Merge pull request #8 from Techtonique/try-improve-rpy2-usage

Align with R version 0.6.2

Author: thierrymoudiki

CHANGES.md

@@ -1,3 +1,11 @@
+# version 0.6.2
+
+- Add Block Bootstrap to `ridge2f` and `basicf`
+- Add external regressors to `ridge2f`
+- Add clustering with _K-Means_ and hierarchical clustering to `ridge2f`
+- Install R or rpy2 if necessary (? weird)
+- Refactor code for `rpy2` and R imports
+
 # version 0.6.1
 
 - Reduce number of required packages depencies

Makefile

@@ -79,7 +79,7 @@ dist: clean ## builds source and wheel package
 	ls -l dist
 
 install: clean ## install the package to the active Python's site-packages
-	python setup.py install
+	python3 -m pip install .
 
 build-site: docs ## put docs website in a directory
 	cd docs&&mkdocs build

ahead/ARMAGARCH/ArmaGarch.py

@@ -1,48 +1,9 @@
-import os
 import numpy as np
-import pandas as pd
-
-import rpy2.robjects as robjects
-import rpy2.robjects.packages as rpackages
-from rpy2.robjects.packages import importr
-from rpy2.robjects import FloatVector
-from datetime import datetime
-from rpy2.robjects.packages import importr
-from rpy2.robjects.vectors import StrVector
+from .. import config
 
 from ..utils import univariate as uv
 from ..utils import unimultivariate as umv
 
-required_packages = ["ahead"]  # list of required R packages
-
-if all(rpackages.isinstalled(x) for x in required_packages):
-    check_packages = True  # True if packages are already installed
-else:
-    check_packages = False  # False if packages are not installed
-
-if check_packages == False:  # Not installed? Then install.
-
-    packages_to_install = [
-        x for x in required_packages if not rpackages.isinstalled(x)
-    ]
-
-    if len(packages_to_install) > 0:
-        base = importr("base")
-        utils = importr("utils")
-        base.options(
-            repos=base.c(
-                techtonique="https://techtonique.r-universe.dev",
-                CRAN="https://cloud.r-project.org",
-            )
-        )
-        utils.install_packages(StrVector(packages_to_install))
-        check_packages = True
-
-
-stats = importr("stats")
-ahead = importr("ahead")
-
-
 class ArmaGarch(object):
     """ ARMA(1, 1)-GARCH(1, 1) forecasting (with simulation)
 
@@ -112,7 +73,12 @@ def __init__(
         dist = "student",
         seed = 123,
         date_formatting="original",
-    ):
+    ): 
+        if not config.R_IS_INSTALLED:
+            raise ImportError("R is not installed! \n" + config.USAGE_MESSAGE)
+        
+        if not config.RPY2_IS_INSTALLED:
+            raise ImportError(config.RPY2_ERROR_MESSAGE + config.USAGE_MESSAGE)
 
         self.h = h
         self.level = level
@@ -121,6 +87,7 @@ def __init__(
         self.dist = dist
         self.seed = seed 
         self.date_formatting = date_formatting
+        self.input_df = None
 
         self.fcast_ = None
         self.averages_ = None
@@ -154,7 +121,7 @@ def forecast(self, df):
 
         y = uv.compute_y_ts(df=self.input_df, df_frequency=frequency)
 
-        self.fcast_ = ahead.armagarchf(
+        self.fcast_ = config.AHEAD_PACKAGE.armagarchf(
             y=y,
             h=self.h,
             level=self.level,

ahead/Basic/BasicForecaster.py

@@ -1,48 +1,10 @@
-import os
 import numpy as np
-import pandas as pd
-
-from rpy2.robjects.packages import importr
-from rpy2.robjects import FloatVector, numpy2ri
-import rpy2.robjects as robjects
-import rpy2.robjects.packages as rpackages
-from datetime import datetime
-from rpy2.robjects.vectors import StrVector
+from .. import config
 
 from ..utils import multivariate as mv
 from ..utils import unimultivariate as umv
 
-required_packages = ["ahead"]  # list of required R packages
-
-if all(rpackages.isinstalled(x) for x in required_packages):
-    check_packages = True  # True if packages are already installed
-else:
-    check_packages = False  # False if packages are not installed
-
-if check_packages == False:  # Not installed? Then install.
-
-    packages_to_install = [
-        x for x in required_packages if not rpackages.isinstalled(x)
-    ]
-
-    if len(packages_to_install) > 0:
-        base = importr("base")
-        utils = importr("utils")
-        base.options(
-            repos=base.c(
-                techtonique="https://techtonique.r-universe.dev",
-                CRAN="https://cloud.r-project.org",
-            )
-        )
-        utils.install_packages(StrVector(packages_to_install))
-        check_packages = True
-
-base = importr("base")
-stats = importr("stats")
-ahead = importr("ahead")
-
-
-class BasicForecaster(object):
+class BasicForecaster():
     """Basic forecasting functions for multivariate time series (mean, median, random walk)
 
     Parameters:
@@ -60,6 +22,9 @@ class BasicForecaster(object):
             Type of prediction interval (currently "gaussian",
             or "bootstrap")
 
+        block_length: an integer
+            length of block for multivariate circular block bootstrap (`type_pi == blockbootstrap`)            
+            
         B: an integer;
             Number of bootstrap replications for `type_pi == bootstrap`
 
