def forecast(self, forecast_horizon: int = 96):
super().forecast(forecast_horizon)
print("Running Prophet forecast for Currency-pair: {} using forecast horizon: {}", self.currency_pair.upper(),
forecast_horizon)
print("Dataset: ", self.currency_pair.upper())
print(self.training_data.head(5))
print(".....\t.........\t...")
print(self.training_data.tail(5))
# model = Prophet(interval_width=0.99, mcmc_samples=60)
model = Prophet(interval_width=0.99)
model.fit(self.training_data)
future = model.make_future_dataframe(periods=forecast_horizon)
future.tail()
_forecast = model.predict(future)
last_n = _forecast.tail(forecast_horizon)
last_n.to_csv(f"output/{self.currency_pair}__{self.model_name.lower()}__{forecast_horizon}__forecasts.csv")
# last_n = _forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]].tail(n)
print(last_n)
self.forecasts = last_n["yhat"]
self.forecasts_upper = last_n["yhat_upper"]
self.forecasts_lower = last_n["yhat_lower"]
self.errors = [(abs(upper - lower) / 2) for upper, lower in zip(self.forecasts_upper, self.forecasts_lower)]
self.forecasts_raw = last_n
def test_prophet_trend_onnx(self):
df = self._get_data()
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=365)
future_np = (future.values -
np.datetime64("1970-01-01T00:00:00.000000000")).astype(
np.int64) / 1000000000
# Convert with Hummingbird.
hb_model = hummingbird.ml.convert(m, "onnx", future_np)
# Predictions.
prophet_trend = m.predict(future)["trend"]
hb_trend = hb_model.predict(future_np)
import onnx
onnx.save(hb_model.model, "prophet.onnx")
np.testing.assert_allclose(prophet_trend,
hb_trend,
rtol=1e-06,
atol=1e-06)
def test_logistic_floor(self):
m = Prophet(growth='logistic')
N = DATA.shape[0]
history = DATA.head(N // 2).copy()
history['floor'] = 10.
history['cap'] = 80.
future = DATA.tail(N // 2).copy()
future['cap'] = 80.
future['floor'] = 10.
m.fit(history, algorithm='Newton')
self.assertTrue(m.logistic_floor)
self.assertTrue('floor' in m.history)
self.assertAlmostEqual(m.history['y_scaled'][0], 1.)
self.assertEqual(m.fit_kwargs, {'algorithm': 'Newton'})
fcst1 = m.predict(future)
m2 = Prophet(growth='logistic')
history2 = history.copy()
history2['y'] += 10.
history2['floor'] += 10.
history2['cap'] += 10.
future['cap'] += 10.
future['floor'] += 10.
m2.fit(history2, algorithm='Newton')
self.assertAlmostEqual(m2.history['y_scaled'][0], 1.)
def predict_in_france(data,
country,
vaccination_metric,
future_days,
plot=True):
df = data[(data['country'] == country)]
df = df[['date', vaccination_metric]]
df.columns = ['ds', 'y']
model = Prophet(interval_width=0.95)
model.fit(df)
layout = dict(
title=
'Prediction : Nombre de personnes vaccinées par cent dans les 60 prochains jours (jusqu'
"'"
'au 28/06/21)',
xaxis=dict(title='Dates'),
yaxis=dict(title='Pourcentage'))
future = model.make_future_dataframe(periods=future_days)
forecast = model.predict(future)
if plot:
fig = plot_plotly(model, forecast)
fig.layout = layout
fig.write_html("analyse/Prediction_France.html")
else:
return forecast
def _estimate_weekly_pattern(self, time, energy_per_day, daily_masks):
epd = energy_per_day.copy()
for i in range(daily_masks.shape[0]):
if daily_masks[i] < 1:
epd[i] = np.nan
daily_times = [i*self.vpd for i in range(daily_masks.shape[0])]
try:
time_index = pd.to_datetime(
time.take(daily_times), format='%Y-%m-%d %H:%M:%S')
except ValueError:
time_index = pd.to_datetime(
time.take(daily_times), format='%d-%b-%Y %H:%M:%S')
time_index.reset_index(drop=True, inplace=True)
daily_data = pd.concat([time_index, pd.Series(
epd)], keys=['ds', 'y'], axis=1)
prophet_model = Prophet(weekly_seasonality=True,
yearly_seasonality=True)
if daily_data.count()['y'] > 1:
prophet_model.fit(daily_data)
