PROPHET_PLOT
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The PROPHET_PLOT node plots the forecasted trend of the time series data that was passed in. This is the output plotly graph from the 'plot_plotly' function from 'prophet.plot'.
It expects the trained Prophet model from the PROPHET_PREDICT node as input.
If 'run_forecast' was True in that node, the forecasted dataframe will be available as the 'm' attribute of the default input.
Otherwise, this will make the predictions on the raw dataframe (in which case it will be the 'm' attribute of the default input).
You can tell if that forecasted dataframe is available via the 'extra' field of data input, 'run_forecast' (data.extra["run_forecast"]).
Inputs
------
default : DataFrame
the DataContainer to be visualized
data : DataContainer
the DataContainer that holds the prophet model and forecast data in the 'extra' field Params: periods : int The number of periods out to predict.
Only used if the node passed into this node (i.e. PROPHET_PREDICT) did NOT return the forecast.
If the forecast was included in the DataContainer, this parameter will be ignored.
Default = 365 Returns: out : Plotly the DataContainer containing the Plotly visualization of the prophet model
Python Code
import os
import sys
import numpy as np
import pandas as pd
from flojoy import DataFrame, Plotly, flojoy
@flojoy(deps={"prophet": "1.1.5"})
def PROPHET_PLOT(default: DataFrame, periods: int = 365) -> Plotly:
"""The PROPHET_PLOT node plots the forecasted trend of the time series data that was passed in.
This is the output plotly graph from the 'plot_plotly' function from 'prophet.plot'.
It expects the trained Prophet model from the PROPHET_PREDICT node as input.
If 'run_forecast' was True in that node, the forecasted dataframe will be available as the 'm' attribute of the default input.
Otherwise, this will make the predictions on the raw dataframe (in which case it will be the 'm' attribute of the default input).
You can tell if that forecasted dataframe is available via the 'extra' field of data input, 'run_forecast' (data.extra["run_forecast"]).
Inputs
------
default : DataFrame
the DataContainer to be visualized
data : DataContainer
the DataContainer that holds the prophet model and forecast data in the 'extra' field
Parameters
----------
periods : int
The number of periods out to predict.
Only used if the node passed into this node (i.e. PROPHET_PREDICT) did NOT return the forecast.
If the forecast was included in the DataContainer, this parameter will be ignored.
Default = 365
Returns
-------
Plotly
the DataContainer containing the Plotly visualization of the prophet model
"""
import prophet
from prophet.plot import plot_plotly
from prophet.serialize import model_from_json
def _make_dummy_dataframe_for_prophet():
"""Generate random time series data to test if prophet works"""
start_date = pd.Timestamp("2023-01-01")
end_date = pd.Timestamp("2023-07-20")
num_days = (end_date - start_date).days + 1
timestamps = pd.date_range(start=start_date, end=end_date, freq="D")
data = np.random.randn(num_days) # Random data points
df = pd.DataFrame({"ds": timestamps, "ys": data})
df.rename(
columns={df.columns[0]: "ds", df.columns[1]: "y"}, inplace=True
) # PROPHET model expects first column to be `ds` and second to be `y`
return df
def _apply_macos_prophet_hotfix():
"""This is a hotfix for MacOS. See https://github.com/facebook/prophet/issues/2250#issuecomment-1559516328 for more detail"""
if sys.platform != "darwin":
return
# Test if prophet works (i.e. if the hotfix had already been applied)
try:
_dummy_df = _make_dummy_dataframe_for_prophet()
prophet.Prophet().fit(_dummy_df)
except RuntimeError:
print("Could not run prophet, applying hotfix...")
else:
return
prophet_dir = prophet.__path__[0] # type: ignore
# Get stan dir
stan_dir = os.path.join(prophet_dir, "stan_model")
# Find cmdstan-xxxxx dir
cmdstan_basename = [x for x in os.listdir(stan_dir) if x.startswith("cmdstan")]
assert len(cmdstan_basename) == 1, "Could not find cmdstan dir"
cmdstan_basename = cmdstan_basename[0]
# Run (from stan_dir) : install_name_tool -add_rpath @executable_path/<CMDSTAN_BASENAME>/stan/lib/stan_math/lib/tbb prophet_model.bin
cmd = f"install_name_tool -add_rpath @executable_path/{cmdstan_basename}/stan/lib/stan_math/lib/tbb prophet_model.bin"
cwd = os.getcwd()
os.chdir(stan_dir)
return_code = os.system(cmd)
os.chdir(cwd)
if return_code != 0:
raise RuntimeError("Could not apply hotfix")
_apply_macos_prophet_hotfix()
extra = default.extra
if not extra or "prophet" not in extra:
raise ValueError(
"Prophet model must be available in DataContainer 'extra' key to plot"
)
node_name = __name__.split(".")[-1]
model = model_from_json(extra["prophet"])
if extra.get("run_forecast") is not None:
forecast = default.m
else:
future = model.make_future_dataframe(periods=periods)
forecast = model.predict(future)
fig = plot_plotly(model, forecast)
fig.update_layout(
dict(title=node_name, autosize=True, template={}, height=None, width=None),
overwrite=True,
)
return Plotly(fig=fig)
Example App
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In this example, the TIMESERIES node generates random time series data
This dataframe is then passed to the PROPHET_PREDICT node, with the default parameters
of run_forecast=True and periods=365. This node trains a Prophet model and runs a prediction
forecast over a 365 period.
It returns a DataContainer with the following
type:dataframem: The forecasted dataframeextra:run_forecast:True(because that’s what was passed in)prophet: The trainedProphetmodeloriginal: The dataframe passed into the node
Finally, this is passed to 2 nodes, PROPHET_PLOT and PROPHET_COMPONENTS, wherein
the forecast and the trend components are plotted in Plotly. Because a forecast was already run,
the PROPHET_PLOT and PROPHET_COMPONENTS nodes know to use the already predicted dataframe.