data/sklearn-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "454cf152-f0f2-41e7-876f-cfe8241b043f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "simple_train = ['hello', 'hi', 'good morning', 'good afternoon', 'it is raining', 'I am playing baseball', 'watching tv']\n",
    "greeting_train = ['yes', 'yes', 'yes', 'yes', 'no', 'no', 'no']\n",
    "greeting_df = pd.DataFrame({\n",
    "    'text': simple_train,\n",
    "    'greeting': greeting_train,\n",
    "});\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2efed064-22dd-4b2b-8503-1754005ea5bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>text</th>\n",
       "      <th>greeting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hello</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hi</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good morning</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good afternoon</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>it is raining</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             text greeting\n",
       "0           hello      yes\n",
       "1              hi      yes\n",
       "2    good morning      yes\n",
       "3  good afternoon      yes\n",
       "4   it is raining       no"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greeting_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90989557-4f5c-49f7-aab3-34285564b20d",
   "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>text</th>\n",
       "      <th>greeting</th>\n",
       "      <th>label_greeting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hello</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hi</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good morning</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>good afternoon</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>it is raining</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             text greeting  label_greeting\n",
       "0           hello      yes               1\n",
       "1              hi      yes               1\n",
       "2    good morning      yes               1\n",
       "3  good afternoon      yes               1\n",
       "4   it is raining       no               0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greeting_df['label_greeting'] = greeting_df.greeting.map({'no':0, 'yes':1})\n",
    "greeting_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a4b877d-e080-467d-8411-f09f39fa0be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = greeting_df.text\n",
    "y = greeting_df.label_greeting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b995359-f52c-47a2-86ce-252b570d7561",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "(2,)\n",
      "(5,)\n",
      "(2,)\n"
     ]
    }
   ],
   "source": [
    "# split X and y into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2c1ec2f-d5be-4913-87b5-010b4af753c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vect = CountVectorizer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "56410be3-272d-4f16-9b12-e729fdfb3c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<5x10 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 10 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_dtm = vect.fit_transform(X_train)\n",
    "X_train_dtm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "900f20ec-b6c7-48e9-a15f-871ce3ef9759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2x10 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 1 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transform testing data (using fitted vocabulary) into a document-term matrix\n",
    "X_test_dtm = vect.transform(X_test)\n",
    "X_test_dtm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a67703bc-79cd-4dd8-80af-1170f06eaab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import and instantiate a Multinomial Naive Bayes model\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4739968d-3783-4cb2-8ef1-476208c0700b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.23 ms, sys: 2.83 ms, total: 11.1 ms\n",
      "Wall time: 9.82 ms\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model using X_train_dtm (timing it with an IPython \"magic command\")\n",
    "%time nb.fit(X_train_dtm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "178918bb-1156-49b9-aa54-a1b641b2caa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make class predictions for X_test_dtm\n",
    "y_pred_class = nb.predict(X_test_dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e8c11f56-8808-4ae3-b169-295d436ad6c0",
   "metadata": {},
   "outputs": [
    {
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       "0.5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate accuracy of class predictions\n",
    "from sklearn import metrics\n",
    "metrics.accuracy_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "16bd86a5-1a3d-4a95-8948-d532f0a0e07c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = ['sing']\n",
    "test_xtm = vect.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "361e9d4b-b827-4272-a271-cb4d51d1541a",
   "metadata": {},
   "outputs": [
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     "data": {
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       "array([1])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred = nb.predict(test_xtm)\n",
    "test_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c207b890-45c1-47fd-a676-b4351c99b839",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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