Class: Prophet::Forecaster

Inherits:
Object
  • Object
show all
Includes:
Holidays, Plot
Defined in:
lib/prophet/forecaster.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Methods included from Plot

#add_changepoints_to_plot, #plot, #plot_components, plot_cross_validation_metric, plt

Methods included from Holidays

#get_holiday_names, #holidays_df, #make_holidays_df

Constructor Details

#initialize(growth: "linear", changepoints: nil, n_changepoints: 25, changepoint_range: 0.8, yearly_seasonality: "auto", weekly_seasonality: "auto", daily_seasonality: "auto", holidays: nil, seasonality_mode: "additive", seasonality_prior_scale: 10.0, holidays_prior_scale: 10.0, changepoint_prior_scale: 0.05, mcmc_samples: 0, interval_width: 0.80, uncertainty_samples: 1000, scaling: "absmax") ⇒ Forecaster

Returns a new instance of Forecaster.



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# File 'lib/prophet/forecaster.rb', line 15

def initialize(
  growth: "linear",
  changepoints: nil,
  n_changepoints: 25,
  changepoint_range: 0.8,
  yearly_seasonality: "auto",
  weekly_seasonality: "auto",
  daily_seasonality: "auto",
  holidays: nil,
  seasonality_mode: "additive",
  seasonality_prior_scale: 10.0,
  holidays_prior_scale: 10.0,
  changepoint_prior_scale: 0.05,
  mcmc_samples: 0,
  interval_width: 0.80,
  uncertainty_samples: 1000,
  scaling: "absmax"
)
  @growth = growth

  @changepoints = to_datetime(changepoints)
  if !@changepoints.nil?
    @n_changepoints = @changepoints.size
    @specified_changepoints = true
  else
    @n_changepoints = n_changepoints
    @specified_changepoints = false
  end

  @changepoint_range = changepoint_range
  @yearly_seasonality = yearly_seasonality
  @weekly_seasonality = weekly_seasonality
  @daily_seasonality = daily_seasonality
  @holidays = convert_df(holidays)

  @seasonality_mode = seasonality_mode
  @seasonality_prior_scale = seasonality_prior_scale.to_f
  @changepoint_prior_scale = changepoint_prior_scale.to_f
  @holidays_prior_scale = holidays_prior_scale.to_f

  @mcmc_samples = mcmc_samples
  @interval_width = interval_width
  @uncertainty_samples = uncertainty_samples
  if !["absmax", "minmax"].include?(scaling)
    raise ArgumentError, "scaling must be one of \"absmax\" or \"minmax\""
  end
  @scaling = scaling

  # Set during fitting or by other methods
  @start = nil
  @y_scale = nil
  @logistic_floor = false
  @t_scale = nil
  @changepoints_t = nil
  @seasonalities = {}
  @extra_regressors = {}
  @country_holidays = nil
  @stan_fit = nil
  @params = {}
  @history = nil
  @history_dates = nil
  @train_component_cols = nil
  @component_modes = nil
  @train_holiday_names = nil
  @fit_kwargs = {}
  validate_inputs

  @logger = ::Logger.new($stderr)
  @logger.level = ::Logger::WARN
  @logger.formatter = proc do |severity, datetime, progname, msg|
    "[prophet] #{msg}\n"
  end
  @stan_backend = StanBackend.new(@logger)
end

Instance Attribute Details

#changepoint_prior_scale ⇒ Object (readonly)

Returns the value of attribute changepoint_prior_scale.



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# File 'lib/prophet/forecaster.rb', line 6

def changepoint_prior_scale
  @changepoint_prior_scale
end

#changepoint_range ⇒ Object (readonly)

Returns the value of attribute changepoint_range.



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# File 'lib/prophet/forecaster.rb', line 6

def changepoint_range
  @changepoint_range
end

#changepoints ⇒ Object (readonly)

Returns the value of attribute changepoints.



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# File 'lib/prophet/forecaster.rb', line 6

def changepoints
  @changepoints
end

#country_holidays ⇒ Object

Returns the value of attribute country_holidays.



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# File 'lib/prophet/forecaster.rb', line 13

def country_holidays
  @country_holidays
end

#extra_regressors ⇒ Object

Returns the value of attribute extra_regressors.



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# File 'lib/prophet/forecaster.rb', line 13

def extra_regressors
  @extra_regressors
end

#fit_kwargs ⇒ Object (readonly)

Returns the value of attribute fit_kwargs.



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# File 'lib/prophet/forecaster.rb', line 6

def fit_kwargs
  @fit_kwargs
end

#growth ⇒ Object (readonly)

Returns the value of attribute growth.



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# File 'lib/prophet/forecaster.rb', line 6

def growth
  @growth
end

#history ⇒ Object (readonly)

Returns the value of attribute history.



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# File 'lib/prophet/forecaster.rb', line 6

def history
  @history
end

#holidays ⇒ Object (readonly)

Returns the value of attribute holidays.



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# File 'lib/prophet/forecaster.rb', line 6

def holidays
  @holidays
end

#holidays_prior_scale ⇒ Object (readonly)

Returns the value of attribute holidays_prior_scale.



