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Saturating minimum for logistic growth Py
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bletham
parent
8d27643339
commit
cc3238acb7
3 changed files with 72 additions and 17 deletions
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@ -91,7 +91,11 @@ def simulated_historical_forecasts(model, horizon, k, period=None):
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m.fit(df[df['ds'] <= cutoff])
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# Calculate yhat
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index_predicted = (df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)
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columns = ['ds'] + (['cap'] if m.growth == 'logistic' else [])
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columns = ['ds']
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if m.growth == 'logistic':
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columns.append('cap')
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if m.logistic_floor:
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columns.append('floor')
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yhat = m.predict(df[index_predicted][columns])
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# Merge yhat(predicts), y(df, original data) and cutoff
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predicts.append(pd.concat([
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@ -134,6 +134,7 @@ class Prophet(object):
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# Set during fitting
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self.start = None
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self.y_scale = None
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self.logistic_floor = False
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self.t_scale = None
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self.changepoints_t = None
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self.seasonalities = {}
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@ -184,7 +185,8 @@ class Prophet(object):
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rn_u = [n + '_upper' for n in reserved_names]
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reserved_names.extend(rn_l)
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reserved_names.extend(rn_u)
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reserved_names.extend(['ds', 'y'])
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reserved_names.extend([
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'ds', 'y', 'cap', 'floor', 'y_scaled', 'cap_scaled'])
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if name in reserved_names:
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raise ValueError('Name "{}" is reserved.'.format(name))
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if (check_holidays and self.holidays is not None and
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@ -231,7 +233,12 @@ class Prophet(object):
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df.reset_index(inplace=True, drop=True)
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if initialize_scales:
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self.y_scale = df['y'].abs().max()
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if self.growth == 'logistic' and 'floor' in df:
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self.logistic_floor = True
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floor = df['floor']
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else:
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floor = 0.
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self.y_scale = (df['y'] - floor).abs().max()
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if self.y_scale == 0:
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self.y_scale = 1
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self.start = df['ds'].min()
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@ -252,13 +259,18 @@ class Prophet(object):
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self.extra_regressors[name]['mu'] = mu
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self.extra_regressors[name]['std'] = std
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df['t'] = (df['ds'] - self.start) / self.t_scale
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if 'y' in df:
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df['y_scaled'] = df['y'] / self.y_scale
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if self.logistic_floor:
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if 'floor' not in df:
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raise ValueError("Expected column 'floor'.")
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else:
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df['floor'] = 0
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if self.growth == 'logistic':
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assert 'cap' in df
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df['cap_scaled'] = df['cap'] / self.y_scale
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df['cap_scaled'] = (df['cap'] - df['floor']) / self.y_scale
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df['t'] = (df['ds'] - self.start) / self.t_scale
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if 'y' in df:
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df['y_scaled'] = (df['y'] - df['floor']) / self.y_scale
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for name, props in self.extra_regressors.items():
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df[name] = pd.to_numeric(df[name])
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@ -694,9 +706,14 @@ class Prophet(object):
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i0, i1 = df['ds'].idxmin(), df['ds'].idxmax()
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T = df['t'].iloc[i1] - df['t'].iloc[i0]
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# Force valid values, in case y > cap.
