joseph-allen

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import GridSearchCV, StratifiedKFold, learning_curve
from sklearn.ensemble import GradientBoostingClassifier


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):
    """Generate a simple plot of the test and training learning curve"""
    plt.figure()

joseph-allen

import seaborn as sns


g = sns.distplot(dataset["Feature"], color="m", label="Skewness : %.2f"%(dataset["Feature"].skew()))
g = g.legend(loc="best")

# Apply log to Feature to reduce skewness distribution
# dataset["Feature"] = dataset["Feature"].map(lambda i: np.log(i) if i > 0 else 0)

joseph-allen

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

# Handle table-like data and matrices
import numpy as np
import pandas as pd

# Modelling Algorithms
from sklearn.tree import DecisionTreeClassifier

joseph-allen

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.tree import DecisionTreeClassifier


def plot_histograms(df, variables, n_rows, n_cols):
    fig = plt.figure(figsize=(16, 12))
    for i, var_name in enumerate(variables):
        ax = fig.add_subplot(n_rows, n_cols, i+1)

joseph-allen

@pytest.fixture(params=[('input', 'expected')])
def get_input(request):
    return request.param


@pytest.fixture
def get_function_result(get_input):
    return test_function(get_input[0])

joseph-allen

jupyter nbconvert --to pdf Report.ipynb  --TemplateExporter.exclude_input=True

joseph-allen

import pandas as pd
import sys
import itertools


def permute(df):
    columns = [df[column] for column in list(df)]
    uniq_columns = [columns[x].unique() for x in range(0, len(columns))]

    return pd.DataFrame(list(itertools.product(*uniq_columns)),

joseph-allen

#!/bin/bash

echo -n "GitHub User: "
read USER

echo -n "GitHub Password: "
read -s PASS

echo ""
echo -n "GitHub Repo (e.g. foo/bar): "

joseph-allen

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

joseph-allen

/**
 * @fileoverview Rule to flag use of _.cloneDeep
 * @author Joseph Allen
 */

//------------------------------------------------------------------------------
// Rule Definition
//------------------------------------------------------------------------------

module.exports = function(context) {