ericmjl

name: pymc4-dev
channels:
- defaults
- conda-forge
- ericmjl
dependencies:
- python=3.6
- jupyter
- jupyterlab
- conda

ericmjl

ericmjl

============================= test session starts ==============================
platform darwin -- Python 3.6.7, pytest-4.0.2, py-1.7.0, pluggy-0.8.0
rootdir: /Users/ericmjl/github/software/autograd-sparse, inifile:
collected 3 items / 2 deselected

tests/test_sparse.py F                                                   [100%]

=================================== FAILURES ===================================
_____________________________ test_sparse_dot_grad _____________________________

ericmjl

ericmjl

ericmjl

name: dl-workshop
channels:
- defaults
- conda-forge
- ericmjl
dependencies:
- python=3.7
- jupyter
- jupyterlab
- conda

ericmjl

ericmjl

import datashader as ds
import holoviews as hv
from holoviews.operation.datashader import datashade
hv.extension('bokeh')

def scatter(dim1, dim2):
    def _scatter(data):
        return hv.Scatter(data, kdims=[dim1], vdims=[dim2], extents=(-10, -10, 10, 10))
    return _scatter

ericmjl

from sklearn.metrics import explained_variance_score

def var_explained(preds, actual):
    """
    Implementation taken directly from the formula on this page:
    http://scikit-learn.org/stable/modules/model_evaluation.html#explained-variance-score
    """
    return 1 - ((preds - actual).var() / actual.var())

y_pred = np.array([3, -0.5, 2, 7])

ericmjl

from pathlib import Path
import os

# We will use the example of creating a .directory under home.

home = Path.home()
dirname = home / '.dir'

if not dirname.exists():
    os.mkdir(dirname)