DuplicateDetectionFeatureEngineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh0ooGCE7Peg"
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_VEuwSD-t8v0"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import nltk\n",
    "import string\n",
    "import re\n",
    "import gensim.downloader\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn import random_projection\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold, ShuffleSplit\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import preprocessing\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "!pip3 install fuzzywuzzy\n",
    "from fuzzywuzzy import fuzz\n",
    "import xgboost\n",
    "\n",
    "from collections import Counter\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "try:\n",
    "    from bayes_opt import BayesianOptimization\n",
    "except :\n",
    "     !pip3 install bayesian-optimization\n",
    "     from bayes_opt import BayesianOptimization\n",
    "\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('averaged_perceptron_tagger')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YcGT4rAfzJE-"
   },
   "source": [
    "# Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wiklkDUZ0cYv"
   },
   "source": [
    "Define the preprocessing function(s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wdHa1pm4zFjO"
   },
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "def benchmark_clean_text(text):\n",
    "    # Lower words\n",
    "    try:\n",
    "        cleaned = text.lower()\n",
    "    except AttributeError:\n",
    "        return  \"this is an empty question\".split()\n",
    "    cleaned = text\n",
    "    # Remove stopwords. Needed to be done before the apostrophes are removed\n",
    "    cleaned = ' '.join([w for w in cleaned.split(' ') if not w in stop_words])\n",
    "    \n",
    "    # Remove punctuation\n",
    "    cleaned = cleaned.translate(str.maketrans('', '', string.punctuation +'–—‘’“”…')) \n",
    "    \n",
    "    # Replace all the remaining numbers that did not match any of the above categories  \n",
    "    number_pattern = re.compile(r'(\\d{1,3},)?(\\d{3},)*\\d+(\\.\\d+)?')\n",
    "    cleaned = number_pattern.sub(r' ', cleaned)\n",
    "\n",
    "    # lemmatization\n",
    "    cleaned = \" \".join([lemmatizer.lemmatize(word) for word in cleaned.split()])\n",
    "    \n",
    "    cleaned = cleaned.split() #note the added .split() here. Returns a list of list of words\n",
    "    \n",
    "    if cleaned == [] or cleaned == ['nan'] or cleaned == ['null'] or cleaned == ['a']:\n",
    "        cleaned = \"this is an empty question\".split()\n",
    "        \n",
    "    return cleaned    \n",
    "\n",
    "def quora_clean_text(text):\n",
    "    text = str(text)\n",
    "    text = text.lower()\n",
    "\n",
    "    # Clean the text\n",
    "    text = re.sub(r\"[^A-Za-z0-9^,!.\\/'+-=]\", \" \", text)\n",
    "    text = re.sub(r\"what's\", \"what is \", text)\n",
    "    text = re.sub(r\"\\'s\", \" \", text)\n",
    "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
    "    text = re.sub(r\"can't\", \"cannot \", text)\n",
    "    text = re.sub(r\"n't\", \" not \", text)\n",
    "    text = re.sub(r\"i'm\", \"i am \", text)\n",
    "    text = re.sub(r\"\\'re\", \" are \", text)\n",
    "    text = re.sub(r\"\\'d\", \" would \", text)\n",
    "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
    "    text = re.sub(r\",\", \" \", text)\n",
    "    text = re.sub(r\"\\.\", \" \", text)\n",
    "    text = re.sub(r\"!\", \" ! \", text)\n",
    "    text = re.sub(r\"\\/\", \" \", text)\n",
    "    text = re.sub(r\"\\^\", \" ^ \", text)\n",
    "    text = re.sub(r\"\\+\", \" + \", text)\n",
    "    text = re.sub(r\"\\-\", \" - \", text)\n",
    "    text = re.sub(r\"\\=\", \" = \", text)\n",
    "    text = re.sub(r\"'\", \" \", text)\n",
    "    text = re.sub(r\"(\\d+)(k)\", r\"\\g<1>000\", text)\n",
    "    text = re.sub(r\":\", \" : \", text)\n",
    "    text = re.sub(r\" e g \", \" eg \", text)\n",
    "    text = re.sub(r\" b g \", \" bg \", text)\n",
    "    text = re.sub(r\" u s \", \" american \", text)\n",
    "    text = re.sub(r\"\\0s\", \"0\", text)\n",
    "    text = re.sub(r\" 9 11 \", \"911\", text)\n",
    "    text = re.sub(r\"e - mail\", \"email\", text)\n",
    "    text = re.sub(r\"j k\", \"jk\", text)\n",
    "    text = re.sub(r\"\\s{2,}\", \" \", text)\n",
    "\n",
    "    text = text.split()\n",
    "    \n",
    "    # Check for empty texts\n",
    "    if text == [] or text == ['nan'] or text == ['null'] or text == ['a']:\n",
    "        text = \"this is an empty question\".split()\n",
    "        \n",
    "    return text\n",
    "\n",
    "def preprocess(text):\n",
    "    return benchmark_clean_text(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ztqLcwic0fl0"
   },
   "source": [
    "Load, prepocess and store the data in a proper form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Zj2dNh3VtLJA"
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")#.dropna()\n",
    "test  = pd.read_csv(\"test_without_labels.csv\")#.dropna()\n",
