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+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":196309,"status":"ok","timestamp":1670510422399,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"},"user_tz":300},"id":"REugZMxlFCvU","outputId":"2b6a1767-4296-4d10-cc7a-fcefdb7d6113"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive/\n"]}],"source":["from google.colab import drive\n","drive.mount(\"/content/gdrive/\")"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"ceMmp0oL8PyI","executionInfo":{"status":"ok","timestamp":1670510423386,"user_tz":300,"elapsed":990,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import shutil\n","import sys \n","import os\n","import time\n","from sklearn.model_selection import train_test_split"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"qAYz11Vr-k5b","executionInfo":{"status":"ok","timestamp":1670510423386,"user_tz":300,"elapsed":3,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["dir_path = 'gdrive/Shareddrives/CS5024 Ethics Project'\n","sys.path.append(dir_path)"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10280,"status":"ok","timestamp":1670510433664,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"},"user_tz":300},"id":"jmTuxI2t8PwQ","outputId":"d312c306-e195-45bc-95de-fed482fa0869"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (5,11) have mixed types.Specify dtype option on import or set low_memory=False.\n"," exec(code_obj, self.user_global_ns, self.user_ns)\n"]}],"source":["# Create DF for train and test set\n","train_df = pd.read_csv(f'{dir_path}/DrivenDataCompetition_DataFiles/TrainingData.csv')\n","test_df = pd.read_csv(f'{dir_path}/DrivenDataCompetition_DataFiles/TestData.csv')"]},{"cell_type":"code","source":["print (train_df.shape)\n","print (test_df.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0NPhp8WQ1YA0","executionInfo":{"status":"ok","timestamp":1670510433665,"user_tz":300,"elapsed":7,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}},"outputId":"33582c11-9cd6-4a16-ef75-957d55f8c906"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["(400277, 26)\n","(50064, 17)\n"]}]},{"cell_type":"markdown","source":["# Data Preprocessing"],"metadata":{"id":"Y558pu6Bdbbw"}},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":797},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1670510433665,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"},"user_tz":300},"id":"Q9Jzqrlwiu83","outputId":"cf12f590-0d36-4704-de43-6193bc7fcf82"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Function Use \\\n","Unnamed: 0 \n","134338 Teacher Compensation Instruction \n","206341 NO_LABEL NO_LABEL \n","326408 Teacher Compensation Instruction \n","364634 Substitute Compensation Instruction \n","47683 Substitute Compensation Instruction \n","... ... ... \n","109283 Professional Development ISPD \n","102430 Substitute Compensation Instruction \n","413949 Parent & Community Relations NO_LABEL \n","433672 Library & Media Instruction \n","415831 Substitute Compensation Instruction \n","\n"," Sharing Reporting Student_Type \\\n","Unnamed: 0 \n","134338 School Reported School NO_LABEL \n","206341 NO_LABEL NO_LABEL NO_LABEL \n","326408 School Reported School Unspecified \n","364634 School Reported School Unspecified \n","47683 School Reported School Unspecified \n","... ... ... ... \n","109283 Shared Services Non-School Unspecified \n","102430 School Reported School Unspecified \n","413949 School Reported School NO_LABEL \n","433672 School on Central Budgets Non-School Unspecified \n","415831 School Reported School Poverty \n","\n"," Position_Type Object_Type Pre_K \\\n","Unnamed: 0 \n","134338 Teacher NO_LABEL NO_LABEL \n","206341 NO_LABEL NO_LABEL NO_LABEL \n","326408 Teacher Base Salary/Compensation Non PreK \n","364634 Substitute Benefits NO_LABEL \n","47683 Teacher Substitute Compensation NO_LABEL \n","... ... ... ... \n","109283 Instructional Coach Other Compensation/Stipend NO_LABEL \n","102430 Substitute Base Salary/Compensation NO_LABEL \n","413949 Other NO_LABEL NO_LABEL \n","433672 Librarian Benefits NO_LABEL \n","415831 Substitute Substitute Compensation Non PreK \n","\n"," Operating_Status \\\n","Unnamed: 0 \n","134338 PreK-12 Operating \n","206341 Non-Operating \n","326408 PreK-12 Operating \n","364634 PreK-12 Operating \n","47683 PreK-12 Operating \n","... ... \n","109283 PreK-12 Operating \n","102430 PreK-12 Operating \n","413949 PreK-12 Operating \n","433672 PreK-12 Operating \n","415831 PreK-12 Operating \n","\n"," Object_Description ... \\\n","Unnamed: 0 ... \n","134338 NaN ... \n","206341 CONTRACTOR SERVICES ... \n","326408 Personal Services - Teachers ... \n","364634 EMPLOYEE BENEFITS ... \n","47683 TEACHER COVERAGE FOR TEACHER ... \n","... ... ... \n","109283 WORKSHOP PARTICIPANT ... \n","102430 SALARIES OF PART TIME EMPLOYEE ... \n","413949 NaN ... \n","433672 EMPLOYEE BENEFITS ... \n","415831 Salaries And Wages For Substitute Professionals ... \n","\n"," Sub_Object_Description \\\n","Unnamed: 0 \n","134338 NaN \n","206341 NaN \n","326408 NaN \n","364634 NaN 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style=\"text-align: right;\">\n"," <th></th>\n"," <th>0</th>\n"," <th>Function_Aides Compensation</th>\n"," <th>Function_Career & Academic Counseling</th>\n"," <th>Function_Communications</th>\n"," <th>Function_Curriculum Development</th>\n"," <th>Function_Data Processing & Information Services</th>\n"," <th>Function_Development & Fundraising</th>\n"," <th>Function_Enrichment</th>\n"," <th>Function_Extended Time & Tutoring</th>\n"," <th>Function_Facilities & Maintenance</th>\n"," <th>...</th>\n"," <th>Student_Type_Special Education</th>\n"," <th>Student_Type_Unspecified</th>\n"," <th>Use_Business Services</th>\n"," <th>Use_ISPD</th>\n"," <th>Use_Instruction</th>\n"," <th>Use_Leadership</th>\n"," <th>Use_NO_LABEL</th>\n"," <th>Use_O&M</th>\n"," <th>Use_Pupil Services & Enrichment</th>\n"," <th>Use_Untracked Budget Set-Aside</th>\n"," </tr>\n"," <tr>\n"," <th>Unnamed: 0</th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," 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'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-efe9ca42-7ce1-4fd4-a880-234b5631bd03');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":7}],"source":["# set target Cols\n","target_cols = ['Function', 'Object_Type', 'Operating_Status', 'Position_Type', 'Pre_K', 'Reporting', 'Sharing', 'Student_Type', 'Use']\n","train_df_dummies = pd.DataFrame()\n","# drop train int cols\n","train_df.drop(['FTE', 'Total'], axis = 1, inplace=True)\n","# get dummies for target cols\n","col = pd.get_dummies(train_df[target_cols])\n","# drop target cols\n","train_df = train_df.drop(target_cols, axis=1)\n","# fill NaN with space\n","train_df.fillna(' ', inplace=True)\n","# combine all text into single col\n","combined = pd.DataFrame([' '.join(row) for row in train_df[train_df.columns[1:]].values])\n","# join combined text col with dummy labels\n","train_df_dummies = pd.concat([combined, col], axis = 1)\n","# drop test int cols\n","test_df.drop(['FTE', 'Total'], axis = 1, inplace=True)\n","# fill NaN with space\n","test_df.fillna(' ', inplace=True)\n","# combine all text into single col\n","test_df_cleaned = pd.DataFrame([' '.join(row) for row in test_df[test_df.columns[1:]].values])\n","# reset indices to original\n","test_df_cleaned.set_index(test_df['Unnamed: 0'])\n","train_df_dummies.set_index(train_df['Unnamed: 0'])"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"sIdbRlvsE2Fn","executionInfo":{"status":"ok","timestamp":1670510435809,"user_tz":300,"elapsed":5,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["cols = col.columns"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"5ZhHV0028PjT","executionInfo":{"status":"ok","timestamp":1670510435809,"user_tz":300,"elapsed":4,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["# rename text col\n","test_df_cleaned.rename(columns={0: \"text\"}, inplace=True)\n","train_df_dummies.rename(columns={0: \"text\"}, inplace=True)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1292,"status":"ok","timestamp":1670510437097,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"},"user_tz":300},"id":"DSMXH2Qmc0Y_","outputId":"fe62771e-2bf1-44f4-88e1-d56dae02b300"},"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data] Unzipping corpora/stopwords.zip.\n","[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data] Unzipping tokenizers/punkt.zip.\n"]}],"source":["import nltk\n","nltk.download('stopwords')\n","nltk.download('punkt')\n","from nltk import word_tokenize\n","from nltk.corpus import stopwords\n","from nltk.stem.snowball import SnowballStemmer\n","import re"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"Xly7pR2-CZhx","executionInfo":{"status":"ok","timestamp":1670510437098,"user_tz":300,"elapsed":4,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["def text_processing(df):\n"," stop_words = set(stopwords.words('english'))\n"," st = SnowballStemmer('english')\n"," # lower and trim spaces\n"," df['text'] = df['text'].apply(lambda x: x.lower().strip())\n"," # remove other spaces\n"," df['text'] = df['text'].apply(lambda x: re.sub(' +', ' ', x))\n"," # remove punctuation\n"," df['text'] = df['text'].apply(lambda x: re.sub('[^a-zA-Z]', ' ', x))\n"," # remove stopwords stem\n"," df['text'] = df['text'].apply(lambda x: ' '.join(st.stem(text) for text in x.split() if text not in stop_words))"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"rR1Z3FPoh7c3","executionInfo":{"status":"ok","timestamp":1670510527234,"user_tz":300,"elapsed":90139,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["# clean df\n","text_processing(train_df_dummies)\n","text_processing(test_df_cleaned)"]},{"cell_type":"markdown","source":["# Embedding"],"metadata":{"id":"4DkHoAlAdS8j"}},{"cell_type":"code","execution_count":13,"metadata":{"id":"iQAX3AoUnrtv","executionInfo":{"status":"ok","timestamp":1670510527235,"user_tz":300,"elapsed":4,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["from sklearn.feature_extraction.text import TfidfVectorizer"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"dpLEeR7dC9Bd","executionInfo":{"status":"ok","timestamp":1670510527655,"user_tz":300,"elapsed":423,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["X = train_df_dummies['text'].values\n","y = train_df_dummies[list(train_df_dummies.columns[1:])].values\n","X_train, X_val, y_train, y_val = train_test_split(X,y,test_size = 0.2,train_size =0.8)"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"J4KQiBI0oZUp","executionInfo":{"status":"ok","timestamp":1670511912627,"user_tz":300,"elapsed":9889,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["def tfidf_features(X_train, X_val, X_test):\n"," \"\"\"\n"," X_train, X_test — samples \n"," return TF-IDF vectorized representation of each sample and vocabulary\n"," \"\"\"\n"," # Create TF-IDF vectorizer with a proper parameters choice\n"," tfidf_vectorizer = TfidfVectorizer(ngram_range=(1,2), max_df=1.0, min_df=1, token_pattern='(\\S+)')\n"," # Fit the vectorizer on the train set\n"," X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n"," # Transform the train, test set and return the result\n"," X_val_tfidf = tfidf_vectorizer.transform(X_val)\n"," X_test_tfidf = tfidf_vectorizer.transform(X_test)\n"," \n"," return X_train_tfidf, X_val_tfidf, X_test_tfidf, tfidf_vectorizer.vocabulary_\n","timer = time.time()\n","X_train_tfidf, X_val_tfidf, X_test_tfidf, tfidf_vocab = tfidf_features(X_train, X_val, test_df_cleaned.text)\n","tfidf_reversed_vocab = {i:word for word,i in tfidf_vocab.items()}"]},{"cell_type":"markdown","source":["# Logistic Regression"],"metadata":{"id":"ydQ-7nJdT7Ad"}},{"cell_type":"code","execution_count":25,"metadata":{"id":"nwyPgHYMplz3","executionInfo":{"status":"ok","timestamp":1670513291331,"user_tz":300,"elapsed":1378713,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"045b6532-7308-4018-b3c3-af14efe9aec5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Time for NLP training\" 1387.7084062099457\n","Time for NLP inference\" 0.6320400238037109\n"]}],"source":["from sklearn.multiclass import OneVsRestClassifier\n","from sklearn.linear_model import LogisticRegression, RidgeClassifier\n","\n","def train_logreg(X_train, y_train, C, regularisation):\n"," \"\"\"\n"," X_train, y_train — training data\n"," \n"," return: trained classifier\n"," \"\"\"\n"," \n"," # Create and fit LogisticRegression wraped into OneVsRestClassifier.\n","\n"," model = OneVsRestClassifier(LogisticRegression(penalty=regularisation, C=C, max_iter=10000)).fit(X_train, y_train)\n"," return model\n","\n","logreg_tfidf = train_logreg(X_train_tfidf, y_train, C = 2, regularisation = 'l2')\n","print (f'Time for NLP training\" {time.time()-timer}')\n","timer = time.time()\n","y_val_predicted_labels_logreg = logreg_tfidf.predict(X_val_tfidf)\n","print (f'Time for NLP inference\" {time.time()-timer}')\n"]},{"cell_type":"markdown","source":["# XG Boost"],"metadata":{"id":"SOFP4vZFUJiu"}},{"cell_type":"code","source":["# from sklearn.multiclass import OneVsRestClassifier\n","# from xgboost import XGBClassifier\n","\n","# def train_xgb(X_train, y_train, params):\n","# \"\"\"\n","# X_train, y_train — training data\n"," \n","# return: trained classifier\n","# \"\"\"\n"," \n","# # Create and fit XGBoost wraped into OneVsRestClassifier.\n","\n","# model = OneVsRestClassifier(XGBClassifier(**params)).fit(X_train, y_train)\n","# return model\n","# xgb_params = {'eta': 0.3, \n","# 'max_depth': 5, \n","# 'subsample': 0.8, \n","# 'colsample_bytree': 0.8, \n","# 'tree_method' : 'gpu_hist',\n","# 'objective': 'binary:logistic', \n","# 'eval_metric': 'auc', \n","# 'seed': 42\n","# }\n","# xgb_tfidf = train_xgb(X_train_tfidf, y_train, xgb_params)\n","# y_val_predicted_labels_xgb = xgb_tfidf.predict(X_val_tfidf)"],"metadata":{"id":"r42fPuwwURXN","executionInfo":{"status":"ok","timestamp":1670513291331,"user_tz":300,"elapsed":10,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":["# SGD"],"metadata":{"id":"WeL9xN2WLpyO"}},{"cell_type":"code","source":["# from sklearn.multiclass import OneVsRestClassifier\n","# from sklearn.linear_model import SGDClassifier\n","# def train_sgd(X_train, y_train):\n","# \"\"\"\n","# X_train, y_train — training data\n"," \n","# return: trained classifier\n","# \"\"\"\n"," \n","# # Create and fit LogisticRegression wraped into OneVsRestClassifier.