dt
)
} else {
initial.dt <- as.difftime(initial, units = units)
if (initial.dt < seasonality.dt) {
warning(paste0('Seasonality has period of ', period.max, ' days which ',
'is larger than initial window. Consider increasing initial.'))
}
}
cutoffs <- generate_cutoffs(df, horizon.dt, initial.dt, period.dt)
predicts <- data.frame()
for (i in 1:length(cutoffs)) {
cutoff <- cutoffs[i]

25
python/fbprophet/diagnostics.py
View file

@ -42,8 +42,10 @@ def generate_cutoffs(df, horizon, initial, period):
# If data does not exist in data range (cutoff, cutoff + horizon]
if not (((df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)).any()):
# Next cutoff point is 'last date before cutoff in data - horizon'
closest_date = df[df['ds'] <= cutoff].max()['ds']
cutoff = closest_date - horizon
if cutoff > df['ds'].min():
closest_date = df[df['ds'] <= cutoff].max()['ds']
cutoff = closest_date - horizon
# else no data left, leave cutoff as is, it will be dropped.
result.append(cutoff)
result = result[:-1]
if len(result) == 0:
@ -82,11 +84,24 @@ def cross_validation(model, horizon, period=None, initial=None):
A pd.DataFrame with the forecast, actual value and cutoff.
"""
df = model.history.copy().reset_index(drop=True)
te = df['ds'].max()
ts = df['ds'].min()
horizon = pd.Timedelta(horizon)
# Set period
period = 0.5 * horizon if period is None else pd.Timedelta(period)
initial = 3 * horizon if initial is None else pd.Timedelta(initial)
# Identify largest seasonality period
period_max = 0.
for s in model.seasonalities.values():
period_max = max(period_max, s['period'])
seasonality_dt = pd.Timedelta(str(period_max) + ' days')
# Set initial
if initial is None:
initial = max(3 * horizon, seasonality_dt)
else:
initial = pd.Timedelta(initial)
if initial < seasonality_dt:
msg = 'Seasonality has period of {} days '.format(period_max)
msg += 'which is larger than initial window. '
msg += 'Consider increasing initial.'
logger.warning(msg)
cutoffs = generate_cutoffs(df, horizon, initial, period)
predicts = []