# `future` starts with the first day of the time series.
# This allows us to select the corresponding pattern value
# for day `i` by accessing `weekly[i % 7]`.
future = prophet_model.make_future_dataframe(periods=0)
forecast = prophet_model.predict(future)
return forecast.get('weekly').head(7)
return pd.Series(np.zeros(7))
def get_prophet_forecast(df, country="ALL", save_model=True):
df_ts = df.copy() if country == "ALL" else df[df['country'] ==
country].copy()
if len(df_ts) <= 180:
return None
df_month = df_ts.groupby('inv_month').sum().reset_index()
df_ts['inv_date'] = pd.to_datetime(df_ts['inv_date'])
df_ts.rename(columns={'inv_date': 'ds', 'value': 'y'}, inplace=True)
m = Prophet(yearly_seasonality=20)
m.fit(df_ts)
if save_model:
with open(fr'models\{country}_model.json', 'w') as fout:
json.dump(model_to_json(m), fout) # Save model
future = m.make_future_dataframe(periods=180, freq='D')
forecast = m.predict(future)
forecast['inv_month'] = forecast['ds'].apply(
lambda v: date(v.year, v.month, 1).isoformat())
monthly_forecast = forecast[['inv_month', 'yhat'
]].groupby('inv_month').sum().reset_index()
df_forecast = pd.merge(monthly_forecast,
df_month,
on='inv_month',
how='left')
return df_forecast
def predict(ticker, start_date):
today = datetime.date.today()
end_date = today.strftime("%Y-%m-%d")
data = yf.download(ticker, start_date, end_date)
df_forecast = data.copy()
df_forecast.reset_index(inplace=True)
df_forecast["ds"] = df_forecast["Date"]
df_forecast["y"] = df_forecast["Adj Close"]
df_forecast = df_forecast[["ds", "y"]]
df_forecast
model = Prophet()
model.fit(df_forecast)
future = pd.to_datetime(end_date) + pd.DateOffset(days=7)
future_date = future.strftime("%Y-%m-%d")
dates = pd.date_range(start=end_date, end=future_date)
df_pred = pd.DataFrame({"ds": dates})
forecast = model.predict(df_pred)
prediction_list = forecast.tail(7).to_dict("records")
output = {}
for data in prediction_list:
date = data["ds"].strftime("%Y-%m-%d")
output[date] = data["trend"]
return output
def test_check_single_cutoff_forecast_func_calls(self):
m = Prophet()
m.fit(self.__df)
mock_predict = pd.DataFrame({
'ds':
pd.date_range(start='2012-09-17', periods=3),
'yhat':
np.arange(16, 19),
'yhat_lower':
np.arange(15, 18),
'yhat_upper':
np.arange(17, 20),
'y':
np.arange(16.5, 19.5),
'cutoff': [datetime.date(2012, 9, 15)] * 3
})
# cross validation with 3 and 7 forecasts
for args, forecasts in ((['4 days', '10 days', '115 days'], 3),
(['4 days', '4 days', '115 days'], 7)):
with patch(
'prophet.diagnostics.single_cutoff_forecast') as mock_func:
mock_func.return_value = mock_predict
df_cv = diagnostics.cross_validation(m, *args)
# check single forecast function called expected number of times
self.assertEqual(diagnostics.single_cutoff_forecast.call_count,
forecasts)
def _prophet_fit_and_predict( # pylint: disable=too-many-arguments
df: DataFrame,
confidence_interval: float,
yearly_seasonality: Union[bool, str, int],
weekly_seasonality: Union[bool, str, int],
daily_seasonality: Union[bool, str, int],
periods: int,
freq: str,
) -> DataFrame:
"""
Fit a prophet model and return a DataFrame with predicted results.
