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the transformers library**\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H8NKlQOUwdC1","executionInfo":{"status":"ok","timestamp":1621293522062,"user_tz":-60,"elapsed":7963,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"4098a0f5-1148-4fad-d03f-cd84ec3a7c29"},"source":["!pip install transformers"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting transformers\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/9e/5b80becd952d5f7250eaf8fc64b957077b12ccfe73e9c03d37146ab29712/transformers-4.6.0-py3-none-any.whl (2.3MB)\n","\u001b[K     |████████████████████████████████| 2.3MB 6.7MB/s \n","\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n","Collecting sacremoses\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n","\u001b[K     |████████████████████████████████| 901kB 28.8MB/s \n","\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n","\u001b[K     |████████████████████████████████| 3.3MB 41.8MB/s \n","\u001b[?25hCollecting huggingface-hub==0.0.8\n","  Downloading 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sacremoses->transformers) (1.15.0)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n","Installing collected packages: sacremoses, tokenizers, huggingface-hub, transformers\n","Successfully installed huggingface-hub-0.0.8 sacremoses-0.0.45 tokenizers-0.10.2 transformers-4.6.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2RTRSs0dH-V-"},"source":["##**Importing the tools**"]},{"cell_type":"code","metadata":{"id":"vhax5oUnPyKg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620049642844,"user_tz":-60,"elapsed":9090,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"9880b490-de38-4a14-be7b-596638494fcf"},"source":["pip install bioinfokit"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting bioinfokit\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/98/b48976d1c2cc8cc455db68d7e18a9d56294e965da7eb7f5fab62d9b346e5/bioinfokit-2.0.3.tar.gz (81kB)\n","\u001b[K     |████████████████████████████████| 81kB 3.5MB/s \n","\u001b[?25hRequirement already satisfied: pandas in 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textwrap3\n","  Downloading https://files.pythonhosted.org/packages/77/9c/a53e561d496ee5866bbeea4d3a850b3b545ed854f8a21007c1e0d872e94d/textwrap3-0.9.2-py2.py3-none-any.whl\n","Collecting adjustText\n","  Downloading https://files.pythonhosted.org/packages/9e/15/4157718bf323fd5f5b81c891c660d0f388e042d2689a558bf1389632dc44/adjustText-0.7.3.tar.gz\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->bioinfokit) (2.8.1)\n","Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->bioinfokit) (2018.9)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bioinfokit) (2.4.7)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bioinfokit) (1.3.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->bioinfokit) (0.10.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->bioinfokit) (1.0.1)\n","Requirement already satisfied: patsy>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from statsmodels->bioinfokit) (0.5.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->bioinfokit) (1.15.0)\n","Building wheels for collected packages: bioinfokit, adjustText\n","  Building wheel for bioinfokit (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for bioinfokit: filename=bioinfokit-2.0.3-cp37-none-any.whl size=55622 sha256=fbcf072e16dc9322698c2cc81a1405dda2341b2ef2f6d4adf901328e3d8adf2e\n","  Stored in directory: /root/.cache/pip/wheels/94/f6/36/f2562d2c83714b882d83a97e56925b363c7b7a4a9eba9d64ad\n","  Building wheel for adjustText (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for adjustText: filename=adjustText-0.7.3-cp37-none-any.whl size=7100 sha256=8dae3226986687d7990bf536aea177c4e27c1ca041229ef6a803332283b99d8a\n","  Stored in directory: /root/.cache/pip/wheels/41/95/74/7d347e136d672f8bc28e937032bc92baf4f80856763a7e7b72\n","Successfully built bioinfokit adjustText\n","Installing collected packages: textwrap3, adjustText, bioinfokit\n","Successfully installed adjustText-0.7.3 bioinfokit-2.0.3 textwrap3-0.9.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"p7WXSnQowxsP"},"source":["\n","import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import cross_val_score\n","import torch\n","import transformers as ppb\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.model_selection import train_test_split\n","import warnings\n","import re\n","warnings.filterwarnings('ignore')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YcCtVBHw6OyL"},"source":["##**Importing the dataset from Drive**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dDgA4lpHw24H","executionInfo":{"status":"ok","timestamp":1621293573841,"user_tz":-60,"elapsed":39517,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"478a6108-712d-41ad-f1ec-478e13f586d9"},"source":["\n","from google.colab import drive \n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"P4GSW5B5wMKV"},"source":["#Mozilla\n","df1=pd.read_csv('gdrive/My Drive/cln_copie_dup_total1.csv',delimiter=';')\n","df2=pd.read_csv('gdrive/My Drive/cln_copie_nndup_total1.csv',delimiter=';')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"N8mxwPmXwQLw"},"source":["#Thunderbird\n","df1=pd.read_csv('gdrive/My Drive/test_dup_TB.csv',delimiter=';')\n","df2=pd.read_csv('gdrive/My Drive/test_Nondup_TB.csv',delimiter=';')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"B4TAd4Li4Mwu"},"source":["df3=pd.read_csv('gdrive/My Drive/test_ten_ec2.csv',delimiter=';')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"gmmQx4iNXmp2"},"source":["#Eclipse\n","df1=pd.read_csv('gdrive/My Drive/EP_test_dup.csv',delimiter=';')\n","df2=pd.read_csv('gdrive/My Drive/EP_test_nondup.csv',delimiter=';')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IxNxIhawCSgc"},"source":["df1['Label'] = 1\n","df2['Label'] = 0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lFJXjbkgxdrZ"},"source":["df = pd.concat([df1,df2], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"oXDpKzHH68eY"},"source":["##**Loading the Pre-trained BERT model**"]},{"cell_type":"code","metadata":{"id":"NQu-ZrK0zIpZ","colab":{"base_uri":"https://localhost:8080/","height":333,"referenced_widgets":["a11e942a58954a509aeeb896621cc390","72eae357fbe2427b9cc0dd37cac2bda4","05563b1052464569b8cb15809add7a30","eaeb0d7566e24f5583d0e5c61ca73946","267d446deaf14ceea199d5628e67f121","d3ca8bd704184cddb8ecabd5576ee7c4","e8465ee96b3b46d69cc71e24a5a02804","6c1fef838e694a6ba1c9cf509759d7d7","6ab683cc908e4d67ae94676fb733b7a8","116a3e6871964e09a8174191b56f193b","5421868949dd48d8b9b0e4cc589138bb","01bfe0961f02429b8c0c2d8d0a7fe16b","7068d00d9a2f42a6857d01b839c6ea89","fbe5f370ff774a41995d4df5b0f1c05c","057c507070624f86863e200f0742625c","9d9bc1e7009f4f489489fee16266f636","3c14d6456ca24e1ca9dcef0eab2393ba","e52f704757d5482ca20ae9573c3cc375","03b7c4aebead4317b2ac6d6bd90b8f77","b48d03dbd9cd4429969c742b65b15bfe","ef7d5570ee2b4356bdaace7de37ee934","1075a09427fd437cb4d26b39b54ebd3c","b352e811c6144156b506371bfb81a451","55b780fa05ff41129d74d6c34daae735","0e0a81c1247048dba246155749fe5e0e","345b159b4c0149f6a37e3fcec9886494","2ddc22ac708f459289f725495725e284","a3331ab7202343e58e5f38596ff8f15d","3067bb32448a4ee3b2aa3a5ecf35cee1","1c72e2673a904ec98e9fb4a1ba5d5ad3","f24c99cc424d403c9325d0313dc482ab","4830071a917648ff8735d6ec895b2ce9","fd39559ed34a407e814fbf1047a59ef6","36895c3ec5cd4f04881a3cf58aabb53e","5892b77310cb45028f8889f8c4bcaa3f","f3d2bb4cdc6d4c53845cbdd929db2ef1","0e095ab6ca784df4be50ac658ebedacb","68061b784fb4457aa7a1c469947c1704","fb0cdf0a1f7b4882b413c7933d04f24b","ec62b0bfa1704d9aae1f280de91f8504"]},"executionInfo":{"status":"ok","timestamp":1621293622474,"user_tz":-60,"elapsed":34927,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"e8f7632b-21bd-4393-89b3-894cf6ddc6d9"},"source":["import time\n","start = time.time()\n","\n","model_class, tokenizer_class, pretrained_weights = (ppb.BertModel, ppb.BertTokenizer, 'bert-base-uncased')\n","tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n","model = model_class.from_pretrained(pretrained_weights)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a11e942a58954a509aeeb896621cc390","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6ab683cc908e4d67ae94676fb733b7a8","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3c14d6456ca24e1ca9dcef0eab2393ba","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0e0a81c1247048dba246155749fe5e0e","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=570.0, style=ProgressStyle(description_…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fd39559ed34a407e814fbf1047a59ef6","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"stream","text":["Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias']\n","- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"yXePBDNnYoaC"},"source":["# **Remove stop words**"]},{"cell_type":"code","metadata":{"id":"JVNkRAxRluFC"},"source":["df1['Title1']= df1['Title1'].str.replace(r'i' 'me' 'my' 'myself' 'we' \n","                                         'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she' 'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that'\n","                                         'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an' 'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' \n","                                         'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once'  'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' 'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","\n","df1['Title2']= df1['Title2'].str.replace(r'i' 'me' 'my' 'myself' 'we' \n","                                         'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she' 'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that'\n","                                         'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' 'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' 'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' \n","                                         'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","df2['Title1']= df2['Title1'].str.replace(r'i' 'me' 'my' 'myself' 'we' 'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' \n","                                         'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' 'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' 'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","\n","df2['Title2']= df2['Title2'].str.replace(r'i' 'me' 'my' 'myself' 'we' 'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' \n","                                         'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' 'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' \n","                                         'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","df1['Description1']= df1['Description1'].str.replace(r'i' 'me' 'my' 'myself' 'we'  'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an' 'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' \n","                                         'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' \n","                                         'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' 'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","df1['Description2']= df1['Description2'].str.replace(r'i' 'me' 'my' 'myself' 'we' 'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' \n","                                         'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' 'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' \n","                                         'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","df2['Description1']= df2['Description1'].str.replace(r'i' 'me' 'my' 'myself' 'we' 'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' 'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' \n","                                         'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own' 'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","df2['Description2']= df2['Description2'].str.replace(r'i' 'me' 'my' 'myself' 'we' 'our' 'ours' 'ourselves' 'you' 'your' 'yours' 'yourself''yourselves' 'they' 'we' 'him' 'he' 'him' 'his' 'himself' 'she'\n","                                         'her' 'hers' 'herself' 'it''its' 'itself' 'they' 'them' 'their' 'theirs' 'themselves'  'what' 'which' 'who' 'whom' 'this' 'that' 'these' 'those' 'am' 'is' 'are' 'was' 'were' 'be' 'been' 'being' 'have' 'has' 'had' 'having' 'do' 'does' 'did' 'doing' 'a' 'an'\n","                                         'the' 'and' 'but' 'if' 'or' 'because' 'as' 'until' 'while' 'of' 'at' 'by' 'for' 'with' 'about' 'against' 'between' 'into' 'through' 'during' 'before' 'after' 'above' 'below' 'to' 'from' 'up' 'down' 'in' 'out' 'on' 'off' 'over' 'under' 'again' 'further' 'then' 'once' \n","                                         'here' 'there' 'when' 'where' 'why' 'how' 'all' 'any' 'both' 'each' 'few' 'more' 'most' 'other' 'some' 'such' 'no' 'nor' 'not' 'only' 'own'  'same' 'so' 'than' 'too' 'very' 's' 't' 'can' 'will' 'just' 'don' 'should' 'now'\n","                                         'java' 'com' 'org' ,'')\n","\n","\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f-GEFOyziCKS"},"source":["#**Batch ThunderBird**"]},{"cell_type":"code","metadata":{"id":"HzsZ95IlEJKQ"},"source":["batch_31=df1[:500]\n","batch_32=df2[:500]\n","df3 = pd.concat([batch_31,batch_32], ignore_index=True)\n","batch_41=df1[500:1000]\n","batch_42=df2[500:1000]\n","df4 = pd.concat([batch_41,batch_42], ignore_index=True)\n","batch_51=df1[1000:1500]\n","batch_52=df2[1000:1500]\n","df5 = pd.concat([batch_51,batch_52], ignore_index=True)\n","batch_61=df1[1500:2000]\n","batch_62=df2[1500:2000]\n","df6 = pd.concat([batch_61,batch_62], ignore_index=True)\n","batch_71=df1[2000:2500]\n","batch_72=df2[2000:2500]\n","df7 = pd.concat([batch_71,batch_72], ignore_index=True)\n","batch_81=df1[2500:3000]\n","batch_82=df2[2500:3000]\n","df8 = pd.concat([batch_81,batch_82], ignore_index=True)\n","batch_91=df1[3000:3486]\n","batch_92=df2[3000:3486]\n","df9 = pd.concat([batch_91,batch_92], ignore_index=True)\n","\n","\n","#Testing\n","batch_101=df1[3486:3900]\n","batch_102=df2[3486:3900]\n","df10 = pd.concat([batch_101,batch_102], ignore_index=True)\n","batch_111=df1[3900:4338]\n","batch_112=df2[3900:4374]\n","df11 = pd.concat([batch_111,batch_112], ignore_index=True)\n","#df12 = df2[4500:5500]\n","#df13=df2[5500:6000]\n","#df14=df2[6000:6500]\n","#df15=df2[6500:7000]\n","#df16=df2[7000:7500]\n","#df17=df2[7500:8000]\n","#df18=df2[8000:8500]\n","#df19=df2[8500:9000]\n","#df20=df2[9000:9500]\n","#df21=df2[9500:]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U-w8E1PFLR2n"},"source":["batch_121=df1[4500:]\n","batch_122=df2[4500:5500]\n","df12 = pd.concat([batch_121,batch_122], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cDqEIaitiQpP"},"source":["#**Batch EP**"]},{"cell_type":"code","metadata":{"id":"CmoBT_kR-qEz"},"source":["#training\n","batch_31=df1[:500]\n","batch_32=df2[:500]\n","df3 = pd.concat([batch_31,batch_32], ignore_index=True)\n","batch_41=df1[500:1000]\n","batch_42=df2[500:1000]\n","df4 = pd.concat([batch_41,batch_42], ignore_index=True)\n","batch_51=df1[1000:1500]\n","batch_52=df2[1000:1500]\n","df5 = pd.concat([batch_51,batch_52], ignore_index=True)\n","batch_61=df1[1500:2000]\n","batch_62=df2[1500:2000]\n","df6 = pd.concat([batch_61,batch_62], ignore_index=True)\n","batch_71=df1[2000:2500]\n","batch_72=df2[2000:2500]\n","df7 = pd.concat([batch_71,batch_72], ignore_index=True)\n","batch_81=df1[2500:3000]\n","batch_82=df2[2500:3000]\n","df8 = pd.concat([batch_81,batch_82], ignore_index=True)\n","batch_91=df1[3000:3500]\n","batch_92=df2[3000:3500]\n","df9 = pd.concat([batch_91,batch_92], ignore_index=True)\n","batch_101=df1[3500:4000]\n","batch_102=df2[3500:4000]\n","df10 = pd.concat([batch_101,batch_102], ignore_index=True)\n","batch_111=df1[4000:4500]\n","batch_112=df2[4000:4500]\n","df11 = pd.concat([batch_111,batch_112], ignore_index=True)\n","batch_121=df1[4500:5000]\n","batch_122=df2[4500:5000]\n","df12 = pd.concat([batch_121,batch_122], ignore_index=True)\n","batch_131=df1[5000:5250]\n","batch_132=df2[5000:5250]\n","df13 = pd.concat([batch_131,batch_132], ignore_index=True)\n","batch_141=df1[5250:5500]\n","batch_142=df2[5250:5500]\n","df14 = pd.concat([batch_141,batch_142], ignore_index=True)\n","batch_151=df1[5500:5750]\n","batch_152=df2[5500:5750]\n","df15 = pd.concat([batch_151,batch_152], ignore_index=True)\n","batch_161=df1[5750:6000]\n","batch_162=df2[5750:6000]\n","df16 = pd.concat([batch_161,batch_162], ignore_index=True)\n","batch_171=df1[6000:6250]\n","batch_172=df2[6000:6250]\n","df17 = pd.concat([batch_171,batch_172], ignore_index=True)\n","batch_181=df1[6250:6500]\n","batch_182=df2[6250:6500]\n","df18 = pd.concat([batch_181,batch_182], ignore_index=True)\n","batch_191=df1[6500:6750]\n","batch_192=df2[6500:6750]\n","df19 = pd.concat([batch_191,batch_192], ignore_index=True)\n","batch_201=df1[6750:7000]\n","batch_202=df2[6750:7000]\n","df20 = pd.concat([batch_201,batch_202], ignore_index=True)\n","batch_211=df1[7000:7250]\n","batch_212=df2[7000:7250]\n","df21 = pd.concat([batch_211,batch_212], ignore_index=True)\n","batch_221=df1[7250:7400]\n","batch_222=df2[7250:7400]\n","df22 = pd.concat([batch_221,batch_222], ignore_index=True)\n","\n","\n","#testing : df1: D: 8103 / df2: Non: 10397\n","batch_231=df1[7400:7750]\n","batch_232=df2[7400:7750]\n","df23 = pd.concat([batch_231,batch_232], ignore_index=True)\n","batch_241=df1[7750:8000]\n","batch_242=df2[7750:8000]\n","df24 = pd.concat([batch_241,batch_242], ignore_index=True)\n","batch_251=df1[8000:8103]\n","batch_252=df2[8000:8250]\n","df25 = pd.concat([batch_251,batch_252], ignore_index=True)\n","#batch_261=df[8250:8500]\n","batch_262=df2[8250:8500]\n","df26=batch_262\n","#df26 = pd.concat([batch_261,batch_262], ignore_index=True)\n","#batch_271=df1[8500:8750]\n","batch_272=df2[8500:9000]\n","df27= batch_272\n","#df27 = pd.concat([batch_271,batch_272], ignore_index=True)\n","#batch_281=df1[8750:9000]\n","#batch_282=df2[8750:9000]\n","#df28= batch_282\n","#df28 = pd.concat([batch_281,batch_282], ignore_index=True)\n","#batch_291=df1[9000:9500]\n","batch_292=df2[9000:9500]\n","df29= batch_292\n","#df29 = pd.concat([batch_291,batch_292], ignore_index=True)\n","#batch_301=df1[9250:9500]\n","batch_302=df2[9500:9900]\n","df30 = batch_302\n","#df30 = pd.concat([batch_301,batch_302], ignore_index=True)\n","#batch_311=df1[9500:9750]\n","batch_312=df2[9900:10397]\n","df31= batch_312\n","#df31 = pd.concat([batch_311,batch_312], ignore_index=True)\n","#atch_321=df1[9750:10000]\n","#batch_322=df2[9750:10000]\n","#df32 = pd.concat([batch_321,batch_322], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-e0njZ3G85WM"},"source":["#**Mozilla**"]},{"cell_type":"code","metadata":{"id":"hcQ0Nxd75rg8"},"source":["#training\n","batch_31=df1[:500]\n","batch_32=df2[:500]\n","df3 = pd.concat([batch_31,batch_32], ignore_index=True)\n","batch_41=df1[500:1000]\n","batch_42=df2[500:1000]\n","df4 = pd.concat([batch_41,batch_42], ignore_index=True)\n","batch_51=df1[1000:1500]\n","batch_52=df2[1000:1500]\n","df5 = pd.concat([batch_51,batch_52], ignore_index=True)\n","batch_61=df1[1500:2000]\n","batch_62=df2[1500:2000]\n","df6 = pd.concat([batch_61,batch_62], ignore_index=True)\n","batch_71=df1[2000:2500]\n","batch_72=df2[2000:2500]\n","df7 = pd.concat([batch_71,batch_72], ignore_index=True)\n","batch_81=df1[2500:3000]\n","batch_82=df2[2500:3000]\n","df8 = pd.concat([batch_81,batch_82], ignore_index=True)\n","batch_91=df1[3000:3500]\n","batch_92=df2[3000:3500]\n","df9 = pd.concat([batch_91,batch_92], ignore_index=True)\n","batch_101=df1[3500:4000]\n","batch_102=df2[3500:4000]\n","df10 = pd.concat([batch_101,batch_102], ignore_index=True)\n","batch_111=df1[4000:4500]\n","batch_112=df2[4000:4500]\n","df11 = pd.concat([batch_111,batch_112], ignore_index=True)\n","batch_121=df1[4500:5000]\n","batch_122=df2[4500:5000]\n","df12 = pd.concat([batch_121,batch_122], ignore_index=True)\n","batch_131=df1[5000:5250]\n","batch_132=df2[5000:5250]\n","df13 = pd.concat([batch_131,batch_132], ignore_index=True)\n","batch_141=df1[5250:5500]\n","batch_142=df2[5250:5500]\n","df14 = pd.concat([batch_141,batch_142], ignore_index=True)\n","batch_151=df1[5500:5750]\n","batch_152=df2[5500:5750]\n","df15 = pd.concat([batch_151,batch_152], ignore_index=True)\n","batch_161=df1[5750:6000]\n","batch_162=df2[5750:6000]\n","df16 = pd.concat([batch_161,batch_162], ignore_index=True)\n","batch_171=df1[6000:6250]\n","batch_172=df2[6000:6250]\n","df17 = pd.concat([batch_171,batch_172], ignore_index=True)\n","batch_181=df1[6250:6500]\n","batch_182=df2[6250:6500]\n","df18 = pd.concat([batch_181,batch_182], ignore_index=True)\n","batch_191=df1[6500:6750]\n","batch_192=df2[6500:6750]\n","df19 = pd.concat([batch_191,batch_192], ignore_index=True)\n","batch_201=df1[6750:7000]\n","batch_202=df2[6750:7000]\n","df20 = pd.concat([batch_201,batch_202], ignore_index=True)\n","batch_211=df1[7000:7250]\n","batch_212=df2[7000:7250]\n","df21 = pd.concat([batch_211,batch_212], ignore_index=True)\n","batch_221=df1[7250:7400]\n","batch_222=df2[7250:7400]\n","df22 = pd.concat([batch_221,batch_222], ignore_index=True)\n","\n","\n","\n","batch_231=df1[7400:7600]\n","batch_232=df2[7400:7600]\n","df23 = pd.concat([batch_231,batch_232], ignore_index=True)\n","\n","#testing : df1: D: 9044 / df2: Non: 9956\n","batch_241=df1[7600:7900]\n","batch_242=df2[7600:7900]\n","df24 = pd.concat([batch_241,batch_242], ignore_index=True)\n","batch_251=df1[7900:8250]\n","batch_252=df2[7900:8250]\n","df25 = pd.concat([batch_251,batch_252], ignore_index=True)\n","batch_261=df1[8250:8500]\n","batch_262=df2[8250:8500]\n","#df26=batch_262\n","df26 = pd.concat([batch_261,batch_262], ignore_index=True)\n","batch_271=df1[8500:8750]\n","batch_272=df2[8500:8750]\n","#df27= batch_272\n","df27 = pd.concat([batch_271,batch_272], ignore_index=True)\n","batch_281=df1[8750:9044]\n","batch_282=df2[8750:9000]\n","#df28= batch_282\n","df28 = pd.concat([batch_281,batch_282], ignore_index=True)\n","#batch_291=df1[9000:9500]\n","batch_292=df2[9000:9500]\n","df29= batch_292\n","#df29 = pd.concat([batch_291,batch_292], ignore_index=True)\n","#batch_301=df1[9250:9500]\n","batch_302=df2[9500:9956]\n","df30 = batch_302\n","#df30 = pd.concat([batch_301,batch_302], ignore_index=True)\n","#batch_311=df1[9500:9750]\n","#batch_312=df2[9900:10103]\n","#df31= batch_312\n","#df31 = pd.concat([batch_311,batch_312], ignore_index=True)\n","#atch_321=df1[9750:10000]\n","#batch_322=df2[9750:10000]\n","#df32 = pd.concat([batch_321,batch_322], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9CicZVkjA6bK"},"source":["#**Batch CrossProject**"]},{"cell_type":"markdown","metadata":{"id":"aTcVvZLxicB_"},"source":["### **_get_segments3**"]},{"cell_type":"code","metadata":{"id":"XCoiTguWgxeg"},"source":["def _get_segments3(tokens, max_seq_length):\n","    \"\"\"Segments: 0 for the first sequence, 1 for the second\"\"\"\n","    if len(tokens)>max_seq_length:\n","        raise IndexError(\"Token length more than max seq length!