282 lines
8.1 KiB
Plaintext
282 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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"import sklearn\n",
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"from sklearn import metrics, preprocessing\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"import pickle\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create dataframes from our csv files and set indeces\n",
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"\n",
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"df = pd.read_csv('data/train.csv')\n",
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"df.set_index('PassengerId', inplace=True)\n",
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"\n",
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"testdf = pd.read_csv('data/test.csv')\n",
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"PassengerId = testdf['PassengerId']\n",
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"testdf.set_index('PassengerId', inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Preprocess the data by converting non numerical features into numerical categorical features \n",
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"# and applying mean imputation to deal with NaN values\n",
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"\n",
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"le = preprocessing.LabelEncoder()\n",
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"df[\"Sex\"] = le.fit_transform(list(df[\"Sex\"]))\n",
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"df[\"Cabin\"] = le.fit_transform(list(df[\"Cabin\"]))\n",
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"df[\"Embarked\"] = le.fit_transform(list(df[\"Embarked\"]))\n",
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"df.fillna(df.mean(), inplace=True)\n",
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"\n",
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"testdf[\"Sex\"] = le.fit_transform(list(testdf[\"Sex\"]))\n",
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"testdf[\"Cabin\"] = le.fit_transform(list(testdf[\"Cabin\"]))\n",
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"testdf[\"Embarked\"] = le.fit_transform(list(testdf[\"Embarked\"]))\n",
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"testdf.fillna(testdf.mean(), inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_titles = df.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
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"type(train_titles)\n",
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"train_titles.value_counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Extract titles from the names and create a feature vector\n",
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"\n",
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"data = [df, testdf]\n",
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"titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
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"\n",
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"for dataset in data:\n",
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" # extract titles\n",
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" dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
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" # replace titles with a more common title or as Rare\n",
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" dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n",
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" 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
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" dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
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" dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
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" dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
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" # filling NaN with 0, to get safe\n",
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" dataset['Title'] = dataset['Title'].fillna(\"NA\")\n",
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"df = df.drop(['Name'], axis=1)\n",
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"testdf = testdf.drop(['Name'], axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df[\"Title\"] = le.fit_transform(list(df[\"Title\"]))\n",
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"testdf[\"Title\"] = le.fit_transform(list(testdf[\"Title\"]))\n",
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"print(df.head)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create our input matrix, label vector, and test input matrix\n",
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"\n",
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"X = df.drop(['Survived', 'Ticket'], axis=1)\n",
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"y = df['Survived']\n",
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"X_test = testdf.drop(['Ticket'], axis=1)\n",
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"print(X.head)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"TODO\n",
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"REPLACE THE LETTERS AND NUMBERS OF A DECK WITH JUST DECK LETTER\n",
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"DEAL WITH NANS\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"# X['Cabin'] = X.Cabin.fillna('')\n",
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"# print(X['Cabin'])\n",
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"# print(X['Cabin'].shape)\n",
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"# temp = X['Cabin']\n",
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"# for i, row in X.iterrows():\n",
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"# value = row['Cabin']\n",
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"# decks = {\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"U\"}\n",
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"# for deck in decks:\n",
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"# if deck in value:\n",
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"# X['Cabin'][i] = deck\n",
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" \n",
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"# print(X['Cabin'])\n",
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"\n",
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"\"\"\"\n",
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"TODO\n",
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"Clean up and fill in nans with mode\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X=(X-X.mean())/X.std()\n",
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"X_test=(X_test-X_test.mean())/X_test.std()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a classifier using logistic regression, opting for liblinear solver\n",
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"# because of how small our dataset is\n",
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"\n",
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"clfRAW = LogisticRegression(solver='liblinear', max_iter = 1000).fit(X, y)\n",
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"clfRAW.score(X,y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create our predictions matrix and save to csv\n",
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"predictions = np.c_[PassengerId, clfRAW.predict(X_test)]\n",
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"submission = pd.DataFrame(predictions, columns = ['PassengerId', 'Survived'])\n",
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"submission['PassengerId'] = PassengerId\n",
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"submission['Survived'] = clfRAW.predict(X_test)\n",
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"submission.to_csv(\"submissions/LogisticSubmissionRAW.csv\", index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Split our labeled data into train and dev sets\n",
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"\n",
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"X_train, X_dev, y_train, y_dev = sklearn.model_selection.train_test_split(\n",
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" X,y,test_size=0.2)\n",
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"\n",
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"# Setup range of values for tuning of C\n",
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"n=np.arange(-3,6)\n",
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"r=pow(float(10),n)\n",
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"\n",
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"# Tune C\n",
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"best = 0\n",
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"for C in r:\n",
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" clf = LogisticRegression(solver='liblinear', max_iter = 1000, C = C).fit(X_train, y_train)\n",
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" acc = clf.score(X_dev, y_dev)\n",
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" print(\"For C = \", C, \", acc = \", acc)\n",
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" if acc > best:\n",
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" best = acc\n",
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" with open('models/liblinearLogisticRegression.model','wb') as f:\n",
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" pickle.dump(clf,f)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load in our best performing model and check train/dev accuracy\n",
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"\n",
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"pickle_in = open('models/liblinearLogisticRegression.model','rb')\n",
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"clf = pickle.load(pickle_in)\n",
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"print(clf.score(X_train, y_train))\n",
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"print(clf.score(X_dev, y_dev))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create submission matrix and save to csv file\n",
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"\n",
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"predictions = np.c_[PassengerId, clf.predict(X_test)]\n",
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"submission = pd.DataFrame(predictions, columns = ['PassengerId', 'Survived'])\n",
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"submission['PassengerId'] = PassengerId\n",
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"submission['Survived'] = clf.predict(X_test)\n",
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"submission.to_csv(\"submissions/LogisticSubmission.csv\", index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Note that in submitting our normal submission (with a train/dev split and tuning of C) to kaggle, we perform worse (0.75) than our RAW submission with no tuning of C (0.77990). Likely as a result of how small the dataset is."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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