Facebook-Prophet/python/fbprophet/forecaster.py

# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from collections import defaultdict
from datetime import timedelta
import pickle

from matplotlib import pyplot as plt
from matplotlib.dates import DateFormatter, MonthLocator, num2date
from matplotlib.ticker import FuncFormatter

import numpy as np
import pandas as pd

# fb-block 1 start
import pkg_resources
# fb-block 1 end

try:
    import pystan
except ImportError:
    print('You cannot run prophet without pystan installed')
    raise

# fb-block 2

class Prophet(object):
    """Prophet forecaster.

    Parameters
    ----------
    growth: String 'linear' or 'logistic' to specify a linear or logistic
        trend.
    changepoints: List of dates at which to include potential changepoints. If
        not specified, potential changepoints are selected automatically.
    n_changepoints: Number of potential changepoints to include. Not used
        if input `changepoints` is supplied. If `changepoints` is not supplied,
        then n.changepoints potential changepoints are selected uniformly from
        the first 80 percent of the history.
    yearly_seasonality: Boolean, fit yearly seasonality.
    weekly_seasonality: Boolean, fit weekly seasonality.
    holidays: pd.DataFrame with columns holiday (string) and ds (date type)
        and optionally columns lower_window and upper_window which specify a
        range of days around the date to be included as holidays.
        lower_window=-2 will include 2 days prior to the date as holidays.
    seasonality_prior_scale: Parameter modulating the strength of the
        seasonality model. Larger values allow the model to fit larger seasonal
        fluctuations, smaller values dampen the seasonality.
    holidays_prior_scale: Parameter modulating the strength of the holiday
        components model.
    changepoint_prior_scale: Parameter modulating the flexibility of the
        automatic changepoint selection. Large values will allow many
        changepoints, small values will allow few changepoints.
    mcmc_samples: Integer, if great than 0, will do full Bayesian inference
        with the specified number of MCMC samples. If 0, will do MAP
        estimation.
    interval_width: Float, width of the uncertainty intervals provided
        for the forecast. If mcmc_samples=0, this will be only the uncertainty
        in the trend using the MAP estimate of the extrapolated generative
        model. If mcmc.samples>0, this will be integrated over all model
        parameters, which will include uncertainty in seasonality.
    uncertainty_samples: Number of simulated draws used to estimate
        uncertainty intervals.
    """

    def __init__(
            self,
            growth='linear',
            changepoints=None,
            n_changepoints=25,
            yearly_seasonality=True,
            weekly_seasonality=True,
            holidays=None,
            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,
    ):
        self.growth = growth

        self.changepoints = pd.to_datetime(changepoints)
        if self.changepoints is not None:
            self.n_changepoints = len(self.changepoints)
        else:
            self.n_changepoints = n_changepoints

        self.yearly_seasonality = yearly_seasonality
        self.weekly_seasonality = weekly_seasonality

        if holidays is not None:
            if not (
                isinstance(holidays, pd.DataFrame)
                and 'ds' in holidays
                and 'holiday' in holidays
            ):
                raise ValueError("holidays must be a DataFrame with 'ds' and "
                                 "'holiday' columns.")
            holidays['ds'] = pd.to_datetime(holidays['ds'])
        self.holidays = holidays

        self.seasonality_prior_scale = float(seasonality_prior_scale)
        self.changepoint_prior_scale = float(changepoint_prior_scale)
        self.holidays_prior_scale = float(holidays_prior_scale)

        self.mcmc_samples = mcmc_samples
        self.interval_width = interval_width
        self.uncertainty_samples = uncertainty_samples

