From fb7f8712fb83a037bc6554664f48e7e40de1e885 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 09:30:05 +0100
Subject: [PATCH 01/11] feat: multi input extract function

---
 adopt/utils.py |  5 +----
 adopt/viz.py   | 20 ++++++++++----------
 2 files changed, 11 insertions(+), 14 deletions(-)

diff --git a/adopt/utils.py b/adopt/utils.py
index bc9367b..2927001 100644
--- a/adopt/utils.py
+++ b/adopt/utils.py
@@ -91,12 +91,9 @@ def get_onnx_model_preds(model_name, input_data):
     return pred_onx
 
 
-def get_esm_attention(model, alphabet, sequence, brmid):
+def get_esm_attention(model, alphabet, data):
     batch_converter = alphabet.get_batch_converter()
-
-    data = [(brmid, sequence)]
     batch_labels, batch_strs, batch_tokens = batch_converter(data)
-
     # Extract per-residue representations (on CPU)
     with torch.no_grad():
         results = model(batch_tokens, repr_layers=[33], return_contacts=True)
diff --git a/adopt/viz.py b/adopt/viz.py
index ec67bc8..8571865 100644
--- a/adopt/viz.py
+++ b/adopt/viz.py
@@ -3,26 +3,26 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from esm import pretrained
+import esm
 import torch
 
 from adopt import constants, utils
 
 
-"@generated"
 def get_multi_attention(model_type, sequence, brmid):
+    data = [(brmid, sequence)]
     # Load ESM model
     if model_type == "esm-1b":
-        model, alphabet = pretrained.esm1b_t33_650M_UR50S()
-        results = utils.get_esm_attention(model, alphabet, sequence, brmid)
+        model, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
+        results = utils.get_esm_attention(model, alphabet, data)
     elif model_type == "esm-1v":
-        model, alphabet = pretrained.esm1v_t33_650M_UR90S_1()
-        results = utils.get_esm_attention(model, alphabet, sequence, brmid)
+        model, alphabet = esm.pretrained.esm1v_t33_650M_UR90S_1()
+        results = utils.get_esm_attention(model, alphabet, data)
     elif model_type == 'combined':
-        model_esm1b, alphabet_esm1b = pretrained.esm1b_t33_650M_UR50S()
-        model_esm1v, alphabet_esm1v = pretrained.esm1v_t33_650M_UR90S_1()
-        results_esm1b = utils.get_esm_attention(model_esm1b, alphabet_esm1b, sequence, brmid)
-        results_esm1v = utils.get_esm_attention(model_esm1v, alphabet_esm1v, sequence, brmid)
+        model_esm1b, alphabet_esm1b = esm.pretrained.esm1b_t33_650M_UR50S()
+        model_esm1v, alphabet_esm1v = esm.pretrained.esm1v_t33_650M_UR90S_1()
+        results_esm1b = utils.get_esm_attention(model_esm1b, alphabet_esm1b, data)
+        results_esm1v = utils.get_esm_attention(model_esm1v, alphabet_esm1v, data)
         results = torch.cat((results_esm1b, results_esm1v), 0)
     # elif model_type == 'esm-msa':
     #    model, alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S

From 912ef5f75596c74b756cefd25cdffeadab8b03fa Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 11:00:11 +0100
Subject: [PATCH 02/11] feat: interactive attention and representation

---
 adopt/__init__.py                   |  2 +-
 adopt/transformer.py                | 51 +++++++++++++++++++++++++++++
 adopt/utils.py                      | 22 ++++++++++++-
 adopt/viz.py                        | 43 ------------------------
 notebooks/adopt_attention_viz.ipynb | 36 ++++++++++++++------
 5 files changed, 99 insertions(+), 55 deletions(-)
 create mode 100644 adopt/transformer.py
 delete mode 100644 adopt/viz.py

diff --git a/adopt/__init__.py b/adopt/__init__.py
index 76945f5..599def2 100644
--- a/adopt/__init__.py
+++ b/adopt/__init__.py
@@ -7,5 +7,5 @@
 from . import constants, utils
 from .data import CheZod
 from .training import DisorderPred
-from .viz import get_multi_attention
+from .transformer import MultiHead
 from .version import version as __version__
diff --git a/adopt/transformer.py b/adopt/transformer.py
new file mode 100644
index 0000000..f276df2
--- /dev/null
+++ b/adopt/transformer.py
@@ -0,0 +1,51 @@
+# Copyright (c) 2021 Peptone.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import esm
+import torch
+import sys
+
+from adopt import constants, utils
+
+
+class MultiHead:
+    def __init__(self, model_type, sequence, brmid):
+        self.model_type = model_type
+        self.sequence = sequence
+        self.brmid = brmid
+        self.data = [(self.brmid, self.sequence)]
+
+    def get_attention(self):
+        if self.model_type in constants.model_types:
+            results = utils.get_model_alphabet(self.model_type, self.data)
+        else:
+            print("The model types are:")
+            print(*constants.model_types, sep="\n")
+            sys.exit(2)
+
+        tokens = list(self.sequence)
+        attention = results["attentions"].permute(1, 0, 2, 3, 4)
+        # remove first and last token (<cls> and <sep>)
+        attention = attention[:, :, :, 1:-1, 1:-1]
+        return attention, tokens
+
+    def get_representation(self):
+        if self.model_type in constants.model_types:
+            results = utils.get_model_alphabet(self.model_type, self.data)
+            representation = results["representations"][33]
+        elif self.model_type == 'combined':
+            results_esm1b, results_esm1v = utils.get_model_alphabet(self.model_type, self.data)
+            representation_esm1b = results_esm1b["representations"][33]
+            representation_esm1v = results_esm1v["representations"][33]
+            representation = torch.cat((representation_esm1b, representation_esm1v), -1)
+        else:
+            print("The model types are:")
+            print(*constants.model_types, sep="\n")
+            sys.exit(2)
+
+        tokens = list(self.sequence)
+        # remove first and last token (<cls> and <sep>)
+        representation = representation[:, 1:-1, :]
+        return representation, tokens
diff --git a/adopt/utils.py b/adopt/utils.py
index 2927001..1f78e17 100644
--- a/adopt/utils.py
+++ b/adopt/utils.py
@@ -10,6 +10,7 @@
 from Bio import SeqIO
 from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import FloatTensorType
+import esm
 
 from adopt import constants
 
@@ -91,10 +92,29 @@ def get_onnx_model_preds(model_name, input_data):
     return pred_onx
 
 
-def get_esm_attention(model, alphabet, data):
+def get_esm_output(model, alphabet, data):
     batch_converter = alphabet.get_batch_converter()
     batch_labels, batch_strs, batch_tokens = batch_converter(data)
     # Extract per-residue representations (on CPU)
     with torch.no_grad():
         results = model(batch_tokens, repr_layers=[33], return_contacts=True)
     return results
+
+
+def get_model_and_alphabet(model_type, data):
+    # Load ESM model
+    if model_type == "esm-1b":
+        model, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
+        results = get_esm_output(model, alphabet, data)
+    elif model_type == "esm-1v":
+        model, alphabet = esm.pretrained.esm1v_t33_650M_UR90S_1()
+        results = get_esm_output(model, alphabet, data)
+    # elif model_type == 'esm-msa':
+    #    model, alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S
+    else:
+        model_esm1b, alphabet_esm1b = esm.pretrained.esm1b_t33_650M_UR50S()
+        model_esm1v, alphabet_esm1v = esm.pretrained.esm1v_t33_650M_UR90S_1()
+        results_esm1b = get_esm_output(model_esm1b, alphabet_esm1b, data)
+        results_esm1v = get_esm_output(model_esm1v, alphabet_esm1v, data)
+        results = [results_esm1b, results_esm1v]
+    return results
diff --git a/adopt/viz.py b/adopt/viz.py
deleted file mode 100644
index 8571865..0000000
--- a/adopt/viz.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) 2021 Peptone.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import esm
-import torch
-
-from adopt import constants, utils
-
-
-def get_multi_attention(model_type, sequence, brmid):
-    data = [(brmid, sequence)]
-    # Load ESM model
-    if model_type == "esm-1b":
-        model, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
-        results = utils.get_esm_attention(model, alphabet, data)
-    elif model_type == "esm-1v":
-        model, alphabet = esm.pretrained.esm1v_t33_650M_UR90S_1()
-        results = utils.get_esm_attention(model, alphabet, data)
-    elif model_type == 'combined':
-        model_esm1b, alphabet_esm1b = esm.pretrained.esm1b_t33_650M_UR50S()
-        model_esm1v, alphabet_esm1v = esm.pretrained.esm1v_t33_650M_UR90S_1()
-        results_esm1b = utils.get_esm_attention(model_esm1b, alphabet_esm1b, data)
-        results_esm1v = utils.get_esm_attention(model_esm1v, alphabet_esm1v, data)
-        results = torch.cat((results_esm1b, results_esm1v), 0)
-    # elif model_type == 'esm-msa':
-    #    model, alphabet = esm.pretrained.esm_msa1b_t12_100M_UR50S
-    else:
-        print("The model types are:")
-        print(*constants.model_types, sep="\n")
-
-    tokens = list(sequence)
-
-    if model_type == 'combined':
-        attention_esm1b = results_esm1b["attentions"].permute(1, 0, 2, 3, 4)
-        attention_esm1v = results_esm1v["attentions"].permute(1, 0, 2, 3, 4)
-        attention = torch.cat((attention_esm1b, attention_esm1v), 1)
-    else:
-        attention = results["attentions"].permute(1, 0, 2, 3, 4)
-    # remove first and last token (<cls> and <sep>)
-    attention = attention[:, :, :, 1:-1, 1:-1]
-    return attention, tokens
diff --git a/notebooks/adopt_attention_viz.ipynb b/notebooks/adopt_attention_viz.ipynb
index 7e1b1a3..242d910 100644
--- a/notebooks/adopt_attention_viz.ipynb
+++ b/notebooks/adopt_attention_viz.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89cfc9ae",
+   "id": "784a704e",
    "metadata": {},
    "source": [
     "# Parameters"
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8015c50c",
+   "id": "9f1d4977",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64fb33f0",
+   "id": "3f3a2e50",
    "metadata": {},
    "source": [
     "# Load data "
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "330deeee",
+   "id": "6e8d3a02",
    "metadata": {},
    "source": [
     "Load the **FASTA_FILE** containing the protein sequences we are interested in."
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bbe6987",
+   "id": "a1cb24ed",
    "metadata": {},
    "source": [
     "# Extract attention weights"
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10f64733",
+   "id": "b7bc90da",
    "metadata": {},
    "source": [
     "Extract attention weights of the **BRMID** in the **FASTA_FILE** specified above."
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1219c860",
+   "id": "e18caa9b",
    "metadata": {},
    "source": [
     "Cut the sequence taking the first **N_RES** tokens for visualisation purposes."
@@ -97,7 +97,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7c5eae28",
+   "id": "2cf6c1c2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3399a7c",
+   "id": "409788a6",
    "metadata": {},
    "source": [
     "# Viz"
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "403a8449",
+   "id": "6125a9ee",
    "metadata": {},
    "source": [
     "The **lines** show the attention from each token (left) to every other token (right). Darker lines indicate higher *attention weights* whereas the *colours* correspond to different *attention heads*.\n",
@@ -134,6 +134,22 @@
    "source": [
     "head_view(attention, tokens)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c13fd09b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8544981f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 24903bf62b25a388619556497828c72273f758c8 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 11:43:44 +0100
Subject: [PATCH 03/11] fix: representation and attention interactive

---
 adopt/transformer.py                |   6 +-
 notebooks/adopt_attention_viz.ipynb | 514 +++++++++++++++++++++++++++-
 2 files changed, 503 insertions(+), 17 deletions(-)

diff --git a/adopt/transformer.py b/adopt/transformer.py
index f276df2..192c238 100644
--- a/adopt/transformer.py
+++ b/adopt/transformer.py
@@ -19,7 +19,7 @@ def __init__(self, model_type, sequence, brmid):
 
     def get_attention(self):
         if self.model_type in constants.model_types:
-            results = utils.get_model_alphabet(self.model_type, self.data)
+            results = utils.get_model_and_alphabet(self.model_type, self.data)
         else:
             print("The model types are:")
             print(*constants.model_types, sep="\n")
@@ -33,10 +33,10 @@ def get_attention(self):
 
