p-m-m-c

name: template-env
channels:
  - conda-forge
  - defaults
dependencies:
  - bzip2=1.0.6=1
  - ca-certificates=2018.10.15=ha4d7672_0
  - certifi=2018.10.15=py36_1000
  - jupyterlab=0.35.4=py36_0
  - jupyterlab_server=0.2.0=py_0

p-m-m-c

# Name, domain and location are different colors
export PS1="\n\[\033[1;32m\]\u\[\033[1;30m\]@\[\033[34m\]\h \[\033[1;30m\]: \[\033[1;33m\]\w\[\e[0;31m\]\$(parse_git_branch)\n \[\033[m\]$ "
export CLICOLOR=1
export LSCOLORS=ExFxBxDxCxegedabagacad

# Define aliases for frequent commands
alias ls='ls -GFh'
alias ..='cd ..'
alias ....='cd ../..'
alias lal='ls -al'

p-m-m-c

syntax on

set tabstop=4
set shiftwidth=4

set autoindent

set expandtab
set smarttab

p-m-m-c

# Code for plotting multiple plots

n_subplots = 8
assert n_subplots%2 == 0, 'Use even number of subplots'
fig, axarr = plt.subplots(n_subplots//2,2)
fig.set_figwidth(15)
fig.set_figheight(4*axarr.shape[0])

for numb, ax in enumerate(axarr.ravel()):
    X = np.arange(10)

p-m-m-c

# INPUT: pandas DataFrame

# Compute the correlation matrix
corr = df.corr()

# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# Set up the matplotlib figure

p-m-m-c

def extract_results_GS(grid_scores):
    """
    After applying gridsearch to a dataset, for all different combinations of parameters, an array of scores is
    given in the grid_scores object of the estimator. The length of this array is equal to the number of fits of 
    the model. E.g. a model that is fit on three different train/test combinations, as happens in cross-validation. From that, the 
    mean validation score and standard deviation of the scores can be obtained. This function converts a 
    GridSearchCV.grid_scores_ object to a pandas DataFrame object. The frame is unsorted, because the best parameter
    setting can be verified in the GridSearchCV.best_estimator_ and best_score_ properties.
    """