        # Set during fitting
        self.start = None
        self.y_scale = None
        self.t_scale = None
        self.changepoints_t = None
        self.stan_fit = None
        self.params = {}
        self.history = None
        self.history_dates = None
        self.validate_inputs()

    def validate_inputs(self):
        """Validates the inputs to Prophet."""
        if self.growth not in ('linear', 'logistic'):
            raise ValueError(
                "Parameter 'growth' should be 'linear' or 'logistic'.")
        if self.holidays is not None:
            has_lower = 'lower_window' in self.holidays
            has_upper = 'upper_window' in self.holidays
            if has_lower + has_upper == 1:
                raise ValueError('Holidays must have both lower_window and ' +
                                 'upper_window, or neither')
            if has_lower:
                if max(self.holidays['lower_window']) > 0:
                    raise ValueError('Holiday lower_window should be <= 0')
                if min(self.holidays['upper_window']) < 0:
                    raise ValueError('Holiday upper_window should be >= 0')
            for h in self.holidays['holiday'].unique():
                if '_delim_' in h:
                    raise ValueError('Holiday name cannot contain "_delim_"')
                if h in ['zeros', 'yearly', 'weekly', 'yhat', 'seasonal',
                         'trend']:
                    raise ValueError('Holiday name {} reserved.'.format(h))

    @classmethod
    def get_linear_model(cls):
        """Load compiled linear trend Stan model"""
        # fb-block 3
        # fb-block 4 start
        model_file = pkg_resources.resource_filename(
            'fbprophet',
            'stan_models/linear_growth.pkl'
        )
        # fb-block 4 end
        with open(model_file, 'rb') as f:
            return pickle.load(f)

    @classmethod
    def get_logistic_model(cls):
        """Load compiled logistic trend Stan model"""
        # fb-block 5
        # fb-block 6 start
        model_file = pkg_resources.resource_filename(
            'fbprophet',
            'stan_models/logistic_growth.pkl'
        )
        # fb-block 6 end
        with open(model_file, 'rb') as f:
            return pickle.load(f)

    def setup_dataframe(self, df, initialize_scales=False):
        """Prepare dataframe for fitting or predicting.

        Adds a time index and scales y. Creates auxillary columns 't', 't_ix',
        'y_scaled', and 'cap_scaled'. These columns are used during both
        fitting and predicting.

        Parameters
        ----------
        df: pd.DataFrame with columns ds, y, and cap if logistic growth.
        initialize_scales: Boolean set scaling factors in self from df.

        Returns
        -------
        pd.DataFrame prepared for fitting or predicting.
        """
        if 'y' in df:
            df['y'] = pd.to_numeric(df['y'])
        df['ds'] = pd.to_datetime(df['ds'])

        df = df.sort_values('ds')
        df.reset_index(inplace=True, drop=True)

        if initialize_scales:
            self.y_scale = df['y'].max()
            self.start = df['ds'].min()
            self.t_scale = df['ds'].max() - self.start

        df['t'] = (df['ds'] - self.start) / self.t_scale
        if 'y' in df:
            df['y_scaled'] = df['y'] / self.y_scale

        if self.growth == 'logistic':
            assert 'cap' in df
            df['cap_scaled'] = df['cap'] / self.y_scale

        return df

    def set_changepoints(self):
        """Set changepoints

        Sets m$changepoints to the dates of changepoints. Either:
        1) The changepoints were passed in explicitly.
            A) They are empty.
            B) They are not empty, and need validation.
        2) We are generating a grid of them.
        3) The user prefers no changepoints be used.
        """
        if self.changepoints is not None:
            if len(self.changepoints) == 0:
                pass
            else:
                too_low = min(self.changepoints) < self.history['ds'].min()
                too_high = max(self.changepoints) > self.history['ds'].max()
                if too_low or too_high:
                    raise ValueError('Changepoints must fall within training data.')
        elif self.n_changepoints > 0:
            # Place potential changepoints evenly throuh first 80% of history
            max_ix = np.floor(self.history.shape[0] * 0.8)
            cp_indexes = (
                np.linspace(0, max_ix, self.n_changepoints + 1)
                .round()
                .astype(np.int)
            )
            self.changepoints = self.history.ix[cp_indexes]['ds'].tail(-1)
        else:
            # set empty changepoints
            self.changepoints = []
        if len(self.changepoints) > 0:
            self.changepoints_t = np.sort(np.array(
                (self.changepoints - self.start) / self.t_scale))
        else:
            self.changepoints_t = np.array([0])  # dummy changepoint

    def get_changepoint_matrix(self):
        """Gets changepoint matrix for history dataframe."""
        A = np.zeros((self.history.shape[0], len(self.changepoints_t)))
        for i, t_i in enumerate(self.changepoints_t):
            A[self.history['t'].values >= t_i, i] = 1
        return A

    @staticmethod
    def fourier_series(dates, period, series_order):
        """Provides Fourier series components with the specified frequency
        and order.