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# File 'lib/prophet/forecaster.rb', line 6

def holidays_prior_scale
  @holidays_prior_scale
end

#interval_width ⇒ Object (readonly)

Returns the value of attribute interval_width.



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# File 'lib/prophet/forecaster.rb', line 6

def interval_width
  @interval_width
end

#logger ⇒ Object (readonly)

Returns the value of attribute logger.



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# File 'lib/prophet/forecaster.rb', line 6

def logger
  @logger
end

#mcmc_samples ⇒ Object (readonly)

Returns the value of attribute mcmc_samples.



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# File 'lib/prophet/forecaster.rb', line 6

def mcmc_samples
  @mcmc_samples
end

#n_changepoints ⇒ Object (readonly)

Returns the value of attribute n_changepoints.



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# File 'lib/prophet/forecaster.rb', line 6

def n_changepoints
  @n_changepoints
end

#params ⇒ Object (readonly)

Returns the value of attribute params.



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# File 'lib/prophet/forecaster.rb', line 6

def params
  @params
end

#seasonalities ⇒ Object

Returns the value of attribute seasonalities.



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# File 'lib/prophet/forecaster.rb', line 6

def seasonalities
  @seasonalities
end

#seasonality_mode ⇒ Object (readonly)

Returns the value of attribute seasonality_mode.



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# File 'lib/prophet/forecaster.rb', line 6

def seasonality_mode
  @seasonality_mode
end

#seasonality_prior_scale ⇒ Object (readonly)

Returns the value of attribute seasonality_prior_scale.



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# File 'lib/prophet/forecaster.rb', line 6

def seasonality_prior_scale
  @seasonality_prior_scale
end

#specified_changepoints ⇒ Object (readonly)

Returns the value of attribute specified_changepoints.



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# File 'lib/prophet/forecaster.rb', line 6

def specified_changepoints
  @specified_changepoints
end

#train_holiday_names ⇒ Object (readonly)

Returns the value of attribute train_holiday_names.



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# File 'lib/prophet/forecaster.rb', line 6

def train_holiday_names
  @train_holiday_names
end

#uncertainty_samples ⇒ Object (readonly)

Returns the value of attribute uncertainty_samples.



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# File 'lib/prophet/forecaster.rb', line 6

def uncertainty_samples
  @uncertainty_samples
end

Instance Method Details

#add_country_holidays(country_name) ⇒ Object

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# File 'lib/prophet/forecaster.rb', line 422

def add_country_holidays(country_name)
  raise Error, "Country holidays must be added prior to model fitting." if @history

  # Fix for previously documented keyword argument
  if country_name.is_a?(Hash) && country_name[:country_name]
    country_name = country_name[:country_name]
  end

  # Validate names.
  get_holiday_names(country_name).each do |name|
    # Allow merging with existing holidays
    validate_column_name(name, check_holidays: false)
  end
  # Set the holidays.
  if @country_holidays
    logger.warn "Changing country holidays from #{@country_holidays.inspect} to #{country_name.inspect}."
  end
  @country_holidays = country_name
  self
end

#add_group_component(components, name, group) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 530

def add_group_component(components, name, group)
  new_comp = components[components["component"].in?(group)].dup
  group_cols = new_comp["col"].uniq
  if group_cols.size > 0
    new_comp = Rover::DataFrame.new({"col" => group_cols, "component" => name})
    components = components.concat(new_comp)
  end
  components
end

#add_regressor(name, prior_scale: nil, standardize: "auto", mode: nil) ⇒ Object

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# File 'lib/prophet/forecaster.rb', line 374

def add_regressor(name, prior_scale: nil, standardize: "auto", mode: nil)
  raise Error, "Regressors must be added prior to model fitting." if @history
  validate_column_name(name, check_regressors: false)
  prior_scale ||= @holidays_prior_scale.to_f
  mode ||= @seasonality_mode
  raise ArgumentError, "Prior scale must be > 0" if prior_scale <= 0
  if !["additive", "multiplicative"].include?(mode)
    raise ArgumentError, "mode must be \"additive\" or \"multiplicative\""
  end
  @extra_regressors[name] = {
    prior_scale: prior_scale,
    standardize: standardize,
    mu: 0.0,
    std: 1.0,
    mode: mode
  }
  self
end

#add_seasonality(name:, period:, fourier_order:, prior_scale: nil, mode: nil, condition_name: nil) ⇒ Object

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# File 'lib/prophet/forecaster.rb', line 393

def add_seasonality(name:, period:, fourier_order:, prior_scale: nil, mode: nil, condition_name: nil)
  raise Error, "Seasonality must be added prior to model fitting." if @history

  if !["daily", "weekly", "yearly"].include?(name)
    # Allow overwriting built-in seasonalities
    validate_column_name(name, check_seasonalities: false)
  end
  if prior_scale.nil?
    ps = @seasonality_prior_scale
  else
    ps = prior_scale.to_f
  end
  raise ArgumentError, "Prior scale must be > 0" if ps <= 0
  raise ArgumentError, "Fourier Order must be > 0" if fourier_order <= 0
  mode ||= @seasonality_mode
  if !["additive", "multiplicative"].include?(mode)
    raise ArgumentError, "mode must be \"additive\" or \"multiplicative\""
  end
  validate_column_name(condition_name) if condition_name
  @seasonalities[name] = {
    period: period,
    fourier_order: fourier_order,
    prior_scale: ps,
    mode: mode,
    condition_name: condition_name
  }
  self
end

