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optimaladj's Introduction

Hello, I'm Facundo (Facu) Sapienza 👋!

I am a PhD candidate in the Department of Statistics at the University of California Berkeley working under the supervision of Fernando Pérez and Jonathan Taylor. Previourly finished my Bsc/Msc (Licenciatura) in Physics and Mathematics in the University of Buenos Aires🇦🇷.

  • 🔭 I’m currently working on:
    • 🧊 ODINN.jl: Global glacier model using Universal Differential Equations for climate-glacier interactions. Integrating the Julia differential programming tools with the Open Global Glacier Model (OGGM) in Python.
    • 🌎 PolarWandering: Statistical modelling of Paleomagnetic data.
  • 🌱 I’m currently learning about sensitivity methods for differential equations. Interested in participating? Check out our review paper and contribute to it!
  • 👨‍🏫 I am interested in teaching machine learning for geophysisc and good practices fordeveloping open, reproducible and collaborative scientific software.
  • 👯 I’m looking to collaborate with people interested in the intersection between machine learning, geophysics and scientific computing.
  • 📫 How to reach me: feel free to send me an email to fsapienza@berkeley@edu.
  • 😄 Pronouns: He/Him

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kirtanp

optimaladj's Issues

If condition not cleared when all nodes are passed as N

With reference, to the following line, wouldn't the condition N == self.nodes() always be False? Because N is passed as an array, and self.nodes() has the type NodeView.

optimaladj/optimaladj/CausalGraph.py

Line 363 in d8f9133

elif N == self.nodes() or set(N).issubset(

Here is a minimal example I thought should work but doesn't. But perhaps I'm missing something. Let me know if this makes sense and I can PR a simple fix.

import numpy as np
import networkx as nx
from optimaladj.CausalGraph import CausalGraph

A = np.array(([[0., 0., 0., 1., 1., 0., 1.],
        [0., 0., 1., 1., 0., 1., 1.],
        [0., 0., 0., 1., 0., 1., 1.],
        [0., 0., 0., 0., 0., 0., 1.],
        [0., 0., 0., 0., 0., 0., 0.],
        [0., 0., 0., 0., 0., 0., 1.],
        [0., 0., 0., 0., 0., 0., 0.]]))
G_temp = nx.from_numpy_array(A, create_using=nx.DiGraph)

G = CausalGraph()

G.add_nodes_from(G_temp.nodes(data=True))
G.add_edges_from(G_temp.edges(data=True))

nodes = list(G.nodes)

treatment = nodes[-2]
outcome = nodes[-1]
L = []
N = nodes

G.optimal_adj_set(treatment, outcome, L, N)

The result is

ConditionException: Conditions to guarantee the existence of an optimal adjustment set are not satisfied

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