        Parameters
        ----------
        dates: pd.Series containing timestamps.
        period: Number of days of the period.
        series_order: Number of components.

        Returns
        -------
        Matrix with seasonality features.
        """
        # convert to days since epoch
        t = np.array(
            (dates - pd.datetime(1970, 1, 1))
            .dt.days
            .astype(np.float)
        )
        return np.column_stack([
            fun((2.0 * (i + 1) * np.pi * t / period))
            for i in range(series_order)
            for fun in (np.sin, np.cos)
        ])

    @classmethod
    def make_seasonality_features(cls, dates, period, series_order, prefix):
        """Data frame with seasonality features.

        Parameters
        ----------
        cls: Prophet class.
        dates: pd.Series containing timestamps.
        period: Number of days of the period.
        series_order: Number of components.
        prefix: Column name prefix.

        Returns
        -------
        pd.DataFrame with seasonality features.
        """
        features = cls.fourier_series(dates, period, series_order)
        columns = [
            '{}_delim_{}'.format(prefix, i + 1)
            for i in range(features.shape[1])
        ]
        return pd.DataFrame(features, columns=columns)

    def make_holiday_features(self, dates):
        """Construct a dataframe of holiday features.

        Parameters
        ----------
        dates: pd.Series containing timestamps used for computing seasonality.

        Returns
        -------
        pd.DataFrame with a column for each holiday.
        """
        # A smaller prior scale will shrink holiday estimates more
        scale_ratio = self.holidays_prior_scale / self.seasonality_prior_scale
        # Holds columns of our future matrix.
        expanded_holidays = defaultdict(lambda: np.zeros(dates.shape[0]))
        # Makes an index so we can perform `get_loc` below.
        row_index = pd.DatetimeIndex(dates)

        for ix, row in self.holidays.iterrows():
            dt = row.ds.date()
            try:
                lw = int(row.get('lower_window', 0))
                uw = int(row.get('upper_window', 0))
            except ValueError:
                lw = 0
                uw = 0
            for offset in range(lw, uw + 1):
                occurrence = dt + timedelta(days=offset)
                try:
                    loc = row_index.get_loc(occurrence)
                except KeyError:
                    loc = None

                key = '{}_delim_{}{}'.format(
                    row.holiday,
                    '+' if offset >= 0 else '-',
                    abs(offset)
                )
                if loc is not None:
                    expanded_holidays[key][loc] = scale_ratio
                else:
                    # Access key to generate value
                    expanded_holidays[key]

        # This relies pretty importantly on pandas keeping the columns in order.
        return pd.DataFrame(expanded_holidays)

    def make_all_seasonality_features(self, df):
        """Dataframe with seasonality features.

        Parameters
        ----------
        df: pd.DataFrame with dates for computing seasonality features.

        Returns
        -------
        pd.DataFrame with seasonality.
        """
        seasonal_features = [
            # Add a column of zeros in case no seasonality is used.
            pd.DataFrame({'zeros': np.zeros(df.shape[0])})
        ]

        # Seasonality features
        if self.yearly_seasonality:
            seasonal_features.append(self.make_seasonality_features(
                df['ds'],
                365.25,
                10,
                'yearly',
            ))

        if self.weekly_seasonality:
            seasonal_features.append(self.make_seasonality_features(
                df['ds'],
                7,
                3,
                'weekly',
            ))

        if self.holidays is not None:
            seasonal_features.append(self.make_holiday_features(df['ds']))
        return pd.concat(seasonal_features, axis=1)

    @staticmethod
    def linear_growth_init(df):
        """Initialize linear growth.

        Provides a strong initialization for linear growth by calculating the
        growth and offset parameters that pass the function through the first
        and last points in the time series.

        Parameters
        ----------
        df: pd.DataFrame with columns ds (date), y_scaled (scaled time series),
            and t (scaled time).