"""
try:
prophet_logger = logging.getLogger("prophet.plot")
prophet_logger.setLevel(logging.CRITICAL)
from prophet import Prophet # pylint: disable=import-error
prophet_logger.setLevel(logging.NOTSET)
except ModuleNotFoundError:
raise QueryObjectValidationError(_("`prophet` package not installed"))
model = Prophet(
interval_width=confidence_interval,
yearly_seasonality=yearly_seasonality,
weekly_seasonality=weekly_seasonality,
daily_seasonality=daily_seasonality,
)
if df["ds"].dt.tz:
df["ds"] = df["ds"].dt.tz_convert(None)
model.fit(df)
future = model.make_future_dataframe(periods=periods, freq=freq)
forecast = model.predict(future)[[
"ds", "yhat", "yhat_lower", "yhat_upper"
]]
return forecast.join(df.set_index("ds"), on="ds").set_index(["ds"])
def test_custom_seasonality(self):
holidays = pd.DataFrame({
'ds': pd.to_datetime(['2017-01-02']),
'holiday': ['special_day'],
'prior_scale': [4.],
})
m = Prophet(holidays=holidays)
m.add_seasonality(name='monthly',
period=30,
fourier_order=5,
prior_scale=2.)
self.assertEqual(
m.seasonalities['monthly'],
{
'period': 30,
'fourier_order': 5,
'prior_scale': 2.,
'mode': 'additive',
'condition_name': None
},
)
with self.assertRaises(ValueError):
m.add_seasonality(name='special_day', period=30, fourier_order=5)
with self.assertRaises(ValueError):
m.add_seasonality(name='trend', period=30, fourier_order=5)
m.add_seasonality(name='weekly', period=30, fourier_order=5)
# Test fourier order <= 0
m = Prophet()
with self.assertRaises(ValueError):
m.add_seasonality(name='weekly', period=7, fourier_order=0)
with self.assertRaises(ValueError):
m.add_seasonality(name='weekly', period=7, fourier_order=-1)
# Test priors
m = Prophet(holidays=holidays,
yearly_seasonality=False,
seasonality_mode='multiplicative')
m.add_seasonality(name='monthly',
period=30,
fourier_order=5,
prior_scale=2.,
mode='additive')
m.fit(DATA.copy())
self.assertEqual(m.seasonalities['monthly']['mode'], 'additive')
self.assertEqual(m.seasonalities['weekly']['mode'], 'multiplicative')
seasonal_features, prior_scales, component_cols, modes = (
m.make_all_seasonality_features(m.history))
self.assertEqual(sum(component_cols['monthly']), 10)
self.assertEqual(sum(component_cols['special_day']), 1)
self.assertEqual(sum(component_cols['weekly']), 6)
self.assertEqual(sum(component_cols['additive_terms']), 10)
self.assertEqual(sum(component_cols['multiplicative_terms']), 7)
if seasonal_features.columns[0] == 'monthly_delim_1':
true = [2.] * 10 + [10.] * 6 + [4.]
self.assertEqual(sum(component_cols['monthly'][:10]), 10)
self.assertEqual(sum(component_cols['weekly'][10:16]), 6)
else:
true = [10.] * 6 + [2.] * 10 + [4.]
