chore: improve and add some missing docstrings

python/prophet/diagnostics.py

@@ -81,19 +81,19 @@ def cross_validation(model, horizon, period=None, initial=None, parallel=None, c
     cutoffs: list of pd.Timestamp specifying cutoffs to be used during
         cross validation. If not provided, they are generated as described
         above.
-    parallel : {None, 'processes', 'threads', 'dask', object}
+    parallel: {None, 'processes', 'threads', 'dask', object}
         How to parallelize the forecast computation. By default no parallelism
         is used.
 
-        * None : No parallelism.
-        * 'processes' : Parallelize with concurrent.futures.ProcessPoolExectuor.
-        * 'threads' : Parallelize with concurrent.futures.ThreadPoolExecutor.
+        * None: No parallelism.
+        * 'processes': Parallelize with concurrent.futures.ProcessPoolExecutor.
+        * 'threads': Parallelize with concurrent.futures.ThreadPoolExecutor.
             Note that some operations currently hold Python's Global Interpreter
             Lock, so parallelizing with threads may be slower than training
             sequentially.
         * 'dask': Parallelize with Dask.
            This requires that a dask.distributed Client be created.
-        * object : Any instance with a `.map` method. This method will
+        * object: Any instance with a `.map` method. This method will
           be called with :func:`single_cutoff_forecast` and a sequence of
           iterables where each element is the tuple of arguments to pass to
           :func:`single_cutoff_forecast`
@@ -132,7 +132,7 @@ def map(self, func, *iterables):
             extra_output_columns = [extra_output_columns]
         predict_columns.extend([c for c in extra_output_columns if c not in predict_columns])
 
-    # Identify largest seasonality period
+    # Identify the largest seasonality period
     period_max = 0.
     for s in model.seasonalities.values():
         period_max = max(period_max, s['period'])
@@ -214,7 +214,7 @@ def map(self, func, *iterables):
 
 def single_cutoff_forecast(df, model, cutoff, horizon, predict_columns):
     """Forecast for single cutoff. Used in cross validation function
-    when evaluating for multiple cutoffs either sequentially or in parallel .
+    when evaluating for multiple cutoffs either sequentially or in parallel.
 
     Parameters
     ----------
@@ -274,7 +274,7 @@ def prophet_copy(m, cutoff=None):
     ----------
     m: Prophet model.
     cutoff: pd.Timestamp or None, default None.
-        cuttoff Timestamp for changepoints member variable.
+        cutoff Timestamp for changepoints member variable.
         changepoints are only retained if 'changepoints <= cutoff'
 
     Returns
@@ -344,7 +344,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.1, monthly=False):
     """Compute performance metrics from cross-validation results.
 
     Computes a suite of performance metrics on the output of cross-validation.
-    By default the following metrics are included:
+    By default, the following metrics are included:
     'mse': mean squared error
     'rmse': root mean squared error
     'mae': mean absolute error

python/prophet/forecaster.py

@@ -75,7 +75,8 @@ class Prophet(object):
         uncertainty intervals. Settings this value to 0 or False will disable
         uncertainty estimation and speed up the calculation.
     stan_backend: str as defined in StanBackendEnum default: None - will try to
-        iterate over all available backends and find the working one
+        iterate over all available backends and find the working one.
+    scaling: 'absmax' (default) or 'minmax'.
     holidays_mode: 'additive' or 'multiplicative'. Defaults to seasonality_mode.
     """
 
@@ -1541,6 +1542,15 @@ def sample_model_vectorized(
     ) -> List[Dict[str, np.ndarray]]:
         """Simulate observations from the extrapolated generative model. Vectorized version of sample_model().
 
+        Parameters
+        ----------
+        df: Prediction dataframe.
+        seasonal_features: pd.DataFrame of seasonal features.
+        iteration: Int sampling iteration to use parameters from.
+        s_a: Indicator vector for additive components.
+        s_m: Indicator vector for multiplicative components.
+        n_samples: Number of future paths of the trend to simulate.
+
         Returns
         -------
         List (length n_samples) of dictionaries with arrays for trend and yhat, each ordered like df['t'].
@@ -1619,6 +1629,12 @@ def sample_predictive_trend(self, df, iteration):
     def sample_predictive_trend_vectorized(self, df: pd.DataFrame, n_samples: int, iteration: int = 0) -> np.ndarray:
         """Sample draws of the future trend values. Vectorized version of sample_predictive_trend().
 
+        Parameters
+        ----------
+        df: Prediction dataframe.
+        iteration: Int sampling iteration to use parameters from.
+        n_samples: Number of future paths of the trend to simulate.
+
         Returns
         -------
         Draws of the trend values with shape (n_samples, len(df)). Values are on the scale of the original data.

python/prophet/make_holidays.py

@@ -56,6 +56,7 @@ def make_holidays_df(year_list, country, province=None, state=None):
     ----------
     year_list: a list of years
     country: country name
+    province: province name
 
     Returns
     -------

python/prophet/plot.py

@@ -486,7 +486,7 @@ def plot_cross_validation_metric(
     for each prediction, and aggregated over a rolling window with horizon.
 
     This uses prophet.diagnostics.performance_metrics to compute the metrics.
-    Valid values of metric are 'mse', 'rmse', 'mae', 'mape', and 'coverage'.
+    Valid values of metric are 'mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 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
@@ -499,7 +499,7 @@ def plot_cross_validation_metric(
     Parameters
     ----------
     df_cv: The output from prophet.diagnostics.cross_validation.
-    metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'coverage'].
+    metric: Metric name, one of ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', '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
@@ -579,6 +579,7 @@ def plot_plotly(m, fcst, uncertainty=True, plot_cap=True, trend=False, changepoi
     changepoints_threshold: Threshold on trend change magnitude for significance.
     xlabel: Optional label name on X-axis
     ylabel: Optional label name on Y-axis
+    figsize: The plot's size (in px).
 
     Returns
     -------
@@ -775,7 +776,7 @@ def plot_components_plotly(
 
 
 def plot_forecast_component_plotly(m, fcst, name, uncertainty=True, plot_cap=False, figsize=(900, 300)):
-    """Plot an particular component of the forecast using Plotly.
+    """Plot a particular component of the forecast using Plotly.
     See plot_plotly() for Plotly setup instructions
 
     Parameters

python/prophet/utilities.py

@@ -49,7 +49,7 @@ def regressor_coefficients(m):
         Only different to `coef` if `mcmc_samples > 0`.
     - `coef`: Expected value of the coefficient.
     - `coef_upper`: Upper bound for the coefficient, estimated from MCMC samples.
-        Only to different to `coef` if `mcmc_samples > 0`.
+        Only different to `coef` if `mcmc_samples > 0`.
     """
     assert len(m.extra_regressors) > 0, 'No extra regressors found.'
     coefs = []