#construct_holiday_dataframe(dates) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 301

def construct_holiday_dataframe(dates)
  all_holidays = Rover::DataFrame.new
  if @holidays
    all_holidays = @holidays.dup
  end
  if @country_holidays
    year_list = dates.map(&:year)
    country_holidays_df = make_holidays_df(year_list, @country_holidays)
    all_holidays = all_holidays.concat(country_holidays_df)
  end
  # Drop future holidays not previously seen in training data
  if @train_holiday_names
    # Remove holiday names didn't show up in fit
    all_holidays = all_holidays[all_holidays["holiday"].in?(@train_holiday_names)]

    # Add holiday names in fit but not in predict with ds as NA
    holidays_to_add = Rover::DataFrame.new({
      "holiday" => @train_holiday_names[!@train_holiday_names.in?(all_holidays["holiday"])]
    })
    all_holidays = all_holidays.concat(holidays_to_add)
  end

  all_holidays
end

#fit(df, **kwargs) ⇒ Object

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# File 'lib/prophet/forecaster.rb', line 652

def fit(df, **kwargs)
  raise Error, "Prophet object can only be fit once" if @history

  df = convert_df(df)
  raise ArgumentError, "Must be a data frame" unless df.is_a?(Rover::DataFrame)

  unless df.include?("ds") && df.include?("y")
    raise ArgumentError, "Data frame must have ds and y columns"
  end

  history = df[!df["y"].missing]
  raise Error, "Data has less than 2 non-nil rows" if history.size < 2

  @history_dates = to_datetime(df["ds"]).sort
  history = setup_dataframe(history, initialize_scales: true)
  @history = history
  set_auto_seasonalities
  seasonal_features, prior_scales, component_cols, modes = make_all_seasonality_features(history)
  @train_component_cols = component_cols
  @component_modes = modes
  @fit_kwargs = kwargs.dup # TODO deep dup?

  set_changepoints

  trend_indicator = {"linear" => 0, "logistic" => 1, "flat" => 2}

  dat = {
    "T" => history.shape[0],
    "K" => seasonal_features.shape[1],
    "S" => @changepoints_t.size,
    "y" => history["y_scaled"],
    "t" => history["t"],
    "t_change" => @changepoints_t,
    "X" => seasonal_features,
    "sigmas" => prior_scales,
    "tau" => @changepoint_prior_scale,
    "trend_indicator" => trend_indicator[@growth],
    "s_a" => component_cols["additive_terms"],
    "s_m" => component_cols["multiplicative_terms"]
  }

  if @growth == "linear"
    dat["cap"] = Numo::DFloat.zeros(@history.shape[0])
    kinit = linear_growth_init(history)
  elsif @growth == "flat"
    dat["cap"] = Numo::DFloat.zeros(@history.shape[0])
    kinit = flat_growth_init(history)
  else
    dat["cap"] = history["cap_scaled"]
    kinit = logistic_growth_init(history)
  end

  stan_init = {
    "k" => kinit[0],
    "m" => kinit[1],
    "delta" => Numo::DFloat.zeros(@changepoints_t.size),
    "beta" => Numo::DFloat.zeros(seasonal_features.shape[1]),
    "sigma_obs" => 1
  }

  if history["y"].min == history["y"].max && (@growth == "linear" || @growth == "flat")
    # Nothing to fit.
    @params = stan_init
    @params["sigma_obs"] = 1e-9
    @params.each do |par, _|
      @params[par] = Numo::NArray.asarray([@params[par]])
    end
  elsif @mcmc_samples > 0
    @params = @stan_backend.sampling(stan_init, dat, @mcmc_samples, **kwargs)
  else
    @params = @stan_backend.fit(stan_init, dat, **kwargs)
  end

  # If no changepoints were requested, replace delta with 0s
  if @changepoints.size == 0
    # Fold delta into the base rate k
    # Numo doesn't support -1 with reshape
    negative_one = @params["delta"].shape.inject(&:*)
    @params["k"] = @params["k"] + @params["delta"].reshape(negative_one)
    @params["delta"] = Numo::DFloat.zeros(@params["delta"].shape).reshape(negative_one, 1)
  end

  self
end

#flat_growth_init(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 646

def flat_growth_init(df)
  k = 0
  m = df["y_scaled"].mean
  [k, m]
end

#flat_trend(t, m) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 798

def flat_trend(t, m)
  m_t = m * t.new_ones
  m_t
end

#fourier_series(dates, period, series_order) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 285

def fourier_series(dates, period, series_order)
  t = dates.map(&:to_i).to_numo / (3600 * 24.0)

  # no need for column_stack
  series_order.times.flat_map do |i|
    [Numo::DFloat::Math.method(:sin), Numo::DFloat::Math.method(:cos)].map do |fun|
      fun.call(2.0 * (i + 1) * Math::PI * t / period)
    end
  end
end

