ch11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n",
      "/Users/matt/.env/364/lib/python3.6/site-packages/sklearn/ensemble/base.py:154: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = min(effective_n_jobs(n_jobs), n_estimators)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "from sklearn.experimental import (\n",
    "    enable_iterative_imputer,\n",
    ")\n",
    "from sklearn import (\n",
    "    ensemble,\n",
    "    impute,\n",
    "    model_selection,    \n",
    "    preprocessing,\n",
    "    tree,\n",
    ")\n",
    "from sklearn.ensemble import (\n",
    "    RandomForestClassifier,\n",
    ")\n",
    "from yellowbrick.model_selection import (\n",
    "    ValidationCurve,\n",
    ")\n",
    "\n",
    "url = (\n",
    "    \"http://biostat.mc.vanderbilt.edu/\"\n",
    "    \"wiki/pub/Main/DataSets/titanic3.xls\"\n",
    ")\n",
    "df = pd.read_excel(url)\n",
    "def tweak_titanic(df):\n",
    "    df = df.drop(\n",
    "        columns=[\n",
    "            \"name\",\n",
    "            \"ticket\",\n",
    "            \"home.dest\",\n",
    "            \"boat\",\n",
    "            \"body\",\n",
    "            \"cabin\",\n",
    "        ]\n",
    "    ).pipe(pd.get_dummies, drop_first=True)\n",
    "    return df\n",
    "\n",
    "def get_train_test_X_y(\n",
    "    df, y_col, size=0.3, std_cols=None\n",
    "):\n",
    "    y = df[y_col]\n",
    "    X = df.drop(columns=y_col)\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "        X, y, test_size=size, random_state=42\n",
    "    )\n",
    "    cols = X.columns\n",
    "    num_cols = [\n",
    "        \"pclass\",\n",
    "        \"age\",\n",
    "        \"sibsp\",\n",
    "        \"parch\",\n",
    "        \"fare\",\n",
    "    ]\n",
    "    fi = impute.IterativeImputer()\n",
    "\n",
    "    fitted = fi.fit_transform(X_train[num_cols])\n",
    "    X_train = X_train.assign(**{c:fitted[:,i] for i, c in enumerate(num_cols)})\n",
    "    test_fit = fi.transform(X_test[num_cols])\n",
    "    X_test = X_test.assign(**{c:test_fit[:,i] for i, c in enumerate(num_cols)})\n",
    "    if std_cols:\n",
    "        std = preprocessing.StandardScaler()\n",
    "        fitted = std.fit_transform(X_train[std_cols])\n",
    "        X_train = X_train.assign(**{c:fitted[:,i] for i, c in enumerate(std_cols)})\n",
    "        test_fit = std.transform(X_test[std_cols])\n",
    "        X_test = X_test.assign(**{c:test_fit[:,i] for i, c in enumerate(std_cols)})\n",
    "\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "ti_df = tweak_titanic(df)\n",
    "std_cols = \"pclass,age,sibsp,fare\".split(\",\")\n",
    "X_train, X_test, y_train, y_test = get_train_test_X_y(\n",
    "    ti_df, \"survived\", std_cols=std_cols\n",
    ")\n",
    "\n",
    "X = pd.concat([X_train, X_test])\n",
    "y = pd.concat([y_train, y_test])\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "vc_viz = ValidationCurve(\n",
    "    RandomForestClassifier(n_estimators=100),\n",
    "    param_name=\"max_depth\",\n",
    "    param_range=np.arange(1, 11),\n",
    "    cv=10,\n",
    "    n_jobs=-1,\n",
    ")\n",
    "vc_viz.fit(X, y)\n",
    "vc_viz.poof()\n",
    "#fig.savefig(\"images/mlpr_1101.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.model_selection import (\n",
    "    LearningCurve,\n",
    ")\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "lc3_viz = LearningCurve(\n",
    "    RandomForestClassifier(n_estimators=100),\n",
    "    cv=10,\n",
    ")\n",
    "lc3_viz.fit(X, y)\n",
    "lc3_viz.poof()\n",
    "#fig.savefig(\"images/mlpr_1102.png\", dpi=300)"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}