prophet/python/prophet/plot.py at main · facebook/prophet · GitHub

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# Copyright (c) Facebook, Inc. and its affiliates.

# This source code is licensed under the MIT license found in the

# LICENSE file in the root directory of this source tree.

from __future__ import annotations

import logging

from typing import TYPE_CHECKING, cast

import numpy as np

import pandas as pd

# TODO: separate performance_metrics into a different module. there is an implicit circular import between forecaster.py and diagnostics.py

from prophet.diagnostics import performance_metrics

if TYPE_CHECKING:

from typing import Literal, Sequence, TypeVar, type_check_only

from typing_extensions import TypedDict

from prophet.forecaster import Prophet

import matplotlib.pyplot as plt

import plotly.graph_objs as go

_AxT = TypeVar('_AxT', bound=plt.Axes)

@type_check_only

class _PlotlyProps(TypedDict):

traces: list[go.Scatter]

xaxis: go.layout.XAxis

yaxis: go.layout.YAxis

logger: logging.Logger = logging.getLogger('prophet.plot')

try:

from matplotlib import pyplot as plt

from matplotlib.dates import (

MonthLocator,

num2date,

AutoDateLocator,

AutoDateFormatter,

)

from matplotlib.ticker import FuncFormatter

from pandas.plotting import deregister_matplotlib_converters

deregister_matplotlib_converters()

except ImportError:

logger.error('Importing matplotlib failed. Plotting will not work.')

try:

import plotly.graph_objs as go

from plotly.subplots import make_subplots

except ImportError:

logger.error('Importing plotly failed. Interactive plots will not work.')

def plot(

m: Prophet,

fcst: pd.DataFrame,

ax: plt.Axes | None = None,

uncertainty: bool = True,

plot_cap: bool = True,

xlabel: str = "ds",

ylabel: str = "y",

figsize: tuple[int, int] = (10, 6),

include_legend: bool = False,

) -> plt.Figure:

"""Plot the Prophet forecast.

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

ax: Optional matplotlib axes on which to plot.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

xlabel: Optional label name on X-axis

ylabel: Optional label name on Y-axis

figsize: Optional tuple width, height in inches.

include_legend: Optional boolean to add legend to the plot.

Returns

-------

A matplotlib figure.

"""

user_provided_ax = False if ax is None else True

if ax is None:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

else:

fig = cast('plt.Figure', ax.get_figure())

fcst_t = fcst['ds']

history = cast('pd.DataFrame', m.history)

ax.plot(history['ds'], history['y'], 'k.', label='Observed data points')

ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2', label='Forecast')

if 'cap' in fcst and plot_cap:

ax.plot(fcst_t, fcst['cap'], ls='--', c='k', label='Maximum capacity')

if m.logistic_floor and 'floor' in fcst and plot_cap:

ax.plot(fcst_t, fcst['floor'], ls='--', c='k', label='Minimum capacity')

if uncertainty and m.uncertainty_samples:

ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'],

color='#0072B2', alpha=0.2, label='Uncertainty interval')

# Specify formatting to workaround matplotlib issue #12925

locator = AutoDateLocator(interval_multiples=False)

formatter = AutoDateFormatter(locator)

ax.xaxis.set_major_locator(locator)

ax.xaxis.set_major_formatter(formatter)

ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)

ax.set_xlabel(xlabel)

ax.set_ylabel(ylabel)

if include_legend:

ax.legend()

if not user_provided_ax:

try:

if fig.get_layout_engine() is None:

fig.tight_layout()

except AttributeError:

fig.tight_layout()

return fig

def plot_components(

m: Prophet,

fcst: pd.DataFrame,

uncertainty: bool = True,

plot_cap: bool = True,

weekly_start: int = 0,

yearly_start: int = 0,

figsize: tuple[int, int] | None = None,

) -> plt.Figure:

"""Plot the Prophet forecast components.

Will plot whichever are available of: trend, holidays, weekly

seasonality, yearly seasonality, and additive and multiplicative extra

regressors.

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

weekly_start: Optional int specifying the start day of the weekly

seasonality plot. 0 (default) starts the week on Sunday. 1 shifts

by 1 day to Monday, and so on.

yearly_start: Optional int specifying the start day of the yearly

seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts

by 1 day to Jan 2, and so on.

figsize: Optional tuple width, height in inches.

Returns

-------

A matplotlib figure.

"""

# Identify components to be plotted

components = ['trend']

if m.train_holiday_names is not None and 'holidays' in fcst:

components.append('holidays')

# Plot weekly seasonality, if present

if 'weekly' in m.seasonalities and 'weekly' in fcst:

components.append('weekly')

# Yearly if present

if 'yearly' in m.seasonalities and 'yearly' in fcst:

components.append('yearly')

# Other seasonalities

components.extend([

name for name in sorted(m.seasonalities)

if name in fcst and name not in ['weekly', 'yearly']

])

regressors = {'additive': False, 'multiplicative': False}

for name, props in m.extra_regressors.items():

regressors[props['mode']] = True

for mode in ['additive', 'multiplicative']:

if regressors[mode] and 'extra_regressors_{}'.format(mode) in fcst:

components.append('extra_regressors_{}'.format(mode))

npanel = len(components)

figsize = figsize if figsize else (9, 3 * npanel)

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

if npanel == 1:

axes = [axes]

multiplicative_axes = []

dt = cast('pd.DataFrame', m.history)['ds'].diff()

min_dt = dt.iloc[cast('np.ndarray', dt.values).nonzero()[0]].min()

for ax, plot_name in zip(axes, components):

if plot_name == 'trend':

plot_forecast_component(

m=m, fcst=fcst, name='trend', ax=ax, uncertainty=uncertainty,

plot_cap=plot_cap,

)

elif plot_name in m.seasonalities:

if (

(plot_name == 'weekly' or m.seasonalities[plot_name]['period'] == 7)

and (min_dt == pd.Timedelta(days=1))

plot_weekly(

m=m, name=plot_name, ax=ax, uncertainty=uncertainty, weekly_start=weekly_start

)

elif plot_name == 'yearly' or m.seasonalities[plot_name]['period'] == 365.25:

plot_yearly(

m=m, name=plot_name, ax=ax, uncertainty=uncertainty, yearly_start=yearly_start

)

else:

plot_seasonality(

m=m, name=plot_name, ax=ax, uncertainty=uncertainty,

)

elif plot_name in [

'holidays',

'extra_regressors_additive',

'extra_regressors_multiplicative',

plot_forecast_component(

m=m, fcst=fcst, name=plot_name, ax=ax, uncertainty=uncertainty,

plot_cap=False,

)

assert m.component_modes is not None

if plot_name in m.component_modes['multiplicative']:

multiplicative_axes.append(ax)

try:

if fig.get_layout_engine() is None:

fig.tight_layout()

except AttributeError:

fig.tight_layout()

# Reset multiplicative axes labels after tight_layout adjustment

for ax in multiplicative_axes:

ax = set_y_as_percent(ax)

return fig

def plot_forecast_component(

m: Prophet,

fcst: pd.DataFrame,

ax: plt.Axes | None = None,

uncertainty: bool = True,

plot_cap: bool = False,

figsize: tuple[int, int] = (10, 6),

) -> Sequence[plt.Artist]:

"""Plot a particular component of the forecast.

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

ax: Optional matplotlib Axes to plot on.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

figsize: Optional tuple width, height in inches.

Returns

-------

a list of matplotlib artists

"""

artists = []

if not ax:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

fcst_t = fcst['ds']

artists += ax.plot(fcst_t, fcst[name], ls='-', c='#0072B2')

if 'cap' in fcst and plot_cap:

artists += ax.plot(fcst_t, fcst['cap'], ls='--', c='k')

if m.logistic_floor and 'floor' in fcst and plot_cap:

ax.plot(fcst_t, fcst['floor'], ls='--', c='k')

if uncertainty and m.uncertainty_samples:

artists += [ax.fill_between(

fcst_t, fcst[name + '_lower'], fcst[name + '_upper'],

color='#0072B2', alpha=0.2)]

# Specify formatting to workaround matplotlib issue #12925

locator = AutoDateLocator(interval_multiples=False)

formatter = AutoDateFormatter(locator)

ax.xaxis.set_major_locator(locator)

ax.xaxis.set_major_formatter(formatter)

ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)

ax.set_xlabel('ds')

ax.set_ylabel(name)

assert m.component_modes

if name in m.component_modes['multiplicative']:

ax = set_y_as_percent(ax)

return artists

def seasonality_plot_df(

m: Prophet,

ds: Sequence[pd.Timestamp] | pd.DatetimeIndex,

) -> pd.DataFrame:

"""Prepare dataframe for plotting seasonal components.

Parameters

----------

m: Prophet model.

ds: List of dates for column ds.

Returns

-------

A dataframe with seasonal components on ds.

"""

df_dict = {'ds': ds, 'cap': 1., 'floor': 0.}

for name in m.extra_regressors:

df_dict[name] = 0.

# Activate all conditional seasonality columns

for props in m.seasonalities.values():

if props['condition_name'] is not None:

df_dict[props['condition_name']] = True

df = pd.DataFrame(df_dict)

df = m.setup_dataframe(df)

return df

def plot_weekly(

m: Prophet,

ax: plt.Axes | None = None,

uncertainty: bool = True,

weekly_start: int = 0,

figsize: tuple[int, int] = (10, 6),

) -> Sequence[plt.Artist]:

"""Plot the weekly component of the forecast.

Parameters

----------

m: Prophet model.

ax: Optional matplotlib Axes to plot on. One will be created if this

is not provided.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

weekly_start: Optional int specifying the start day of the weekly

seasonality plot. 0 (default) starts the week on Sunday. 1 shifts

by 1 day to Monday, and so on.

figsize: Optional tuple width, height in inches.

Returns

-------

a list of matplotlib artists

"""

artists = []

if not ax:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

# Compute weekly seasonality for a Sun-Sat sequence of dates.

days = (pd.date_range(start='2017-01-01', periods=7) +

pd.Timedelta(days=weekly_start))

df_w = seasonality_plot_df(m, days)

seas = m.predict_seasonal_components(df_w)

days = days.day_name()

artists += ax.plot(range(len(days)), seas[name], ls='-',

c='#0072B2')

if uncertainty and m.uncertainty_samples:

artists += [ax.fill_between(range(len(days)),

seas[name + '_lower'], seas[name + '_upper'],

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(name)

if m.seasonalities[name]['mode'] == 'multiplicative':

ax = set_y_as_percent(ax)

return artists

def plot_yearly(

m: Prophet,

ax: plt.Axes | None = None,

uncertainty: bool = True,

yearly_start: int = 0,

figsize: tuple[int, int] = (10, 6),

) -> Sequence[plt.Artist]:

"""Plot the yearly component of the forecast.

Parameters

----------

m: Prophet model.

ax: Optional matplotlib Axes to plot on. One will be created if

this is not provided.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

yearly_start: Optional int specifying the start day of the yearly

seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts

by 1 day to Jan 2, and so on.

figsize: Optional tuple width, height in inches.

Returns

-------

a list of matplotlib artists

"""

artists = []

if not ax:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

# Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.

days = (pd.date_range(start='2017-01-01', periods=365) +

pd.Timedelta(days=yearly_start))

df_y = seasonality_plot_df(m, days)

seas = m.predict_seasonal_components(df_y)

artists += ax.plot(

df_y['ds'], seas[name], ls='-', c='#0072B2')

if uncertainty and m.uncertainty_samples:

artists += [ax.fill_between(

df_y['ds'], seas[name + '_lower'],

seas[name + '_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(name)

if m.seasonalities[name]['mode'] == 'multiplicative':

ax = set_y_as_percent(ax)

return artists

def plot_seasonality(

m: Prophet,

ax: plt.Axes | None = None,

uncertainty: bool = True,

figsize: tuple[int, int] = (10, 6),

) -> Sequence[plt.Artist]:

"""Plot a custom seasonal component.

Parameters

----------

m: Prophet model.

ax: Optional matplotlib Axes to plot on. One will be created if

this is not provided.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

figsize: Optional tuple width, height in inches.