        Returns
        -------
        A tuple (k, m) with the rate (k) and offset (m) of the linear growth
        function.
        """
        i0, i1 = df['ds'].idxmin(), df['ds'].idxmax()
        T = df['t'].ix[i1] - df['t'].ix[i0]
        k = (df['y_scaled'].ix[i1] - df['y_scaled'].ix[i0]) / T
        m = df['y_scaled'].ix[i0] - k * df['t'].ix[i0]
        return (k, m)

    @staticmethod
    def logistic_growth_init(df):
        """Initialize logistic growth.

        Provides a strong initialization for logistic growth by calculating the
        growth and offset parameters that pass the function through the first
        and last points in the time series.

        Parameters
        ----------
        df: pd.DataFrame with columns ds (date), cap_scaled (scaled capacity),
            y_scaled (scaled time series), and t (scaled time).

        Returns
        -------
        A tuple (k, m) with the rate (k) and offset (m) of the logistic growth
        function.
        """
        i0, i1 = df['ds'].idxmin(), df['ds'].idxmax()
        T = df['t'].ix[i1] - df['t'].ix[i0]

        # Force valid values, in case y > cap.
        r0 = max(1.01, df['cap_scaled'].ix[i0] / df['y_scaled'].ix[i0])
        r1 = max(1.01, df['cap_scaled'].ix[i1] / df['y_scaled'].ix[i1])

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

        L0 = np.log(r0 - 1)
        L1 = np.log(r1 - 1)

        # Initialize the offset
        m = L0 * T / (L0 - L1)
        # And the rate
        k = L0 / m
        return (k, m)

    # fb-block 7
    def fit(self, df, **kwargs):
        """Fit the Prophet model.

        This sets self.params to contain the fitted model parameters. It is a
        dictionary parameter names as keys and the following items:
            k (Mx1 array): M posterior samples of the initial slope.
            m (Mx1 array): The initial intercept.
            delta (MxN array): The slope change at each of N changepoints.
            beta (MxK matrix): Coefficients for K seasonality features.
            sigma_obs (Mx1 array): Noise level.
        Note that M=1 if MAP estimation.

        Parameters
        ----------
        df: pd.DataFrame containing the history. Must have columns ds (date
            type) and y, the time series. If self.growth is 'logistic', then
            df must also have a column cap that specifies the capacity at
            each ds.
        kwargs: Additional arguments passed to the optimizing or sampling
            functions in Stan.

        Returns
        -------
        The fitted Prophet object.
        """
        history = df[df['y'].notnull()].copy()
        self.history_dates = pd.to_datetime(df['ds']).sort_values()

        history = self.setup_dataframe(history, initialize_scales=True)
        self.history = history
        seasonal_features = self.make_all_seasonality_features(history)

        self.set_changepoints()
        A = self.get_changepoint_matrix()

        dat = {
            'T': history.shape[0],
            'K': seasonal_features.shape[1],
            'S': len(self.changepoints_t),
            'y': history['y_scaled'],
            't': history['t'],
            'A': A,
            't_change': self.changepoints_t,
            'X': seasonal_features,
            'sigma': self.seasonality_prior_scale,
            'tau': self.changepoint_prior_scale,
        }

        if self.growth == 'linear':
            kinit = self.linear_growth_init(history)
            model = self.get_linear_model()
        else:
            dat['cap'] = history['cap_scaled']
            kinit = self.logistic_growth_init(history)
            model = self.get_logistic_model()

        def stan_init():
            return {
                'k': kinit[0],
                'm': kinit[1],
                'delta': np.zeros(len(self.changepoints_t)),
                'beta': np.zeros(seasonal_features.shape[1]),
                'sigma_obs': 1,
            }

        if self.mcmc_samples > 0:
            stan_fit = model.sampling(
                dat,
                init=stan_init,
                iter=self.mcmc_samples,
                **kwargs
            )
            for par in stan_fit.model_pars:
                self.params[par] = stan_fit[par]

        else:
            params = model.optimizing(dat, init=stan_init, iter=1e4, **kwargs)
            for par in params:
                self.params[par] = params[par].reshape((1, -1))

        # If no changepoints were requested, replace delta with 0s
        if len(self.changepoints) == 0:
            # Fold delta into the base rate k
            params['k'] = params['k'] + params['delta']
            params['delta'] = np.zeros(params['delta'].shape)

        return self

    # fb-block 8
    def predict(self, df=None):
        """Predict using the prophet model.

        Parameters
        ----------
        df: pd.DataFrame with dates for predictions (column ds), and capacity
            (column cap) if logistic growth. If not provided, predictions are
            made on the history.

