def test_performance_metrics(self):
m = Prophet()
m.fit(self.__df)
df_cv = diagnostics.cross_validation(
m, horizon='4 days', period='10 days', initial='90 days')
# Aggregation level none
df_none = diagnostics.performance_metrics(df_cv, rolling_window=0)
self.assertEqual(
set(df_none.columns),
{'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
)
self.assertEqual(df_none.shape[0], 16)
# Aggregation level 0.2
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
self.assertEqual(len(df_horizon['horizon'].unique()), 4)
self.assertEqual(df_horizon.shape[0], 14)
# Aggregation level all
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
self.assertEqual(df_all.shape[0], 1)
for metric in ['mse', 'mape', 'mae', 'coverage']:
self.assertEqual(df_all[metric].values[0], df_none[metric].mean())
# Custom list of metrics
df_horizon = diagnostics.performance_metrics(
df_cv, metrics=['coverage', 'mse'],
)
self.assertEqual(
set(df_horizon.columns),
{'coverage', 'mse', 'horizon'},
)
def test_performance_metrics(self):
m = Prophet()
m.fit(self.__df)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='90 days')
# Aggregation level none
df_none = diagnostics.performance_metrics(df_cv, rolling_window=0)
self.assertEqual(
set(df_none.columns),
{'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
)
self.assertEqual(df_none.shape[0], 16)
# Aggregation level 0.2
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
self.assertEqual(len(df_horizon['horizon'].unique()), 4)
self.assertEqual(df_horizon.shape[0], 14)
# Aggregation level all
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
self.assertEqual(df_all.shape[0], 1)
for metric in ['mse', 'mape', 'mae', 'coverage']:
self.assertEqual(df_all[metric].values[0], df_none[metric].mean())
# Custom list of metrics
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=['coverage', 'mse'],
)
self.assertEqual(
set(df_horizon.columns),
{'coverage', 'mse', 'horizon'},
)
def test_performance_metrics(self):
m = Prophet()
m.fit(self.__df)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='90 days')
# Aggregation level none
df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
self.assertEqual(
set(df_none.columns),
{'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse'},
)
self.assertEqual(df_none.shape[0], 16)
# Aggregation level 0
df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)
self.assertEqual(len(df_0), 4)
self.assertEqual(len(df_0['horizon'].unique()), 4)
# Aggregation level 0.2
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
self.assertEqual(len(df_horizon), 4)
self.assertEqual(len(df_horizon['horizon'].unique()), 4)
# Aggregation level all
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
self.assertEqual(df_all.shape[0], 1)
for metric in ['mse', 'mape', 'mae', 'coverage']:
self.assertAlmostEqual(df_all[metric].values[0],
df_none[metric].mean())
self.assertAlmostEqual(df_all['mdape'].values[0],
df_none['mdape'].median())
# Custom list of metrics
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=['coverage', 'mse'],
)
self.assertEqual(
set(df_horizon.columns),
{'coverage', 'mse', 'horizon'},
)
# Skip MAPE
df_cv.loc[0, 'y'] = 0.
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=['coverage', 'mape'],
)
self.assertEqual(
set(df_horizon.columns),
{'coverage', 'horizon'},
)
df_horizon = diagnostics.performance_metrics(
df_cv,
metrics=['mape'],
)
self.assertIsNone(df_horizon)
# List of metrics containing non-valid metrics
with self.assertRaises(ValueError):
diagnostics.performance_metrics(
df_cv,
metrics=['mse', 'error_metric'],
)
def FB_Model(train_x, train_y,train_ratio,predict_period,changepoint_prior_scale,analysisornot,holiday):
time_split = int((train_x.shape[0]) * train_ratio)
df = pd.DataFrame(columns=['ds', 'y'])
df['ds'] = train_x[:time_split];df['y'] = train_y[:time_split]
df['y'] = np.log((np.asarray(df['y'], dtype=float))) #log能让预测效果更好
Hoday = pd.DataFrame({
'holiday': 'weekend',
'ds': pd.to_datetime(holiday),
'lower_window': 0,
