Code/orpheus/analysis/chart_nb_x_alpha.ipynb

{
 "cells": [
  {
   "source": [
    "Copyright (C) 2020 Ralph \"Blake\" Vente and Anthony Chen\n",
    "\n",
    "orpheus is free software: you can redistribute it and/or modify it under the\n",
    "terms of the GNU General Public License as published by the Free Software\n",
    "Foundation, either version 3 of the License, or (at your option) any later\n",
    "version.\n",
    "\n",
    "This program queries the filesysytem's logs and produces charts based on the\n",
    "experiments in those logs.\n",
    "\n",
    "This one in particular performs things for our naive bayes experiment."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "FileSystemExperimentLoader(\"../logs\")"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('.')\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import incense\n",
    "from incense.experiment_loader import FileSystemExperimentLoader\n",
    "\n",
    "basedir = Path(\"../logs/\")\n",
    "loader = FileSystemExperimentLoader(basedir)\n",
    "loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hack: only run this cell _after_ generating some other graph first.\n",
    "matplotlib.use(\"pgf\")\n",
    "matplotlib.rcParams.update({\n",
    "    \"pgf.texsystem\": \"pdflatex\",\n",
    "    'font.family': 'serif',\n",
    "    'text.usetex': True,\n",
    "    'pgf.rcfonts': False,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(dataframe, title=\"Title\", xlabel=\"xlabel\", ylabel=\"ylabel\"):\n",
    "    graph = dataframe.rename(columns={\"review_contents\": \"review contents\", \"pos_tags\": \"POS tags L0\", \"path_pos_bigrams\": \"POS paths L1\", \"nested_pos_bigrams\" : 'POS nested pairs', 'path_pos_trigrams': 'POS paths L2'}).plot(title=title)\n",
    "    graph.set_xlabel(xlabel)\n",
    "    graph.set_ylabel(ylabel)\n",
    "    return graph.get_figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         review_contents  pos_tags  nested_pos_bigrams  path_pos_bigrams  \\\n",
       "0.00010         0.564000  0.398667            0.319333          0.390667   \n",
       "0.00020         0.588000  0.441333            0.350000          0.428000   \n",
       "0.00030         0.601333  0.460000            0.370000          0.454667   \n",
       "0.00050         0.623333  0.495333            0.399333          0.494667   \n",
       "0.00075         0.648667  0.520667            0.424667          0.523333   \n",
       "0.00100         0.666667  0.538000            0.446000          0.550000   \n",
       "0.00200         0.704000  0.586667            0.502667          0.605333   \n",
       "0.00300         0.726000  0.615333            0.532000          0.622000   \n",
       "0.00400         0.734667  0.630000            0.560000          0.638000   \n",
       "0.00500         0.746000  0.638667            0.575333          0.647333   \n",
       "0.00600         0.752000  0.644000            0.582000          0.652667   \n",
       "0.01000         0.764000  0.638667            0.589333          0.646667   \n",
       "0.02000         0.760000  0.585333            0.564000          0.575333   \n",
       "\n",
       "         path_pos_trigrams  \n",
       "0.00010           0.273333  \n",
       "0.00020           0.303333  \n",
       "0.00030           0.330667  \n",
       "0.00050           0.359333  \n",
       "0.00075           0.394667  \n",
       "0.00100           0.411333  \n",
       "0.00200           0.468000  \n",
       "0.00300           0.495333  \n",
       "0.00400           0.507333  \n",
       "0.00500           0.504667  \n",
       "0.00600           0.502000  \n",
       "0.01000           0.472667  \n",
       "0.02000           0.381333  "
      ],
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     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "start = 447\n",
    "end = 512\n",
    "df = pd.DataFrame()\n",
    "for i in range(start,end):\n",
    "    exp = loader.find_by_id(i)\n",
    "    cfg = exp.config\n",
    "    feat = cfg['dataset']['feature_column']\n",
    "    alpha = cfg['naivebayes']['alpha']\n",
    "    df.loc[alpha, feat] = exp.result.value\n",
    "\n",
    "df.to_pickle('../results/nb_x_alpha_df_acc.pkl')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dataplot = plot(df, title=\"Naive Bayes TfIDF(min\\_df=.003)\", xlabel=\"Degree Of Smoothing (alpha)\", ylabel=\"Test Accuracy\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataplot.savefig('../figures/nb_x_alpha.pdf')"
   ]
  }
 ],
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