Returns

-------

a list of matplotlib artists

"""

artists = []

if not ax:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

# Compute seasonality from Jan 1 through a single period.

start = pd.to_datetime('2017-01-01 0000')

period = m.seasonalities[name]['period']

end = start + pd.Timedelta(days=period)

plot_points = 200

# https://github.com/pandas-dev/pandas-stubs/issues/1645

days = pd.to_datetime(np.linspace(start.value, end.value, plot_points)) # pyrefly:ignore[no-matching-overload]

df_y = seasonality_plot_df(m, days)

seas = m.predict_seasonal_components(df_y)

artists += ax.plot(df_y['ds'], seas[name], ls='-',

c='#0072B2')

if uncertainty and m.uncertainty_samples:

artists += [ax.fill_between(

df_y['ds'], seas[name + '_lower'],

seas[name + '_upper'], color='#0072B2', alpha=0.2)]

ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)

n_ticks = 8

# https://github.com/pandas-dev/pandas-stubs/issues/1645

xticks = pd.to_datetime(np.linspace(start.value, end.value, n_ticks) # pyrefly:ignore[no-matching-overload]

).to_pydatetime()

ax.set_xticks(xticks)

if name == 'yearly':

fmt = FuncFormatter(

lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x)))

ax.set_xlabel('Day of year')

elif name == 'weekly':

fmt = FuncFormatter(

lambda x, pos=None: '{dt:%A}'.format(dt=num2date(x)))

ax.set_xlabel('Day of Week')

elif name == 'daily':

fmt = FuncFormatter(

lambda x, pos=None: '{dt:%T}'.format(dt=num2date(x)))

ax.set_xlabel('Hour of day')

elif period <= 2:

fmt = FuncFormatter(

lambda x, pos=None: '{dt:%T}'.format(dt=num2date(x)))

ax.set_xlabel('Hours')

else:

fmt = FuncFormatter(

lambda x, pos=None: '{:.0f}'.format(pos * period / (n_ticks - 1)))

ax.set_xlabel('Days')

ax.xaxis.set_major_formatter(fmt)

ax.set_ylabel(name)

if m.seasonalities[name]['mode'] == 'multiplicative':

ax = set_y_as_percent(ax)

return artists

def set_y_as_percent(ax: _AxT) -> _AxT:

yticks = 100 * ax.get_yticks()

yticklabels = ['{0:.4g}%'.format(y) for y in yticks]

ax.set_yticks(ax.get_yticks().tolist())

ax.set_yticklabels(yticklabels)

return ax

def add_changepoints_to_plot(

ax: plt.Axes,

m: Prophet,

fcst: pd.DataFrame,

threshold: float = 0.01,

cp_color: str = 'r',

cp_linestyle: str = '--',

trend: bool = True,

) -> list[plt.Line2D]:

"""Add markers for significant changepoints to prophet forecast plot.

Example:

fig = m.plot(forecast)

add_changepoints_to_plot(fig.gca(), m, forecast)

Parameters

----------

ax: axis on which to overlay changepoint markers.

m: Prophet model.

fcst: Forecast output from m.predict.

threshold: Threshold on trend change magnitude for significance.

cp_color: Color of changepoint markers.

cp_linestyle: Linestyle for changepoint markers.

trend: If True, will also overlay the trend.

Returns

-------

a list of matplotlib artists

"""

artists = []

if trend:

artists.extend(ax.plot(fcst['ds'], fcst['trend'], c=cp_color))

assert m.changepoints is not None

signif_changepoints = m.changepoints[

np.abs(np.nanmean(m.params['delta'], axis=0)) >= threshold

] if len(m.changepoints) > 0 else []

for cp in signif_changepoints:

# Matplotlib stubs type axvline x as float; pandas Timestamp is accepted at runtime.

artists.append(ax.axvline(x=cp, c=cp_color, ls=cp_linestyle)) # pyrefly:ignore[bad-argument-type]

return artists

def plot_cross_validation_metric(

df_cv: pd.DataFrame,

metric: str,

rolling_window: float = 0.1,

ax: plt.Axes | None = None,

figsize: tuple[int, int] = (10, 6),

color: str = 'b',

point_color: str = 'gray',

) -> plt.Figure:

"""Plot a performance metric vs. forecast horizon from cross validation.

