Split (1)

train · 12.6k rows

repo_name stringlengths 8 38	pr_number int64 3 47.1k	pr_title stringlengths 8 175	pr_description stringlengths 2 19.8k ⌀	author null	date_created stringlengths 25 25	date_merged stringlengths 25 25	filepath stringlengths 6 136	before_content stringlengths 54 884k ⌀	after_content stringlengths 56 884k	pr_author stringlengths 3 21	previous_commit stringlengths 40 40	pr_commit stringlengths 40 40	comment stringlengths 2 25.4k	comment_author stringlengths 3 29	__index_level_0__ int64 0 5.1k
airbnb/knowledge-repo	767	[KP] support more sort by & UI improvement	Description of changeset: as title Test Plan: local dev ![image](https://user-images.githubusercontent.com/64947033/224917135-549707a4-2cba-4294-8ed6-24c4d083fa98.png) Reviewers:	null	2023-03-14 06:42:17+00:00	2023-03-19 03:03:33+00:00	knowledge_repo/app/templates/index-base.html	{% extends "base.html" %} {% macro format_authors(authors) %} {% for author in authors %} <a href='/feed?authors={{ author.identifier\|urlencode }}'> {{ author.format_name }} </a> {% if not loop.last %} , {% endif %} {% endfor %} {% endmacro %} {% macro pagination(max_pages=20, extremes...	{% extends "base.html" %} {% macro format_authors(authors) %} {% for author in authors %} <a href='/feed?authors={{ author.identifier\|urlencode }}'> {{ author.format_name }} </a> {% if not loop.last %} , {% endif %} {% endfor %} {% endmacro %} {% macro pagination(max_pages=20, extremes=True, autohide=True) %} {% if...	mengting1010	9a3fac2c2e8cdd295b53fe38a446d535e4f2bcb5	0d7aba9ddd3336f00f42000a5c2f17ad76ef570d	The above code and some code below will be uncommented in the next PR	mengting1010	0
airbnb/knowledge-repo	735	[kp] fix md data error and ipynb upload	Description of changeset: fix md data error and ipynb upload Test Plan: [x] local test Reviewers: @csharplus @mengting1010	null	2023-01-29 09:14:49+00:00	2023-02-02 07:41:26+00:00	knowledge_repo/app/routes/editor.py	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session, s3_client, notion_client from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image impor...	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session, s3_client, notion_client from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image impor...	JJJ000	43635f53c93ceb0f1568c9fdda796e88a80bb2d0	1070404b926ac5f1e1c27a246d4fac95d3cd3518	Use `os.path.join()` as explained in: https://www.geeksforgeeks.org/python-os-path-join-method/ ?	csharplus	1
airbnb/knowledge-repo	725	update notion db id	Description of changeset: as title Test Plan: local dev Reviewers: @csharplus @JJJ000	null	2023-01-20 01:59:00+00:00	2023-01-21 19:36:20+00:00	knowledge_repo/utils/notion.py	from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: B...	from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: B...	mengting1010	582fc923aff96253440a15de493f950f8195cca6	58155530b5fe639d54f6797cd6e8b55ac4a753fe	we probably need to add it to config	JJJ000	2
airbnb/knowledge-repo	725	update notion db id	Description of changeset: as title Test Plan: local dev Reviewers: @csharplus @JJJ000	null	2023-01-20 01:59:00+00:00	2023-01-21 19:36:20+00:00	knowledge_repo/utils/notion.py	from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: B...	from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: B...	mengting1010	582fc923aff96253440a15de493f950f8195cca6	58155530b5fe639d54f6797cd6e8b55ac4a753fe	Updated, PTAL again. Thanks!	mengting1010	3
airbnb/knowledge-repo	706	[kp] update s3 repo	Description of changeset: update s3 repo Test Plan: [x] CI Reviewers: @csharplus @mengting1010	null	2023-01-02 01:35:26+00:00	2023-01-06 03:37:10+00:00	requirements.txt	boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthl...	boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthl...	JJJ000	c255ede148aef3f804a293972a21b9d7b2419326	00d51151f35a0dccf7dae17812331fdc0065f1ca	Please lock the version of the new library to avoid unexpected breaks when the library updates in the future.	csharplus	4
airbnb/knowledge-repo	704	Add Notion Integration	Description of changeset: as title. Test Plan: local dev Reviewers:	null	2022-12-31 20:38:03+00:00	2023-01-11 04:00:33+00:00	requirements.txt	boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthl...	boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthl...	mengting1010	a875df6b4cc47024d8b3133776c7c6e8213f9daa	1ad529a84dcf923fdde97a3b7e804936f1d14007	Please add the current version number of `notion-client` as well to avoid future break changes from this library.	csharplus	5
airbnb/knowledge-repo	698	Update Jupyter Notebook Upload Related	Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000	null	2022-12-28 23:02:53+00:00	2022-12-29 07:10:50+00:00	knowledge_repo/app/routes/editor.py	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	mengting1010	451577868d66570a463260c67dff7034214beafd	6edad5351bf3f4f0abc457b6d9532ca25c62c952	You got Lint error in CI: ``` knowledge_repo/app/routes/editor.py:198:57: E502 the backslash is redundant between brackets Error: Process completed with exit code 1. ```	csharplus	6
