saymrwulf/prophet
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Merge pull request #84 from lemonlaug/better_mpl

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Refactoring mpl code to address #62, #63
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Sean J. Taylor 2017-03-03 15:57:39 -08:00 committed by GitHub
commit f287a57cca
1 changed files with 149 additions and 77 deletions
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226
python/fbprophet/forecaster.py
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@ -2,7 +2,7 @@
# 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
# 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
@ -188,7 +188,6 @@ class Prophet(object):
else:
self.changepoints_t = np.array([0]) # dummy changepoint
def get_changepoint_matrix(self):
A = np.zeros((self.history.shape[0], len(self.changepoints_t)))
for i, t_i in enumerate(self.changepoints_t):
@ -269,7 +268,6 @@ class Prophet(object):
# This relies pretty importantly on pandas keeping the columns in order.
return pd.DataFrame(expanded_holidays)
def make_all_seasonality_features(self, df):
seasonal_features = [
# Add a column of zeros in case no seasonality is used.
@ -626,16 +624,16 @@ class Prophet(object):
-------
a matplotlib figure.
"""
forecast_color = '#0072B2'
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=forecast_color)
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=forecast_color, alpha=0.2)
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)
@ -658,87 +656,161 @@ class Prophet(object):
a matplotlib figure.
"""
# Identify components to be plotted
plot_trend = True
plot_holidays = self.holidays is not None
plot_weekly = 'weekly' in fcst
plot_yearly = 'yearly' in fcst
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)
npanel = plot_trend + plot_holidays + plot_weekly + plot_yearly
forecast_color = '#0072B2'
fig = plt.figure(facecolor='w', figsize=(9, 3 * npanel))
panel_num = 1
ax = fig.add_subplot(npanel, 1, panel_num)
ax.plot(fcst['ds'].values, fcst['trend'], ls='-', c=forecast_color)
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:
ax = fig.add_subplot(111)
artists += ax.plot(fcst['ds'].values, fcst['trend'], ls='-',
c='#0072B2')
if 'cap' in fcst:
ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
artists += ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
if uncertainty:
ax.fill_between(
artists += [ax.fill_between(
fcst['ds'].values, fcst['trend_lower'], fcst['trend_upper'],
color=forecast_color, alpha=0.2)
color='#0072B2', alpha=0.2)]
ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
ax.xaxis.set_major_locator(MaxNLocator(nbins=7))
ax.set_xlabel('ds')
ax.set_ylabel('trend')
return artists
if plot_holidays:
panel_num += 1
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:
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.xaxis.set_major_locator(MaxNLocator(nbins=7))
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:
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:
ax = fig.add_subplot(npanel, 1, panel_num)
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.
ax.plot(fcst['ds'].values, y_holiday, ls='-', c=forecast_color)
if uncertainty:
ax.fill_between(fcst['ds'].values, y_holiday_l, y_holiday_u,
color=forecast_color, alpha=0.2)
ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
ax.xaxis.set_major_locator(MaxNLocator(nbins=7))
ax.set_xlabel('ds')
ax.set_ylabel('holidays')
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(DateFormatter('%B %-d'))
ax.xaxis.set_major_locator(months)
ax.set_xlabel('Day of year')
ax.set_ylabel('yearly')
return artists
if plot_weekly:
panel_num += 1
ax = fig.add_subplot(npanel, 1, panel_num)
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]
ax.plot(range(len(days)), y_weekly, ls='-', c=forecast_color)
if uncertainty:
ax.fill_between(range(len(days)), y_weekly_l, y_weekly_u,
color=forecast_color, 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')
if plot_yearly:
panel_num += 1
ax = fig.add_subplot(npanel, 1, panel_num)
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()
ax.plot(pd.to_datetime(df_s.index), df_s['yearly'], ls='-',
c=forecast_color)
if uncertainty:
ax.fill_between(
pd.to_datetime(df_s.index), df_s['yearly_lower'],
df_s['yearly_upper'], color=forecast_color, 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(DateFormatter('%B %-d'))
ax.xaxis.set_major_locator(months)
ax.set_xlabel('Day of year')
ax.set_ylabel('yearly')
fig.tight_layout()
return fig
# fb-block 9