@@ -88,7 +53,7 @@ class BasicForecaster(object):
         mean_: a numpy array
             contains series mean forecast as a numpy array 
 
-        lower_: a numpy array 
+        lower_: a numpy array
             contains series lower bound forecast as a numpy array   
 
         upper_: a numpy array 
@@ -131,27 +96,37 @@ def __init__(
         level=95,
         method="mean",
         type_pi="gaussian",
+        block_length=5,
         B=100,        
         date_formatting="original",
         seed=123,
     ):
 
+        if not config.R_IS_INSTALLED:
+            raise ImportError("R is not installed! \n" + config.USAGE_MESSAGE)
+        
+        if not config.RPY2_IS_INSTALLED:
+            raise ImportError(config.RPY2_ERROR_MESSAGE + config.USAGE_MESSAGE)                
+
         self.h = h
         self.level = level
         self.method = method
         self.type_pi = type_pi
+        self.block_length = block_length
         self.B = B
         self.date_formatting = date_formatting
+        self.input_df = None 
         self.seed = seed
 
+        self.fcast_ = None
         self.averages_ = None
         self.ranges_ = None
         self.output_dates_ = []
         self.mean_ = None
         self.lower_ = None
         self.upper_ = None
-        self.result_df_s_ = None
-        self.sims_ = None
+        self.result_dfs_ = None
+        self.sims_ = None        
 
     def forecast(self, df):
         """Forecasting method from `BasicForecaster` class
@@ -164,7 +139,7 @@ def forecast(self, df):
         """
 
         self.input_df = df
-        n_series = len(df.columns)
+        n_series = len(df.columns)        
 
         # obtain dates 'forecast' -----
 
@@ -175,12 +150,17 @@ def forecast(self, df):
         # obtain time series forecast -----
 
         y = mv.compute_y_mts(self.input_df, frequency)
-        self.fcast_ = ahead.basicf(
+
+        if self.type_pi is "blockbootstrap":            
+            assert self.block_length is not None, "For `type_pi == 'blockbootstrap'`, `block_length` must be not None"
+
+        self.fcast_ = config.AHEAD_PACKAGE.basicf(
             y,
             h=self.h,
             level=self.level,
             method=self.method,
             type_pi=self.type_pi,
+            block_length=self.block_length,
             B=self.B,
             seed=self.seed,
         )
@@ -208,7 +188,7 @@ def forecast(self, df):
             for i in range(n_series)
         )
 
-        if self.type_pi == "bootstrap":
+        if self.type_pi == "bootstrap" or self.type_pi == "blockbootstrap":
             self.sims_ = tuple(
                 np.asarray(self.fcast_.rx2["sims"][i]) for i in range(self.B)
             )

ahead/DynamicRegressor/DynamicRegressor.py

@@ -1,47 +1,10 @@
-import os
 import numpy as np
-import pandas as pd
+from .. import config
 
-import rpy2.robjects as robjects
-import rpy2.robjects.packages as rpackages
-from rpy2.robjects.packages import importr
-from rpy2.robjects import FloatVector
-from datetime import datetime
-from rpy2.robjects.vectors import StrVector
-
-from ..utils import univariate as uv
+from ..utils import univariate as uv 
 from ..utils import unimultivariate as umv
 
-required_packages = ["ahead"]  # list of required R packages
-
-if all(rpackages.isinstalled(x) for x in required_packages):
-    check_packages = True  # True if packages are already installed
-else:
-    check_packages = False  # False if packages are not installed
-
-if check_packages == False:  # Not installed? Then install.
-
-    packages_to_install = [
-        x for x in required_packages if not rpackages.isinstalled(x)
-    ]
-
-    if len(packages_to_install) > 0:
-        base = importr("base")
-        utils = importr("utils")
-        base.options(
-            repos=base.c(
-                techtonique="https://techtonique.r-universe.dev",
-                CRAN="https://cloud.r-project.org",
-            )
-        )
-        utils.install_packages(StrVector(packages_to_install))
-        check_packages = True
-
-stats = importr("stats")
-ahead = importr("ahead")
-
-
-class DynamicRegressor(object):
+class DynamicRegressor():
     """Dynamic Regression Model adapted from R's `forecast::nnetar`
 
     Parameters:
@@ -112,10 +75,17 @@ class DynamicRegressor(object):
 
     def __init__(self, h=5, level=95, type_pi="E", date_formatting="original"):
 
+        if not config.R_IS_INSTALLED:
+            raise ImportError("R is not installed! \n" + config.USAGE_MESSAGE)
+        
+        if not config.RPY2_IS_INSTALLED:
+            raise ImportError(config.RPY2_ERROR_MESSAGE + config.USAGE_MESSAGE)
+
         self.h = h
         self.level = level
         self.type_pi = type_pi
         self.date_formatting = date_formatting
+        self.input_df = None 
 
         self.fcast_ = None
         self.averages_ = None
@@ -136,7 +106,7 @@ def forecast(self, df):
 
         """
 
-        self.input_df = df
+        self.input_df = df        
 
         # obtain dates 'forecast' -----
 
@@ -148,7 +118,7 @@ def forecast(self, df):
 
         y = uv.compute_y_ts(df=self.input_df, df_frequency=frequency)
 
-        self.fcast_ = ahead.dynrmf(
+        self.fcast_ = config.AHEAD_PACKAGE.dynrmf(
             y=y, h=self.h, level=self.level, type_pi=self.type_pi
         )