Add stl() function

- `stl()` wraps R's "stl" function in Python
- STL is a decomposition method for time series

src/urban_meal_delivery/__init__.py

@@ -6,11 +6,12 @@ Example:
     True
 """
 # The config object must come before all other project-internal imports.
-from urban_meal_delivery.configuration import config  # noqa:F401 isort:skip
+from urban_meal_delivery.configuration import config  # isort:skip
 
 from importlib import metadata as _metadata
 
-from urban_meal_delivery import db  # noqa:F401
+from urban_meal_delivery import db
+from urban_meal_delivery import forecasts
 
 
 try:

src/urban_meal_delivery/forecasts/__init__.py

@@ -1,3 +1,4 @@
 """Demand forecasting utilities."""
 
+from urban_meal_delivery.forecasts import decomposition
 from urban_meal_delivery.forecasts import timify

src/urban_meal_delivery/forecasts/decomposition.py

@@ -0,0 +1,174 @@
+"""Seasonal-trend decomposition procedure based on LOESS (STL).
+
+This module defines a `stl()` function that wraps R's STL decomposition function
+using the `rpy2` library.
+"""
+
+import math
+
+import pandas as pd
+from rpy2 import robjects
+from rpy2.robjects import pandas2ri
+
+
+def stl(  # noqa:C901,WPS210,WPS211,WPS231
+    time_series: pd.Series,
+    *,
+    frequency: int,
+    ns: int,
+    nt: int = None,
+    nl: int = None,
+    ds: int = 0,
+    dt: int = 1,
+    dl: int = 1,
+    js: int = None,
+    jt: int = None,
+    jl: int = None,
+    ni: int = 2,
+    no: int = 0,  # noqa:WPS110
+) -> pd.DataFrame:
+    """Decompose a time series into seasonal, trend, and residual components.
+
+    This is a Python wrapper around the corresponding R function.
+
+    Further info on the STL method:
+        https://www.nniiem.ru/file/news/2016/stl-statistical-model.pdf
+        https://otexts.com/fpp2/stl.html
+
+    Further info on the R's "stl" function:
+        https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/stl
+
+    Args:
+        time_series: time series with a `DateTime` based index;
+            must not contain `NaN` values
+        frequency: frequency of the observations in the `time_series`
+        ns: smoothing parameter for the seasonal component
+            (= window size of the seasonal smoother);
+            must be odd and `>= 7` so that the seasonal component is smooth;
+            the greater `ns`, the smoother the seasonal component;
+            so, this is a hyper-parameter optimized in accordance with the application
+        nt: smoothing parameter for the trend component
+            (= window size of the trend smoother);
+            must be odd and `>= (1.5 * frequency) / [1 - (1.5 / ns)]`;
+            the latter threshold is the default value;
+            the greater `nt`, the smoother the trend component
+        nl: smoothing parameter for the low-pass filter;
+            must be odd and `>= frequency`;
+            the least odd number `>= frequency` is the default
+        ds: degree of locally fitted polynomial in seasonal smoothing;
+            must be `0` or `1`
+        dt: degree of locally fitted polynomial in trend smoothing;
+            must be `0` or `1`
+        dl: degree of locally fitted polynomial in low-pass smoothing;
+            must be `0` or `1`
+        js: number of steps by which the seasonal smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `js` is the smallest integer `>= 0.1 * ns`
+        jt: number of steps by which the trend smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `jt` is the smallest integer `>= 0.1 * nt`
+        jl: number of steps by which the low-pass smoother skips ahead
+            and then linearly interpolates between observations;
+            if set to `1`, the smoother is evaluated at all points;
+            to make the STL decomposition faster, increase this value;
+            by default, `jl` is the smallest integer `>= 0.1 * nl`
+        ni: number of iterations of the inner loop that updates the
+            seasonal and trend components;
+            usually, a low value (e.g., `2`) suffices
+        no: number of iterations of the outer loop that handles outliers;
+            also known as the "robustness" loop;
+            if no outliers need to be handled, set `no=0`;
+            otherwise, `no=5` or `no=10` combined with `ni=1` is a good choice
+
+    Returns:
+        result: a DataFrame with three columns ("seasonal", "trend", and "residual")
+            providing time series of the individual components
+
+    Raises:
+        ValueError: some argument does not adhere to the specifications above
+    """
+    # Re-seed R every time the process does something.
+    robjects.r('set.seed(42)')
+
+    # Validate all arguments and set default values.
+
+    if time_series.isnull().any():
+        raise ValueError('`time_series` must not contain `NaN` values')
+
+    if ns % 2 == 0 or ns < 7:
+        raise ValueError('`ns` must be odd and `>= 7`')
+
+    default_nt = math.ceil((1.5 * frequency) / (1 - (1.5 / ns)))  # noqa:WPS432
+    if nt is not None:
+        if nt % 2 == 0 or nt < default_nt:
+            raise ValueError(
+                '`nt` must be odd and `>= (1.5 * frequency) / [1 - (1.5 / ns)]`, '
+                + 'which is {0}'.format(default_nt),
+            )
+    else:
+        nt = default_nt
+        if nt % 2 == 0:  # pragma: no cover => hard to construct edge case
+            nt += 1
+
+    if nl is not None:
+        if nl % 2 == 0 or nl < frequency:
+            raise ValueError('`nl` must be odd and `>= frequency`')
+    elif frequency % 2 == 0:
+        nl = frequency + 1
+    else:  # pragma: no cover => hard to construct edge case
+        nl = frequency
+
+    if ds not in {0, 1}:
+        raise ValueError('`ds` must be either `0` or `1`')
+    if dt not in {0, 1}:
+        raise ValueError('`dt` must be either `0` or `1`')
+    if dl not in {0, 1}:
+        raise ValueError('`dl` must be either `0` or `1`')
+
+    if js is not None:
+        if js <= 0:
+            raise ValueError('`js` must be positive')
+    else:
+        js = math.ceil(ns / 10)
+
+    if jt is not None:
+        if jt <= 0:
+            raise ValueError('`jt` must be positive')
+    else:
+        jt = math.ceil(nt / 10)
+
+    if jl is not None:
+        if jl <= 0:
+            raise ValueError('`jl` must be positive')
+    else:
+        jl = math.ceil(nl / 10)
+
+    if ni <= 0:
+        raise ValueError('`ni` must be positive')
+
+    if no < 0:
+        raise ValueError('`no` must be non-negative')
+    elif no > 0:
+        robust = True
+    else:
+        robust = False
+
+    # Call the STL function in R.
+    ts = robjects.r['ts'](pandas2ri.py2rpy(time_series), frequency=frequency)
+    result = robjects.r['stl'](
+        ts, ns, ds, nt, dt, nl, dl, js, jt, jl, robust, ni, no,  # noqa:WPS221
+    )
+
+    # Unpack the result to a `pd.DataFrame`.
+    result = pandas2ri.rpy2py(result[0])
+    result = {
+        'seasonal': pd.Series(result[:, 0], index=time_series.index),
+        'trend': pd.Series(result[:, 1], index=time_series.index),
+        'residual': pd.Series(result[:, 2], index=time_series.index),
+    }
+
+    return pd.DataFrame(result)