Cross validation produces a collection of out-of-sample model predictions

that can be compared to actual values, at a range of different horizons

(distance from the cutoff). This computes a specified performance metric

for each prediction, and aggregated over a rolling window with horizon.

This uses prophet.diagnostics.performance_metrics to compute the metrics.

Valid values of metric are 'mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', and 'coverage'.

rolling_window is the proportion of data included in the rolling window of

aggregation. The default value of 0.1 means 10% of data are included in the

aggregation for computing the metric.

As a concrete example, if metric='mse', then this plot will show the

squared error for each cross validation prediction, along with the MSE

averaged over rolling windows of 10% of the data.

Parameters

----------

df_cv: The output from prophet.diagnostics.cross_validation.

metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].

rolling_window: Proportion of data to use for rolling average of metric.

In [0, 1]. Defaults to 0.1.

ax: Optional matplotlib axis on which to plot. If not given, a new figure

will be created.

figsize: Optional tuple width, height in inches.

color: Optional color for plot and error points, useful when plotting

multiple model performances on one axis for comparison.

Returns

-------

a matplotlib figure.

"""

if ax is None:

fig = plt.figure(facecolor='w', figsize=figsize)

ax = fig.add_subplot(111)

else:

fig = cast('plt.Figure', ax.get_figure())

# Get the metric at the level of individual predictions, and with the rolling window.

df_none = performance_metrics(df_cv, metrics=[metric], rolling_window=-1)

df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)

assert df_none is not None

assert df_h is not None

# Some work because matplotlib does not handle timedelta

# Target ~10 ticks.

tick_w = max(df_none['horizon'].astype('timedelta64[ns]')) / 10.

# Find the largest time resolution that has <1 unit per bin.

dts: list[Literal["D", "h", "m", "s", "ms", "us", "ns"]]

dts = ['D', 'h', 'm', 's', 'ms', 'us', 'ns']

dt_names = [

'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds',

'nanoseconds'

]

dt_conversions = [

24 * 60 * 60 * 10 ** 9,

60 * 60 * 10 ** 9,

60 * 10 ** 9,

10 ** 9,

10 ** 6,

10 ** 3,

1.,

]

for i, dt in enumerate(dts):

if np.timedelta64(1, dt) < np.timedelta64(tick_w, 'ns'):

break

x_plt = np.asarray(df_none['horizon'].astype('timedelta64[ns]')).view(np.int64) / float(dt_conversions[i])

x_plt_h = np.asarray(df_h['horizon'].astype('timedelta64[ns]')).view(np.int64) / float(dt_conversions[i])

ax.plot(x_plt, df_none[metric], '.', alpha=0.1, c=point_color)

ax.plot(x_plt_h, df_h[metric], '-', c=color)

ax.grid(True)

ax.set_xlabel('Horizon ({})'.format(dt_names[i]))

ax.set_ylabel(metric)

return fig

def plot_plotly(

m: Prophet,

fcst: pd.DataFrame,

uncertainty: bool = True,

plot_cap: bool = True,

trend: bool = False,

changepoints: bool = False,

changepoints_threshold: float = 0.01,

xlabel: str = 'ds',

ylabel: str = 'y',

figsize: tuple[int, int] = (900, 600)

) -> go.Figure:

"""Plot the Prophet forecast with Plotly offline.

Plotting in Jupyter Notebook requires initializing plotly.offline.init_notebook_mode():

>>> import plotly.offline as py

>>> py.init_notebook_mode()

Then the figure can be displayed using plotly.offline.iplot(...):

>>> fig = plot_plotly(m, fcst)

>>> py.iplot(fig)

see https://plot.ly/python/offline/ for details

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

uncertainty: Optional boolean to plot uncertainty intervals.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

trend: Optional boolean to plot trend

changepoints: Optional boolean to plot changepoints

changepoints_threshold: Threshold on trend change magnitude for significance.

xlabel: Optional label name on X-axis

ylabel: Optional label name on Y-axis

figsize: The plot's size (in px).

Returns

-------

A Plotly Figure.