'upper_window': 1,
})
#训练
model = Prophet(changepoint_prior_scale=changepoint_prior_scale,mcmc_samples=0,holidays=Hoday)#默认为0，增大后将最大后验估计取代为马尔科夫蒙特卡洛取样，但是会极大地延长训练时间
model.fit(df)
future = model.make_future_dataframe(freq='D', periods=predict_period) # 建立数据预测框架，数据粒度为天，预测步长为20天
forecast = model.predict(future)
model.plot(forecast).show() # 绘制预测效果图
model.plot_components(forecast).show()
#分析结果
if analysisornot:
df_cv = cross_validation(model, initial='1100 days', period='20 days', horizon='20 days')
df_p = performance_metrics(df_cv)
print(df_cv.head())
print(df_p.head())
fig = plot_cross_validation_metric(df_cv, metric='mape')
fig.show()
def fit_cv(
self,
param_grid=None,
initial="80 days",
horizon="14 days",
period="14 day",
rolling_window=1,
):
if param_grid is None:
param_grid = {
"changepoint_prior_scale": [0.001, 0.01, 0.1, 0.5],
"seasonality_prior_scale": [0.01, 0.1, 1.0, 10.0],
"holidays_prior_scale": [0.01, 0.1, 1.0, 10.0],
}
all_params = [
dict(zip(param_grid.keys(), v))
for v in itertools.product(*param_grid.values())
]
# We manually set this cap otherwise are not able
# to see the plots clearly. If we were to set the correct cap,
# we would have used the total pop:
# self.df['cap'] = get_region_pop(province, pop_prov_df, prov_list_df)
if self.growth == "logistic":
if self.cap is None:
self.cap = self.df["y"].max() + 20000
self.df["cap"] = self.cap
rmses, mae, mse = [], [], []
for params in all_params:
m = Prophet(self.growth, **params)
if self.holidays:
m.add_country_holidays(country_name="IT")
m.fit(self.df)
df_cv = cross_validation(m,
initial=initial,
horizon=horizon,
period=period,
parallel="processes")
df_p = performance_metrics(df_cv, rolling_window=rolling_window)
rmses.append(df_p["rmse"].values[0])
mae.append(df_p["mae"].values[0])
mse.append(df_p["mse"].values[0])
tuning_results = pd.DataFrame(all_params)
tuning_results["rmse"] = rmses
tuning_results["mae"] = mae
tuning_results["mse"] = mse
self.tuning_results = tuning_results
self.best_params = all_params[np.argmin(rmses)]
self.fit(**self.best_params)
def validate_model(model,dates):
"""
Background:
This model validation function is still under construction and will be updated during a future release.
"""
count_of_time_units = len(dates)
#print(count_of_time_units)
initial_size = str(int(count_of_time_units * 0.20)) + " days"
horizon_size = str(int(count_of_time_units * 0.10)) + " days"
period_size = str(int(count_of_time_units * 0.05)) + " days"
df_cv = cross_validation(model, initial=initial_size, horizon=horizon_size, period=period_size)
#df_cv = cross_validation(model,initial='730 days', period='180 days', horizon = '365 days')
df_p = performance_metrics(df_cv)
#print(df_cv.head(100))
#print(df_p.head(100))
mape_score_avg = str(round(df_p['mape'].mean()*100,2)) + "%"
return mape_score_avg
def getparameters_4ph(
self): #return best_params and overide default parameters
param_grid = {
'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.3, 0.5, 1],
'seasonality_prior_scale': [0.01, 0.1, 1, 3, 6, 10, 20],
}
all_params = [
dict(zip(param_grid.keys(), v))
for v in itertools.product(*param_grid.values())
]
rmses = []
# Use cross validation to evaluate all parameters
for params in all_params:
m = Prophet(**params,
daily_seasonality=True,
weekly_seasonality=True,
yearly_seasonality=True,
growth='linear')
m.add_seasonality(name='yearly', period=365.25, fourier_order=20)
m = m.fit(self.ph_train)
df_cv = cross_validation(m,
initial='730 days',
period='90 days',
horizon='180 days')
df_p = performance_metrics(df_cv, rolling_window=1)
rmses.append(df_p['rmse'].values[0])
# Find the best parameters
tuning_results = pd.DataFrame(all_params)
tuning_results['rmse'] = rmses
if disp: print(tuning_results)
best_params = all_params[np.argmin(rmses)]
if disp: print(best_params)
self.ph_bestparameters = best_params
return best_params
def train(self, metric_dict, oldest_data_datetime):
"""Train the Prophet model and store the predictions in predicted_df."""
