Andreas Mueller amueller

## tree_plotting.py
import numpy as np
from numbers import Integral

from sklearn.externals import six
from sklearn.tree.export import _color_brew, _criterion, _tree


def plot_tree(decision_tree, max_depth=None, feature_names=None,
              class_names=None, label='all', filled=False,
              leaves_parallel=False, impurity=True, node_ids=False,

## student_groups.py
import cvxpy as cvx

n_students = 130
n_projects = 30
assignment = cvx.Int(rows=n_students, cols=n_projects)

import numpy as np
rng = np.random.RandomState(0)
project_preferences = rng.rand(n_students, n_projects)

## parsing_in_preparation.ipynb

      
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                amueller
                / parsing_in_preparation.ipynb
            
            
              Created
              September 28, 2018 16:29
            
              
                parsing in preparation datasets on openml
              
          
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## commits.py
from github import Github


gh = Github("SECRETKEY")
rep = gh.get_repo("scikit-learn/scikit-learn")
org = gh.get_organization("scikit-learn")
org_members = list(org.get_members())
import datetime
n_commits = {}
limit = datetime.datetime(2017, 1, 1)

## precondition_logistic_regression.ipynb

      
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                amueller
                / precondition_logistic_regression.ipynb
            
            
              Created
              November 12, 2019 21:00
            
          
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## block-coroutine.ipynb

      
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                amueller
                / block-coroutine.ipynb
            
            
              Created
              February 25, 2021 23:18
                — forked from minrk/block-coroutine.ipynb
            
          
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	import numpy as np
	from numbers import Integral

	from sklearn.externals import six
	from sklearn.tree.export import _color_brew, _criterion, _tree


	def plot_tree(decision_tree, max_depth=None, feature_names=None,
	class_names=None, label='all', filled=False,
	leaves_parallel=False, impurity=True, node_ids=False,
	import cvxpy as cvx

	n_students = 130
	n_projects = 30
	assignment = cvx.Int(rows=n_students, cols=n_projects)

	import numpy as np
	rng = np.random.RandomState(0)
	project_preferences = rng.rand(n_students, n_projects)
	from github import Github


	gh = Github("SECRETKEY")
	rep = gh.get_repo("scikit-learn/scikit-learn")
	org = gh.get_organization("scikit-learn")
	org_members = list(org.get_members())
	import datetime
	n_commits = {}
	limit = datetime.datetime(2017, 1, 1)