        Returns
        -------
        A pd.DataFrame with the forecast components.
        """
        if df is None:
            df = self.history.copy()
        else:
            df = self.setup_dataframe(df)

        df['trend'] = self.predict_trend(df)
        seasonal_components = self.predict_seasonal_components(df)
        intervals = self.predict_uncertainty(df)

        df2 = pd.concat((df, intervals, seasonal_components), axis=1)
        df2['yhat'] = df2['trend'] + df2['seasonal']
        return df2

    @staticmethod
    def piecewise_linear(t, deltas, k, m, changepoint_ts):
        """Evaluate the piecewise linear function.

        Parameters
        ----------
        t: np.array of times on which the function is evaluated.
        deltas: np.array of rate changes at each changepoint.
        k: Float initial rate.
        m: Float initial offset.
        changepoint_ts: np.array of changepoint times.

        Returns
        -------
        Vector y(t).
        """
        # Intercept changes
        gammas = -changepoint_ts * deltas
        # Get cumulative slope and intercept at each t
        k_t = k * np.ones_like(t)
        m_t = m * np.ones_like(t)
        for s, t_s in enumerate(changepoint_ts):
            indx = t >= t_s
            k_t[indx] += deltas[s]
            m_t[indx] += gammas[s]
        return k_t * t + m_t

    @staticmethod
    def piecewise_logistic(t, cap, deltas, k, m, changepoint_ts):
        """Evaluate the piecewise logistic function.

        Parameters
        ----------
        t: np.array of times on which the function is evaluated.
        cap: np.array of capacities at each t.
        deltas: np.array of rate changes at each changepoint.
        k: Float initial rate.
        m: Float initial offset.
        changepoint_ts: np.array of changepoint times.

        Returns
        -------
        Vector y(t).
        """
        # Compute offset changes
        k_cum = np.concatenate((np.atleast_1d(k), np.cumsum(deltas) + k))
        gammas = np.zeros(len(changepoint_ts))
        for i, t_s in enumerate(changepoint_ts):
            gammas[i] = (
                (t_s - m - np.sum(gammas))
                * (1 - k_cum[i] / k_cum[i + 1])
            )
        # Get cumulative rate and offset at each t
        k_t = k * np.ones_like(t)
        m_t = m * np.ones_like(t)
        for s, t_s in enumerate(changepoint_ts):
            indx = t >= t_s
            k_t[indx] += deltas[s]
            m_t[indx] += gammas[s]
        return cap / (1 + np.exp(-k_t * (t - m_t)))

    def predict_trend(self, df):
        """Predict trend using the prophet model.

        Parameters
        ----------
        df: Prediction dataframe.

        Returns
        -------
        Vector with trend on prediction dates.
        """
        k = np.nanmean(self.params['k'])
        m = np.nanmean(self.params['m'])
        deltas = np.nanmean(self.params['delta'], axis=0)

        t = np.array(df['t'])
        if self.growth == 'linear':
            trend = self.piecewise_linear(t, deltas, k, m, self.changepoints_t)
        else:
            cap = df['cap_scaled']
            trend = self.piecewise_logistic(
                t, cap, deltas, k, m, self.changepoints_t)

        return trend * self.y_scale

    def predict_seasonal_components(self, df):
        """Predict seasonality broken down into components.

        Parameters
        ----------
        df: Prediction dataframe.

