Sequence_Mining.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DM_08_04\n",
    "## Import packages\n",
    "We'll create a hidden Markov model to examine the state-shifting in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting hmmlearn\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/c5/91b43156b193d180ed94069269bcf88d3c7c6e54514a8482050fa9995e10/hmmlearn-0.2.2.tar.gz (146kB)\n",
      "\u001b[K     |████████████████████████████████| 153kB 3.4MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.10 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from hmmlearn) (1.16.4)\n",
      "Requirement already satisfied: scikit-learn>=0.16 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from hmmlearn) (0.21.2)\n",
      "Requirement already satisfied: joblib>=0.11 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikit-learn>=0.16->hmmlearn) (0.13.2)\n",
      "Requirement already satisfied: scipy>=0.17.0 in /srv/conda/envs/notebook/lib/python3.7/site-packages (from scikit-learn>=0.16->hmmlearn) (1.3.0)\n",
      "Building wheels for collected packages: hmmlearn\n",
      "  Building wheel for hmmlearn (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jovyan/.cache/pip/wheels/2c/b6/0e/63a865a30e21e01d04f417d8995fbfb793d6bd464707efc546\n",
      "Successfully built hmmlearn\n",
      "Installing collected packages: hmmlearn\n",
      "Successfully installed hmmlearn-0.2.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install hmmlearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pylab\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from hmmlearn.hmm import GaussianHMM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data\n",
    "Read CSV file into \"df.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row</th>\n",
       "      <th>rt</th>\n",
       "      <th>corr</th>\n",
       "      <th>Pacc</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.456770</td>\n",
       "      <td>cor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.602119</td>\n",
       "      <td>cor</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.253829</td>\n",
       "      <td>inc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5.451038</td>\n",
       "      <td>inc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.872118</td>\n",
       "      <td>inc</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row        rt corr  Pacc prev\n",
       "0    1  6.456770  cor   0.0  inc\n",
       "1    2  5.602119  cor   0.0  cor\n",
       "2    3  6.253829  inc   0.0  cor\n",
       "3    4  5.451038  inc   0.0  inc\n",
       "4    5  5.872118  inc   0.0  inc"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"speed.csv\", sep = \",\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the row number and \"corr\" so we can focus on the influence of \"prev\" and \"Pacc\" on \"rt.\" Also define \"prev\" as a factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop([\"row\", \"corr\"], axis = 1)\n",
    "x[\"prev\"] = pd.factorize(x[\"prev\"])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create model\n",
    "Make an HMM with 2 states. (The choice of 2 is based on theory.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConvergenceMonitor(\n",
       "    history=[-116.44940310318694, -116.44670518278312],\n",
       "    iter=8,\n",
       "    n_iter=10000,\n",
       "    tol=0.01,\n",
       "    verbose=False,\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GaussianHMM(n_components=2, n_iter=10000, random_state=1).fit(x)\n",
    "model.monitor_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the hidden state for each record and get count of predicted states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    243\n",
       "1    196\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = model.predict(x)\n",
    "pd.Series(states).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the mean reaction time (rt) for each of the two states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.40098754, 5.56434105])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.means_[:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize results\n",
    "Make a graph to show the change of states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f97fdc68ef0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pylab.figure(figsize=(20, 1))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.grid(True)\n",
    "ax.set_xlabel(\"Record number\")\n",
    "ax.set_ylabel(\"State\")\n",
    "ax.plot(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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