#initialize_scales(initialize_scales, df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 226

def initialize_scales(initialize_scales, df)
  return unless initialize_scales

  if @growth == "logistic" && df.include?("floor")
    @logistic_floor = true
    if @scaling == "absmax"
      @y_min = (df["y"] - df["floor"]).abs.min.to_f
      @y_scale = (df["y"] - df["floor"]).abs.max.to_f
    elsif @scaling == "minmax"
      @y_min = df["floor"].min
      @y_scale = (df["cap"].max - @y_min).to_f
    end
  else
    if @scaling == "absmax"
      @y_min = 0.0
      @y_scale = df["y"].abs.max.to_f
    elsif @scaling == "minmax"
      @y_min = df["y"].min
      @y_scale =  (df["y"].max - @y_min).to_f
    end
  end
  @y_scale = 1 if @y_scale == 0
  @start = df["ds"].min
  @t_scale = df["ds"].max - @start
end

#linear_growth_init(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 609

def linear_growth_init(df)
  i0 = 0
  i1 = df.size - 1
  t = df["t"][i1] - df["t"][i0]
  k = (df["y_scaled"][i1] - df["y_scaled"][i0]) / t
  m = df["y_scaled"][i0] - k * df["t"][i0]
  [k, m]
end

#logistic_growth_init(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 618

def logistic_growth_init(df)
  i0 = 0
  i1 = df.size - 1
  t = df["t"][i1] - df["t"][i0]

  # Force valid values, in case y > cap or y < 0
  c0 = df["cap_scaled"][i0]
  c1 = df["cap_scaled"][i1]
  y0 = [0.01 * c0, [0.99 * c0, df["y_scaled"][i0]].min].max
  y1 = [0.01 * c1, [0.99 * c1, df["y_scaled"][i1]].min].max

  r0 = c0 / y0
  r1 = c1 / y1

  if (r0 - r1).abs <= 0.01
    r0 = 1.05 * r0
  end

  l0 = Math.log(r0 - 1)
  l1 = Math.log(r1 - 1)

  # Initialize the offset
  m = l0 * t / (l0 - l1)
  # And the rate
  k = (l0 - l1) / t
  [k, m]
end

#make_all_seasonality_features(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 443

def make_all_seasonality_features(df)
  seasonal_features = []
  prior_scales = []
  modes = {"additive" => [], "multiplicative" => []}

  # Seasonality features
  @seasonalities.each do |name, props|
    features = make_seasonality_features(
      df["ds"],
      props[:period],
      props[:fourier_order],
      name
    )
    if props[:condition_name]
      features[!df.where(props[:condition_name])] = 0
    end
    seasonal_features << features
    prior_scales.concat([props[:prior_scale]] * features.shape[1])
    modes[props[:mode]] << name
  end

  # Holiday features
  holidays = construct_holiday_dataframe(df["ds"])
  if holidays.size > 0
    features, holiday_priors, holiday_names = make_holiday_features(df["ds"], holidays)
    seasonal_features << features
    prior_scales.concat(holiday_priors)
    modes[@seasonality_mode].concat(holiday_names)
  end

  # Additional regressors
  @extra_regressors.each do |name, props|
    seasonal_features << Rover::DataFrame.new({name => df[name]})
    prior_scales << props[:prior_scale]
    modes[props[:mode]] << name
  end

  # Dummy to prevent empty X
  if seasonal_features.size == 0
    seasonal_features << Rover::DataFrame.new({"zeros" => [0] * df.shape[0]})
    prior_scales << 1.0
  end

  seasonal_features = df_concat_axis_one(seasonal_features)

  component_cols, modes = regressor_column_matrix(seasonal_features, modes)

  [seasonal_features, prior_scales, component_cols, modes]
end

#make_future_dataframe(periods:, freq: "D", include_history: true) ⇒ Object

Raises:



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# File 'lib/prophet/forecaster.rb', line 961

def make_future_dataframe(periods:, freq: "D", include_history: true)
  raise Error, "Model has not been fit" unless @history_dates
  last_date = @history_dates.max
  # TODO add more freq
  # https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
  case freq
  when /\A\d+S\z/
    secs = freq.to_i
    dates = (periods + 1).times.map { |i| last_date + i * secs }
  when "H"
    hour = 3600
    dates = (periods + 1).times.map { |i| last_date + i * hour }
  when "D"
    # days have constant length with UTC (no DST or leap seconds)
    day = 24 * 3600
    dates = (periods + 1).times.map { |i| last_date + i * day }
  when "W"
    week = 7 * 24 * 3600
    dates = (periods + 1).times.map { |i| last_date + i * week }
  when "MS"
    dates = [last_date]
    # TODO reset day from last date, but keep time
    periods.times do
      dates << dates.last.to_datetime.next_month.to_time.utc
    end
  when "QS"
    dates = [last_date]
    # TODO reset day and month from last date, but keep time
    periods.times do
      dates << dates.last.to_datetime.next_month.next_month.next_month.to_time.utc
    end
  when "YS"
    dates = [last_date]
    # TODO reset day and month from last date, but keep time
    periods.times do
      dates << dates.last.to_datetime.next_year.to_time.utc
    end
  else
    raise ArgumentError, "Unknown freq: #{freq}"
  end
  dates.select! { |d| d > last_date }
  dates = dates.last(periods)
  dates = @history_dates.to_numo.concatenate(Numo::NArray.cast(dates)) if include_history
  Rover::DataFrame.new({"ds" => dates})
end

#make_holiday_features(dates, holidays) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 326

def make_holiday_features(dates, holidays)
  expanded_holidays = Hash.new { |hash, key| hash[key] = Numo::DFloat.zeros(dates.size) }
  prior_scales = {}
  # Makes an index so we can perform `get_loc` below.
  # Strip to just dates.
  row_index = dates.map(&:to_date)

  holidays.each_row do |row|
    dt = row["ds"]
    lw = nil
    uw = nil
    begin
      lw = row["lower_window"].to_i
      uw = row["upper_window"].to_i
    rescue IndexError
      lw = 0
      uw = 0
    end
    ps = @holidays_prior_scale
    if prior_scales[row["holiday"]] && prior_scales[row["holiday"]] != ps
      raise ArgumentError, "Holiday #{row["holiday"].inspect} does not have consistent prior scale specification."
    end
    raise ArgumentError, "Prior scale must be > 0" if ps <= 0
    prior_scales[row["holiday"]] = ps

    lw.upto(uw).each do |offset|
      occurrence = dt ? dt + offset : nil
      loc = occurrence ? row_index.to_a.index(occurrence) : nil
      key = "#{row["holiday"]}_delim_#{offset >= 0 ? "+" : "-"}#{offset.abs}"
      if loc
        expanded_holidays[key][loc] = 1.0
      else
        expanded_holidays[key]  # Access key to generate value
      end
    end
  end
  holiday_features = Rover::DataFrame.new(expanded_holidays)
  # Make sure column order is consistent
  holiday_features = holiday_features[holiday_features.vector_names.sort]
  prior_scale_list = holiday_features.vector_names.map { |h| prior_scales[h.split("_delim_")[0]] }
  holiday_names = prior_scales.keys
  # Store holiday names used in fit
  if @train_holiday_names.nil?
    @train_holiday_names = Rover::Vector.new(holiday_names)
  end
  [holiday_features, prior_scale_list, holiday_names]
end

#make_seasonality_features(dates, period, series_order, prefix) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 296

def make_seasonality_features(dates, period, series_order, prefix)
  features = fourier_series(dates, period, series_order)
  Rover::DataFrame.new(features.map.with_index { |v, i| ["#{prefix}_delim_#{i + 1}", v] }.to_h)
end

#parse_seasonality_args(name, arg, auto_disable, default_order) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 540

def parse_seasonality_args(name, arg, auto_disable, default_order)
  case arg
  when "auto"
    fourier_order = 0
    if @seasonalities.include?(name)
      logger.info "Found custom seasonality named #{name.inspect}, disabling built-in #{name.inspect}seasonality."
    elsif auto_disable
      logger.info "Disabling #{name} seasonality. Run prophet with #{name}_seasonality: true to override this."
    else
      fourier_order = default_order
    end
  when true
    fourier_order = default_order
  when false
    fourier_order = 0
  else
    fourier_order = arg.to_i
  end
  fourier_order
end

#piecewise_linear(t, deltas, k, m, changepoint_ts) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 765

def piecewise_linear(t, deltas, k, m, changepoint_ts)
  # Intercept changes
  gammas = -changepoint_ts * deltas
  # Get cumulative slope and intercept at each t
  k_t = t.new_ones * k
  m_t = t.new_ones * m
  changepoint_ts.each_with_index do |t_s, s|
    indx = t >= t_s
    k_t[indx] += deltas[s]
    m_t[indx] += gammas[s]
  end
  k_t * t + m_t
end

#piecewise_logistic(t, cap, deltas, k, m, changepoint_ts) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 779

def piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
  k_1d = Numo::NArray.asarray(k)
  k_1d = k_1d.reshape(1) if k_1d.ndim < 1
  k_cum = k_1d.concatenate(deltas.cumsum + k)
  gammas = Numo::DFloat.zeros(changepoint_ts.size)
  changepoint_ts.each_with_index do |t_s, i|
    gammas[i] = (t_s - m - gammas.sum) * (1 - k_cum[i] / k_cum[i + 1])
  end
  # Get cumulative rate and offset at each t
  k_t = t.new_ones * k
  m_t = t.new_ones * m
  changepoint_ts.each_with_index do |t_s, s|
    indx = t >= t_s
    k_t[indx] += deltas[s]
    m_t[indx] += gammas[s]
  end
  cap.to_numo / (1 + Numo::NMath.exp(-k_t * (t - m_t)))
end

#predict(df = nil) ⇒ Object

Raises:



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# File 'lib/prophet/forecaster.rb', line 737

def predict(df = nil)
  raise Error, "Model has not been fit." unless @history

  if df.nil?
    df = @history.dup
  else
    raise ArgumentError, "Dataframe has no rows." if df.shape[0] == 0
    df = setup_dataframe(df.dup)
  end

  df["trend"] = predict_trend(df)
  seasonal_components = predict_seasonal_components(df)
  if @uncertainty_samples
    intervals = predict_uncertainty(df)
  else
    intervals = nil
  end

  # Drop columns except ds, cap, floor, and trend
  cols = ["ds", "trend"]
  cols << "cap" if df.include?("cap")
  cols << "floor" if @logistic_floor
  # Add in forecast components
  df2 = df_concat_axis_one([df[cols], intervals, seasonal_components])
  df2["yhat"] = df2["trend"] * (df2["multiplicative_terms"] + 1) + df2["additive_terms"]
  df2
end

#predict_seasonal_components(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 821

def predict_seasonal_components(df)
  seasonal_features, _, component_cols, _ = make_all_seasonality_features(df)
  if @uncertainty_samples
    lower_p = 100 * (1.0 - @interval_width) / 2
    upper_p = 100 * (1.0 + @interval_width) / 2
  end

  x = seasonal_features.to_numo
  data = {}
  (component_cols.vector_names - ["col"]).each do |component|
    beta_c =  @params["beta"] * component_cols[component].to_numo

    comp = x.dot(beta_c.transpose)
    if @component_modes["additive"].include?(component)
      comp *= @y_scale
    end
    data[component] = comp.mean(axis: 1, nan: true)
    if @uncertainty_samples
      data["#{component}_lower"] = comp.percentile(lower_p, axis: 1)
      data["#{component}_upper"] = comp.percentile(upper_p, axis: 1)
    end
  end
  Rover::DataFrame.new(data)
end

#predict_trend(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 803

def predict_trend(df)
  k = @params["k"].mean(nan: true)
  m = @params["m"].mean(nan: true)
  deltas = @params["delta"].mean(axis: 0, nan: true)

  t = Numo::NArray.asarray(df["t"].to_a)
  if @growth == "linear"
    trend = piecewise_linear(t, deltas, k, m, @changepoints_t)
  elsif @growth == "logistic"
    cap = df["cap_scaled"]
    trend = piecewise_logistic(t, cap, deltas, k, m, @changepoints_t)
  elsif @growth == "flat"
    trend = flat_trend(t, m)
  end

  trend * @y_scale + Numo::NArray.asarray(df["floor"].to_a)
end

#predict_uncertainty(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 885

def predict_uncertainty(df)
  sim_values = sample_posterior_predictive(df)

  lower_p = 100 * (1.0 - @interval_width) / 2
  upper_p = 100 * (1.0 + @interval_width) / 2

  series = {}
  ["yhat", "trend"].each do |key|
    series["#{key}_lower"] = sim_values[key].percentile(lower_p, axis: 1)
    series["#{key}_upper"] = sim_values[key].percentile(upper_p, axis: 1)
  end

  Rover::DataFrame.new(series)
end

#predictive_samples(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 879

def predictive_samples(df)
  df = setup_dataframe(df.dup)
  sim_values = sample_posterior_predictive(df)
  sim_values
end

#regressor_column_matrix(seasonal_features, modes) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 493

def regressor_column_matrix(seasonal_features, modes)
  components = Rover::DataFrame.new(
    "col" => seasonal_features.shape[1].times.to_a,
    "component" => seasonal_features.vector_names.map { |x| x.split("_delim_")[0] }
  )

  # Add total for holidays
  if @train_holiday_names
    components = add_group_component(components, "holidays", @train_holiday_names.uniq)
  end
  # Add totals additive and multiplicative components, and regressors
  ["additive", "multiplicative"].each do |mode|
    components = add_group_component(components, "#{mode}_terms", modes[mode])
    regressors_by_mode = @extra_regressors.select { |r, props| props[:mode] == mode }
      .map { |r, props| r }
    components = add_group_component(components, "extra_regressors_#{mode}", regressors_by_mode)

    # Add combination components to modes
    modes[mode] << "#{mode}_terms"
    modes[mode] << "extra_regressors_#{mode}"
  end
  # After all of the additive/multiplicative groups have been added,
  modes[@seasonality_mode] << "holidays"
  # Convert to a binary matrix
  component_cols = components["col"].crosstab(components["component"])
  component_cols["col"] = component_cols.delete("_")

  # Add columns for additive and multiplicative terms, if missing
  ["additive_terms", "multiplicative_terms"].each do |name|
    component_cols[name] = 0 unless component_cols.include?(name)
  end

  # TODO validation

  [component_cols, modes]
end

#sample_model(df, seasonal_features, iteration, s_a, s_m) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 900

def sample_model(df, seasonal_features, iteration, s_a, s_m)
  trend = sample_predictive_trend(df, iteration)

  beta = @params["beta"][iteration, true]
  xb_a = seasonal_features.dot(beta * s_a) * @y_scale
  xb_m = seasonal_features.dot(beta * s_m)

  sigma = @params["sigma_obs"][iteration]
  noise = Numo::DFloat.new(*df.shape[0]).rand_norm(0, sigma) * @y_scale

  # skip data frame for performance
  {
    "yhat" => trend * (1 + xb_m) + xb_a + noise,
    "trend" => trend
  }
end

#sample_posterior_predictive(df) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 846

def sample_posterior_predictive(df)
  n_iterations = @params["k"].shape[0]
  samp_per_iter = [1, (@uncertainty_samples / n_iterations.to_f).ceil].max

  # Generate seasonality features once so we can re-use them.
  seasonal_features, _, component_cols, _ = make_all_seasonality_features(df)