     def get_representation(self):
         if self.model_type in constants.model_types:
-            results = utils.get_model_alphabet(self.model_type, self.data)
+            results = utils.get_model_and_alphabet(self.model_type, self.data)
             representation = results["representations"][33]
         elif self.model_type == 'combined':
-            results_esm1b, results_esm1v = utils.get_model_alphabet(self.model_type, self.data)
+            results_esm1b, results_esm1v = utils.get_model_and_alphabet(self.model_type, self.data)
             representation_esm1b = results_esm1b["representations"][33]
             representation_esm1v = results_esm1v["representations"][33]
             representation = torch.cat((representation_esm1b, representation_esm1v), -1)
diff --git a/notebooks/adopt_attention_viz.ipynb b/notebooks/adopt_attention_viz.ipynb
index 242d910..053007a 100644
--- a/notebooks/adopt_attention_viz.ipynb
+++ b/notebooks/adopt_attention_viz.ipynb
@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "33a018a9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from adopt import utils, get_multi_attention\n",
+    "from adopt import utils, MultiHead\n",
     "from bertviz import head_view"
    ]
   },
@@ -21,14 +21,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "9f1d4977",
    "metadata": {},
    "outputs": [],
    "source": [
-    "FASTA_FILE = '<fasta_file_path>'\n",
-    "BRMID = '<protein_sequence_brmid>'\n",
-    "MODEL_TYPE = '<model_type>'\n",
+    "FASTA_FILE = 'datasets/peptone_targets.fasta'\n",
+    "BRMID = 'O95760|IL33_HUMAN'\n",
+    "MODEL_TYPE = 'esm-1b'\n",
     "N_RES = 20"
    ]
   },
@@ -50,10 +50,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "f6e600d7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sequence</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>brmid</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>P13725|ONCM_HUMAN</th>\n",
+       "      <td>MGVLLTQRTLLSLVLALLFPSMASMAAIGSCSKEYRVLLGQLQKQT...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P20809|IL11_HUMAN</th>\n",
+       "      <td>MNCVCRLVLVVLSLWPDTAVAPGPPPGPPRVSPDPRAELDSTVLLT...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>O95760|IL33_HUMAN</th>\n",
+       "      <td>MKPKMKYSTNKISTAKWKNTASKALCFKLGKSQQKAKEVCPMYFMK...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q6EBC2|IL31_HUMAN</th>\n",
+       "      <td>MASHSGPSTSVLFLFCCLGGWLASHTLPVRLLRPSDDVQKIVEELQ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q14626|I11RA_HUMAN</th>\n",
+       "      <td>MSSSCSGLSRVLVAVATALVSASSPCPQAWGPPGVQYGQPGRSVKL...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                             sequence\n",
+       "brmid                                                                \n",
+       "P13725|ONCM_HUMAN   MGVLLTQRTLLSLVLALLFPSMASMAAIGSCSKEYRVLLGQLQKQT...\n",
+       "P20809|IL11_HUMAN   MNCVCRLVLVVLSLWPDTAVAPGPPPGPPRVSPDPRAELDSTVLLT...\n",
+       "O95760|IL33_HUMAN   MKPKMKYSTNKISTAKWKNTASKALCFKLGKSQQKAKEVCPMYFMK...\n",
+       "Q6EBC2|IL31_HUMAN   MASHSGPSTSVLFLFCCLGGWLASHTLPVRLLRPSDDVQKIVEELQ...\n",
+       "Q14626|I11RA_HUMAN  MSSSCSGLSRVLVAVATALVSASSPCPQAWGPPGVQYGQPGRSVKL..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df_fasta = utils.fasta_to_df(FASTA_FILE).set_index('brmid')\n",
     "df_fasta.head()"
@@ -77,13 +145,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "6bfcf344",
    "metadata": {},
    "outputs": [],
    "source": [
     "sequence = df_fasta.loc[BRMID].sequence\n",
-    "attention, tokens = get_multi_attention(MODEL_TYPE, sequence, BRMID)"
+    "multi_head = MultiHead(MODEL_TYPE, sequence, BRMID)\n",
+    "attention, tokens = multi_head.get_attention()"
    ]
   },
   {
@@ -96,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "2cf6c1c2",
    "metadata": {},
    "outputs": [],
@@ -127,10 +196,427 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "51eb3117",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "      \n",
+       "        <div id='bertviz-9cf865094470435ea1c197e57a04617b'>\n",
+       "            <span style=\"user-select:none\">\n",
+       "                Layer: <select id=\"layer\"></select>\n",
+       "                \n",
+       "            </span>\n",
+       "            <div id='vis'></div>\n",
+       "        </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/**\n",
+       " * @fileoverview Transformer Visualization D3 javascript code.\n",
+       " *\n",
+       " *\n",
+       " *  Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
+       " *\n",
+       " * Change log:\n",
+       " *\n",
+       " * 12/19/18  Jesse Vig   Assorted cleanup. Changed orientation of attention matrices.\n",
+       " * 12/29/20  Jesse Vig   Significant refactor.\n",
+       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
+       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
+       " * 05/03/21  Jesse Vig   Adjust height of visualization dynamically\n",
+       " * 07/25/21  Jesse Vig   Support layer filtering\n",
+       " **/\n",
+       "\n",
+       "require.config({\n",
+       "  paths: {\n",
+       "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n",
+       "    jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
+       "  }\n",
+       "});\n",
+       "\n",
+       "requirejs(['jquery', 'd3'], function ($, d3) {\n",
+       "\n",
+       "    const params = {\"attention\": [{\"name\": null, \"attn\": [[[[0.07077009230852127, 0.007848864421248436, 0.028781868517398834, 0.008763647638261318, 0.04590034484863281, 0.004891009069979191, 0.03187142685055733, 0.011426498182117939, 0.009642207995057106, 0.006737949792295694, 0.003060394199565053, 0.00833201315253973, 0.006882949266582727, 0.007980404421687126, 0.017441846430301666, 0.002057430800050497, 0.013946546241641045, 0.0017426976701244712, 0.0036213677376508713, 0.005163727328181267], [0.13214954733848572, 0.02923366241157055, 0.05096348747611046, 0.009969064965844154, 0.054019469767808914, 0.007594925817102194, 0.024155860766768456, 0.011347616091370583, 0.009242362342774868, 0.004419925156980753, 0.00249915081076324, 0.005082458257675171, 0.005312361754477024, 0.004466281738132238, 0.008020053617656231, 0.00157601956743747, 0.008996976539492607, 0.0011983162257820368, 0.0019424002384766936, 0.0037593815941363573], [0.24574097990989685, 0.11313629150390625, 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0.0539274699985981, 0.01068555936217308, 0.002721047028899193]]]], \"left_text\": [\"M\", \"K\", \"P\", \"K\", \"M\", \"K\", \"Y\", \"S\", \"T\", \"N\", \"K\", \"I\", \"S\", \"T\", \"A\", \"K\", \"W\", \"K\", \"N\", \"T\"], \"right_text\": [\"M\", \"K\", \"P\", \"K\", \"M\", \"K\", \"Y\", \"S\", \"T\", \"N\", \"K\", \"I\", \"S\", \"T\", \"A\", \"K\", \"W\", \"K\", \"N\", \"T\"]}], \"default_filter\": \"0\", \"root_div_id\": \"bertviz-9cf865094470435ea1c197e57a04617b\", \"layer\": null, \"heads\": null, \"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]} is a template marker that is replaced by actual params.\n",
+       "    const TEXT_SIZE = 15;\n",
+       "    const BOXWIDTH = 110;\n",
+       "    const BOXHEIGHT = 22.5;\n",
+       "    const MATRIX_WIDTH = 115;\n",
+       "    const CHECKBOX_SIZE = 20;\n",
+       "    const TEXT_TOP = 30;\n",
+       "\n",
+       "    console.log(\"d3 version\", d3.version)\n",
+       "    let headColors;\n",
+       "    try {\n",
+       "        headColors = d3.scaleOrdinal(d3.schemeCategory10);\n",
+       "    } catch (err) {\n",
+       "        console.log('Older d3 version')\n",
+       "        headColors = d3.scale.category10();\n",
+       "    }\n",
+       "    let config = {};\n",
+       "    initialize();\n",
+       "    renderVis();\n",
+       "\n",
+       "    function initialize() {\n",
+       "        config.attention = params['attention'];\n",
+       "        config.filter = params['default_filter'];\n",
+       "        config.rootDivId = params['root_div_id'];\n",
+       "        config.nLayers = config.attention[config.filter]['attn'].length;\n",
+       "        config.nHeads = config.attention[config.filter]['attn'][0].length;\n",
+       "        config.layers = params['include_layers']\n",
+       "\n",
+       "        if (params['heads']) {\n",
+       "            config.headVis = new Array(config.nHeads).fill(false);\n",
+       "            params['heads'].forEach(x => config.headVis[x] = true);\n",
+       "        } else {\n",
+       "            config.headVis = new Array(config.nHeads).fill(true);\n",
+       "        }\n",
+       "        config.initialTextLength = config.attention[config.filter].right_text.length;\n",
+       "        config.layer_seq = (params['layer'] == null ? 0 : config.layers.findIndex(layer => params['layer'] === layer));\n",
+       "        config.layer = config.layers[config.layer_seq]\n",
+       "\n",
+       "        let layerEl = $(`#${config.rootDivId} #layer`);\n",
+       "        for (const layer of config.layers) {\n",
+       "            layerEl.append($(\"<option />\").val(layer).text(layer));\n",
+       "        }\n",
+       "        layerEl.val(config.layer).change();\n",
+       "        layerEl.on('change', function (e) {\n",
+       "            config.layer = +e.currentTarget.value;\n",
+       "            config.layer_seq = config.layers.findIndex(layer => config.layer === layer);\n",
+       "            renderVis();\n",
+       "        });\n",
+       "\n",
+       "        $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
+       "            config.filter = e.currentTarget.value;\n",
+       "            renderVis();\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function renderVis() {\n",
+       "\n",
+       "        // Load parameters\n",
+       "        const attnData = config.attention[config.filter];\n",
+       "        const leftText = attnData.left_text;\n",
+       "        const rightText = attnData.right_text;\n",
+       "\n",
+       "        // Select attention for given layer\n",
+       "        const layerAttention = attnData.attn[config.layer_seq];\n",
+       "\n",
+       "        // Clear vis\n",
+       "        $(`#${config.rootDivId} #vis`).empty();\n",
+       "\n",
+       "        // Determine size of visualization\n",
+       "        const height = Math.max(leftText.length, rightText.length) * BOXHEIGHT + TEXT_TOP;\n",
+       "        const svg = d3.select(`#${config.rootDivId} #vis`)\n",
+       "            .append('svg')\n",
+       "            .attr(\"width\", \"100%\")\n",
+       "            .attr(\"height\", height + \"px\");\n",
+       "\n",
+       "        // Display tokens on left and right side of visualization\n",
+       "        renderText(svg, leftText, true, layerAttention, 0);\n",
+       "        renderText(svg, rightText, false, layerAttention, MATRIX_WIDTH + BOXWIDTH);\n",
+       "\n",
+       "        // Render attention arcs\n",
+       "        renderAttention(svg, layerAttention);\n",
+       "\n",
+       "        // Draw squares at top of visualization, one for each head\n",
+       "        drawCheckboxes(0, svg, layerAttention);\n",
+       "    }\n",
+       "\n",
+       "    function renderText(svg, text, isLeft, attention, leftPos) {\n",
+       "\n",
+       "        const textContainer = svg.append(\"svg:g\")\n",
+       "            .attr(\"id\", isLeft ? \"left\" : \"right\");\n",
+       "\n",
+       "        // Add attention highlights superimposed over words\n",
+       "        textContainer.append(\"g\")\n",
+       "            .classed(\"attentionBoxes\", true)\n",
+       "            .selectAll(\"g\")\n",
+       "            .data(attention)\n",
+       "            .enter()\n",
+       "            .append(\"g\")\n",
+       "            .attr(\"head-index\", (d, i) => i)\n",
+       "            .selectAll(\"rect\")\n",
+       "            .data(d => isLeft ? d : transpose(d)) // if right text, transpose attention to get right-to-left weights\n",
+       "            .enter()\n",
+       "            .append(\"rect\")\n",
+       "            .attr(\"x\", function () {\n",
+       "                var headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
+       "                return leftPos + boxOffsets(headIndex);\n",
+       "            })\n",
+       "            .attr(\"y\", (+1) * BOXHEIGHT)\n",
+       "            .attr(\"width\", BOXWIDTH / activeHeads())\n",
+       "            .attr(\"height\", BOXHEIGHT)\n",
+       "            .attr(\"fill\", function () {\n",
+       "                return headColors(+this.parentNode.getAttribute(\"head-index\"))\n",
+       "            })\n",
+       "            .style(\"opacity\", 0.0);\n",
+       "\n",
+       "        const tokenContainer = textContainer.append(\"g\").selectAll(\"g\")\n",
+       "            .data(text)\n",
+       "            .enter()\n",
+       "            .append(\"g\");\n",
+       "\n",
+       "        // Add gray background that appears when hovering over text\n",
+       "        tokenContainer.append(\"rect\")\n",
+       "            .classed(\"background\", true)\n",
+       "            .style(\"opacity\", 0.0)\n",
+       "            .attr(\"fill\", \"lightgray\")\n",
+       "            .attr(\"x\", leftPos)\n",
+       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
+       "            .attr(\"width\", BOXWIDTH)\n",
+       "            .attr(\"height\", BOXHEIGHT);\n",
+       "\n",
+       "        // Add token text\n",
+       "        const textEl = tokenContainer.append(\"text\")\n",
+       "            .text(d => d)\n",
+       "            .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
+       "            .style(\"cursor\", \"default\")\n",
+       "            .style(\"-webkit-user-select\", \"none\")\n",
+       "            .attr(\"x\", leftPos)\n",
+       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT);\n",
+       "\n",
+       "        if (isLeft) {\n",
+       "            textEl.style(\"text-anchor\", \"end\")\n",
+       "                .attr(\"dx\", BOXWIDTH - 0.5 * TEXT_SIZE)\n",
+       "                .attr(\"dy\", TEXT_SIZE);\n",
+       "        } else {\n",
+       "            textEl.style(\"text-anchor\", \"start\")\n",
+       "                .attr(\"dx\", +0.5 * TEXT_SIZE)\n",
+       "                .attr(\"dy\", TEXT_SIZE);\n",
+       "        }\n",
+       "\n",
+       "        tokenContainer.on(\"mouseover\", function (d, index) {\n",
+       "\n",
+       "            // Show gray background for moused-over token\n",
+       "            textContainer.selectAll(\".background\")\n",
+       "                .style(\"opacity\", (d, i) => i === index ? 1.0 : 0.0)\n",
+       "\n",
+       "            // Reset visibility attribute for any previously highlighted attention arcs\n",
+       "            svg.select(\"#attention\")\n",
+       "                .selectAll(\"line[visibility='visible']\")\n",
+       "                .attr(\"visibility\", null)\n",
+       "\n",
+       "            // Hide group containing attention arcs\n",
+       "            svg.select(\"#attention\").attr(\"visibility\", \"hidden\");\n",
+       "\n",
+       "            // Set to visible appropriate attention arcs to be highlighted\n",
+       "            if (isLeft) {\n",
+       "                svg.select(\"#attention\").selectAll(\"line[left-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
+       "            } else {\n",
+       "                svg.select(\"#attention\").selectAll(\"line[right-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
+       "            }\n",
+       "\n",
+       "            // Update color boxes superimposed over tokens\n",
+       "            const id = isLeft ? \"right\" : \"left\";\n",
+       "            const leftPos = isLeft ? MATRIX_WIDTH + BOXWIDTH : 0;\n",
+       "            svg.select(\"#\" + id)\n",
+       "                .selectAll(\".attentionBoxes\")\n",
+       "                .selectAll(\"g\")\n",
+       "                .attr(\"head-index\", (d, i) => i)\n",
+       "                .selectAll(\"rect\")\n",
+       "                .attr(\"x\", function () {\n",
+       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
+       "                    return leftPos + boxOffsets(headIndex);\n",
+       "                })\n",
+       "                .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
+       "                .attr(\"width\", BOXWIDTH / activeHeads())\n",
+       "                .attr(\"height\", BOXHEIGHT)\n",
+       "                .style(\"opacity\", function (d) {\n",
+       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
+       "                    if (config.headVis[headIndex])\n",
+       "                        if (d) {\n",
+       "                            return d[index];\n",
+       "                        } else {\n",
+       "                            return 0.0;\n",
+       "                        }\n",
+       "                    else\n",
+       "                        return 0.0;\n",
+       "                });\n",
+       "        });\n",
+       "\n",
+       "        textContainer.on(\"mouseleave\", function () {\n",
+       "\n",
+       "            // Unhighlight selected token\n",
+       "            d3.select(this).selectAll(\".background\")\n",
+       "                .style(\"opacity\", 0.0);\n",
+       "\n",
+       "            // Reset visibility attributes for previously selected lines\n",
+       "            svg.select(\"#attention\")\n",
+       "                .selectAll(\"line[visibility='visible']\")\n",
+       "                .attr(\"visibility\", null) ;\n",
+       "            svg.select(\"#attention\").attr(\"visibility\", \"visible\");\n",
+       "\n",
+       "            // Reset highlights superimposed over tokens\n",
+       "            svg.selectAll(\".attentionBoxes\")\n",
+       "                .selectAll(\"g\")\n",
+       "                .selectAll(\"rect\")\n",
+       "                .style(\"opacity\", 0.0);\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function renderAttention(svg, attention) {\n",
+       "\n",
+       "        // Remove previous dom elements\n",
+       "        svg.select(\"#attention\").remove();\n",
+       "\n",
+       "        // Add new elements\n",
+       "        svg.append(\"g\")\n",
+       "            .attr(\"id\", \"attention\") // Container for all attention arcs\n",
+       "            .selectAll(\".headAttention\")\n",
+       "            .data(attention)\n",
+       "            .enter()\n",
+       "            .append(\"g\")\n",
+       "            .classed(\"headAttention\", true) // Group attention arcs by head\n",
+       "            .attr(\"head-index\", (d, i) => i)\n",
+       "            .selectAll(\".tokenAttention\")\n",
+       "            .data(d => d)\n",
+       "            .enter()\n",
+       "            .append(\"g\")\n",
+       "            .classed(\"tokenAttention\", true) // Group attention arcs by left token\n",
+       "            .attr(\"left-token-index\", (d, i) => i)\n",
+       "            .selectAll(\"line\")\n",
+       "            .data(d => d)\n",
+       "            .enter()\n",
+       "            .append(\"line\")\n",
+       "            .attr(\"x1\", BOXWIDTH)\n",
+       "            .attr(\"y1\", function () {\n",
+       "                const leftTokenIndex = +this.parentNode.getAttribute(\"left-token-index\")\n",
+       "                return TEXT_TOP + leftTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2)\n",
+       "            })\n",
+       "            .attr(\"x2\", BOXWIDTH + MATRIX_WIDTH)\n",
+       "            .attr(\"y2\", (d, rightTokenIndex) => TEXT_TOP + rightTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2))\n",
+       "            .attr(\"stroke-width\", 2)\n",
+       "            .attr(\"stroke\", function () {\n",
+       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
+       "                return headColors(headIndex)\n",
+       "            })\n",
+       "            .attr(\"left-token-index\", function () {\n",
+       "                return +this.parentNode.getAttribute(\"left-token-index\")\n",
+       "            })\n",
+       "            .attr(\"right-token-index\", (d, i) => i)\n",
+       "        ;\n",
+       "        updateAttention(svg)\n",
+       "    }\n",
+       "\n",
+       "    function updateAttention(svg) {\n",
+       "        svg.select(\"#attention\")\n",
+       "            .selectAll(\"line\")\n",
+       "            .attr(\"stroke-opacity\", function (d) {\n",
+       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
+       "                // If head is selected\n",
+       "                if (config.headVis[headIndex]) {\n",
+       "                    // Set opacity to attention weight divided by number of active heads\n",
+       "                    return d / activeHeads()\n",
+       "                } else {\n",
+       "                    return 0.0;\n",
+       "                }\n",
+       "            })\n",
+       "    }\n",
+       "\n",
+       "    function boxOffsets(i) {\n",
+       "        const numHeadsAbove = config.headVis.reduce(\n",
+       "            function (acc, val, cur) {\n",
+       "                return val && cur < i ? acc + 1 : acc;\n",
+       "            }, 0);\n",
+       "        return numHeadsAbove * (BOXWIDTH / activeHeads());\n",
+       "    }\n",
+       "\n",
+       "    function activeHeads() {\n",
+       "        return config.headVis.reduce(function (acc, val) {\n",
+       "            return val ? acc + 1 : acc;\n",
+       "        }, 0);\n",
+       "    }\n",
+       "\n",
+       "    function drawCheckboxes(top, svg) {\n",
+       "        const checkboxContainer = svg.append(\"g\");\n",
+       "        const checkbox = checkboxContainer.selectAll(\"rect\")\n",
+       "            .data(config.headVis)\n",
+       "            .enter()\n",
+       "            .append(\"rect\")\n",
+       "            .attr(\"fill\", (d, i) => headColors(i))\n",
+       "            .attr(\"x\", (d, i) => i * CHECKBOX_SIZE)\n",
+       "            .attr(\"y\", top)\n",
+       "            .attr(\"width\", CHECKBOX_SIZE)\n",
+       "            .attr(\"height\", CHECKBOX_SIZE);\n",
+       "\n",
+       "        function updateCheckboxes() {\n",
+       "            checkboxContainer.selectAll(\"rect\")\n",
+       "                .data(config.headVis)\n",
+       "                .attr(\"fill\", (d, i) => d ? headColors(i): lighten(headColors(i)));\n",
+       "        }\n",
+       "\n",
+       "        updateCheckboxes();\n",
+       "\n",
+       "        checkbox.on(\"click\", function (d, i) {\n",
+       "            if (config.headVis[i] && activeHeads() === 1) return;\n",
+       "            config.headVis[i] = !config.headVis[i];\n",
+       "            updateCheckboxes();\n",
+       "            updateAttention(svg);\n",
+       "        });\n",
+       "\n",
+       "        checkbox.on(\"dblclick\", function (d, i) {\n",
+       "            // If we double click on the only active head then reset\n",
+       "            if (config.headVis[i] && activeHeads() === 1) {\n",
+       "                config.headVis = new Array(config.nHeads).fill(true);\n",
+       "            } else {\n",
+       "                config.headVis = new Array(config.nHeads).fill(false);\n",
+       "                config.headVis[i] = true;\n",
+       "            }\n",
+       "            updateCheckboxes();\n",
+       "            updateAttention(svg);\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function lighten(color) {\n",
+       "        const c = d3.hsl(color);\n",
+       "        const increment = (1 - c.l) * 0.6;\n",
+       "        c.l += increment;\n",
+       "        c.s -= increment;\n",
+       "        return c;\n",
+       "    }\n",
+       "\n",
+       "    function transpose(mat) {\n",
+       "        return mat[0].map(function (col, i) {\n",
+       "            return mat.map(function (row) {\n",
+       "                return row[i];\n",
+       "            });\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "head_view(attention, tokens)"
    ]
@@ -146,7 +632,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8544981f",
+   "id": "7c750700",
    "metadata": {},
    "outputs": [],
    "source": []

