{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mushroom Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of this activity is to employ the grid and randomized search strategies to find an optimal model capable of discerning whether a particular mushroom species is poisonous or not given attributed relating to its appearance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Load the data into Python and call the object mushrooms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data. Note the lack of header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mushrooms = pd.read_csv('./agaricus-lepiota.data', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mushrooms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.\tSeparate the target y and features X from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_raw = mushrooms.iloc[:,0]\n",
    "\n",
    "X_raw = mushrooms.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.\tRecode the target y such that poisonous mushrooms are represented as 1, edible mushrooms as 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = (y_raw == 'p') * 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.\tThe featureset X will need to have its columns transformed into a numpy array with a binary representation. This is known as ‘one hot encoding.’"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create and fit the encoder then transform the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = preprocessing.OneHotEncoder()\n",
    "\n",
    "encoder.fit(X_raw)\n",
    "\n",
    "X = encoder.transform(X_raw).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.\tConduct both a grid and random search to find an optimal hyperparameterization for a random forest classifier. Use accuracy as your method of model evaluation. Which method of tuning is more effective?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the random forest classifer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = ensemble.RandomForestClassifier(n_estimators=100, random_state=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import model select, define the grid, set up the grid search, start the grid search and visualise the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_features': [2, 4, 6, 8, 10, 12, 14]\n",
    "}\n",
    "\n",
    "gscv = model_selection.GridSearchCV(estimator=rfc, param_grid=grid, cv=5, scoring='accuracy')\n",
    "\n",
    "gscv.fit(X,y)\n",
    "\n",
    "results = pd.DataFrame(gscv.cv_results_)\n",
    "\n",
    "results.sort_values('rank_test_score', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.\tPlot mean test score vs hyperparameterization for the top 10 models found. Can you spot any obvious patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "    results\n",
    "    .sort_values('rank_test_score', ascending=False)\n",
    "    .loc[:,['params','mean_test_score']]\n",
    "    .head(10).plot.barh(x='params', xlim=(0.8))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the stats models, define the parameter dictionary an any distributions, conduct a randomized search and visualise the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "max_features = X.shape[1]\n",
    "\n",
    "param_dist = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_features': stats.randint(low=1, high=max_features)\n",
    "}\n",
    "\n",
    "rscv = model_selection.RandomizedSearchCV(estimator=rfc, param_distributions=param_dist, n_iter=50, cv=5, scoring='accuracy', random_state=100)\n",
    "\n",
    "rscv.fit(X,y)\n",
    "\n",
    "results = pd.DataFrame(rscv.cv_results_)\n",
    "\n",
    "results.sort_values('rank_test_score', ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.loc[:,'params'] = results.loc[:,'params'].astype(str)\n",
    "\n",
    "(\n",
    "    results.sort_values('rank_test_score', ascending=False)\n",
    "    .loc[:,['params','mean_test_score']]\n",
    "    .drop_duplicates()\n",
    "    .head(10)\n",
    "    .plot.barh(x='params', xlim=(0.8))\n",
    ")"
   ]
  }
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