bbengfort

#!/usr/bin/env python3

import os
import glob
import argparse
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

bbengfort

type Bitmask uint8

const (
	F0 bitmask = 1 << iota
	F1
	F2
)

func Set(b, flag Bitmask) Bitmask    { return b | flag }
func Clear(b, flag Bitmask) Bitmask  { return b &^ flag }

bbengfort

from yellowbrick.features.projection import ProjectionVisualizer

from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from sklearn.preprocessing import  StandardScaler
from sklearn.datasets import make_regression


class Visualizer(ProjectionVisualizer):

bbengfort

package main

import (
	"context"
	"fmt"
	"log"
	"time"

	"github.com/BTrDB/btrdb"
	"github.com/PingThingsIO/smartgridstore/modules/creds"

bbengfort

from sklearn.model_selection import train_test_split as tts


def visualizer(X, y, model, ax=None, hyperparam=42, random_state=None, tts_kwargs=None, poof=True, **kwargs):
    """
    This is the visualizer quick method and it is intended to perform the most common
    usage of the Visualizer in a single step. In this particular case, the quick method
    demonstrates the reduction of 5 lines of code into a single line, however, it also
    demonstrates how inflexible the quick method is. For example, the **kwargs can only
    be passed to ``Visualizer`` not to any of the other methods; additionally the kwargs

bbengfort

import os
import time
import btrdb

from tabulate import tabulate

db = btrdb.connect("api.research.predictivegrid.com:4411", "48BE21A7A13661B1EED5AB87")


def list_collection(prefix):

bbengfort

{% set name = "yellowbrick" %}
{% set version = "0.9.1" %}
{% set file_ext = "tar.gz" %}
{% set hash_type = "sha256" %}
{% set hash_value = "aa85f1f3898e972e39cf8934cca37a35b7b8d376f16606681a20c95b59e9c71c" %}

package:
  name: '{{ name|lower }}'
  version: '{{ version }}'

bbengfort

#!/usr/bin/env python

def hex2rgb(color):
    color = color.lstrip("#")
    return tuple(
        int(color[idx:idx+2], 16)
        for idx in (0, 2, 4)
    )

bbengfort

#!/usr/bin/env python3

"""
Visual reports for classification models using matplotlib and scikit-learn.
"""

import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split as tts

bbengfort

import numpy as np
import matplotlib.pyplot as plt

def plot_classification_report(cr, title='Classification report ', with_avg_total=False, cmap=plt.cm.Blues):

    lines = cr.split('\n')

    classes = []
    plotMat = []
    for line in lines[2 : (len(lines) - 3)]: