11 fix rpy2 depraction of conversion rules

README.md

@@ -4,10 +4,12 @@
 [![Code style: ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/format.json)](https://github.com/astral-sh/ruff)
 
 This repo provides details regarding $\texttt{causalAssembly}$, a causal discovery benchmark data tool based on complex production data.
-Theoretical details and information regarding construction are presented in the [paper](https://arxiv.org/abs/2306.10816):
+Theoretical details and information regarding construction are presented in the [paper](https://proceedings.mlr.press/v236/gobler24a.html):
+
+    Göbler, K., Windisch, T., Drton, M., Pychynski, T., Roth, M., & Sonntag, S. (2024). causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. In Proceedings of the Third Conference on Causal Learning and Reasoning (pp. 609–642). PMLR.
 
-    Göbler, K., Windisch, T., Pychynski, T., Sonntag, S., Roth, M., & Drton, M. causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery, to appear in Proceedings of the 3rd Conference on Causal Learning and Reasoning (CLeaR), 2024,
 ## Authors
+
 * [Konstantin Goebler](mailto:konstantin.goebler@de.bosch.com)
 * [Steffen Sonntag](mailto:steffen.sonntag@de.bosch.com)
 
@@ -26,14 +28,13 @@ The package can be installed as follows
 
     pip install causalAssembly
 
-[comment]: <> (git+https://github.com/boschresearch/causalAssembly.git)
 
 ## <a name="using">How to use</a>
 
 This is how $\texttt{causalAssembly}$'s functionality may be used. Be sure to read the [documentation](https://boschresearch.github.io/causalAssembly/) for more in-depth details regarding available functions and classes.
 
 In case you want to train a distributional random forests yourself (see [how to semisynthetsize](#how-to-semisynthesize)),
-you need an R installation as well as the corresponding [drf](https://cran.r-project.org/web/packages/drf/index.html) R package.
+you need an R installation.
 Sampling has first been proposed in [[2]](#2).
 
 *Note*: For Windows users the python package [rpy2](https://github.com/rpy2/rpy2) might cause issues.
@@ -69,7 +70,9 @@ assembly_line.Station3.drf = fit_drf(assembly_line.Station3, data=assembly_line_
 station3_sample = assembly_line.Station3.sample_from_drf(size=n_select)
 
 ```
+
 ### <a name="Interventional data">Interventional data</a>
+
 In case you want to create interventional data, we currently support hard and soft interventions.
 For soft interventions we use `sympy`'s `RandomSymbol` class. Essentially, soft interventions should
 be declared by choosing your preferred random variable with associated distribution from [here](https://docs.sympy.org/latest/modules/stats.html#continuous-types). Simple examples include:
@@ -158,7 +161,6 @@ if nx.is_directed_acyclic_graph(s_graph):
 
 ### <a name="how-to-rand">How to generate random production DAGs</a>
 
-
 The `ProductionLineGraph` class can further be used to generate completely random DAGs that follow an assembly line logic. Consider the following example:
 
 ```python
@@ -183,12 +185,11 @@ example_line.connect_cells(forward_probs= [.1])
 example_line.show()
 
 ```
-### <a name="how-to-fcm">How to generate FCMs</a>
 
+### <a name="how-to-fcm">How to generate FCMs</a>
 
 $\texttt{causalAssembly}$ also allows creating structural causal models (SCM) or synonymously functional causal models (FCM). In particular, we employ symbolic programming to allow for a seamless interplay between readability and performance. The `FCM` class is completely general and inherits no production data logic. See the example below for construction and usage.
 
-
 ```python
 
 import numpy as np
@@ -279,6 +280,7 @@ Please feel free to contact one of the authors in case you wish to contribute.
 | [matplotlib](https://github.com/matplotlib/matplotlib) | [Other](https://github.com/matplotlib/matplotlib/tree/main/LICENSE)                 | Dependency |
 | [sympy](https://github.com/sympy/sympy)                | [BSD-3-Clause License](https://github.com/sympy/sympy/blob/master/LICENSE)          | Dependency |
 | [rpy2](https://github.com/rpy2/rpy2)                   | [GNU General Public License v2.0](https://github.com/rpy2/rpy2/blob/master/LICENSE) | Dependency |
+
 ### Development dependency
 
 | Name                                                            | License                                                                           | Type       |

VERSION

@@ -1 +1 @@
-1.2.0
+1.2.1

causalAssembly/drf_fitting.py

@@ -19,17 +19,26 @@
 import numpy as np
 import pandas as pd
 import rpy2.robjects as ro
-import rpy2.robjects.numpy2ri
-from rpy2.robjects import pandas2ri
+import rpy2.robjects.packages as rpackages
+from rpy2.robjects import numpy2ri, pandas2ri
+from rpy2.robjects.conversion import localconverter
 from rpy2.robjects.packages import importr
 from scipy.stats import gaussian_kde
 
 from causalAssembly.dag import DAG
 from causalAssembly.models_dag import ProcessCell, ProductionLineGraph
 