prediction_freq = "1MIN"
# convert incoming metric to Metric Object
metric = Metric(metric_dict, oldest_data_datetime)
self._model = Prophet(daily_seasonality=True,
weekly_seasonality=True,
yearly_seasonality=True)
_LOGGER.info("training data range: %s - %s", metric.start_time,
metric.end_time)
_LOGGER.debug("begin training")
df_fit = self._model.fit(metric.metric_values)
if True:
df_cv = cross_validation(self._model,
horizon="1 day",
period="8 hours",
initial="4 days")
df_p = performance_metrics(df_cv)
_LOGGER.info("Performance data: %s %s", metric.metric_name, df_p)
def cross_validation(self):
if self.cv is None:
LOGGER.info("Cross validation not configured, skipping")
return None
try:
horizon = self._get_timedelta(self.cv["horizon"])
period = self._get_timedelta(self.cv.get("period"))
initial = self._get_timedelta(self.cv.get("initial"))
except KeyError:
raise ValueError(
"Horizon is the required config for cross validation")
self.cv_data = cross_validation(self.model,
horizon=horizon,
period=period,
initial=initial)
if self.cv_output_uri is not None:
self.save(self.cv_data, "cv_output_uri")
LOGGER.info(f"Cross validation data saved to {self.cv_output_uri}")
rolling_window = self.cv.get("rolling_window") or 0.1
metrics = self.cv.get("metrics")
self.cv_metrics = performance_metrics(self.cv_data,
rolling_window=rolling_window,
metrics=metrics)
if self.cv_metrics_uri is not None:
self.save(self.cv_metrics, "cv_metrics_uri")
LOGGER.info(
f"Cross validation metrics saved to {self.cv_metrics_uri}")
def prophet_cv_performance(model, train, minmax_pipe):
#fit the model
model.fit(train)
#run cross validation
#cv_results = cross_validation(model, initial='365 days', period='24 hours', horizon='24 hours')
cv_results = cross_validation(model,
initial='366 days',
period='24 hours',
horizon='24 hours')
#inverse transform the target and prediction columns
cv_results[['yhat',
'y']] = minmax_pipe.inverse_transform(cv_results[['yhat',
'y']])
#get performance results on the cv output
cv_performance = performance_metrics(cv_results)
#display the results
print('Model Mean Hourly MAE {0:.2f}'.format(np.mean(
cv_performance['mae'])))
print('Model Mean Hourly MAPE {0:.2f}'.format(
np.mean(cv_performance['mape'])))
return cv_results, cv_performance
def evaluate_model(model):
df_cv = cross_validation(model,
initial="700 days",
period="92 days",
horizon="8 days")
df_p = performance_metrics(df_cv)
return df_cv, df_p
def fbp(df, p, freq):
model = fbprophet.Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=p,
freq=freq,
include_history=True)
# future.tail()
forecast = model.predict(future)
# model.plot(forecast)
# model.plot_components(forecast)
# print(forecast)
if freq == 'Y':
time_format = '%Y'
elif freq == 'M':
time_format = '%Y-%m'
elif freq == 'D':
time_format = '%Y-%m-%d'
df_cv = cross_validation(model, horizon='30 days')
df_pe = performance_metrics(df_cv)
df_cv.to_csv('C:/Users/47135/Desktop/df_cv.csv', encoding='UTF-8')
df_pe.to_csv('C:/Users/47135/Desktop/df_pe.csv', encoding='UTF-8')
forecast['ds'] = forecast['ds'].dt.strftime(time_format)
result = forecast.to_dict(orient='list')
# print(result)
return result
def random_search(df, param_grid, max_evals=MAX_EVALS):
"""Random search for hyperparameter optimization"""
results = pd.DataFrame(columns=['mape', 'params', 'iteration', 'model'],
index=list(range(MAX_EVALS)))
for i in range(max_evals):
# Choose random hyperparameters
hyperparameters = {k: sample(v, 1)[0] for k, v in param_grid.items()}
# Evaluate randomly selected hyperparameters
eval_results = objective(df, hyperparameters, i)
results.loc[i, :] = eval_results
# Model without param tuning
m = Prophet()
m.fit(df)
df_cv = cross_validation(m,
initial='{} days'.format(round(len(df) * 0.75)),
horizon='{} days'.format(round(len(df) * 0.1)))
df_p = performance_metrics(df_cv)
mape = df_p.mape.mean()
results.loc[max_evals, :] = [mape, None, None, m]
# Sort with best score on top
results.sort_values('mape', inplace=True)
results.reset_index(inplace=True)
return results
def model_prophet(series,
holidays,
weekly=0,
monthly=0,
dev_length=729,
period='1 days',
horizon='1 days',
cvf=True,
perf=True,
val_dates=None):
#define model
modelf = Prophet(interval_width=0.95,
daily_seasonality=False,