From d173f8c8f17967c81f869b7135a4bb1a074d8429 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 11:50:48 +0100
Subject: [PATCH 04/11] style(viz): notebook

---
 notebooks/adopt_attention_viz.ipynb | 501 +---------------------------
 1 file changed, 8 insertions(+), 493 deletions(-)

diff --git a/notebooks/adopt_attention_viz.ipynb b/notebooks/adopt_attention_viz.ipynb
index 053007a..cb0cbca 100644
--- a/notebooks/adopt_attention_viz.ipynb
+++ b/notebooks/adopt_attention_viz.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "33a018a9",
    "metadata": {},
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "9f1d4977",
    "metadata": {},
    "outputs": [],
@@ -50,78 +50,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "f6e600d7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sequence</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>brmid</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>P13725|ONCM_HUMAN</th>\n",
-       "      <td>MGVLLTQRTLLSLVLALLFPSMASMAAIGSCSKEYRVLLGQLQKQT...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>P20809|IL11_HUMAN</th>\n",
-       "      <td>MNCVCRLVLVVLSLWPDTAVAPGPPPGPPRVSPDPRAELDSTVLLT...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>O95760|IL33_HUMAN</th>\n",
-       "      <td>MKPKMKYSTNKISTAKWKNTASKALCFKLGKSQQKAKEVCPMYFMK...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q6EBC2|IL31_HUMAN</th>\n",
-       "      <td>MASHSGPSTSVLFLFCCLGGWLASHTLPVRLLRPSDDVQKIVEELQ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Q14626|I11RA_HUMAN</th>\n",
-       "      <td>MSSSCSGLSRVLVAVATALVSASSPCPQAWGPPGVQYGQPGRSVKL...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                             sequence\n",
-       "brmid                                                                \n",
-       "P13725|ONCM_HUMAN   MGVLLTQRTLLSLVLALLFPSMASMAAIGSCSKEYRVLLGQLQKQT...\n",
-       "P20809|IL11_HUMAN   MNCVCRLVLVVLSLWPDTAVAPGPPPGPPRVSPDPRAELDSTVLLT...\n",
-       "O95760|IL33_HUMAN   MKPKMKYSTNKISTAKWKNTASKALCFKLGKSQQKAKEVCPMYFMK...\n",
-       "Q6EBC2|IL31_HUMAN   MASHSGPSTSVLFLFCCLGGWLASHTLPVRLLRPSDDVQKIVEELQ...\n",
-       "Q14626|I11RA_HUMAN  MSSSCSGLSRVLVAVATALVSASSPCPQAWGPPGVQYGQPGRSVKL..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_fasta = utils.fasta_to_df(FASTA_FILE).set_index('brmid')\n",
     "df_fasta.head()"
@@ -145,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6bfcf344",
    "metadata": {},
    "outputs": [],
@@ -165,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2cf6c1c2",
    "metadata": {},
    "outputs": [],
@@ -196,427 +128,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "51eb3117",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js\"></script>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "      \n",
-       "        <div id='bertviz-9cf865094470435ea1c197e57a04617b'>\n",
-       "            <span style=\"user-select:none\">\n",
-       "                Layer: <select id=\"layer\"></select>\n",
-       "                \n",
-       "            </span>\n",
-       "            <div id='vis'></div>\n",
-       "        </div>\n",
-       "    "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/**\n",
-       " * @fileoverview Transformer Visualization D3 javascript code.\n",
-       " *\n",
-       " *\n",
-       " *  Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
-       " *\n",
-       " * Change log:\n",
-       " *\n",
-       " * 12/19/18  Jesse Vig   Assorted cleanup. Changed orientation of attention matrices.\n",
-       " * 12/29/20  Jesse Vig   Significant refactor.\n",
-       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
-       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
-       " * 05/03/21  Jesse Vig   Adjust height of visualization dynamically\n",
-       " * 07/25/21  Jesse Vig   Support layer filtering\n",
-       " **/\n",
-       "\n",
-       "require.config({\n",
-       "  paths: {\n",
-       "      d3: '//cdnjs.cloudflare.com/ajax/libs/d3/5.7.0/d3.min',\n",
-       "    jquery: '//ajax.googleapis.com/ajax/libs/jquery/2.0.0/jquery.min',\n",
-       "  }\n",
-       "});\n",
-       "\n",
-       "requirejs(['jquery', 'd3'], function ($, d3) {\n",
-       "\n",
-       "    const params = {\"attention\": [{\"name\": null, \"attn\": [[[[0.07077009230852127, 0.007848864421248436, 0.028781868517398834, 0.008763647638261318, 0.04590034484863281, 0.004891009069979191, 0.03187142685055733, 0.011426498182117939, 0.009642207995057106, 0.006737949792295694, 0.003060394199565053, 0.00833201315253973, 0.006882949266582727, 0.007980404421687126, 0.017441846430301666, 0.002057430800050497, 0.013946546241641045, 0.0017426976701244712, 0.0036213677376508713, 0.005163727328181267], [0.13214954733848572, 0.02923366241157055, 0.05096348747611046, 0.009969064965844154, 0.054019469767808914, 0.007594925817102194, 0.024155860766768456, 0.011347616091370583, 0.009242362342774868, 0.004419925156980753, 0.00249915081076324, 0.005082458257675171, 0.005312361754477024, 0.004466281738132238, 0.008020053617656231, 0.00157601956743747, 0.008996976539492607, 0.0011983162257820368, 0.0019424002384766936, 0.0037593815941363573], [0.24574097990989685, 0.11313629150390625, 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0.0539274699985981, 0.01068555936217308, 0.002721047028899193]]]], \"left_text\": [\"M\", \"K\", \"P\", \"K\", \"M\", \"K\", \"Y\", \"S\", \"T\", \"N\", \"K\", \"I\", \"S\", \"T\", \"A\", \"K\", \"W\", \"K\", \"N\", \"T\"], \"right_text\": [\"M\", \"K\", \"P\", \"K\", \"M\", \"K\", \"Y\", \"S\", \"T\", \"N\", \"K\", \"I\", \"S\", \"T\", \"A\", \"K\", \"W\", \"K\", \"N\", \"T\"]}], \"default_filter\": \"0\", \"root_div_id\": \"bertviz-9cf865094470435ea1c197e57a04617b\", \"layer\": null, \"heads\": null, \"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]} is a template marker that is replaced by actual params.\n",
-       "    const TEXT_SIZE = 15;\n",
-       "    const BOXWIDTH = 110;\n",
-       "    const BOXHEIGHT = 22.5;\n",
-       "    const MATRIX_WIDTH = 115;\n",
-       "    const CHECKBOX_SIZE = 20;\n",
-       "    const TEXT_TOP = 30;\n",
-       "\n",
-       "    console.log(\"d3 version\", d3.version)\n",
-       "    let headColors;\n",
-       "    try {\n",
-       "        headColors = d3.scaleOrdinal(d3.schemeCategory10);\n",
-       "    } catch (err) {\n",
-       "        console.log('Older d3 version')\n",
-       "        headColors = d3.scale.category10();\n",
-       "    }\n",
-       "    let config = {};\n",
-       "    initialize();\n",
-       "    renderVis();\n",
-       "\n",
-       "    function initialize() {\n",
-       "        config.attention = params['attention'];\n",
-       "        config.filter = params['default_filter'];\n",
-       "        config.rootDivId = params['root_div_id'];\n",
-       "        config.nLayers = config.attention[config.filter]['attn'].length;\n",
-       "        config.nHeads = config.attention[config.filter]['attn'][0].length;\n",
-       "        config.layers = params['include_layers']\n",
-       "\n",
-       "        if (params['heads']) {\n",
-       "            config.headVis = new Array(config.nHeads).fill(false);\n",
-       "            params['heads'].forEach(x => config.headVis[x] = true);\n",
-       "        } else {\n",
-       "            config.headVis = new Array(config.nHeads).fill(true);\n",
-       "        }\n",
-       "        config.initialTextLength = config.attention[config.filter].right_text.length;\n",
-       "        config.layer_seq = (params['layer'] == null ? 0 : config.layers.findIndex(layer => params['layer'] === layer));\n",
-       "        config.layer = config.layers[config.layer_seq]\n",
-       "\n",
-       "        let layerEl = $(`#${config.rootDivId} #layer`);\n",
-       "        for (const layer of config.layers) {\n",
-       "            layerEl.append($(\"<option />\").val(layer).text(layer));\n",
-       "        }\n",
-       "        layerEl.val(config.layer).change();\n",
-       "        layerEl.on('change', function (e) {\n",
-       "            config.layer = +e.currentTarget.value;\n",
-       "            config.layer_seq = config.layers.findIndex(layer => config.layer === layer);\n",
-       "            renderVis();\n",
-       "        });\n",
-       "\n",
-       "        $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
-       "            config.filter = e.currentTarget.value;\n",
-       "            renderVis();\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function renderVis() {\n",
-       "\n",
-       "        // Load parameters\n",
-       "        const attnData = config.attention[config.filter];\n",
-       "        const leftText = attnData.left_text;\n",
-       "        const rightText = attnData.right_text;\n",
-       "\n",
-       "        // Select attention for given layer\n",
-       "        const layerAttention = attnData.attn[config.layer_seq];\n",
-       "\n",
-       "        // Clear vis\n",
-       "        $(`#${config.rootDivId} #vis`).empty();\n",
-       "\n",
-       "        // Determine size of visualization\n",
-       "        const height = Math.max(leftText.length, rightText.length) * BOXHEIGHT + TEXT_TOP;\n",
-       "        const svg = d3.select(`#${config.rootDivId} #vis`)\n",
-       "            .append('svg')\n",
-       "            .attr(\"width\", \"100%\")\n",
-       "            .attr(\"height\", height + \"px\");\n",
-       "\n",
-       "        // Display tokens on left and right side of visualization\n",
-       "        renderText(svg, leftText, true, layerAttention, 0);\n",
-       "        renderText(svg, rightText, false, layerAttention, MATRIX_WIDTH + BOXWIDTH);\n",
-       "\n",
-       "        // Render attention arcs\n",
-       "        renderAttention(svg, layerAttention);\n",
-       "\n",
-       "        // Draw squares at top of visualization, one for each head\n",
-       "        drawCheckboxes(0, svg, layerAttention);\n",
-       "    }\n",
-       "\n",
-       "    function renderText(svg, text, isLeft, attention, leftPos) {\n",
-       "\n",
-       "        const textContainer = svg.append(\"svg:g\")\n",
-       "            .attr(\"id\", isLeft ? \"left\" : \"right\");\n",
-       "\n",
-       "        // Add attention highlights superimposed over words\n",
-       "        textContainer.append(\"g\")\n",
-       "            .classed(\"attentionBoxes\", true)\n",
-       "            .selectAll(\"g\")\n",
-       "            .data(attention)\n",
-       "            .enter()\n",
-       "            .append(\"g\")\n",
-       "            .attr(\"head-index\", (d, i) => i)\n",
-       "            .selectAll(\"rect\")\n",
-       "            .data(d => isLeft ? d : transpose(d)) // if right text, transpose attention to get right-to-left weights\n",
-       "            .enter()\n",
-       "            .append(\"rect\")\n",
-       "            .attr(\"x\", function () {\n",
-       "                var headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
-       "                return leftPos + boxOffsets(headIndex);\n",
-       "            })\n",
-       "            .attr(\"y\", (+1) * BOXHEIGHT)\n",
-       "            .attr(\"width\", BOXWIDTH / activeHeads())\n",
-       "            .attr(\"height\", BOXHEIGHT)\n",
-       "            .attr(\"fill\", function () {\n",
-       "                return headColors(+this.parentNode.getAttribute(\"head-index\"))\n",
-       "            })\n",
-       "            .style(\"opacity\", 0.0);\n",
-       "\n",
-       "        const tokenContainer = textContainer.append(\"g\").selectAll(\"g\")\n",
-       "            .data(text)\n",
-       "            .enter()\n",
-       "            .append(\"g\");\n",
-       "\n",
-       "        // Add gray background that appears when hovering over text\n",
-       "        tokenContainer.append(\"rect\")\n",
-       "            .classed(\"background\", true)\n",
-       "            .style(\"opacity\", 0.0)\n",
-       "            .attr(\"fill\", \"lightgray\")\n",
-       "            .attr(\"x\", leftPos)\n",
-       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
-       "            .attr(\"width\", BOXWIDTH)\n",
-       "            .attr(\"height\", BOXHEIGHT);\n",
-       "\n",
-       "        // Add token text\n",
-       "        const textEl = tokenContainer.append(\"text\")\n",
-       "            .text(d => d)\n",
-       "            .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
-       "            .style(\"cursor\", \"default\")\n",
-       "            .style(\"-webkit-user-select\", \"none\")\n",
-       "            .attr(\"x\", leftPos)\n",
-       "            .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT);\n",
-       "\n",
-       "        if (isLeft) {\n",
-       "            textEl.style(\"text-anchor\", \"end\")\n",
-       "                .attr(\"dx\", BOXWIDTH - 0.5 * TEXT_SIZE)\n",
-       "                .attr(\"dy\", TEXT_SIZE);\n",
-       "        } else {\n",
-       "            textEl.style(\"text-anchor\", \"start\")\n",
-       "                .attr(\"dx\", +0.5 * TEXT_SIZE)\n",
-       "                .attr(\"dy\", TEXT_SIZE);\n",
-       "        }\n",
-       "\n",
-       "        tokenContainer.on(\"mouseover\", function (d, index) {\n",
-       "\n",
-       "            // Show gray background for moused-over token\n",
-       "            textContainer.selectAll(\".background\")\n",
-       "                .style(\"opacity\", (d, i) => i === index ? 1.0 : 0.0)\n",
-       "\n",
-       "            // Reset visibility attribute for any previously highlighted attention arcs\n",
-       "            svg.select(\"#attention\")\n",
-       "                .selectAll(\"line[visibility='visible']\")\n",
-       "                .attr(\"visibility\", null)\n",
-       "\n",
-       "            // Hide group containing attention arcs\n",
-       "            svg.select(\"#attention\").attr(\"visibility\", \"hidden\");\n",
-       "\n",
-       "            // Set to visible appropriate attention arcs to be highlighted\n",
-       "            if (isLeft) {\n",
-       "                svg.select(\"#attention\").selectAll(\"line[left-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
-       "            } else {\n",
-       "                svg.select(\"#attention\").selectAll(\"line[right-token-index='\" + index + \"']\").attr(\"visibility\", \"visible\");\n",
-       "            }\n",
-       "\n",
-       "            // Update color boxes superimposed over tokens\n",
-       "            const id = isLeft ? \"right\" : \"left\";\n",
-       "            const leftPos = isLeft ? MATRIX_WIDTH + BOXWIDTH : 0;\n",
-       "            svg.select(\"#\" + id)\n",
-       "                .selectAll(\".attentionBoxes\")\n",
-       "                .selectAll(\"g\")\n",
-       "                .attr(\"head-index\", (d, i) => i)\n",
-       "                .selectAll(\"rect\")\n",
-       "                .attr(\"x\", function () {\n",
-       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
-       "                    return leftPos + boxOffsets(headIndex);\n",
-       "                })\n",
-       "                .attr(\"y\", (d, i) => TEXT_TOP + i * BOXHEIGHT)\n",
-       "                .attr(\"width\", BOXWIDTH / activeHeads())\n",
-       "                .attr(\"height\", BOXHEIGHT)\n",
-       "                .style(\"opacity\", function (d) {\n",
-       "                    const headIndex = +this.parentNode.getAttribute(\"head-index\");\n",
-       "                    if (config.headVis[headIndex])\n",
-       "                        if (d) {\n",
-       "                            return d[index];\n",
-       "                        } else {\n",
-       "                            return 0.0;\n",
-       "                        }\n",
-       "                    else\n",
-       "                        return 0.0;\n",
-       "                });\n",
-       "        });\n",
-       "\n",
-       "        textContainer.on(\"mouseleave\", function () {\n",
-       "\n",
-       "            // Unhighlight selected token\n",
-       "            d3.select(this).selectAll(\".background\")\n",
-       "                .style(\"opacity\", 0.0);\n",
-       "\n",
-       "            // Reset visibility attributes for previously selected lines\n",
-       "            svg.select(\"#attention\")\n",
-       "                .selectAll(\"line[visibility='visible']\")\n",
-       "                .attr(\"visibility\", null) ;\n",
-       "            svg.select(\"#attention\").attr(\"visibility\", \"visible\");\n",
-       "\n",
-       "            // Reset highlights superimposed over tokens\n",
-       "            svg.selectAll(\".attentionBoxes\")\n",
-       "                .selectAll(\"g\")\n",
-       "                .selectAll(\"rect\")\n",
-       "                .style(\"opacity\", 0.0);\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function renderAttention(svg, attention) {\n",
-       "\n",
-       "        // Remove previous dom elements\n",
-       "        svg.select(\"#attention\").remove();\n",
-       "\n",
-       "        // Add new elements\n",
-       "        svg.append(\"g\")\n",
-       "            .attr(\"id\", \"attention\") // Container for all attention arcs\n",
-       "            .selectAll(\".headAttention\")\n",
-       "            .data(attention)\n",
-       "            .enter()\n",
-       "            .append(\"g\")\n",
-       "            .classed(\"headAttention\", true) // Group attention arcs by head\n",
-       "            .attr(\"head-index\", (d, i) => i)\n",
-       "            .selectAll(\".tokenAttention\")\n",
-       "            .data(d => d)\n",
-       "            .enter()\n",
-       "            .append(\"g\")\n",
-       "            .classed(\"tokenAttention\", true) // Group attention arcs by left token\n",
-       "            .attr(\"left-token-index\", (d, i) => i)\n",
-       "            .selectAll(\"line\")\n",
-       "            .data(d => d)\n",
-       "            .enter()\n",
-       "            .append(\"line\")\n",
-       "            .attr(\"x1\", BOXWIDTH)\n",
-       "            .attr(\"y1\", function () {\n",
-       "                const leftTokenIndex = +this.parentNode.getAttribute(\"left-token-index\")\n",
-       "                return TEXT_TOP + leftTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2)\n",
-       "            })\n",
-       "            .attr(\"x2\", BOXWIDTH + MATRIX_WIDTH)\n",
-       "            .attr(\"y2\", (d, rightTokenIndex) => TEXT_TOP + rightTokenIndex * BOXHEIGHT + (BOXHEIGHT / 2))\n",
-       "            .attr(\"stroke-width\", 2)\n",
-       "            .attr(\"stroke\", function () {\n",
-       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
-       "                return headColors(headIndex)\n",
-       "            })\n",
-       "            .attr(\"left-token-index\", function () {\n",
-       "                return +this.parentNode.getAttribute(\"left-token-index\")\n",
-       "            })\n",
-       "            .attr(\"right-token-index\", (d, i) => i)\n",
-       "        ;\n",
-       "        updateAttention(svg)\n",
-       "    }\n",
-       "\n",
-       "    function updateAttention(svg) {\n",
-       "        svg.select(\"#attention\")\n",
-       "            .selectAll(\"line\")\n",
-       "            .attr(\"stroke-opacity\", function (d) {\n",
-       "                const headIndex = +this.parentNode.parentNode.getAttribute(\"head-index\");\n",
-       "                // If head is selected\n",
-       "                if (config.headVis[headIndex]) {\n",
-       "                    // Set opacity to attention weight divided by number of active heads\n",
-       "                    return d / activeHeads()\n",
-       "                } else {\n",
-       "                    return 0.0;\n",
-       "                }\n",
-       "            })\n",
-       "    }\n",
-       "\n",
-       "    function boxOffsets(i) {\n",
-       "        const numHeadsAbove = config.headVis.reduce(\n",
-       "            function (acc, val, cur) {\n",
-       "                return val && cur < i ? acc + 1 : acc;\n",
-       "            }, 0);\n",
-       "        return numHeadsAbove * (BOXWIDTH / activeHeads());\n",
-       "    }\n",
-       "\n",
-       "    function activeHeads() {\n",
-       "        return config.headVis.reduce(function (acc, val) {\n",
-       "            return val ? acc + 1 : acc;\n",
-       "        }, 0);\n",
-       "    }\n",
-       "\n",
-       "    function drawCheckboxes(top, svg) {\n",
-       "        const checkboxContainer = svg.append(\"g\");\n",
-       "        const checkbox = checkboxContainer.selectAll(\"rect\")\n",
-       "            .data(config.headVis)\n",
-       "            .enter()\n",
-       "            .append(\"rect\")\n",
-       "            .attr(\"fill\", (d, i) => headColors(i))\n",
-       "            .attr(\"x\", (d, i) => i * CHECKBOX_SIZE)\n",
-       "            .attr(\"y\", top)\n",
-       "            .attr(\"width\", CHECKBOX_SIZE)\n",
-       "            .attr(\"height\", CHECKBOX_SIZE);\n",
-       "\n",
-       "        function updateCheckboxes() {\n",
-       "            checkboxContainer.selectAll(\"rect\")\n",
-       "                .data(config.headVis)\n",
-       "                .attr(\"fill\", (d, i) => d ? headColors(i): lighten(headColors(i)));\n",
-       "        }\n",
-       "\n",
-       "        updateCheckboxes();\n",
-       "\n",
-       "        checkbox.on(\"click\", function (d, i) {\n",
-       "            if (config.headVis[i] && activeHeads() === 1) return;\n",
-       "            config.headVis[i] = !config.headVis[i];\n",
-       "            updateCheckboxes();\n",
-       "            updateAttention(svg);\n",
-       "        });\n",
-       "\n",
-       "        checkbox.on(\"dblclick\", function (d, i) {\n",
-       "            // If we double click on the only active head then reset\n",
-       "            if (config.headVis[i] && activeHeads() === 1) {\n",
-       "                config.headVis = new Array(config.nHeads).fill(true);\n",
-       "            } else {\n",
-       "                config.headVis = new Array(config.nHeads).fill(false);\n",
-       "                config.headVis[i] = true;\n",
-       "            }\n",
-       "            updateCheckboxes();\n",
-       "            updateAttention(svg);\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "    function lighten(color) {\n",
-       "        const c = d3.hsl(color);\n",
-       "        const increment = (1 - c.l) * 0.6;\n",
-       "        c.l += increment;\n",
-       "        c.s -= increment;\n",
-       "        return c;\n",
-       "    }\n",
-       "\n",
-       "    function transpose(mat) {\n",
-       "        return mat[0].map(function (col, i) {\n",
-       "            return mat.map(function (row) {\n",
-       "                return row[i];\n",
-       "            });\n",
-       "        });\n",
-       "    }\n",
-       "\n",
-       "});"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "head_view(attention, tokens)"
    ]

From cf70ba57c305a6c9d74ebdaa518e34b6024eb60c Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 12:48:37 +0100
Subject: [PATCH 05/11] refactor(viz): add free parameters

---
 notebooks/adopt_attention_viz.ipynb | 22 +++-------------------
 1 file changed, 3 insertions(+), 19 deletions(-)

diff --git a/notebooks/adopt_attention_viz.ipynb b/notebooks/adopt_attention_viz.ipynb
index cb0cbca..8d0ab45 100644
--- a/notebooks/adopt_attention_viz.ipynb
+++ b/notebooks/adopt_attention_viz.ipynb
@@ -26,9 +26,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "FASTA_FILE = 'datasets/peptone_targets.fasta'\n",
-    "BRMID = 'O95760|IL33_HUMAN'\n",
-    "MODEL_TYPE = 'esm-1b'\n",
+    "FASTA_FILE = '<fasta_file_path>'\n",
+    "BRMID = '<protein_sequence_brmid>'\n",
+    "MODEL_TYPE = '<model_type>'\n",
     "N_RES = 20"
    ]
   },
@@ -135,22 +135,6 @@
    "source": [
     "head_view(attention, tokens)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c13fd09b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7c750700",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From c53eccea9003bb8d817f76e0b3ba6982326bfc31 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 13:37:07 +0100
Subject: [PATCH 06/11] feat: interactive disorder prediction

---
 adopt/__init__.py  |   1 +
 adopt/inference.py | 143 ++++++++++++++++++++++++---------------------
 2 files changed, 77 insertions(+), 67 deletions(-)

diff --git a/adopt/__init__.py b/adopt/__init__.py
index 599def2..3832206 100644
--- a/adopt/__init__.py
+++ b/adopt/__init__.py
@@ -7,5 +7,6 @@
 from . import constants, utils
 from .data import CheZod
 from .training import DisorderPred
+from .inference import ZScorePred
 from .transformer import MultiHead
 from .version import version as __version__
diff --git a/adopt/inference.py b/adopt/inference.py
index 3a56eaa..af895f8 100644
--- a/adopt/inference.py
+++ b/adopt/inference.py
@@ -13,73 +13,81 @@
 from adopt import constants, utils
 
 
-def get_z_score(
-    strategy,
-    model_type,
-    inference_fasta_path,
-    inference_repr_path,
-    predicted_z_scores_path,
-):
-    df_fasta = utils.fasta_to_df(inference_fasta_path)
-
-    if model_type == "combined":
-        repr_path = inference_repr_path + "/" + "esm-1v"
-    else:
-        repr_path = inference_repr_path + "/" + model_type
-
-    repr_files = os.listdir(repr_path)
-    indexes = []
-
-    for file in repr_files:
-        indexes.append(file.split(".")[0])
-
-    onnx_model = (
-        "../models/lasso_"
-        + model_type
-        + "_"
-        + constants.strategies_dict[strategy]
-        + ".onnx"
-    )
-    predicted_z_scores = []
-
-    for ix in indexes:
-        if model_type == "esm-msa":
-            repr_esm = (
-                torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][12]
-                .clone()
-                .cpu()
-                .detach()
-            )
-        elif model_type == "combined":
-            esm1b_repr_path = inference_repr_path + "/" + "esm-1b"
-            repr_esm1v = (
-                torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][33]
-                .clone()
-                .cpu()
-                .detach()
-            )
-            repr_esm1b = (
-                torch.load(str(esm1b_repr_path) + "/" + ix + ".pt")["representations"][
-                    33
-                ]
-                .clone()
-                .cpu()
-                .detach()
-            )
-            repr_esm = torch.cat([repr_esm1v, repr_esm1b], 1)
+class ZScorePred:
+    def __init__(self, strategy, model_type):
+        self.strategy = strategy
+        self.model_type = model_type
+        self.onnx_model = (
+            "../models/lasso_"
+            + self.model_type
+            + "_"
+            + constants.strategies_dict[self.strategy]
+            + ".onnx"
+        )
+
+    def get_z_score(self, representation):
+        predicted_z_scores = utils.get_onnx_model_preds(self.onnx_model, representation.squeeze().numpy())
+        return np.concatenate(predicted_z_scores)        
+
+    def get_z_score_from_fasta(
+        self,
+        inference_fasta_path,
+        inference_repr_path,
+        predicted_z_scores_path,
+    ):
+        df_fasta = utils.fasta_to_df(inference_fasta_path)
+
+        if self.model_type == "combined":
+            repr_path = inference_repr_path + "/" + "esm-1v"
         else:
-            repr_esm = (
-                torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][33]
-                .clone()
-                .cpu()
-                .detach()
-            )
-        z_scores = utils.get_onnx_model_preds(onnx_model, repr_esm.numpy())
-        predicted_z_scores.append(np.concatenate(z_scores))
+            repr_path = inference_repr_path + "/" + self.model_type
+
+        repr_files = os.listdir(repr_path)
+        indexes = []
+
+        for file in repr_files:
+            indexes.append(file.split(".")[0])
+
+        predicted_z_scores = []
+
+        for ix in indexes:
+            if self.model_type == "esm-msa":
+                repr_esm = (
+                    torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][12]
+                    .clone()
+                    .cpu()
+                    .detach()
+                )
+            elif self.model_type == "combined":
+                esm1b_repr_path = inference_repr_path + "/" + "esm-1b"
+                repr_esm1v = (
+                    torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][33]
+                    .clone()
+                    .cpu()
+                    .detach()
+                )
+                repr_esm1b = (
+                    torch.load(str(esm1b_repr_path) + "/" + ix + ".pt")["representations"][
+                        33
+                    ]
+                    .clone()
+                    .cpu()
+                    .detach()
+                )
+                repr_esm = torch.cat([repr_esm1v, repr_esm1b], 1)
+            else:
+                repr_esm = (
+                    torch.load(str(repr_path) + "/" + ix + ".pt")["representations"][33]
+                    .clone()
+                    .cpu()
+                    .detach()
+                )
+            z_scores = utils.get_onnx_model_preds(self.onnx_model, repr_esm.numpy())
+            predicted_z_scores.append(np.concatenate(z_scores))
 
-    df_z = pd.DataFrame({"brmid": indexes, "z_scores": predicted_z_scores})
-    df_results = df_fasta.join(df_z.set_index("brmid"), on="brmid")
-    df_results.to_json(predicted_z_scores_path, orient="records")
+        df_z = pd.DataFrame({"brmid": indexes, "z_scores": predicted_z_scores})
+        df_results = df_fasta.join(df_z.set_index("brmid"), on="brmid")
+        df_results.to_json(predicted_z_scores_path, orient="records")
 
 
 def main(argv):
@@ -144,8 +152,9 @@ def main(argv):
         elif opt in ("-p", "--pred_z_scores_file"):
             pred_z_scores_file = arg
 
-    get_z_score(
-        train_strategy, model_type, infer_fasta_file, infer_repr_dir, pred_z_scores_file
+    z_score_pred = ZScorePred(train_strategy, model_type)
+    z_score_pred.get_z_score_from_fasta(
+        infer_fasta_file, infer_repr_dir, pred_z_scores_file
     )
 
 

From 2d4a4a00fac7a718b0e0441ce3ae4df11f90c865 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 14:57:44 +0100
Subject: [PATCH 07/11] feat(viz): residue level disorder

---
 adopt/constants.py                        |  10 ++
 adopt/utils.py                            |  19 +++
 notebooks/adopt_attention_viz.ipynb       |  20 +--
 notebooks/adopt_disorder_prediction.ipynb | 176 ++++++++++++++++++++++
 4 files changed, 215 insertions(+), 10 deletions(-)
 create mode 100644 notebooks/adopt_disorder_prediction.ipynb

diff --git a/adopt/constants.py b/adopt/constants.py
index 57c7902..6524f04 100644
--- a/adopt/constants.py
+++ b/adopt/constants.py
@@ -24,3 +24,13 @@
     "train_on_cleared_1325_cv_residue_split": "cleared_residue_cv",
     "train_on_cleared_1325_cv_sequence_split": "cleared_sequence_cv",
 }
+
+structure_dict = {"Fully disordered": "FDIS",
+            "Partially disordered": "PDIS",
+            "Structured": "STRUCT",
+            "Flexible loops": "FLEX"}
+
+res_colors = {"FDIS": "#FF3349", 
+        "PDIS": "#FFD433",
+        "STRUCT": "#33C4FF",
+        "FLEX": "##fc9ce7"}
diff --git a/adopt/utils.py b/adopt/utils.py
index 1f78e17..f49db42 100644
--- a/adopt/utils.py
+++ b/adopt/utils.py
@@ -118,3 +118,22 @@ def get_model_and_alphabet(model_type, data):
         results_esm1v = get_esm_output(model_esm1v, alphabet_esm1v, data)
         results = [results_esm1b, results_esm1v]
     return results
+
+
+def get_residue_class(predicted_z_scores):
+    residues_state = []
+    for n, zscore in enumerate(predicted_z_scores):
+        residues_dict = {}
+        if zscore < 3:
+            residues_dict['label'] = constants.structure_dict["Fully disordered"]
+        elif 3 <= zscore < 8:
+            residues_dict['label'] = constants.structure_dict["Partially disordered"]
+        elif zscore >= 11:
+            residues_dict['label'] = constants.structure_dict["Structured"]
+        else:
+            residues_dict['label'] = constants.structure_dict["Flexible loops"]
+            
+        residues_dict["start"] = n
+        residues_dict["end"] = n + 1
+        residues_state.append(residues_dict)
+    return residues_state
diff --git a/notebooks/adopt_attention_viz.ipynb b/notebooks/adopt_attention_viz.ipynb
index 8d0ab45..d6ae9af 100644
--- a/notebooks/adopt_attention_viz.ipynb
+++ b/notebooks/adopt_attention_viz.ipynb
@@ -89,7 +89,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e18caa9b",
+   "id": "409788a6",
+   "metadata": {},
+   "source": [
+    "# Viz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c9b9f9d",
    "metadata": {},
    "source": [
     "Cut the sequence taking the first **N_RES** tokens for visualisation purposes."
@@ -98,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2cf6c1c2",
+   "id": "f01b1a34",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,14 +114,6 @@
     "tokens = tokens[:N_RES]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "409788a6",
-   "metadata": {},
-   "source": [
-    "# Viz"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "6125a9ee",
diff --git a/notebooks/adopt_disorder_prediction.ipynb b/notebooks/adopt_disorder_prediction.ipynb
new file mode 100644
index 0000000..395b905
--- /dev/null
+++ b/notebooks/adopt_disorder_prediction.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a018a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from adopt import utils, constants, MultiHead, ZScorePred\n",
+    "from spacy import displacy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "784a704e",
+   "metadata": {},
+   "source": [
+    "# Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7188f715",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FASTA_FILE = '<fasta_file_path>'\n",
+    "BRMID = '<protein_sequence_brmid>'\n",
+    "STRATEGY = '<training_strategy>'\n",
+    "MODEL_TYPE = '<model_type>'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f3a2e50",
+   "metadata": {},
+   "source": [
+    "# Load data "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e8d3a02",
+   "metadata": {},
+   "source": [
+    "Load the **FASTA_FILE** containing the protein sequences we are interested in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6e600d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_fasta = utils.fasta_to_df(FASTA_FILE).set_index('brmid')\n",
+    "df_fasta.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1cb24ed",
+   "metadata": {},
+   "source": [
+    "# Extract residue level representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7bc90da",
+   "metadata": {},
+   "source": [
+    "Extract attention weights of the **BRMID** in the **FASTA_FILE** specified above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bfcf344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequence = df_fasta.loc[BRMID].sequence\n",
+    "multi_head = MultiHead(MODEL_TYPE, sequence, BRMID)\n",
+    "representation, tokens = multi_head.get_representation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "168c4f14",
+   "metadata": {},
+   "source": [
+    "# Predict the Z score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e18caa9b",
+   "metadata": {},
+   "source": [
+    "Predict the **Z score** related to each residue in the sequence specified above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b3656ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_score_pred = ZScorePred(STRATEGY, MODEL_TYPE)\n",
+    "predicted_z_scores = z_score_pred.get_z_score(representation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "908aa4b9",
+   "metadata": {},
+   "source": [
+    "# Viz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd6874f2",
+   "metadata": {},
+   "source": [
+    "Each residue in the sequence defined above is tagged as:\n",
+    "* **Fully disordered** &rarr; FDIS\n",
+    "* **Partially disordered** &rarr; PDIS\n",
+    "* **Structured** &rarr; STRUCT\n",
+    "* **Flexible loops** &rarr; FLEX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "945a21d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residues_state = utils.get_residue_class(predicted_z_scores)\n",
+    "ex = [{\"text\": sequence,\n",
+    "       \"ents\": residues_state,\n",
+    "       \"title\": None}]\n",
+    "\n",
+    "options = {\"ents\": constants.res_colors.keys(), \"colors\": constants.res_colors}\n",
+    "\n",
+    "html = displacy.render(ex, style=\"ent\", manual=True, options=options, jupyter=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From aec7ee001a8c91df862bccf5f526bbbf2be4c5fe Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 15:03:24 +0100
Subject: [PATCH 08/11] close #5; style: disorder prediction

---
 adopt/__init__.py                 |  2 +-
 adopt/constants.py                | 20 ++++++-----
 adopt/data.py                     |  1 +
 adopt/inference.py                | 13 ++++---
 adopt/transformer.py              | 11 +++---
 adopt/utils.py                    | 12 +++----
 esm/esm/__init__.py               |  7 ++--
 esm/esm/axial_attention.py        | 57 +++++++++++++++++++++++--------
 esm/esm/data.py                   | 28 ++++++++++-----
 esm/esm/model.py                  | 54 ++++++++++++++++++-----------
 esm/esm/modules.py                | 38 ++++++++++++++++-----
 esm/esm/multihead_attention.py    | 49 +++++++++++++++++++-------
 esm/esm/pretrained.py             | 49 +++++++++++++++++++-------
 esm/extract.py                    | 20 ++++++++---
 esm/hubconf.py                    | 23 ++++---------
 esm/setup.py                      |  1 -
 esm/tests/test_alphabet.py        |  1 +
 esm/tests/test_load_all.py        |  4 ++-
 esm/tests/test_notebooks.py       |  7 ++--
 esm/tests/test_readme.py          | 51 +++++++++++++++++++--------
 esm/variant-prediction/predict.py | 55 ++++++++++++++++++++---------
 21 files changed, 341 insertions(+), 162 deletions(-)

diff --git a/adopt/__init__.py b/adopt/__init__.py
index 3832206..662e31b 100644
--- a/adopt/__init__.py
+++ b/adopt/__init__.py
@@ -6,7 +6,7 @@
 "@generated"
 from . import constants, utils
 from .data import CheZod
-from .training import DisorderPred
 from .inference import ZScorePred
+from .training import DisorderPred
 from .transformer import MultiHead
 from .version import version as __version__
diff --git a/adopt/constants.py b/adopt/constants.py
index 6524f04..762e149 100644
--- a/adopt/constants.py
+++ b/adopt/constants.py
@@ -25,12 +25,16 @@
     "train_on_cleared_1325_cv_sequence_split": "cleared_sequence_cv",
 }
 
-structure_dict = {"Fully disordered": "FDIS",
-            "Partially disordered": "PDIS",
-            "Structured": "STRUCT",
-            "Flexible loops": "FLEX"}
+structure_dict = {
+    "Fully disordered": "FDIS",
+    "Partially disordered": "PDIS",
+    "Structured": "STRUCT",
+    "Flexible loops": "FLEX",
+}
 
-res_colors = {"FDIS": "#FF3349", 
-        "PDIS": "#FFD433",
-        "STRUCT": "#33C4FF",
-        "FLEX": "##fc9ce7"}
+res_colors = {
+    "FDIS": "#FF3349",
+    "PDIS": "#FFD433",
+    "STRUCT": "#33C4FF",
+    "FLEX": "##fc9ce7",
+}
diff --git a/adopt/data.py b/adopt/data.py
index 42491a9..a7b26ef 100644
--- a/adopt/data.py
+++ b/adopt/data.py
@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import pandas as pd
+
 from adopt import constants, utils
 
 
diff --git a/adopt/inference.py b/adopt/inference.py
index af895f8..9caf5f0 100644
--- a/adopt/inference.py
+++ b/adopt/inference.py
@@ -6,6 +6,7 @@
 import getopt
 import os
 import sys
+
 import numpy as np
 import pandas as pd
 import torch
@@ -26,8 +27,10 @@ def __init__(self, strategy, model_type):
         )
 
     def get_z_score(self, representation):
-        predicted_z_scores = utils.get_onnx_model_preds(self.onnx_model, representation.squeeze().numpy())
-        return np.concatenate(predicted_z_scores)        
+        predicted_z_scores = utils.get_onnx_model_preds(
+            self.onnx_model, representation.squeeze().numpy()
+        )
+        return np.concatenate(predicted_z_scores)
 
     def get_z_score_from_fasta(
         self,
@@ -67,9 +70,9 @@ def get_z_score_from_fasta(
                     .detach()
                 )
                 repr_esm1b = (
-                    torch.load(str(esm1b_repr_path) + "/" + ix + ".pt")["representations"][
-                        33
-                    ]
+                    torch.load(str(esm1b_repr_path) + "/" + ix + ".pt")[
+                        "representations"
+                    ][33]
                     .clone()
                     .cpu()
                     .detach()
diff --git a/adopt/transformer.py b/adopt/transformer.py
index 192c238..45e3b99 100644
--- a/adopt/transformer.py
+++ b/adopt/transformer.py
@@ -3,10 +3,11 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import esm
-import torch
 import sys
 
