{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Google AI4Code – EDA as Binary Classification Task\n\n\n![image.png](attachment:72d3f5c2-c3fe-4a8b-8e91-2e045f56b624.png)\n\n\nThank you for your checking this notebook.\n\nThis is my EDA for \"Google AI4Code – Understand Code in Python Notebooks\" competition [(Link)](https://www.kaggle.com/competitions/AI4Code).\n\nThe task is to identify correct position of Markdown cells in sequence of code cells which are ordered correctly.\n\nSo my first approach is to prepare two types \"Correctly order\" & \"Not correctly order\" of data for training and solve task as binary classification.\n\n![image.png](attachment:73c96f9b-dea7-4d2c-b703-a500d705339c.png)\n\nIf you think this notebook is useful, please give your comment or question and I appreciate your upvote as well. :) \n\n<a id='top'></a>\n## Contents\n1. [Import Library & Set Config](#config)\n2. [Load Data](#load)\n3. [Preprocessing Data](#prep)\n4. [tf-idf](#tfidf)\n5. [Latent Dirichlet Allocation](#lda)\n6. [Wordcloud](#wordcloud)\n7. [TruncatedSVD](#svd)\n8. [t-sne](#tsne)\n9. [UMAP](#umap)\n10. [Cosine Similality](#cosi)\n11. [Conclusion](#conclusion)\n12. [Reference](#ref)\n","metadata":{},"attachments":{"72d3f5c2-c3fe-4a8b-8e91-2e045f56b624.png":{"image/png":"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"},"73c96f9b-dea7-4d2c-b703-a500d705339c.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a id='config'></a>\n\n---\n# 1. Import Library & Set Config\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"!pip install mglearn","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:23:19.933175Z","iopub.execute_input":"2022-07-03T00:23:19.933697Z","iopub.status.idle":"2022-07-03T00:23:29.275518Z","shell.execute_reply.started":"2022-07-03T00:23:19.93359Z","shell.execute_reply":"2022-07-03T00:23:29.272855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# General\nimport sys, warnings, time, os, copy, gc, re, random, json\nimport pickle as pkl\nwarnings.filterwarnings('ignore')\nfrom IPython.display import display\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\npd.set_option('display.max_rows', 100)\npd.set_option('display.max_columns', None)\n# pd.set_option(\"display.max_colwidth\", 10000)\nimport seaborn as sns\nsns.set()\nfrom pandas.io.json import json_normalize\nfrom pprint import pprint\nfrom pathlib import Path\nfrom tqdm import tqdm\ntqdm.pandas()\nfrom datetime import datetime, timedelta\nfrom scipy import sparse\nimport mglearn\n\n# Pre-Processing\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import LatentDirichletAllocation, TruncatedSVD\nfrom sklearn.manifold import TSNE\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom jrstc_util import cleanse_text_new, text_cleaning, clean\nfrom wordcloud import WordCloud, STOPWORDS\nimport umap\n\n# Model\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import make_pipeline","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:23:29.280932Z","iopub.execute_input":"2022-07-03T00:23:29.281529Z","iopub.status.idle":"2022-07-03T00:23:59.399129Z","shell.execute_reply.started":"2022-07-03T00:23:29.281474Z","shell.execute_reply":"2022-07-03T00:23:59.398331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Configuration\nDEBUG = False\nPATH_INPUT = Path('../input/AI4Code')\nSAMPLE_ID = '051d049a469e47'\n\nif DEBUG:\n    NUM_SAMPLE = 10\n    # NUM_SAMPLE = 1000\n    \nelse:\n    NUM_SAMPLE = 2000","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:26:56.408265Z","iopub.execute_input":"2022-07-03T00:26:56.409742Z","iopub.status.idle":"2022-07-03T00:26:56.416702Z","shell.execute_reply.started":"2022-07-03T00:26:56.409686Z","shell.execute_reply":"2022-07-03T00:26:56.415319Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dump_load(obj, fileName, mode):\n    if mode == 'wb':\n        with open(fileName, mode=mode) as f:\n            pkl.dump(obj, f)\n            \n    elif mode == 'rb':\n        with open(fileName, mode=mode) as f:\n            x = pkl.load(f)\n            \n            return x\n            \n    else:\n        print('Please give \"wb\" or \"rb\" as mode.')","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:23:59.411739Z","iopub.execute_input":"2022-07-03T00:23:59.412711Z","iopub.status.idle":"2022-07-03T00:23:59.430723Z","shell.execute_reply.started":"2022-07-03T00:23:59.412671Z","shell.execute_reply":"2022-07-03T00:23:59.429889Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='load'></a>\n\n---\n# 2. Load Data\n\nData discription page [Link](https://www.kaggle.com/competitions/AI4Code/data) explained that their mainly two folders for training and test are prepared. \n\nThe train folder contains about 140,000 JSON files which contains the code and markdown cells. The correct order of them are described in train_orders.csv.\n\nThe test folder contains only 4 JSON files for testing our code. The contents inside of this folder will be replaced by actual test set when the notebook is submitted.\n\nIn this notebook, as demostration checking the data based on sampling from train data.\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"def read_notes(path):\n    df = pd.read_json(path,\n                     dtype={'cell_type': 'category', 'source': 'str'}\n                     )\n    df = df.assign(id=path.stem).rename_axis('cell_id')\n    \n    return df","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:23:59.431873Z","iopub.execute_input":"2022-07-03T00:23:59.43259Z","iopub.status.idle":"2022-07-03T00:23:59.442337Z","shell.execute_reply.started":"2022-07-03T00:23:59.43255Z","shell.execute_reply":"2022-07-03T00:23:59.441299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listTrainPaths = list((PATH_INPUT / 'train').glob('*.json'))[:NUM_SAMPLE]\nlistTrainNotes = [read_notes(path) for path in tqdm(listTrainPaths)]\ndfTrain = pd.concat(listTrainNotes)\ndfTrain = dfTrain.set_index('id', append=True)\ndfTrain = dfTrain.swaplevel().sort_index(level='id', sort_remaining=False)\ndfTrain","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:23:59.44387Z","iopub.execute_input":"2022-07-03T00:23:59.444475Z","iopub.status.idle":"2022-07-03T00:24:00.422494Z","shell.execute_reply.started":"2022-07-03T00:23:59.444438Z","shell.execute_reply":"2022-07-03T00:24:00.421279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfOrders = pd.read_csv((PATH_INPUT / 'train_orders.csv'), index_col='id', squeeze=True)\ndfOrders = dfOrders.str.split()\ndfOrders","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:00.423849Z","iopub.execute_input":"2022-07-03T00:24:00.424296Z","iopub.status.idle":"2022-07-03T00:24:02.970013Z","shell.execute_reply.started":"2022-07-03T00:24:00.424252Z","shell.execute_reply":"2022-07-03T00:24:02.968808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfSample = dfTrain.loc[SAMPLE_ID, :]\nnumCode = dfSample[dfSample['cell_type'] == 'code'].shape[0]\nnumMark = dfSample[dfSample['cell_type'] == 'markdown'].shape[0]\nprint(f'Notebook {SAMPLE_ID} has {numCode} code cells and {numMark} markdown cells. \\n')\ndfSample","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:02.971503Z","iopub.execute_input":"2022-07-03T00:24:02.972047Z","iopub.status.idle":"2022-07-03T00:24:03.00551Z","shell.execute_reply.started":"2022-07-03T00:24:02.971999Z","shell.execute_reply":"2022-07-03T00:24:03.004558Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listOrders = dfOrders.loc[SAMPLE_ID]\ndfSample.loc[listOrders, :]","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:03.006766Z","iopub.execute_input":"2022-07-03T00:24:03.007126Z","iopub.status.idle":"2022-07-03T00:24:03.026395Z","shell.execute_reply.started":"2022-07-03T00:24:03.007092Z","shell.execute_reply":"2022-07-03T00:24:03.025339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfSample\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:03.027716Z","iopub.execute_input":"2022-07-03T00:24:03.028561Z","iopub.status.idle":"2022-07-03T00:24:03.476085Z","shell.execute_reply.started":"2022-07-03T00:24:03.028522Z","shell.execute_reply":"2022-07-03T00:24:03.474843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='prep'></a>\n\n---\n# 3. Preprocessing Data\n\nFirst, text in source column is cleansed by my util file.\n\nSecond, dataframe which have correctly ordered data is prepared and pick up markdown cell and two code cell before/after that. This data would have label, True (1).\n\nNext, dataframe which have shuffled markdown cell position is prepared and pick up markdown cell and two code cell before/after that. This data would have label, False (0).\n\nThen two dataframe are concatenated.\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"dfTrain['source'] = dfTrain['source'].progress_apply(cleanse_text_new)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:03.477601Z","iopub.execute_input":"2022-07-03T00:24:03.478221Z","iopub.status.idle":"2022-07-03T00:24:07.578666Z","shell.execute_reply.started":"2022-07-03T00:24:03.47818Z","shell.execute_reply":"2022-07-03T00:24:07.577699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listID = set(dfTrain.reset_index()['id'].tolist())\n\nn = 1\nfor nbid in tqdm(listID):\n    dfTemp = dfTrain.loc[nbid,:]\n    listMD = dfTemp[dfTemp['cell_type'] == 'markdown'].reset_index()['cell_id'].to_list()\n    listOrders = dfOrders.loc[nbid]\n    \n    for mdid in listMD:\n        pos = listOrders.index(mdid)\n        \n        if pos == 0:\n            x = dfTemp.loc[listOrders[:2],:].T\n            x.columns = ['markdown','code2']\n            x = x.drop('cell_type')\n            x['code1'] = 'start'\n            x = x.reindex(columns=['code1', 'markdown','code2'])\n            \n        elif pos == (len(listOrders)-1):\n            x = dfTemp.loc[listOrders[-2:],:].T\n            x.columns = ['code1', 'markdown']\n            x = x.drop('cell_type')\n            x['code2'] = 'end'\n            \n        else:\n            x = dfTemp.loc[listOrders[(pos-1):(pos+2)],:].T\n            x.columns = ['code1', 'markdown','code2']\n            x = x.drop('cell_type')\n            \n            \n        if n == 1:\n            dfTrue = x\n            \n        else:\n            dfTrue = pd.concat([dfTrue, x], axis=0)\n            \n        n += 1\n        \ndfTrue['label'] = 'True'\ndfTrue","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:07.579799Z","iopub.execute_input":"2022-07-03T00:24:07.580105Z","iopub.status.idle":"2022-07-03T00:24:07.829762Z","shell.execute_reply.started":"2022-07-03T00:24:07.580077Z","shell.execute_reply":"2022-07-03T00:24:07.8288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfFalse = dfTrue.copy()\ndfFalse['markdown'] = dfTrue['markdown'].sample(frac=1) \ndfFalse['label'] = 'False'\ndfFalse","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:07.832877Z","iopub.execute_input":"2022-07-03T00:24:07.833178Z","iopub.status.idle":"2022-07-03T00:24:07.851362Z","shell.execute_reply.started":"2022-07-03T00:24:07.833152Z","shell.execute_reply":"2022-07-03T00:24:07.850324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfAll = pd.concat([dfTrue, dfFalse], axis=0)\ndfAll['textAll'] = dfAll['code1'] + ' ' + dfAll['markdown'] + ' ' + dfAll['code2']","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:07.85297Z","iopub.execute_input":"2022-07-03T00:24:07.853448Z","iopub.status.idle":"2022-07-03T00:24:07.862723Z","shell.execute_reply.started":"2022-07-03T00:24:07.853412Z","shell.execute_reply":"2022-07-03T00:24:07.862028Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfTrue, dfFalse, dfTrain, dfTemp\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:07.864331Z","iopub.execute_input":"2022-07-03T00:24:07.864751Z","iopub.status.idle":"2022-07-03T00:24:08.289593Z","shell.execute_reply.started":"2022-07-03T00:24:07.864703Z","shell.execute_reply":"2022-07-03T00:24:08.288525Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='tfidf'></a>\n\n---\n# 4. tf-idf\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"xTrain = dfAll['textAll']\nyTrain = dfAll['label']\n\npipe = make_pipeline(TfidfVectorizer(min_df=5, norm=None),\n                     LogisticRegression())\nparam_grid = {'logisticregression__C': [0.001, 0.01, 0.1, 1, 10]}\n\ngrid = GridSearchCV(pipe, param_grid, cv=5)\ngrid.fit(xTrain, yTrain)\nprint(\"Best cross-validation score: {:.2f}\".format(grid.best_score_))","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:08.290637Z","iopub.execute_input":"2022-07-03T00:24:08.291275Z","iopub.status.idle":"2022-07-03T00:24:09.913784Z","shell.execute_reply.started":"2022-07-03T00:24:08.29122Z","shell.execute_reply":"2022-07-03T00:24:09.912752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"vectorizer = grid.best_estimator_.named_steps[\"tfidfvectorizer\"]\ndump_load(vectorizer, 'vectorizer.pkl', 'wb')\n\n# transform the training dataset:\nxTrain = vectorizer.transform(xTrain)\n# find maximum value for each of the features over dataset:\nmax_value = xTrain.max(axis=0).toarray().ravel()\nsorted_by_tfidf = max_value.argsort()\n# get feature names\nfeature_names = np.array(vectorizer.get_feature_names())\n\nprint(\"Features with lowest tfidf:\\n{}\".format(\n      feature_names[sorted_by_tfidf[:20]]))\n\nprint(\"Features with highest tfidf: \\n{}\".format(\n      feature_names[sorted_by_tfidf[-20:]]))","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:09.914957Z","iopub.execute_input":"2022-07-03T00:24:09.915631Z","iopub.status.idle":"2022-07-03T00:24:09.949121Z","shell.execute_reply.started":"2022-07-03T00:24:09.915596Z","shell.execute_reply":"2022-07-03T00:24:09.947957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='lda'></a>\n\n---\n# 5. Latent Dirichlet Allocation\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"lda = LatentDirichletAllocation(n_components=10, learning_method=\"batch\",\n                                max_iter=25, random_state=0)\n\ndocument_topics = lda.fit_transform(xTrain)\ndump_load(lda, 'lda.pkl', 'wb')\nprint(\"lda.components_.shape: {}\".format(lda.components_.shape))","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:09.950409Z","iopub.execute_input":"2022-07-03T00:24:09.950772Z","iopub.status.idle":"2022-07-03T00:24:11.270759Z","shell.execute_reply.started":"2022-07-03T00:24:09.950744Z","shell.execute_reply":"2022-07-03T00:24:11.269427Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"document_topics.shape","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.271915Z","iopub.execute_input":"2022-07-03T00:24:11.272282Z","iopub.status.idle":"2022-07-03T00:24:11.278241Z","shell.execute_reply.started":"2022-07-03T00:24:11.272251Z","shell.execute_reply":"2022-07-03T00:24:11.27718Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sorting = np.argsort(lda.components_, axis=1)[:, ::-1]\n\nmglearn.tools.print_topics(topics=range(10), feature_names=feature_names,\n                           sorting=sorting, topics_per_chunk=5, n_words=10)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.279526Z","iopub.execute_input":"2022-07-03T00:24:11.27983Z","iopub.status.idle":"2022-07-03T00:24:11.290044Z","shell.execute_reply.started":"2022-07-03T00:24:11.279796Z","shell.execute_reply":"2022-07-03T00:24:11.289045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listLDA = ['lda_'+str(i) for i in range(1, 11)]\ndfLDA = pd.DataFrame(document_topics, columns=listLDA)\ndfAll = pd.concat([dfAll.reset_index(drop=True), dfLDA], axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.291377Z","iopub.execute_input":"2022-07-03T00:24:11.291693Z","iopub.status.idle":"2022-07-03T00:24:11.303307Z","shell.execute_reply.started":"2022-07-03T00:24:11.291667Z","shell.execute_reply":"2022-07-03T00:24:11.302529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del feature_names, dfLDA, document_topics, listLDA\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.304297Z","iopub.execute_input":"2022-07-03T00:24:11.305124Z","iopub.status.idle":"2022-07-03T00:24:11.755466Z","shell.execute_reply.started":"2022-07-03T00:24:11.305086Z","shell.execute_reply":"2022-07-03T00:24:11.754194Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='wordcloud'></a>\n\n---\n# 6. Word Cloud\n\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"def show_wordcloud(text):\n    wordcloud = WordCloud(\n                          background_color='white',\n                          stopwords=set(STOPWORDS),\n                          max_words=200,\n                          max_font_size=40, \n                          random_state=42\n                         ).generate(text)\n\n    print(wordcloud)\n    fig = plt.figure(1)\n    plt.imshow(wordcloud)\n    plt.axis('off')\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.757228Z","iopub.execute_input":"2022-07-03T00:24:11.757636Z","iopub.status.idle":"2022-07-03T00:24:11.76602Z","shell.execute_reply.started":"2022-07-03T00:24:11.757604Z","shell.execute_reply":"2022-07-03T00:24:11.765097Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"textTrue = str(dfAll['textAll'][dfAll['label'] == 'True'])\ntextFalse = str(dfAll['textAll'][dfAll['label'] == 'False'])\nshow_wordcloud(textTrue)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:11.769563Z","iopub.execute_input":"2022-07-03T00:24:11.770183Z","iopub.status.idle":"2022-07-03T00:24:12.244243Z","shell.execute_reply.started":"2022-07-03T00:24:11.770147Z","shell.execute_reply":"2022-07-03T00:24:12.243296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'Wordcloud of False data.\\n')\nshow_wordcloud(textFalse)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:12.245395Z","iopub.execute_input":"2022-07-03T00:24:12.245708Z","iopub.status.idle":"2022-07-03T00:24:12.670681Z","shell.execute_reply.started":"2022-07-03T00:24:12.245679Z","shell.execute_reply":"2022-07-03T00:24:12.669578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del textFalse, textTrue\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:12.672189Z","iopub.execute_input":"2022-07-03T00:24:12.672695Z","iopub.status.idle":"2022-07-03T00:24:13.103638Z","shell.execute_reply.started":"2022-07-03T00:24:12.672656Z","shell.execute_reply":"2022-07-03T00:24:13.102652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='svd'></a>\n\n---\n# 7. TruncatedSVD\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"scaler = StandardScaler(with_mean=False)  # https://www.haya-programming.com/entry/2020/02/08/073241\nscaler.fit(xTrain)\ndump_load(scaler, 'scaler.pkl', 'wb')\n\nxTrainScaled = scaler.transform(xTrain)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.104971Z","iopub.execute_input":"2022-07-03T00:24:13.105814Z","iopub.status.idle":"2022-07-03T00:24:13.122016Z","shell.execute_reply.started":"2022-07-03T00:24:13.105767Z","shell.execute_reply":"2022-07-03T00:24:13.121062Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"svd = TruncatedSVD(n_components=2)\nsvd.fit(xTrainScaled)\ndump_load(svd, 'svd.pkl', 'wb')\n\nxTrainSVD = svd.transform(xTrainScaled)\nprint(\"Original shape: {}\".format(str(xTrainScaled.shape)))\nprint(\"Reduced shape: {}\".format(str(xTrainSVD.shape)))","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.123905Z","iopub.execute_input":"2022-07-03T00:24:13.124483Z","iopub.status.idle":"2022-07-03T00:24:13.147722Z","shell.execute_reply.started":"2022-07-03T00:24:13.124436Z","shell.execute_reply":"2022-07-03T00:24:13.14658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(15, 10))\nmglearn.discrete_scatter(xTrainSVD[:, 0], xTrainSVD[:, 1], yTrain)\nplt.legend(['True', 'False'], loc=\"best\")\nplt.gca().set_aspect(\"equal\")\nplt.xlabel(\"First principal component\")\nplt.ylabel(\"Second principal component\")","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.15017Z","iopub.execute_input":"2022-07-03T00:24:13.151662Z","iopub.status.idle":"2022-07-03T00:24:13.421051Z","shell.execute_reply.started":"2022-07-03T00:24:13.151601Z","shell.execute_reply":"2022-07-03T00:24:13.420012Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listSVD = ['SVD_'+str(i) for i in range(1, 3)]\ndfSVD = pd.DataFrame(xTrainSVD, columns=listSVD)\ndfAll = pd.concat([dfAll, dfSVD], axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.422316Z","iopub.execute_input":"2022-07-03T00:24:13.423087Z","iopub.status.idle":"2022-07-03T00:24:13.430751Z","shell.execute_reply.started":"2022-07-03T00:24:13.423048Z","shell.execute_reply":"2022-07-03T00:24:13.429792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfSVD, listSVD, xTrainSVD\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.432213Z","iopub.execute_input":"2022-07-03T00:24:13.432721Z","iopub.status.idle":"2022-07-03T00:24:13.87208Z","shell.execute_reply.started":"2022-07-03T00:24:13.432676Z","shell.execute_reply":"2022-07-03T00:24:13.870922Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='tsne'></a>\n\n---\n# 8. t-sne\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"tsne = TSNE(random_state=42)\nxTrainTsne = tsne.fit_transform(xTrainScaled)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:13.873427Z","iopub.execute_input":"2022-07-03T00:24:13.873864Z","iopub.status.idle":"2022-07-03T00:24:14.769149Z","shell.execute_reply.started":"2022-07-03T00:24:13.873832Z","shell.execute_reply":"2022-07-03T00:24:14.768322Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nmglearn.discrete_scatter(xTrainTsne[:, 0], xTrainTsne[:, 1], yTrain)\nplt.legend(['True', 'False'], loc=\"best\")\nplt.gca().set_aspect(\"equal\")\nplt.xlabel(\"t-SNE feature 0\")\nplt.ylabel(\"t-SNE feature 1\")","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:14.770391Z","iopub.execute_input":"2022-07-03T00:24:14.771037Z","iopub.status.idle":"2022-07-03T00:24:15.035706Z","shell.execute_reply.started":"2022-07-03T00:24:14.77099Z","shell.execute_reply":"2022-07-03T00:24:15.034713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='umap'></a>\n\n---\n# 9. UMAP\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"um = umap.UMAP(n_components=2)\num.fit(xTrainScaled)\ndump_load(um, 'umap.pkl', 'wb')\n\nxTrainUM = um.transform(xTrainScaled)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:15.036925Z","iopub.execute_input":"2022-07-03T00:24:15.037298Z","iopub.status.idle":"2022-07-03T00:24:29.461642Z","shell.execute_reply.started":"2022-07-03T00:24:15.037257Z","shell.execute_reply":"2022-07-03T00:24:29.460445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nmglearn.discrete_scatter(xTrainUM[:, 0], xTrainUM[:, 1], yTrain)\nplt.legend(['True', 'False'], loc=\"best\")\nplt.gca().set_aspect(\"equal\")\nplt.xlabel(\"UMAP feature 0\")\nplt.ylabel(\"UMAP feature 1\")","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:29.463296Z","iopub.execute_input":"2022-07-03T00:24:29.463732Z","iopub.status.idle":"2022-07-03T00:24:29.743217Z","shell.execute_reply.started":"2022-07-03T00:24:29.463685Z","shell.execute_reply":"2022-07-03T00:24:29.742243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"listUMAP = ['UMAP_'+str(i) for i in range(1, 3)]\ndfUMAP = pd.DataFrame(xTrainUM, columns=listUMAP)\ndfAll = pd.concat([dfAll, dfUMAP], axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:29.744669Z","iopub.execute_input":"2022-07-03T00:24:29.745026Z","iopub.status.idle":"2022-07-03T00:24:29.751788Z","shell.execute_reply.started":"2022-07-03T00:24:29.744994Z","shell.execute_reply":"2022-07-03T00:24:29.750615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del dfUMAP, xTrainUM, listUMAP\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:29.753397Z","iopub.execute_input":"2022-07-03T00:24:29.753752Z","iopub.status.idle":"2022-07-03T00:24:30.292842Z","shell.execute_reply.started":"2022-07-03T00:24:29.753722Z","shell.execute_reply":"2022-07-03T00:24:30.292185Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='cosi'></a>\n\n---\n# 10. Cosine Similality\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"vectCode1 = vectorizer.transform(dfAll['code1'])\nvectMD = vectorizer.transform(dfAll['markdown'])\nvectCode2 = vectorizer.transform(dfAll['code2'])","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:30.29376Z","iopub.execute_input":"2022-07-03T00:24:30.294459Z","iopub.status.idle":"2022-07-03T00:24:30.324892Z","shell.execute_reply.started":"2022-07-03T00:24:30.294426Z","shell.execute_reply":"2022-07-03T00:24:30.324222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(dfAll.shape[0]):\n    cosSim1 = cosine_similarity(vectCode1[i], vectMD[i])\n    cosSim2 = cosine_similarity(vectMD[i], vectCode2[i])\n    cosSimRow = np.append(cosSim1, cosSim2).reshape(-1,2)\n    if i == 0:\n        cosSimAll = cosSimRow\n    else:\n        cosSimAll = np.concatenate([cosSimAll, cosSimRow], 0)\n\ndfCosSim = pd.DataFrame(data=cosSimAll, columns=['cos_sim1', 'cos_sim2'])\ndfAll = pd.concat([dfAll, dfCosSim], axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:30.326374Z","iopub.execute_input":"2022-07-03T00:24:30.327807Z","iopub.status.idle":"2022-07-03T00:24:30.647012Z","shell.execute_reply.started":"2022-07-03T00:24:30.327754Z","shell.execute_reply":"2022-07-03T00:24:30.646329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nsns.scatterplot(data=dfAll, x='cos_sim1', y='cos_sim2', hue='label')","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:30.650675Z","iopub.execute_input":"2022-07-03T00:24:30.651465Z","iopub.status.idle":"2022-07-03T00:24:30.95503Z","shell.execute_reply.started":"2022-07-03T00:24:30.651423Z","shell.execute_reply":"2022-07-03T00:24:30.954314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dfAll.to_csv('dfAll.csv')\ndfAll","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:30.956069Z","iopub.execute_input":"2022-07-03T00:24:30.956599Z","iopub.status.idle":"2022-07-03T00:24:31.000526Z","shell.execute_reply.started":"2022-07-03T00:24:30.956561Z","shell.execute_reply":"2022-07-03T00:24:30.99959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Show variables and their memory usage\n'''\nprint(\"{}{: >25}{}{: >10}{}\".format('|','Variable Name','|','Memory','|'))\nprint(\" ------------------------------------ \")\nfor var_name in dir():\n    if not var_name.startswith(\"_\"):\n        print(\"{}{: >25}{}{: >10}{}\".format('|',var_name,'|',sys.getsizeof(eval(var_name)),'|'))\n'''","metadata":{"execution":{"iopub.status.busy":"2022-07-03T00:24:31.001708Z","iopub.execute_input":"2022-07-03T00:24:31.002046Z","iopub.status.idle":"2022-07-03T00:24:31.00787Z","shell.execute_reply.started":"2022-07-03T00:24:31.002017Z","shell.execute_reply":"2022-07-03T00:24:31.007244Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id='conclusion'></a>\n\n---\n# 11. Conclusion\n\nThank you for your checking this notebook.\n\nIf you find an interesting thing here, please click upvote :)\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"markdown","source":"<a id='ref'></a>\n\n---\n# 12. Reference\n1. [Getting Started with AI4Code](https://www.kaggle.com/code/ryanholbrook/getting-started-with-ai4code)\n2. [introduction_to_ml_with_python: 07-working-with-text-data](https://github.com/amueller/introduction_to_ml_with_python/blob/master/07-working-with-text-data.ipynb)\n\n---\n\n[Back to Contents](#top)","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}