weekly_seasonality=False,
yearly_seasonality=False,
holidays=holidays)
if weekly != 0:
modelf.add_seasonality(name='weeklyx', period=7, fourier_order=weekly)
if monthly != 0:
modelf.add_seasonality(name='monthlyx',
period=30.5,
fourier_order=monthly)
modelf.fit(series)
#test for horizon
if cvf:
cvf = cross_validation(modelf,
initial=str(dev_length) + ' days',
period=period,
horizon=horizon)
if perf: perf = performance_metrics(cvf)
return modelf, cvf, perf
def evaluate_metrics(model):
df_cv = cross_validation(model,
initial='730 days',
period='90 days',
horizon='180 days')
df_p = performance_metrics(df_cv)
return df_p
def plot_residuals(self):
print("CROSS VALIDATION RESULTS")
df_cv = cross_validation(self.m,
initial='365.25 days',
period='365.25 days',
horizon='365.25 days')
# df_cv = cross_validation(self.m, initial='180 days', period='180 days', horizon='180 days')
# self.out_table = df_cv
print(pdtabulate(df_cv))
print("PERFORMANCE METRICS")
df_p = performance_metrics(df_cv)
print(pdtabulate(df_p))
# Mean absolute percentage error
fig3 = plot_cross_validation_metric(df_cv,
metric='mape',
figsize=(11, 6))
ax3 = fig3.gca()
blue_patch = mpatches.Patch(color='#5F86BC',
label='Accuracy ~= ((1 - MAPE) * 100) %')
plt.legend(handles=[blue_patch])
# ax3.legend("Accuracy ~= ((1 - MAPE) * 100) %")
ax3.set_title(
label="Prophet Forecast - Mean Absolute Percentage Error - " +
self.name,
fontsize=24)
ax3.set_xlabel(xlabel="Forecast Horizon (days)", fontsize=16)
ax3.set_ylabel(ylabel="MAPE", fontsize=16)
ax3.set_ylim([0.0, 0.8])
plt.show()
def cross_validation_worker(model, initial, period, horizon, metric):
df_cv = cross_validation(model,
initial=initial,
period=period,
horizon=horizon)
df_p = performance_metrics(df_cv)
average_metric = df_p[metric].mean()
return average_metric
def test_model(model): # Making 90 forecasts with cutoffs between 2008-03-11 00:00:00 and 2010-08-18 00:00:00 df_cv = cross_validation(model, initial='450 days', period='10 days', horizon = '100 days') df_cv.head() df_p = performance_metrics(df_cv) df_p.head()
def __cv_run(self, params):
#交叉验证过程
model = Prophet(**params, holidays=self.holidays)
model.fit(self.data)
future = model.make_future_dataframe(freq=self.freq, periods=1)
forecast = model.predict(future)
data_merage = pd.concat([self.data, forecast[['yhat']]],
axis=1,
join='inner')
mape_train = np.abs(data_merage['yhat'] / data_merage['y'] - 1).mean()
initial_value = (self.data['ds'].max() -
self.data['ds'].min()).days / 1.9
if 1.5 < initial_value < 5 or 7 < initial_value < 194 or initial_value > 368:
initial_value = (self.data['ds'].max() -
self.data['ds'].min()) / 1.9
horizon_weight = 1 - 1 / 1.9
df_cv = cross_validation(model, initial=(self.data['ds'].max()-sef.data['ds'].min())/1.9, \
period=(self.data['ds'].max()-self.data['ds'].min())/4.2, \
horizon =(self.data['ds'].max()-self.data['ds'].min())/2.2)
elif initial_value <= 1.5:
initial_value = '25 hours'
period_value = '25 hours'
horizon_value = self.data['ds'].max() - self.data['ds'].min(
) - datetime.timedelta(hours=25)
horizon_weight = horizon_value / (self.data['ds'].max() -
self.data['ds'].min())
df_cv = cross_validation(model,
initial=initial_value,
period=period_value,
horizon=horizon_value)
elif initial_value <= 7:
initial_value = '8 days'
period_value = '8 days'
horizon_value = self.data['ds'].max() - self.data['ds'].min(
) - datetime.timedelta(days=8)
horizon_weight = horizon_value / (self.data['ds'].max() -
self.data['ds'].min())
df_cv = cross_validation(model,
initial=initial_value,
period=period_value,
horizon=horizon_value)
else:
initial_value = '367 days'
period_value = '367 days'
horizon_value = self.data['ds'].max() - self.data['ds'].min(
) - datetime.timedelta(days=367)
horizon_weight = horizon_value / (self.data['ds'].max() -
self.data['ds'].min())
df_cv = cross_validation(model,
initial=initial_value,
period=period_value,
horizon=horizon_value)
mape = horizon_weight * performance_metrics(df_cv, metrics=[
'mape'
])['mape'].mean() + (1 - horizon_weight) * mape_train
return mape
def ts_diagnose(self):
"""Diagnoses the fitted model"""
try:
assert self.model_fit is not None
except AssertionError:
self._prophet_logger.exception(
"Model has to be fitted first! Please call ts_fit(...)")