airbnb/knowledge-repo	698	Update Jupyter Notebook Upload Related	Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000	null	2022-12-28 23:02:53+00:00	2022-12-29 07:10:50+00:00	knowledge_repo/app/routes/editor.py	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	mengting1010	451577868d66570a463260c67dff7034214beafd	6edad5351bf3f4f0abc457b6d9532ca25c62c952	Same lint issue here.	csharplus	7
airbnb/knowledge-repo	698	Update Jupyter Notebook Upload Related	Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000	null	2022-12-28 23:02:53+00:00	2022-12-29 07:10:50+00:00	knowledge_repo/app/routes/editor.py	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_f...	mengting1010	451577868d66570a463260c67dff7034214beafd	6edad5351bf3f4f0abc457b6d9532ca25c62c952	Same lint issue here.	csharplus	8
airbnb/knowledge-repo	697	Enable Jupyter Notebook Upload Post Editor	Description of changeset: - Enable jupyter notebook upload post editor ![image](https://user-images.githubusercontent.com/64947033/209613316-41bf9581-2f77-4dc4-8f79-afb17f578269.png) - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download t...	null	2022-12-27 04:44:23+00:00	2022-12-27 20:24:27+00:00	docker/config.py	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	mengting1010	b15c759e7d17b6a52c502b8bfdc174181da83789	451577868d66570a463260c67dff7034214beafd	this could cause security issue, you might need to remove it from your commit.	JJJ000	9
airbnb/knowledge-repo	697	Enable Jupyter Notebook Upload Post Editor	Description of changeset: - Enable jupyter notebook upload post editor ![image](https://user-images.githubusercontent.com/64947033/209613316-41bf9581-2f77-4dc4-8f79-afb17f578269.png) - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download t...	null	2022-12-27 04:44:23+00:00	2022-12-27 20:24:27+00:00	docker/config.py	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	mengting1010	b15c759e7d17b6a52c502b8bfdc174181da83789	451577868d66570a463260c67dff7034214beafd	I add this file in gitignore, somehow it does not works looks like.	JJJ000	10
airbnb/knowledge-repo	697	Enable Jupyter Notebook Upload Post Editor	Description of changeset: - Enable jupyter notebook upload post editor ![image](https://user-images.githubusercontent.com/64947033/209613316-41bf9581-2f77-4dc4-8f79-afb17f578269.png) - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download t...	null	2022-12-27 04:44:23+00:00	2022-12-27 20:24:27+00:00	docker/config.py	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication provide...	mengting1010	b15c759e7d17b6a52c502b8bfdc174181da83789	451577868d66570a463260c67dff7034214beafd	removed	mengting1010	11
airbnb/knowledge-repo	673	fix index and db bugs	Description of changeset: 1. index_failure when service start 2. PostgreSQL close unexpectedly the connection issue Test Plan: local build @csharplus	null	2022-06-12 16:39:05+00:00	2022-06-13 05:23:21+00:00	requirements.txt	cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3	cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess	JJJ000	5dfd384612a54adf846cc1e81cc09690f1f942a6	f08ced24fda6cee63874b0921869d8405e894358	Is this the correct library name? I searched for it and could not find it. I found the following instead: https://pypi.org/project/multiprocess/	csharplus	12
airbnb/knowledge-repo	673	fix index and db bugs	Description of changeset: 1. index_failure when service start 2. PostgreSQL close unexpectedly the connection issue Test Plan: local build @csharplus	null	2022-06-12 16:39:05+00:00	2022-06-13 05:23:21+00:00	requirements.txt	cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3	cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess	JJJ000	5dfd384612a54adf846cc1e81cc09690f1f942a6	f08ced24fda6cee63874b0921869d8405e894358	@csharplus yeah, you are right, I past the wrong package name	JJJ000	13
airbnb/knowledge-repo	669	fix package dependency to support local server	Description of changeset: fix package dependency to support local server Test Plan: [x]ci @csharplus	null	2022-05-23 06:36:29+00:00	2022-05-25 02:29:21+00:00	docker/Dockerfile	FROM ubuntu:18.04 LABEL description="knowledge-repo service" # Define some build time variables ARG PIP=pip3 ARG VERSION=0.9.1 # Install required tools and libraries RUN apt-get update && \ apt-get -y install \ wget \ zip \ python3-pip \ python3-dev \ git \ && cd /usr/local/bin \ && l...	FROM ubuntu:18.04 LABEL description="knowledge-repo service" # Define some build time variables ARG PIP=pip3 ARG VERSION=0.9.0 # Install required tools and libraries RUN apt-get update && \ apt-get -y install \ wget \ zip \ python3-pip \ python3-dev \ git \ && cd /usr/local/bin \ && l...	JJJ000	09fe061460e3463c33b7eb811899ae6ee4a959c9	2eafc06a4c57ff710283e2fdc3abf0cb17d15e1d	Avoid reducing the version number?	csharplus	14