self.assertEqual(sum(component_cols['weekly'][:6]), 6)
self.assertEqual(sum(component_cols['monthly'][6:16]), 10)
self.assertEqual(prior_scales, true)
def test_cross_validation_extra_regressors(self):
df = self.__df.copy()
df['extra'] = range(df.shape[0])
df['is_conditional_week'] = np.arange(df.shape[0]) // 7 % 2
m = Prophet()
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.add_seasonality(name='conditional_weekly',
period=7,
fourier_order=3,
prior_scale=2.,
condition_name='is_conditional_week')
m.add_regressor('extra')
m.fit(df)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='4 days',
initial='135 days')
self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
period = pd.Timedelta('4 days')
dc = df_cv['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= period)
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
def get_forecast(df, month, country="ALL"):
df_ts = df.copy() if country=="ALL" else df[df['country']==country].copy()
if len(df_ts) <= 180:
return None
actual = df_ts[df_ts['inv_month'] == month.isoformat()]['value'].sum()
df_ts = df_ts[['inv_month', 'inv_date', 'value']].groupby(['inv_month', 'inv_date']).sum().reset_index()
df_train = df_ts[df_ts['inv_month'] < month.isoformat()]
df_train['inv_date'] = pd.to_datetime(df_train['inv_date'])
df_train.rename(columns={'inv_date':'ds', 'value':'y'}, inplace=True)
m = Prophet(yearly_seasonality=20)
m.fit(df_train)
future = m.make_future_dataframe(periods=60, freq='D')
df_forecast = m.predict(future)
df_forecast['inv_month'] = df_forecast['ds'].apply(lambda v: date(v.year, v.month, 1).isoformat())
forecast = df_forecast[df_forecast['inv_month'] == month.isoformat()]['yhat'].sum()
exp_model = SimpleExpSmoothing(df_ts[['inv_month', 'inv_date', 'value']].set_index(['inv_month', 'inv_date'])).fit(smoothing_level=0.2, optimized=False)
exp_forecast = forecast_df = sum(exp_model.forecast(30))
return actual, forecast, exp_forecast
def make_forecast(df, len_forecast: int, time_series_label: str):
"""
Function for making time series forecasting with Prophet library
:param df: dataframe to process
:param len_forecast: forecast length
:param time_series_label: name of time series to process
:return predicted_values: forecast
:return model_name: name of the model (always 'AutoTS')
"""
df['ds'] = df['datetime']
df['y'] = df[time_series_label]
prophet_model = Prophet()
prophet_model.fit(df)
future = prophet_model.make_future_dataframe(periods=len_forecast,
include_history=False)
forecast = prophet_model.predict(future)
predicted_values = np.array(forecast['yhat'])
model_name = 'Prophet'
return predicted_values, model_name
def fit_prophet(dtf_train, dtf_test, lst_exog=None, model=None, freq="D", conf=0.95, figsize=(15,10)):
## setup prophet
if model is None:
model = Prophet(growth="linear", changepoints=None, n_changepoints=25, seasonality_mode="multiplicative",
yearly_seasonality="auto", weekly_seasonality="auto", daily_seasonality="auto",
holidays=None, interval_width=conf)
if lst_exog != None:
for regressor in lst_exog:
model.add_regressor(regressor)
## train
model.fit(dtf_train)
## test
dtf_prophet = model.make_future_dataframe(periods=len(dtf_test)+10, freq=freq, include_history=True)
if model.growth == "logistic":
dtf_prophet["cap"] = dtf_train["cap"].unique()[0]
if lst_exog != None:
dtf_prophet = dtf_prophet.merge(dtf_train[["ds"]+lst_exog], how="left")
dtf_prophet.iloc[-len(dtf_test):][lst_exog] = dtf_test[lst_exog].values
dtf_prophet = model.predict(dtf_prophet)
dtf_train = dtf_train.merge(dtf_prophet[["ds","yhat"]], how="left").rename(
columns={'yhat':'model', 'y':'ts'}).set_index("ds")
dtf_test = dtf_test.merge(dtf_prophet[["ds","yhat","yhat_lower","yhat_upper"]], how="left").rename(
columns={'yhat':'forecast', 'y':'ts', 'yhat_lower':'lower', 'yhat_upper':'upper'}).set_index("ds")
## evaluate
dtf = dtf_train.append(dtf_test)
dtf = utils_evaluate_ts_model(dtf, conf=conf, figsize=figsize, title="Prophet")
return dtf, model
def generate_forecast(df: pd.DataFrame, length: int) -> pd.DataFrame:
"""Takes in a training set (data - value) and a length to
return a forecast DataFrame"""
model = Prophet(interval_width=0.50)
model.fit(df)
return model.predict(model.make_future_dataframe(periods=length))
def test_fit_predict_no_changepoints_mcmc(self):
N = DATA.shape[0]
train = DATA.head(N // 2)
future = DATA.tail(N // 2)
forecaster = Prophet(n_changepoints=0, mcmc_samples=100)
forecaster.fit(train)
forecaster.predict(future)
def test_fit_predict_no_seasons(self):
N = DATA.shape[0]
train = DATA.head(N // 2)
future = DATA.tail(N // 2)
forecaster = Prophet(weekly_seasonality=False,
yearly_seasonality=False)
forecaster.fit(train)
forecaster.predict(future)
def fit(df, args):
m = Prophet(changepoint_prior_scale=args.changepoint_prior_scale,
interval_width=args.interval_width,
mcmc_samples=args.mcmc_samples)
m.add_country_holidays(country_name='US')
