Sebastian Pölsterl sebp

## 6.ex.Breast.csv

          
            id
            entage
            follow
            pd
            fate
            fh

            
              1
              30
              14.863791465759277
              0
              0
              0

            
              2
              45
              24.16974639892578
              0
              0
              0

            
              3
              35
              23.32375144958496
              0
              0
              0

            
              4
              47
              17.4757022857666
              0
              0
              0

            
              5
              30
              28.43805694580078
              1
              0
              0

            
              6
              44
              26.376455307006836
              1
              0
              0

            
              7
              50
              28.331279754638672
              1
              0
              0

            
              8
              47
              29.3223819732666
              0
              0
              0

            
              9
              39
              27.299110412597656
              1
              0
              0

## export.py
"""
This module defines export functions for survival trees.

It is based on the sklearn.tree.export module.
"""

# Authors: Gilles Louppe <g.louppe@gmail.com>
#          Peter Prettenhofer <peter.prettenhofer@gmail.com>
#          Brian Holt <bdholt1@gmail.com>
#          Noel Dawe <noel@dawe.me>

## coefficient_rescaler.py
import numpy as np


class CoefficientRescaler:
    """Inverse transform of coefficients of a linear model.

    Parameters
    ==========
    scalar_mean : ndarray, shape=(n_features,)
        Feature-wise mean.

## missing_values.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.backends.backend_pdf import PdfPages


def index_to_binary_matrix(index):
    return index.to_series().apply(lambda x: pd.Series(map(int, x.split(":")), name=x))

## simulation.r
# simulation study related to https://stats.stackexchange.com/questions/362194/data-how-smart-are-models-is-my-dummy-redundant
library(ROCR)

gen.features <- function(n.samples) {
    age <- runif(n.samples, min=6, max=89)
    sex <- factor(rbinom(n.samples, 1, 0.5), c(0, 1), c("male", "female"))
    data <- data.frame(age, sex)
    return(data)
}

## coxnet-gridsearch.ipynb

      
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                sebp
                / coxnet-gridsearch.ipynb
            
            
              Created
              June 9, 2018 19:39
            
              
                GridSearchCV example to select hyper-parameters of CoxnetSurvivalAnalysis
              
          
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## python-scientific-computing.md

      
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                sebp
                / python-scientific-computing.md
            
            
              Last active
              March 14, 2024 02:40
            
              
                Overview of Python Packages for Scientific Computing
              
          
    Overview of Python Packages for Scientific Computing

Resources


The Python Package Index (PyPI) is a repository of software for the Python programming language.


NumFOCUS is a nonprofit that supports open source scientific computing.


Best Practices in Scientific Computing.


## pymc3-bayesian-cor.py
from theano.printing import Print
import pymc3 as pm
import numpy as np
import theano.tensor as T


def covariance(sigma, rho):
    C = T.fill_diagonal(T.alloc(rho, 2, 2), 1.)
    S = T.diag(sigma)
    M = S.dot(C).dot(S)
id	entage	follow	pd	fate	fh
1	30	14.863791465759277	0	0	0
2	45	24.16974639892578	0	0	0
3	35	23.32375144958496	0	0	0
4	47	17.4757022857666	0	0	0
5	30	28.43805694580078	1	0	0
6	44	26.376455307006836	1	0	0
7	50	28.331279754638672	1	0	0
8	47	29.3223819732666	0	0	0
9	39	27.299110412597656	1	0	0
	"""
	This module defines export functions for survival trees.

	It is based on the sklearn.tree.export module.
	"""

	# Authors: Gilles Louppe <g.louppe@gmail.com>
	# Peter Prettenhofer <peter.prettenhofer@gmail.com>
	# Brian Holt <bdholt1@gmail.com>
	# Noel Dawe <noel@dawe.me>
	import numpy as np


	class CoefficientRescaler:
	"""Inverse transform of coefficients of a linear model.

	Parameters
	==========
	scalar_mean : ndarray, shape=(n_features,)
	Feature-wise mean.
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import seaborn as sns
	from matplotlib.backends.backend_pdf import PdfPages


	def index_to_binary_matrix(index):
	return index.to_series().apply(lambda x: pd.Series(map(int, x.split(":")), name=x))
	# simulation study related to https://stats.stackexchange.com/questions/362194/data-how-smart-are-models-is-my-dummy-redundant
	library(ROCR)

	gen.features <- function(n.samples) {
	age <- runif(n.samples, min=6, max=89)
	sex <- factor(rbinom(n.samples, 1, 0.5), c(0, 1), c("male", "female"))
	data <- data.frame(age, sex)
	return(data)
	}
	from theano.printing import Print
	import pymc3 as pm
	import numpy as np
	import theano.tensor as T


	def covariance(sigma, rho):
	C = T.fill_diagonal(T.alloc(rho, 2, 2), 1.)
	S = T.diag(sigma)
	M = S.dot(C).dot(S)