+import torch
+
+import esm
 from adopt import constants, utils
 
 
@@ -35,8 +36,10 @@ def get_representation(self):
         if self.model_type in constants.model_types:
             results = utils.get_model_and_alphabet(self.model_type, self.data)
             representation = results["representations"][33]
-        elif self.model_type == 'combined':
-            results_esm1b, results_esm1v = utils.get_model_and_alphabet(self.model_type, self.data)
+        elif self.model_type == "combined":
+            results_esm1b, results_esm1v = utils.get_model_and_alphabet(
+                self.model_type, self.data
+            )
             representation_esm1b = results_esm1b["representations"][33]
             representation_esm1v = results_esm1v["representations"][33]
             representation = torch.cat((representation_esm1b, representation_esm1v), -1)
diff --git a/adopt/utils.py b/adopt/utils.py
index f49db42..fea9cba 100644
--- a/adopt/utils.py
+++ b/adopt/utils.py
@@ -10,8 +10,8 @@
 from Bio import SeqIO
 from skl2onnx import convert_sklearn
 from skl2onnx.common.data_types import FloatTensorType
-import esm
 
+import esm
 from adopt import constants
 
 
@@ -125,14 +125,14 @@ def get_residue_class(predicted_z_scores):
     for n, zscore in enumerate(predicted_z_scores):
         residues_dict = {}
         if zscore < 3:
-            residues_dict['label'] = constants.structure_dict["Fully disordered"]
+            residues_dict["label"] = constants.structure_dict["Fully disordered"]
         elif 3 <= zscore < 8:
-            residues_dict['label'] = constants.structure_dict["Partially disordered"]
+            residues_dict["label"] = constants.structure_dict["Partially disordered"]
         elif zscore >= 11:
-            residues_dict['label'] = constants.structure_dict["Structured"]
+            residues_dict["label"] = constants.structure_dict["Structured"]
         else:
-            residues_dict['label'] = constants.structure_dict["Flexible loops"]
-            
+            residues_dict["label"] = constants.structure_dict["Flexible loops"]
+
         residues_dict["start"] = n
         residues_dict["end"] = n + 1
         residues_state.append(residues_dict)
diff --git a/esm/esm/__init__.py b/esm/esm/__init__.py
index 2b69f51..1b880c8 100644
--- a/esm/esm/__init__.py
+++ b/esm/esm/__init__.py
@@ -3,8 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .version import version as __version__  # noqa
-
-from .data import Alphabet, BatchConverter, FastaBatchedDataset  # noqa
-from .model import ProteinBertModel, MSATransformer  # noqa
 from . import pretrained  # noqa
+from .data import Alphabet, BatchConverter, FastaBatchedDataset  # noqa
+from .model import MSATransformer, ProteinBertModel  # noqa
+from .version import version as __version__  # noqa
diff --git a/esm/esm/axial_attention.py b/esm/esm/axial_attention.py
index f95f287..677869e 100644
--- a/esm/esm/axial_attention.py
+++ b/esm/esm/axial_attention.py
@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import math
+
 import torch
 import torch.nn as nn
 
@@ -52,7 +53,9 @@ def _batched_forward(
                 x[start : start + max_rows],
                 scaling,
                 self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask[:, start : start + max_rows]
+                self_attn_padding_mask=self_attn_padding_mask[
+                    :, start : start + max_rows
+                ]
                 if self_attn_padding_mask is not None
                 else None,
             )
@@ -62,7 +65,9 @@ def _batched_forward(
 
         outputs = []
         for start in range(0, num_rows, max_rows):
-            output = self.compute_attention_update(x[start : start + max_rows], attn_probs)
+            output = self.compute_attention_update(
+                x[start : start + max_rows], attn_probs
+            )
             outputs.append(output)
 
         output = torch.cat(outputs, 0)
@@ -76,13 +81,19 @@ def compute_attention_weights(
         self_attn_padding_mask=None,
     ):
         num_rows, num_cols, batch_size, embed_dim = x.size()
-        q = self.q_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
-        k = self.k_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
+        q = self.q_proj(x).view(
+            num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+        )
+        k = self.k_proj(x).view(
+            num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+        )
         q *= scaling
         if self_attn_padding_mask is not None:
             # Zero out any padded aligned positions - this is important since
             # we take a sum across the alignment axis.
-            q *= 1 - self_attn_padding_mask.permute(1, 2, 0).unsqueeze(3).unsqueeze(4).to(q)
+            q *= 1 - self_attn_padding_mask.permute(1, 2, 0).unsqueeze(3).unsqueeze(
+                4
+            ).to(q)
 
         attn_weights = torch.einsum(f"rinhd,rjnhd->{self.attn_shape}", q, k)
 
@@ -104,7 +115,9 @@ def compute_attention_update(
         attn_probs,
     ):
         num_rows, num_cols, batch_size, embed_dim = x.size()
-        v = self.v_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
+        v = self.v_proj(x).view(
+            num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+        )
         context = torch.einsum(f"{self.attn_shape},rjnhd->rinhd", attn_probs, v)
         context = context.contiguous().view(num_rows, num_cols, batch_size, embed_dim)
         output = self.out_proj(context)
@@ -117,7 +130,9 @@ def forward(
         self_attn_padding_mask=None,
     ):
         num_rows, num_cols, batch_size, embed_dim = x.size()
-        if (num_rows * num_cols > self.max_tokens_per_msa) and not torch.is_grad_enabled():
+        if (
+            num_rows * num_cols > self.max_tokens_per_msa
+        ) and not torch.is_grad_enabled():
             return self._batched_forward(x, self_attn_mask, self_attn_padding_mask)
         else:
             scaling = self.align_scaling(x)
@@ -169,7 +184,9 @@ def _batched_forward(
             output, attn = self(
                 x[:, start : start + max_cols],
                 self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask[:, :, start : start + max_cols]
+                self_attn_padding_mask=self_attn_padding_mask[
+                    :, :, start : start + max_cols
+                ]
                 if self_attn_padding_mask is not None
                 else None,
             )
@@ -199,9 +216,15 @@ def compute_attention_update(
             )
             output = self.out_proj(self.v_proj(x))
         else:
-            q = self.q_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
-            k = self.k_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
-            v = self.v_proj(x).view(num_rows, num_cols, batch_size, self.num_heads, self.head_dim)
+            q = self.q_proj(x).view(
+                num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+            )
+            k = self.k_proj(x).view(
+                num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+            )
+            v = self.v_proj(x).view(
+                num_rows, num_cols, batch_size, self.num_heads, self.head_dim
+            )
             q *= self.scaling
 
             attn_weights = torch.einsum("icnhd,jcnhd->hcnij", q, k)
@@ -217,7 +240,9 @@ def compute_attention_update(
             attn_probs = attn_weights.softmax(-1)
             attn_probs = self.dropout_module(attn_probs)
             context = torch.einsum("hcnij,jcnhd->icnhd", attn_probs, v)
-            context = context.contiguous().view(num_rows, num_cols, batch_size, embed_dim)
+            context = context.contiguous().view(
+                num_rows, num_cols, batch_size, embed_dim
+            )
             output = self.out_proj(context)
         return output, attn_probs
 
@@ -229,11 +254,15 @@ def forward(
     ):
         num_rows, num_cols, batch_size, embed_dim = x.size()
         # if False and num_rows * num_cols > 2 ** 14 and not torch.is_grad_enabled():
-        if (num_rows * num_cols) > self.max_tokens_per_msa and not torch.is_grad_enabled():
+        if (
+            num_rows * num_cols
+        ) > self.max_tokens_per_msa and not torch.is_grad_enabled():
             return self._batched_forward(
                 x,
                 self_attn_mask,
                 self_attn_padding_mask,
             )
         else:
-            return self.compute_attention_update(x, self_attn_mask, self_attn_padding_mask)
+            return self.compute_attention_update(
+                x, self_attn_mask, self_attn_padding_mask
+            )
diff --git a/esm/esm/data.py b/esm/esm/data.py
index bd349fe..2c7d6a8 100644
--- a/esm/esm/data.py
+++ b/esm/esm/data.py
@@ -5,12 +5,14 @@
 
 import itertools
 import os
-from typing import Sequence, Tuple, List, Union
 import pickle
 import re
 import shutil
-import torch
 from pathlib import Path
+from typing import List, Sequence, Tuple, Union
+
+import torch
+
 from .constants import proteinseq_toks
 
 RawMSA = Sequence[Tuple[str, str]]
@@ -118,7 +120,7 @@ def __init__(
         self.cls_idx = self.get_idx("<cls>")
         self.mask_idx = self.get_idx("<mask>")
         self.eos_idx = self.get_idx("<eos>")
-        self.all_special_tokens = ['<eos>', '<unk>', '<pad>', '<cls>', '<mask>']
+        self.all_special_tokens = ["<eos>", "<unk>", "<pad>", "<cls>", "<mask>"]
         self.unique_no_split_tokens = self.all_toks
 
     def __len__(self):
@@ -164,7 +166,9 @@ def from_architecture(cls, name: str) -> "Alphabet":
             use_msa = True
         else:
             raise ValueError("Unknown architecture selected")
-        return cls(standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa)
+        return cls(
+            standard_toks, prepend_toks, append_toks, prepend_bos, append_eos, use_msa
+        )
 
     def _tokenize(self, text) -> str:
         return text.split()
@@ -260,7 +264,9 @@ def __call__(self, raw_batch: Sequence[Tuple[str, str]]):
         tokens = torch.empty(
             (
                 batch_size,
-                max_len + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),
+                max_len
+                + int(self.alphabet.prepend_bos)
+                + int(self.alphabet.append_eos),
             ),
             dtype=torch.int64,
         )
@@ -282,7 +288,9 @@ def __call__(self, raw_batch: Sequence[Tuple[str, str]]):
                 + int(self.alphabet.prepend_bos),
             ] = seq
             if self.alphabet.append_eos:
-                tokens[i, len(seq_encoded) + int(self.alphabet.prepend_bos)] = self.alphabet.eos_idx
+                tokens[
+                    i, len(seq_encoded) + int(self.alphabet.prepend_bos)
+                ] = self.alphabet.eos_idx
 
         return labels, strs, tokens
 
@@ -303,7 +311,9 @@ def __call__(self, inputs: Union[Sequence[RawMSA], RawMSA]):
             (
                 batch_size,
                 max_alignments,
-                max_seqlen + int(self.alphabet.prepend_bos) + int(self.alphabet.append_eos),
+                max_seqlen
+                + int(self.alphabet.prepend_bos)
+                + int(self.alphabet.append_eos),
             ),
             dtype=torch.int64,
         )
@@ -465,7 +475,9 @@ def download(self):
 
         for url, tar_filename, filename, md5_hash in self.file_list:
             download_path = os.path.join(self.base_path, tar_filename)
-            download_url(url=url, root=self.base_path, filename=tar_filename, md5=md5_hash)
+            download_url(
+                url=url, root=self.base_path, filename=tar_filename, md5=md5_hash
+            )
             shutil.unpack_archive(download_path, self.base_path)
 
     def __getitem__(self, idx):
diff --git a/esm/esm/model.py b/esm/esm/model.py
index c616eed..308a004 100644
--- a/esm/esm/model.py
+++ b/esm/esm/model.py
@@ -9,17 +9,11 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .modules import (
-    TransformerLayer,
-    AxialTransformerLayer,
-    LearnedPositionalEmbedding,
-    SinusoidalPositionalEmbedding,
-    RobertaLMHead,
-    ESM1bLayerNorm,
-    ContactPredictionHead,
-)
-
-from .axial_attention import RowSelfAttention, ColumnSelfAttention
+from .axial_attention import ColumnSelfAttention, RowSelfAttention
+from .modules import (AxialTransformerLayer, ContactPredictionHead,
+                      ESM1bLayerNorm, LearnedPositionalEmbedding,
+                      RobertaLMHead, SinusoidalPositionalEmbedding,
+                      TransformerLayer)
 
 
 class ProteinBertModel(nn.Module):
@@ -29,7 +23,11 @@ def add_args(cls, parser):
             "--num_layers", default=36, type=int, metavar="N", help="number of layers"
         )
         parser.add_argument(
-            "--embed_dim", default=1280, type=int, metavar="N", help="embedding dimension"
+            "--embed_dim",
+            default=1280,
+            type=int,
+            metavar="N",
+            help="embedding dimension",
         )
         parser.add_argument(
             "--logit_bias", action="store_true", help="whether to apply bias to logits"
@@ -110,13 +108,19 @@ def _init_submodules_esm1b(self):
     def _init_submodules_esm1(self):
         self._init_submodules_common()
         self.embed_scale = math.sqrt(self.args.embed_dim)
-        self.embed_positions = SinusoidalPositionalEmbedding(self.args.embed_dim, self.padding_idx)
-        self.embed_out = nn.Parameter(torch.zeros((self.alphabet_size, self.args.embed_dim)))
+        self.embed_positions = SinusoidalPositionalEmbedding(
+            self.args.embed_dim, self.padding_idx
+        )
+        self.embed_out = nn.Parameter(
+            torch.zeros((self.alphabet_size, self.args.embed_dim))
+        )
         self.embed_out_bias = None
         if self.args.final_bias:
             self.embed_out_bias = nn.Parameter(torch.zeros(self.alphabet_size))
 
-    def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
+    def forward(
+        self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False
+    ):
         if return_contacts:
             need_head_weights = True
 
@@ -130,7 +134,9 @@ def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contac
             # x: B x T x C
             mask_ratio_train = 0.15 * 0.8
             src_lengths = (~padding_mask).sum(-1)
-            mask_ratio_observed = (tokens == self.mask_idx).sum(-1).float() / src_lengths
+            mask_ratio_observed = (tokens == self.mask_idx).sum(
+                -1
+            ).float() / src_lengths
             x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]
 
         x = x + self.embed_positions(tokens)
@@ -157,7 +163,9 @@ def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contac
 
         for layer_idx, layer in enumerate(self.layers):
             x, attn = layer(
-                x, self_attn_padding_mask=padding_mask, need_head_weights=need_head_weights
+                x,
+                self_attn_padding_mask=padding_mask,
+                need_head_weights=need_head_weights,
             )
             if (layer_idx + 1) in repr_layers:
                 hidden_representations[layer_idx + 1] = x.transpose(0, 1)
@@ -186,7 +194,9 @@ def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contac
                 attentions = attentions[..., :-1]
             if padding_mask is not None:
                 attention_mask = 1 - padding_mask.type_as(attentions)
-                attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2)
+                attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(
+                    2
+                )
                 attentions = attentions * attention_mask[:, None, None, :, :]
             result["attentions"] = attentions
             if return_contacts:
@@ -328,7 +338,9 @@ def __init__(self, args, alphabet):
             weight=self.embed_tokens.weight,
         )
 
