{ "cells": [ { "cell_type": "markdown", "id": "fab9b725-438b-49ab-b935-a0eab8900afc", "metadata": {}, "source": [ "# 11 Text Features Extraction" ] }, { "cell_type": "markdown", "id": "02b1488a-5e0b-4d55-a773-4211a54db2e4", "metadata": {}, "source": [ "Most classic machine learning algorithms can't take in raw text.\n", "\n", "\n", "It's necessary to perform a feature extraction from the raw text in order to pass numerical features to the machine learning algorithm." ] }, { "cell_type": "markdown", "id": "8cf1a7d1-e7d4-4ab5-805b-9fa48aad2d11", "metadata": {}, "source": [ "## CountVectorization" ] }, { "cell_type": "markdown", "id": "cdaa0238-a7ab-479e-b895-f53a190731eb", "metadata": {}, "source": [ "Text vectorization algorithm that transforms a text into a count of unique occurrences in the text." ] }, { "cell_type": "code", "execution_count": 1, "id": "439bb3a2-3862-46c0-aa30-4cb2f581328f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

CountVectorizer()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

" ], "text/plain": [ "CountVectorizer()" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "vect = CountVectorizer()\n", "\n", "messages = [\n", " \"Hey, let's go to the game today!\",\n", " \"Call your sister\",\n", " \"Want to go walk your dogs?\"\n", "]\n", "\n", "vect.fit(messages)" ] }, { "cell_type": "markdown", "id": "f4f9ee6d-32d4-4c46-b06d-df8549b62f47", "metadata": {}, "source": [ "Each unique word becomes a feature:" ] }, { "cell_type": "code", "execution_count": 3, "id": "f477ebc2-162a-4e00-8b5f-fe55238422fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['call', 'dogs', 'game', 'go', 'hey', 'let', 'sister', 'the', 'to',\n", " 'today', 'walk', 'want', 'your'], dtype=object)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vect.get_feature_names_out()" ] }, { "cell_type": "code", "execution_count": 6, "id": "04702527-6166-4e03-aa24-95d38eed1ded", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (0, 2)\t1\n", " (0, 3)\t1\n", " (0, 4)\t1\n", " (0, 5)\t1\n", " (0, 7)\t1\n", " (0, 8)\t1\n", " (0, 9)\t1\n", " (1, 0)\t1\n", " (1, 6)\t1\n", " (1, 12)\t1\n", " (2, 1)\t1\n", " (2, 3)\t1\n", " (2, 8)\t1\n", " (2, 10)\t1\n", " (2, 11)\t1\n", " (2, 12)\t1\n" ] } ], "source": [ "print(vect.transform(messages))" ] }, { "cell_type": "markdown", "id": "8f2d6a76-93ce-4779-8884-3b7ba6606f24", "metadata": {}, "source": [ "## FrequencyVectorization" ] }, { "cell_type": "markdown", "id": "fc17c88c-8b41-4f3d-9938-928bbe891494", "metadata": {}, "source": [ "FrequencyVectorization consists in taking the words frequency for each unique word.\n", "\n", "\n", "It's important to remember that stop words will have an high frequency." ] }, { "cell_type": "markdown", "id": "c53300ad-7936-4f41-971b-ef6948c7cf74", "metadata": {}, "source": [ "## Inverse Frequency Vectorization" ] }, { "cell_type": "markdown", "id": "0900ee3d-c953-4957-9b17-28897ac5f9a2", "metadata": {}, "source": [ "Inverse Frequency Vectorization consists in taking the inverce frequence of each unique word in order to give more value to low frequency words." ] }, { "cell_type": "code", "execution_count": 13, "id": "3641d176-5c5e-4b11-87fc-c7b3bdfab58c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (0, 9)\t0.40301621080355077\n", " (0, 2)\t0.40301621080355077\n", " (0, 7)\t0.40301621080355077\n", " (0, 8)\t0.3065042162415877\n", " (0, 3)\t0.3065042162415877\n", " (0, 5)\t0.40301621080355077\n", " (0, 4)\t0.40301621080355077\n", " (1, 6)\t0.6227660078332259\n", " (1, 12)\t0.4736296010332684\n", " (1, 0)\t0.6227660078332259\n", " (2, 1)\t0.45954803293870056\n", " (2, 10)\t0.45954803293870056\n", " (2, 11)\t0.45954803293870056\n", " (2, 12)\t0.3494981241087058\n", " (2, 8)\t0.3494981241087058\n", " (2, 3)\t0.3494981241087058\n" ] } ], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "vect = TfidfVectorizer()\n", "dtm = vect.fit_transform(messages)\n", "\n", "print(dtm)" ] }, { "cell_type": "markdown", "id": "453d875c-13f8-4141-820e-6ed386b7f286", "metadata": {}, "source": [ "## Building a vocabulary" ] }, { "cell_type": "code", "execution_count": 14, "id": "a9c6f894-d4c8-4b00-a28a-9e22b24092a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing 1.txt\n" ] } ], "source": [ "%%writefile 1.txt\n", "This is a story about cats\n", "Our feline pets\n", "Cats are furry animals" ] }, { "cell_type": "code", "execution_count": 15, "id": "1fc841b6-68e4-469f-a7a3-d70b2faacc71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing 2.txt\n" ] } ], "source": [ "%%writefile 2.txt\n", "This story is about surfing\n", "Catching waves is fun\n", "Surfing is a popular water sport" ] }, { "cell_type": "code", "execution_count": 16, "id": "cab2d278-1b74-47bc-b5d5-3b7e14f18d1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'this': 1, 'is': 2, 'a': 3, 'story': 4, 'about': 5, 'cats': 6, 'our': 7, 'feline': 8, 'pets': 9, 'are': 10, 'furry': 11, 'animals': 12}\n" ] } ], "source": [ "vocab = {}\n", "i = 1\n", "\n", "with open('1.txt') as f:\n", " # array of lower case words\n", " x = f.read().lower().split()\n", "\n", "for word in x:\n", " if word in vocab:\n", " continue\n", " else:\n", " # assign a unique id to each word\n", " vocab[word] = i\n", " i+=1\n", "\n", "print(vocab)" ] }, { "cell_type": "markdown", "id": "8e0fc62b-5386-4bda-867b-3f09edfcd54e", "metadata": {}, "source": [ "Assign a unique id to each word also for 2nd doc:" ] }, { "cell_type": "code", "execution_count": 17, "id": "744d4552-cca2-42b6-9bf6-9efae5556fd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'this': 1, 'is': 2, 'a': 3, 'story': 4, 'about': 5, 'cats': 6, 'our': 7, 'feline': 8, 'pets': 9, 'are': 10, 'furry': 11, 'animals': 12, 'surfing': 13, 'catching': 14, 'waves': 15, 'fun': 16, 'popular': 17, 'water': 18, 'sport': 19}\n" ] } ], "source": [ "with open('2.txt') as f:\n", " # array of lower case words\n", " x = f.read().lower().split()\n", "\n", "for word in x:\n", " if word in vocab:\n", " continue\n", " else:\n", " # assign a unique id to each word\n", " vocab[word] = i\n", " i+=1\n", "\n", "print(vocab)" ] }, { "cell_type": "code", "execution_count": 19, "id": "a9dca426-330f-4236-ac81-92d55d3e45f2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1.txt', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create an array containing a zero for each word in the vocab, in order to set an initial state to count words.\n", "one = ['1.txt']+[0]*len(vocab)\n", "one" ] }, { "cell_type": "markdown", "id": "9f64c65c-8a77-4084-836f-d556833b3322", "metadata": {}, "source": [ "Map to each zero in the array the count of occurrences of the term:" ] }, { "cell_type": "code", "execution_count": 20, "id": "de2043cd-3d50-4fd9-99cc-a8b1bfeb677f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['1.txt', 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('1.txt') as f:\n", " x = f.read().lower().split()\n", "\n", "for word in x:\n", " one[vocab[word]]+=1\n", "\n", "one" ] }, { "cell_type": "code", "execution_count": 22, "id": "9ae0f6aa-0858-4682-a043-b401efbeb9ed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['2.txt', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "two = ['2.txt']+[0]*len(vocab)\n", "two" ] }, { "cell_type": "code", "execution_count": 23, "id": "7ee740cb-d9c5-48f2-b36c-812b2a35e7b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['2.txt', 1, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('2.txt') as f:\n", " x = f.read().lower().split()\n", "\n", "for word in x:\n", " two[vocab[word]]+=1\n", "\n", "two" ] }, { "cell_type": "markdown", "id": "f86c7561-3f2b-47ca-8804-91790a276014", "metadata": {}, "source": [ "Comparing the 2 vectors:" ] }, { "cell_type": "code", "execution_count": 24, "id": "e6013375-71a4-4557-916a-fd6b57acd5ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1.txt', 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]\n", "['2.txt', 1, 3, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 1, 1, 1, 1]\n" ] } ], "source": [ "print(f'{one}\\n{two}')" ] }, { "cell_type": "markdown", "id": "295861a0-c82f-48d3-9dc6-a20b9dce3663", "metadata": {}, "source": [ "## Scikit-Learn Features Extraction" ] }, { "cell_type": "code", "execution_count": 25, "id": "83ffa703-99b8-42d1-a427-bfdbe3287322", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 26, "id": "56fdd9ec-98a7-4589-8dc6-d1a0fa574f58", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "

	label	message	length	punct
0	ham	Go until jurong point, crazy.. Available only ...	111	9
1	ham	Ok lar... Joking wif u oni...	29	6
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...	155	6
3	ham	U dun say so early hor... U c already then say...	49	6
4	ham	Nah I don't think he goes to usf, he lives aro...	61	2

\n", "

" ], "text/plain": [ " label message length punct\n", "0 ham Go until jurong point, crazy.. Available only ... 111 9\n", "1 ham Ok lar... Joking wif u oni... 29 6\n", "2 spam Free entry in 2 a wkly comp to win FA Cup fina... 155 6\n", "3 ham U dun say so early hor... U c already then say... 49 6\n", "4 ham Nah I don't think he goes to usf, he lives aro... 61 2" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('./smsspamcollection.tsv', sep='\\t')\n", "df.head()" ] }, { "cell_type": "markdown", "id": "ef80b730-6670-4b58-926b-019c25960a93", "metadata": {}, "source": [ "Check null values:" ] }, { "cell_type": "code", "execution_count": 27, "id": "ab8b0a2d-b61e-4117-be4e-e5f4eacdbaa8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "label 0\n", "message 0\n", "length 0\n", "punct 0\n", "dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 28, "id": "51034f05-0cd5-4dca-bf1c-4d7f6b470c39", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "label\n", "ham 4825\n", "spam 747\n", "Name: count, dtype: int64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['label'].value_counts()" ] }, { "cell_type": "code", "execution_count": 29, "id": "61ffc47d-8f9d-47dc-b3a4-3f098c0fdb4e", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 30, "id": "fce13ad6-3d21-4b8e-a04c-646033223b4b", "metadata": {}, "outputs": [], "source": [ "X = df['message']\n", "y = df['label']" ] }, { "cell_type": "code", "execution_count": 33, "id": "1ecab0d3-ec83-445d-a330-24246ed195b2", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 34, "id": "f7208a34-419d-4335-b9ce-b2f9f345e184", "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "count_vect = CountVectorizer()" ] }, { "cell_type": "code", "execution_count": 35, "id": "ee531060-cdc7-4d8e-96d5-52512a351c90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Go until jurong point, crazy.. Available only ...\n", "1 Ok lar... Joking wif u oni...\n", "2 Free entry in 2 a wkly comp to win FA Cup fina...\n", "3 U dun say so early hor... U c already then say...\n", "4 Nah I don't think he goes to usf, he lives aro...\n", " ... \n", "5567 This is the 2nd time we have tried 2 contact u...\n", "5568 Will ü b going to esplanade fr home?\n", "5569 Pity, * was in mood for that. So...any other s...\n", "5570 The guy did some bitching but I acted like i'd...\n", "5571 Rofl. Its true to its name\n", "Name: message, Length: 5572, dtype: object" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "cell_type": "code", "execution_count": 36, "id": "db647759-369d-40d2-a8b9-a6e9daf76e4b", "metadata": {}, "outputs": [], "source": [ "X_train_counts = count_vect.fit_transform(X_train)" ] }, { "cell_type": "code", "execution_count": 38, "id": "3b0d924c-78ee-4d31-8a6b-378943c2c431", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (0, 7069)\t1\n", " (0, 4415)\t1\n", " (0, 1736)\t1\n", " (1, 5512)\t1\n", " (1, 4270)\t1\n", " (1, 5443)\t1\n", " (1, 6330)\t2\n", " (1, 5791)\t1\n", " (1, 878)\t1\n", " (1, 3008)\t1\n", " (1, 2156)\t2\n", " (1, 5468)\t1\n", " (1, 935)\t1\n", " (1, 2677)\t1\n", " (1, 2566)\t1\n", " (1, 5437)\t1\n", " (1, 6247)\t1\n", " (1, 3280)\t1\n", " (1, 7048)\t1\n", " (1, 957)\t1\n", " (1, 6250)\t1\n", " (1, 4470)\t1\n", " (1, 938)\t1\n", " (1, 5243)\t1\n", " (1, 4513)\t1\n", " :\t:\n", " (3728, 6913)\t1\n", " (3728, 2287)\t1\n", " (3728, 3769)\t1\n", " (3729, 1454)\t1\n", " (3729, 5795)\t1\n", " (3729, 3794)\t1\n", " (3729, 3674)\t1\n", " (3730, 5141)\t1\n", " (3730, 3085)\t1\n", " (3730, 2743)\t1\n", " (3730, 4902)\t1\n", " (3730, 5800)\t1\n", " (3730, 5799)\t1\n", " (3731, 6345)\t1\n", " (3731, 4429)\t1\n", " (3731, 5520)\t1\n", " (3731, 3505)\t1\n", " (3732, 3416)\t1\n", " (3732, 3532)\t1\n", " (3732, 2090)\t1\n", " (3732, 5423)\t1\n", " (3732, 3073)\t1\n", " (3732, 6119)\t1\n", " (3732, 5763)\t1\n", " (3732, 4285)\t1\n" ] } ], "source": [ "print(X_train_counts)" ] }, { "cell_type": "code", "execution_count": 39, "id": "6c3bc4ee-358c-4a3a-bfea-0a5841c6eaf7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3733, 7082)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_counts.shape" ] }, { "cell_type": "code", "execution_count": 40, "id": "d08add87-147e-4a3a-886b-9e5e83b44578", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3733,)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "markdown", "id": "e6c8bb9f-0fa2-4663-8b5e-24b963fcdb9b", "metadata": {}, "source": [ "It is common to perform a CountVectorization and then a Tfidf transformation:" ] }, { "cell_type": "code", "execution_count": 41, "id": "437d6f05-42be-43ba-93c8-35dc9003ef83", "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfTransformer\n", "\n", "tfidf_transformer = TfidfTransformer()" ] }, { "cell_type": "code", "execution_count": 44, "id": "c23911b7-f1b8-440a-a4c1-7b09e3032330", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3733, 7082)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n", "X_train_tfidf.shape" ] }, { "cell_type": "code", "execution_count": 45, "id": "de1a915a-d63a-4f32-9298-97bc2c4a8988", "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer" ] }, { "cell_type": "code", "execution_count": 46, "id": "7a242901-8065-4435-b315-748fd10af272", "metadata": {}, "outputs": [], "source": [ "vect = TfidfVectorizer()" ] }, { "cell_type": "markdown", "id": "13a5479a-2753-4a86-9ab1-44d1518d101c", "metadata": {}, "source": [ "This is a shortcut to combine the CountVectorization with Tfidf transformation:" ] }, { "cell_type": "code", "execution_count": 48, "id": "75a411c0-ff32-483a-bdfb-7c3da8af713b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3733, 7082)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_tfidf = vect.fit_transform(X_train)\n", "X_train_counts.shape" ] }, { "cell_type": "markdown", "id": "25e2eb69-7737-4737-8d02-84fba5e7405d", "metadata": {}, "source": [ "Training a classifier:" ] }, { "cell_type": "code", "execution_count": 49, "id": "242fec28-0b6b-46c6-a474-3ea24a6d7537", "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import LinearSVC" ] }, { "cell_type": "code", "execution_count": 50, "id": "9196b981-a04e-4c32-843a-696a3d6f9edb", "metadata": {}, "outputs": [], "source": [ "clf = LinearSVC()" ] }, { "cell_type": "code", "execution_count": 51, "id": "ffc321b5-2905-4b0b-8bb1-c56bb42dab1f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.11/site-packages/sklearn/svm/_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