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r0 = max(1.01, df['cap_scaled'].iloc[i0] / df['y_scaled'].iloc[i0])
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r1 = max(1.01, df['cap_scaled'].iloc[i1] / df['y_scaled'].iloc[i1])
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# Force valid values, in case y > cap or y < 0
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C0 = df['cap_scaled'].iloc[i0]
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C1 = df['cap_scaled'].iloc[i1]
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y0 = max(0.01 * C0, min(0.99 * C0, df['y_scaled'].iloc[i0]))
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y1 = max(0.01 * C1, min(0.99 * C1, df['y_scaled'].iloc[i1]))
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r0 = C0 / y0
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r1 = C1 / y1
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if abs(r0 - r1) <= 0.01:
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r0 = 1.05 * r0
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@ -840,11 +857,12 @@ class Prophet(object):
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seasonal_components = self.predict_seasonal_components(df)
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intervals = self.predict_uncertainty(df)
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# Drop columns except ds, cap, and trend
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# Drop columns except ds, cap, floor, and trend
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cols = ['ds', 'trend']
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if 'cap' in df:
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cols = ['ds', 'cap', 'trend']
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else:
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cols = ['ds', 'trend']
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cols.append('cap')
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if self.logistic_floor:
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cols.append('floor')
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# Add in forecast components
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df2 = pd.concat((df[cols], intervals, seasonal_components), axis=1)
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df2['yhat'] = df2['trend'] + df2['seasonal']
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@ -934,7 +952,7 @@ class Prophet(object):
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trend = self.piecewise_logistic(
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t, cap, deltas, k, m, self.changepoints_t)
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return trend * self.y_scale
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return trend * self.y_scale + df['floor']
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def predict_seasonal_components(self, df):
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"""Predict seasonality components, holidays, and added regressors.
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@ -1162,7 +1180,7 @@ class Prophet(object):
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trend = self.piecewise_logistic(t, cap, deltas, k, m,
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changepoint_ts)
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return trend * self.y_scale
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return trend * self.y_scale + df['floor']
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def make_future_dataframe(self, periods, freq='D', include_history=True):
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"""Simulate the trend using the extrapolated generative model.
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@ -1219,6 +1237,8 @@ class Prophet(object):
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ax.plot(fcst['ds'].values, fcst['yhat'], ls='-', c='#0072B2')
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if 'cap' in fcst and plot_cap:
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ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
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if self.logistic_floor and 'floor' in fcst and plot_cap :
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ax.plot(fcst['ds'].values, fcst['floor'], ls='--', c='k')
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if uncertainty:
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ax.fill_between(fcst['ds'].values, fcst['yhat_lower'],
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fcst['yhat_upper'], color='#0072B2',
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@ -1313,6 +1333,8 @@ class Prophet(object):
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artists += ax.plot(fcst['ds'].values, fcst[name], ls='-', c='#0072B2')
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if 'cap' in fcst and plot_cap:
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artists += ax.plot(fcst['ds'].values, fcst['cap'], ls='--', c='k')
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if self.logistic_floor and 'floor' in fcst and plot_cap :
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ax.plot(fcst['ds'].values, fcst['floor'], ls='--', c='k')
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if uncertainty:
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artists += [ax.fill_between(
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fcst['ds'].values, fcst[name + '_lower'],
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@ -109,6 +109,35 @@ class TestProphet(TestCase):
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self.assertTrue('y_scaled' in history)
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self.assertEqual(history['y_scaled'].max(), 1.0)
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def test_logistic_floor(self):
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m = Prophet(growth='logistic')
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history['floor'] = 10.
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history['cap'] = 80.
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future = DATA.tail(N // 2).copy()
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future['cap'] = 80.
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future['floor'] = 10.
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m.fit(history)
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self.assertTrue(m.logistic_floor)
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self.assertTrue('floor' in m.history)
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self.assertAlmostEqual(m.history['y_scaled'][0], 1.)
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fcst1 = m.predict(future)
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m2 = Prophet(growth='logistic')
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history2 = history.copy()
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history2['y'] += 10.
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history2['floor'] += 10.
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history2['cap'] += 10.
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future['cap'] += 10.
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future['floor'] += 10.
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m2.fit(history2)
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self.assertAlmostEqual(m2.history['y_scaled'][0], 1.)
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fcst2 = m2.predict(future)
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fcst2['yhat'] -= 10.
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# Check for approximate shift invariance
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self.assertTrue((np.abs(fcst1['yhat'] - fcst2['yhat']) < 1).all())
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def test_get_changepoints(self):
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m = Prophet()
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N = DATA.shape[0]
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