-rpy2.robjects.numpy2ri.activate()
-pandas2ri.activate()
+# Converter for numpy + pandas instead of using deprecated activate()
+R_CONVERTER = ro.default_converter + numpy2ri.converter + pandas2ri.converter
+
 base_r_package = importr("base")
+utils = importr("utils")
+
+if not rpackages.isinstalled("drf"):
+    # select a mirror for R packages
+    utils.chooseCRANmirror(graphics=False)
+    utils.install_packages("drf", repos="https://cloud.r-project.org/")
+
 drf_r_package = importr("drf")
 
 
@@ -41,22 +50,24 @@ class DRF:
     """
 
     def __init__(self, **fit_params):
-        """Initialize DRF object."""
+        """Initialize the DRF object with fit parameters."""
         self.fit_params = fit_params
 
     def fit(self, X: pd.DataFrame, Y: pd.DataFrame | pd.Series):
         """Fit DRF in order to estimate conditional distribution P(Y|X=x).
 
         Args:
-            X (pd.DataFrame): Conditioning set.
-            Y (pd.DataFrame): Variable of interest (can be vector-valued).
+            X (pd.DataFrame): Predictor variables.
+            Y (pd.DataFrame | pd.Series): Response variable(s).
         """
         self.X_train = X
         self.Y_train = Y
 
-        X_r = ro.conversion.py2rpy(X)
-        Y_r = ro.conversion.py2rpy(Y)
-        self.r_fit_object = drf_r_package.drf(X_r, Y_r, **self.fit_params)
+        # Use localconverter
+        with localconverter(R_CONVERTER):
+            X_r = ro.conversion.py2rpy(X)
+            Y_r = ro.conversion.py2rpy(Y)
+            self.r_fit_object = drf_r_package.drf(X_r, Y_r, **self.fit_params)
 
     def produce_sample(
         self,
@@ -72,15 +83,18 @@ def produce_sample(
             n (int, optional): Number of n-samples to draw. Defaults to 1.
 
         Returns:
-            np.ndarray: New predicted samlpe of Y.
+            np.ndarray: New predicted sample of Y.
         """
-        newdata_r = ro.conversion.py2rpy(newdata)
-        r_output = drf_r_package.predict_drf(self.r_fit_object, newdata_r)
+        with localconverter(R_CONVERTER):
+            newdata_r = ro.conversion.py2rpy(newdata)
+            r_output = drf_r_package.predict_drf(self.r_fit_object, newdata_r)
 
-        weights = base_r_package.as_matrix(r_output[0])
+            # Convert back to Python
+            weights = ro.conversion.rpy2py(base_r_package.as_matrix(r_output[0]))
+            Y = ro.conversion.rpy2py(base_r_package.as_matrix(r_output[1]))
 
-        Y = pd.DataFrame(base_r_package.as_matrix(r_output[1]))
-        Y = Y.apply(pd.Series)
+        if not isinstance(Y, pd.DataFrame):
+            Y = pd.DataFrame(Y)
 
         sample = np.zeros((newdata.shape[0], Y.shape[1], n))
         for i in range(newdata.shape[0]):
@@ -98,17 +112,14 @@ def fit_drf(graph: ProductionLineGraph | ProcessCell | DAG, data: pd.DataFrame):
         graph (ProductionLineGraph | ProcessCell | DAG): Graph to fit the DRF to.
         data (pd.DataFrame): Columns of dataframe need to match name and order of the graph
 
-    Raises:
-        ValueError: Raises error if columns don't meet this requirement
+    Raises: ValueError: Raises error if columns don't meet this requirement
 
-    Returns:
-        (dict): dict of fitted DRFs.
+    Returns: (dict): dict of fitted DRFs.
     """
     tempdata = data.copy()
 
     if set(graph.nodes).issubset(tempdata.columns):
         tempdata = tempdata[graph.nodes]
-
     else:
         raise ValueError("Data columns don't match node names.")
 
@@ -118,9 +129,8 @@ def fit_drf(graph: ProductionLineGraph | ProcessCell | DAG, data: pd.DataFrame):
         if not parents:
             drf_dict[node] = gaussian_kde(tempdata[node].to_numpy())
         elif parents:
-            drf_object = DRF(
-                min_node_size=15, num_trees=2000, splitting_rule="FourierMMD"
-            )  # default setting as suggested in the paper
+            # default setting as suggested in the paper
+            drf_object = DRF(min_node_size=15, num_trees=2000, splitting_rule="FourierMMD")
             X = tempdata[parents]
             Y = tempdata[node]
             drf_object.fit(X, Y)