sys.exit("STOP")
self.plot_residuals()
if self._diagnose:
if input(
"Run cross validation y/n? Note, depending on parameters provided "
"this can take some time...").strip().lower() == 'y':
start = time()
self._prophet_logger.info(
"Running cross validation using parameters provided....")
if self._history is not None:
try:
self._prophet_cv = cross_validation(
self.model_fit,
initial=self._history,
period=self._step,
horizon=self._horizon)
except Exception:
self._prophet_logger.exception(
"Prophet cross validation error: check your "
"parameters 'history', 'horizon', 'step'!")
else:
try:
self._prophet_cv = cross_validation(
self.model_fit,
period=self._step,
horizon=self._horizon)
except Exception:
self._prophet_logger.exception(
"Prophet cross validation error: "
"check your parameters 'horizon', 'step'!")
self._prophet_logger.info("Time elapsed: {}".format(time() -
start))
simu_intervals = self._prophet_cv.groupby('cutoff')['ds'].agg([
('forecast_start', 'min'), ('forecast_till', 'max')
])
self._prophet_logger.info(
"Following time windows and cutoffs have been set-up:\n")
print(simu_intervals)
#
plot_cross_validation_metric(self._prophet_cv, metric='mape')
#
self._prophet_logger.info("Running performance metrics...")
self._prophet_p = performance_metrics(self._prophet_cv)
else:
self._prophet_logger.info("OK")
return
def cross_val(self):
df_cv = cross_validation(self.m,
initial='62 days',
period='1 days',
horizon='7 days')
# for col in ['yhat', 'yhat_lower', 'yhat_upper', 'y']:
# df_cv[col] = inv_boxcox(df_cv[col], lmbda)
print(df_cv.sort_values('ds').tail())
df_p = performance_metrics(df_cv)
print(df_p)
def cross_validate(fitted_model, training_range, forecast_range, cv_interval):
'''
Input --> 1 already defined model
Output --> avg MAPE
'''
# nee dot make sure that each initial, horizon covers full week??
df_cv = cross_validation(fitted_model, initial=training_range, horizon=forecast_range, period=cv_interval)
df_p = performance_metrics(df_cv, rolling_window = 1/7)
return df_p['mape'].mean()
def test_cross_validation_uncertainty_disabled(self):
df = self.__df.copy()
for uncertainty in [0, False]:
m = Prophet(uncertainty_samples=uncertainty)
m.fit(df, algorithm='Newton')
df_cv = diagnostics.cross_validation(
m, horizon='4 days', period='4 days', initial='115 days')
expected_cols = ['ds', 'yhat', 'y', 'cutoff']
self.assertTrue(all(col in expected_cols for col in df_cv.columns.tolist()))
df_p = diagnostics.performance_metrics(df_cv)
self.assertTrue('coverage' not in df_p.columns)
def validator(self):
self.__cv_metrics.df_cv = cross_validation(self.__model,
initial="1 days",
period="120 days",
horizon="15 days")
self.__cv_metrics.df_perf = performance_metrics(
self.__cv_metrics.df_cv)
self.__cv_metrics.manual_mape = self.mean_absolute_percentage_error(
self.__cv_metrics.df_cv.y, self.__cv_metrics.df_cv.yhat)
def test_prophecy(ticker):
df = get_daily_data(ticker, 90)
df.rename(columns={'time': 'ds', 'close': 'y'}, inplace=True)
m.fit(df)
df_cv = cross_validation(m, horizon='10 days')
df_p = performance_metrics(df_cv)
df_p.head(5)
plot_cross_validation_metric(df_cv, metric='mape')
plt.show()
def cross_validate(df):
prophet = Prophet()
prophet.fit(df)
df_cv = cross_validation(prophet, initial='30 days', period='4 days', horizon='7 days')
df_performance = performance_metrics(df_cv)
fig_performance = plot_cross_validation_metric(df_cv, metric='mape')
return df_performance
#print(predict(df_cases_fb))
def cross_validate(df):
prophet = Prophet()
prophet.fit(df)
df_cv = cross_validation(
prophet, initial="30 days", period="4 days", horizon="7 days"
)
df_performance = performance_metrics(df_cv)
fig_performance = plot_cross_validation_metric(df_cv, metric="mape")
return df_performance