# m.add_regressor('low', prior_scale=0.5, mode='multiplicative') ?
print(f'Model is trained on {len(df)} data')
m.fit(df)
return m
def test_subdaily_holidays(self):
holidays = pd.DataFrame({
'ds': pd.to_datetime(['2017-01-02']),
'holiday': ['special_day'],
})
m = Prophet(holidays=holidays)
m.fit(DATA2)
fcst = m.predict()
self.assertEqual(sum(fcst['special_day'] == 0), 575)
def run_prophet_train(df):
m = Prophet(daily_seasonality=True, weekly_seasonality=True)
m.add_country_holidays(country_name='CN')
m.add_seasonality(name='weekly',
period=7,
fourier_order=3,
prior_scale=0.1)
m.fit(df)
pass
def test_fit_predict_duplicates(self):
N = DATA.shape[0]
train1 = DATA.head(N // 2).copy()
train2 = DATA.head(N // 2).copy()
train2['y'] += 10
train = train1.append(train2)
future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
forecaster = Prophet()
forecaster.fit(train)
forecaster.predict(future)
def test_fit_with_holidays(self):
holidays = pd.DataFrame({
'ds':
pd.to_datetime(['2012-06-06', '2013-06-06']),
'holiday': ['seans-bday'] * 2,
'lower_window': [0] * 2,
'upper_window': [1] * 2,
})
model = Prophet(holidays=holidays, uncertainty_samples=0)
model.fit(DATA).predict()
def test_cross_validation(self):
m = Prophet()
m.fit(self.__df)
# Calculate the number of cutoff points(k)
horizon = pd.Timedelta('4 days')
period = pd.Timedelta('10 days')
initial = pd.Timedelta('115 days')
methods = [None, 'processes', 'threads', CustomParallelBackend()]
try:
from dask.distributed import Client
client = Client(processes=False) # noqa
methods.append("dask")
except ImportError:
pass
for parallel in methods:
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='115 days',
parallel=parallel)
self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)
self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
self.assertTrue(
min(df_cv['cutoff']) >= min(self.__df['ds']) + initial)
dc = df_cv['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= period)
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
# Each y in df_cv and self.__df with same ds should be equal
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='135 days')
self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)
with self.assertRaises(ValueError):
diagnostics.cross_validation(m,
horizon='10 days',
period='10 days',
initial='140 days')
# invalid alias
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m, horizon="4 days", parallel="bad")
# no map method
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m,
horizon="4 days",
parallel=object())
def prophet_forecast(self, train_start, train_end, test_start, test_end):
from sys import platform
if platform == "linux" or platform == "linux2":
from prophet import Prophet # linux
elif platform == "darwin":
from fbprophet import Prophet # OS X
elif platform == "win32":
from fbprophet import Prophet # Windows...