airbnb/knowledge-repo	631	reformat unit test code	Description of changeset: reformat code Test Plan: [x]CI @csharplus	null	2022-03-23 02:45:22+00:00	2022-03-26 21:37:54+00:00	knowledge_repo/config.py	from .constants import PY_EXTENSION, YML_EXTENSION from .utils.files import read_yaml import functools import importlib import logging import os import sys import time import types logger = logging.getLogger(__name__) class KnowledgeRepositoryConfig(dict): def __init__(self, repo, args, *kwargs): self...	from .constants import PY_EXTENSION, YML_EXTENSION from .utils.files import read_yaml import functools import importlib import logging import os import sys import time import types logger = logging.getLogger(__name__) class KnowledgeRepositoryConfig(dict): def __init__(self, repo, args, *kwargs): sel...	JJJ000	48416e89dbdc485efd7d95fa99f9d28adcd79d12	ce89b54b9c18b8f639facb122b7427d0b4d7c369	Let's keep all import statements sorted in alphabetical order for easy maintenance.	csharplus	15
py-why/dowhy	1,120	Rename classes in test folder	The TestEstimator and TestRefuter were falsely interpreted as unit test classes due to their "Test" prefix.	null	2023-12-11 15:43:46+00:00	2023-12-11 18:52:07+00:00	tests/gcm/test_stochastic_models.py	import numpy as np import pytest from flaky import flaky from pytest import approx from scipy import stats from dowhy.gcm import BayesianGaussianMixtureDistribution, EmpiricalDistribution, ScipyDistribution def test_when_fitting_bayesian_gaussian_mixture_distribution_then_samples_should_be_drawn_correctly(): tes...	import numpy as np import pytest from flaky import flaky from pytest import approx from scipy import stats from dowhy.gcm import BayesianGaussianMixtureDistribution, EmpiricalDistribution, ScipyDistribution def test_when_fitting_bayesian_gaussian_mixture_distribution_then_samples_should_be_drawn_correctly(): tes...	bloebp	711e6ba9025f42b20c6f7b423bafae2913f94d2d	1f9ee6be46ad35ce5a710acc3e259fc8360f8a38	Recommend not to introduce many changes in one PR (specially as the change here should only concern class renaming).	kailashbuki	0
py-why/dowhy	1,114	Fix bug in networkx plot function with 0 error strenghts	null	null	2023-12-08 20:17:35+00:00	2023-12-11 18:56:00+00:00	dowhy/utils/plotting.py	import logging import os import tempfile from typing import Any, Dict, List, Optional, Tuple, Union import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from matplotlib import image _logger = logging.getLogger(__name__) def plot( causal_graph: nx.Graph, layout_prog: O...	import logging import os import tempfile from typing import Any, Dict, List, Optional, Tuple, Union import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from matplotlib import image _logger = logging.getLogger(__name__) def plot( causal_graph: nx.Graph, layout_prog: O...	bloebp	4f317345dcc5fc8763b13105bd5906e3434ace2f	72986a859db309b1afef92a542955569669bd8ea	Recommend a check beforehand to ensure that the `strength` is less than or equal to `max_strength`.	kailashbuki	1
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	is there a specific reason for removing the conda installation guide? There is a currently an issue with conda build that we can fix. It is also nice to share how to get the development version.	amit-sharma	2
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	is there a specific reason for removing the conda installation guide? There is a currently an issue with conda build that we can fix.	amit-sharma	3
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	Let's make it: 'Effect identification and estimation.' since identification happens first.	amit-sharma	4
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	"follows modern concepts" -- feels awkward. Modern can be vague. how about, "follows best practices"?	amit-sharma	5
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	"or checkout `Jupyter...." "the other" can be removed?	amit-sharma	6
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	The reason was due to the very outdated conda version (still on 0.8). I will put it back in, but let's try to fix the build issues there.	bloebp	7
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	Agree, what does "modern" even mean and is also subjective to the application. I changed it to "It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via causal mechanisms to support a wide range of causal algorithms."	bloebp	8
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	conda issue is now resolved: https://github.com/conda-forge/dowhy-feedstock/pull/10	amit-sharma	9
py-why/dowhy	1,088	Overhauled readme	The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable c...	null	2023-11-26 05:56:29+00:00	2023-12-01 14:58:26+00:00	README.rst	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	\|BuildStatus\|_ \|PyPiVersion\|_ \|PythonSupport\|_ \|Downloads\|_ \|discord\|_ .. \|PyPiVersion\| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. \|PythonSupport\| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. \|B...	bloebp	591e9d916e19bcad07e28687aeb8c81e1db26e8d	f455c569db0e7d0c1e1e7b54d1f8c85f026ec611	Awesome! I already put the conda installation guide back in.	bloebp	10
py-why/dowhy	1,050	Add new method to estimate KL divergence using classifier	This should work better with multivariate data and mixed data types. However, it is slower than the knn approach.	null	2023-10-20 02:13:28+00:00	2023-11-15 15:00:17+00:00	dowhy/gcm/stochastic_models.py	"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np ...	"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np ...	bloebp	395d1fa46743234fdbaf765e548b85e8089d0c7e	eb887358064d72611387b41510a77223952bfdb8	I assume you've run into import cycles and that's why you had to do a local import. This is likely a smell. I'm sure this problem is related to [this change in PR 932](https://github.com/py-why/dowhy/pull/932/files#diff-62eae184e3560bffa3e7cbc57e06f30910651732d961cf7e34c1a032e3c541fd). `PredictionModel` is an interface...	petergtz	11