def fit_hex_model(hex_data: pd.DataFrame) -> Prophet:
fit_data = hex_data[['timestamp', 'y']].copy()
fit_data.rename(columns={'timestamp': 'ds'}, inplace=True)
fit_data['cap'] = 1
fit_data['floor'] = 0
model = Prophet(n_changepoints=0, growth='logistic')
forecaster = model.fit(fit_data, algorithm='Newton')
df_cv = cross_validation(model,
initial='40 days',
period='7 days',
horizon='7 days')
df_metrics = performance_metrics(df_cv)
print(df_metrics.mdape.mean())
def process_job(conn, job):
assert job['type'].lower() in ['cases', 'deaths', 'tests']
print(f"{time.strftime('%H:%M:%S')} Starting job={job}")
data = query_data(conn, job)
df = prepare_data(job, data)
m = create_model(job)
m.fit(df)
# predict a third into the future of what we looked back
future_days = round(job['days_to_look_back'] / 3)
future = m.make_future_dataframe(periods=future_days)
future['cap'] = df['cap'][0]
forecast = m.predict(future)
# region debug
if os.getenv('DOCKER_ACTIVE') is None:
fig = m.plot(forecast)
add_changepoints_to_plot(fig.gca(), m, forecast)
fig.savefig(f"../job/prediction-{job['id']}.png")
# endregion
change_points = m.changepoints.dt.date.tolist()
store_prediction(conn, job, forecast, change_points)
# cross validate and create score
if job['with_score']:
# compute period to have 5-6 simulated forecasts
horizon = pd.Timedelta("14 days")
initial = horizon * 3
period = (df.iloc[-1]['ds'] - df.iloc[0]['ds'] - horizon - initial) / 5
df_cv = cross_validation(m,
initial=initial,
horizon=horizon,
period=period,
parallel='processes')
df_p = performance_metrics(df_cv)
# region debug
if os.getenv('DOCKER_ACTIVE') is None:
fig = plot_cross_validation_metric(df_cv, metric='mape')
fig.savefig(f"../job/score-{job['id']}.png")
# endregion
score = df_p.iloc[-1]['mape']
store_score(conn, job, score)
def cv_cases(state):
df = prepdata_cases(state)
prophet = Prophet()
prophet.fit(df)
df_cv = cross_validation(
prophet, initial="50 days", period="4 days", horizon="7 days"
)
df_performance = performance_metrics(df_cv)
fig_performance = plot_cross_validation_metric(df_cv, metric="mape")
return plt.show()
def single_cv_run(history_df, metrics, param_dict, parallel):
m = Prophet(**param_dict)
m.add_country_holidays(country_name='BR')
history_df['cap'] = 2 * history_df["y"].max()
m.fit(history_df)
df_cv = cross_validation(m,
initial='3600 days',
horizon='1200 days',
parallel=parallel)
df_p = performance_metrics(df_cv, rolling_window=1)
df_p['params'] = str(param_dict)
print(df_p.head())
df_p = df_p.loc[:, metrics]
return df_p
def plot_cross_validation_metric(df_cv, metric, rolling_window=0.1, ax=None):
"""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 fbprophet.diagnostics.performance_metrics to compute the metrics.
Valid values of metric are 'mse', 'rmse', 'mae', 'mape', 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 fbprophet.diagnostics.cross_validation.
metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', '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.
Returns
-------
a matplotlib figure.
"""
if ax is None:
fig = plt.figure(facecolor='w', figsize=(10, 6))
ax = fig.add_subplot(111)
else:
fig = 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=0)
df_h = performance_metrics(df_cv, metrics=[metric], rolling_window=rolling_window)
# 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 = ['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 = df_none['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])
x_plt_h = df_h['horizon'].astype('timedelta64[ns]').astype(np.int64) / float(dt_conversions[i])
ax.plot(x_plt, df_none[metric], '.', alpha=0.5, c='gray')
ax.plot(x_plt_h, df_h[metric], '-', c='b')
ax.grid(True)
ax.set_xlabel('Horizon ({})'.format(dt_names[i]))
ax.set_ylabel(metric)
return fig