"""

prediction_color = '#0072B2'

error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity

actual_color = 'black'

cap_color = 'black'

trend_color = '#B23B00'

line_width = 2

marker_size = 4

data = []

# Add actual

assert m.history

data.append(go.Scatter(

name='Actual',

x=m.history['ds'],

y=m.history['y'],

marker=dict(color=actual_color, size=marker_size),

mode='markers'

))

# Add lower bound

if uncertainty and m.uncertainty_samples:

data.append(go.Scatter(

x=fcst['ds'],

y=fcst['yhat_lower'],

mode='lines',

line=dict(width=0),

hoverinfo='skip'

))

# Add prediction

data.append(go.Scatter(

name='Predicted',

x=fcst['ds'],

y=fcst['yhat'],

mode='lines',

line=dict(color=prediction_color, width=line_width),

fillcolor=error_color,

fill='tonexty' if uncertainty and m.uncertainty_samples else 'none'

))

# Add upper bound

if uncertainty and m.uncertainty_samples:

data.append(go.Scatter(

x=fcst['ds'],

y=fcst['yhat_upper'],

mode='lines',

line=dict(width=0),

fillcolor=error_color,

fill='tonexty',

hoverinfo='skip'

))

# Add caps

if 'cap' in fcst and plot_cap:

data.append(go.Scatter(

name='Cap',

x=fcst['ds'],

y=fcst['cap'],

mode='lines',

line=dict(color=cap_color, dash='dash', width=line_width),

))

if m.logistic_floor and 'floor' in fcst and plot_cap:

data.append(go.Scatter(

name='Floor',

x=fcst['ds'],

y=fcst['floor'],

mode='lines',

line=dict(color=cap_color, dash='dash', width=line_width),

))

# Add trend

if trend:

data.append(go.Scatter(

name='Trend',

x=fcst['ds'],

y=fcst['trend'],

mode='lines',

line=dict(color=trend_color, width=line_width),

))

# Add changepoints

assert m.changepoints

if changepoints and len(m.changepoints) > 0:

signif_changepoints = m.changepoints[

np.abs(np.nanmean(m.params['delta'], axis=0)) >= changepoints_threshold

]

data.append(go.Scatter(

x=signif_changepoints,

y=fcst.loc[fcst['ds'].isin(signif_changepoints), 'trend'],

marker=dict(size=50, symbol='line-ns-open', color=trend_color,

line=dict(width=line_width)),

mode='markers',

hoverinfo='skip'

))

layout = dict(

showlegend=False,

width=figsize[0],

height=figsize[1],

yaxis=dict(

title=ylabel

xaxis=dict(

title=xlabel,

type='date',

rangeselector=dict(

buttons=list([

dict(count=7,

label='1w',

step='day',

stepmode='backward'),

dict(count=1,

label='1m',

step='month',

stepmode='backward'),

dict(count=6,

label='6m',

step='month',

stepmode='backward'),

dict(count=1,

label='1y',

step='year',

stepmode='backward'),

dict(step='all')

])

rangeslider=dict(

visible=True

)

fig = go.Figure(data=data, layout=layout)

return fig

def plot_components_plotly(

m: Prophet,

fcst: pd.DataFrame,

uncertainty: bool = True,

plot_cap: bool = True,

figsize: tuple[int, int] = (900, 200),

) -> go.Figure:

"""Plot the Prophet forecast components using Plotly.

See plot_plotly() for Plotly setup instructions

Will plot whichever are available of: trend, holidays, weekly

seasonality, yearly seasonality, and additive and multiplicative extra

regressors.

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

figsize: Set the size for the subplots (in px).

Returns

-------

A Plotly Figure.

"""

# Identify components to plot and get their Plotly props

components = {}

components['trend'] = get_forecast_component_plotly_props(

m, fcst, 'trend', uncertainty, plot_cap)

if m.train_holiday_names is not None and 'holidays' in fcst:

components['holidays'] = get_forecast_component_plotly_props(

m, fcst, 'holidays', uncertainty)

regressors = {'additive': False, 'multiplicative': False}

for name, props in m.extra_regressors.items():

regressors[props['mode']] = True

for mode in ['additive', 'multiplicative']:

if regressors[mode] and 'extra_regressors_{}'.format(mode) in fcst:

components['extra_regressors_{}'.format(mode)] = get_forecast_component_plotly_props(

m, fcst, 'extra_regressors_{}'.format(mode))

for seasonality in m.seasonalities:

components[seasonality] = get_seasonality_plotly_props(m, seasonality)

