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Fix requirements #118

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Fix requirements #118

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0637773
Update requirements.txt
amrzv Sep 29, 2025
a70ee30
Fix notebook:
amrzv Sep 29, 2025
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2 changes: 1 addition & 1 deletion .github/workflows/build_and_test.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ jobs:
runs-on: ubuntu-20.04
strategy:
matrix:
python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
python-version: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]

steps:
- uses: actions/checkout@v2
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8 changes: 4 additions & 4 deletions README.rst
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Expand Up @@ -61,7 +61,7 @@ To use the project:
# as well as neuron activations and input saliency values.

# To view the input saliency
output.saliency()
output.primary_attributions()

This does the following:

Expand All @@ -70,17 +70,17 @@ This does the following:
3. The model returns an ecco ``OutputSeq`` object. This object holds the output sequence, but also a lot of data generated by the generation run, including the input sequence and input saliency values. If we set ``activations=True`` in ``from_pretrained()``, then this would also contain neuron activation values.
4. ``output`` can now produce various interactive explorables. Examples include:

- ``output.saliency()`` to generate input saliency explorable [`Input Saliency Colab Notebook <https://colab.research.google.com/github/jalammar/ecco/blob/main/notebooks/Ecco_Input_Saliency.ipynb>`_]
- ``output.primary_attributions()`` to generate input saliency explorable [`Input Saliency Colab Notebook <https://colab.research.google.com/github/jalammar/ecco/blob/main/notebooks/Ecco_Input_Saliency.ipynb>`_]
- ``output.run_nmf()`` to to explore non-negative matrix factorization of neuron activations [`Neuron Activation Colab Notebook <https://colab.research.google.com/github/jalammar/ecco/blob/main/notebooks/Ecco_Neuron_Factors.ipynb>`_]


.. code-block:: python

# To view the input saliency explorable
output.saliency()
output.primary_attributions()

# to view input saliency with more details (a bar and % value for each token)
output.saliency(style="detailed")
output.primary_attributions(style="detailed")