py-why/dowhy	1,050	Add new method to estimate KL divergence using classifier	This should work better with multivariate data and mixed data types. However, it is slower than the knn approach.	null	2023-10-20 02:13:28+00:00	2023-11-15 15:00:17+00:00	dowhy/gcm/stochastic_models.py	"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np ...	"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np ...	bloebp	395d1fa46743234fdbaf765e548b85e8089d0c7e	eb887358064d72611387b41510a77223952bfdb8	It is unfortunately a bit more complicated because the new KL divergence method now also relies on the auto module (for optional automatic selection of the classifier model), which in turn imports the `EmpiricalDistribution` from the `stochastic_models` module. We might need to move these implementations into separate ...	bloebp	12
py-why/dowhy	1,049	GCM auto assignment now returns a summary object	The summary object contains information about the evaluated models and model choices. This object is printable to provide quick summary.	null	2023-10-16 19:40:03+00:00	2023-11-02 18:42:28+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning ...	bloebp	86fcb28ba23269922dad2c2b93a00fca3c611e0f	d83fc3f022c9ce4bc8f3ebfc7eee099a3c5db74b	`.keys()` is redundant here	kailashbuki	13
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	"We use the CDT library" should be "we use the causal-learn library"	emrekiciman	14
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	Consider adding a link to the top-level documentation to help readers explore all the other methods supported by causal-learn	emrekiciman	15
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	the sachs dataset is still being loaded via CDT. Rather than require installing all of CDT, can we just copy the load_sachs function, giving credit and following license requirements. I believe it's a short function, though we'd also need an abbreviated version of the read_list_edges function. https://github.com/Fen...	emrekiciman	16
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	Oh thanks for reminding this!	kunwuz	17
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	Sure, that's a great idea.	kunwuz	18
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	Yeah, you are totally right. It could be even better to have a causal-learn function that could automatically load various real-world datasets. I will code it up and update it in causal-learn.	kunwuz	19
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	There's still a reference to CDT in the beginning of the sentence.	emrekiciman	20
py-why/dowhy	1,026	Update the causal discovery notebook with examples using causal-learn	Updating the old notebook as mentioned in #1021.	null	2023-08-30 21:25:09+00:00	2023-10-05 21:26:19+00:00	docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/Causal...	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will ...	kunwuz	67b305db5224bf718067a21acfe4baa92a7d2c8c	7eb4a0c253514a920588d1ab222e1aeb5e07cb51	Also, 1 final reference to CDT on an earlier cell "Experiments on the Auto-MPG dataset")	emrekiciman	21
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Good idea taking this is out as a separate function! It is probably useful for other use cases as well. Just a minor suggestion; consider renaming it to `assign_causal_mechanism_node` to be more consistent with the names of other methods.	bloebp	22
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Instead of allowing `None` and setting a default value, let's make this explicit in the function definition by setting `quality: AssignmentQuality = AssignmentQuality.GOOD` and remove the `Optional` accordingly.	bloebp	23
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Here, If you see where the _remove_invariant_nodes is called, the quality can be None as well and so to handle that case, I chose to write it like that. If I make the default to be the value you have said above, I will have to handle in the upstream _remove_invariant_nodes to make sure that None does n...	bhatt-priyadutt	24
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	I see. The reason why `None` is an option in `distribution_change` is to indicate to use the existing models and not do an auto assignment. However, at the point of calling `assign_causal_mechanism_node`, a `None` parameter doesn't make sense anymore. So, I would then rather check before calling this whether it is `Non...	bloebp	25
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/auto.py	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearR...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	done	bhatt-priyadutt	26
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Instead of 'constant' what about something like: "List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed"	bloebp	27