# Create Plotly subplot figure and add the components to it

fig = make_subplots(rows=len(components), cols=1, print_grid=False)

fig['layout'].update(go.Layout(

showlegend=False,

width=figsize[0],

height=figsize[1] * len(components)

))

for i, name in enumerate(components):

if i == 0:

xaxis = fig['layout']['xaxis']

yaxis = fig['layout']['yaxis']

else:

xaxis = fig['layout']['xaxis{}'.format(i + 1)]

yaxis = fig['layout']['yaxis{}'.format(i + 1)]

xaxis.update(components[name]['xaxis'])

yaxis.update(components[name]['yaxis'])

for trace in components[name]['traces']:

fig.append_trace(trace, i + 1, 1)

return fig

def plot_forecast_component_plotly(

m: Prophet,

fcst: pd.DataFrame,

uncertainty: bool = True,

plot_cap: bool = False,

figsize: tuple[int, int] = (900, 300)

) -> go.Figure:

"""Plot a particular component of the forecast using Plotly.

See plot_plotly() for Plotly setup instructions

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

figsize: The plot's size (in px).

Returns

-------

A Plotly Figure.

"""

props = get_forecast_component_plotly_props(m, fcst, name, uncertainty, plot_cap)

layout = go.Layout(

width=figsize[0],

height=figsize[1],

showlegend=False,

xaxis=props['xaxis'],

yaxis=props['yaxis']

)

fig = go.Figure(data=props['traces'], layout=layout)

return fig

def plot_seasonality_plotly(

m: Prophet,

uncertainty: bool = True,

figsize: tuple[int, int] = (900, 300)

) -> go.Figure:

"""Plot a custom seasonal component using Plotly.

See plot_plotly() for Plotly setup instructions

Parameters

----------

m: Prophet model.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

figsize: Set the plot's size (in px).

Returns

-------

A Plotly Figure.

"""

props = get_seasonality_plotly_props(m, name, uncertainty)

layout = go.Layout(

width=figsize[0],

height=figsize[1],

showlegend=False,

xaxis=props['xaxis'],

yaxis=props['yaxis']

)

fig = go.Figure(data=props['traces'], layout=layout)

return fig

def get_forecast_component_plotly_props(

m: Prophet,

fcst: pd.DataFrame,

uncertainty: bool = True,

plot_cap: bool = False,

) -> _PlotlyProps:

"""Prepares a dictionary for plotting the selected forecast component with Plotly

Parameters

----------

m: Prophet model.

fcst: pd.DataFrame output of m.predict.

uncertainty: Optional boolean to plot uncertainty intervals, which will

only be done if m.uncertainty_samples > 0.

plot_cap: Optional boolean indicating if the capacity should be shown

in the figure, if available.

Returns

-------

A dictionary with Plotly traces, xaxis and yaxis

"""

prediction_color = '#0072B2'

error_color = 'rgba(0, 114, 178, 0.2)' # '#0072B2' with 0.2 opacity

cap_color = 'black'

zeroline_color = '#AAA'

line_width = 2

range_margin = (fcst['ds'].max() - fcst['ds'].min()) * 0.05

range_x = [fcst['ds'].min() - range_margin, fcst['ds'].max() + range_margin]

text = None

mode = 'lines'

if name == 'holidays':

# Combine holidays into one hover text

holidays = m.construct_holiday_dataframe(fcst['ds'])

holiday_features, _, _ = m.make_holiday_features(fcst['ds'], holidays)

holiday_features.columns = holiday_features.columns.str.replace('_delim_', '', regex=False)

holiday_features.columns = holiday_features.columns.str.replace('+0', '', regex=False)

text = pd.Series(data='', index=holiday_features.index)

for holiday_feature, idxs in holiday_features.items():

# https://github.com/facebook/pyrefly/issues/2248

# pyrefly:ignore[unsupported-operation]

text[idxs.astype(bool) & (text != '')] += '<br>' # Add newline if additional holiday

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