NoRaincheck

[Background]
Color=39,40,34

[BackgroundIntense]
Color=65,67,57

[Foreground]
Color=225,225,218

[ForegroundIntense]

NoRaincheck

# The goal of this is to try to make use of Shap to explain a tree built in river.
# https://github.com/online-ml/river/issues/437

from functools import reduce
import operator

import numpy as np
import pandas as pd
import pprint
from sklearn import datasets

NoRaincheck

#!/usr/bin/env python
"""
Simple example of a full screen application with a vertical split.

This will show a window on the left for user input. When the user types, the
reversed input is shown on the right. Pressing Ctrl-Q will quit the application.
"""
from prompt_toolkit.application import Application
from prompt_toolkit.buffer import Buffer
from prompt_toolkit.key_binding import KeyBindings

NoRaincheck

import pandas as pd
import statsmodels.api as sm


def test_rolling_mode():
    """
    The goal of this application level monitoring is to react in light of changes
    to re-model fits and movement in the modelling process. In the linear model
    scenario, confidence intervals around the coefficients can be provided which
    can be used as a measure for model drift over time.

NoRaincheck

"""
This is a manual implementaiton of grucell so that it will work in more
general envrionments...
"""

import tensorflow as tf


input_size = 64
cell_size = 32

NoRaincheck

<script src='https://unpkg.com/[email protected]/dist/tesseract.min.js'></script>
<input type="file" onchange="getRego(this.files)">

<script>
function getRego(files) {
    const worker = new Tesseract.TesseractWorker();
    worker.recognize(files[0]).then(function(data){
        // construct something which glues all the words together...
        console.log(data.text);
        })

NoRaincheck

import numpy as np

def linear_part(x, w):
    return x * w

def non_linear(x, p=0.001):
    multi = (x > 0).astype(np.float64)
    multi[multi == 0] = 0.001
    return x * multi

NoRaincheck

pareto_principle <- function(x){
  return (x ^ (log(0.2)/log(0.8)))
}

sprintf("%.2f of the effort comes from %.2f of the causes", 0.99, pareto_principle(0.99))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.95, pareto_principle(0.95))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.8, pareto_principle(0.8))
sprintf("%.2f of the effort comes from %.2f of the causes", 0.5, pareto_principle(0.5))

NoRaincheck

FROM jupyter/r-notebook

# docker build --rm -t jupyter/extended-notebook .
# docker run --rm -it -p 8888:8888 jupyter/extended-notebook


RUN conda install --quiet --yes -c r rstudio=1.1*
RUN conda install --quiet --yes -c anaconda pandas scipy gensim scikit-learn

# install nbrsessionproxy

NoRaincheck

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#