\")\n","    segments = []\n","    first_sep = False\n","    current_segment_id = 0 \n","    for token in tokens:\n","        segments.append(current_segment_id)\n","        #print(token)\n","        if token == 102:\n","            #if first_sep:\n","                #first_sep = False \n","            #else:\n","           current_segment_id = 1\n","    return segments + [0] * (max_seq_length - len(tokens))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gOFGJdemfgnG"},"source":["#**df3**"]},{"cell_type":"code","metadata":{"id":"aZSPfF7rglgG"},"source":["pair3_1= df3['Title1'] + df3['Description1']\n","tokenized3_1 = pair3_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair3_2=  df3['Title2'] + df3['Description2'] + [\" [SEP] \"]\n","tokenized3_2 = pair3_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized3 = tokenized3_1 + tokenized3_2 \n","\n","max_len3 = 0                 # padding all lists to the same size\n","for i in tokenized3.values:\n","    if len(i) > max_len3:\n","        max_len3 = len(i)\n","max_len3 =300\n","padded3 = np.array([i + [0]*(max_len3-len(i)) for i in tokenized3.values])\n","\n","np.array(padded3).shape \n","\n","attention_mask3 = np.where(padded3 != 0, 1, 0)\n","attention_mask3.shape\n","input_ids3 = torch.tensor(padded3)  \n","attention_mask3 = torch.tensor(attention_mask3)\n","input_segments3= np.array([_get_segments3(token, max_len3)for token in tokenized3.values])\n","token_type_ids3 = torch.tensor(input_segments3)\n","input_segments3 = torch.tensor(input_segments3)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1Nulz336hBcB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621293710028,"user_tz":-60,"elapsed":75266,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"fb58d82f-1aef-496f-962a-3e90703d5072"},"source":["with torch.no_grad():\n","    last_hidden_states3 = model(input_ids3, attention_mask=attention_mask3, token_type_ids=input_segments3)    # <<< 600 rows only !!!\n","features3 = last_hidden_states3[0][:,0,:].numpy()\n","features3    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.1917540e+00, -2.7251735e-01, -3.0222225e-01, ...,\n","        -8.3550316e-01,  2.5618803e-02,  6.2426007e-01],\n","       [-1.1158620e+00, -3.1189933e-01, -5.9667796e-01, ...,\n","        -1.1258570e+00, -1.4045573e-03,  6.0073900e-01],\n","       [-1.4388194e+00, -3.8521490e-01, -3.6954167e-01, ...,\n","        -8.8833499e-01, -1.4637592e-01,  3.8774931e-01],\n","       ...,\n","       [-8.1014574e-01, -3.0788603e-01, -1.5076555e-01, ...,\n","        -6.1479270e-01,  5.5893880e-01,  5.5536240e-01],\n","       [-5.0958288e-01, -4.3499950e-01,  6.0237968e-01, ...,\n","        -1.1628953e+00,  4.7574383e-01,  7.3822087e-01],\n","       [-7.5887567e-01, -2.3363902e-01, -1.7179585e-01, ...,\n","        -9.7637200e-01,  2.3601519e-01,  6.3248944e-01]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"PgWWChRB9Gd-"},"source":["from sklearn.manifold import TSNE\n","m=TSNE(learning_rate=50,n_components=2, verbose=1)\n","#features= m.fit_transform(df_numeric)\n","\n","tsne_em = m.fit_transform(features3) \n","\n","\n","from bioinfokit.visuz import cluster\n","#cluster.tsneplot(score=tsne_em)\n","cluster.tsneplot(score=tsne_em)\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"73Lh8Rol4PZd"},"source":["color_class = df3['Label'].to_numpy()\n","#color_class= '1', '0'\n","cluster.tsneplot(score=tsne_em, colorlist=color_class,colordot=( '#b0413e',  '#736ced'), \n","    legendpos='upper right', legendanchor=(1.15, 1) )"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"11le9CiUVDCO"},"source":["#**df4**"]},{"cell_type":"code","metadata":{"id":"-VsXGOj8IaZy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621293893653,"user_tz":-60,"elapsed":79499,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2ed0ad34-a43f-418d-855e-c3bc8ecee9e1"},"source":["pair4_1= df4['Title1'] + df4['Description1']\n","tokenized4_1 = pair4_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair4_2=  df4['Title2'] + df4['Description2'] + [\" [SEP] \"]\n","tokenized4_2 = pair4_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized4 = tokenized4_1 + tokenized4_2 \n","\n","\n","#tokenized4= np.array(tokenized4)\n","max_len4 = 0                 # padding all lists to the same size\n","for i in tokenized4.values:\n","    if len(i) > max_len4:\n","        max_len4 = len(i)\n","max_len4 =300\n","padded4 = np.array([i + [0]*(max_len4-len(i)) for i in tokenized4.values])\n","#padded4= torch.from_numpy(padded4)\n","#np.array(padded4).shape\n","\n","attention_mask4 = np.where(padded4 != 0, 1, 0)\n","attention_mask4.shape\n","input_ids4 = torch.tensor(padded4)  \n","attention_mask4 = torch.tensor(attention_mask4)\n","input_segments4= np.array([_get_segments3(token, max_len4)for token in tokenized4.values])\n","token_type_ids4 = torch.tensor(input_segments4)\n","input_segments4 = torch.tensor(input_segments4)\n","\n","with torch.no_grad():\n","    last_hidden_states4 = model(input_ids4, attention_mask=attention_mask4, token_type_ids=input_segments4)   \n","features4 = last_hidden_states4[0][:,0,:].numpy()\n","features4  "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.0074065 ,  0.00903689, -0.16027234, ..., -0.9727307 ,\n","         0.17978334,  0.58984923],\n","       [-0.5074217 , -0.5942143 ,  0.1708871 , ..., -0.61933416,\n","        -0.06042692,  0.58103824],\n","       [-0.9273993 , -0.39810988, -0.29337618, ..., -0.89548934,\n","         0.33331445,  0.30819273],\n","       ...,\n","       [-0.57721615,  0.4543554 , -0.49093696, ..., -1.083432  ,\n","         0.647508  ,  0.3420691 ],\n","       [-0.48825482, -0.12333166,  0.0897423 , ..., -1.0772939 ,\n","         0.3861007 ,  0.44875968],\n","       [-0.57685316, -0.21282074,  0.20056196, ..., -0.8152186 ,\n","        -0.43966672,  0.40351158]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"markdown","metadata":{"id":"udmOsWumVKOa"},"source":["#**df5**"]},{"cell_type":"code","metadata":{"id":"z4RItiAaFNF2"},"source":["pair5_1= df5['Title1'] + df5['Description1']\n","tokenized5_1 = pair5_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair5_2=  df5['Title2'] + df5['Description2'] + [\" [SEP] \"]\n","tokenized5_2 = pair5_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized5 = tokenized5_1 + tokenized5_2 \n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QvzhBBP081nL"},"source":["##**Padding**"]},{"cell_type":"code","metadata":{"id":"lR8b3SBtz6HA"},"source":["max_len5 = 0                 # padding all lists to the same size\n","for i in tokenized5.values:\n","    if len(i) > max_len5:\n","        max_len5 = len(i)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"OppURTFyz-pp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621293914315,"user_tz":-60,"elapsed":4765,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"f5c7a451-370e-4778-d4e4-0b09758a5b47"},"source":["max_len5 =300\n","padded5 = np.array([i + [0]*(max_len5-len(i)) for i in tokenized5.values])\n","\n","np.array(padded5).shape        # Dimensions of the padded variable"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 300)"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"sOZ4VGE99GuU"},"source":["##**Masking**"]},{"cell_type":"code","metadata":{"id":"c9j3ZYcZ0BGt"},"source":["attention_mask5 = np.where(padded5 != 0, 1, 0)\n","attention_mask5.shape\n","input_ids5 = torch.tensor(padded5)  \n","attention_mask5 = torch.tensor(attention_mask5)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zqZuEvAPElvT"},"source":["##**Running the `model()` function through BERT**"]},{"cell_type":"code","metadata":{"id":"BijBFjIs581u"},"source":["input_segments5= np.array([_get_segments3(token, max_len5)for token in tokenized5.values])\n","token_type_ids5 = torch.tensor(input_segments5)\n","input_segments5 = torch.tensor(input_segments5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pd5IRqMXiMGm"},"source":["with torch.no_grad():\n","    last_hidden_states5 = model(input_ids5, attention_mask=attention_mask5, token_type_ids=input_segments5)    # <<< 600 rows only !!!"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mlP88s-1FPZW"},"source":["##**Slicing the part of the output of BERT : [cls]**"]},{"cell_type":"code","metadata":{"id":"iag0CM1N0Xhk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621293991082,"user_tz":-60,"elapsed":71420,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"a548a7f5-07b3-4e29-a607-55c6f24d6135"},"source":["features5 = last_hidden_states5[0][:,0,:].numpy()\n","features5"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.7150868 , -0.02143804, -0.53712595, ..., -0.96465296,\n","         0.49361002,  0.5210915 ],\n","       [-0.4751579 , -0.12975042, -0.21995719, ..., -0.7899966 ,\n","         0.5517666 ,  0.43111968],\n","       [-0.54855496,  0.00473771, -0.50283426, ..., -1.0070364 ,\n","         0.6967882 ,  0.66924685],\n","       ...,\n","       [-0.43534502, -0.23190856, -0.07703763, ..., -0.85549664,\n","         0.12206236,  0.3770517 ],\n","       [-0.63622326, -0.28681666,  0.4257244 , ..., -0.42866865,\n","         0.09889748,  0.3313467 ],\n","       [-0.336238  , -0.7268026 ,  0.15072565, ..., -0.7417214 ,\n","        -0.26674798,  0.42967662]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"vCmfgIy9frZR"},"source":["#**df6**"]},{"cell_type":"code","metadata":{"id":"CRnTQdzMFJ6I"},"source":["pair6_1= df6['Title1'] + df6['Description1']\n","tokenized6_1 = pair6_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair6_2=  df6['Title2'] + df6['Description2'] + [\" [SEP] \"]\n","tokenized6_2 = pair6_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized6 = tokenized6_1 + tokenized6_2 \n","\n","max_len6 = 0                 # padding all lists to the same size\n","for i in tokenized6.values:\n","    if len(i) > max_len6:\n","        max_len6 = len(i)\n","\n","max_len6=300\n","padded6 = np.array([i + [0]*(max_len6-len(i)) for i in tokenized6.values])\n","\n","np.array(padded6).shape        # Dimensions of the padded variable        \n","\n","attention_mask6 = np.where(padded6 != 0, 1, 0)\n","attention_mask6.shape\n","input_ids6 = torch.tensor(padded6)  \n","attention_mask6 = torch.tensor(attention_mask6)\n","input_segments6= np.array([_get_segments3(token, max_len6)for token in tokenized6.values])\n","token_type_ids6 = torch.tensor(input_segments6)\n","input_segments6 = torch.tensor(input_segments6)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ggFLgBpQjCn2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294124447,"user_tz":-60,"elapsed":75209,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"0cf1df3a-c91a-4095-e76e-948978c4fa2d"},"source":["with torch.no_grad():\n","    last_hidden_states6 = model(input_ids6, attention_mask=attention_mask6, token_type_ids=input_segments6)   \n","features6 = last_hidden_states6[0][:,0,:].numpy()\n","features6    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.70732516, -0.5288594 , -0.24584115, ..., -0.8108048 ,\n","         0.17355457,  0.7004055 ],\n","       [-0.6506675 , -0.6261653 , -0.27850452, ..., -0.79061395,\n","         0.02959961,  0.4444836 ],\n","       [-0.8070857 , -0.5031243 , -0.38067722, ..., -0.8365173 ,\n","         0.12972401,  0.4878298 ],\n","       ...,\n","       [-0.46825522, -0.02428632, -0.3221991 , ..., -0.66942424,\n","         0.6531059 ,  0.4078506 ],\n","       [-0.86163706, -0.71258837, -0.30841464, ..., -0.67918557,\n","        -0.5018392 ,  0.47072276],\n","       [-0.9294154 , -0.7963693 , -0.19163138, ..., -0.70108855,\n","         0.11923506,  0.8842914 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"Mj5OSBQMf4-d"},"source":["#**df7**"]},{"cell_type":"code","metadata":{"id":"VWIWi1oAGaAO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294214487,"user_tz":-60,"elapsed":78591,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"616ac976-b5d3-4b73-b53e-a33bcfd64be2"},"source":["pair7_1= df7['Title1'] + df7['Description1']\n","tokenized7_1 = pair7_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair7_2=  df7['Title2'] + df7['Description2'] + [\" [SEP] \"]\n","tokenized7_2 = pair7_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized7 = tokenized7_1 + tokenized7_2 \n","\n","max_len7 = 0                 # padding all lists to the same size\n","for i in tokenized7.values:\n","    if len(i) > max_len7:\n","        max_len7 = len(i)\n","\n","max_len7=300\n","padded7 = np.array([i + [0]*(max_len7-len(i)) for i in tokenized7.values])\n","\n","np.array(padded7).shape        # Dimensions of the padded variable    \n","\n","attention_mask7 = np.where(padded7 != 0, 1, 0)\n","attention_mask7.shape\n","input_ids7 = torch.tensor(padded7)  \n","attention_mask7 = torch.tensor(attention_mask7)\n","input_segments7= np.array([_get_segments3(token, max_len7)for token in tokenized7.values])\n","token_type_ids7 = torch.tensor(input_segments7)\n","input_segments7 = torch.tensor(input_segments7)\n","\n","with torch.no_grad():\n","    last_hidden_states7 = model(input_ids7, attention_mask=attention_mask7, token_type_ids=input_segments7)  \n","features7 = last_hidden_states7[0][:,0,:].numpy()\n","features7  "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.0016961 , -0.1429964 , -0.43225417, ..., -0.89050096,\n","         0.26834354,  0.3012184 ],\n","       [-1.2596343 , -0.21117105, -0.869571  , ..., -0.9034431 ,\n","         0.34412763,  0.19730861],\n","       [-0.9392237 , -0.2350783 , -0.7561936 , ..., -0.9042811 ,\n","         0.26033762,  0.3057428 ],\n","       ...,\n","       [-0.43330994,  0.03957942,  0.3698622 , ..., -0.7907169 ,\n","         0.31549135,  0.47611699],\n","       [-0.49524376,  0.28503007, -0.43448916, ..., -0.6576232 ,\n","         0.44116557,  0.6529278 ],\n","       [-0.23445708, -0.28388354,  0.18275483, ..., -0.6729698 ,\n","         0.23269467,  0.5694868 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"markdown","metadata":{"id":"dJ3RvBPQf7fT"},"source":["#**df8**"]},{"cell_type":"code","metadata":{"id":"5VoQeXl4HEjN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294290270,"user_tz":-60,"elapsed":149883,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"50e48d86-4ba8-4664-a0b2-99da2394f20c"},"source":["pair8_1= df8['Title1'] + df8['Description1']\n","tokenized8_1 = pair8_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair8_2=  df8['Title2'] + df8['Description2'] + [\" [SEP] \"]\n","tokenized8_2 = pair8_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized8 = tokenized8_1 + tokenized8_2 \n","\n","max_len8 = 0                 # padding all lists to the same size\n","for i in tokenized8.values:\n","    if len(i) > max_len8:\n","        max_len8 = len(i)\n","max_len8=300\n","padded8 = np.array([i + [0]*(max_len8-len(i)) for i in tokenized8.values])\n","\n","np.array(padded8).shape        # Dimensions of the padded variable  \n","\n","\n","attention_mask8 = np.where(padded8 != 0, 1, 0)\n","attention_mask8.shape\n","input_ids8 = torch.tensor(padded8)  \n","attention_mask8 = torch.tensor(attention_mask8)\n","input_segments8= np.array([_get_segments3(token, max_len8)for token in tokenized8.values])\n","token_type_ids8 = torch.tensor(input_segments8)\n","input_segments8 = torch.tensor(input_segments8)\n","\n","with torch.no_grad():\n","    last_hidden_states8 = model(input_ids8, attention_mask=attention_mask8, token_type_ids=input_segments8)   \n","features8 = last_hidden_states8[0][:,0,:].numpy()\n","features8 "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.57219756, -0.19104268, -0.29295504, ..., -0.9924922 ,\n","         0.35121664,  0.673953  ],\n","       [-0.6965745 , -0.17319888, -0.28782362, ..., -1.0402663 ,\n","         0.4613169 ,  0.61206377],\n","       [-0.63936394, -0.28378955, -0.17343408, ..., -0.8304699 ,\n","         0.3297921 ,  0.7071261 ],\n","       ...,\n","       [-0.85080385,  0.2128815 , -0.20193015, ..., -0.8751407 ,\n","         0.19648312,  0.26859182],\n","       [-0.72485876,  0.0556468 ,  0.19721866, ..., -0.6981154 ,\n","         0.65800685,  0.42406514],\n","       [-0.38661766, -0.1474134 , -0.06704615, ..., -0.856245  ,\n","         0.21856555,  0.46101326]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"rNXTxV1l0pIJ"},"source":["#**df9**"]},{"cell_type":"code","metadata":{"id":"sk-H7hcUH7cg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294368110,"user_tz":-60,"elapsed":221550,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"a57a219b-1e3e-47f5-d5af-25b1ab65ceff"},"source":["pair9_1= df9['Title1'] + df9['Description1']\n","tokenized9_1 = pair9_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair9_2=  df9['Title2'] + df9['Description2'] + [\" [SEP] \"]\n","tokenized9_2 = pair9_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized9 = tokenized9_1 + tokenized9_2 \n","\n","max_len9 = 0                 # padding all lists to the same size\n","for i in tokenized9.values:\n","    if len(i) > max_len9:\n","        max_len9 = len(i)\n","max_len9=300\n","padded9 = np.array([i + [0]*(max_len9-len(i)) for i in tokenized9.values])\n","\n","np.array(padded9).shape        # Dimensions of the padded variable    \n","\n","attention_mask9 = np.where(padded9 != 0, 1, 0)\n","attention_mask9.shape\n","input_ids9 = torch.tensor(padded9)  \n","attention_mask9 = torch.tensor(attention_mask9)\n","input_segments9= np.array([_get_segments3(token, max_len9)for token in tokenized9.values])\n","token_type_ids9 = torch.tensor(input_segments9)\n","input_segments9 = torch.tensor(input_segments9)\n","\n","with torch.no_grad():\n","    last_hidden_states9 = model(input_ids9, attention_mask=attention_mask9, token_type_ids=input_segments9)    \n","features9 = last_hidden_states9[0][:,0,:].numpy()\n","features9"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.2880924 , -0.18822992, -0.79702693, ..., -0.6162087 ,\n","         0.2414326 ,  0.54821324],\n","       [-0.94441354, -0.03251145, -0.74263215, ..., -0.65941155,\n","         0.26729995,  0.4164142 ],\n","       [-0.7975459 , -0.12546346, -0.23241737, ..., -0.63911545,\n","         0.2462435 ,  0.6830209 ],\n","       ...,\n","       [-0.48609257, -0.5872942 ,  0.23615816, ..., -0.52832294,\n","         0.18745112,  0.5525364 ],\n","       [-0.7446838 , -0.18541873, -0.49343872, ..., -0.8192452 ,\n","         0.38214442,  0.4427576 ],\n","       [-0.51682675, -0.28136814,  0.08318645, ..., -0.6633832 ,\n","         0.21209669,  0.05075266]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"markdown","metadata":{"id":"0ok-IVZl0xID"},"source":["#**df10**"]},{"cell_type":"code","metadata":{"id":"bfnW68FxIlN1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294445292,"user_tz":-60,"elapsed":294685,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"08c19381-2477-4fd5-a7d5-24d63a4944e2"},"source":["pair10_1= df10['Title1'] + df10['Description1']\n","tokenized10_1 = pair10_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair10_2=  df10['Title2'] + df10['Description2'] + [\" [SEP] \"]\n","tokenized10_2 = pair10_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized10 = tokenized10_1 + tokenized10_2 \n","\n","max_len10 = 0                 # padding all lists to the same size\n","for i in tokenized10.values:\n","    if len(i) > max_len10:\n","        max_len10 = len(i)\n","max_len10=300\n","padded10 = np.array([i + [0]*(max_len10-len(i)) for i in tokenized10.values])\n","\n","np.array(padded10).shape        # Dimensions of the padded variable\n","\n","attention_mask10 = np.where(padded10 != 0, 1, 0)\n","attention_mask10.shape\n","input_ids10 = torch.tensor(padded10)  \n","attention_mask10 = torch.tensor(attention_mask10)\n","input_segments10= np.array([_get_segments3(token, max_len10)for token in tokenized10.values])\n","token_type_ids10 = torch.tensor(input_segments10)\n","input_segments10 = torch.tensor(input_segments10)\n","\n","with