        Returns
        -------
        Dataframe with seasonal components.
        """
        seasonal_features = self.make_all_seasonality_features(df)
        lower_p = 100 * (1.0 - self.interval_width) / 2
        upper_p = 100 * (1.0 + self.interval_width) / 2

        components = pd.DataFrame({
            'col': np.arange(seasonal_features.shape[1]),
            'component': [x.split('_delim_')[0] for x in seasonal_features.columns],
        })
        # Remove the placeholder
        components = components[components['component'] != 'zeros']

        if components.shape[0] > 0:
            X = seasonal_features.as_matrix()
            data = {}
            for component, features in components.groupby('component'):
                cols = features.col.tolist()
                comp_beta = self.params['beta'][:, cols]
                comp_features = X[:, cols]
                comp = (
                    np.matmul(comp_features, comp_beta.transpose())
                    * self.y_scale
                )
                data[component] = np.nanmean(comp, axis=1)
                data[component + '_lower'] = np.nanpercentile(comp, lower_p,
                                                              axis=1)
                data[component + '_upper'] = np.nanpercentile(comp, upper_p,
                                                              axis=1)

            component_predictions = pd.DataFrame(data)
            component_predictions['seasonal'] = (
                component_predictions[components['component'].unique()].sum(1))
        else:
            component_predictions = pd.DataFrame(
                {'seasonal': np.zeros(df.shape[0])})
        return component_predictions

    def predict_uncertainty(self, df):
        """Predict seasonality broken down into components.

        Parameters
        ----------
        df: Prediction dataframe.

        Returns
        -------
        Dataframe with uncertainty intervals.
        """
        n_iterations = self.params['k'].shape[0]
        samp_per_iter = max(1, int(np.ceil(
            self.uncertainty_samples / float(n_iterations)
        )))

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

        sim_values = {'yhat': [], 'trend': [], 'seasonal': []}
        for i in range(n_iterations):
            for j in range(samp_per_iter):
                sim = self.sample_model(df, seasonal_features, i)
                for key in sim_values:
                    sim_values[key].append(sim[key])

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

        series = {}
        for key, value in sim_values.items():
            mat = np.column_stack(value)
            series['{}_lower'.format(key)] = np.nanpercentile(mat, lower_p,
                                                              axis=1)
            series['{}_upper'.format(key)] = np.nanpercentile(mat, upper_p,
                                                              axis=1)

        return pd.DataFrame(series)

    def sample_model(self, df, seasonal_features, iteration):
        """Simulate observations from the extrapolated generative model.

        Parameters
        ----------
        df: Prediction dataframe.
        seasonal_features: pd.DataFrame of seasonal features.
        iteration: Int sampling iteration to use parameters from.

        Returns
        -------
        Dataframe with trend, seasonality, and yhat, each like df['t'].
        """
        trend = self.sample_predictive_trend(df, iteration)

        beta = self.params['beta'][iteration]
        seasonal = np.matmul(seasonal_features.as_matrix(), beta) * self.y_scale

        sigma = self.params['sigma_obs'][iteration]
        noise = np.random.normal(0, sigma, df.shape[0]) * self.y_scale

        return pd.DataFrame({
            'yhat': trend + seasonal + noise,
            'trend': trend,
            'seasonal': seasonal,
        })

    def sample_predictive_trend(self, df, iteration):
        """Simulate the trend using the extrapolated generative model.

        Parameters
        ----------
        df: Prediction dataframe.
        seasonal_features: pd.DataFrame of seasonal features.
        iteration: Int sampling iteration to use parameters from.

        Returns
        -------
        np.array of simulated trend over df['t'].
        """
        k = self.params['k'][iteration]
        m = self.params['m'][iteration]
        deltas = self.params['delta'][iteration]

        t = np.array(df['t'])
        T = t.max()

        if T > 1:
            # Get the time discretization of the history
            dt = np.diff(self.history['t'])
            dt = np.min(dt[dt > 0])
            # Number of time periods in the future
            N = np.ceil((T - 1) / float(dt))
            S = len(self.changepoints_t)

            prob_change = min(1, (S * (T - 1)) / N)
            n_changes = np.random.binomial(N, prob_change)

            # Sample ts
            changepoint_ts_new = sorted(np.random.uniform(1, T, n_changes))
        else:
            # Case where we're not extrapolating.
            changepoint_ts_new = []
            n_changes = 0

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

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

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

        if self.growth == 'linear':
            trend = self.piecewise_linear(t, deltas, k, m, changepoint_ts)
        else:
            cap = df['cap_scaled']
            trend = self.piecewise_logistic(t, cap, deltas, k, m,
                                            changepoint_ts)

        return trend * self.y_scale

    def make_future_dataframe(self, periods, freq='D', include_history=True):
        """Simulate the trend using the extrapolated generative model.

