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)]:

bbengfort

#!/usr/bin/env python
# codehead
# Scans the local directory if it's a git repository and adds the ID line.
#
# Author:   Benjamin Bengfort <benjamin@bengfort.com>
# Created:  Mon Mar 07 17:53:36 2016 -0500
#
# Copyright (C) 2016 Bengfort.com
# For license information, see LICENSE.txt
#

bbengfort

import numpy as np
import matplotlib.pyplot as plt

from yellowbrick.datasets import load_credit

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Load data from yellowbrick.datasets
X, y = load_credit()

bbengfort

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as grid

from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size


def joint_plot(x, y, ax=None):
    """
    Create a square joint plot of x and y.

bbengfort

type Blast struct {
	requests  uint64          // the number of successful requests
	failures  uint64          // the number of failed requests
	started   time.Time       // the time the benchmark was started
	duration  time.Duration   // the duration of the benchmark period
	latencies []time.Duration // observed latencies in the number of requests
}

// Run N operations against the server at addr by putting a unique key with
// random values of size S. Returns a Benchmark object whose objects can be