LinearSVC()

" ], "text/plain": [ "LinearSVC()" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X_train_tfidf, y_train)" ] }, { "cell_type": "markdown", "id": "a113aded-75a0-4fa7-92ce-ada02f9da674", "metadata": {}, "source": [ "Combining all the previous steps into a single pipeline object:" ] }, { "cell_type": "code", "execution_count": 52, "id": "ad926f82-3afc-4f24-9030-eb37d3eca912", "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline" ] }, { "cell_type": "code", "execution_count": 53, "id": "56bc468e-b9c3-4576-b6b4-7535c05387e7", "metadata": {}, "outputs": [], "source": [ "text_clf = Pipeline([\n", " ('tfidf', TfidfVectorizer()),\n", " ('clf', LinearSVC())\n", "])" ] }, { "cell_type": "code", "execution_count": 54, "id": "3819fed8-83f7-404a-88ef-4a2ab97d9e25", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.11/site-packages/sklearn/svm/_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "

Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])

" ], "text/plain": [ "Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', LinearSVC())])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text_clf.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 55, "id": "8573a194-a334-48e7-b7c3-e660a693788b", "metadata": {}, "outputs": [], "source": [ "p = text_clf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 56, "id": "98c5ec2f-b2e8-4b40-a8b8-3601dee2e6b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['ham', 'ham', 'ham', ..., 'ham', 'ham', 'ham'], dtype=object)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p" ] }, { "cell_type": "code", "execution_count": 58, "id": "ad357804-70c1-4188-bfa0-65395d8c6cc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1586 7]\n", " [ 12 234]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report\n", "\n", "print(confusion_matrix(y_test, p))" ] }, { "cell_type": "code", "execution_count": 59, "id": "347d2fa2-7b12-41b2-88c8-7a1a24c135e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " ham 0.99 1.00 0.99 1593\n", " spam 0.97 0.95 0.96 246\n", "\n", " accuracy 0.99 1839\n", " macro avg 0.98 0.97 0.98 1839\n", "weighted avg 0.99 0.99 0.99 1839\n", "\n" ] } ], "source": [ "print(classification_report(y_test, p))" ] }, { "cell_type": "code", "execution_count": 60, "id": "ba28a7d6-85a3-4e75-ba64-f4b384121535", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.989668297988037\n" ] } ], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "print(accuracy_score(y_test, p))" ] }, { "cell_type": "code", "execution_count": 61, "id": "f04e7303-7307-47fc-850d-7ca921c021c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['ham'], dtype=object)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text_clf.predict([\"Hi how are you doing today?\"])" ] }, { "cell_type": "code", "execution_count": 62, "id": "71d9f9bc-d60d-4dd5-b522-77cf13f0b8c8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['spam'], dtype=object)" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text_clf.predict([\"Congratulations! you have been selected as a winner. TEXT WON to 44255 congratulations free entry to contest.\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "f60df050-e3f7-461f-8898-118b1640e263", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }