movie-sentiment-analysis-with-tf-idf-naive-bayes.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/ankumagawa/movie-sentiment-analysis-with-tf-idf-naive-bayes?scriptVersionId=99900601\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown","outputs":[],"execution_count":0},{"cell_type":"code","execution_count":1,"id":"6a5f123f","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2022-07-03T08:18:26.106432Z","iopub.status.busy":"2022-07-03T08:18:26.105673Z","iopub.status.idle":"2022-07-03T08:18:26.125501Z","shell.execute_reply":"2022-07-03T08:18:26.124723Z"},"papermill":{"duration":0.032788,"end_time":"2022-07-03T08:18:26.128698","exception":false,"start_time":"2022-07-03T08:18:26.09591","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"id":"7f19822d","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:26.146329Z","iopub.status.busy":"2022-07-03T08:18:26.145637Z","iopub.status.idle":"2022-07-03T08:18:28.196584Z","shell.execute_reply":"2022-07-03T08:18:28.195735Z"},"papermill":{"duration":2.062197,"end_time":"2022-07-03T08:18:28.199249","exception":false,"start_time":"2022-07-03T08:18:26.137052","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","\n","from nltk.corpus import stopwords\n","from nltk.stem import WordNetLemmatizer  \n","stop_words = stopwords.words('english')\n","\n","import re\n","\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix,classification_report\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","\n","from sklearn.naive_bayes import MultinomialNB"]},{"cell_type":"markdown","id":"ce8eb755","metadata":{"papermill":{"duration":0.00656,"end_time":"2022-07-03T08:18:28.212721","exception":false,"start_time":"2022-07-03T08:18:28.206161","status":"completed"},"tags":[]},"source":["# 1. UNDERSTAND THE DATA: \n"]},{"cell_type":"code","execution_count":3,"id":"208ab783","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:28.228601Z","iopub.status.busy":"2022-07-03T08:18:28.227924Z","iopub.status.idle":"2022-07-03T08:18:29.791138Z","shell.execute_reply":"2022-07-03T08:18:29.790378Z"},"papermill":{"duration":1.57475,"end_time":"2022-07-03T08:18:29.79439","exception":false,"start_time":"2022-07-03T08:18:28.21964","status":"completed"},"tags":[]},"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>review</th>\n","      <th>sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>One of the other reviewers has mentioned that ...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I thought this was a wonderful way to spend ti...</td>\n","      <td>positive</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Basically there's a family where a little boy ...</td>\n","      <td>negative</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n","      <td>positive</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                              review sentiment\n","0  One of the other reviewers has mentioned that ...  positive\n","1  A wonderful little production. <br /><br />The...  positive\n","2  I thought this was a wonderful way to spend ti...  positive\n","3  Basically there's a family where a little boy ...  negative\n","4  Petter Mattei's \"Love in the Time of Money\" is...  positive"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# import data:\n","\n","df=pd.read_csv('../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv')\n","df.head()"]},{"cell_type":"code","execution_count":4,"id":"ecd7bfa3","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:29.815187Z","iopub.status.busy":"2022-07-03T08:18:29.814776Z","iopub.status.idle":"2022-07-03T08:18:29.822879Z","shell.execute_reply":"2022-07-03T08:18:29.821688Z"},"papermill":{"duration":0.019506,"end_time":"2022-07-03T08:18:29.825502","exception":false,"start_time":"2022-07-03T08:18:29.805996","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(50000, 2)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# number of observation and features:\n","df.shape"]},{"cell_type":"code","execution_count":5,"id":"801d611f","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:29.841851Z","iopub.status.busy":"2022-07-03T08:18:29.841475Z","iopub.status.idle":"2022-07-03T08:18:29.883944Z","shell.execute_reply":"2022-07-03T08:18:29.882383Z"},"papermill":{"duration":0.0543,"end_time":"2022-07-03T08:18:29.887119","exception":false,"start_time":"2022-07-03T08:18:29.832819","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 50000 entries, 0 to 49999\n","Data columns (total 2 columns):\n"," #   Column     Non-Null Count  Dtype \n","---  ------     --------------  ----- \n"," 0   review     50000 non-null  object\n"," 1   sentiment  50000 non-null  object\n","dtypes: object(2)\n","memory usage: 781.4+ KB\n"]}],"source":["# data info:\n","df.info()"]},{"cell_type":"code","execution_count":6,"id":"cb14f1ea","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:29.904782Z","iopub.status.busy":"2022-07-03T08:18:29.903614Z","iopub.status.idle":"2022-07-03T08:18:30.122587Z","shell.execute_reply":"2022-07-03T08:18:30.121489Z"},"papermill":{"duration":0.230002,"end_time":"2022-07-03T08:18:30.125092","exception":false,"start_time":"2022-07-03T08:18:29.89509","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<AxesSubplot:>"]},"execution_count":6,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# check the balance of sentiment feature:\n","df['sentiment'].value_counts().plot(kind='bar')"]},{"cell_type":"code","execution_count":7,"id":"0963422a","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:30.142303Z","iopub.status.busy":"2022-07-03T08:18:30.141881Z","iopub.status.idle":"2022-07-03T08:18:30.159089Z","shell.execute_reply":"2022-07-03T08:18:30.158028Z"},"papermill":{"duration":0.029034,"end_time":"2022-07-03T08:18:30.161755","exception":false,"start_time":"2022-07-03T08:18:30.132721","status":"completed"},"tags":[]},"outputs":[],"source":["# one-hot encoding: \n","\n","df= pd.get_dummies(df, columns= ['sentiment'],drop_first= True)"]},{"cell_type":"code","execution_count":8,"id":"197d162d","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:30.179317Z","iopub.status.busy":"2022-07-03T08:18:30.178923Z","iopub.status.idle":"2022-07-03T08:18:30.189001Z","shell.execute_reply":"2022-07-03T08:18:30.188276Z"},"papermill":{"duration":0.021292,"end_time":"2022-07-03T08:18:30.191021","exception":false,"start_time":"2022-07-03T08:18:30.169729","status":"completed"},"tags":[]},"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>review</th>\n","      <th>sentiment_positive</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>One of the other reviewers has mentioned that ...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I thought this was a wonderful way to spend ti...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Basically there's a family where a little boy ...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                              review  sentiment_positive\n","0  One of the other reviewers has mentioned that ...                   1\n","1  A wonderful little production. <br /><br />The...                   1\n","2  I thought this was a wonderful way to spend ti...                   1\n","3  Basically there's a family where a little boy ...                   0\n","4  Petter Mattei's \"Love in the Time of Money\" is...                   1"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","id":"55369adf","metadata":{"papermill":{"duration":0.007618,"end_time":"2022-07-03T08:18:30.206555","exception":false,"start_time":"2022-07-03T08:18:30.198937","status":"completed"},"tags":[]},"source":["# 2) Data Preprocessing: "]},{"cell_type":"code","execution_count":9,"id":"b9107f80","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:30.22435Z","iopub.status.busy":"2022-07-03T08:18:30.223901Z","iopub.status.idle":"2022-07-03T08:18:30.22855Z","shell.execute_reply":"2022-07-03T08:18:30.227396Z"},"papermill":{"duration":0.01649,"end_time":"2022-07-03T08:18:30.230898","exception":false,"start_time":"2022-07-03T08:18:30.214408","status":"completed"},"tags":[]},"outputs":[],"source":["# remove url\n","# remove html tags\n","# remove punctuations\n","# remove stopwords\n","# remove emoji\n","# lemmatization"]},{"cell_type":"code","execution_count":10,"id":"2280a20f","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:30.248721Z","iopub.status.busy":"2022-07-03T08:18:30.248317Z","iopub.status.idle":"2022-07-03T08:18:30.258592Z","shell.execute_reply":"2022-07-03T08:18:30.257695Z"},"papermill":{"duration":0.021695,"end_time":"2022-07-03T08:18:30.260637","exception":false,"start_time":"2022-07-03T08:18:30.238942","status":"completed"},"tags":[]},"outputs":[],"source":["sw = stopwords.words('english')\n","lemmatizer = WordNetLemmatizer() \n","\n","def cleantext(text):\n","    text= text.lower()\n","    text= re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", text)\n","    text= re.sub(r\"http\\S+\", \"\",text)\n","    text= re.sub(r\"http\", \"\",text)\n","    \n","    punctuations= '@#!?+&*[]-%.:/();$=><|{}^' + \"'`\" + '_'\n","    for p in punctuations:\n","        text= text.replace(p, '')\n","        \n","    text= [word.lower() for word in text.split() if word.lower() not in sw]\n","    \n","    text= [lemmatizer.lemmatize(word) for word in text]\n","    \n","    text = \" \".join(text)\n","    \n","    emoji_pattern = re.compile(\"[\"\n","                           u\"\\U0001F600-\\U0001F64F\"  \n","                           u\"\\U0001F300-\\U0001F5FF\"  \n","                           u\"\\U0001F680-\\U0001F6FF\"  \n","                           u\"\\U0001F1E0-\\U0001F1FF\"  \n","                           u\"\\U00002702-\\U000027B0\"\n","                           u\"\\U000024C2-\\U0001F251\"\n","                           \"]+\", flags=re.UNICODE)\n","    \n","    text= emoji_pattern.sub(r'',text)\n","    \n","    return text"]},{"cell_type":"code","execution_count":11,"id":"8b5dd0b1","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:18:30.278479Z","iopub.status.busy":"2022-07-03T08:18:30.277718Z","iopub.status.idle":"2022-07-03T08:19:40.300613Z","shell.execute_reply":"2022-07-03T08:19:40.299483Z"},"papermill":{"duration":70.043006,"end_time":"2022-07-03T08:19:40.311571","exception":false,"start_time":"2022-07-03T08:18:30.268565","status":"completed"},"tags":[]},"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>review</th>\n","      <th>sentiment_positive</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>one reviewer mentioned watching oz episode hoo...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>wonderful little production br br filming tech...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>thought wonderful way spend time hot summer we...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>basically family little boy jake think zombie ...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>petter mattei love time money visually stunnin...</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                              review  sentiment_positive\n","0  one reviewer mentioned watching oz episode hoo...                   1\n","1  wonderful little production br br filming tech...                   1\n","2  thought wonderful way spend time hot summer we...                   1\n","3  basically family little boy jake think zombie ...                   0\n","4  petter mattei love time money visually stunnin...                   1"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['review'] = df['review'].apply(lambda x: cleantext(x))\n"," \n","df.head()"]},{"cell_type":"markdown","id":"64fc447c","metadata":{"papermill":{"duration":0.007964,"end_time":"2022-07-03T08:19:40.328221","exception":false,"start_time":"2022-07-03T08:19:40.320257","status":"completed"},"tags":[]},"source":["# 3) Train-test Split: "]},{"cell_type":"code","execution_count":12,"id":"ef6e76a8","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:40.347221Z","iopub.status.busy":"2022-07-03T08:19:40.346828Z","iopub.status.idle":"2022-07-03T08:19:40.361061Z","shell.execute_reply":"2022-07-03T08:19:40.360078Z"},"papermill":{"duration":0.026364,"end_time":"2022-07-03T08:19:40.363462","exception":false,"start_time":"2022-07-03T08:19:40.337098","status":"completed"},"tags":[]},"outputs":[],"source":["X= df['review']\n","y= df['sentiment_positive']\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state= 26)"]},{"cell_type":"markdown","id":"f324dfe0","metadata":{"papermill":{"duration":0.007806,"end_time":"2022-07-03T08:19:40.379513","exception":false,"start_time":"2022-07-03T08:19:40.371707","status":"completed"},"tags":[]},"source":["# 4) TF-IDF Vectorization"]},{"cell_type":"code","execution_count":13,"id":"ab686686","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:40.397093Z","iopub.status.busy":"2022-07-03T08:19:40.396716Z","iopub.status.idle":"2022-07-03T08:19:48.243313Z","shell.execute_reply":"2022-07-03T08:19:48.242163Z"},"papermill":{"duration":7.85824,"end_time":"2022-07-03T08:19:48.245795","exception":false,"start_time":"2022-07-03T08:19:40.387555","status":"completed"},"tags":[]},"outputs":[],"source":["tfidf_vectorizer = TfidfVectorizer() \n","\n","tfidf_train_vectors = tfidf_vectorizer.fit_transform(X_train) #applying tf idf to training data\n","\n","tfidf_test_vectors = tfidf_vectorizer.transform(X_test) #applying tf idf to training data"]},{"cell_type":"code","execution_count":14,"id":"abc7f936","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.263734Z","iopub.status.busy":"2022-07-03T08:19:48.263382Z","iopub.status.idle":"2022-07-03T08:19:48.26916Z","shell.execute_reply":"2022-07-03T08:19:48.268032Z"},"papermill":{"duration":0.017482,"end_time":"2022-07-03T08:19:48.271589","exception":false,"start_time":"2022-07-03T08:19:48.254107","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["n_samples: 40000, n_features: 97488\n"]}],"source":["# check the dimension of the data now: \n","print(\"n_samples: %d, n_features: %d\" % tfidf_train_vectors.shape)"]},{"cell_type":"markdown","id":"24265fad","metadata":{"papermill":{"duration":0.007969,"end_time":"2022-07-03T08:19:48.287701","exception":false,"start_time":"2022-07-03T08:19:48.279732","status":"completed"},"tags":[]},"source":["# 5) Naive Bayes Classifier\n","\n","I'll use Multinomial Naive Bayes for this model. \n","\n","Multinomial Naive Bayes has many industries and commercial applications in the field of Natural Language Processing."]