GPT-2 Agents

• Search the db for the appropriate “from to” text snippet (e.g. “Black moves pawn from e2 to e3”), with a count of the number of times this move was done using that piece. Done!
• Add a “fewest hops” (A* – traditional network approach), closest (each step finds the closest node to the target) in addition to the line following algorithm. There will have to be some user testing to see what makes the most sense, if any
• Played around a bit with Summarization, but it didn’t work that well
• TextHero came across my Twitter feed. It might be good for topic extraction? Trying it out, but the documentation is… sparse. Checking out
  • Installing many things:
    • unidecode
    • spacy (which installs many other things)
    • plotly (I thought you had to pay to use this?)
    • wordcloud
  • Working through the example, which is broken. Trying to fix based on the Getting Started

GOES

• 11:00 Meeting with Vadim
• Got the DataDictionary streaming to InfluxDBL

• More Satern – one more course down

(Re)Discovering Protein Structure and Function Through Language Modeling (ArXiv)(Code)

• In our study, we show how a language model, trained simply to predict a masked (hidden) amino acid in a protein sequence, recovers high-level structural and functional properties of proteins. In particular, we show how the Transformer language model uses attention (1) to capture the folding structure of proteins, connecting regions that are apart in the underlying sequence but spatially close in the protein structure, and (2) targets binding sites, a key functional component of proteins. We also introduce a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with biological processes and provide a tool to aid scientific discovery. The code for the visualization tool and experiments is available at https://github.com/salesforce/provis.
• TL;DR: Trained solely on language modeling, the Transformer’s attention mechanism recovers high-level structural and functional properties of proteins.
• We explored the degree to which attention captures these contact relationships by analyzing the attention patterns of 5,000 protein sequences and comparing them to ground-truth contact maps. Our analysis revealed that one particular head — the 12th layer’s 4^th head, denoted as head 12-4 — aligned remarkably well with the contact map. For “high confidence” attention (> .9 ), 76% of this head’s total attention connected amino acids that were in contact. In contrast, the background frequency of contacts among all amino acid pairs in the dataset is just 1.3%.

GPT-2 Agents

• Add a menu that writes node spatial information to the DB
• Add a “Graph from DB” menu that assembles the edge information from the move table and the node information from the new table, above.
• Continue on path finding
  • Distance between a point and a line using numpy  (stackoverflow). Not exactly what I need, which is the point of intersection and  the distance. There is a stackoverflow post that is close, but here’s a version that tests the results and plots it:

    import numpy as np
    import math
    import matplotlib.pyplot as plt
    
    p1 = np.array([1.0, 1.0])
    l1 = np.array([0.0, 1.0])
    l2 = np.array([1.0, 0.0])
    
    lvec = l2 - l1
    lvec /= np.linalg.norm(lvec, 2)
    
    p2 = l1 + lvec * np.dot(p1 - l1, lvec)
    print("intesection = {}".format(p2)) #0.2 1.
    
    pvec = p2 - p1
    dist = np.linalg.norm(pvec, 2)
    pvec /= dist
    det = np.linalg.det([lvec, pvec])
    dot = np.dot(lvec, pvec)
    rads = math.atan2(det, dot)
    print("distance = {}, angle = {}".format(dist, math.degrees(rads)))
    
    plt.plot([l1[0], l2[0]],[l1[1], l2[1]])
    plt.plot([p1[0], p2[0]],[p1[1], p2[1]])
    plt.show()

• Here’s the test for seeing if a point is on a line. Again, loosely based on a stackoverflow post:

  def is_between(self, l1:[int, int], l2:[int, int], p1:[int, int], epsilon:float = .1) -> bool:
      p1 = np.array(p1).astype(np.float)
      l1 = np.array(l1).astype(np.float)
      l2 = np.array(l2).astype(np.float)
      
      s1 = np.linalg.norm(l1-p1)
      s2 = np.linalg.norm(l2-p1)
      d = np.linalg.norm(l2-l1)
      # print("d = {}, s1 + s2 = {}".format(d, s1+s2))
      if abs(d - (s1+s2)) < epsilon:
          return True
      return False

• Got graphical node selection working. Need to tie that back into the menus for start and stop

Proposal

• Looks like no writing today. Done, maybe?

GOES

• 10:00 CASSIE demo – really good
• 12:00 All Hands – need to catch up on my training. Something for the afternoons?

ML Seminar

• Status report
• Participated in some some triage on Arpita’s and Fatima’s paper