        Parameters
        ----------
        periods: Int number of periods to forecast forward.
        freq: Any valid frequency for pd.date_range, such as 'D' or 'M'.
        include_history: Boolean to include the historical dates in the data
            frame for predictions.

        Returns
        -------
        pd.Dataframe that extends forward from the end of self.history for the
        requested number of periods.
        """
        last_date = self.history_dates.max()
        dates = pd.date_range(
            start=last_date,
            periods=periods + 1,  # An extra in case we include start
            freq=freq)
        dates = dates[dates > last_date]  # Drop start if equals last_date
        dates = dates[:periods]  # Return correct number of periods

        if include_history:
            dates = np.concatenate((np.array(self.history_dates), dates))

        return pd.DataFrame({'ds': dates})

    def plot(self, fcst, uncertainty=True, xlabel='ds', ylabel='y'):
        """Plot the Prophet forecast.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        uncertainty: Optional boolean to plot uncertainty intervals.
        xlabel: Optional label name on X-axis
        ylabel: Optional label name on Y-axis

        Returns
        -------
        a matplotlib figure.
        """
        fig = plt.figure(facecolor='w', figsize=(10, 6))
        ax = fig.add_subplot(111)
        ax.plot(self.history['ds'].values, self.history['y'], 'k.')
        ax.plot(fcst['ds'].values, fcst['yhat'], ls='-', c='#0072B2')
        if 'cap' in fcst:
            ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
        if uncertainty:
            ax.fill_between(fcst['ds'].values, fcst['yhat_lower'],
                            fcst['yhat_upper'], color='#0072B2',
                            alpha=0.2)
        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
        ax.set_xlabel(xlabel)
        ax.set_ylabel(ylabel)
        fig.tight_layout()
        return fig

    def plot_components(self, fcst, uncertainty=True):
        """Plot the Prophet forecast components.

        Will plot whichever are available of: trend, holidays, weekly
        seasonality, and yearly seasonality.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        uncertainty: Optional boolean to plot uncertainty intervals.

        Returns
        -------
        a matplotlib figure.
        """
        # Identify components to be plotted
        components = [('plot_trend', True),
                      ('plot_holidays', self.holidays is not None),
                      ('plot_weekly', 'weekly' in fcst),
                      ('plot_yearly', 'yearly' in fcst)]
        components = [(plot, cond) for plot, cond in components if cond]
        npanel = len(components)

        fig, axes = plt.subplots(npanel, 1, facecolor='w',
                                 figsize=(9, 3 * npanel))

        artists = []
        for ax, plot in zip(axes,
                            [getattr(self, plot) for plot, _ in components]):
            artists += plot(fcst, ax=ax, uncertainty=uncertainty)

        fig.tight_layout()
        return artists

    def plot_trend(self, fcst, ax=None, uncertainty=True):
        """Plot the trend component of the forecast.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        ax: Optional matplotlib Axes to plot on.
        uncertainty: Optional boolean to plot uncertainty intervals.

        Returns
        -------
        a list of matplotlib artists
        """

        artists = []
        if not ax:
            fig = plt.figure(facecolor='w', figsize=(10, 6))
            ax = fig.add_subplot(111)
        artists += ax.plot(fcst['ds'].values, fcst['trend'], ls='-',
                           c='#0072B2')
        if 'cap' in fcst:
            artists += ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
        if uncertainty:
            artists += [ax.fill_between(
                fcst['ds'].values, fcst['trend_lower'], fcst['trend_upper'],
                color='#0072B2', alpha=0.2)]
        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
        ax.set_xlabel('ds')
        ax.set_ylabel('trend')
        return artists

    def plot_holidays(self, fcst, ax=None, uncertainty=True):
        """Plot the holidays component of the forecast.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        ax: Optional matplotlib Axes to plot on. One will be created if this 
            is not provided.
        uncertainty: Optional boolean to plot uncertainty intervals.