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	While there might be use cases outside of the distribution change, let's for now keep it as an internal function here, i.e., add an `_` in front of the name. Also consider moving it at the bottom of the function so that the public facing methods are at the top.	bloebp	28
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	The type for `invariant_nodes` should be `List[Any]` for consistency.	bloebp	29
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Similar as above: The type for `invariant_nodes` should be `List[Any]` for consistency.	bloebp	30
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	What about skipping this if `auto_assignment_quality` is `None` since you allow it to be optional. In that way, we can also use this method to just get the new graph.	bloebp	31
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Check explicitly: `if invariant_nodes is not None:`	bloebp	32
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	@bloebp can you please explain more on this....i didnt understood here!	bhatt-priyadutt	33
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	What I meant is something like: ``` if auto_assignment_quality is not None: for child in children: set_causal_mechanism_for_node(causal_model, child, old_data, quality=auto_assignment_quality) ``` In that way, one can still get the modified model without running the auto assignment on the children no...	bloebp	34
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	If we do this then we will not be able to get updated model for child nodes when assignment quality is None right? and this is which we dont want. Or i am still unable to understand the reason. Also, please see the below image where this function is getting called, its called after the assigning of the old causal graph...	bhatt-priyadutt	35
py-why/dowhy	1,013	Invariant nodes removal	Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis	null	2023-08-15 12:59:48+00:00	2023-08-24 16:12:46+00:00	dowhy/gcm/distribution_change.py	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import ...	"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np i...	bhatt-priyadutt	734b61671d130cf21a4e437084b9083dbd5c4a39	b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73	Ok yes, nevermind my comment, I was thinking of a use-case outside of this method where a user just wants the corrected graph structure without necessarily having a new mechanism assigned. But then we would also need to make the data optional. Let's keep the logic as is, but to avoid confusion, the assignment qualit...	bloebp	36
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	What about using `nan` instead of `-1`? That makes it clearer.	bloebp	37
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	What does `tpa` stand for? Isn't `dsep` already a good hint what this method validates?	bloebp	38
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Wondering if there is really a use case where someone wants to use different data for the tests? To make things simpler, I would probably leave this as required and pass it to the methods. Or are there tests that do not require data (i.e., we cannot blindly pass it)?	bloebp	39
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Isn't the `n_jobs` also method dependent or does every method expect this?	bloebp	40
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Similar comments as for `validate_graph`.	bloebp	41
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	What about the following alternative: Remove all the method specific parameters here and set a default method partial function. This is, something like: ``` def validate_graph( causal_graph: DirectedGraph, methods=( wrap_partial(validate_lmc, data=data, independence_test=kernel_based, conditional_ind...	bloebp	42
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Can you add this reference to the notebook as well in case it is not in there?	bloebp	43
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Make this an internal function with `_`. Can you also add a docstring why we need this `wrap_partial` instead of `partial` directly (due to the `__name__` attribute). Maybe briefly explain how the `__name__` attribute is used there.	bloebp	44
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	`tpa` stands for parental triple. This is how we call it in the paper and I think this unifies it a bit. I explained it in the docstring now!	eeulig	45
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	Yes, the `validate_tpa` method doesn't require data. I now included an optional `data` argument there which is ignored, so that we can blindly pass it.	eeulig	46
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	I used `wrap_partial` because we needed the `__name__` attribute. I now included a `FalsifyConst.METHOD` key in the validation summary of each method so that this is not necessary anymore. Methods can now be simply wrapped using `partial`.	eeulig	47
py-why/dowhy	967	Remove kwargs from falsify, improve docstrings	This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on ob...	null	2023-07-04 13:58:30+00:00	2023-07-18 14:19:10+00:00	dowhy/gcm/falsify.py	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import pa...	eeulig	379f188d674d5be6d644559f9f54852b87403f7c	4e30c63fe1113e35259e7fb3400a6f09bca4d699	I made data non-optional. Defining the default methods similar to how we do it for `validate_graph` now is a bit tricky though because of `causal_graph_reference` for `validate_tpa` and the p-value cache that is initialized after calling the function.	eeulig	48
py-why/dowhy	963	Removing autolguon as optional dependency	The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.	null	2023-06-22 14:55:47+00:00	2023-06-26 16:58:14+00:00	pyproject.toml	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	bloebp	21a37a9453d0a018318e16af22ffabab5d6d4fc6	a1c6c042ca5bdfba98063e78b348854cf1709307	`>=0.13.2,<1.0` isn't this the same as `^0.13.2` in poetry?	Zethson	49
py-why/dowhy	963	Removing autolguon as optional dependency	The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.	null	2023-06-22 14:55:47+00:00	2023-06-26 16:58:14+00:00	pyproject.toml	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	bloebp	21a37a9453d0a018318e16af22ffabab5d6d4fc6	a1c6c042ca5bdfba98063e78b348854cf1709307	I thought so too, but somehow it behaved slightly differently. Might be that it was something local, but just wanted to make sure :)	bloebp	50
py-why/dowhy	963	Removing autolguon as optional dependency	The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.	null	2023-06-22 14:55:47+00:00	2023-06-26 16:58:14+00:00	pyproject.toml	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	bloebp	21a37a9453d0a018318e16af22ffabab5d6d4fc6	a1c6c042ca5bdfba98063e78b348854cf1709307	we can change this to >=3.8,<3.12. the latest version of numba supports 3.11. can update line 45 too.	amit-sharma	51
py-why/dowhy	963	Removing autolguon as optional dependency	The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.	null	2023-06-22 14:55:47+00:00	2023-06-26 16:58:14+00:00	pyproject.toml	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <a...	bloebp	21a37a9453d0a018318e16af22ffabab5d6d4fc6	a1c6c042ca5bdfba98063e78b348854cf1709307	Both should be fine! :)	amit-sharma	52
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/auto_identifier.py	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand imp...	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstiman...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	nice, like the nomenclature of observed_nodes, action_nodes and outcome_nodes.	amit-sharma	53
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/auto_identifier.py	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand imp...	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstiman...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	thanks for simplifying this function	amit-sharma	54
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/auto_identifier.py	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand imp...	import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstiman...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	to be consistent with naming, we can call it "mediator_nodes".	amit-sharma	55
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/efficient_backdoor.py	import networkx as nx import numpy as np EXCEPTION_NO_ADJ = "An adjustment set formed by observable variables does not exist" EXCEPTION_COND_NO_OPT = "Conditions to guarantee the existence of an optimal adjustment set are not satisfied" class EfficientBackdoor: """ Implements methods for finding optimal (eff...	from typing import List import networkx as nx import numpy as np EXCEPTION_NO_ADJ = "An adjustment set formed by observable variables does not exist" EXCEPTION_COND_NO_OPT = "Conditions to guarantee the existence of an optimal adjustment set are not satisfied" class EfficientBackdoor: """ Implements methods...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	why not use `self.graph` in lines 48, 49? Not a strong preference since they are equivalent in the current code.	amit-sharma	56
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/id_identifier.py	from typing import Dict, List, Optional, Set, Union import networkx as nx import numpy as np from dowhy.causal_graph import CausalGraph from dowhy.utils.api import parse_state from dowhy.utils.graph_operations import find_ancestor, find_c_components, induced_graph from dowhy.utils.ordered_set import OrderedSet clas...	from typing import Dict, List, Optional, Set, Union import networkx as nx import numpy as np from dowhy.graph import get_adjacency_matrix from dowhy.utils.api import parse_state from dowhy.utils.graph_operations import find_ancestor, find_c_components, induced_graph from dowhy.utils.ordered_set import OrderedSet cl...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	nice! good way to remove the node_names param	amit-sharma	57
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/identify_effect.py	from typing import List, Protocol, Union from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Proto...	from typing import List, Protocol, Union import networkx as nx from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a Caus...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	can change "CausalGraph" to simply, "causal graph"	amit-sharma	58