  # convert to Numo for performance
  seasonal_features = seasonal_features.to_numo
  additive_terms = component_cols["additive_terms"].to_numo
  multiplicative_terms = component_cols["multiplicative_terms"].to_numo

  sim_values = {"yhat" => [], "trend" => []}
  n_iterations.times do |i|
    samp_per_iter.times do
      sim = sample_model(
        df,
        seasonal_features,
        i,
        additive_terms,
        multiplicative_terms
      )
      sim_values.each_key do |key|
        sim_values[key] << sim[key]
      end
    end
  end
  sim_values.each do |k, v|
    sim_values[k] = Numo::NArray.column_stack(v)
  end
  sim_values
end

#sample_predictive_trend(df, iteration) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 917

def sample_predictive_trend(df, iteration)
  k = @params["k"][iteration]
  m = @params["m"][iteration]
  deltas = @params["delta"][iteration, true]

  t = Numo::NArray.asarray(df["t"].to_a)
  upper_t = t.max

  # New changepoints from a Poisson process with rate S on [1, T]
  if upper_t > 1
    s = @changepoints_t.size
    n_changes = poisson(s * (upper_t - 1))
  else
    n_changes = 0
  end
  if n_changes > 0
    changepoint_ts_new = 1 + Numo::DFloat.new(n_changes).rand * (upper_t - 1)
    changepoint_ts_new.sort
  else
    changepoint_ts_new = []
  end

  # Get the empirical scale of the deltas, plus epsilon to avoid NaNs.
  lambda_ = deltas.abs.mean + 1e-8

  # Sample deltas
  deltas_new = laplace(0, lambda_, n_changes)

  # Prepend the times and deltas from the history
  changepoint_ts = @changepoints_t.concatenate(changepoint_ts_new)
  deltas = deltas.concatenate(deltas_new)

  if @growth == "linear"
    trend = piecewise_linear(t, deltas, k, m, changepoint_ts)
  elsif @growth == "logistic"
    cap = df["cap_scaled"]
    trend = piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
  elsif @growth == "flat"
    trend = flat_trend(t, m)
  end

  trend * @y_scale + Numo::NArray.asarray(df["floor"].to_a)
end

#set_auto_seasonalities ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 561

def set_auto_seasonalities
  first = @history["ds"].min
  last = @history["ds"].max
  dt = @history["ds"].diff
  min_dt = dt.min

  days = 86400

  # Yearly seasonality
  yearly_disable = last - first < 730 * days
  fourier_order = parse_seasonality_args("yearly", @yearly_seasonality, yearly_disable, 10)
  if fourier_order > 0
    @seasonalities["yearly"] = {
      period: 365.25,
      fourier_order: fourier_order,
      prior_scale: @seasonality_prior_scale,
      mode: @seasonality_mode,
      condition_name: nil
    }
  end

  # Weekly seasonality
  weekly_disable = last - first < 14 * days || min_dt >= 7 * days
  fourier_order = parse_seasonality_args("weekly", @weekly_seasonality, weekly_disable, 3)
  if fourier_order > 0
    @seasonalities["weekly"] = {
      period: 7,
      fourier_order: fourier_order,
      prior_scale: @seasonality_prior_scale,
      mode: @seasonality_mode,
      condition_name: nil
    }
  end

  # Daily seasonality
  daily_disable = last - first < 2 * days || min_dt >= 1 * days
  fourier_order = parse_seasonality_args("daily", @daily_seasonality, daily_disable, 4)
  if fourier_order > 0
    @seasonalities["daily"] = {
      period: 1,
      fourier_order: fourier_order,
      prior_scale: @seasonality_prior_scale,
      mode: @seasonality_mode,
      condition_name: nil
    }
  end
end

#set_changepoints ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 252

def set_changepoints
  if @changepoints
    if @changepoints.size > 0
      too_low = @changepoints.min < @history["ds"].min
      too_high = @changepoints.max > @history["ds"].max
      if too_low || too_high
        raise ArgumentError, "Changepoints must fall within training data."
      end
    end
  else
    hist_size = (@history.shape[0] * @changepoint_range).floor

    if @n_changepoints + 1 > hist_size
      @n_changepoints = hist_size - 1
      logger.info "n_changepoints greater than number of observations. Using #{@n_changepoints}"
    end

    if @n_changepoints > 0
      step = (hist_size - 1) / @n_changepoints.to_f
      cp_indexes = (@n_changepoints + 1).times.map { |i| (i * step).round }
      @changepoints = Rover::Vector.new(@history["ds"].to_a.values_at(*cp_indexes)).tail(-1)
    else
      @changepoints = []
    end
  end

  if @changepoints.size > 0
    @changepoints_t = (@changepoints.map(&:to_i).sort.to_numo.cast_to(Numo::DFloat) - @start.to_i) / @t_scale.to_f
  else
    @changepoints_t = Numo::NArray.asarray([0])
  end
end

#setup_dataframe(df, initialize_scales: false) ⇒ Object

Raises:

  • (ArgumentError) 


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# File 'lib/prophet/forecaster.rb', line 156

def setup_dataframe(df, initialize_scales: false)
  if df.include?("y")
    df["y"] = df["y"].map(&:to_f)
    raise ArgumentError, "Found infinity in column y." unless df["y"].all?(&:finite?)
  end
  # TODO support integers

  df["ds"] = to_datetime(df["ds"])

  raise ArgumentError, "Found NaN in column ds." if df["ds"].any?(&:nil?)

  @extra_regressors.each_key do |name|
    if !df.include?(name)
      raise ArgumentError, "Regressor #{name.inspect} missing from dataframe"
    end
    df[name] = df[name].map(&:to_f)
    if df[name].any?(&:nil?)
      raise ArgumentError, "Found NaN in column #{name.inspect}"
    end
  end
  @seasonalities.each_value do |props|
    condition_name = props[:condition_name]
    if condition_name
      if !df.include?(condition_name)
        raise ArgumentError, "Condition #{condition_name.inspect} missing from dataframe"
      end
      if df.where(!df[condition_name].in([true, false])).any?
        raise ArgumentError, "Found non-boolean in column #{condition_name.inspect}"
      end
    end
  end

  df = df.sort_by { |r| r["ds"] }

  initialize_scales(initialize_scales, df)

  if @logistic_floor
    unless df.include?("floor")
      raise ArgumentError, "Expected column \"floor\"."
    end
  else
    if @scaling == "absmax"
      df["floor"] = 0
    elsif @scaling == "minmax"
      df["floor"] = @y_min
    end
  end

  if @growth == "logistic"
    unless df.include?("cap")
      raise ArgumentError, "Capacities must be supplied for logistic growth in column \"cap\""
    end
    if df[df["cap"] <= df["floor"]].size > 0
      raise ArgumentError, "cap must be greater than floor (which defaults to 0)."
    end
    df["cap_scaled"] = (df["cap"] - df["floor"]) / @y_scale.to_f
  end

  df["t"] = (df["ds"] - @start) / @t_scale.to_f
  if df.include?("y")
    df["y_scaled"] = (df["y"] - df["floor"]) / @y_scale.to_f
  end

  @extra_regressors.each do |name, props|
    df[name] = (df[name] - props[:mu]) / props[:std].to_f
  end

  df
end

#to_json ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 1007

def to_json
  require "json"

  JSON.generate(as_json)
end

#validate_column_name(name, check_holidays: true, check_seasonalities: true, check_regressors: true) ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 125

def validate_column_name(name, check_holidays: true, check_seasonalities: true, check_regressors: true)
  if name.include?("_delim_")
    raise ArgumentError, "Name cannot contain \"_delim_\""
  end
  reserved_names = [
    "trend", "additive_terms", "daily", "weekly", "yearly",
    "holidays", "zeros", "extra_regressors_additive", "yhat",
    "extra_regressors_multiplicative", "multiplicative_terms"
  ]
  rn_l = reserved_names.map { |n| "#{n}_lower" }
  rn_u = reserved_names.map { |n| "#{n}_upper" }
  reserved_names.concat(rn_l)
  reserved_names.concat(rn_u)
  reserved_names.concat(["ds", "y", "cap", "floor", "y_scaled", "cap_scaled"])
  if reserved_names.include?(name)
    raise ArgumentError, "Name #{name.inspect} is reserved."
  end
  if check_holidays && @holidays && @holidays["holiday"].uniq.to_a.include?(name)
    raise ArgumentError, "Name #{name.inspect} already used for a holiday."
  end
  if check_holidays && @country_holidays && get_holiday_names(@country_holidays).to_a.include?(name)
    raise ArgumentError, "Name #{name.inspect} is a holiday name in #{@country_holidays.inspect}."
  end
  if check_seasonalities && @seasonalities[name]
    raise ArgumentError, "Name #{name.inspect} already used for a seasonality."
  end
  if check_regressors && @extra_regressors[name]
    raise ArgumentError, "Name #{name.inspect} already used for an added regressor."
  end
end

#validate_inputs ⇒ Object 



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# File 'lib/prophet/forecaster.rb', line 90

def validate_inputs
  if !["linear", "logistic", "flat"].include?(@growth)
    raise ArgumentError, "Parameter \"growth\" should be \"linear\", \"logistic\", or \"flat\"."
  end
  if @changepoint_range < 0 || @changepoint_range > 1
    raise ArgumentError, "Parameter \"changepoint_range\" must be in [0, 1]"
  end
  if @holidays
    if !(@holidays.is_a?(Rover::DataFrame) && @holidays.include?("ds") && @holidays.include?("holiday"))
      raise ArgumentError, "holidays must be a DataFrame with \"ds\" and \"holiday\" columns."
    end
    @holidays["ds"] = to_datetime(@holidays["ds"])
    has_lower = @holidays.include?("lower_window")
    has_upper = @holidays.include?("upper_window")
    if has_lower ^ has_upper # xor
      raise ArgumentError, "Holidays must have both lower_window and upper_window, or neither"
    end
    if has_lower
      if @holidays["lower_window"].max > 0
        raise ArgumentError, "Holiday lower_window should be <= 0"
      end
      if @holidays["upper_window"].min < 0
        raise ArgumentError, "Holiday upper_window should be >= 0"
      end
    end
    @holidays["holiday"].uniq.each do |h|
      validate_column_name(h, check_holidays: false)
    end
  end

  if !["additive", "multiplicative"].include?(@seasonality_mode)
    raise ArgumentError, "seasonality_mode must be \"additive\" or \"multiplicative\""
  end
end