torch.no_grad():\n","    last_hidden_states10 = model(input_ids10, attention_mask=attention_mask10, token_type_ids=input_segments10) \n","features10 = last_hidden_states10[0][:,0,:].numpy()\n","features10\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.92287654,  0.09879377, -0.21756157, ..., -0.9992561 ,\n","         0.31372535,  0.4032963 ],\n","       [-0.89550596, -0.42283508, -0.17069414, ..., -0.7713603 ,\n","         0.29041082,  0.37330768],\n","       [-0.8750404 ,  0.11880389, -0.5262182 , ..., -0.8546316 ,\n","         0.6586568 ,  0.63991576],\n","       ...,\n","       [-0.3293789 , -0.12245314, -0.33827764, ..., -1.0260113 ,\n","         0.04016275,  0.7023087 ],\n","       [-0.80368096, -0.46616802, -0.06866035, ..., -0.6966629 ,\n","         0.1788419 ,  0.46562386],\n","       [-0.86321205, -0.24407259, -0.02506274, ..., -0.84817237,\n","        -0.2299144 ,  0.43663958]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"dJ_ER9DhCQGj"},"source":["#**df11**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kmG3KS5TJffr","executionInfo":{"status":"ok","timestamp":1621294522510,"user_tz":-60,"elapsed":366265,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"6f077bc8-7819-438f-ed1b-9999f492d3b5"},"source":["pair11_1= df11['Title1'] + df11['Description1']\n","tokenized11_1 = pair11_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair11_2=  df11['Title2'] + df11['Description2'] + [\" [SEP] \"]\n","tokenized11_2 = pair11_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized11 = tokenized11_1 + tokenized11_2 \n","\n","max_len11 = 0                 # padding all lists to the same size\n","for i in tokenized11.values:\n","    if len(i) > max_len11:\n","        max_len11 = len(i)\n","max_len11=300\n","padded11 = np.array([i + [0]*(max_len11-len(i)) for i in tokenized11.values])\n","\n","np.array(padded11).shape        # Dimensions of the padded variable   \n","\n","attention_mask11 = np.where(padded11 != 0, 1, 0)\n","attention_mask11.shape\n","input_ids11 = torch.tensor(padded11)  \n","attention_mask11 = torch.tensor(attention_mask11)\n","input_segments11= np.array([_get_segments3(token, max_len11)for token in tokenized11.values])\n","token_type_ids11 = torch.tensor(input_segments11)\n","input_segments11 = torch.tensor(input_segments11)\n","\n","\n","with torch.no_grad():\n","    last_hidden_states11 = model(input_ids11, attention_mask=attention_mask11, token_type_ids=input_segments11)   \n","features11 = last_hidden_states11[0][:,0,:].numpy()\n","features11    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.6444266 ,  0.14766087, -0.6265677 , ..., -0.82210374,\n","         0.49804857,  0.4426836 ],\n","       [-0.60791767,  0.16495073, -0.6926536 , ..., -1.0188453 ,\n","         0.45899355,  0.48626038],\n","       [-0.7974473 ,  0.23354901, -0.6727306 , ..., -1.0123372 ,\n","         0.3378023 ,  0.35920295],\n","       ...,\n","       [-0.5652498 , -0.25174397,  0.10305873, ..., -0.9278619 ,\n","         0.4564897 ,  0.40928823],\n","       [-0.90839016, -0.70828366, -0.6166792 , ..., -0.8851601 ,\n","         0.26777136,  0.5479087 ],\n","       [-0.7892092 , -0.27814952, -0.26179987, ..., -0.9758322 ,\n","        -0.22188179,  0.5490474 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"markdown","metadata":{"id":"Akm6P3kv5_8j"},"source":["#**df12**"]},{"cell_type":"code","metadata":{"id":"2pyD5tXKKU0s","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621294630553,"user_tz":-60,"elapsed":77497,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"4dfadf48-6ac7-46c4-dec6-38482194c89c"},"source":["pair12_1= df12['Title1'] + df12['Description1']\n","tokenized12_1 = pair12_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair12_2=  df12['Title2'] + df12['Description2'] + [\" [SEP] \"]\n","tokenized12_2 = pair12_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized12 = tokenized12_1 + tokenized12_2 \n","\n","max_len12 = 0                 # padding all lists to the same size\n","for i in tokenized12.values:\n","    if len(i) > max_len12:\n","        max_len12 = len(i)\n","max_len12=300\n","padded12 = np.array([i + [0]*(max_len12-len(i)) for i in tokenized12.values])\n","\n","np.array(padded12).shape        # Dimensions of the padded variable   \n","\n","attention_mask12 = np.where(padded12 != 0, 1, 0)\n","attention_mask12.shape\n","input_ids12 = torch.tensor(padded12)  \n","attention_mask12 = torch.tensor(attention_mask12)\n","input_segments12= np.array([_get_segments3(token, max_len12)for token in tokenized12.values])\n","token_type_ids12 = torch.tensor(input_segments12)\n","input_segments12 = torch.tensor(input_segments12)\n","\n","\n","with torch.no_grad():\n","    last_hidden_states12 = model(input_ids12, attention_mask=attention_mask12, token_type_ids=input_segments12)  \n","features12 = last_hidden_states12[0][:,0,:].numpy()\n","features12    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.0735995 , -0.1923016 , -0.47442412, ..., -0.89130265,\n","         0.49215153,  0.759457  ],\n","       [-0.89668524, -0.22152005, -0.27283043, ..., -0.61521524,\n","         0.18424058,  0.6948311 ],\n","       [-0.5661337 ,  0.16488978, -0.64580345, ..., -0.9932497 ,\n","         0.65060866,  0.45075616],\n","       ...,\n","       [-0.4946508 , -0.06287071, -0.02177085, ..., -0.9843518 ,\n","         0.49477184,  0.31902546],\n","       [-0.73068666, -0.4428062 ,  0.06826377, ..., -0.61992997,\n","         0.06909189,  0.54237413],\n","       [-1.0755062 , -0.28629768,  0.22673316, ..., -0.664705  ,\n","         0.21154751,  0.70127374]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"PIb7HOBxEyVz"},"source":["#**df13**"]},{"cell_type":"code","metadata":{"id":"9tc3z0e4RWpi"},"source":["#df13=df2[5500:6000]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kMl3VKD7Fhaq","executionInfo":{"status":"ok","timestamp":1621294670441,"user_tz":-60,"elapsed":39852,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3c4c27c8-cc21-4ac2-82d4-579f595a4ed7"},"source":["pair13_1= df13['Title1'] + df13['Description1']\n","tokenized13_1 = pair13_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair13_2=  df13['Title2'] + df13['Description2'] + [\" [SEP] \"]\n","tokenized13_2 = pair13_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized13 = tokenized13_1 + tokenized13_2 \n","\n","max_len13 = 0                 # padding all lists to the same size\n","for i in tokenized13.values:\n","    if len(i) > max_len13:\n","        max_len13 = len(i)\n","max_len13=300\n","padded13 = np.array([i + [0]*(max_len13-len(i)) for i in tokenized13.values])\n","\n","np.array(padded13).shape        # Dimensions of the padded variable\n","\n","attention_mask13 = np.where(padded13 != 0, 1, 0)\n","attention_mask13.shape\n","input_ids13 = torch.tensor(padded13)  \n","attention_mask13 = torch.tensor(attention_mask13)\n","input_segments13= np.array([_get_segments3(token, max_len13)for token in tokenized13.values])\n","token_type_ids13 = torch.tensor(input_segments13)\n","input_segments13 = torch.tensor(input_segments13)\n","\n","with torch.no_grad():\n","    last_hidden_states13 = model(input_ids13, attention_mask=attention_mask13, token_type_ids=input_segments13)  \n","features13 = last_hidden_states13[0][:,0,:].numpy()\n","features13   "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.84586275, -0.29329702, -0.18363795, ..., -0.74361074,\n","        -0.04235256,  0.51925707],\n","       [-0.74942523, -0.10529888, -0.13210417, ..., -0.8098796 ,\n","         0.4452805 ,  0.46759093],\n","       [-1.0349753 , -0.12392537, -0.6363263 , ..., -0.80865395,\n","         0.64386445,  0.34398606],\n","       ...,\n","       [-0.83905953, -0.5268834 ,  0.5088448 , ..., -0.5507948 ,\n","         0.04140781, -0.06669161],\n","       [-0.8615075 , -0.00717248, -0.2696669 , ..., -1.0141423 ,\n","         0.35323018,  0.47003973],\n","       [-0.6577108 , -0.67795086, -0.3680778 , ..., -0.9813665 ,\n","         0.22296506,  0.731669  ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"markdown","metadata":{"id":"rrURrafXSSHX"},"source":["#**df14**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cqNWaeC7SVOW","executionInfo":{"status":"ok","timestamp":1621294709249,"user_tz":-60,"elapsed":78639,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"232d5b65-ed85-41fa-a0ab-c54de3138f8f"},"source":["pair14_1= df14['Title1'] + df14['Description1']\n","tokenized14_1 = pair14_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair14_2=  df14['Title2'] + df14['Description2'] + [\" [SEP] \"]\n","tokenized14_2 = pair14_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized14 = tokenized14_1 + tokenized14_2 \n","\n","max_len14 = 0                 # padding all lists to the same size\n","for i in tokenized14.values:\n","    if len(i) > max_len14:\n","        max_len14 = len(i)\n","max_len14=300\n","padded14 = np.array([i + [0]*(max_len14-len(i)) for i in tokenized14.values])\n","\n","np.array(padded14).shape     \n","\n","attention_mask14 = np.where(padded14 != 0, 1, 0)\n","attention_mask14.shape\n","input_ids14 = torch.tensor(padded14)  \n","attention_mask14 = torch.tensor(attention_mask14)\n","input_segments14= np.array([_get_segments3(token, max_len14)for token in tokenized14.values])\n","token_type_ids14 = torch.tensor(input_segments14)\n","input_segments14 = torch.tensor(input_segments14)\n","\n","with torch.no_grad():\n","    last_hidden_states14 = model(input_ids14, attention_mask=attention_mask14, token_type_ids=input_segments14)  \n","features14 = last_hidden_states14[0][:,0,:].numpy()\n","features14    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.4382822 ,  0.1319151 , -0.22264527, ..., -0.85487574,\n","         0.67548203,  0.6253792 ],\n","       [-0.56488657,  0.07524411, -0.32411373, ..., -0.95759845,\n","         0.5780279 ,  0.66118693],\n","       [-0.835055  , -0.19509742, -0.03488394, ..., -0.7988464 ,\n","         0.39954567,  0.8884008 ],\n","       ...,\n","       [ 0.00237992, -0.03051847,  0.5203918 , ..., -0.8232539 ,\n","         0.34302324,  0.29655576],\n","       [-0.65640277, -0.19521101, -0.28471646, ..., -0.54120123,\n","         0.26860672,  0.6602464 ],\n","       [-0.65587133, -0.22902943, -0.13357252, ..., -0.80128306,\n","         0.28232118,  0.42396054]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"IXx11g-u69uK"},"source":["#**df15**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RJWqIhRR6_LJ","executionInfo":{"status":"ok","timestamp":1621294749322,"user_tz":-60,"elapsed":118697,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"11bb31ca-2321-4f8a-f7b3-b1b23a46a70c"},"source":["pair15_1= df15['Title1'] + df15['Description1']\n","tokenized15_1 = pair15_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair15_2=  df15['Title2'] + df15['Description2'] + [\" [SEP] \"]\n","tokenized15_2 = pair15_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized15 = tokenized15_1 + tokenized15_2 \n","\n","max_len15 = 0                 # padding all lists to the same size\n","for i in tokenized15.values:\n","    if len(i) > max_len15:\n","        max_len15 = len(i)\n","max_len15=300\n","padded15 = np.array([i + [0]*(max_len15-len(i)) for i in tokenized15.values])\n","\n","np.array(padded15).shape      \n","\n","attention_mask15 = np.where(padded15 != 0, 1, 0)\n","attention_mask15.shape\n","input_ids15 = torch.tensor(padded15)  \n","attention_mask15 = torch.tensor(attention_mask15)\n","input_segments15= np.array([_get_segments3(token, max_len15)for token in tokenized15.values])\n","token_type_ids15 = torch.tensor(input_segments15)\n","input_segments15 = torch.tensor(input_segments15)\n","\n","with torch.no_grad():\n","    last_hidden_states15 = model(input_ids15, attention_mask=attention_mask15, token_type_ids=input_segments15)  \n","features15 = last_hidden_states15[0][:,0,:].numpy()\n","features15    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.5899792 ,  0.16364564, -0.576838  , ..., -1.1116666 ,\n","         0.6742583 ,  0.30382776],\n","       [-0.7531738 ,  0.19286473, -0.73669285, ..., -1.112627  ,\n","         0.6092787 ,  0.32141748],\n","       [-0.6660772 ,  0.24692722, -0.5840642 , ..., -1.0853012 ,\n","         0.65590775,  0.24422836],\n","       ...,\n","       [-0.57201827, -0.679884  , -0.28683627, ..., -0.8067721 ,\n","         0.01363039,  0.5901763 ],\n","       [-0.6014906 , -0.3258594 ,  0.46142426, ..., -0.93038756,\n","         0.25484458,  0.24946323],\n","       [-0.58562684, -0.13684392, -0.3209806 , ..., -0.90065265,\n","         0.15474507,  0.39913133]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"markdown","metadata":{"id":"00u5IAxw_sev"},"source":["#**df16**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uayZ5uUx_vIP","executionInfo":{"status":"ok","timestamp":1621294817044,"user_tz":-60,"elapsed":40460,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"8cd19017-289d-465b-ad42-95a912763e3c"},"source":["pair16_1= df16['Title1'] + df16['Description1']\n","tokenized16_1 = pair16_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair16_2=  df16['Title2'] + df16['Description2'] + [\" [SEP] \"]\n","tokenized16_2 = pair16_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized16 = tokenized16_1 + tokenized16_2 \n","\n","max_len16 = 0                 # padding all lists to the same size\n","for i in tokenized16.values:\n","    if len(i) > max_len16:\n","        max_len16 = len(i)\n","max_len16=300\n","padded16 = np.array([i + [0]*(max_len16-len(i)) for i in tokenized16.values])\n","\n","np.array(padded16).shape   \n","\n","attention_mask16 = np.where(padded16 != 0, 1, 0)\n","attention_mask16.shape\n","input_ids16 = torch.tensor(padded16)  \n","attention_mask16 = torch.tensor(attention_mask16)\n","input_segments16= np.array([_get_segments3(token, max_len16)for token in tokenized16.values])\n","token_type_ids16 = torch.tensor(input_segments16)\n","input_segments16 = torch.tensor(input_segments16)\n","\n","with torch.no_grad():\n","    last_hidden_states16 = model(input_ids16, attention_mask=attention_mask16, token_type_ids=input_segments16) \n","features16 = last_hidden_states16[0][:,0,:].numpy()\n","features16    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.1645118 , -0.08159038, -0.28868464, ..., -1.1377177 ,\n","         0.19367465,  0.4910545 ],\n","       [-1.0563453 , -0.14460485, -0.5375086 , ..., -1.2010411 ,\n","        -0.02683641,  0.5968748 ],\n","       [-0.75979847, -0.03825461,  0.10768998, ..., -0.93488234,\n","         0.13480282,  0.71413827],\n","       ...,\n","       [-0.51698416, -0.17140767, -0.5833566 , ..., -0.8569129 ,\n","         0.20680729,  0.51323295],\n","       [-0.5848157 , -0.01480818, -0.0695256 , ..., -1.0982612 ,\n","         0.17077155,  0.5416513 ],\n","       [-0.211759  , -0.08804377,  0.28183404, ..., -0.71317226,\n","         0.79882747,  0.5844663 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"jfq3WVjwLee9"},"source":["#**df17**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BwAuoQlaLion","executionInfo":{"status":"ok","timestamp":1621294857527,"user_tz":-60,"elapsed":76691,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3e60f755-feb2-444e-a9c4-ac74f55d7c2f"},"source":["pair17_1= df17['Title1'] + df17['Description1']\n","tokenized17_1 = pair17_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair17_2=  df17['Title2'] + df17['Description2'] + [\" [SEP] \"]\n","tokenized17_2 = pair17_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized17 = tokenized17_1 + tokenized17_2 \n","\n","max_len17 = 0                 # padding all lists to the same size\n","for i in tokenized17.values:\n","    if len(i) > max_len17:\n","        max_len17 = len(i)\n","max_len17=300\n","padded17 = np.array([i + [0]*(max_len17-len(i)) for i in tokenized17.values])\n","\n","np.array(padded17).shape       \n","\n","attention_mask17 = np.where(padded17 != 0, 1, 0)\n","attention_mask17.shape\n","input_ids17 = torch.tensor(padded17)  \n","attention_mask17 = torch.tensor(attention_mask17)\n","input_segments17= np.array([_get_segments3(token, max_len17)for token in tokenized17.values])\n","token_type_ids17 = torch.tensor(input_segments17)\n","input_segments17 = torch.tensor(input_segments17)\n","\n","with torch.no_grad():\n","    last_hidden_states17 = model(input_ids17, attention_mask=attention_mask17, token_type_ids=input_segments17)\n","features17 = last_hidden_states17[0][:,0,:].numpy()\n","features17    "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.78435516,  0.09102303, -0.40447772, ..., -1.0782113 ,\n","         0.16395526,  0.5756747 ],\n","       [-0.36878583,  0.33088037, -0.3502967 , ..., -1.029018  ,\n","         0.21755946,  0.5142426 ],\n","       [-0.7790864 ,  0.05711417, -0.45339012, ..., -1.2646732 ,\n","         0.15215172,  0.4933091 ],\n","       ...,\n","       [-0.5204744 , -0.11495806,  0.18309072, ..., -0.6571749 ,\n","         0.21333776,  0.66483474],\n","       [-0.4854606 , -0.20248346, -0.10727064, ..., -0.7088102 ,\n","         0.07561126,  0.50668705],\n","       [-0.4726083 , -0.3006615 ,  0.01131171, ..., -0.751304  ,\n","         0.26760477,  0.5989091 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"markdown","metadata":{"id":"2Fp-Cbw_e2TW"},"source":["#**df18**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RG2dmIavgBVQ","executionInfo":{"status":"ok","timestamp":1621294896325,"user_tz":-60,"elapsed":112133,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"1b040bf0-a949-412a-f3ab-959acb4d25d5"},"source":["pair18_1= df18['Title1'] + df18['Description1']\n","tokenized18_1 = pair18_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair18_2=  df18['Title2'] + df18['Description2'] + [\" [SEP] \"]\n","tokenized18_2 = pair18_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized18 = tokenized18_1 + tokenized18_2 \n","\n","max_len18 = 0                 # padding all lists to the same size\n","for i in tokenized18.values:\n","    if len(i) > max_len18:\n","        max_len18 = len(i)\n","max_len18=300\n","padded18 = np.array([i + [0]*(max_len18-len(i)) for i in tokenized18.values])\n","\n","np.array(padded18).shape     \n","\n","attention_mask18 = np.where(padded18 != 0, 1, 0)\n","attention_mask18.shape\n","input_ids18 = torch.tensor(padded18)  \n","attention_mask18 = torch.tensor(attention_mask18)\n","input_segments18= np.array([_get_segments3(token, max_len18)for token in tokenized18.values])\n","token_type_ids18 = torch.tensor(input_segments18)\n","input_segments18 = torch.tensor(input_segments18)\n","\n","with torch.no_grad():\n","    last_hidden_states18 = model(input_ids18, attention_mask=attention_mask18, token_type_ids=input_segments18)\n","features18 = last_hidden_states18[0][:,0,:].numpy()\n","features18"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.7717108 , -0.1240691 , -0.622713  , ..., -0.6479922 ,\n","         0.3295504 ,  0.35852036],\n","       [-1.1258471 , -0.3479055 , -1.0686003 , ..., -0.9400008 ,\n","         0.23932102,  0.14423761],\n","       [-1.3704703 , -0.18974589, -0.367852  , ..., -1.029429  ,\n","         0.14457959,  0.20676291],\n","       ...,\n","       [-0.19269753,  0.00453271,  0.1035491 , ..., -0.8124571 ,\n","         0.8087191 ,  0.48013112],\n","       [-0.42821676, -0.13646783, -0.2527251 , ..., -0.7837628 ,\n","         0.31582963,  0.6569925 ],\n","       [-0.6312388 , -0.46364272,  0.05273023, ..., -0.7532062 ,\n","         0.10419462,  0.5087463 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"iCIVAvMVe5eO"},"source":["#**df19**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kIv9C1kygDC9","executionInfo":{"status":"ok","timestamp":1621294938160,"user_tz":-60,"elapsed":150616,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2c5438ab-e45b-40f7-a409-5238e55dfbe2"},"source":["pair19_1= df19['Title1'] + df19['Description1']\n","tokenized19_1 = pair19_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair19_2=  df19['Title2'] + df19['Description2'] + [\" [SEP] \"]\n","tokenized19_2 = pair19_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized19 = tokenized19_1 + tokenized19_2 \n","\n","max_len19 = 0                 # padding all lists to the same size\n","for i in tokenized19.values:\n","    if len(i) > max_len19:\n","        max_len19 = len(i)\n","max_len19=300\n","padded19 = np.array([i + [0]*(max_len19-len(i)) for i in tokenized19.values])\n","\n","np.array(padded19).shape    \n","\n","attention_mask19 = np.where(padded19 != 0, 1, 0)\n","attention_mask19.shape\n","input_ids19 = torch.tensor(padded19)  \n","attention_mask19 = torch.tensor(attention_mask19)\n","input_segments19= np.array([_get_segments3(token, max_len19)for token in tokenized19.values])\n","token_type_ids19 = torch.tensor(input_segments19)\n","input_segments19 = torch.tensor(input_segments19)\n","\n","with torch.no_grad():\n","    last_hidden_states19 = model(input_ids19, attention_mask=attention_mask19, token_type_ids=input_segments19)\n","features19 = last_hidden_states19[0][:,0,:].numpy()\n","features19"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.98667634, -0.11157274, -0.80741   , ..., -0.7810394 ,\n","         0.46052873,  0.37985256],\n","       [-0.9708489 , -0.05238465, -0.7727669 , ..., -0.8805591 ,\n","         0.4431736 ,  0.44717863],\n","       [-0.70604604, -0.38538772, -0.19615647, ..., -0.697482  ,\n","         0.24916638,  0.55227894],\n","       ...,\n","       [-0.6923005 , -0.39972755,  0.43625608, ..., -0.9277213 ,\n","         0.07440467,  0.64886457],\n","       [-0.21035576, -0.13203467, -0.00480144, ..., -0.7742965 ,\n","         0.00856865,  0.54349333],\n","       [-0.5868797 , -0.45248738,  0.2773639 , ..., -0.8644068 ,\n","         0.1035508 ,  0.7737859 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"markdown","metadata":{"id":"abr35-RAe8dV"},"source":["#**df20**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aLeD6Gyuf8KD","executionInfo":{"status":"ok","timestamp":1621294978299,"user_tz":-60,"elapsed":187935,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"79983c68-acbd-470f-a333-275e49b8a9cd"},"source":["\n","pair20_1= df20['Title1'] + df20['Description1']\n","tokenized20_1 = pair20_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair20_2=  df20['Title2'] + df20['Description2'] + [\" [SEP] \"]\n","tokenized20_2 = pair20_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized20 = tokenized20_1 + tokenized20_2 \n","\n","max_len20 = 0                 # padding all lists to the same size\n","for i in tokenized20.values:\n","    if len(i) > max_len20:\n","        max_len20 = len(i)\n","max_len20=300\n","padded20 = np.array([i + [0]*(max_len20-len(i)) for i in tokenized20.values])\n","\n","np.array(padded20).shape        \n","\n","attention_mask20 = np.where(padded20 != 0, 1, 0)\n","attention_mask20.shape\n","input_ids20 = torch.tensor(padded20)  \n","attention_mask20 = torch.tensor(attention_mask20)\n","input_segments20= np.array([_get_segments3(token, max_len20)for token in tokenized20.values])\n","token_type_ids20 = torch.tensor(input_segments20)\n","input_segments20 = torch.tensor(input_segments20)\n","\n","with torch.no_grad():\n","    last_hidden_states20 = model(input_ids20, attention_mask=attention_mask20, token_type_ids=input_segments20)\n","features20 = last_hidden_states20[0][:,0,:].numpy()\n","features20"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.0150967 , -0.25400913, -0.5187902 , ..., -0.9620519 ,\n","         0.43129852,  0.41579223],\n","       [-1.1338849 , -0.09580976, -0.3502366 , ..., -1.0419962 ,\n","         0.60616744,  0.4900453 ],\n","       [-1.0451201 , -0.34236193, -0.6952826 , ..., -1.2241607 ,\n","         0.51094   ,  0.6073863 ],\n","       ...,\n","       [-0.4568721 , -0.70066416, -0.07922   , ..., -0.54369605,\n","         0.6131975 ,  0.2863331 ],\n","       [-0.8584459 , -0.1671505 ,  0.27942142, ..., -0.4921005 ,\n","         0.05619491,  0.4128188 ],\n","       [-0.4910427 ,  0.03321358, -0.03531507, ..., -0.8886709 ,\n","         0.5044633 ,  0.47409007]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"OfUSgZhjfI-A"},"source":["#**df21**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w0_IhvLof9c5","executionInfo":{"status":"ok","timestamp":1621295018896,"user_tz":-60,"elapsed":225763,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2238b527-8761-45ea-f807-61888673e840"},"source":["\n","pair21_1= df21['Title1'] + df21['Description1']\n","tokenized21_1 = pair21_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair21_2=  df21['Title2'] + df21['Description2'] + [\" [SEP] \"]\n","tokenized21_2 = pair21_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized21 = tokenized21_1 + tokenized21_2 \n","\n","\n","max_len21 = 0                 # padding all lists to the same size\n","for i in tokenized21.values:\n","    if len(i) > max_len21:\n","        max_len21 = len(i)\n","max_len21=300\n","padded21 = np.array([i + [0]*(max_len21-len(i)) for i in tokenized21.values])\n","\n","np.array(padded21).shape   \n","\n","attention_mask21 = np.where(padded21 != 0, 1, 0)\n","attention_mask21.shape\n","input_ids21 = torch.tensor(padded21)  \n","attention_mask21 = torch.tensor(attention_mask21)\n","input_segments21= np.array([_get_segments3(token, max_len21)for token in tokenized21.values])\n","token_type_ids21 = torch.tensor(input_segments21)\n","input_segments21 = torch.tensor(input_segments21)\n","\n","with torch.no_grad():\n","    last_hidden_states21 = model(input_ids21, attention_mask=attention_mask21, token_type_ids=input_segments21)\n","features21 = last_hidden_states21[0][:,0,:].numpy()\n","features21"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.3319684 , -0.3229488 , -0.787826  , ..., -0.64126456,\n","         0.17553517,  0.5133847 ],\n","       [-1.046668  , -0.15479995, -0.695106  , ..., -0.76472914,\n","         0.38943988,  0.44666377],\n","       [-1.0500628 , -0.48266345, -0.40872374, ..., -0.99589294,\n","         0.398098  ,  0.4770009 ],\n","       ...,\n","       [-0.8337978 , -0.5396768 , -0.38175556, ..., -0.5450827 ,\n","         0.17122209,  0.9228525 ],\n","       [-0.52883863, -0.07382848,  0.09855226, ..., -0.879496  ,\n","         0.66332036,  0.6050807 ],\n","       [-0.69276965, -0.2245767 , -0.28113693, ..., -0.9234267 ,\n","         0.25257722,  0.4384909 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"markdown","metadata":{"id":"zr_YIvreub21"},"source":["#**df22**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IYWTfJkpvYhH","executionInfo":{"status":"ok","timestamp":1621295043632,"user_tz":-60,"elapsed":246276,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"e5f414e1-21f8-4dbe-e072-107349b0f0ff"},"source":["pair22_1= df22['Title1'] + df22['Description1']\n","tokenized22_1 = pair22_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair22_2=  df22['Title2'] + df22['Description2'] + [\" [SEP] \"]\n","tokenized22_2 = pair22_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized22 = tokenized22_1 + tokenized22_2 \n","\n","max_len22 = 0                 # padding all lists to the same size\n","for i in tokenized22.values:\n","    if len(i) > max_len22:\n","        max_len22 = len(i)\n","max_len22=300\n","padded22 = np.array([i + [0]*(max_len22-len(i)) for i in tokenized22.values])\n","np.array(padded22).shape   \n","\n","attention_mask22 = np.where(padded22 != 0, 1, 0)\n","attention_mask22.shape\n","input_ids22 = torch.tensor(padded22)  \n","attention_mask22 = torch.tensor(attention_mask22)\n","input_segments22= np.array([_get_segments3(token, max_len22)for token in tokenized22.values])\n","token_type_ids22 = torch.tensor(input_segments22)\n","input_segments22 = torch.tensor(input_segments22)\n","\n","with torch.no_grad():\n","    last_hidden_states22 = model(input_ids22, attention_mask=attention_mask22, token_type_ids=input_segments22)\n","features22 = last_hidden_states22[0][:,0,:].numpy()\n","features22"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.47512624, -0.11340275, -0.04376292, ..., -0.4552569 ,\n","         0.37455752,  0.7926645 ],\n","       [-0.564063  , -0.2897263 , -0.17914487, ..., -0.6681819 ,\n","         0.37395516,  0.60282964],\n","       [-1.2417847 , -0.5371647 , -0.7787334 , ..., -0.8983654 ,\n","         0.5286114 ,  0.39187244],\n","       ...,\n","       [-0.24298175,  0.02814092, -0.19516206, ..., -0.911193  ,\n","         0.59410024,  0.47344208],\n","       [-0.53201354,  0.02557845, -0.12477157, ..., -0.99282795,\n","         0.1700637 ,  0.64021075],\n","       [-0.90918595, -0.7125028 , -0.08294608, ..., -0.93787235,\n","         0.37896106,  0.5667769 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"WsasMBu0ud9R"},"source":["#**df23**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ciD4eUC0vZ0d","executionInfo":{"status":"ok","timestamp":1621295115552,"user_tz":-60,"elapsed":32590,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"33986a41-c428-44e6-ee77-f4bdd1d424ed"},"source":["\n","pair23_1= df23['Title1'] + df23['Description1']\n","tokenized23_1 = pair23_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair23_2=  df23['Title2'] + df23['Description2'] + [\" [SEP] \"]\n","tokenized23_2 = pair23_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized23 = tokenized23_1 + tokenized23_2 \n","\n","max_len23 = 0                 # padding all lists to the same size\n","for i in tokenized23.values:\n","    if len(i) > max_len23:\n","        max_len23 = len(i)\n","max_len23=300\n","padded23 = np.array([i + [0]*(max_len23-len(i)) for i in tokenized23.values])\n","\n","np.array(padded23).shape   \n","\n","attention_mask23 = np.where(padded23 != 0, 1, 0)\n","attention_mask23.shape\n","input_ids23 = torch.tensor(padded23)  \n","attention_mask23 = torch.tensor(attention_mask23)\n","input_segments23= np.array([_get_segments3(token, max_len23)for token in tokenized23.values])\n","token_type_ids23 = torch.tensor(input_segments23)\n","input_segments23 = torch.tensor(input_segments23)\n","\n","with torch.no_grad():\n","    last_hidden_states23 = model(input_ids23, attention_mask=attention_mask23, token_type_ids=input_segments23)\n","features23 = last_hidden_states23[0][:,0,:].numpy()\n","features23"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.1697934 , -0.39938816, -0.57404083, ..., -0.899161  ,\n","        -0.06948874,  0.27391982],\n","       [-0.9393645 , -0.30244058, -0.46869898, ..., -0.9505654 ,\n","         0.13768162,  0.37148646],\n","       [-0.97729427, -0.07341684, -0.18122584, ..., -0.8524337 ,\n","         0.31165802,  0.28807884],\n","       ...,\n","       [-0.693733  , -0.25388074, -0.21483818, ..., -1.1454256 ,\n","         0.37241054,  0.63211316],\n","       [-0.23243003,  0.14514066,  0.1357318 , ..., -0.67860085,\n","         0.5776253 ,  0.503997  ],\n","       [-0.5976079 ,  0.08768367, -0.298696  , ..., -0.8817897 ,\n","         0.43359783,  0.5436856 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"markdown","metadata":{"id":"Wh3F7eOvugt3"},"source":["#**df24**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GzL-3zj2vbCz","executionInfo":{"status":"ok","timestamp":1621295165327,"user_tz":-60,"elapsed":49756,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"af1474b3-85bb-4348-c16c-65b613170e84"},"source":["\n","pair24_1= df24['Title1'] + df24['Description1']\n","tokenized24_1 = pair24_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair24_2=  df24['Title2'] + df24['Description2'] + [\" [SEP] \"]\n","tokenized24_2 = pair24_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized24 = tokenized24_1 + tokenized24_2 \n","\n","max_len24 = 0                 # padding all lists to the same size\n","for i in tokenized24.values:\n","    if len(i) > max_len24:\n","        max_len24 = len(i)\n","max_len24=300\n","padded24 = np.array([i + [0]*(max_len24-len(i)) for i in tokenized24.values])\n","np.array(padded24).shape   \n","\n","attention_mask24 = np.where(padded24 != 0, 1, 0)\n","attention_mask24.shape\n","input_ids24 = torch.tensor(padded24)  \n","attention_mask24 = torch.tensor(attention_mask24)\n","input_segments24= np.array([_get_segments3(token, max_len24)for token in tokenized24.values])\n","token_type_ids24 = torch.tensor(input_segments24)\n","input_segments24 = torch.tensor(input_segments24)\n","\n","with torch.no_grad():\n","    last_hidden_states24 = model(input_ids24, attention_mask=attention_mask24, token_type_ids=input_segments24)\n","features24 = last_hidden_states24[0][:,0,:].numpy()\n","features24"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.7535611 ,  0.19774744, -0.2017633 , ..., -0.8773727 ,\n","         0.5283627 ,  0.30960515],\n","       [-0.8252229 , -0.03530721, -0.29539856, ..., -1.0501251 ,\n","         0.56057376,  0.5650473 ],\n","       [-0.7987763 ,  0.1449089 , -0.01526497, ..., -1.0057086 ,\n","         0.67298514,  0.46888915],\n","       ...,\n","       [-0.36776283, -0.2897454 ,  0.14100806, ..., -0.74842757,\n","         0.4470607 ,  0.4224291 ],\n","       [-0.97080976, -0.5489768 , -0.29368505, ..., -0.73443043,\n","         0.00880346,  0.6028512 ],\n","       [-0.4384143 , -0.2677996 ,  0.10872191, ..., -0.8961506 ,\n","         0.32807347,  0.3567047 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"yv0oIj3fujmJ"},"source":["#**df25**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JDXWdzfOvFgq","executionInfo":{"status":"ok","timestamp":1621295220151,"user_tz":-60,"elapsed":104562,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3356106f-d0c1-49ed-cdeb-98b48d7b9776"},"source":["\n","pair25_1= df25['Title1'] + df25['Description1']\n","tokenized25_1 = pair25_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair25_2=  df25['Title2'] + df25['Description2'] + [\" [SEP] \"]\n","tokenized25_2 = pair25_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized25 = tokenized25_1 + tokenized25_2 \n","\n","\n","max_len25 = 0                 # padding all lists to the same size\n","for i in tokenized25.values:\n","    if len(i) > max_len25:\n","        max_len25 = len(i)\n","max_len25=300\n","padded25 = np.array([i + [0]*(max_len25-len(i)) for i in tokenized25.values])\n","\n","np.array(padded25).shape   \n","\n","attention_mask25 = np.where(padded25 != 0, 1, 0)\n","attention_mask25.shape\n","input_ids25 = torch.tensor(padded25)  \n","attention_mask25 = torch.tensor(attention_mask25)\n","input_segments25= np.array([_get_segments3(token, max_len25)for token in tokenized25.values])\n","token_type_ids25 = torch.tensor(input_segments25)\n","input_segments25 = torch.tensor(input_segments25)\n","\n","with torch.no_grad():\n","    last_hidden_states25 = model(input_ids25, attention_mask=attention_mask25, token_type_ids=input_segments25)\n","features25 = last_hidden_states25[0][:,0,:].numpy()\n","features25"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.0390332 ,  0.01969299, -0.59267473, ..., -0.7710288 ,\n","         0.17892659,  0.80974853],\n","       [-1.1648864 , -0.47875407, -0.6029652 , ..., -0.6611392 ,\n","         0.25938013,  0.61807734],\n","       [-0.9970298 , -0.12700206, -0.5901304 , ..., -0.7755313 ,\n","         0.24222064,  0.68291354],\n","       ...,\n","       [-0.46649033, -0.28420627, -0.0247592 , ..., -0.71058714,\n","         0.15861082,  0.5015246 ],\n","       [-0.56439596, -0.1636404 , -0.09751624, ..., -0.58296525,\n","         0.37247625,  0.49289796],\n","       [-0.9009748 , -0.64925593,  0.4526653 , ..., -0.8855894 ,\n","        -0.1245965 ,  0.02678629]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"markdown","metadata":{"id":"bdt_480Cul5Y"},"source":["#**df26**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y8nMJ1KQvHt3","executionInfo":{"status":"ok","timestamp":1621295260069,"user_tz":-60,"elapsed":144468,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3ec2044a-e662-47a4-8a36-9b687c4a3ce1"},"source":["pair26_1= df26['Title1'] + df26['Description1']\n","tokenized26_1 = pair26_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair26_2=  df26['Title2'] + df26['Description2'] + [\" [SEP] \"]\n","tokenized26_2 = pair26_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized26 = tokenized26_1 + tokenized26_2 \n","\n","max_len26 = 0                 # padding all lists to the same size\n","for i in tokenized26.values:\n","    if len(i) > max_len26:\n","        max_len26 = len(i)\n","max_len26=300\n","padded26 = np.array([i + [0]*(max_len26-len(i)) for i in tokenized26.values])\n","\n","np.array(padded26).shape   \n","\n","attention_mask26 = np.where(padded26 != 0, 1, 0)\n","attention_mask26.shape\n","input_ids26 = torch.tensor(padded26)  \n","attention_mask26 = torch.tensor(attention_mask26)\n","input_segments26= np.array([_get_segments3(token, max_len26)for token in tokenized26.values])\n","token_type_ids26 = torch.tensor(input_segments26)\n","input_segments26 = torch.tensor(input_segments26)\n","\n","with torch.no_grad():\n","    last_hidden_states26 = model(input_ids26, attention_mask=attention_mask26, token_type_ids=input_segments26)\n","features26 = last_hidden_states26[0][:,0,:].numpy()\n","features26"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.78322977, -0.33811417, -0.3422635 , ..., -0.76769614,\n","        -0.08031312,  0.7175272 ],\n","       [-0.89401466, -0.1431471 , -0.45462698, ..., -0.92973477,\n","         0.19659013,  0.59760374],\n","       [-1.1356161 , -0.2847324 , -0.48872843, ..., -0.9545001 ,\n","         0.27668464,  0.51989204],\n","       ...,\n","       [-0.44618037, -0.34087825, -0.03756937, ..., -0.7246386 ,\n","         0.19576843,  0.48222452],\n","       [-0.8666359 , -0.31289792,  0.22294344, ..., -0.47105864,\n","         0.08765298,  0.21511568],\n","       [-0.92532164, -0.46061158, -0.07328541, ..., -0.8741189 ,\n","        -0.2875784 ,  0.78856146]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"Us8kmV9suo7q"},"source":["#**df27**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QkgDrZddvelN","executionInfo":{"status":"ok","timestamp":1621295299928,"user_tz":-60,"elapsed":54550,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2db64cc2-e4b9-4f7e-ea02-c75bac404827"},"source":["\n","pair27_1= df27['Title1'] + df27['Description1']\n","tokenized27_1 = pair27_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair27_2=  df27['Title2'] + df27['Description2'] + [\" [SEP] \"]\n","tokenized27_2 = pair27_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized27 = tokenized27_1 + tokenized27_2 \n","\n","max_len27 = 0                 # padding all lists to the same size\n","for i in tokenized27.values:\n","    if len(i) > max_len27:\n","        max_len27 = len(i)\n","max_len27=300\n","padded27 = np.array([i + [0]*(max_len27-len(i)) for i in tokenized27.values])\n","\n","np.array(padded27).shape   \n","\n","attention_mask27 = np.where(padded27 != 0, 1, 0)\n","attention_mask27.shape\n","input_ids27 = torch.tensor(padded27)  \n","attention_mask27 = torch.tensor(attention_mask27)\n","input_segments27= np.array([_get_segments3(token, max_len27)for token in tokenized27.values])\n","token_type_ids27 = torch.tensor(input_segments27)\n","input_segments27 = torch.tensor(input_segments27)\n","\n","with torch.no_grad():\n","    last_hidden_states27 = model(input_ids27, attention_mask=attention_mask27, token_type_ids=input_segments27)\n","features27 = last_hidden_states27[0][:,0,:].numpy()\n","features27"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.7513677 , -0.35763142, -0.6980059 , ..., -1.0390716 ,\n","         0.26798323,  0.61422056],\n","       [-1.0063967 , -0.47107723, -0.48950505, ..., -0.978051  ,\n","         0.13629137,  0.49008244],\n","       [-0.88592464, -0.48334095, -0.46533874, ..., -1.0271544 ,\n","         0.1333541 ,  0.51148236],\n","       ...,\n","       [-0.911986  , -0.3462242 , -0.10145118, ..., -0.5805561 ,\n","        -0.11432099,  0.23959145],\n","       [-0.9002802 , -0.2490642 ,  0.28323624, ..., -0.8922608 ,\n","         0.36913162,  0.6854232 ],\n","       [-0.680914  , -0.79856163, -0.06113893, ..., -0.9368848 ,\n","         0.256337  ,  0.4251777 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"Uph3ij1pusnG"},"source":["#**df28**"]},{"cell_type":"code","metadata":{"id":"zZPSypq4ZjTb"},"source":["pair28_1= df28['Title1'] + df28['Description1']\n","tokenized28_1 = pair28_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair28_2=  df28['Title2'] + df28['Description2'] + [\" [SEP] \"]\n","tokenized28_2 = pair28_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized28 = tokenized28_1 + tokenized28_2 \n","\n","max_len28 = 0                 # padding all lists to the same size\n","for i in tokenized28.values:\n","    if len(i) > max_len28:\n","        max_len28 = len(i)\n","max_len28=300\n","padded28 = np.array([i + [0]*(max_len28-len(i)) for i in tokenized28.values])\n","\n","np.array(padded28).shape   \n","\n","attention_mask28 = np.where(padded28 != 0, 1, 0)\n","attention_mask28.shape\n","input_ids28 = torch.tensor(padded28)  \n","attention_mask28 = torch.tensor(attention_mask28)\n","input_segments28= np.array([_get_segments3(token, max_len28)for token in tokenized28.values])\n","token_type_ids28 = torch.tensor(input_segments28)\n","input_segments28 = torch.tensor(input_segments28)\n","\n","with torch.no_grad():\n","    last_hidden_states28 = model(input_ids28, attention_mask=attention_mask28, token_type_ids=input_segments28)\n","features28 = last_hidden_states28[0][:,0,:].numpy()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"4Wd2GPEhuuxN"},"source":["#**df29**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VOahLiBNmqCz","executionInfo":{"status":"ok","timestamp":1621295393617,"user_tz":-60,"elapsed":40454,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"8ffb9870-4c32-4340-c850-77fa397ea5ae"},"source":["pair29_1= df29['Title1'] + df29['Description1']\n","tokenized29_1 = pair29_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair29_2=  df29['Title2'] + df29['Description2'] + [\" [SEP] \"]\n","tokenized29_2 = pair29_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized29 = tokenized29_1 + tokenized29_2 \n","\n","max_len29 = 0                 # padding all lists to the same size\n","for i in tokenized29.values:\n","    if len(i) > max_len29:\n","        max_len29 = len(i)\n","max_len29=300\n","padded29 = np.array([i + [0]*(max_len29-len(i)) for i in tokenized29.values])\n","\n","np.array(padded29).shape   \n","\n","attention_mask29 = np.where(padded29 != 0, 1, 0)\n","attention_mask29.shape\n","input_ids29 = torch.tensor(padded29)  \n","attention_mask29 = torch.tensor(attention_mask29)\n","input_segments29= np.array([_get_segments3(token, max_len29)for token in tokenized29.values])\n","token_type_ids29 = torch.tensor(input_segments29)\n","input_segments29 = torch.tensor(input_segments29)\n","\n","with torch.no_grad():\n","    last_hidden_states29 = model(input_ids29, attention_mask=attention_mask29, token_type_ids=input_segments29)\n","features29 = last_hidden_states29[0][:,0,:].numpy()\n","features29"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.6442252 , -0.02765663, -0.09096211, ..., -1.1406581 ,\n","         0.3638043 ,  0.30675036],\n","       [-0.7302427 , -0.26206082, -0.32128623, ..., -1.0565829 ,\n","         0.19180779,  0.5764701 ],\n","       [-0.65673065, -0.96012205,  0.2668043 , ..., -0.82881415,\n","        -0.06950548,  0.70626915],\n","       ...,\n","       [-0.6483737 , -0.20846057, -0.1105257 , ..., -0.8626772 ,\n","         0.43319142,  0.47293282],\n","       [-0.8211483 ,  0.0738892 ,  0.1241888 , ..., -0.90354365,\n","        -0.02187373,  0.5640604 ],\n","       [-0.4484957 , -0.05985481, -0.09319164, ..., -0.8906582 ,\n","         0.31014693,  0.3659551 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"markdown","metadata":{"id":"VhtNowb-uw75"},"source":["#**df30**"]},{"cell_type":"code","metadata":{"id":"5K2h0TYgvjDd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621295442378,"user_tz":-60,"elapsed":86042,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"a070f75f-fbe3-43a3-b022-24bee4eba689"},"source":["\n","\n","pair30_1= df30['Title1'] + df30['Description1']\n","tokenized30_1 = pair30_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair30_2=  df30['Title2'] + df30['Description2'] + [\" [SEP] \"]\n","tokenized30_2 = pair30_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized30 = tokenized30_1 + tokenized30_2 \n","\n","max_len30 = 0                 # padding all lists to the same size\n","for i in tokenized30.values:\n","    if len(i) > max_len30:\n","        max_len30 = len(i)\n","max_len30=400\n","padded30 = np.array([i + [0]*(max_len30-len(i)) for i in tokenized30.values])\n","\n","np.array(padded30).shape   \n","\n","attention_mask30 = np.where(padded30 != 0, 1, 0)\n","attention_mask30.shape\n","input_ids30 = torch.tensor(padded30)  \n","attention_mask30 = torch.tensor(attention_mask30)\n","input_segments30= np.array([_get_segments3(token, max_len30)for token in tokenized30.values])\n","token_type_ids30 = torch.tensor(input_segments30)\n","input_segments30 = torch.tensor(input_segments30)\n","\n","with torch.no_grad():\n","    last_hidden_states30 = model(input_ids30, attention_mask=attention_mask30, token_type_ids=input_segments30)\n","features30 = last_hidden_states30[0][:,0,:].numpy()\n","features30"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.4453059 , -0.14734341, -0.07163807, ..., -1.050628  ,\n","        -0.14134096,  0.6420791 ],\n","       [-0.78169966,  0.06717974, -0.16056736, ..., -0.7080422 ,\n","         0.11159071,  0.6453743 ],\n","       [-0.3924361 ,  0.01668293, -0.3393484 , ..., -0.9771536 ,\n","         0.1338701 ,  0.43819675],\n","       ...,\n","       [-0.58877724, -0.51785   , -0.02094198, ..., -0.8006971 ,\n","         0.40676105,  0.46239504],\n","       [ 0.06780341,  0.01076564,  0.23148802, ..., -0.74949974,\n","         0.526388  ,  0.5184697 ],\n","       [-0.6755459 , -0.4624003 ,  0.50151443, ..., -0.5038084 ,\n","         0.07889253,  0.384119  ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2q_qQwvxuy--"},"source":["#**df32**"]},{"cell_type":"code","metadata":{"id":"9A5DutfSvO3r"},"source":["#pair31= df31['Title1'] + [\" [SEP] \"] + df31['Title2'] \n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J-b3kFUXvk08","executionInfo":{"status":"ok","timestamp":1620000026472,"user_tz":-60,"elapsed":330065,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2b318a39-763d-4a64-9c96-9c272339c1a1"},"source":["pair31_1= df31['Title1'] + df31['Description1']\n","tokenized31_1 = pair31_1.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=150)))\n","\n","pair31_2=  df31['Title2'] + df31['Description2'] + [\" [SEP] \"]\n","tokenized31_2 = pair31_2.apply((lambda x: tokenizer.encode(x, add_special_tokens=False,truncation=True, max_length=150)))\n","\n","tokenized31 = tokenized31_1 + tokenized31_2 \n","\n","max_len31 = 0                 # padding all lists to the same size\n","for i in tokenized31.values:\n","    if len(i) > max_len31:\n","        max_len31 = len(i)\n","max_len31=300\n","padded31 = np.array([i + [0]*(max_len31-len(i)) for i in tokenized31.values])\n","\n","np.array(padded31).shape   \n","\n","attention_mask31 = np.where(padded31 != 0, 1, 0)\n","attention_mask31.shape\n","input_ids31 = torch.tensor(padded31)  \n","attention_mask31 = torch.tensor(attention_mask31)\n","input_segments31= np.array([_get_segments3(token, max_len31)for token in tokenized31.values])\n","token_type_ids31 = torch.tensor(input_segments31)\n","input_segments31 = torch.tensor(input_segments31)\n","\n","with torch.no_grad():\n","    last_hidden_states31 = model(input_ids31, attention_mask=attention_mask31, token_type_ids=input_segments31)\n","features31 = last_hidden_states31[0][:,0,:].numpy()\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.65374637, -0.25770128, -0.27137473, ..., -1.055062  ,\n","         0.27409405,  0.5575747 ],\n","       [-0.9090233 , -0.67389584, -0.16870375, ..., -0.9468148 ,\n","         0.02032401,  0.6758954 ],\n","       [-0.86842585, -0.39240825,  0.48261124, ..., -0.8012018 ,\n","         0.08503567,  0.53989166],\n","       ...,\n","       [-0.4355205 , -0.27902588,  0.21604487, ..., -1.0098826 ,\n","        -0.19656669,  0.82929754],\n","       [-0.68936384, -0.39621878, -0.33253226, ..., -0.87420696,\n","         0.70330465,  0.85867214],\n","       [-0.7667994 , -0.06452248,  0.49026033, ..., -0.7198353 ,\n","        -0.33611625,  0.5437544 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"markdown","metadata":{"id":"T-y98h_O17Es"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"z6m1jSq0fvSK"},"source":["#**Classification**"]},{"cell_type":"code","metadata":{"id":"n_ehQvqlzpKb"},"source":["features=np.concatenate([features3,features4,features5,features6,features7,features8,features9,features10,features11,features12,features13,features14,features15,features16,features17,features18,features19,features20,features21,features22,features23,features24,features25,features26,features27,features29, features30, features28])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_fQOGOczXPSr"},"source":["features=np.concatenate([features3,features4,features5,features6,features7,features8,features9,features10,features11])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eWLJczI21W7N","executionInfo":{"status":"ok","timestamp":1619394572887,"user_tz":-60,"elapsed":712,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"a7badac4-12d0-4841-ff79-8257a989c50f"},"source":["features.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(18500, 768)"]},"metadata":{"tags":[]},"execution_count":72}]},{"cell_type":"code","metadata":{"id":"eoP6quJLy7xr"},"source":["Total = pd.concat([df3,df4,df5,df6,df7,df8,df9,df10,df11,df12,df13,df14,df15,df16,df17,df18,df19,df20,df21,df22,df23,df24,df25,df26,df27,df29,df30, df28], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"axi2Jeq5zfpG"},"source":["Total = pd.concat([df3,df4,df5,df6,df7,df8,df9,df10,df11], ignore_index=True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N_15I5LWzLoQ","executionInfo":{"status":"ok","timestamp":1621295713383,"user_tz":-60,"elapsed":611,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"01de33db-f1d0-441f-fbf1-301dd18c0f74"},"source":["labels =Total['Label']\n","labels"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0        1\n","1        1\n","2        1\n","3        1\n","4        1\n","        ..\n","18995    0\n","18996    0\n","18997    0\n","18998    0\n","18999    0\n","Name: Label, Length: 19000, dtype: int64"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"id":"S0o3eJqySYM9"},"source":["corrmat = Total.corr()\n","import seaborn as sns\n","  \n","f, ax = plt.subplots(figsize =(9, 8))\n","sns.heatmap(corrmat, ax = ax, cmap =\"YlGnBu\", linewidths = 0.1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":299},"id":"s2v0urb_swV_","executionInfo":{"status":"ok","timestamp":1619357792844,"user_tz":-60,"elapsed":3143,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"c7638aa5-1fdf-4e43-eb70-4b37b14c7687"},"source":["from numpy import mean\n","from numpy import std\n","from numpy.random import randn\n","from numpy.random import seed\n","from matplotlib import pyplot\n","# seed random number generator\n","seed(1)\n","# prepare data\n","data1= features3\n","data2= features4\n","# summarize\n","print('data1: mean=%.3f stdv=%.3f' % (mean(data1), std(data1)))\n","print('data2: mean=%.3f stdv=%.3f' % (mean(data2), std(data2)))\n","# plot\n","pyplot.scatter(data1, data2)\n","pyplot.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["data1: mean=-0.015 stdv=0.601\n","data2: mean=-0.015 stdv=0.600\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KYXzWvCX0Oi4","executionInfo":{"status":"ok","timestamp":1619749727549,"user_tz":-60,"elapsed":159549,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"48a73bc7-d864-4e84-9851-f01cdf0e77e5"},"source":["from sklearn.manifold import TSNE\n","m=TSNE(learning_rate=50 , perplexity=30,  verbose=1)\n","#features= m.fit_transform(df_numeric)\n","\n","tsne_train = m.fit_transform(features) "],"execution_count":null,"outputs":[{"output_type":"stream","text":["[t-SNE] Computing 91 nearest neighbors...\n","[t-SNE] Indexed 8712 samples in 0.966s...\n","[t-SNE] Computed neighbors for 8712 samples in 138.660s...\n","[t-SNE] Computed conditional probabilities for sample 1000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 2000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 3000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 4000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 5000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 6000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 7000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 8000 / 8712\n","[t-SNE] Computed conditional probabilities for sample 8712 / 8712\n","[t-SNE] Mean sigma: 1.410274\n","[t-SNE] KL divergence after 250 iterations with early exaggeration: 86.063347\n","[t-SNE] KL divergence after 1000 iterations: 2.498612\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TQ9mf2Ig4o1N"},"source":["from sklearn.manifold import TSNE\n","m=TSNE(learning_rate=50,  verbose=1)\n","#features= m.fit_transform(df_numeric)\n","\n","tsne_em = m.fit_transform(test_features) \n","\n","\n","from bioinfokit.visuz import cluster\n","#cluster.tsneplot(score=tsne_em)\n","cluster.tsneplot(score=tsne_em)\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kjRRFyhMLc7b","executionInfo":{"status":"ok","timestamp":1620000465704,"user_tz":-60,"elapsed":101996,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"e114c97e-115a-462f-9eff-e680a884e3d2"},"source":["from sklearn.manifold import TSNE\n","from bioinfokit.visuz import cluster\n","m=TSNE(learning_rate=50, verbose=1)\n","tsne_em = m.fit_transform(train_features) \n","cluster.tsneplot(score=tsne_em)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[t-SNE] Computing 91 nearest neighbors...\n","[t-SNE] Indexed 15200 samples in 1.871s...\n","[t-SNE] Computed neighbors for 15200 samples in 353.528s...\n","[t-SNE] Computed conditional probabilities for sample 1000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 2000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 3000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 4000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 5000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 6000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 7000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 8000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 9000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 10000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 11000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 12000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 13000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 14000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 15000 / 15200\n","[t-SNE] Computed conditional probabilities for sample 15200 / 15200\n","[t-SNE] Mean sigma: 1.418431\n","[t-SNE] KL divergence after 250 iterations with early exaggeration: 89.682816\n"],"name":"stdout"},{"output_type":"stream","text":["findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"],"name":"stderr"},{"output_type":"stream","text":["[t-SNE] KL divergence after 1000 iterations: 2.924733\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XUdlWDAELf-M"},"source":["color_class = train_labels.to_numpy()\n","cluster.tsneplot(score=tsne_em, colorlist=color_class, colordot=( '#B0413E','#4381C1'), legendpos='upper right' )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WomzSTKDPOLL","executionInfo":{"status":"ok","timestamp":1619827441053,"user_tz":-60,"elapsed":956,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3c1d3c48-b95d-4a72-ea10-4aa97962e209"},"source":["train_features"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-0.5893915 , -0.37842947,  0.2480471 , ..., -0.70812   ,\n","         0.5106978 ,  0.5359587 ],\n","       [-1.0844721 , -0.27785385, -0.5645715 , ..., -1.1021416 ,\n","         0.18319096,  0.56342137],\n","       [-1.507599  , -0.26869115, -0.5008892 , ..., -0.9228428 ,\n","         0.0713805 ,  0.63753456],\n","       ...,\n","       [-0.26079848, -0.04462294, -0.08351172, ..., -0.7957429 ,\n","         0.382503  ,  0.5617432 ],\n","       [-0.53201354,  0.02557845, -0.12477157, ..., -0.99282795,\n","         0.1700637 ,  0.64021075],\n","       [-0.90918595, -0.7125028 , -0.08294608, ..., -0.93787235,\n","         0.37896106,  0.5667769 ]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":55}]},{"cell_type":"markdown","metadata":{"id":"qs5eiuYdG2pW"},"source":["hold out "]},{"cell_type":"code","metadata":{"id":"j_hjZHRuiiD6"},"source":["train_features = features[0:3486]\n","train_labels = labels[0:3486]\n","test_features = features[3486:]\n","test_labels = labels[3486:]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"M4JfwFka81mM"},"source":["train_features = features[0:15200]\n","train_labels = labels[0:15200]\n","test_features = features[15200:]\n","test_labels = labels[15200:]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mQjTndYjcJTV","executionInfo":{"status":"ok","timestamp":1619394609233,"user_tz":-60,"elapsed":1310,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"afd228ec-253c-4b83-ae0e-9b417f876370"},"source":["test_features.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3700, 768)"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"UqHZPrNpGsfQ"},"source":["train_features, test_features, train_labels, test_labels = train_test_split(features, labels,test_size=0.2,random_state=42)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nG_ymUrw3ozc"},"source":["#**LogisticRegression Optimized**\n","\n","---\n","\n"]},{"cell_type":"code","metadata":{"id":"kM9yUlFcybbL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621295827411,"user_tz":-60,"elapsed":62603,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"6a5d2f30-ad5c-4d62-baa0-45c3434e3ad9"},"source":["#n_splits=2\n","#cross_val_score=5\n","parameters = {'C': np.linspace(0.0001, 100, 20)}\n","grid_search = GridSearchCV(LogisticRegression(), parameters, cv=5)\n","grid_search.fit(train_features, train_labels)\n","print('best parameters: ', grid_search.best_params_)\n","print('best scrores: ', grid_search.best_score_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["best parameters:  {'C': 5.263252631578947}\n","best scrores:  0.9099342105263158\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DKr7gDRz5S2L","executionInfo":{"status":"ok","timestamp":1621282555218,"user_tz":-60,"elapsed":16186,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"0cba8424-d961-4f14-d4e3-a0ba1ea6f1ce"},"source":["#n_splits=2\n","#cross_val_score=5\n","parameters = {'C': np.linspace(0.0001, 100, 20)}\n","grid_search = GridSearchCV(LogisticRegression(), parameters, cv=5)\n","grid_search.fit(train_features, train_labels)\n","print('best parameters: ', grid_search.best_params_)\n","print('best scrores: ', grid_search.best_score_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["best parameters:  {'C': 47.36847368421053}\n","best scrores:  0.9552638610829055\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7rm32RA-HgBd","executionInfo":{"status":"ok","timestamp":1621295850823,"user_tz":-60,"elapsed":2419,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"d63cb765-9b05-473a-8044-91b25b835a9f"},"source":["lr_clf = LogisticRegression(C=5.26)\n","lr_clf.fit(train_features, train_labels)\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LogisticRegression(C=5.26, class_weight=None, dual=False, fit_intercept=True,\n","                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n","                   multi_class='auto', n_jobs=None, penalty='l2',\n","                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n","                   warm_start=False)"]},"metadata":{"tags":[]},"execution_count":52}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8sm8PZlaHo-b","executionInfo":{"status":"ok","timestamp":1621295856896,"user_tz":-60,"elapsed":1795,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"7d18db43-b609-4440-f5c7-8a49f1d3f924"},"source":["lr_clf.score(test_features, test_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9171052631578948"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"id":"0ICHUVuhRs3X","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621295869378,"user_tz":-60,"elapsed":1281,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3d1f9255-1500-4c00-ab0b-2293d9c8a1e4"},"source":["y_predLr = lr_clf.predict(test_features)\n","np.set_printoptions(threshold=np.inf)\n","y_predLr"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n","       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0,\n","       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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confusion_matrix\n","print(classification_report(test_labels,y_predLr))\n","print(confusion_matrix(test_labels, y_predLr))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.92      0.93      2356\n","           1       0.88      0.90      0.89      1444\n","\n","    accuracy                           0.92      3800\n","   macro avg       0.91      0.91      0.91      3800\n","weighted avg       0.92      0.92      0.92      3800\n","\n","[[2179  177]\n"," [ 138 1306]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W6uV_wgzvP53","executionInfo":{"status":"ok","timestamp":1621295879610,"user_tz":-60,"elapsed":1118,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"8fd9a087-92fb-406e-9be0-d4252e965717"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, y_predLr))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9171052631578948\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dZ6fZqC6Wo7v","executionInfo":{"elapsed":6985,"status":"ok","timestamp":1617645365302,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"e0daa004-b4c6-4848-db02-d802aef951e6"},"source":["scores = cross_val_score(lr_clf, features, labels,cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.888 (std: 0.008)\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"OwB70CQCpWsQ","executionInfo":{"status":"ok","timestamp":1621295884056,"user_tz":-60,"elapsed":2348,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"96997e8f-6f47-47de-9e09-86f7c080c8ad"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_predLr)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"GklbE2mcyFKb"},"source":["#**Decision tree**"]},{"cell_type":"code","metadata":{"id":"9tW7vrR9yQaZ"},"source":["from sklearn.tree import DecisionTreeClassifier"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2AphF0d1yX37"},"source":["clf = DecisionTreeClassifier(max_depth = 500, random_state = 0,criterion='gini')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"cLU-AQqIxzJI"},"source":["clf = DecisionTreeClassifier(max_depth = 500, random_state = 0)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ygtC-1A9ycDW","executionInfo":{"status":"ok","timestamp":1621295944682,"user_tz":-60,"elapsed":49241,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"8d2494e9-fdb3-4252-f6e9-2f5e5ff6ea93"},"source":["clf.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n","                       max_depth=500, max_features=None, max_leaf_nodes=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, presort='deprecated',\n","                       random_state=0, splitter='best')"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"id":"JvKjL68bSqi2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621295944683,"user_tz":-60,"elapsed":47100,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"0fe68cfc-5f9f-493b-c79e-9b4c21bf2c40"},"source":["y_preddt = clf.predict(test_features)\n","np.set_printoptions(threshold=np.inf)\n","y_preddt"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,\n","       1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       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classification_report, confusion_matrix\n","print(classification_report(test_labels,y_preddt))\n","print(confusion_matrix(test_labels, y_preddt))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.91      0.84      0.87      2356\n","           1       0.76      0.86      0.81      1444\n","\n","    accuracy                           0.85      3800\n","   macro avg       0.84      0.85      0.84      3800\n","weighted avg       0.85      0.85      0.85      3800\n","\n","[[1970  386]\n"," [ 197 1247]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ehJDtCspyJLW","colab":{"base_uri":"https://localhost:8080/","height":525},"executionInfo":{"status":"ok","timestamp":1621295945162,"user_tz":-60,"elapsed":31035,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"89fb71a1-8f83-4284-9fa1-4ef837339b61"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_preddt)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7eIUnRYivoEP","executionInfo":{"status":"ok","timestamp":1621295945164,"user_tz":-60,"elapsed":27474,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"8cfdcfd2-9b62-447b-cc12-4b7ddf61c8ad"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, y_preddt))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.8465789473684211\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"WrVSkqR0y71G","executionInfo":{"elapsed":650,"status":"ok","timestamp":1601919492440,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"d6d6b039-14f0-4d2a-b01b-d2999188066a"},"source":["# The score method returns the accuracy of the model\n","score = clf.score(test_features, test_labels)\n","print(score)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.5765\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9tR2H-yHmxqJ","executionInfo":{"elapsed":295086,"status":"ok","timestamp":1617645691484,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"77a23a1b-40cc-4330-c167-7afbf8a085cc"},"source":["scores = cross_val_score(clf, features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.814 (std: 0.005)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZkQR7uLE22Y0"},"source":["#**SVM**"]},{"cell_type":"code","metadata":{"id":"T16t0BN2thID"},"source":["from sklearn.svm import SVC"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q6CgvC0n21oc","executionInfo":{"status":"ok","timestamp":1621296065692,"user_tz":-60,"elapsed":102980,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"942a317c-1bb7-42af-9ba7-a00cca41daf1"},"source":["\n","svclassifier = SVC(kernel='linear')\n","svclassifier.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n","    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n","    max_iter=-1, probability=False, random_state=None, shrinking=True,\n","    tol=0.001, verbose=False)"]},"metadata":{"tags":[]},"execution_count":67}]},{"cell_type":"code","metadata":{"id":"DLyTKn6K3yqj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621296076415,"user_tz":-60,"elapsed":111455,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"6e76049d-f11f-40bd-e629-a7dcadf3c3a2"},"source":["y_pred = svclassifier.predict(test_features)\n","np.set_printoptions(threshold=np.inf)\n","y_pred"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0,\n","       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 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0])"]},"metadata":{"tags":[]},"execution_count":68}]},{"cell_type":"markdown","metadata":{"id":"WrkMmKZUkQPE"},"source":["Kernel : Sigmoid: 0.88 / Linear: 0.92/ rbf:0.9 / poly : 0.9"]},{"cell_type":"code","metadata":{"id":"qmHE07TI4HCh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621296076416,"user_tz":-60,"elapsed":107804,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"60b9f573-4df0-498a-fe67-5824a97e6451"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(confusion_matrix(test_labels,y_pred))\n","print(classification_report(test_labels,y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[2178  178]\n"," [ 138 1306]]\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.92      0.93      2356\n","           1       0.88      0.90      0.89      1444\n","\n","    accuracy                           0.92      3800\n","   macro avg       0.91      0.91      0.91      3800\n","weighted avg       0.92      0.92      0.92      3800\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"RyhSG9fIyGym","colab":{"base_uri":"https://localhost:8080/","height":520},"executionInfo":{"status":"ok","timestamp":1621296077272,"user_tz":-60,"elapsed":106443,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"eb338aeb-61d8-488a-a8c5-0b904c04aa13"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_pred)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rQXTzIAKvxpe","executionInfo":{"status":"ok","timestamp":1621296077272,"user_tz":-60,"elapsed":103458,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"1945b5cb-b0bb-4044-8b24-10c50f8abdd1"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9168421052631579\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1Cg9zFCCZRDD"},"source":["param_grid = {'C':[1,10,100,1000],'gamma':[1,0.1,0.001,0.0001], 'kernel':['linear','rbf']}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0aIAteOTZVFI"},"source":["grid = GridSearchCV(SVC(),param_grid,refit = True, verbose=2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SvsdclfXZYtf"},"source":["grid.fit(train_features,train_labels)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Aq--IG7WZo7W"},"source":["grid.best_params_"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9g5eOxMYZvlh"},"source":["predic = grid.predict(test_features)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1bp4AxfJZ4SL"},"source":["print(classification_report(test_labels,predic))\n","print(confusion_matrix(test_labels, predic))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ry43MpkWaTix"},"source":["#Cross_Val"]},{"cell_type":"code","metadata":{"id":"NYWTVLNknWXg"},"source":["from sklearn.model_selection import cross_val_score"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"B6LUaMOyrMPF"},"source":["from sklearn import svm"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JOd-QdsqnXPE"},"source":["clf = svm.SVC(kernel='linear')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ArvTazd6qpr9","executionInfo":{"elapsed":906844,"status":"ok","timestamp":1617646830623,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"6da6ce8a-5397-48cf-ee15-cb27bddafb9f"},"source":["scores = cross_val_score(svclassifier,features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.885 (std: 0.006)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-GfWBgit7kKc"},"source":["#**MLP Best params**"]},{"cell_type":"code","metadata":{"id":"2jo__QQE7omY"},"source":["from sklearn.neural_network import MLPClassifier\n","mlp = MLPClassifier(max_iter=100)\n","from sklearn.datasets import make_classification"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fS44TAenh4zR"},"source":["parameter_space = {\n","    'hidden_layer_sizes': [(50,100,50), (50,10,50), (100,)],\n","    'activation': ['tanh', 'relu'],\n","    'solver': ['sgd', 'adam'],\n","    'alpha': [0.0001, 0.05],\n","    'learning_rate': ['constant','adaptive'],\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dnwD6IhehjFW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621296439735,"user_tz":-60,"elapsed":452055,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"14a4c5c7-bd0a-47cf-a1f7-d7d537dbdaee"},"source":["from sklearn.model_selection import GridSearchCV\n","\n","clf = GridSearchCV(mlp, parameter_space, n_jobs=-1, cv=3)\n","clf.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=3, error_score=nan,\n","             estimator=MLPClassifier(activation='relu', alpha=0.0001,\n","                                     batch_size='auto', beta_1=0.9,\n","                                     beta_2=0.999, early_stopping=False,\n","                                     epsilon=1e-08, hidden_layer_sizes=(100,),\n","                                     learning_rate='constant',\n","                                     learning_rate_init=0.001, max_fun=15000,\n","                                     max_iter=100, momentum=0.9,\n","                                     n_iter_no_change=10,\n","                                     nesterovs_momentum=True, power_t=0.5,\n","                                     random_state...\n","                                     solver='adam', tol=0.0001,\n","                                     validation_fraction=0.1, verbose=False,\n","                                     warm_start=False),\n","             iid='deprecated', n_jobs=-1,\n","             param_grid={'activation': ['tanh', 'relu'],\n","                         'alpha': [0.0001, 0.05],\n","                         'hidden_layer_sizes': [(50, 100, 50), (50, 10, 50),\n","                                                (100,)],\n","                         'learning_rate': ['constant', 'adaptive'],\n","                         'solver': ['sgd', 'adam']},\n","             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n","             scoring=None, verbose=0)"]},"metadata":{"tags":[]},"execution_count":74}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q3R7Hhe-KUWC","executionInfo":{"status":"ok","timestamp":1621296439740,"user_tz":-60,"elapsed":309276,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"1ce103b4-f852-4fc7-8292-1fd04b0eb6aa"},"source":["# Best paramete set\n","print('Best parameters found:\\n', clf.best_params_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Best parameters found:\n"," {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"D4iS97yMiCXi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621296439742,"user_tz":-60,"elapsed":307689,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"f534256c-b7b7-46a6-84ee-5dbe5bbaf49d"},"source":["\n","\n","# All results\n","means = clf.cv_results_['mean_test_score']\n","stds = clf.cv_results_['std_test_score']\n","for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n","    print(\"%0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.910 (+/-0.007) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.897 (+/-0.017) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.907 (+/-0.009) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.900 (+/-0.018) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.910 (+/-0.013) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.899 (+/-0.019) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.908 (+/-0.014) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.899 (+/-0.012) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.906 (+/-0.016) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.905 (+/-0.018) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.906 (+/-0.011) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.902 (+/-0.010) for {'activation': 'tanh', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.908 (+/-0.012) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.900 (+/-0.019) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.906 (+/-0.018) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.899 (+/-0.015) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.908 (+/-0.012) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.892 (+/-0.015) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.909 (+/-0.013) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.901 (+/-0.017) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.907 (+/-0.015) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.906 (+/-0.021) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.907 (+/-0.014) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.906 (+/-0.004) for {'activation': 'tanh', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.909 (+/-0.013) for {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.895 (+/-0.013) for {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.906 (+/-0.013) for {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (50, 100, 50), 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'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.905 (+/-0.012) for {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.898 (+/-0.009) for {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.911 (+/-0.007) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.896 (+/-0.012) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.909 (+/-0.016) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.890 (+/-0.029) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 100, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.907 (+/-0.008) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.891 (+/-0.026) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.910 (+/-0.012) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.893 (+/-0.001) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (50, 10, 50), 'learning_rate': 'adaptive', 'solver': 'adam'}\n","0.909 (+/-0.015) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'sgd'}\n","0.898 (+/-0.017) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'constant', 'solver': 'adam'}\n","0.904 (+/-0.014) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'sgd'}\n","0.905 (+/-0.015) for {'activation': 'relu', 'alpha': 0.05, 'hidden_layer_sizes': (100,), 'learning_rate': 'adaptive', 'solver': 'adam'}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ur092BrFxETo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1621296536305,"user_tz":-60,"elapsed":547,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"bd689004-786b-4e28-d363-fbc34324e5c0"},"source":["import time\n","start = time.time()\n","\n","y_true, y_pred = test_labels , clf.predict(test_features)\n","np.set_printoptions(threshold=np.inf)\n","y_pred"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n","       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n"," 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0])"]},"metadata":{"tags":[]},"execution_count":77}]},{"cell_type":"code","metadata":{"id":"B0AnzZ3ExKis"},"source":["tsne_em"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hCVtqVsfizFy","executionInfo":{"status":"ok","timestamp":1621296540441,"user_tz":-60,"elapsed":776,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3dbe6750-ac99-44e3-b1c2-fecaaa1849b6"},"source":["\n","\n","from sklearn.metrics import classification_report, confusion_matrix\n","print('Results on the test set:')\n","print(classification_report(y_true, y_pred))\n","print(confusion_matrix(y_true, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Results on the test set:\n","              precision    recall  f1-score   support\n","\n","           0       0.95      0.92      0.94      2356\n","           1       0.88      0.92      0.90      1444\n","\n","    accuracy                           0.92      3800\n","   macro avg       0.91      0.92      0.92      3800\n","weighted avg       0.92      0.92      0.92      3800\n","\n","[[2168  188]\n"," [ 112 1332]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cZAV-_g_XWcR"},"source":["history = mlp.fit(train_features, train_labels)\n","\n","plt.plot(history.history['loss'])\n","#plt.plot(model.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train'], loc='upper right')\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"CS7AUmsf6yeX","executionInfo":{"status":"ok","timestamp":1621296685512,"user_tz":-60,"elapsed":116214,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3dd460e5-c602-4dbf-ae45-0602dd7ee48f"},"source":["import matplotlib.pyplot as plt\n","mlp = MLPClassifier(hidden_layer_sizes=(50, 100, 50), max_iter=100, alpha=0.0001,\n","                    solver='sgd', verbose=0, tol=0.0001, random_state=1,\n","                    learning_rate_init=0.001)\n","\n","\"\"\" Home-made mini-batch learning\n","    -> not to be used in out-of-core setting!\n","\"\"\"\n","N_TRAIN_SAMPLES = train_features.shape[0]\n","N_EPOCHS = 100\n","N_BATCH = 128\n","N_CLASSES = np.unique(train_labels)\n","\n","scores_train = []\n","scores_test = []\n","\n","# EPOCH\n","epoch = 0\n","while epoch < N_EPOCHS:\n","    print('epoch: ', epoch)\n","    # SHUFFLING\n","    random_perm = np.random.permutation(train_features.shape[0])\n","    mini_batch_index = 0\n","    while True:\n","        # MINI-BATCH\n","        indices = random_perm[mini_batch_index:mini_batch_index + N_BATCH]\n","        mlp.partial_fit(train_features[indices], train_labels[indices], classes=N_CLASSES)\n","        mini_batch_index += N_BATCH\n","\n","        if mini_batch_index >= N_TRAIN_SAMPLES:\n","            break\n","\n","    # SCORE TRAIN\n","    scores_train.append(mlp.score(train_features, train_labels))\n","\n","    # SCORE TEST\n","    scores_test.append(mlp.score(test_features, test_labels))\n","\n","    epoch += 1\n","\n","\"\"\" Plot \"\"\"\n","fig, ax = plt.subplots(2, sharex=True, sharey=True)\n","ax[0].plot(scores_train)\n","ax[0].set_title('Train')\n","ax[1].plot(scores_test)\n","ax[1].set_title('Test')\n","fig.suptitle(\"Accuracy over epochs\", fontsize=14)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["epoch:  0\n","epoch:  1\n","epoch:  2\n","epoch:  3\n","epoch:  4\n","epoch:  5\n","epoch:  6\n","epoch:  7\n","epoch:  8\n","epoch:  9\n","epoch:  10\n","epoch:  11\n","epoch:  12\n","epoch:  13\n","epoch:  14\n","epoch:  15\n","epoch:  16\n","epoch:  17\n","epoch:  18\n","epoch:  19\n","epoch:  20\n","epoch:  21\n","epoch:  22\n","epoch:  23\n","epoch:  24\n","epoch:  25\n","epoch:  26\n","epoch:  27\n","epoch:  28\n","epoch:  29\n","epoch:  30\n","epoch:  31\n","epoch:  32\n","epoch:  33\n","epoch:  34\n","epoch:  35\n","epoch:  36\n","epoch:  37\n","epoch:  38\n","epoch:  39\n","epoch:  40\n","epoch:  41\n","epoch:  42\n","epoch:  43\n","epoch:  44\n","epoch:  45\n","epoch:  46\n","epoch:  47\n","epoch:  48\n","epoch:  49\n","epoch:  50\n","epoch:  51\n","epoch:  52\n","epoch:  53\n","epoch:  54\n","epoch:  55\n","epoch:  56\n","epoch:  57\n","epoch:  58\n","epoch:  59\n","epoch:  60\n","epoch:  61\n","epoch:  62\n","epoch:  63\n","epoch:  64\n","epoch:  65\n","epoch:  66\n","epoch:  67\n","epoch:  68\n","epoch:  69\n","epoch:  70\n","epoch:  71\n","epoch:  72\n","epoch:  73\n","epoch:  74\n","epoch:  75\n","epoch:  76\n","epoch:  77\n","epoch:  78\n","epoch:  79\n","epoch:  80\n","epoch:  81\n","epoch:  82\n","epoch:  83\n","epoch:  84\n","epoch:  85\n","epoch:  86\n","epoch:  87\n","epoch:  88\n","epoch:  89\n","epoch:  90\n","epoch:  91\n","epoch:  92\n","epoch:  93\n","epoch:  94\n","epoch:  95\n","epoch:  96\n","epoch:  97\n","epoch:  98\n","epoch:  99\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"id":"YQt35NyVAFDY","executionInfo":{"status":"ok","timestamp":1621297180794,"user_tz":-60,"elapsed":1274,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"18e881c0-31af-43a4-8145-7b97cfc65cde"},"source":["plt.plot(scores_train, alpha=0.8, label='Train')\n","plt.plot(scores_test,  alpha=0.8, label='Test')\n","plt.title(\"Model Accuracy \", fontsize=14)\n","plt.xlabel('Epochs')\n","plt.legend(loc='upper left')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"k1EPDtYux58q","executionInfo":{"status":"ok","timestamp":1621296748494,"user_tz":-60,"elapsed":22746,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"452a50a5-9e8a-4d66-f881-d5eeb23e102b"},"source":["clf = MLPClassifier(hidden_layer_sizes=(10,10,10), max_iter=100)\n","clf.fit(train_features,train_labels)\n","#plt.ylabel('cost')\n","plt.xlabel('Epochs')\n","plt.title(\"Loss\" )\n","plt.plot(clf.loss_curve_)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"lOC0ef2ZOE0j","executionInfo":{"elapsed":1533,"status":"ok","timestamp":1618064157531,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"e06f422b-6e0d-4769-d262-85b2dbbc4400"},"source":["import matplotlib.pyplot as plt\n","loss_values = clf.loss_curve_\n","plt.plot(loss_values)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"VlY9gX2voVUC","executionInfo":{"elapsed":10097,"status":"ok","timestamp":1618358823689,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"7c992b20-5f0b-464b-be9e-6588a434ac03"},"source":["    mlp=MLPClassifier(\n","        activation=\"relu\",\n","        max_iter=100, \n","        validation_fraction=0.2, \n","        early_stopping=True)\n","    mlp.fit(train_features,train_labels)\n","    #print (mlp.score(X_train,y_train))\n","    plt.plot(mlp.loss_curve_)\n","    mlp.score(test_features, test_labels)\n","    plt.plot(mlp.loss_curve_)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fe10f11b450>]"]},"metadata":{"tags":[]},"execution_count":51},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":445},"id":"8XCCK0rUSbQ8","executionInfo":{"status":"error","timestamp":1621296718361,"user_tz":-60,"elapsed":1472,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"cb702f11-eada-4403-ffa8-13e064ac6060"},"source":["plt.plot(mlp.loss_curve_,label=\"train\")\n","plt.plot(mlp.validation_scores_,label=\"test\")   #line changed\n","plt.legend()"],"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-81-919426954637>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_curve_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidation_scores_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#line changed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'MLPClassifier' object has no attribute 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"iBykQ0AKvYsL"},"source":["history= mlp=MLPClassifier(\n","        activation=\"relu\",\n","        max_iter=100, \n","        validation_fraction=0.2, \n","        early_stopping=True)\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'test'], loc='upper left')\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":313},"id":"TfezkGHdBjld","executionInfo":{"status":"ok","timestamp":1621297147390,"user_tz":-60,"elapsed":55736,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"2244ac7b-48dd-4050-d231-1b9c6b2e45af"},"source":["  import matplotlib.pyplot as plt\n","  mlp.fit(train_features,train_labels)\n","  #mlp.partial_fit(test_features,test_labels)\n","  #print (mlp.score(train_features,train_labels))\n","  #print (mlp.score(test_features,test_labels))\n","\n","  plt.plot(mlp.loss_curve_, label=\"train\")\n","  mlp.fit(test_features,test_labels)\n","  plt.title(\"Model Loss \", fontsize=14)\n","  plt.xlabel('Epochs')\n","  plt.legend(loc='upper right')\n","  plt.plot(mlp.loss_curve_, label=\"test\")\n","  plt.legend()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7fb479897a10>"]},"metadata":{"tags":[]},"execution_count":87},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":529},"id":"SNypUonLBiXI","executionInfo":{"elapsed":974,"status":"ok","timestamp":1618359310000,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"bc0a170c-7179-4df5-8afe-0f3eacd236a1"},"source":["from matplotlib import pyplot as pyplot\n","\n","loss_values = mlp.loss_curve_\n","pyplot.title('Loss / Mean Squared Error')\n","#mlp.history['val_loss']\n","pyplot.plot(loss_values)\n","pyplot.show()\n","#pyplot.plot(history['loss'], label='train')\n","pyplot.plot(['val_loss'], label='test')\n","pyplot.legend()\n","pyplot.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":495},"id":"3y8BWvX2On2c","executionInfo":{"elapsed":1779,"status":"error","timestamp":1618359213286,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"30858eb4-b0ef-43ee-fc5f-489c6cbc8b5e"},"source":["from matplotlib import pyplot as pyplot\n","# plot loss during training\n","pyplot.title('Loss / Mean Squared Error')\n","mlp.history['val_loss']\n","pyplot.plot(loss_values)\n","pyplot.show()\n","pyplot.plot(history['loss'], label='train')\n","pyplot.plot(history.history['val_loss'], label='test')\n","pyplot.legend()\n","pyplot.show()"],"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-57-44f84aa405b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# plot loss during training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Loss / Mean Squared Error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'MLPClassifier' object has no attribute 'history'"]},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7FQKuu8b9Nps","executionInfo":{"elapsed":1169,"status":"ok","timestamp":1618359222357,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"2d3cca2b-2d4c-406e-d94a-47821f6f22ed"},"source":["end = time.time()\n","print(f\"Runtime of the program is {end - start}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Runtime of the program is 680.0419142246246\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":425},"id":"6qGiLGIzfQqh","executionInfo":{"elapsed":1021,"status":"error","timestamp":1617888001446,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"f56aae86-afc6-4091-a964-a11144ee87a4"},"source":["y_pred = clf.predict(test_features)\n","indices = [i for i,v in enumerate(y_pred) if y_pred[i]!=y_true[i]]\n","subset_of_wrongly_predicted = [test_features[i] for i in indices ]"],"execution_count":null,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    354\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: 0 is not in range","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-43-15b4e2306b9b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msubset_of_wrongly_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtest_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-43-15b4e2306b9b>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msubset_of_wrongly_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtest_features\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    880\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 882\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    884\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/range.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    355\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 357\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    358\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 0"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"xm3_8wnnxafJ","executionInfo":{"status":"ok","timestamp":1621297201387,"user_tz":-60,"elapsed":1336,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"5fcd8183-9a07-4973-a977-c6ad485aad85"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(y_true,y_pred)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"OfQ3_jlMLZTJ"},"source":["y_true, y_pred = test_labels , clf.predict(test_features)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hG0MW3HzGa8T"},"source":["import operator\n","for test_input ,pred, label in zip(test_features, y_pred, test_labels):\n","  if pred != label:\n","    print(test_input, 'has been classified as' , y_pred, 'should be', y_true)\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HV96Dn8xwINN","executionInfo":{"status":"ok","timestamp":1621297217958,"user_tz":-60,"elapsed":1094,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"9d6ff697-6cf1-45eb-9d65-3e4a7efdd188"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(y_true, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9210526315789473\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W7XXMqpz8kI5","executionInfo":{"elapsed":875,"status":"ok","timestamp":1616615168623,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"54ae4da9-eaf5-480e-f896-44f0036f1e4d"},"source":["clf.score(test_features, test_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8694046654057462"]},"metadata":{"tags":[]},"execution_count":86}]},{"cell_type":"code","metadata":{"id":"qJbrz3Cb9mfo"},"source":["from sklearn.model_selection import cross_val_score"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jhUP__Kx94rC"},"source":["scores = cross_val_score(clf, test_labels, y_pred, cv=5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m49YtVHa99I9","executionInfo":{"elapsed":1794792,"status":"ok","timestamp":1617649428827,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"87f516ec-f5cc-4d95-ab1a-be17d49b43c4"},"source":["\n","scores = cross_val_score(clf, features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.892 (std: 0.006)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rkStE77lVPjB"},"source":["#**Random Forest**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Cvq-n5HVUH2","executionInfo":{"status":"ok","timestamp":1621297234106,"user_tz":-60,"elapsed":12644,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"fce25938-12df-4159-9436-a83a34781c4e"},"source":["from sklearn.ensemble import RandomForestClassifier\n","\n","rf = RandomForestClassifier(n_estimators=20, random_state=0)\n","rf.fit(train_features, train_labels)\n","y_pred1 = rf.predict(test_features)\n","y_pred1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n","       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 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0])"]},"metadata":{"tags":[]},"execution_count":92}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ORiXr8DQI5E5","executionInfo":{"status":"ok","timestamp":1621297240311,"user_tz":-60,"elapsed":553,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"4ebb406e-ae33-4535-cb32-5119569d9e41"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,y_pred1))\n","print(confusion_matrix(test_labels, y_pred1))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.95      0.92      0.93      2356\n","           1       0.88      0.91      0.89      1444\n","\n","    accuracy                           0.92      3800\n","   macro avg       0.91      0.92      0.91      3800\n","weighted avg       0.92      0.92      0.92      3800\n","\n","[[2168  188]\n"," [ 124 1320]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"vRIz1HBfx5Xv","executionInfo":{"status":"ok","timestamp":1621297248786,"user_tz":-60,"elapsed":1307,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"c509ec1d-fb8c-401b-e109-1a3f30c344a6"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_pred1)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3AOvVQIjwSQ_","executionInfo":{"status":"ok","timestamp":1621297258319,"user_tz":-60,"elapsed":590,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"daed0576-d195-4317-b0cc-a8501e814bdc"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, y_pred1))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9178947368421052\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":306},"id":"76vce9ZUVdLB","executionInfo":{"elapsed":10243,"status":"error","timestamp":1617650588815,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"2504303a-9484-47d5-c884-701bdb61c981"},"source":["scores = cross_val_score(rf, features, labels, cv=5)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-107-7abc6d5e02f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    388\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m                                 error_score=error_score)\n\u001b[0m\u001b[1;32m    391\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             error_score=error_score)\n\u001b[0;32m--> 236\u001b[0;31m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m             \u001b[0;32mif\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m 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to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    513\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    516\u001b[0m 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to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'balanced'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    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       \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 877\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m    878\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    365\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DQ-gblPatPEi","executionInfo":{"elapsed":551,"status":"ok","timestamp":1614703262384,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"6793c17e-4331-4795-8154-4c423918837b"},"source":["print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.867 (std: 0.013)\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yLS7LQUBVlgo","executionInfo":{"elapsed":66915,"status":"ok","timestamp":1617650659712,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"f2876ffb-2039-4d52-8e6a-036f14c86ce4"},"source":["scores = cross_val_score(rf, features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.872 (std: 0.012)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"UK1v4gA6-ssL"},"source":["#**Naive Bayes**"]},{"cell_type":"markdown","metadata":{"id":"fEsrF7jnB8ck"},"source":["#Gaussian"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x4-7126r-110","executionInfo":{"status":"ok","timestamp":1621297265478,"user_tz":-60,"elapsed":496,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"9a40db70-0e0b-4171-e7bb-4076e568d774"},"source":["from sklearn.naive_bayes import GaussianNB\n","gnb = GaussianNB()\n","gnb.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["GaussianNB(priors=None, var_smoothing=1e-09)"]},"metadata":{"tags":[]},"execution_count":96}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lD6bQGaX_Ch8","executionInfo":{"status":"ok","timestamp":1621297267886,"user_tz":-60,"elapsed":506,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"b53ca77c-4e74-42a5-e517-5c855f64bf49"},"source":["y_pred = gnb.predict(test_features)\n","y_pred"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n","       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n","       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n","       0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n","       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n","       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0,\n","       0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n","       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0])"]},"metadata":{"tags":[]},"execution_count":97}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gNCBuGYI_HeA","executionInfo":{"status":"ok","timestamp":1621297273056,"user_tz":-60,"elapsed":646,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"d6574f2e-665d-401f-b95a-5e5579396780"},"source":["from sklearn import metrics\n","print(\"Accuracy:\",metrics.accuracy_score(test_labels, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Accuracy: 0.8276315789473684\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZNq7MJ3eG4r1","executionInfo":{"status":"ok","timestamp":1621297280474,"user_tz":-60,"elapsed":1397,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"ad75ad46-0518-4ddb-d752-3b04fd1269b7"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,y_pred))\n","print(confusion_matrix(test_labels, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      0.76      0.84      2356\n","           1       0.70      0.94      0.81      1444\n","\n","    accuracy                           0.83      3800\n","   macro avg       0.83      0.85      0.83      3800\n","weighted avg       0.86      0.83      0.83      3800\n","\n","[[1782  574]\n"," [  81 1363]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"B5rFujwdx6_O","executionInfo":{"status":"ok","timestamp":1621297287439,"user_tz":-60,"elapsed":947,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"fdf6c612-5f3a-4f26-ab9f-b8a84c6be847"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_pred)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sfE0-AH4DltJ"},"source":["*Cross Validation*"]},{"cell_type":"code","metadata":{"id":"-mpqZDu9SJA-"},"source":["scores = cross_val_score(gnb, test_labels, y_pred, cv=5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k9n7Rtxd7avI","executionInfo":{"elapsed":1568,"status":"ok","timestamp":1617650719346,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"7a22e97b-cba5-45f3-8225-49fde862446e"},"source":["\n","scores = cross_val_score(gnb, features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.817 (std: 0.005)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KMtKlGiKkglz"},"source":["#**Optimized SVC**"]},{"cell_type":"code","metadata":{"id":"iy2vI2U3klp-"},"source":["from sklearn.svm import SVC"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BHXJjiAjkzGv"},"source":["model = SVC()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":85},"id":"QVaipTgdk1yG","executionInfo":{"elapsed":43449,"status":"ok","timestamp":1603311275655,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"0741843a-230a-4ba0-f5d9-58eb48a34526"},"source":["model.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n","    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n","    max_iter=-1, probability=False, random_state=None, shrinking=True,\n","    tol=0.001, verbose=False)"]},"metadata":{"tags":[]},"execution_count":300}]},{"cell_type":"code","metadata":{"id":"3pykStcPk-3y"},"source":["prediction = model.predict(test_features)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"8fIiNAjklJKh","executionInfo":{"elapsed":963,"status":"ok","timestamp":1603311428421,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"af081b60-7fac-4897-dadd-6f840f4d7f5d"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,prediction))\n","print(confusion_matrix(test_labels, prediction))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.90      0.97      0.94      1559\n","           1       0.94      0.83      0.88       913\n","\n","    accuracy                           0.92      2472\n","   macro avg       0.92      0.90      0.91      2472\n","weighted avg       0.92      0.92      0.92      2472\n","\n","[[1513   46]\n"," [ 159  754]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"oS0abKaklbv9"},"source":["param_grid = {'C':[1,10],'gamma':[1,0.1], 'kernel':['linear','rbf']}\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jhHpD4Gtleqr"},"source":["grid = GridSearchCV(SVC(),param_grid,refit = True, verbose=2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"aC1ULpsvlh4J","executionInfo":{"elapsed":1351373,"status":"ok","timestamp":1603315653807,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"8b18abcf-e346-40e8-de7b-b4c399a9e211"},"source":["grid.fit(train_features,train_labels)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Fitting 5 folds for each of 8 candidates, totalling 40 fits\n","[CV] C=1, gamma=1, kernel=linear .....................................\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"],"name":"stderr"},{"output_type":"stream","text":["[CV] ...................... C=1, gamma=1, kernel=linear, total=  32.9s\n","[CV] C=1, gamma=1, kernel=linear .....................................\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   32.9s remaining:    0.0s\n"],"name":"stderr"},{"output_type":"stream","text":["[CV] ...................... C=1, gamma=1, kernel=linear, total=  33.1s\n","[CV] C=1, gamma=1, kernel=linear .....................................\n","[CV] ...................... C=1, gamma=1, kernel=linear, total=  33.8s\n","[CV] C=1, gamma=1, kernel=linear .....................................\n","[CV] ...................... C=1, gamma=1, kernel=linear, total=  33.6s\n","[CV] C=1, gamma=1, kernel=linear .....................................\n","[CV] ...................... C=1, gamma=1, kernel=linear, total=  32.4s\n","[CV] C=1, gamma=1, kernel=rbf ........................................\n","[CV] ......................... C=1, gamma=1, kernel=rbf, total= 2.4min\n","[CV] C=1, gamma=1, kernel=rbf ........................................\n","[CV] ......................... C=1, gamma=1, kernel=rbf, total= 2.4min\n","[CV] C=1, gamma=1, kernel=rbf ........................................\n","[CV] ......................... C=1, gamma=1, kernel=rbf, total= 2.4min\n","[CV] C=1, gamma=1, kernel=rbf ........................................\n","[CV] ......................... C=1, gamma=1, kernel=rbf, total= 2.3min\n","[CV] C=1, gamma=1, kernel=rbf ........................................\n","[CV] ......................... C=1, gamma=1, kernel=rbf, total= 2.4min\n","[CV] C=1, gamma=0.1, kernel=linear ...................................\n","[CV] .................... C=1, gamma=0.1, kernel=linear, total=  32.4s\n","[CV] C=1, gamma=0.1, kernel=linear ...................................\n","[CV] .................... C=1, gamma=0.1, kernel=linear, total=  31.7s\n","[CV] C=1, gamma=0.1, kernel=linear ...................................\n","[CV] .................... C=1, gamma=0.1, kernel=linear, total=  31.8s\n","[CV] C=1, gamma=0.1, kernel=linear ...................................\n","[CV] .................... C=1, gamma=0.1, kernel=linear, total=  31.9s\n","[CV] C=1, gamma=0.1, kernel=linear ...................................\n","[CV] .................... C=1, gamma=0.1, kernel=linear, total=  30.6s\n","[CV] C=1, gamma=0.1, kernel=rbf ......................................\n","[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 1.4min\n","[CV] C=1, gamma=0.1, kernel=rbf ......................................\n","[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 1.4min\n","[CV] C=1, gamma=0.1, kernel=rbf ......................................\n","[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 1.4min\n","[CV] C=1, gamma=0.1, kernel=rbf ......................................\n","[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 1.4min\n","[CV] C=1, gamma=0.1, kernel=rbf ......................................\n","[CV] ....................... C=1, gamma=0.1, kernel=rbf, total= 1.4min\n","[CV] C=10, gamma=1, kernel=linear ....................................\n","[CV] ..................... C=10, gamma=1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=1, kernel=linear ....................................\n","[CV] ..................... C=10, gamma=1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=1, kernel=linear ....................................\n","[CV] ..................... C=10, gamma=1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=1, kernel=linear ....................................\n","[CV] ..................... C=10, gamma=1, kernel=linear, total= 1.2min\n","[CV] C=10, gamma=1, kernel=linear ....................................\n","[CV] ..................... C=10, gamma=1, kernel=linear, total= 1.0min\n","[CV] C=10, gamma=1, kernel=rbf .......................................\n","[CV] ........................ C=10, gamma=1, kernel=rbf, total= 4.7min\n","[CV] C=10, gamma=1, kernel=rbf .......................................\n","[CV] ........................ C=10, gamma=1, kernel=rbf, total= 4.6min\n","[CV] C=10, gamma=1, kernel=rbf .......................................\n","[CV] ........................ C=10, gamma=1, kernel=rbf, total= 4.4min\n","[CV] C=10, gamma=1, kernel=rbf .......................................\n","[CV] ........................ C=10, gamma=1, kernel=rbf, total= 4.9min\n","[CV] C=10, gamma=1, kernel=rbf .......................................\n","[CV] ........................ C=10, gamma=1, kernel=rbf, total= 4.8min\n","[CV] C=10, gamma=0.1, kernel=linear ..................................\n","[CV] ................... C=10, gamma=0.1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=0.1, kernel=linear ..................................\n","[CV] ................... C=10, gamma=0.1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=0.1, kernel=linear ..................................\n","[CV] ................... C=10, gamma=0.1, kernel=linear, total= 1.1min\n","[CV] C=10, gamma=0.1, kernel=linear ..................................