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/causal_identifier/identify_effect.py	from typing import List, Protocol, Union from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Proto...	from typing import List, Protocol, Union import networkx as nx from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a Caus...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	good to change all mentions of CausalGraph in docs/comments to causal graph.	amit-sharma	59
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/do_sampler.py	import logging import numpy as np import pandas as pd from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, data, params=None, variable_types=None, num_cores=1, causal_model=None, keep_original_treatment=Fal...	import logging from typing import List import networkx as nx import numpy as np import pandas as pd from dowhy import EstimandType, identify_effect_auto from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, ...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	any specific reason you removed the `set_identifier_method` call?	amit-sharma	60
py-why/dowhy	943	Proposal: Finalize functional API refactor - deprecate causal graph	- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks....	null	2023-05-16 16:07:18+00:00	2023-11-27 17:48:56+00:00	dowhy/do_sampler.py	import logging import numpy as np import pandas as pd from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, data, params=None, variable_types=None, num_cores=1, causal_model=None, keep_original_treatment=Fal...	import logging from typing import List import networkx as nx import numpy as np import pandas as pd from dowhy import EstimandType, identify_effect_auto from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, ...	bloebp	4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e	2a8e49a77eb43a74d7ee6fc0925a376cd60335f2	I put it back in. I think my thought here was that the `identify_effect_auto` method should set the "identifier method", but I might mix something up here. Doesn't the `identify_effect_auto` select the identifier method (although it is currently by default the backdoor)?	bloebp	61
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	can remove the first sentence: we're adding.. to dowhy"	amit-sharma	62
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	add a link to CACM. And then move all references to the bottom of the notebook. This is to avoid interrupting the flow	amit-sharma	63
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	what is ERM? expand the acronym and say that you are using it as a baseline	amit-sharma	64
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	is there a way to avoid this verbose output? can you output a progress bar using tqdm?	amit-sharma	65
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	This seems to be an issue with tqdm and jupyter, not PL (https://github.com/Lightning-AI/lightning/issues/330, https://github.com/Lightning-AI/lightning/issues/765). A potential solution I could see in these issues is using custom progress bar as a callback in Jupyter, but these don't seem to be working here.	jivatneet	66
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Co...	{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using Causally Adaptive Constraint Minimization (CACM) (https://arxiv.org...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	okay, it is fine then	amit-sharma	67
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	dowhy/causal_prediction/algorithms/cacm.py	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="...	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, op...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	remove the commented code if not needed	amit-sharma	68
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	dowhy/causal_prediction/algorithms/cacm.py	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="...	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, op...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	Added this to provide greater clarity on the variable shape. Can remove if you feel it's not needed.	jivatneet	69
py-why/dowhy	925	Adding general version of CACM	This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the ge...	null	2023-04-18 10:36:38+00:00	2023-06-15 11:59:01+00:00	dowhy/causal_prediction/algorithms/cacm.py	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="...	import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, op...	jivatneet	c87b511bfd95caec2bc50e4686ed21ca448e9c37	0fb1314c5ddaeb6805109453433ac6248063f3c5	Okay, it is good then.	amit-sharma	70
py-why/dowhy	918	Fix auto assign model unit test	null	null	2023-04-03 14:24:14+00:00	2023-04-03 15:38:01+00:00	tests/gcm/test_auto.py	import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, Logistic...	import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, Logistic...	bloebp	13ed13e8adf7a26a995b2e219103d7a25dacb426	5edb89192864d1684b5b2bde4eb86450a00b093e	Where does pipeline sneak in [here](https://github.com/py-why/dowhy/blob/main/dowhy/gcm/auto.py#L150)?	kailashbuki	71