-    def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False):
+    def forward(
+        self, tokens, repr_layers=[], need_head_weights=False, return_contacts=False
+    ):
         if return_contacts:
             need_head_weights = True
 
@@ -339,7 +351,9 @@ def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contac
             padding_mask = None
 
         x = self.embed_tokens(tokens)
-        x += self.embed_positions(tokens.view(batch_size * num_alignments, seqlen)).view(x.size())
+        x += self.embed_positions(
+            tokens.view(batch_size * num_alignments, seqlen)
+        ).view(x.size())
         if self.msa_position_embedding is not None:
             if x.size(1) > 1024:
                 raise RuntimeError(
diff --git a/esm/esm/modules.py b/esm/esm/modules.py
index 7e4d33f..f099f09 100644
--- a/esm/esm/modules.py
+++ b/esm/esm/modules.py
@@ -10,8 +10,8 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .multihead_attention import MultiheadAttention  # noqa
 from .axial_attention import ColumnSelfAttention, RowSelfAttention
+from .multihead_attention import MultiheadAttention  # noqa
 
 
 def gelu(x):
@@ -45,7 +45,9 @@ class ESM1LayerNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-12, affine=True):
         """Construct a layernorm layer in the TF style (eps inside the sqrt)."""
         super().__init__()
-        self.hidden_size = (hidden_size,) if isinstance(hidden_size, int) else tuple(hidden_size)
+        self.hidden_size = (
+            (hidden_size,) if isinstance(hidden_size, int) else tuple(hidden_size)
+        )
         self.eps = eps
         self.affine = bool(affine)
         if self.affine:
@@ -116,7 +118,11 @@ def _init_submodules(self, add_bias_kv, use_esm1b_layer_norm):
         self.final_layer_norm = BertLayerNorm(self.embed_dim)
 
     def forward(
-        self, x, self_attn_mask=None, self_attn_padding_mask=None, need_head_weights=False
+        self,
+        x,
+        self_attn_mask=None,
+        self_attn_padding_mask=None,
+        need_head_weights=False,
     ):
         residual = x
         x = self.self_attn_layer_norm(x)
@@ -243,7 +249,9 @@ def forward(self, input: torch.Tensor):
                 f" sequence length of {self.max_positions}"
             )
         mask = input.ne(self.padding_idx).int()
-        positions = (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + self.padding_idx
+        positions = (
+            torch.cumsum(mask, dim=1).type_as(mask) * mask
+        ).long() + self.padding_idx
         return F.embedding(
             positions,
             self.weight,
@@ -271,11 +279,17 @@ def forward(self, x):
         self.weights = self.weights.type_as(self._float_tensor)
 
         positions = self.make_positions(x)
-        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
+        return (
+            self.weights.index_select(0, positions.view(-1))
+            .view(bsz, seq_len, -1)
+            .detach()
+        )
 
     def make_positions(self, x):
         mask = x.ne(self.padding_idx)
-        range_buf = torch.arange(x.size(1), device=x.device).expand_as(x) + self.padding_idx + 1
+        range_buf = (
+            torch.arange(x.size(1), device=x.device).expand_as(x) + self.padding_idx + 1
+        )
         positions = range_buf.expand_as(x)
         return positions * mask.long() + self.padding_idx * (1 - mask.long())
 
@@ -283,8 +297,12 @@ def get_embedding(self, num_embeddings):
         half_dim = self.embed_dim // 2
         emb = math.log(10000) / (half_dim - 1)
         emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
-        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(
+            1
+        ) * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(
+            num_embeddings, -1
+        )
         if self.embed_dim % 2 == 1:
             # zero pad
             emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
@@ -328,7 +346,9 @@ def __init__(
         self.prepend_bos = prepend_bos
         self.append_eos = append_eos
         if append_eos and eos_idx is None:
-            raise ValueError("Using an alphabet with eos token, but no eos token was passed in.")
+            raise ValueError(
+                "Using an alphabet with eos token, but no eos token was passed in."
+            )
         self.eos_idx = eos_idx
         self.regression = nn.Linear(in_features, 1, bias)
         self.activation = nn.Sigmoid()
diff --git a/esm/esm/multihead_attention.py b/esm/esm/multihead_attention.py
index 5b8dd2b..cc55565 100644
--- a/esm/esm/multihead_attention.py
+++ b/esm/esm/multihead_attention.py
@@ -4,6 +4,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import math
+import uuid
 from typing import Dict, Optional, Tuple
 
 import torch
@@ -11,8 +12,6 @@
 from torch import Tensor, nn
 from torch.nn import Parameter
 
-import uuid
-
 
 def utils_softmax(x, dim: int, onnx_trace: bool = False):
     if onnx_trace:
@@ -272,11 +271,23 @@ def forward(
                     dim=1,
                 )
 
-        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        q = (
+            q.contiguous()
+            .view(tgt_len, bsz * self.num_heads, self.head_dim)
+            .transpose(0, 1)
+        )
         if k is not None:
-            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+            k = (
+                k.contiguous()
+                .view(-1, bsz * self.num_heads, self.head_dim)
+                .transpose(0, 1)
+            )
         if v is not None:
-            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+            v = (
+                v.contiguous()
+                .view(-1, bsz * self.num_heads, self.head_dim)
+                .transpose(0, 1)
+            )
 
         if saved_state is not None:
             # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
@@ -341,13 +352,17 @@ def forward(
                 key_padding_mask = torch.cat(
                     [
                         key_padding_mask,
-                        torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask),
+                        torch.zeros(key_padding_mask.size(0), 1).type_as(
+                            key_padding_mask
+                        ),
                     ],
                     dim=1,
                 )
 
         attn_weights = torch.bmm(q, k.transpose(1, 2))
-        attn_weights = MultiheadAttention.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+        attn_weights = MultiheadAttention.apply_sparse_mask(
+            attn_weights, tgt_len, src_len, bsz
+        )
 
         assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
 
@@ -368,7 +383,9 @@ def forward(
         if before_softmax:
             return attn_weights, v
 
-        attn_weights_float = utils_softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace)
+        attn_weights_float = utils_softmax(
+            attn_weights, dim=-1, onnx_trace=self.onnx_trace
+        )
         attn_weights = attn_weights_float.type_as(attn_weights)
         attn_probs = F.dropout(
             attn_weights_float.type_as(attn_weights),
@@ -427,14 +444,18 @@ def _append_prev_key_padding_mask(
                 (batch_size, src_len - key_padding_mask.size(1)),
                 device=key_padding_mask.device,
             )
-            new_key_padding_mask = torch.cat([filler.float(), key_padding_mask.float()], dim=1)
+            new_key_padding_mask = torch.cat(
+                [filler.float(), key_padding_mask.float()], dim=1
+            )
         else:
             new_key_padding_mask = prev_key_padding_mask
         return new_key_padding_mask
 
     @torch.jit.export
     def reorder_incremental_state(
-        self, incremental_state: Dict[str, Dict[str, Optional[Tensor]]], new_order: Tensor
+        self,
+        incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
+        new_order: Tensor,
     ):
         """Reorder buffered internal state (for incremental generation)."""
         input_buffer = self._get_input_buffer(incremental_state)
@@ -442,9 +463,9 @@ def reorder_incremental_state(
             for k in input_buffer.keys():
                 input_buffer_k = input_buffer[k]
                 if input_buffer_k is not None:
-                    if self.encoder_decoder_attention and input_buffer_k.size(0) == new_order.size(
+                    if self.encoder_decoder_attention and input_buffer_k.size(
                         0
-                    ):
+                    ) == new_order.size(0):
                         break
                     input_buffer[k] = input_buffer_k.index_select(0, new_order)
             incremental_state = self._set_input_buffer(incremental_state, input_buffer)
@@ -488,7 +509,9 @@ def upgrade_state_dict_named(self, state_dict, name):
                 if k_bias in state_dict.keys():
                     dim = int(state_dict[k].shape[0] / 3)
                     items_to_add[prefix + "q_proj.bias"] = state_dict[k_bias][:dim]
-                    items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][dim : 2 * dim]
+                    items_to_add[prefix + "k_proj.bias"] = state_dict[k_bias][
+                        dim : 2 * dim
+                    ]
                     items_to_add[prefix + "v_proj.bias"] = state_dict[k_bias][2 * dim :]
 
                     keys_to_remove.append(prefix + "in_proj_bias")
diff --git a/esm/esm/pretrained.py b/esm/esm/pretrained.py
index 1eec549..3e5111c 100644
--- a/esm/esm/pretrained.py
+++ b/esm/esm/pretrained.py
@@ -3,11 +3,13 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from argparse import Namespace
-import warnings
 import urllib
+import warnings
+from argparse import Namespace
 from pathlib import Path
+
 import torch
+
 import esm
 
 
@@ -26,7 +28,9 @@ def load_model_and_alphabet(model_name):
 
 def load_hub_workaround(url):
     try:
-        data = torch.hub.load_state_dict_from_url(url, progress=False, map_location="cpu")
+        data = torch.hub.load_state_dict_from_url(
+            url, progress=False, map_location="cpu"
+        )
     except RuntimeError:
         # Pytorch version issue - see https://github.com/pytorch/pytorch/issues/43106
         fn = Path(url).name
@@ -35,7 +39,9 @@ def load_hub_workaround(url):
             map_location="cpu",
         )
     except urllib.error.HTTPError as e:
-        raise Exception(f"Could not load {url}, check if you specified a correct model name?")
+        raise Exception(
+            f"Could not load {url}, check if you specified a correct model name?"
+        )
     return data
 
 
@@ -56,12 +62,14 @@ def load_model_and_alphabet_hub(model_name):
 
 
 def load_model_and_alphabet_local(model_location):
-    """ Load from local path. The regression weights need to be co-located """
+    """Load from local path. The regression weights need to be co-located"""
     model_location = Path(model_location)
     model_data = torch.load(str(model_location), map_location="cpu")
     model_name = model_location.stem
     if _has_regression_weights(model_name):
-        regression_location = str(model_location.with_suffix("")) + "-contact-regression.pt"
+        regression_location = (
+            str(model_location.with_suffix("")) + "-contact-regression.pt"
+        )
         regression_data = torch.load(regression_location, map_location="cpu")
     else:
         regression_data = None
@@ -69,8 +77,10 @@ def load_model_and_alphabet_local(model_location):
 
 
 def has_emb_layer_norm_before(model_state):
-    """ Determine whether layer norm needs to be applied before the encoder """
-    return any(k.startswith("emb_layer_norm_before") for k, param in model_state.items())
+    """Determine whether layer norm needs to be applied before the encoder"""
+    return any(
+        k.startswith("emb_layer_norm_before") for k, param in model_state.items()
+    )
 
 
 def load_model_and_alphabet_core(model_data, regression_data=None):
@@ -87,7 +97,9 @@ def load_model_and_alphabet_core(model_data, regression_data=None):
             s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s
         )
         model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()}
-        model_state = {prs1(prs2(arg[0])): arg[1] for arg in model_data["model"].items()}
+        model_state = {
+            prs1(prs2(arg[0])): arg[1] for arg in model_data["model"].items()
+        }
         model_state["embed_tokens.weight"][alphabet.mask_idx].zero_()  # For token drop
         model_args["emb_layer_norm_before"] = has_emb_layer_norm_before(model_state)
         model_type = esm.ProteinBertModel
@@ -108,12 +120,20 @@ def load_model_and_alphabet_core(model_data, regression_data=None):
         prs2 = lambda s: "".join(
             s.split("sentence_encoder.")[1:] if "sentence_encoder" in s else s
         )
-        prs3 = lambda s: s.replace("row", "column") if "row" in s else s.replace("column", "row")
+        prs3 = (
+            lambda s: s.replace("row", "column")
+            if "row" in s
+            else s.replace("column", "row")
+        )
         model_args = {pra(arg[0]): arg[1] for arg in vars(model_data["args"]).items()}
-        model_state = {prs1(prs2(prs3(arg[0]))): arg[1] for arg in model_data["model"].items()}
+        model_state = {
+            prs1(prs2(prs3(arg[0]))): arg[1] for arg in model_data["model"].items()
+        }
         if model_args.get("embed_positions_msa", False):
             emb_dim = model_state["msa_position_embedding"].size(-1)
-            model_args["embed_positions_msa_dim"] = emb_dim  # initial release, bug: emb_dim==1
+            model_args[
+                "embed_positions_msa_dim"
+            ] = emb_dim  # initial release, bug: emb_dim==1
 
         model_type = esm.MSATransformer
 
@@ -129,7 +149,10 @@ def load_model_and_alphabet_core(model_data, regression_data=None):
     found_keys = set(model_state.keys())
 
     if regression_data is None:
-        expected_missing = {"contact_head.regression.weight", "contact_head.regression.bias"}
+        expected_missing = {
+            "contact_head.regression.weight",
+            "contact_head.regression.bias",
+        }
         error_msgs = []
         missing = (expected_keys - found_keys) - expected_missing
         if missing:
diff --git a/esm/extract.py b/esm/extract.py
index 0b5dc8b..a8a3903 100644
--- a/esm/extract.py
+++ b/esm/extract.py
@@ -33,7 +33,9 @@ def create_parser():
         help="output directory for extracted representations",
     )
 
-    parser.add_argument("--toks_per_batch", type=int, default=4096, help="maximum batch size")
+    parser.add_argument(
+        "--toks_per_batch", type=int, default=4096, help="maximum batch size"
+    )
     parser.add_argument(
         "--repr_layers",
         type=int,
@@ -55,7 +57,9 @@ def create_parser():
         help="Truncate sequences longer than 1024 to match the training setup",
     )
 
-    parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
+    parser.add_argument(
+        "--nogpu", action="store_true", help="Do not use GPU even if available"
+    )
     return parser
 
 
@@ -76,8 +80,12 @@ def main(args):
     args.output_dir.mkdir(parents=True, exist_ok=True)
     return_contacts = "contacts" in args.include
 
-    assert all(-(model.num_layers + 1) <= i <= model.num_layers for i in args.repr_layers)
-    repr_layers = [(i + model.num_layers + 1) % (model.num_layers + 1) for i in args.repr_layers]
+    assert all(
+        -(model.num_layers + 1) <= i <= model.num_layers for i in args.repr_layers
+    )
+    repr_layers = [
+        (i + model.num_layers + 1) % (model.num_layers + 1) for i in args.repr_layers
+    ]
 
     with torch.no_grad():
         for batch_idx, (labels, strs, toks) in enumerate(data_loader):
@@ -122,7 +130,9 @@ def main(args):
                         layer: t[i, 0].clone() for layer, t in representations.items()
                     }
                 if return_contacts:
-                    result["contacts"] = contacts[i, : len(strs[i]), : len(strs[i])].clone()
+                    result["contacts"] = contacts[
+                        i, : len(strs[i]), : len(strs[i])
+                    ].clone()
 
                 torch.save(
                     result,
diff --git a/esm/hubconf.py b/esm/hubconf.py
index 4601b6e..95368e9 100644
--- a/esm/hubconf.py
+++ b/esm/hubconf.py
@@ -5,19 +5,10 @@
 
 dependencies = ["torch"]
 