data = self.data.reset_index()
data.rename(columns={'Date': 'ds', self.column: 'y'}, inplace=True)
# FIXME and take user input
size = len(data)
df_train = data.iloc[int(-size * .10):, :]
df_test = data.iloc[int(-size * .20):, :]
model_prophet = Prophet(seasonality_mode='additive')
model_prophet.add_seasonality(name='monthly',
period=30.5,
fourier_order=5)
model_prophet.fit(df_train)
df_future = model_prophet.make_future_dataframe(periods=365)
df_pred = model_prophet.predict(df_future)
model_prophet.plot(df_pred)
plt.tight_layout()
plt.title('Prophet Forecast')
plt.savefig(os.path.join(img_dirp, f'img/prophet_forecast.png'))
model_prophet.plot_components(df_pred)
plt.tight_layout()
plt.savefig(os.path.join(img_dirp, 'img/components.png'))
# merge test set with predicted data and plot accuracy of model's predictions
selected_columns = ['ds', 'yhat_lower', 'yhat_upper', 'yhat']
df_pred = df_pred.loc[:, selected_columns].reset_index(drop=True)
df_test = df_test.merge(df_pred, on=['ds'], how='left')
df_test.ds = pd.to_datetime(df_test.ds)
df_test.set_index('ds', inplace=True)
fig, ax = plt.subplots(1, 1)
ax = sns.lineplot(
data=df_test[['y', 'yhat_lower', 'yhat_upper', 'yhat']])
ax.fill_between(df_test.index,
df_test.yhat_lower,
df_test.yhat_upper,
alpha=0.3)
ax.set(title=f'{self.column} - actual vs. predicted',
xlabel='Date',
ylabel='{self.column}')
plt.tight_layout()
plt.savefig(os.path.join(img_dirp, 'img/actual_v_predicted.png'))
def getTickerForcast(ticker, period, dataPeriod):
stockTicker = yf.Ticker(ticker)
data = stockTicker.history(period=dataPeriod, interval="1d")
df = pd.DataFrame({'ds': data.index.values, 'y': data['Close'].values})
m = Prophet(daily_seasonality=True)
m.fit(df)
future = m.make_future_dataframe(periods=period)
prediction = m.predict(future)
return pd.DataFrame({
'date': prediction['ds'].values,
'close': prediction['yhat'].values
}).to_dict('records')
def test_cross_validation_custom_cutoffs(self):
m = Prophet()
m.fit(self.__df)
# When specify a list of cutoffs
# the cutoff dates in df_cv are those specified
df_cv1 = diagnostics.cross_validation(
m,
horizon='32 days',
period='10 days',
cutoffs=[pd.Timestamp('2012-07-31'),
pd.Timestamp('2012-08-31')])
self.assertEqual(len(df_cv1['cutoff'].unique()), 2)
def run_prophet(data, predict_period=40):
data = data.reset_index()
print("data columns", data.columns)
data = data[["Date", "Adj Close"]]
data.columns = ["ds", "y"]
m = Prophet()
m.fit(data)
future = m.make_future_dataframe(periods=predict_period)
future.tail()
forecast = m.predict(future)
# forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
forecast = forecast[['ds', 'trend', 'yhat', 'yhat_lower', 'yhat_upper']]
return forecast, m
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 forecast(df, periods, showflag=True):
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=periods)
predict = m.predict(future)
if showflag:
fig = m.plot(predict)
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title("forcasted")
plt.show()
return m, future, predict
class MetricPredictor:
"""docstring for Predictor."""
model_name = "prophet"
model_description = "Forecasted value from Prophet model"
model = None
predicted_df = None
metric = None
def __init__(self, metric, rolling_data_window_size="10d"):
"""Initialize the Metric object."""
self.metric = Metric(metric, rolling_data_window_size)
def train(self, metric_data=None, prediction_duration=15):
"""Train the Prophet model and store the predictions in predicted_df."""
prediction_freq = "1MIN"
# convert incoming metric to Metric Object
if metric_data:
# because the rolling_data_window_size is set, this df should not bloat
self.metric += Metric(metric_data)
# Don't really need to store the model, as prophet models are not retrainable
# But storing it as an example for other models that can be retrained
self.model = Prophet(daily_seasonality=True,
weekly_seasonality=True,
yearly_seasonality=True)
_LOGGER.info("training data range: %s - %s", self.metric.start_time,
self.metric.end_time)
# _LOGGER.info("training data end time: %s", self.metric.end_time)
_LOGGER.debug("begin training")
self.model.fit(self.metric.metric_values)
future = self.model.make_future_dataframe(
periods=int(prediction_duration),
freq=prediction_freq,
include_history=False,
)
forecast = self.model.predict(future)
forecast["timestamp"] = forecast["ds"]
forecast = forecast[["timestamp", "yhat", "yhat_lower", "yhat_upper"]]
forecast = forecast.set_index("timestamp")
self.predicted_df = forecast
_LOGGER.debug(forecast)
def predict_value(self, prediction_datetime):
"""Return the predicted value of the metric for the prediction_datetime."""
nearest_index = self.predicted_df.index.get_loc(prediction_datetime,
method="nearest")
return self.predicted_df.iloc[[nearest_index]]