\n","[CV] ................... C=10, gamma=0.1, kernel=linear, total= 1.2min\n","[CV] C=10, gamma=0.1, kernel=linear ..................................\n","[CV] ................... C=10, gamma=0.1, kernel=linear, total= 1.0min\n","[CV] C=10, gamma=0.1, kernel=rbf .....................................\n","[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 1.6min\n","[CV] C=10, gamma=0.1, kernel=rbf .....................................\n","[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 1.6min\n","[CV] C=10, gamma=0.1, kernel=rbf .....................................\n","[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 1.6min\n","[CV] C=10, gamma=0.1, kernel=rbf .....................................\n","[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 1.6min\n","[CV] C=10, gamma=0.1, kernel=rbf .....................................\n","[CV] ...................... C=10, gamma=0.1, kernel=rbf, total= 1.6min\n"],"name":"stdout"},{"output_type":"stream","text":["[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed: 66.4min finished\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=None, error_score=nan,\n","             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n","                           class_weight=None, coef0=0.0,\n","                           decision_function_shape='ovr', degree=3,\n","                           gamma='scale', kernel='rbf', max_iter=-1,\n","                           probability=False, random_state=None, shrinking=True,\n","                           tol=0.001, verbose=False),\n","             iid='deprecated', n_jobs=None,\n","             param_grid={'C': [1, 10], 'gamma': [1, 0.1],\n","                         'kernel': ['linear', 'rbf']},\n","             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n","             scoring=None, verbose=2)"]},"metadata":{"tags":[]},"execution_count":305}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"VcgVNkPnlr1r","executionInfo":{"elapsed":1172724,"status":"ok","timestamp":1603315653815,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"a280ecab-79c5-47da-ae1b-6851a9e64a23"},"source":["grid.param_grid"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'C': [1, 10], 'gamma': [1, 0.1], 'kernel': ['linear', 'rbf']}"]},"metadata":{"tags":[]},"execution_count":306}]},{"cell_type":"code","metadata":{"id":"YClW2tzulvYg"},"source":["predic = grid.predict(test_features)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"esuC0EeWl2Pi","executionInfo":{"elapsed":943,"status":"ok","timestamp":1603316838448,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"76dcae1a-8e99-47c7-f10d-4758131b1b0c"},"source":["print(classification_report(test_labels,predic))\n","print(confusion_matrix(test_labels, predic))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.90      0.98      0.94      1559\n","           1       0.95      0.81      0.88       913\n","\n","    accuracy                           0.92      2472\n","   macro avg       0.93      0.89      0.91      2472\n","weighted avg       0.92      0.92      0.91      2472\n","\n","[[1524   35]\n"," [ 175  738]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MsvXLipBnrum"},"source":["#**Random Forest Optimized**"]},{"cell_type":"code","metadata":{"id":"9-L23_YdnxQ-"},"source":["from sklearn.ensemble import RandomForestClassifier\n","\n","rfc=RandomForestClassifier(random_state=42)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KQYpYWOin8Zu"},"source":["param_grid = { \n","    'n_estimators': [200, 500],\n","    'max_features': ['auto', 'sqrt', 'log2'],\n","    'max_depth' : [4,5,6,7,8],\n","    'criterion' :['gini', 'entropy']\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rTixtlAbn_Jp"},"source":["CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n","CV_rfc.fit(train_features, train_labels)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":85},"id":"ihYv5YuPoH9c","executionInfo":{"elapsed":1900,"status":"ok","timestamp":1603231151664,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"31ed64d9-3e5e-4fc0-db97-c0198ccf640a"},"source":["CV_rfc.best_params_"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'criterion': 'entropy',\n"," 'max_depth': 8,\n"," 'max_features': 'auto',\n"," 'n_estimators': 500}"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"code","metadata":{"id":"1VvRqQsGoLon"},"source":["rfc1=RandomForestClassifier(random_state=42, max_features='auto', n_estimators= 200, max_depth=8, criterion='gini')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153},"id":"Kh0Kg6VaoPX7","executionInfo":{"elapsed":26575,"status":"ok","timestamp":1603231221862,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"dbdb652b-1d9c-4276-f669-725100723322"},"source":["rfc1.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n","                       criterion='gini', max_depth=8, max_features='auto',\n","                       max_leaf_nodes=None, max_samples=None,\n","                       min_impurity_decrease=0.0, min_impurity_split=None,\n","                       min_samples_leaf=1, min_samples_split=2,\n","                       min_weight_fraction_leaf=0.0, n_estimators=200,\n","                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n","                       warm_start=False)"]},"metadata":{"tags":[]},"execution_count":174}]},{"cell_type":"code","metadata":{"id":"11qWbyQ6oWKc"},"source":["\n","pred=rfc1.predict(test_features)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"L_kXzkJVJCzB","executionInfo":{"elapsed":827,"status":"ok","timestamp":1603231795794,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"6fa137aa-cde4-4d0e-ab6b-5a9a0501de12"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,pred))\n","print(confusion_matrix(test_labels, pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.87      0.94      0.90      1061\n","           1       0.92      0.83      0.88       911\n","\n","    accuracy                           0.89      1972\n","   macro avg       0.90      0.89      0.89      1972\n","weighted avg       0.89      0.89      0.89      1972\n","\n","[[998  63]\n"," [153 758]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"5oIbVhGFocDD","executionInfo":{"elapsed":1065,"status":"ok","timestamp":1603231185119,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"c95f03fe-242e-4672-a793-39d19f114e1c"},"source":["from sklearn.metrics import accuracy_score\n","print(\"Accuracy for Random Forest on CV data: \",accuracy_score(test_labels,pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Accuracy for Random Forest on CV data:  0.8950304259634888\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XAXuL31Rqv2B"},"source":["#**XGBoost**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rS6XD1Wl5aCJ","executionInfo":{"status":"ok","timestamp":1621297368986,"user_tz":-60,"elapsed":73869,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"37fd1e40-7dfe-485e-d0f1-999bec589f7a"},"source":["import xgboost as xgb\n","modelxgb=xgb.XGBClassifier(random_state=1,learning_rate=0.01)\n","modelxgb.fit(train_features, train_labels)\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n","              colsample_bynode=1, colsample_bytree=1, gamma=0,\n","              learning_rate=0.01, max_delta_step=0, max_depth=3,\n","              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n","              nthread=None, objective='binary:logistic', random_state=1,\n","              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n","              silent=None, subsample=1, verbosity=1)"]},"metadata":{"tags":[]},"execution_count":101}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VERUlLMfVcff","executionInfo":{"status":"ok","timestamp":1621297368986,"user_tz":-60,"elapsed":10006,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"39b49255-014c-4037-801e-e2b78b1d69a1"},"source":["predxgb = modelxgb.predict(test_features)\n","predxgb"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n","       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n","       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0, 0])"]},"metadata":{"tags":[]},"execution_count":102}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3VDVkMJ8VxEs","executionInfo":{"status":"ok","timestamp":1621297368987,"user_tz":-60,"elapsed":5778,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"cfa6b0eb-4ebd-4bed-ebc5-c9551043f9f2"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,predxgb))\n","print(confusion_matrix(test_labels, predxgb))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.94      0.89      0.92      2356\n","           1       0.84      0.91      0.88      1444\n","\n","    accuracy                           0.90      3800\n","   macro avg       0.89      0.90      0.90      3800\n","weighted avg       0.91      0.90      0.90      3800\n","\n","[[2108  248]\n"," [ 124 1320]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"hJOb8S9ax9zi","executionInfo":{"status":"ok","timestamp":1621297369816,"user_tz":-60,"elapsed":2058,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"68d6282c-98a9-4597-ea8b-6020289666a7"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,predxgb)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EZvEqHDkh5T9","executionInfo":{"status":"ok","timestamp":1621297375554,"user_tz":-60,"elapsed":1142,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"0dedc6b4-f07b-44b4-b040-0274d0b0c57a"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, predxgb))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9021052631578947\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wHThKNgWELHA","executionInfo":{"elapsed":59955,"status":"ok","timestamp":1617650801300,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"bf7fcafb-b5ac-4bf9-8711-8138a6ff94b1"},"source":["modelxgb.score(test_features,test_labels)\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8831578947368421"]},"metadata":{"tags":[]},"execution_count":120}]},{"cell_type":"code","metadata":{"id":"8ww8gvggEbQ5"},"source":["scores = cross_val_score(model, test_labels, test_features, cv=5)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hUPXhje3Em-x","executionInfo":{"elapsed":434873,"status":"ok","timestamp":1617651180166,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"cda68fdc-2a4c-4c80-e0f9-16b59c19f9cd"},"source":["scores = cross_val_score(modelxgb, features, labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.863 (std: 0.015)\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FJcrg46fq96M"},"source":["#**Optimized DT**"]},{"cell_type":"code","metadata":{"id":"tGXKLf88OWWB"},"source":["from sklearn.tree import DecisionTreeClassifier"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vV3F64KqNrvU"},"source":["# Same 3-step process\n","\n","# 1. Instantiate\n","# default criterion=gini\n","# you can swap to criterion=entropy \n","dtc = DecisionTreeClassifier(random_state=0)\n","\n","# 2. Fit\n","dtc.fit(train_features, train_labels)\n","\n","# 3. Predict, there're 4 features in the iris dataset\n","y_pred_class = dtc.predict(test_features)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CX1K_T-kO7e5"},"source":["from sklearn import metrics"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"eaYZDO_LPDtR","executionInfo":{"elapsed":590,"status":"ok","timestamp":1601506916593,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"63421e7b-f9f0-4b68-ccec-fb211fbc3200"},"source":["# Accuracy\n","metrics.accuracy_score(test_labels, y_pred_class)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.7873333333333333"]},"metadata":{"tags":[]},"execution_count":66}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"68Q9-FlUPOUd","executionInfo":{"elapsed":737,"status":"ok","timestamp":1601506960945,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"8a48eca0-69d6-4bce-d30c-05c3b2a2d585"},"source":["from sklearn.metrics import classification_report, confusion_matrix\n","print(classification_report(test_labels,y_pred_class))\n","print(confusion_matrix(test_labels, y_pred_class))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.79      0.78      0.79      3005\n","           1       0.78      0.79      0.79      2995\n","\n","    accuracy                           0.79      6000\n","   macro avg       0.79      0.79      0.79      6000\n","weighted avg       0.79      0.79      0.79      6000\n","\n","[[2343  662]\n"," [ 614 2381]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":340},"id":"gXuobfEYQck2","executionInfo":{"elapsed":361126,"status":"ok","timestamp":1602187561716,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"50e30c2f-f04b-422f-f017-e72fcde576da"},"source":["# Import\n","from sklearn.model_selection import GridSearchCV\n","\n","# Define the parameter values that should be searched\n","sample_split_range = list(range(1, 50))\n","\n","# Create a parameter grid: map the parameter names to the values that should be searched\n","# Simply a python dictionary\n","# Key: parameter name\n","# Value: list of values that should be searched for that parameter\n","# Single key-value pair for param_grid\n","param_grid = dict(min_samples_split=sample_split_range)\n","\n","# instantiate the grid\n","dtc = DecisionTreeClassifier(random_state=0)\n","grid = GridSearchCV(dtc, param_grid, cv=10, scoring='accuracy')\n","\n","# fit the grid with data\n","grid.fit(train_features, train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["GridSearchCV(cv=10, error_score=nan,\n","             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n","                                              criterion='gini', max_depth=None,\n","                                              max_features=None,\n","                                              max_leaf_nodes=None,\n","                                              min_impurity_decrease=0.0,\n","                                              min_impurity_split=None,\n","                                              min_samples_leaf=1,\n","                                              min_samples_split=2,\n","                                              min_weight_fraction_leaf=0.0,\n","                                              presort='deprecated',\n","                                              random_state=0, splitter='best'),\n","             iid='deprecated', n_jobs=None,\n","             param_grid={'min_samples_split': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n","                                               11, 12, 13, 14, 15, 16, 17, 18,\n","                                               19, 20, 21, 22, 23, 24, 25, 26,\n","                                               27, 28, 29, 30, ...]},\n","             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n","             scoring='accuracy', verbose=0)"]},"metadata":{"tags":[]},"execution_count":163}]},{"cell_type":"code","metadata":{"id":"VPx7qq5MVHuX"},"source":["\n","# Single best score achieved across all params (min_samples_split)\n","print(grid.best_score_)\n","\n","# Dictionary containing the parameters (min_samples_split) used to generate that score\n","print(grid.best_params_)\n","\n","# Actual model object fit with those best parameters\n","# Shows default parameters that we did not specify\n","print(grid.best_estimator_)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"L_dRDZrNVRg5"},"source":["# Entropy of child 1 = 0\n","# Perfect split for this child\n","\n","# Entropy of child 2 = 0.918\n","-(2/3)*np.log2(2/3) - (1/3)*np.log2(1/3)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"t7aCcTzpVS0s"},"source":["# Weighted average of entropy(children)\n","(3/4)*(0.9184) + (1/4)*0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qacxtswJVf9J"},"source":["# Entropy Gain\n","1 - (3/4)*(0.9184) + (1/4)*0"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PtBlFouhFeDi"},"source":["#**KNN**"]},{"cell_type":"code","metadata":{"id":"zXg9cPXey1su"},"source":["#import KNeighborsClassifier\n","from sklearn.neighbors import KNeighborsClassifier\n","\n","#Setup arrays to store training and test accuracies\n","neighbors = np.arange(1,9)\n","train_accuracy =np.empty(len(neighbors))\n","test_accuracy = np.empty(len(neighbors))\n","\n","for i,k in enumerate(neighbors):\n","    #Setup a knn classifier with k neighbors\n","    knn = KNeighborsClassifier(n_neighbors=k)\n","    \n","    #Fit the model\n","    knn.fit(train_features, train_labels)\n","    \n","    #Compute accuracy on the training set\n","    train_accuracy[i] = knn.score(train_features, train_labels)\n","    \n","    #Compute accuracy on the test set\n","    test_accuracy[i] = knn.score(test_features, test_labels) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"PvGnHUIV6KQd"},"source":["import matplotlib.pyplot as plt\n","plt.style.use('ggplot')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Uu5db0IA6AMY"},"source":["#Generate plot\n","plt.title('k-NN Varying number of neighbors')\n","plt.plot(neighbors, test_accuracy, label='Testing Accuracy')\n","plt.plot(neighbors, train_accuracy, label='Training accuracy')\n","plt.legend()\n","plt.xlabel('Number of neighbors')\n","plt.ylabel('Accuracy')\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ka82d7XU6XMu"},"source":["knn = KNeighborsClassifier(n_neighbors=7)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"57Q8uMC66b2n","executionInfo":{"status":"ok","timestamp":1621300721666,"user_tz":-60,"elapsed":3333917,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"e31db2db-99de-4be0-b682-6ce02e4be33b"},"source":["#Fit the model\n","knn.fit(train_features,train_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n","                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n","                     weights='uniform')"]},"metadata":{"tags":[]},"execution_count":108}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jeJ8XtHW6kb5","executionInfo":{"status":"ok","timestamp":1621300808140,"user_tz":-60,"elapsed":3418092,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"6340afd2-c9bb-4ac8-b663-a31eb7628f8a"},"source":["knn.score(test_features,test_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.9160526315789473"]},"metadata":{"tags":[]},"execution_count":109}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Da6HuDZLG6ou","executionInfo":{"status":"ok","timestamp":1621300895714,"user_tz":-60,"elapsed":3503563,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"0d78d437-2d90-4006-d58d-8f93abc70049"},"source":["y_pred = knn.predict(test_features)\n","np.set_printoptions(threshold=np.inf)\n","y_pred"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n","       1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n","       1, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n","       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"]},"metadata":{"tags":[]},"execution_count":110}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D4f66fCe7DI2","executionInfo":{"status":"ok","timestamp":1621300904714,"user_tz":-60,"elapsed":1334,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"1974ddab-004d-49e9-d5df-7a51380ab6b8"},"source":["from sklearn.metrics import confusion_matrix\n","print(confusion_matrix(test_labels,y_pred))\n","from sklearn.metrics import classification_report\n","print(classification_report(test_labels,y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[2182  174]\n"," [ 145 1299]]\n","              precision    recall  f1-score   support\n","\n","           0       0.94      0.93      0.93      2356\n","           1       0.88      0.90      0.89      1444\n","\n","    accuracy                           0.92      3800\n","   macro avg       0.91      0.91      0.91      3800\n","weighted avg       0.92      0.92      0.92      3800\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"Rm9D-fJ7x_zB","executionInfo":{"status":"ok","timestamp":1621300909053,"user_tz":-60,"elapsed":1118,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"20f8451a-8c8e-43bb-b119-0686be7dd788"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","import sklearn\n","from scipy import stats\n","import matplotlib.pyplot as plt\n","import os\n","import seaborn as sns\n","cm=confusion_matrix(test_labels,y_pred)\n","plt.figure(figsize=(9,9))\n","sns.heatmap(cm, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n","plt.ylabel('Actual label');\n","plt.xlabel('Predicted label');"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 648x648 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OyNTSlJyh830","executionInfo":{"status":"ok","timestamp":1621300913402,"user_tz":-60,"elapsed":670,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"}},"outputId":"3bc14299-7aa4-4015-ba3b-912d49a93605"},"source":["from sklearn.metrics import accuracy_score\n","print(accuracy_score(test_labels, y_pred))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["0.9160526315789473\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BIyj4Oah7s-J","executionInfo":{"elapsed":110342,"status":"ok","timestamp":1617627964302,"user":{"displayName":"SSBSE Admin","photoUrl":"","userId":"11167642343971864398"},"user_tz":-60},"outputId":"2adf76af-a47d-496e-d8d7-3a5143176537"},"source":["scores = cross_val_score(knn, test_features, test_labels, cv=5)\n","print(\"mean: {:.3f} (std: {:.3f})\".format(scores.mean(),\n","                                          scores.std()),\n","                                          end=\"\\n\\n\" )\n","#88%"],"execution_count":null,"outputs":[{"output_type":"stream","text":["mean: 0.910 (std: 0.010)\n","\n"],"name":"stdout"}]}]}