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PROPHET_PREDICT

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Run a Prophet time series prediction model on an incoming dataframe. The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type. This node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model. This model can be loaded as follows: Params: run_forecast : bool If True (default case), the dataframe of the returning DataContainer ('m' parameter of the DataContainer) will be the forecasted dataframe. It will also have an 'extra' field with the key 'original', which is the original dataframe passed in. If False, the returning dataframe will be the original data. This node will also always have an 'extra' field, run_forecast, which matches that of the parameters passed in. This is for future nodes to know if a forecast has already been run. Default = True periods : int The number of periods to predict out. Only used if run_forecast is True. Default = 365 Returns: out : DataFrame With parameter as df. Indicates either the original df passed in, or the forecasted df (depending on if run_forecast is True). out : DataContainer With parameter as 'extra'. Contains keys run_forecast which correspond to the input parameter, and potentially 'original' in the event that run_forecast is True.
Python Code
from flojoy import DataFrame, flojoy


@flojoy(deps={"prophet": "1.1.5"})
def PROPHET_PREDICT(
    default: DataFrame, run_forecast: bool = True, periods: int = 365
) -> DataFrame:
    """Run a Prophet time series prediction model on an incoming dataframe.

    The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type.

    This node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model.
    This model can be loaded as follows:

    Parameters
    ----------
    run_forecast : bool
        If True (default case), the dataframe of the returning DataContainer
        ('m' parameter of the DataContainer) will be the forecasted dataframe.
        It will also have an 'extra' field with the key 'original', which is
        the original dataframe passed in.

        If False, the returning dataframe will be the original data.

        This node will also always have an 'extra' field, run_forecast, which
        matches that of the parameters passed in. This is for future nodes
        to know if a forecast has already been run.

        Default = True

    periods : int
        The number of periods to predict out. Only used if run_forecast is True.
        Default = 365

    Returns
    -------
    DataFrame
        With parameter as df.
        Indicates either the original df passed in, or the forecasted df
        (depending on if run_forecast is True).

    DataContainer
        With parameter as 'extra'.
        Contains keys run_forecast which correspond to the input parameter,
        and potentially 'original' in the event that run_forecast is True.
    """

    import os
    import sys

    import numpy as np
    import pandas as pd
    import prophet
    from prophet.serialize import model_to_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 not 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()

    df = default.m
    first_col = df.iloc[:, 0]
    if not pd.api.types.is_datetime64_any_dtype(first_col):
        raise ValueError(
            "First column must be of datetime type data for PROPHET_PREDICT!"
        )
    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`
    model = prophet.Prophet()
    model.fit(df)
    extra = {"prophet": model_to_json(model), "run_forecast": run_forecast}
    # If run_forecast, the return df is the forecast, otherwise the original
    return_df = df.copy()
    if run_forecast:
        future = model.make_future_dataframe(periods)
        forecast = model.predict(future)
        extra["original"] = df
        return_df = forecast

    return DataFrame(df=return_df, extra=extra)

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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: dataframe
  • m: The forecasted dataframe
  • extra:
    • run_forecast: True (because thatโ€™s what was passed in)
    • prophet: The trained Prophet model
    • original: 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.