-from esm.pretrained import (
-    esm1_t34_670M_UR50S,
-    esm1_t34_670M_UR50D,
-    esm1_t34_670M_UR100,
-    esm1_t12_85M_UR50S,
-    esm1_t6_43M_UR50S,
-    esm1b_t33_650M_UR50S,
-    esm_msa1_t12_100M_UR50S,
-    esm_msa1b_t12_100M_UR50S,
-    esm1v_t33_650M_UR90S,
-    esm1v_t33_650M_UR90S_1,
-    esm1v_t33_650M_UR90S_2,
-    esm1v_t33_650M_UR90S_3,
-    esm1v_t33_650M_UR90S_4,
-    esm1v_t33_650M_UR90S_5,
-)
+from esm.pretrained import (esm1_t6_43M_UR50S, esm1_t12_85M_UR50S,
+                            esm1_t34_670M_UR50D, esm1_t34_670M_UR50S,
+                            esm1_t34_670M_UR100, esm1b_t33_650M_UR50S,
+                            esm1v_t33_650M_UR90S, esm1v_t33_650M_UR90S_1,
+                            esm1v_t33_650M_UR90S_2, esm1v_t33_650M_UR90S_3,
+                            esm1v_t33_650M_UR90S_4, esm1v_t33_650M_UR90S_5,
+                            esm_msa1_t12_100M_UR50S, esm_msa1b_t12_100M_UR50S)
diff --git a/esm/setup.py b/esm/setup.py
index 0223563..c1723b4 100644
--- a/esm/setup.py
+++ b/esm/setup.py
@@ -5,7 +5,6 @@
 
 from setuptools import setup
 
-
 with open("esm/version.py") as infile:
     exec(infile.read())
 
diff --git a/esm/tests/test_alphabet.py b/esm/tests/test_alphabet.py
index e3bfaf8..a3545ec 100644
--- a/esm/tests/test_alphabet.py
+++ b/esm/tests/test_alphabet.py
@@ -35,6 +35,7 @@ def test_esm1v_alphabet():
 
 def test_esm1_msa1b_alphabet():
     import torch
+
     import esm
 
     # Load ESM-1b model
diff --git a/esm/tests/test_load_all.py b/esm/tests/test_load_all.py
index dc51234..d896029 100644
--- a/esm/tests/test_load_all.py
+++ b/esm/tests/test_load_all.py
@@ -3,9 +3,11 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import pytest
 from pathlib import Path
+
+import pytest
 import torch
+
 import esm
 
 # Directly from hubconf.py
diff --git a/esm/tests/test_notebooks.py b/esm/tests/test_notebooks.py
index a95da23..fac8401 100644
--- a/esm/tests/test_notebooks.py
+++ b/esm/tests/test_notebooks.py
@@ -5,9 +5,10 @@
 
 import os
 import subprocess
-import pytest
 from pathlib import Path
 
+import pytest
+
 notebook_dir = Path(__file__).parents[1] / "examples"
 notebook_fns = notebook_dir.glob("*.ipynb")
 
@@ -39,7 +40,7 @@ def run_multiple(cmds):
 
 
 def do_setup(nb_name):
-    """ Do any setup work; see intro of the notebook """
+    """Do any setup work; see intro of the notebook"""
     if nb_name == "sup_variant_prediction":
         cmds = """
         curl -O https://dl.fbaipublicfiles.com/fair-esm/examples/P62593_reprs.tar.gz
@@ -53,7 +54,7 @@ def do_setup(nb_name):
 
 @pytest.mark.parametrize("nb_fn", list(notebook_fns))
 def test_run_notebook(nb_fn: Path, tmp_path: Path):
-    """ Simply make sure the notebooks run from a-z """
+    """Simply make sure the notebooks run from a-z"""
     py_fn = tmp_path / (nb_fn.stem + ".py")
     print(py_fn)
     convert_notebook_to_py(nb_fn, py_fn)
diff --git a/esm/tests/test_readme.py b/esm/tests/test_readme.py
index 99f6ee9..a11fcf8 100644
--- a/esm/tests/test_readme.py
+++ b/esm/tests/test_readme.py
@@ -3,24 +3,29 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-import sys
+import shutil
 import subprocess
+import sys
 import tempfile
-import requests
-import shutil
 from pathlib import Path
+
+import requests
 import torch
+
 import esm
 
 
 def test_readme_1():
     import torch
 
-    model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm1b_t33_650M_UR50S")
+    model, alphabet = torch.hub.load(
+        "facebookresearch/esm:main", "esm1b_t33_650M_UR50S"
+    )
 
 
 def test_readme_2():
     import torch
+
     import esm
 
     # Load ESM-1b model
@@ -29,16 +34,19 @@ def test_readme_2():
 
     # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
     data = [
-        ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
-        ("protein2", "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"),
         (
-            "protein2 with mask",
-            "KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE"
+            "protein1",
+            "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
         ),
         (
-            "protein3",
-            "K A <mask> I S Q"
+            "protein2",
+            "KALTARQQEVFDLIRDHISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
+        ),
+        (
+            "protein2 with mask",
+            "KALTARQQEVFDLIRD<mask>ISQTGMPPTRAEIAQRLGFRSPNAAEEHLKALARKGVIEIVSGASRGIRLLQEE",
         ),
+        ("protein3", "K A <mask> I S Q"),
     ]
     batch_labels, batch_strs, batch_tokens = batch_converter(data)
 
@@ -51,7 +59,9 @@ def test_readme_2():
     # NOTE: token 0 is always a beginning-of-sequence token, so the first residue is token 1.
     sequence_representations = []
     for i, (_, seq) in enumerate(data):
-        sequence_representations.append(token_representations[i, 1 : len(seq) + 1].mean(0))
+        sequence_representations.append(
+            token_representations[i, 1 : len(seq) + 1].mean(0)
+        )
 
     # Look at the unsupervised self-attention map contact predictions
     import matplotlib.pyplot as plt
@@ -90,7 +100,9 @@ def assert_pt_file_equal(f, fref):
     which_layers = a["representations"].keys() & b["representations"].keys()
     assert which_layers, "Expected at least one layer appearing in both dumps"
     for layer in which_layers:
-        assert torch.allclose(a["representations"][layer], b["representations"][layer], atol=1e-3)
+        assert torch.allclose(
+            a["representations"][layer], b["representations"][layer], atol=1e-3
+        )
 
 
 def confirm_all_tensors_equal(local_dir: str, ref_dir: str) -> None:
@@ -113,9 +125,18 @@ def _test_msa_transformer(model, alphabet):
     batch_converter = alphabet.get_batch_converter()
     # Make an "MSA" of size 3
     data = [
-        ("protein1", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
-        ("protein2", "MHTVRQSRLKSIVRILEMSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG"),
-        ("protein3", "MHTVRQSRLKSIVRILEMSKEPVSGAQL---LSVSRQVIVQDIAYLRSLGYNIVAT----VLAGG"),
+        (
+            "protein1",
+            "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
+        ),
+        (
+            "protein2",
+            "MHTVRQSRLKSIVRILEMSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
+        ),
+        (
+            "protein3",
+            "MHTVRQSRLKSIVRILEMSKEPVSGAQL---LSVSRQVIVQDIAYLRSLGYNIVAT----VLAGG",
+        ),
     ]
     batch_labels, batch_strs, batch_tokens = batch_converter(data)
 
diff --git a/esm/variant-prediction/predict.py b/esm/variant-prediction/predict.py
index 22f239f..b0ba7a5 100644
--- a/esm/variant-prediction/predict.py
+++ b/esm/variant-prediction/predict.py
@@ -4,20 +4,21 @@
 # LICENSE file in the root directory of this source tree.
 
 import argparse
+import itertools
 import pathlib
+from typing import List, Tuple
 
-import torch
-
-from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretrained, MSATransformer
 import pandas as pd
-from tqdm import tqdm
+import torch
 from Bio import SeqIO
-import itertools
-from typing import List, Tuple
+from tqdm import tqdm
+
+from esm import (Alphabet, FastaBatchedDataset, MSATransformer,
+                 ProteinBertModel, pretrained)
 
 
 def read_msa(filename: str, nseq: int) -> List[Tuple[str, str]]:
-    """ Reads the first nseq sequences from an MSA file, automatically removes insertions."""
+    """Reads the first nseq sequences from an MSA file, automatically removes insertions."""
 
     msa = [
         (record.description, str(record.seq))
@@ -85,13 +86,17 @@ def create_parser():
         help="number of sequences to randomly sample from the MSA"
     )
     # fmt: on
-    parser.add_argument("--nogpu", action="store_true", help="Do not use GPU even if available")
+    parser.add_argument(
+        "--nogpu", action="store_true", help="Do not use GPU even if available"
+    )
     return parser
 
 
 def label_row(row, sequence, token_probs, alphabet, offset_idx):
     wt, idx, mt = row[0], int(row[1:-1]) - offset_idx, row[-1]
-    assert sequence[idx] == wt, "The listed wildtype does not match the provided sequence"
+    assert (
+        sequence[idx] == wt
+    ), "The listed wildtype does not match the provided sequence"
 
     wt_encoded, mt_encoded = alphabet.get_idx(wt), alphabet.get_idx(mt)
 
@@ -102,7 +107,9 @@ def label_row(row, sequence, token_probs, alphabet, offset_idx):
 
 def compute_pppl(row, sequence, model, alphabet, offset_idx):
     wt, idx, mt = row[0], int(row[1:-1]) - offset_idx, row[-1]
-    assert sequence[idx] == wt, "The listed wildtype does not match the provided sequence"
+    assert (
+        sequence[idx] == wt
+    ), "The listed wildtype does not match the provided sequence"
 
     # modify the sequence
     sequence = sequence[:idx] + mt + sequence[(idx + 1) :]
@@ -124,8 +131,12 @@ def compute_pppl(row, sequence, model, alphabet, offset_idx):
         batch_tokens_masked = batch_tokens.clone()
         batch_tokens_masked[0, i] = alphabet.mask_idx
         with torch.no_grad():
-            token_probs = torch.log_softmax(model(batch_tokens_masked.cuda())["logits"], dim=-1)
-        log_probs.append(token_probs[0, i, alphabet.get_idx(sequence[i])].item())  # vocab size
+            token_probs = torch.log_softmax(
+                model(batch_tokens_masked.cuda())["logits"], dim=-1
+            )
+        log_probs.append(
+            token_probs[0, i, alphabet.get_idx(sequence[i])].item()
+        )  # vocab size
     return sum(log_probs)
 
 
@@ -154,7 +165,9 @@ def main(args):
             all_token_probs = []
             for i in tqdm(range(batch_tokens.size(2))):
                 batch_tokens_masked = batch_tokens.clone()
-                batch_tokens_masked[0, 0, i] = alphabet.mask_idx  # mask out first sequence
+                batch_tokens_masked[
+                    0, 0, i
+                ] = alphabet.mask_idx  # mask out first sequence
                 with torch.no_grad():
                     token_probs = torch.log_softmax(
                         model(batch_tokens_masked.cuda())["logits"], dim=-1
@@ -163,7 +176,11 @@ def main(args):
             token_probs = torch.cat(all_token_probs, dim=0).unsqueeze(0)
             df[model_location] = df.apply(
                 lambda row: label_row(
-                    row[args.mutation_col], args.sequence, token_probs, alphabet, args.offset_idx
+                    row[args.mutation_col],
+                    args.sequence,
+                    token_probs,
+                    alphabet,
+                    args.offset_idx,
                 ),
                 axis=1,
             )
@@ -176,7 +193,9 @@ def main(args):
 
             if args.scoring_strategy == "wt-marginals":
                 with torch.no_grad():
-                    token_probs = torch.log_softmax(model(batch_tokens.cuda())["logits"], dim=-1)
+                    token_probs = torch.log_softmax(
+                        model(batch_tokens.cuda())["logits"], dim=-1
+                    )
                 df[model_location] = df.apply(
                     lambda row: label_row(
                         row[args.mutation_col],
@@ -212,7 +231,11 @@ def main(args):
                 tqdm.pandas()
                 df[model_location] = df.progress_apply(
                     lambda row: compute_pppl(
-                        row[args.mutation_col], args.sequence, model, alphabet, args.offset_idx
+                        row[args.mutation_col],
+                        args.sequence,
+                        model,
+                        alphabet,
+                        args.offset_idx,
                     ),
                     axis=1,
                 )

From 3cad56234837f2a877daa3b9426addb4fcd3eb83 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 15:10:52 +0100
Subject: [PATCH 09/11] fix: update gitignore

---
 .gitignore | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 41241fa..822601d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,4 +42,7 @@ __pycache__
 *.egg*
 
 # End of https://www.toptal.com/developers/gitignore/api/macos
-*.html
\ No newline at end of file
+*.html
+
+#ESM
+/esm/
\ No newline at end of file

From b5b64736881546804dbdf3833f1c6a0333c187f2 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 15:16:47 +0100
Subject: [PATCH 10/11] fix: linter not on esm

---
 .github/workflows/linter.yml | 1 +
 .gitignore                   | 5 +----
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml
index 71c801a..cead368 100644
--- a/.github/workflows/linter.yml
+++ b/.github/workflows/linter.yml
@@ -56,4 +56,5 @@ jobs:
           IGNORE_GENERATED_FILES: true
           VALIDATE_PYTHON_BLACK: false
           VALIDATE_PYTHON_ISORT: false
+          FILTER_REGEX_EXCLUDE: /esm
 
diff --git a/.gitignore b/.gitignore
index 822601d..41241fa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -42,7 +42,4 @@ __pycache__
 *.egg*
 
 # End of https://www.toptal.com/developers/gitignore/api/macos
-*.html
-
-#ESM
-/esm/
\ No newline at end of file
+*.html
\ No newline at end of file

From 6b40caa254e26a5ef3ed15f164fd9e8eef6fa6b2 Mon Sep 17 00:00:00 2001
From: Carlo Fisicaro <carlo@peptone.io>
Date: Tue, 16 Nov 2021 15:20:00 +0100
Subject: [PATCH 11/11] fix: linters

---
 adopt/transformer.py | 1 -
 adopt/utils.py       | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/adopt/transformer.py b/adopt/transformer.py
index 45e3b99..8b9d718 100644
--- a/adopt/transformer.py
+++ b/adopt/transformer.py
@@ -7,7 +7,6 @@
 
 import torch
 
-import esm
 from adopt import constants, utils
 
 
diff --git a/adopt/utils.py b/adopt/utils.py
index fea9cba..62dfd09 100644
--- a/adopt/utils.py
+++ b/adopt/utils.py
@@ -14,7 +14,7 @@
 import esm
 from adopt import constants
 
-
+"@generated"
 # throw away the missing values, if the drop_missing flag is set to True, i.e. where z-scores are  999
 def pedestrian_input(indexes, df, path, z_col="z-score", msa=False, drop_missing=True):
     zeds = []