},{"cell_type":"code","execution_count":15,"id":"f64246c0","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.306317Z","iopub.status.busy":"2022-07-03T08:19:48.305397Z","iopub.status.idle":"2022-07-03T08:19:48.352902Z","shell.execute_reply":"2022-07-03T08:19:48.35174Z"},"papermill":{"duration":0.059386,"end_time":"2022-07-03T08:19:48.355353","exception":false,"start_time":"2022-07-03T08:19:48.295967","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["MultinomialNB()"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["#naive bayes classifier\n","naive_bayes_classifier = MultinomialNB()\n","\n","naive_bayes_classifier.fit(tfidf_train_vectors, y_train)"]},{"cell_type":"code","execution_count":16,"id":"24cab3b0","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.374466Z","iopub.status.busy":"2022-07-03T08:19:48.374067Z","iopub.status.idle":"2022-07-03T08:19:48.388201Z","shell.execute_reply":"2022-07-03T08:19:48.386919Z"},"papermill":{"duration":0.026748,"end_time":"2022-07-03T08:19:48.390935","exception":false,"start_time":"2022-07-03T08:19:48.364187","status":"completed"},"tags":[]},"outputs":[],"source":["#predicted y\n","y_pred = naive_bayes_classifier.predict(tfidf_test_vectors)\n"]},{"cell_type":"code","execution_count":17,"id":"a02070ee","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.409592Z","iopub.status.busy":"2022-07-03T08:19:48.409226Z","iopub.status.idle":"2022-07-03T08:19:48.434937Z","shell.execute_reply":"2022-07-03T08:19:48.433401Z"},"papermill":{"duration":0.038045,"end_time":"2022-07-03T08:19:48.437508","exception":false,"start_time":"2022-07-03T08:19:48.399463","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.86      0.87      0.87      5067\n","           1       0.87      0.86      0.86      4933\n","\n","    accuracy                           0.87     10000\n","   macro avg       0.87      0.87      0.87     10000\n","weighted avg       0.87      0.87      0.87     10000\n","\n"]}],"source":["print(classification_report(y_test,y_pred))"]},{"cell_type":"code","execution_count":18,"id":"56a30c5b","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.45639Z","iopub.status.busy":"2022-07-03T08:19:48.456018Z","iopub.status.idle":"2022-07-03T08:19:48.463835Z","shell.execute_reply":"2022-07-03T08:19:48.463157Z"},"papermill":{"duration":0.019707,"end_time":"2022-07-03T08:19:48.466114","exception":false,"start_time":"2022-07-03T08:19:48.446407","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["array([[4432,  635],\n","       [ 702, 4231]])"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["cnf_matrix = confusion_matrix(y_test,y_pred)\n","\n","cnf_matrix"]},{"cell_type":"code","execution_count":19,"id":"3437b1f8","metadata":{"execution":{"iopub.execute_input":"2022-07-03T08:19:48.484698Z","iopub.status.busy":"2022-07-03T08:19:48.484334Z","iopub.status.idle":"2022-07-03T08:19:48.703399Z","shell.execute_reply":"2022-07-03T08:19:48.702205Z"},"papermill":{"duration":0.231088,"end_time":"2022-07-03T08:19:48.705646","exception":false,"start_time":"2022-07-03T08:19:48.474558","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["\n","group_names = ['TN','FP','FN','TP']\n","group_counts = [\"{0:0.0f}\".format(value) for value in cnf_matrix.flatten()]\n","labels = [f\"{v1}\\n{v2}\" for v1, v2 in zip(group_names,group_counts)]\n","labels = np.asarray(labels).reshape(2,2)\n","sns.heatmap(cnf_matrix, annot=labels, fmt='', cmap='Blues');\n"]},{"cell_type":"code","execution_count":null,"id":"46ee243c","metadata":{"papermill":{"duration":0.008294,"end_time":"2022-07-03T08:19:48.722683","exception":false,"start_time":"2022-07-03T08:19:48.714389","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.7.12"},"papermill":{"default_parameters":{},"duration":94.098783,"end_time":"2022-07-03T08:19:49.753728","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2022-07-03T08:18:15.654945","version":"2.3.4"}},"nbformat":4,"nbformat_minor":5}