LSTM_Model2.ipynb

{"cells":[{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","colab":{},"colab_type":"code","id":"vYadQfkXkuB5","outputId":"21eaaa0f-0b64-47f7-a95d-4d95c9ee18af","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/donors-choose/preprocessed_data.csv\n/kaggle/input/glove6b100dtxt/glove.6B.100d.txt\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","colab":{},"colab_type":"code","id":"O6sC7S7mkuCH","trusted":true},"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.preprocessing import LabelEncoder\nfrom tqdm import tqdm\nfrom sklearn.feature_extraction.text import TfidfVectorizer","execution_count":2,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"1FeglN1-kuCS","outputId":"2f60e58c-9c48-42c8-b0b4-fe69aa1a5bb4","trusted":true},"cell_type":"code","source":"embeddings_index = dict()\nf = open('/kaggle/input/glove6b100dtxt/glove.6B.100d.txt',encoding=\"utf8\")\nfor line in f:\n    values = line.split()\n    word = values[0]\n    coefs = np.asarray(values[1:])\n    embeddings_index[word] = coefs\nf.close()\nprint('Loaded %s word vectors.' % len(embeddings_index))","execution_count":3,"outputs":[{"output_type":"stream","text":"Loaded 400000 word vectors.\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"JFAZwvDUkuCb","trusted":true},"cell_type":"code","source":"data=pd.read_csv('/kaggle/input/donors-choose/preprocessed_data.csv')","execution_count":4,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"o3i4T_0hkuCz","trusted":true},"cell_type":"code","source":"y=data['project_is_approved']","execution_count":5,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"zjTrTXiFkuC5","trusted":true},"cell_type":"code","source":"data=data.drop(['project_is_approved'],axis=1)","execution_count":6,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"ZzV6zxPhkuDC","trusted":true},"cell_type":"code","source":"preproc=[]\nfor row in data['project_grade_category']:\n    row=row.replace('grades','')\n    row=row.replace('_prek_2','prek2')\n    row=row.replace('_3_5','3to5')\n    row=row.replace('_6_8','6to8')\n    row=row.replace('_9_12','9to12')\n    preproc.append(row)\n    \ndata['project_grade_category']=preproc","execution_count":7,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"nuYDGk1BkuDO","trusted":true},"cell_type":"code","source":"preproc=[]\nfor row in data['clean_categories']:\n    row=row.replace(' ','')\n    row=row.replace('_','')\n    preproc.append(row)\n    \ndata['clean_categories']=preproc","execution_count":8,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"nbLka2cJkuDX","trusted":true},"cell_type":"code","source":"preproc=[]\nfor row in data['clean_subcategories']:\n    row=row.replace(' ','')\n    row=row.replace('_','')\n    preproc.append(row)\n    \ndata['clean_subcategories']=preproc","execution_count":9,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"WvFAq_3IkuDg","trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":10,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"hY00SMMqkuDl","trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.33, stratify=y)","execution_count":11,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"m-wo3BNwkuDq","outputId":"ce9268ac-c48a-4ba1-85f5-b3da4167b9b0","trusted":true},"cell_type":"code","source":"X_train.columns","execution_count":12,"outputs":[{"output_type":"execute_result","execution_count":12,"data":{"text/plain":"Index(['school_state', 'teacher_prefix', 'project_grade_category',\n       'teacher_number_of_previously_posted_projects', 'clean_categories',\n       'clean_subcategories', 'essay', 'price'],\n      dtype='object')"},"metadata":{}}]},{"metadata":{"colab":{},"colab_type":"code","id":"W48slF1fkuDv","outputId":"8341250e-f9ea-4124-cdcf-992ab6a71e99","trusted":true},"cell_type":"code","source":"from keras.models import Sequential,Model\nfrom keras.layers import Dense,BatchNormalization\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.layers import LSTM,Input,Flatten\nfrom keras.layers.embeddings import Embedding\nfrom keras.preprocessing import sequence\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.layers import BatchNormalization\nfrom keras.initializers import he_normal","execution_count":13,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"colab_type":"text","id":"4wuV-X1akuD1"},"cell_type":"markdown","source":"# Model 2"},{"metadata":{"colab_type":"text","id":"1kQ7MT54kuD2"},"cell_type":"markdown","source":"### Essay"},{"metadata":{"trusted":true},"cell_type":"code","source":"tfidf = TfidfVectorizer(min_df=10,ngram_range=(1,1))\ntfidf.fit(X_train['essay'])\nprint(\"Total number of features \",len(tfidf.get_feature_names()))","execution_count":14,"outputs":[{"output_type":"stream","text":"Total number of features  14219\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"feature_names = np.asarray(tfidf.get_feature_names()) # getting all words\nidf_score = tfidf.idf_\nindex = []\nfor i in range(len(idf_score)):\n    if idf_score[i] >= 2 and idf_score[i] <=10:\n        index.append(i)\n        \nimportant_words = []\nfor i in index:\n    important_words.append(feature_names[i])","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tqdm import tqdm\nx_train_essay_new = []\nfor sentence in tqdm(X_train['essay']):\n    sen = []\n    for word in sentence.split():\n        if word in important_words:\n            sen.append(word)      \n    x_train_essay_new.append(' '.join(sen))","execution_count":17,"outputs":[{"output_type":"stream","text":"100%|██████████| 73196/73196 [32:50<00:00, 37.15it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test_essay_new = []\nfor sentence in tqdm(X_test['essay']):\n    sen = []\n    for word in sentence.split():\n        if word in important_words:\n            sen.append(word)    \n    x_test_essay_new.append(' '.join(sen))","execution_count":18,"outputs":[{"output_type":"stream","text":"100%|██████████| 36052/36052 [16:11<00:00, 37.10it/s]\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"text_essay = Tokenizer()\ntext_essay.fit_on_texts(x_train_essay_new)\nvocab_size = len(text_essay.word_index) + 1\nencoded_train_new = text_essay.texts_to_sequences(x_train_essay_new)\nencoded_test_new = text_essay.texts_to_sequences(x_test_essay_new)","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"max_length = 100\npadded_train_new = pad_sequences(encoded_train_new, maxlen=max_length, padding='post')\npadded_test_new = pad_sequences(encoded_test_new, maxlen=max_length, padding='post')\n","execution_count":20,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"b3eB4H5TkuD8","trusted":true},"cell_type":"code","source":"embedding_matrix = np.zeros((vocab_size, 100))\nfor word, i in text_essay.word_index.items():\n    embedding_vector = embeddings_index.get(word)\n    if embedding_vector is not None:\n        embedding_matrix[i] = embedding_vector","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"input_essay_tfidf = Input(shape=(100,))\n\nembedding = Embedding(vocab_size,100,weights=[embedding_matrix],input_length=100,trainable=False)(input_essay_tfidf)\nlstm=LSTM(100,return_sequences=True)(embedding)\nflatten_21=Flatten()(lstm)","execution_count":22,"outputs":[]},{"metadata":{"colab_type":"text","id":"Gk4s_W-wkuEk"},"cell_type":"markdown","source":"### School State"},{"metadata":{"trusted":true},"cell_type":"code","source":"def self_token(column):\n    unique = list(set(column))\n    total = list(column)\n    size = len(unique)\n    count = []\n    for category in unique:\n        count.append([total.count(category),category])\n    count.sort()\n    rank = {}\n    for i in range(1,len(count)+1):\n        rank.update({count[i-1][1] : i})\n    return (rank , unique,size)","execution_count":23,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"zxFf05qykuEl","trusted":true},"cell_type":"code","source":"state_rank, unique,size = self_token(X_train['school_state'])\nprint(state_rank)\nstate_size = size\nencoded_state_train = []\nencoded_state_test = []\nfor state in X_train['school_state']:\n    encoded_state_train.append(state_rank[state])\n\nfor state in X_test['school_state']:\n    if state in unique:\n        encoded_state_test.append(state_rank[state]) \n    else:\n        encoded_state_test.append(0) \n    \nencoded_state_train = np.asarray(encoded_state_train)\nencoded_state_test = np.asarray(encoded_state_test)\n\nprint(encoded_state_train.shape)\nprint(encoded_state_test.shape)","execution_count":24,"outputs":[{"output_type":"stream","text":"{'vt': 1, 'wy': 2, 'nd': 3, 'mt': 4, 'sd': 5, 'ne': 6, 'ri': 7, 'de': 8, 'ak': 9, 'nh': 10, 'wv': 11, 'dc': 12, 'hi': 13, 'me': 14, 'nm': 15, 'ks': 16, 'ia': 17, 'id': 18, 'ar': 19, 'mn': 20, 'co': 21, 'or': 22, 'ms': 23, 'ky': 24, 'nv': 25, 'md': 26, 'ct': 27, 'tn': 28, 'ut': 29, 'al': 30, 'wi': 31, 'va': 32, 'az': 33, 'nj': 34, 'ok': 35, 'wa': 36, 'la': 37, 'ma': 38, 'oh': 39, 'in': 40, 'mo': 41, 'pa': 42, 'mi': 43, 'sc': 44, 'ga': 45, 'il': 46, 'nc': 47, 'fl': 48, 'ny': 49, 'tx': 50, 'ca': 51}\n(73196,)\n(36052,)\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"arO9_e81kuEt","trusted":true},"cell_type":"code","source":"input_state = Input(shape=(1,))\nembedding=Embedding(state_size,10, input_length=1,trainable=False)(input_state)\nflatten_1=Flatten()(embedding)","execution_count":25,"outputs":[]},{"metadata":{"colab_type":"text","id":"R_ob1mjpkuEx"},"cell_type":"markdown","source":"### Project Grade Category"},{"metadata":{"colab":{},"colab_type":"code","id":"UvaE5Q63kuEy","trusted":true},"cell_type":"code","source":"p_grade_rank, unique,size = self_token(X_train['project_grade_category'])\nprint(p_grade_rank)\nproject_grade_categories_size = size\nencoded_p_grade_train = []\nencoded_p_grade_test = []\nfor grade in X_train['project_grade_category']:\n    encoded_p_grade_train.append(p_grade_rank[grade])\n\nfor grade in X_test['project_grade_category']:\n    if grade in unique:\n        encoded_p_grade_test.append(p_grade_rank[grade]) \n    else:\n        encoded_p_grade_test.append(0) \n    \nencoded_p_grade_train = np.asarray(encoded_p_grade_train)\nencoded_p_grade_test = np.asarray(encoded_p_grade_test)\n\nprint(encoded_p_grade_train.shape)\nprint(encoded_p_grade_test.shape)","execution_count":26,"outputs":[{"output_type":"stream","text":"{'9to12': 1, '6to8': 2, '3to5': 3, 'prek2': 4}\n(73196,)\n(36052,)\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"RAMA6UQvkuE7","trusted":true},"cell_type":"code","source":"input_grade = Input(shape=(1,))\nembedding=Embedding(project_grade_categories_size,10, input_length=1,trainable=False)(input_grade)\nflatten_2=Flatten()(embedding)","execution_count":27,"outputs":[]},{"metadata":{"colab_type":"text","id":"3yo2cFttkuE-"},"cell_type":"markdown","source":"### Clean Categories"},{"metadata":{"colab":{},"colab_type":"code","id":"l7caEHKOkuFA","trusted":true},"cell_type":"code","source":"cat_rank, unique,size = self_token(X_train['clean_categories'])\nprint(cat_rank)\ncategories_size = size\nencoded_cat_train = []\nencoded_cat_test = []\nfor category in X_train['clean_categories']:\n    encoded_cat_train.append(cat_rank[category])\n\nfor category in X_test['clean_categories']:\n    if category in unique:\n        encoded_cat_test.append(cat_rank[category]) \n    else:\n        encoded_cat_test.append(0) \n    \nencoded_cat_train = np.asarray(encoded_cat_train)\nencoded_cat_test = np.asarray(encoded_cat_test)\n\nprint(encoded_cat_train.shape)\nprint(encoded_cat_test.shape)","execution_count":28,"outputs":[{"output_type":"stream","text":"{'musicartswarmthcarehunger': 1, 'historycivicshealthsports': 2, 'mathsciencewarmthcarehunger': 3, 'appliedlearningwarmthcarehunger': 4, 'literacylanguagewarmthcarehunger': 5, 'musicartsappliedlearning': 6, 'specialneedswarmthcarehunger': 7, 'musicartshistorycivics': 8, 'musicartshealthsports': 9, 'healthsportswarmthcarehunger': 10, 'historycivicsappliedlearning': 11, 'healthsportshistorycivics': 12, 'specialneedshealthsports': 13, 'literacylanguagehealthsports': 14, 'musicartsspecialneeds': 15, 'healthsportsmusicarts': 16, 'appliedlearninghistorycivics': 17, 'healthsportsappliedlearning': 18, 'historycivicsspecialneeds': 19, 'healthsportsmathscience': 20, 'historycivicsmathscience': 21, 'specialneedsmusicarts': 22, 'historycivicsmusicarts': 23, 'mathsciencehealthsports': 24, 'appliedlearninghealthsports': 25, 'literacylanguageappliedlearning': 26, 'mathsciencehistorycivics': 27, 'literacylanguagehistorycivics': 28, 'appliedlearningmusicarts': 29, 'healthsportsliteracylanguage': 30, 'appliedlearningmathscience': 31, 'mathscienceappliedlearning': 32, 'warmthcarehunger': 33, 'healthsportsspecialneeds': 34, 'historycivicsliteracylanguage': 35, 'appliedlearningspecialneeds': 36, 'mathsciencemusicarts': 37, 'literacylanguagemusicarts': 38, 'historycivics': 39, 'mathsciencespecialneeds': 40, 'appliedlearningliteracylanguage': 41, 'mathscienceliteracylanguage': 42, 'appliedlearning': 43, 'literacylanguagespecialneeds': 44, 'specialneeds': 45, 'musicarts': 46, 'healthsports': 47, 'literacylanguagemathscience': 48, 'mathscience': 49, 'literacylanguage': 50}\n(73196,)\n(36052,)\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"TwnY2hl0kuFG","trusted":true},"cell_type":"code","source":"input_cat = Input(shape=(1,))\nembedding=Embedding(categories_size,10, input_length=1,trainable=False)(input_cat)\nflatten_3=Flatten()(embedding)","execution_count":29,"outputs":[]},{"metadata":{"colab_type":"text","id":"oI8tnREBkuFJ"},"cell_type":"markdown","source":"### Clean Subcategories"},{"metadata":{"colab":{},"colab_type":"code","id":"_3s1uzy7kuFK","trusted":true},"cell_type":"code","source":"subcat_rank, unique,size = self_token(X_train['clean_subcategories'])\nprint(subcat_rank)\nsubcategories_size = size\nencoded_subcat_train = []\nencoded_subcat_test = []\nfor category in X_train['clean_subcategories']:\n    encoded_subcat_train.append(subcat_rank[category])\n\nfor category in X_test['clean_subcategories']:\n    if category in unique:\n        encoded_subcat_test.append(subcat_rank[category]) \n    else:\n        encoded_subcat_test.append(0) \n    \nencoded_subcat_train = np.asarray(encoded_subcat_train)\nencoded_subcat_test = np.asarray(encoded_subcat_test)\n\nprint(encoded_subcat_train.shape)\nprint(encoded_subcat_test.shape)","execution_count":30,"outputs":[{"output_type":"stream","text":"{'civicsgovernmentextracurricular': 1, 'civicsgovernmentforeignlanguages': 2, 'civicsgovernmenthealthwellness': 3, 'civicsgovernmentnutritioneducation': 4, 'civicsgovernmentparentinvolvement': 5, 'collegecareerprepwarmthcarehunger': 6, 'communityservicefinancialliteracy': 7, 'communityservicemusic': 8, 'earlydevelopmentforeignlanguages': 9, 'earlydevelopmenthistorygeography': 10, 'economicsforeignlanguages': 11, 'economicshealthlifescience': 12, 'economicsother': 13, 'environmentalscienceteamsports': 14, 'esleconomics': 15, 'eslteamsports': 16, 'extracurricularforeignlanguages': 17, 'extracurricularsocialsciences': 18, 'financialliteracyhealthwellness': 19, 'financialliteracyother': 20, 'financialliteracyperformingarts': 21, 'foreignlanguagesgymfitness': 22, 'foreignlanguageshealthlifescience': 23, 'gymfitnessparentinvolvement': 24, 'gymfitnesssocialsciences': 25, 'gymfitnesswarmthcarehunger': 26, 'literaturewritingnutritioneducation': 27, 'otherwarmthcarehunger': 28, 'parentinvolvementwarmthcarehunger': 29, 'visualartswarmthcarehunger': 30, 'charactereducationnutritioneducation': 31, 'charactereducationwarmthcarehunger': 32, 'collegecareerprepteamsports': 33, 'eslgymfitness': 34, 'eslnutritioneducation': 35, 'financialliteracysocialsciences': 36, 'foreignlanguagesperformingarts': 37, 'healthlifesciencewarmthcarehunger': 38, 'healthwellnessparentinvolvement': 39, 'historygeographyparentinvolvement': 40, 'historygeographyteamsports': 41, 'mathematicswarmthcarehunger': 42, 'otherperformingarts': 43, 'othersocialsciences': 44, 'socialsciencesteamsports': 45, 'appliedscienceseconomics': 46, 'appliedsciencesfinancialliteracy': 47, 'civicsgovernmentesl': 48, 'civicsgovernmentmathematics': 49, 'civicsgovernmentperformingarts': 50, 'collegecareerprepgymfitness': 51, 'communityserviceeconomics': 52, 'communityserviceesl': 53, 'communityservicenutritioneducation': 54, 'communityserviceperformingarts': 55, 'earlydevelopmentteamsports': 56, 'earlydevelopmentwarmthcarehunger': 57, 'environmentalsciencegymfitness': 58, 'environmentalsciencemusic': 59, 'environmentalsciencewarmthcarehunger': 60, 'eslextracurricular': 61, 'eslfinancialliteracy': 62, 'extracurricularhealthlifescience': 63, 'extracurricularnutritioneducation': 64, 'financialliteracyhealthlifescience': 65, 'gymfitnesshistorygeography': 66, 'nutritioneducationvisualarts': 67, 'nutritioneducationwarmthcarehunger': 68, 'parentinvolvementperformingarts': 69, 'appliedsciencesnutritioneducation': 70, 'charactereducationfinancialliteracy': 71, 'civicsgovernmentcollegecareerprep': 72, 'earlydevelopmentfinancialliteracy': 73, 'economicsspecialneeds': 74, 'environmentalsciencefinancialliteracy': 75, 'environmentalscienceforeignlanguages': 76, 'extracurriculargymfitness': 77, 'extracurricularhistorygeography': 78, 'healthlifescienceperformingarts': 79, 'literacywarmthcarehunger': 80, 'literaturewritingwarmthcarehunger': 81, 'mathematicsteamsports': 82, 'musicother': 83, 'musicparentinvolvement': 84, 'otherteamsports': 85, 'teamsportsvisualarts': 86, 'appliedsciencesforeignlanguages': 87, 'charactereducationcivicsgovernment': 88, 'charactereducationforeignlanguages': 89, 'charactereducationgymfitness': 90, 'collegecareerprepeconomics': 91, 'communityservicesocialsciences': 92, 'economicsenvironmentalscience': 93, 'extracurricularparentinvolvement': 94, 'financialliteracyvisualarts': 95, 'foreignlanguagesother': 96, 'gymfitnesshealthlifescience': 97, 'gymfitnessperformingarts': 98, 'healthlifesciencemusic': 99, 'musicsocialsciences': 100, 'musicteamsports': 101, 'appliedsciencesteamsports': 102, 'communityservicehealthlifescience': 103, 'communityservicehistorygeography': 104, 'communityserviceparentinvolvement': 105, 'earlydevelopmentextracurricular': 106, 'earlydevelopmentnutritioneducation': 107, 'economicsvisualarts': 108, 'foreignlanguagessocialsciences': 109, 'gymfitnessvisualarts': 110, 'parentinvolvementsocialsciences': 111, 'performingartssocialsciences': 112, 'charactereducationhistorygeography': 113, 'civicsgovernmentcommunityservice': 114, 'communityserviceearlydevelopment': 115, 'communityserviceother': 116, 'economicsliteracy': 117, 'environmentalscienceextracurricular': 118, 'environmentalscienceother': 119, 'foreignlanguageshealthwellness': 120, 'healthlifescienceteamsports': 121, 'literacynutritioneducation': 122, 'literaturewritingteamsports': 123, 'otherparentinvolvement': 124, 'performingartsteamsports': 125, 'collegecareerprepesl': 126, 'collegecareerprepfinancialliteracy': 127, 'communityservicemathematics': 128, 'environmentalscienceparentinvolvement': 129, 'eslother': 130, 'eslparentinvolvement': 131, 'financialliteracyhistorygeography': 132, 'foreignlanguagesmusic': 133, 'gymfitnessother': 134, 'healthlifescienceother': 135, 'healthlifescienceparentinvolvement': 136, 'literacyteamsports': 137, 'mathematicsnutritioneducation': 138, 'nutritioneducationother': 139, 'appliedsciencescivicsgovernment': 140, 'collegecareerprepmusic': 141, 'collegecareerprepnutritioneducation': 142, 'communityservicehealthwellness': 143, 'economicssocialsciences': 144, 'eslsocialsciences': 145, 'extracurricularhealthwellness': 146, 'foreignlanguagesvisualarts': 147, 'historygeographyother': 148, 'appliedsciencesgymfitness': 149, 'civicsgovernmentvisualarts': 150, 'collegecareerprephealthwellness': 151, 'earlydevelopmentsocialsciences': 152, 'healthwellnessperformingarts': 153, 'nutritioneducationteamsports': 154, 'specialneedswarmthcarehunger': 155, 'charactereducationperformingarts': 156, 'civicsgovernmentenvironmentalscience': 157, 'civicsgovernmenthealthlifescience': 158, 'environmentalscienceperformingarts': 159, 'financialliteracyliteraturewriting': 160, 'foreignlanguagesspecialneeds': 161, 'gymfitnessliteraturewriting': 162, 'healthwellnesssocialsciences': 163, 'historygeographyperformingarts': 164, 'charactereducationenvironmentalscience': 165, 'civicsgovernmentfinancialliteracy': 166, 'collegecareerprepforeignlanguages': 167, 'earlydevelopmentperformingarts': 168, 'eslmusic': 169, 'performingartsspecialneeds': 170, 'appliedsciencescommunityservice': 171, 'charactereducationesl': 172, 'civicsgovernmentspecialneeds': 173, 'collegecareerprephistorygeography': 174, 'communityserviceextracurricular': 175, 'eslperformingarts': 176, 'gymfitnessmusic': 177, 'healthwellnesshistorygeography': 178, 'healthwellnesswarmthcarehunger': 179, 'charactereducationhealthlifescience': 180, 'communityserviceliteracy': 181, 'extracurricularperformingarts': 182, 'extracurricularspecialneeds': 183, 'financialliteracyliteracy': 184, 'foreignlanguageshistorygeography': 185, 'charactereducationmusic': 186, 'charactereducationsocialsciences': 187, 'charactereducationteamsports': 188, 'civicsgovernmenteconomics': 189, 'earlydevelopmentmusic': 190, 'economicsmathematics': 191, 'charactereducationparentinvolvement': 192, 'collegecareerprepperformingarts': 193, 'collegecareerprepsocialsciences': 194, 'communityservicespecialneeds': 195, 'collegecareerprepcommunityservice': 196, 'collegecareerprepearlydevelopment': 197, 'economicshistorygeography': 198, 'extracurricularliteraturewriting': 199, 'extracurricularmusic': 200, 'gymfitnessliteracy': 201, 'eslhealthwellness': 202, 'extracurricularother': 203, 'extracurricularteamsports': 204, 'historygeographymusic': 205, 'parentinvolvementspecialneeds': 206, 'mathematicsperformingarts': 207, 'nutritioneducationspecialneeds': 208, 'communityserviceliteraturewriting': 209, 'eslhistorygeography': 210, 'appliedsciencesperformingarts': 211, 'communityservicevisualarts': 212, 'eslenvironmentalscience': 213, 'parentinvolvementvisualarts': 214, 'collegecareerprepenvironmentalscience': 215, 'collegecareerprepextracurricular': 216, 'eslhealthlifescience': 217, 'gymfitnessmathematics': 218, 'healthwellnessvisualarts': 219, 'appliedscienceshealthwellness': 220, 'earlydevelopmenthealthlifescience': 221, 'eslvisualarts': 222, 'foreignlanguagesmathematics': 223, 'earlydevelopmentgymfitness': 224, 'earlydevelopmentparentinvolvement': 225, 'economics': 226, 'healthwellnessmusic': 227, 'collegecareerprepparentinvolvement': 228, 'extracurricularliteracy': 229, 'specialneedsteamsports': 230, 'communityserviceenvironmentalscience': 231, 'environmentalsciencehealthwellness': 232, 'environmentalsciencenutritioneducation': 233, 'othervisualarts': 234, 'financialliteracyspecialneeds': 235, 'literaturewritingmusic': 236, 'musicvisualarts': 237, 'parentinvolvement': 238, 'collegecareerprephealthlifescience': 239, 'communityservice': 240, 'extracurricularmathematics': 241, 'appliedsciencescharactereducation': 242, 'gymfitnessnutritioneducation': 243, 'eslforeignlanguages': 244, 'healthlifesciencenutritioneducation': 245, 'charactereducationextracurricular': 246, 'appliedsciencessocialsciences': 247, 'charactereducationcommunityservice': 248, 'literaturewritingparentinvolvement': 249, 'socialsciencesspecialneeds': 250, 'civicsgovernmentsocialsciences': 251, 'earlydevelopmentenvironmentalscience': 252, 'mathematicsmusic': 253, 'healthlifesciencehistorygeography': 254, 'socialsciencesvisualarts': 255, 'appliedsciencesmusic': 256, 'environmentalsciencesocialsciences': 257, 'appliedsciencesparentinvolvement': 258, 'mathematicsparentinvolvement': 259, 'appliedsciencesesl': 260, 'civicsgovernment': 261, 'eslearlydevelopment': 262, 'foreignlanguagesliteraturewriting': 263, 'healthlifesciencesocialsciences': 264, 'collegecareerprepother': 265, 'appliedscienceshistorygeography': 266, 'charactereducationvisualarts': 267, 'healthlifesciencevisualarts': 268, 'mathematicssocialsciences': 269, 'civicsgovernmentliteraturewriting': 270, 'literaturewritingperformingarts': 271, 'performingartsvisualarts': 272, 'economicsfinancialliteracy': 273, 'extracurricularvisualarts': 274, 'charactereducationmathematics': 275, 'charactereducationother': 276, 'historygeographyspecialneeds': 277, 'historygeographymathematics': 278, 'mathematicsother': 279, 'charactereducationhealthwellness': 280, 'extracurricular': 281, 'musicspecialneeds': 282, 'charactereducationcollegecareerprep': 283, 'literacyperformingarts': 284, 'healthlifesciencespecialneeds': 285, 'collegecareerprepspecialneeds': 286, 'financialliteracymathematics': 287, 'civicsgovernmentliteracy': 288, 'collegecareerprepvisualarts': 289, 'environmentalsciencespecialneeds': 290, 'literaturewritingother': 291, 'appliedsciencesother': 292, 'literacymusic': 293, 'earlydevelopmentother': 294, 'appliedsciencesextracurricular': 295, 'appliedsciencesearlydevelopment': 296, 'financialliteracy': 297, 'literacyparentinvolvement': 298, 'healthlifesciencehealthwellness': 299, 'environmentalsciencehistorygeography': 300, 'healthwellnessother': 301, 'literacyother': 302, 'charactereducationearlydevelopment': 303, 'charactereducationliteraturewriting': 304, 'earlydevelopmentvisualarts': 305, 'gymfitnessspecialneeds': 306, 'historygeographyvisualarts': 307, 'socialsciences': 308, 'charactereducationspecialneeds': 309, 'healthlifescienceliteraturewriting': 310, 'environmentalsciencevisualarts': 311, 'eslspecialneeds': 312, 'healthwellnessmathematics': 313, 'earlydevelopmentliteraturewriting': 314, 'civicsgovernmenthistorygeography': 315, 'foreignlanguagesliteracy': 316, 'collegecareerprepliteracy': 317, 'nutritioneducation': 318, 'eslmathematics': 319, 'otherspecialneeds': 320, 'earlydevelopmentmathematics': 321, 'environmentalscienceliteraturewriting': 322, 'literaturewritingsocialsciences': 323, 'historygeographysocialsciences': 324, 'healthwellnessliteraturewriting': 325, 'healthlifescienceliteracy': 326, 'specialneedsvisualarts': 327, 'collegecareerprepmathematics': 328, 'earlydevelopmenthealthwellness': 329, 'healthwellnessteamsports': 330, 'charactereducation': 331, 'charactereducationliteracy': 332, 'collegecareerprepliteraturewriting': 333, 'appliedsciencesspecialneeds': 334, 'literacysocialsciences': 335, 'foreignlanguages': 336, 'collegecareerprep': 337, 'appliedsciencescollegecareerprep': 338, 'performingarts': 339, 'esl': 340, 'appliedsciencesliteraturewriting': 341, 'environmentalscienceliteracy': 342, 'healthwellnessliteracy': 343, 'mathematicsvisualarts': 344, 'healthlifesciencemathematics': 345, 'literacyvisualarts': 346, 'historygeographyliteracy': 347, 'historygeography': 348, 'gymfitnessteamsports': 349, 'appliedsciencesliteracy': 350, 'appliedscienceshealthlifescience': 351, 'historygeographyliteraturewriting': 352, 'appliedsciencesvisualarts': 353, 'literaturewritingvisualarts': 354, 'earlydevelopmentliteracy': 355, 'eslliteraturewriting': 356, 'earlydevelopmentspecialneeds': 357, 'healthlifescience': 358, 'healthwellnessnutritioneducation': 359, 'other': 360, 'environmentalsciencemathematics': 361, 'earlydevelopment': 362, 'musicperformingarts': 363, 'environmentalsciencehealthlifescience': 364, 'appliedsciencesenvironmentalscience': 365, 'environmentalscience': 366, 'teamsports': 367, 'gymfitness': 368, 'mathematicsspecialneeds': 369, 'healthwellnessspecialneeds': 370, 'literaturewritingspecialneeds': 371, 'warmthcarehunger': 372, 'music': 373, 'eslliteracy': 374, 'visualarts': 375, 'gymfitnesshealthwellness': 376, 'literacyspecialneeds': 377, 'appliedsciences': 378, 'appliedsciencesmathematics': 379, 'healthwellness': 380, 'specialneeds': 381, 'literaturewriting': 382, 'mathematics': 383, 'literacyliteraturewriting': 384, 'literaturewritingmathematics': 385, 'literacymathematics': 386, 'literacy': 387}\n(73196,)\n(36052,)\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"kdtsOE_0kuFQ","trusted":true},"cell_type":"code","source":"input_sub = Input(shape=(1,))\nembedding=Embedding(subcategories_size,10, input_length=1,trainable=False)(input_sub)\nflatten_4=Flatten()(embedding)","execution_count":31,"outputs":[]},{"metadata":{"colab_type":"text","id":"CFSjFr7qkuFZ"},"cell_type":"markdown","source":"### Teacher Prefix"},{"metadata":{"colab":{},"colab_type":"code","id":"oKBA45ROkuFb","trusted":true},"cell_type":"code","source":"t_prefix_rank, unique,size = self_token(X_train['teacher_prefix'])\nprint(t_prefix_rank)\nteacher_prefix_size =size\nencoded_t_prefix_train = []\nencoded_t_prefix_test = []\nfor prefix in X_train['teacher_prefix']:\n    encoded_t_prefix_train.append(t_prefix_rank[prefix])\n\nfor prefix in X_test['teacher_prefix']:\n    if prefix in unique:\n        encoded_t_prefix_test.append(t_prefix_rank[prefix]) \n    else:\n        encoded_t_prefix_test.append(0) \n    \nencoded_t_prefix_train = np.asarray(encoded_t_prefix_train)\nencoded_t_prefix_test = np.asarray(encoded_t_prefix_test)\n\nprint(encoded_t_prefix_train.shape)\nprint(encoded_t_prefix_test.shape)","execution_count":32,"outputs":[{"output_type":"stream","text":"{'dr': 1, 'teacher': 2, 'mr': 3, 'ms': 4, 'mrs': 5}\n(73196,)\n(36052,)\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"emNY1muMkuFg","trusted":true},"cell_type":"code","source":"input_prefix = Input(shape=(1,))\nembedding=Embedding(teacher_prefix_size, 10, input_length=1,trainable=False)(input_prefix)\nflatten_5=Flatten()(embedding)","execution_count":33,"outputs":[]},{"metadata":{"colab_type":"text","id":"JEj3dCg1kuFl"},"cell_type":"markdown","source":"### Remaining Numerical Features"},{"metadata":{"colab":{},"colab_type":"code","id":"M_dzIOfQkuFn","trusted":true},"cell_type":"code","source":"from scipy.sparse import hstack\n\ntrain_price=X_train['price'].values.reshape(-1, 1)\ntrain_number=X_train['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)\n\ntest_price=X_test['price'].values.reshape(-1, 1)\ntest_number=X_test['teacher_number_of_previously_posted_projects'].values.reshape(-1, 1)\n\nrem_train=np.concatenate((train_price,train_number),axis=1)\nrem_test=np.concatenate((test_price,test_number),axis=1)","execution_count":34,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"QXDRcQhWkuFr","trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler\nscaler=MinMaxScaler()\nscaler.fit(rem_train)\nrem_train_standard=scaler.transform(rem_train)\nrem_test_standard=scaler.transform(rem_test)","execution_count":35,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.regularizers import l2","execution_count":36,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"U7AS8nofkuFu","trusted":true},"cell_type":"code","source":"rem_feat = Input(shape=(2,))\nrem_feat_dense= Dense(128, activation='relu',kernel_regularizer=l2(0.0001))(rem_feat)","execution_count":70,"outputs":[]},{"metadata":{"colab_type":"text","id":"OoDO2KdDkuFz"},"cell_type":"markdown","source":"### Concatenating Data"},{"metadata":{"colab":{},"colab_type":"code","id":"LpZQT0fJkuFz","trusted":true},"cell_type":"code","source":"train_2=[padded_train_new,encoded_state_train,encoded_p_grade_train,encoded_cat_train,encoded_subcat_train,encoded_t_prefix_train,rem_train_standard]\ntest_2=[padded_test_new,encoded_state_test,encoded_p_grade_test,encoded_cat_test,encoded_subcat_test,encoded_t_prefix_test,rem_test_standard]","execution_count":71,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"IEIT5Y4dkuF_","trusted":true},"cell_type":"code","source":"# https://www.youtube.com/watch?v=2U6Jl7oqRkM\n# https://www.tensorflow.org/tensorboard/r2/get_started\n\nimport tensorflow as tf\nfrom keras.callbacks import TensorBoard,EarlyStopping\nfrom time import time\n\nearlystop_2 = EarlyStopping(monitor = 'val_loss', mode=\"max\",min_delta = 0, patience = 10,verbose = 1,restore_best_weights = True)\ntensorboard_2 = TensorBoard(\"logs932332\")","execution_count":73,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"4-SL4zmWkuGB","trusted":true},"cell_type":"code","source":"# https://github.com/pranaya-mathur/Donors-Choose-LSTM/blob/master/DonorsChoose_Model_1_13_Aug_19.ipynb\nimport tensorflow as tf\nfrom sklearn.metrics import roc_auc_score\n\ndef auc_score(y_true, y_pred):\n    if len(np.unique(y_true[:])) == 1:\n        return 0.5\n    else:\n        return roc_auc_score(y_true, y_pred)\n\ndef auc_sc(y_true, y_pred):\n    return tf.py_func(auc_score, (y_true, y_pred), tf.double)   ","execution_count":74,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"2A9tnd8AkuG3","trusted":true},"cell_type":"code","source":"from keras.layers import concatenate\nfrom keras.layers import Dropout\nfrom keras.initializers import glorot_normal\nconcat = concatenate([flatten_21,flatten_1,flatten_2,flatten_3,flatten_4,flatten_5,rem_feat_dense])\n\nx = Dense(256, activation='relu',kernel_regularizer=l2(0.001),kernel_initializer='glorot_normal')(concat)\nx=Dropout(0.5)(x)\n\nx = Dense(128, activation='relu',kernel_regularizer=l2(0.001),kernel_initializer='glorot_normal')(x)\nx=Dropout(0.5)(x)\nx = BatchNormalization()(x)\n\nx = Dense(64, activation='relu',kernel_regularizer=l2(0.001),kernel_initializer='glorot_normal')(x)\n\n# And finally we add the main logistic regression layer\nmain_output = Dense(2, activation='softmax')(x)","execution_count":75,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"JB-MS8ewkuGE","trusted":true},"cell_type":"code","source":"from keras.utils import np_utils\nY_train=np_utils.to_categorical(y_train)\nY_test=np_utils.to_categorical(y_test)","execution_count":76,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"olg56BMbkuG7","trusted":true},"cell_type":"code","source":"model2 = Model(inputs=[input_essay_tfidf, input_state, input_grade, input_cat, input_sub, input_prefix,rem_feat], outputs=[main_output])","execution_count":77,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"U10DhdvIkuHD","trusted":true},"cell_type":"code","source":"model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=[auc_sc])","execution_count":78,"outputs":[]},{"metadata":{"colab":{},"colab_type":"code","id":"BISSKyBOkuG9","outputId":"4aec9df4-a93e-4647-a3e9-3107df4d5e99","trusted":true},"cell_type":"code","source":"model2.summary()","execution_count":79,"outputs":[{"output_type":"stream","text":"Model: \"model_5\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            (None, 100)          0                                            \n__________________________________________________________________________________________________\nembedding_1 (Embedding)         (None, 100, 100)     1419500     input_1[0][0]                    \n__________________________________________________________________________________________________\ninput_2 (InputLayer)            (None, 1)            0                                            \n__________________________________________________________________________________________________\ninput_3 (InputLayer)            (None, 1)            0                                            \n__________________________________________________________________________________________________\ninput_4 (InputLayer)            (None, 1)            0                                            \n__________________________________________________________________________________________________\ninput_5 (InputLayer)            (None, 1)            0                                            \n__________________________________________________________________________________________________\ninput_6 (InputLayer)            (None, 1)            0                                            \n__________________________________________________________________________________________________\nlstm_1 (LSTM)                   (None, 100, 100)     80400       embedding_1[0][0]                \n__________________________________________________________________________________________________\nembedding_2 (Embedding)         (None, 1, 10)        510         input_2[0][0]                    \n__________________________________________________________________________________________________\nembedding_3 (Embedding)         (None, 1, 10)        40          input_3[0][0]                    \n__________________________________________________________________________________________________\nembedding_4 (Embedding)         (None, 1, 10)        500         input_4[0][0]                    \n__________________________________________________________________________________________________\nembedding_5 (Embedding)         (None, 1, 10)        3870        input_5[0][0]                    \n__________________________________________________________________________________________________\nembedding_6 (Embedding)         (None, 1, 10)        50          input_6[0][0]                    \n__________________________________________________________________________________________________\ninput_8 (InputLayer)            (None, 2)            0                                            \n__________________________________________________________________________________________________\nflatten_1 (Flatten)             (None, 10000)        0           lstm_1[0][0]                     \n__________________________________________________________________________________________________\nflatten_2 (Flatten)             (None, 10)           0           embedding_2[0][0]                \n__________________________________________________________________________________________________\nflatten_3 (Flatten)             (None, 10)           0           embedding_3[0][0]                \n__________________________________________________________________________________________________\nflatten_4 (Flatten)             (None, 10)           0           embedding_4[0][0]                \n__________________________________________________________________________________________________\nflatten_5 (Flatten)             (None, 10)           0           embedding_5[0][0]                \n__________________________________________________________________________________________________\nflatten_6 (Flatten)             (None, 10)           0           embedding_6[0][0]                \n__________________________________________________________________________________________________\ndense_15 (Dense)                (None, 128)          384         input_8[0][0]                    \n__________________________________________________________________________________________________\nconcatenate_5 (Concatenate)     (None, 10178)        0           flatten_1[0][0]                  \n                                                                 flatten_2[0][0]                  \n                                                                 flatten_3[0][0]                  \n                                                                 flatten_4[0][0]                  \n                                                                 flatten_5[0][0]                  \n                                                                 flatten_6[0][0]                  \n                                                                 dense_15[0][0]                   \n__________________________________________________________________________________________________\ndense_16 (Dense)                (None, 256)          2605824     concatenate_5[0][0]              \n__________________________________________________________________________________________________\ndropout_7 (Dropout)             (None, 256)          0           dense_16[0][0]                   \n__________________________________________________________________________________________________\ndense_17 (Dense)                (None, 128)          32896       dropout_7[0][0]                  \n__________________________________________________________________________________________________\ndropout_8 (Dropout)             (None, 128)          0           dense_17[0][0]                   \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 128)          512         dropout_8[0][0]                  \n__________________________________________________________________________________________________\ndense_18 (Dense)                (None, 64)           8256        batch_normalization_4[0][0]      \n__________________________________________________________________________________________________\ndense_19 (Dense)                (None, 2)            130         dense_18[0][0]                   \n==================================================================================================\nTotal params: 4,152,872\nTrainable params: 2,728,146\nNon-trainable params: 1,424,726\n__________________________________________________________________________________________________\n","name":"stdout"}]},{"metadata":{"colab":{},"colab_type":"code","id":"-vKKWo7skuHH","outputId":"c163d729-ccae-4b84-92b3-9af37f62df48","trusted":true},"cell_type":"code","source":"model2.fit(train_2,Y_train,batch_size=512, epochs=20,validation_data=(test_2,Y_test),verbose=1,class_weight='balanced',callbacks=[tensorboard_2,earlystop_2])","execution_count":80,"outputs":[{"output_type":"stream","text":"Train on 73196 samples, validate on 36052 samples\nEpoch 1/20\n73196/73196 [==============================] - 26s 354us/step - loss: 0.8555 - auc_sc: 0.7045 - val_loss: 0.6101 - val_auc_sc: 0.7238\nEpoch 2/20\n73196/73196 [==============================] - 25s 340us/step - loss: 0.5678 - auc_sc: 0.6977 - val_loss: 0.5283 - val_auc_sc: 0.7212\nEpoch 3/20\n73196/73196 [==============================] - 25s 339us/step - loss: 0.4857 - auc_sc: 0.7207 - val_loss: 0.4711 - val_auc_sc: 0.7226\nEpoch 4/20\n73196/73196 [==============================] - 25s 341us/step - loss: 0.4524 - auc_sc: 0.7318 - val_loss: 0.4436 - val_auc_sc: 0.7287\nEpoch 5/20\n73196/73196 [==============================] - 25s 342us/step - loss: 0.4311 - auc_sc: 0.7382 - val_loss: 0.4296 - val_auc_sc: 0.7346\nEpoch 6/20\n73196/73196 [==============================] - 25s 341us/step - loss: 0.4189 - auc_sc: 0.7435 - val_loss: 0.4185 - val_auc_sc: 0.7342\nEpoch 7/20\n73196/73196 [==============================] - 25s 343us/step - loss: 0.4054 - auc_sc: 0.7528 - val_loss: 0.4132 - val_auc_sc: 0.7322\nEpoch 8/20\n73196/73196 [==============================] - 25s 343us/step - loss: 0.3974 - auc_sc: 0.7559 - val_loss: 0.4133 - val_auc_sc: 0.7305\nEpoch 9/20\n73196/73196 [==============================] - 25s 347us/step - loss: 0.3910 - auc_sc: 0.7588 - val_loss: 0.4023 - val_auc_sc: 0.7366\nEpoch 10/20\n73196/73196 [==============================] - 25s 346us/step - loss: 0.3878 - auc_sc: 0.7631 - val_loss: 0.4002 - val_auc_sc: 0.7370\nEpoch 11/20\n73196/73196 [==============================] - 25s 340us/step - loss: 0.3812 - auc_sc: 0.7701 - val_loss: 0.3996 - val_auc_sc: 0.7343\nRestoring model weights from the end of the best epoch\nEpoch 00011: early stopping\n","name":"stdout"},{"output_type":"execute_result","execution_count":80,"data":{"text/plain":"<keras.callbacks.callbacks.History at 0x7fb1507e6f60>"},"metadata":{}}]},{"metadata":{"ExecuteTime":{"end_time":"2019-10-17T13:18:17.156852Z","start_time":"2019-10-17T13:18:17.142492Z"},"colab":{},"colab_type":"code","id":"rUrv_fMLkuHK","outputId":"c2d77d42-9eb2-4ca7-96c0-758e5032c865","trusted":true},"cell_type":"code","source":"from prettytable import PrettyTable\nx = PrettyTable()\nx.field_names = [\"Model\", \"train_auc\", \"test_auc\"]\nx.add_row([\"Model_2\", 0.7701,0.7343])\nprint(x)","execution_count":82,"outputs":[{"output_type":"stream","text":"+---------+-----------+----------+\n|  Model  | train_auc | test_auc |\n+---------+-----------+----------+\n| Model_2 |   0.7701  |  0.7343  |\n+---------+-----------+----------+\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"%load_ext tensorboard.notebook\n%tensorboard --logdir logs932332","execution_count":83,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<!-- JUPYTER_TENSORBOARD_TEST_MARKER -->\n<script>\n    const req = {\n        method: 'POST',\n        contentType: 'application/json',\n        body: JSON.stringify({ 'logdir': 'logs932332' }),\n        headers: { 'Content-Type': 'application/json' }\n    };\n\n    const baseUrl = Jupyter.notebook.base_url;\n\n    fetch(baseUrl + 'api/tensorboard', req)\n        .then(res => res.json())\n        .then(res => {\n            const iframe = document.getElementById('tensorboard-bc8634aa-7f4f-4714-9d7c-19a87a1e1728');\n            iframe.src = baseUrl + 'tensorboard/' + res.name;\n            iframe.style.display = 'block';\n        });\n</script>\n\n<iframe\n    id=\"tensorboard-bc8634aa-7f4f-4714-9d7c-19a87a1e1728\"\n    style=\"width: 100%; height: 620px; display: none;\"\n    frameBorder=\"0\">\n</iframe>\n"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"colab":{"name":"donors-choose-lstm(6).ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":false},"varInspector":{"cols":{"lenName":16,"lenType":16,"lenVar":40},"kernels_config":{"python":{"delete_cmd_postfix":"","delete_cmd_prefix":"del ","library":"var_list.py","varRefreshCmd":"print(var_dic_list())"},"r":{"delete_cmd_postfix":") ","delete_cmd_prefix":"rm(","library":"var_list.r","varRefreshCmd":"cat(var_dic_list()) "}},"types_to_exclude":["module","function","builtin_function_or_method","instance","_Feature"],"window_display":false}},"nbformat":4,"nbformat_minor":1}