\n","\n","# model = OneVsRestClassifier(SGDClassifier(loss = 'log', penalty = 'l2')).fit(X_train, y_train)\n","# return model\n","\n","# sgd_tfidf = train_sgd(X_train_tfidf, y_train)\n","# print (f'Time for NLP training\" {time.time()-timer}')\n","# timer = time.time()\n","# y_val_predicted_labels_sgd = sgd_tfidf.predict(X_val_tfidf)\n","# print (f'Time for NLP inference\" {time.time()-timer}')\n"],"metadata":{"id":"CxxB9ABSLrs1","executionInfo":{"status":"ok","timestamp":1670513291331,"user_tz":300,"elapsed":8,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":["## Model Evaluation"],"metadata":{"id":"aToqJ94RdO9R"}},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score \n","from sklearn.metrics import precision_score\n","from sklearn.metrics import average_precision_score\n","from sklearn.metrics import recall_score\n","from sklearn.metrics import log_loss"],"metadata":{"id":"a2ULt-H4dAT5","executionInfo":{"status":"ok","timestamp":1670513291332,"user_tz":300,"elapsed":8,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"execution_count":28,"outputs":[]},{"cell_type":"code","source":["def print_evaluation_scores(y_test, predicted):\n"," \n"," print('Accuracy: ', accuracy_score(y_test, predicted, normalize=True))\n"," print('F1-score macro: ', f1_score(y_test, predicted, average='macro', labels=[1]))\n"," print('F1-score micro: ', f1_score(y_test, predicted, average='micro', labels=[1]))\n"," print('F1-score weighted: ', f1_score(y_test, predicted, average='weighted'))\n"," print('Precision macro: ', average_precision_score(y_test, predicted, average='macro'))\n"," print('Precision micro: ', average_precision_score(y_test, predicted, average='micro'))\n"," print('Precision weighted: ', precision_score(y_test, predicted, average='weighted', labels=[1]))\n"," print('Log Loss: ', log_loss(y_test, predicted, normalize=True))\n","\n"," \n","print('Metrics')\n","print_evaluation_scores(y_val, y_val_predicted_labels_logreg)\n","# print_evaluation_scores(y_val, y_val_predicted_labels_xgb)\n","# print_evaluation_scores(y_val, y_val_predicted_labels_sgd)"],"metadata":{"id":"g1xaUV6Ic1kS","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1670513299813,"user_tz":300,"elapsed":8489,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}},"outputId":"e1fb88d6-f5e0-4b75-c656-9b0ee6ea87da"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Metrics\n","Accuracy: 0.898533526531428\n","F1-score macro: 0.857397504456328\n","F1-score micro: 0.857397504456328\n","F1-score weighted: 0.9780481032620225\n","Precision macro: 0.8548790839083935\n","Precision micro: 0.9595717445537028\n","Precision weighted: 0.8809523809523809\n","Log Loss: 27.62320822221433\n"]}]},{"cell_type":"markdown","source":["# Submission"],"metadata":{"id":"4e9yV9CadHwO"}},{"cell_type":"code","execution_count":30,"metadata":{"id":"75VcWGLmvvCi","executionInfo":{"status":"ok","timestamp":1670513300205,"user_tz":300,"elapsed":394,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["test_predictions = logreg_tfidf.predict_proba(X_test_tfidf)"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"GhNpC96cwRmD","executionInfo":{"status":"ok","timestamp":1670513301440,"user_tz":300,"elapsed":1237,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}},"colab":{"base_uri":"https://localhost:8080/","height":554},"outputId":"aa8f5639-897c-4437-aca7-daafddd03797"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" Function__Aides Compensation Function__Career & Academic Counseling \\\n"," \n","180042 0.005369 0.001911 \n","28872 0.000574 0.001182 \n","186915 0.017363 0.000992 \n","412396 0.011996 0.000990 \n","427740 0.001207 0.002310 \n","... ... ... \n","169063 0.004662 0.000559 \n","433255 0.004662 0.000559 \n","232204 0.004662 0.000559 \n","171685 0.004662 0.000559 \n","249087 0.004662 0.000559 \n","\n"," Function__Communications Function__Curriculum Development \\\n"," \n","180042 0.000062 0.000037 \n","28872 0.000243 0.005150 \n","186915 0.000156 0.001177 \n","412396 0.000155 0.001122 \n","427740 0.000585 0.002508 \n","... ... ... \n","169063 0.000271 0.001190 \n","433255 0.000271 0.001190 \n","232204 0.000271 0.001190 \n","171685 0.000271 0.001190 \n","249087 0.000271 0.001190 \n","\n"," Function__Data Processing & Information Services \\\n"," \n","180042 0.000424 \n","28872 0.001041 \n","186915 0.000616 \n","412396 0.000625 \n","427740 0.002355 \n","... ... \n","169063 0.000868 \n","433255 0.000868 \n","232204 0.000868 \n","171685 0.000868 \n","249087 0.000868 \n","\n"," Function__Development & Fundraising Function__Enrichment \\\n"," \n","180042 0.000088 0.000996 \n","28872 0.000150 0.227637 \n","186915 0.000108 0.000500 \n","412396 0.000108 0.000475 \n","427740 0.000135 0.002673 \n","... ... ... \n","169063 0.000236 0.002325 \n","433255 0.000236 0.002325 \n","232204 0.000236 0.002325 \n","171685 0.000236 0.002325 \n","249087 0.000236 0.002325 \n","\n"," Function__Extended Time & Tutoring \\\n"," \n","180042 0.000203 \n","28872 0.003900 \n","186915 0.000463 \n","412396 0.000489 \n","427740 0.000067 \n","... ... \n","169063 0.000239 \n","433255 0.000239 \n","232204 0.000239 \n","171685 0.000239 \n","249087 0.000239 \n","\n"," Function__Facilities & Maintenance Function__Facilities Planning \\\n"," \n","180042 0.001402 0.000029 \n","28872 0.006152 0.000040 \n","186915 0.001457 0.000034 \n","412396 0.001456 0.000034 \n","427740 0.003695 0.000041 \n","... ... ... \n","169063 0.001862 0.000044 \n","433255 0.001862 0.000044 \n","232204 0.001862 0.000044 \n","171685 0.001862 0.000044 \n","249087 0.001862 0.000044 \n","\n"," ... Student_Type__Special Education Student_Type__Unspecified \\\n"," ... \n","180042 ... 0.002347 0.836926 \n","28872 ... 0.005913 0.894910 \n","186915 ... 0.004487 0.282162 \n","412396 ... 0.004447 0.250461 \n","427740 ... 0.004405 0.980520 \n","... ... ... ... \n","169063 ... 0.004677 0.037555 \n","433255 ... 0.004677 0.037555 \n","232204 ... 0.004677 0.037555 \n","171685 ... 0.004677 0.037555 \n","249087 ... 0.004677 0.037555 \n","\n"," Use__Business Services Use__ISPD Use__Instruction Use__Leadership \\\n"," \n","180042 0.000200 0.003739 0.064349 0.003398 \n","28872 0.003721 0.009994 0.011118 0.023945 \n","186915 0.000895 0.012540 0.695857 0.008499 \n","412396 0.000910 0.010479 0.693750 0.007253 \n","427740 0.008272 0.040371 0.001226 0.805063 \n","... ... ... ... ... \n","169063 0.003252 0.003751 0.017954 0.002785 \n","433255 0.003252 0.003751 0.017954 0.002785 \n","232204 0.003252 0.003751 0.017954 0.002785 \n","171685 0.003252 0.003751 0.017954 0.002785 \n","249087 0.003252 0.003751 0.017954 0.002785 \n","\n"," Use__NO_LABEL Use__O&M Use__Pupil Services & Enrichment \\\n"," \n","180042 0.264019 0.006676 0.005294 \n","28872 0.047036 0.023556 0.146622 \n","186915 0.016858 0.003341 0.009452 \n","412396 0.017822 0.003547 0.008566 \n","427740 0.005581 0.037933 0.004177 \n","... ... ... ... \n","169063 0.883382 0.002572 0.044943 \n","433255 0.883382 0.002572 0.044943 \n","232204 0.883382 0.002572 0.044943 \n","171685 0.883382 0.002572 0.044943 \n","249087 0.883382 0.002572 0.044943 \n","\n"," Use__Untracked Budget Set-Aside \n"," \n","180042 0.000372 \n","28872 0.000244 \n","186915 0.000168 \n","412396 0.000168 \n","427740 0.000233 \n","... ... \n","169063 0.000341 \n","433255 0.000341 \n","232204 0.000341 \n","171685 0.000341 \n","249087 0.000341 \n","\n","[50064 rows x 104 columns]"],"text/html":["\n"," <div id=\"df-73e59131-014c-49d1-a337-c844b74a21b5\">\n"," <div class=\"colab-df-container\">\n"," <div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>Function__Aides Compensation</th>\n"," <th>Function__Career & Academic Counseling</th>\n"," <th>Function__Communications</th>\n"," <th>Function__Curriculum Development</th>\n"," <th>Function__Data Processing & Information Services</th>\n"," <th>Function__Development & Fundraising</th>\n"," <th>Function__Enrichment</th>\n"," <th>Function__Extended Time & Tutoring</th>\n"," <th>Function__Facilities & Maintenance</th>\n"," <th>Function__Facilities Planning</th>\n"," <th>...</th>\n"," <th>Student_Type__Special Education</th>\n"," <th>Student_Type__Unspecified</th>\n"," <th>Use__Business Services</th>\n"," <th>Use__ISPD</th>\n"," <th>Use__Instruction</th>\n"," <th>Use__Leadership</th>\n"," <th>Use__NO_LABEL</th>\n"," <th>Use__O&M</th>\n"," <th>Use__Pupil Services & Enrichment</th>\n"," <th>Use__Untracked Budget Set-Aside</th>\n"," </tr>\n"," <tr>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," <th></th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>180042</th>\n"," <td>0.005369</td>\n"," <td>0.001911</td>\n"," <td>0.000062</td>\n"," <td>0.000037</td>\n"," <td>0.000424</td>\n"," <td>0.000088</td>\n"," <td>0.000996</td>\n"," <td>0.000203</td>\n"," <td>0.001402</td>\n"," <td>0.000029</td>\n"," <td>...</td>\n"," <td>0.002347</td>\n"," <td>0.836926</td>\n"," <td>0.000200</td>\n"," <td>0.003739</td>\n"," <td>0.064349</td>\n"," <td>0.003398</td>\n"," <td>0.264019</td>\n"," <td>0.006676</td>\n"," <td>0.005294</td>\n"," <td>0.000372</td>\n"," </tr>\n"," <tr>\n"," <th>28872</th>\n"," <td>0.000574</td>\n"," <td>0.001182</td>\n"," <td>0.000243</td>\n"," <td>0.005150</td>\n"," <td>0.001041</td>\n"," <td>0.000150</td>\n"," <td>0.227637</td>\n"," <td>0.003900</td>\n"," <td>0.006152</td>\n"," <td>0.000040</td>\n"," <td>...</td>\n"," <td>0.005913</td>\n"," <td>0.894910</td>\n"," <td>0.003721</td>\n"," <td>0.009994</td>\n"," <td>0.011118</td>\n"," <td>0.023945</td>\n"," <td>0.047036</td>\n"," <td>0.023556</td>\n"," <td>0.146622</td>\n"," <td>0.000244</td>\n"," </tr>\n"," <tr>\n"," <th>186915</th>\n"," <td>0.017363</td>\n"," <td>0.000992</td>\n"," <td>0.000156</td>\n"," <td>0.001177</td>\n"," <td>0.000616</td>\n"," <td>0.000108</td>\n"," <td>0.000500</td>\n"," <td>0.000463</td>\n"," <td>0.001457</td>\n"," <td>0.000034</td>\n"," <td>...</td>\n"," <td>0.004487</td>\n"," <td>0.282162</td>\n"," <td>0.000895</td>\n"," <td>0.012540</td>\n"," <td>0.695857</td>\n"," <td>0.008499</td>\n"," <td>0.016858</td>\n"," <td>0.003341</td>\n"," <td>0.009452</td>\n"," <td>0.000168</td>\n"," </tr>\n"," <tr>\n"," <th>412396</th>\n"," <td>0.011996</td>\n"," <td>0.000990</td>\n"," <td>0.000155</td>\n"," <td>0.001122</td>\n"," <td>0.000625</td>\n"," <td>0.000108</td>\n"," <td>0.000475</td>\n"," <td>0.000489</td>\n"," <td>0.001456</td>\n"," <td>0.000034</td>\n"," <td>...</td>\n"," <td>0.004447</td>\n"," <td>0.250461</td>\n"," <td>0.000910</td>\n"," <td>0.010479</td>\n"," <td>0.693750</td>\n"," <td>0.007253</td>\n"," <td>0.017822</td>\n"," <td>0.003547</td>\n"," <td>0.008566</td>\n"," <td>0.000168</td>\n"," </tr>\n"," <tr>\n"," <th>427740</th>\n"," <td>0.001207</td>\n"," <td>0.002310</td>\n"," <td>0.000585</td>\n"," <td>0.002508</td>\n"," <td>0.002355</td>\n"," <td>0.000135</td>\n"," <td>0.002673</td>\n"," <td>0.000067</td>\n"," <td>0.003695</td>\n"," <td>0.000041</td>\n"," <td>...</td>\n"," <td>0.004405</td>\n"," <td>0.980520</td>\n"," <td>0.008272</td>\n"," <td>0.040371</td>\n"," <td>0.001226</td>\n"," <td>0.805063</td>\n"," <td>0.005581</td>\n"," <td>0.037933</td>\n"," <td>0.004177</td>\n"," <td>0.000233</td>\n"," </tr>\n"," <tr>\n"," <th>...</th>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," <td>...</td>\n"," </tr>\n"," <tr>\n"," <th>169063</th>\n"," <td>0.004662</td>\n"," <td>0.000559</td>\n"," <td>0.000271</td>\n"," <td>0.001190</td>\n"," <td>0.000868</td>\n"," <td>0.000236</td>\n"," <td>0.002325</td>\n"," <td>0.000239</td>\n"," <td>0.001862</td>\n"," <td>0.000044</td>\n"," <td>...</td>\n"," <td>0.004677</td>\n"," <td>0.037555</td>\n"," <td>0.003252</td>\n"," <td>0.003751</td>\n"," <td>0.017954</td>\n"," <td>0.002785</td>\n"," <td>0.883382</td>\n"," <td>0.002572</td>\n"," <td>0.044943</td>\n"," <td>0.000341</td>\n"," </tr>\n"," <tr>\n"," <th>433255</th>\n"," <td>0.004662</td>\n"," <td>0.000559</td>\n"," <td>0.000271</td>\n"," <td>0.001190</td>\n"," <td>0.000868</td>\n"," <td>0.000236</td>\n"," <td>0.002325</td>\n"," <td>0.000239</td>\n"," <td>0.001862</td>\n"," <td>0.000044</td>\n"," <td>...</td>\n"," <td>0.004677</td>\n"," <td>0.037555</td>\n"," <td>0.003252</td>\n"," <td>0.003751</td>\n"," <td>0.017954</td>\n"," <td>0.002785</td>\n"," <td>0.883382</td>\n"," <td>0.002572</td>\n"," <td>0.044943</td>\n"," <td>0.000341</td>\n"," </tr>\n"," <tr>\n"," <th>232204</th>\n"," <td>0.004662</td>\n"," <td>0.000559</td>\n"," <td>0.000271</td>\n"," <td>0.001190</td>\n"," <td>0.000868</td>\n"," <td>0.000236</td>\n"," <td>0.002325</td>\n"," <td>0.000239</td>\n"," <td>0.001862</td>\n"," <td>0.000044</td>\n"," <td>...</td>\n"," <td>0.004677</td>\n"," <td>0.037555</td>\n"," <td>0.003252</td>\n"," <td>0.003751</td>\n"," 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'block' : 'none';\n","\n"," async function convertToInteractive(key) {\n"," const element = document.querySelector('#df-73e59131-014c-49d1-a337-c844b74a21b5');\n"," const dataTable =\n"," await google.colab.kernel.invokeFunction('convertToInteractive',\n"," [key], {});\n"," if (!dataTable) return;\n","\n"," const docLinkHtml = 'Like what you see? Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n"," </div>\n"," "]},"metadata":{},"execution_count":31}],"source":["submission_cols = pd.read_csv(f'{dir_path}/DrivenDataCompetition_DataFiles/SubmissionFormat.csv')\n","cols_list = list(submission_cols.columns.values)\n","cols_list = cols_list[1:] ## remove the first column which is the index\n","submission = pd.DataFrame(test_predictions, columns=cols_list)\n","submission.set_index(test_df['Unnamed: 0'], inplace=True)\n","submission.index.name = \"\"\n","submission"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"ep-ruNCfyMKH","executionInfo":{"status":"ok","timestamp":1670513308615,"user_tz":300,"elapsed":7178,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":["submission.to_csv('12072022_tfidf_logreg_Cval_2.csv')"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"XwaSy1i13nax","executionInfo":{"status":"ok","timestamp":1670513308616,"user_tz":300,"elapsed":5,"user":{"displayName":"Danny Mathieson","userId":"14123752102169145219"}}},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[{"file_id":"1dLdfXkr3V01-zlCKmWbNew2UXHqO9Vwo","timestamp":1668433872897}],"machine_shape":"hm"},"gpuClass":"premium","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
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