# output.activations contains the neuron activation values. it has the shape: (layer, neuron, token position)

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99 changes: 46 additions & 53 deletions notebooks/Language_Models_and_Ecco_PyData_Khobar.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -87,13 +87,6 @@
"id": "Ixn-FHcdYL_M",
"outputId": "21f706c7-2229-4c4d-c616-5c75a3e269c9"
},
"source": [
"text = \" it was a matter of\"\r\n",
"\r\n",
"# Generate one token\r\n",
"output_1 = lm.generate(text, generate=1, do_sample=False)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -225,6 +218,12 @@
"tags": []
}
}
],
"source": [
"text = \" it was a matter of\"\n",
"\n",
"# Generate one token\n",
"output_1 = lm.generate(text, generate=1, do_sample=False, output_hidden_states=True)"
]
},
{
Expand All @@ -237,12 +236,6 @@
"id": "JzA265wyYL4K",
"outputId": "54f3826b-3e9c-438e-db7c-9a635c1cc4f6"
},
"source": [
"# Show the top 10 candidate output tokens for position #5. \r\n",
"# Layer 5 is the last layer in the model.\r\n",
"output_1.layer_predictions(position=5, layer=5, topk=10)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -363,6 +356,11 @@
"tags": []
}
}
],
"source": [
"# Show the top 10 candidate output tokens for position #5. \n",
"# Layer 5 is the last layer in the model.\n",
"output_1.layer_predictions(position=5, layer=5, topk=10)"
]
},
{
Expand Down Expand Up @@ -415,11 +413,6 @@
"id": "RdC5MO1yvTvC",
"outputId": "86193189-42d6-4d77-a28e-e8f665c2c83e"
},
"source": [
"# Compare the rankings of \"Principle\" and \"Principal\" across layers\n",
"output_1.rankings_watch(watch=[7989, 10033], position=5)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand All @@ -433,6 +426,10 @@
"tags": []
}
}
],
"source": [
"# Compare the rankings of \"Principle\" and \"Principal\" across layers\n",
"output_1.rankings_watch(watch=[7989, 10033], position=5)"
]
},
{
Expand All @@ -457,11 +454,6 @@
},
"outputId": "d5ac06ef-8157-4b89-debe-6d3400b5f848"
},
"source": [
"text = \" Heathrow airport is located in\"\n",
"output_2 = lm.generate(text, generate=5, do_sample=False)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -681,6 +673,10 @@
"tags": []
}
}
],
"source": [
"text = \" Heathrow airport is located in\"\n",
"output_2 = lm.generate(text, generate=5, do_sample=False)"
]
},
{
Expand All @@ -702,11 +698,6 @@
"id": "2E-8avmf8OWc",
"outputId": "df611a7f-5ad4-4f8e-8bcf-8df553245d5a"
},
"source": [
"text = \" Heathrow airport is located in the city of\"\n",
"output_2 = lm.generate(text, generate=1, do_sample=False)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -838,6 +829,10 @@
"tags": []
}
}
],
"source": [
"text = \" Heathrow airport is located in the city of\"\n",
"output_2 = lm.generate(text, generate=1, do_sample=False, output_hidden_states=True)"
]
},
{
Expand All @@ -850,11 +845,6 @@
"id": "Ws7tKJ_K8Wcg",
"outputId": "f045a616-d379-4eff-ab35-818b5bc7fd57"
},
"source": [
"# What other tokens were possible to output in place of \"London\"?\n",
"output_2.layer_predictions(position=9, layer=5, topk=30)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
Expand Down Expand Up @@ -975,6 +965,10 @@
"tags": []
}
}
],
"source": [
"# What other tokens were possible to output in place of \"London\"?\n",
"output_2.layer_predictions(position=9, layer=5, topk=30)"
]
},
{
Expand All @@ -987,12 +981,6 @@
"id": "HrkMe1rI8kCF",
"outputId": "60d9d4f8-7aef-4fa7-edee-d42aa5994db9"
},
"source": [
"# Now that the model has selcted the tokens \"London . \\n\"\n",
"# How did each layer rank these tokens during processing?\n",
"output_2.rankings()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand All @@ -1006,6 +994,11 @@
"tags": []
}
}
],
"source": [
"# Now that the model has selcted the tokens \"London . \\n\"\n",
"# How did each layer rank these tokens during processing?\n",
"output_2.rankings()"
]
},
{
Expand Down Expand Up @@ -1046,12 +1039,6 @@
"id": "X72IPVlJ8_8r",
"outputId": "dba57cae-c14a-4270-bcbe-1fa06bda06a9"
},
"source": [
"text= \"The countries of the European Union are:\\n1. Austria\\n2. Belgium\\n3. Bulgaria\\n4.\"\n",
"\n",
"output_3 = lm.generate(text, generate=20, do_sample=True)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -1601,6 +1588,11 @@
"tags": []
}
}
],
"source": [
"text= \"The countries of the European Union are:\\n1. Austria\\n2. Belgium\\n3. Bulgaria\\n4.\"\n",
"\n",
"output_3 = lm.generate(text, generate=20, do_sample=True, output_hidden_states=True, attribution=[\"grad_x_input\"])"
]
},
{
Expand Down Expand Up @@ -1639,6 +1631,9 @@
"tags": []
}
}
],
"source": [
"output_3.rankings()"
]
},
{
Expand All @@ -1660,10 +1655,6 @@
"id": "D-nKpXW5FPlY",
"outputId": "a476e098-5e50-4f51-9362-990ebb52ade9"
},
"source": [
"output_3.saliency()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -1780,6 +1771,9 @@
"tags": []
}
}
],
"source": [
"output_3.primary_attributions()"
]
},
{
Expand All @@ -1802,10 +1796,6 @@
},
"outputId": "e7208416-9b90-4573-fa4f-185fec23e896"
},
"source": [
"output_3.saliency(style=\"detailed\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
Expand Down Expand Up @@ -1914,6 +1904,9 @@
"tags": []
}
}
],
"source": [
"output_3.primary_attributions(style=\"detailed\")"
]
}
]
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16 changes: 8 additions & 8 deletions requirements.txt
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@@ -1,11 +1,11 @@
matplotlib>=3.3
numpy>=1.19
ipython>=7.16
scikit-learn>=0.24.2,<2
seaborn>=0.11
transformers~=4.6
pytest>=6.1.2
setuptools>=49.6.0
torch>=1.9.0,<3
PyYAML>=6.0
captum~=0.4.1
scikit-learn>=0.25
seaborn>=0.13
transformers<4.47
pytest>=7
setuptools>=50
torch>=2
PyYAML>=6
captum>=0.4
44 changes: 13 additions & 31 deletions setup.py
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@@ -1,26 +1,14 @@
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import absolute_import
from __future__ import print_function

import io
import re
from glob import glob
from os.path import basename
from os.path import dirname
from os.path import join
from os.path import splitext
from pathlib import Path

from setuptools import find_packages
from setuptools import setup


def read(*names, **kwargs):
with io.open(
join(dirname(__file__), *names),
encoding=kwargs.get('encoding', 'utf8')
) as fh:
return fh.read()
def read(file):
return Path(file).read_text(encoding='utf-8')


setup(
Expand All @@ -37,23 +25,20 @@ def read(*names, **kwargs):
url='https://github.com/jalammar/ecco',
packages=find_packages('src'),
package_dir={'': 'src'},
py_modules=[splitext(basename(path))[0] for path in glob('src/*.py')],
py_modules=[p.stem for p in Path('src').glob('*.py')],
include_package_data=True,
zip_safe=False,
classifiers=[
# complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: Unix',
'Operating System :: POSIX',
'Operating System :: Microsoft :: Windows',
'Programming Language :: Python',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
'Programming Language :: Python :: 3.12',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: PyPy',
'Topic :: Utilities',
Expand All @@ -65,21 +50,18 @@ def read(*names, **kwargs):
keywords=[
'Natural Language Processing', 'Explainable AI', 'keyword3',
],
python_requires='!=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*',
python_requires='>=3.7',
install_requires=[
"transformers ~= 4.2",
"seaborn >= 0.11",
"scikit-learn>=0.23,<2",
"PyYAML>=6.0",
"captum ~= 0.4"
"transformers<4.47",
"seaborn>=0.13",
"scikit-learn>=0.25",
"PyYAML>=6",
"captum>=0.4"
],
extras_require={
"dev": [
"pytest>=6.1",
"pytest>=7",
],
# eg:
# 'rst': ['docutils>=0.11'],
# ':python_version=="2.6"': ['argparse'],
},
entry_points={
'console_scripts': [
Expand Down