        Returns
        -------
        a list of matplotlib artists
        """
        artists = []
        if not ax:
            fig = plt.figure(facecolor='w', figsize=(10, 6))
            ax = fig.add_subplot(111)
        holiday_comps = self.holidays['holiday'].unique()
        y_holiday = fcst[holiday_comps].sum(1)
        y_holiday_l = fcst[[h + '_lower' for h in holiday_comps]].sum(1)
        y_holiday_u = fcst[[h + '_upper' for h in holiday_comps]].sum(1)
        # NOTE the above CI calculation is incorrect if holidays overlap
        # in time. Since it is just for the visualization we will not
        # worry about it now.
        artists += ax.plot(fcst['ds'].values, y_holiday, ls='-',
                           c='#0072B2')
        if uncertainty:
            artists += [ax.fill_between(fcst['ds'].values,
                                        y_holiday_l, y_holiday_u,
                                        color='#0072B2', alpha=0.2)]
        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
        ax.set_xlabel('ds')
        ax.set_ylabel('holidays')
        return artists

    def plot_weekly(self, fcst, ax=None, uncertainty=True):
        """Plot the weekly component of the forecast.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        ax: Optional matplotlib Axes to plot on. One will be created if this 
            is not provided.
        uncertainty: Optional boolean to plot uncertainty intervals.

        Returns
        -------
        a list of matplotlib artists
        """
        artists = []
        if not ax:
            fig = plt.figure(facecolor='w', figsize=(10, 6))
            ax = fig.add_subplot(111)
        df_s = fcst.copy()
        df_s['dow'] = df_s['ds'].dt.weekday_name
        df_s = df_s.groupby('dow').first()
        days = pd.date_range(start='2017-01-01', periods=7).weekday_name
        y_weekly = [df_s.loc[d]['weekly'] for d in days]
        y_weekly_l = [df_s.loc[d]['weekly_lower'] for d in days]
        y_weekly_u = [df_s.loc[d]['weekly_upper'] for d in days]
        artists += ax.plot(range(len(days)), y_weekly, ls='-',
                           c='#0072B2')
        if uncertainty:
            artists += [ax.fill_between(range(len(days)),
                                        y_weekly_l, y_weekly_u,
                                        color='#0072B2', alpha=0.2)]
        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
        ax.set_xticks(range(len(days)))
        ax.set_xticklabels(days)
        ax.set_xlabel('Day of week')
        ax.set_ylabel('weekly')
        return artists

    def plot_yearly(self, fcst, ax=None, uncertainty=True):
        """Plot the yearly component of the forecast.

        Parameters
        ----------
        fcst: pd.DataFrame output of self.predict.
        ax: Optional matplotlib Axes to plot on. One will be created if 
            this is not provided.
        uncertainty: Optional boolean to plot uncertainty intervals.

        Returns
        -------
        a list of matplotlib artists
        """
        artists = []
        if not ax:
            fig = plt.figure(facecolor='w', figsize=(10, 6))
            ax = fig.add_subplot(111)
        df_s = fcst.copy()
        df_s['doy'] = df_s['ds'].map(lambda x: x.strftime('2000-%m-%d'))
        df_s = df_s.groupby('doy').first().sort_index()
        artists += ax.plot(pd.to_datetime(df_s.index), df_s['yearly'], ls='-',
                           c='#0072B2')
        if uncertainty:
            artists += [ax.fill_between(
                pd.to_datetime(df_s.index), df_s['yearly_lower'],
                df_s['yearly_upper'], color='#0072B2', alpha=0.2)]
        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
        months = MonthLocator(range(1, 13), bymonthday=1, interval=2)
        ax.xaxis.set_major_formatter(FuncFormatter(
            lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x))))
        ax.xaxis.set_major_locator(months)
        ax.set_xlabel('Day of year')
        ax.set_ylabel('yearly')
        return artists


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