py-why/dowhy	918	Fix auto assign model unit test	null	null	2023-04-03 14:24:14+00:00	2023-04-03 15:38:01+00:00	tests/gcm/test_auto.py	import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, Logistic...	import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, Logistic...	bloebp	13ed13e8adf7a26a995b2e219103d7a25dacb426	5edb89192864d1684b5b2bde4eb86450a00b093e	Here: https://github.com/py-why/dowhy/blob/main/dowhy/gcm/ml/regression.py#L121 It adds polynomial features, which can be equally good as a linear model when the coefficients are zero accordingly.	bloebp	72
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	One statement	petergtz	73
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	Can we use type hints here?	petergtz	74
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	Put this at the top as ```python if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact") ```	petergtz	75
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	directly return this.	petergtz	76
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	dito	petergtz	77
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	this can now be a simple if	petergtz	78
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	else is not needed anymore See also: [Why You Shouldn't Nest Your Code ](https://www.youtube.com/watch?v=CFRhGnuXG-4) Oh, just saw: this code was actually just moved. Feel free to postpone my proposed refactoring. Either way you choose, I think it's worth doing it.	petergtz	79
py-why/dowhy	913	Add method to estimate ICC for single samples	Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.	null	2023-03-29 21:42:31+00:00	2023-03-31 13:39:16+00:00	dowhy/gcm/influence.py	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np impor...	"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy a...	bloebp	45046e141ebd701820ca8a692c8f03ed5dce314c	e734f4e6245d404218b0ba11b14f7cc621bec7a0	return this directly	petergtz	80
py-why/dowhy	896	Change default parameter for estimating continous KL divergence	This should reduce variance in the estimation, increase runtime and, generally, accuracy.	null	2023-03-13 21:42:18+00:00	2023-03-15 19:04:17+00:00	dowhy/gcm/divergence.py	"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import numpy as np from scipy.stats import entropy from sklearn.neighbors import NearestNeighbors from dowhy.gcm.constant import EPS from dowhy.gcm.util.general import is_categorical, shape_int...	"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import numpy as np from scipy.stats import entropy from sklearn.neighbors import NearestNeighbors from dowhy.gcm.constant import EPS from dowhy.gcm.util.general import is_categorical, setdiff2...	bloebp	b8fb120033656688621477768fc7002aba4d3601	58f314c81c98c4e417b49fce990c82f1c535fe42	BTW, there is an elegant construct in Python for this: ```python X, Y = setdiff2d(X, Y, assume_unique=True), setdiff2d(Y, X, assume_unique=True) ```	petergtz	81
py-why/dowhy	896	Change default parameter for estimating continous KL divergence	This should reduce variance in the estimation, increase runtime and, generally, accuracy.	null	2023-03-13 21:42:18+00:00	2023-03-15 19:04:17+00:00	dowhy/gcm/util/general.py	"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import random from typing import Dict import numpy as np from scipy.optimize import minimize from sklearn.preprocessing import OneHotEncoder def shape_into_2d(*args): """If necessary, sh...	"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import random from typing import Dict import numpy as np from scipy.optimize import minimize from sklearn.preprocessing import OneHotEncoder def shape_into_2d(*args): """If necessary, sh...	bloebp	b8fb120033656688621477768fc7002aba4d3601	58f314c81c98c4e417b49fce990c82f1c535fe42	Every local variable is temporary ;-). Just `dtype`?	petergtz	82
py-why/dowhy	882	Revise user guide entry for intrinsic causal influence	null	null	2023-02-24 21:36:44+00:00	2023-03-07 16:13:45+00:00	docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst	Quantifying Intrinsic Causal Influence ====================================== By quantifying intrinsic causal influence, we answer the question: How strong is the causal influence of a source node to a target node that is not inherited from the parents of the source node? Naturally, descendants will have a z...	Quantifying Intrinsic Causal Influence ====================================== By quantifying intrinsic causal influence, we answer the question: How strong is the causal influence of a source node to a target node that is not inherited from the parents of the source node? Naturally, descendants will have a z...	bloebp	a1dcccbc805cb28aa4840fe9bce8338278632a50	12168ea7bd7a30d4c0d6501f69c7161ddb073845	Do X, Y, Z represent delays? If so, then delays can also be negative, trains may arrive faster than usual. A realistic distribution would then be a Gaussian distribution which has a non-zero mean with negative observations ~3 standard deviations away.	kailashbuki	83

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.