    "\n",
    "# We need the test NaN values for ordering when submitting\n",
    "#train_df = pd.read_csv(\"../storage/datasets/q2b/train.csv\")\n",
    "#test_df  = pd.read_csv(\"../storage/datasets/q2b/test_without_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JB0jeQ_wvfd7"
   },
   "outputs": [],
   "source": [
    "# Create the preprocessed text in memory\n",
    "train_q1 = [preprocess(t) for t in train['Question1']]\n",
    "train_q2 = [preprocess(t) for t in train['Question2']]\n",
    "train_labels = np.array(train['IsDuplicate'])\n",
    "\n",
    "test_q1 = [preprocess(t) for t in test['Question1']]\n",
    "\n",
    "test_q2 = [preprocess(t) for t in test['Question2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MhCdUoZYvfd8"
   },
   "outputs": [],
   "source": [
    "# Create the preprocessed train texts and save it in disk (care, we save a string insted of a list of words)\n",
    "cleaned_train_df = train_df.copy(deep=True)\n",
    "\n",
    "cleaned_train_q1 = cleaned_train_df.Question1.apply(lambda text: \" \".join(preprocess(text)))\n",
    "cleaned_train_df = cleaned_train_df.assign(Q1=cleaned_train_q1)\n",
    "\n",
    "cleaned_train_q2 = cleaned_train_df.Question2.apply(lambda text: \" \".join(preprocess(text)))\n",
    "cleaned_train_df = cleaned_train_df.assign(Q2=cleaned_train_q2)\n",
    "                                                       \n",
    "cleaned_train_df.drop(columns=['Question1', 'Question2'], inplace=True)\n",
    "#cleaned_train_df.to_csv('../storage/datasets/q2b/preprocessed/train_benchmark_clean.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_tcJ_Uojvfd9"
   },
   "outputs": [],
   "source": [
    "# Create the preprocessed test texts and save it in disk (care, we save a string insted of a list of words)\n",
    "cleaned_test_df = test_df.copy(deep=True)\n",
    "\n",
    "cleaned_test_q1 = cleaned_test_df.Question1.apply(lambda text: \" \".join(preprocess(text)))\n",
    "cleaned_test_df = cleaned_test_df.assign(Q1=cleaned_test_q1)\n",
    "\n",
    "cleaned_test_q2 = cleaned_test_df.Question2.apply(lambda text: \" \".join(preprocess(text)))\n",
    "cleaned_test_df = cleaned_test_df.assign(Q2=cleaned_test_q2)\n",
    "                                                       \n",
    "cleaned_test_df.drop(columns=['Question1', 'Question2'], inplace=True)\n",
    "cleaned_test_df.to_csv('../storage/datasets/q2b/preprocessed/test_benchmark_clean.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vmXsr5F-6ESu"
   },
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xZjg7CcavfeB"
   },
   "outputs": [],
   "source": [
    "# Reading the preprocessed train and test sets\n",
    "clean_train_df = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "clean_test_df = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m5MNP5bBvfeE"
   },
   "source": [
    "### Feature Class\n",
    "\n",
    "For easier organisation and creation / storing of features we create a class which offers some basic functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SyyGYgzVvfeE"
   },
   "outputs": [],
   "source": [
    "class TextFeature:\n",
    "    def __init__(self, train_arr, test_arr, name):\n",
    "        self.train_arr = train_arr\n",
    "        self.test_arr = test_arr\n",
    "        self.name = name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZPpgWtUX6ODm"
   },
   "source": [
    "## Word2Vec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fykgF960jD8"
   },
   "source": [
    "\n",
    "Create the word2vec model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Gnrgsb-vtv8y"
   },
   "outputs": [],
   "source": [
    "# This is a 1.6GB download for the first time\n",
    "quora_w2v = gensim.downloader.load('word2vec-google-news-300')\n",
    "google_news_emb_dim = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mSy6_FB4vfeH"
   },
   "outputs": [],
   "source": [
    "# This part creates the vocabulary that can be both used in the feature extraction part and as an embedding layer in a NN\n",
    "vocabulary = dict()\n",
    "inverse_vocabulary = ['<unk>']\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def text_to_vec(df, w2v, vocabulary, inverse_vocabulary):\n",
    "    numb_represantations = []\n",
    "    for index, row in df.iterrows():\n",
    "        questions = []\n",
    "        for question in ['Q1', 'Q2']:\n",
    "            q2n = []\n",
    "#             print(row.loc[question])\n",
    "            for word in row.loc[question].split():\n",
    "#                 print(row.loc[question].split())\n",
    "                # Stopwords have not yet been removed since they might be included in the pretrained word2vec\n",
    "                if word in stop_words and word not in w2v.vocab:\n",
    "                    continue\n",
    "                    \n",
    "                if word not in vocabulary:\n",
    "                    vocabulary[word] = len(inverse_vocabulary)\n",
    "                    q2n.append(len(inverse_vocabulary))\n",
    "                    inverse_vocabulary.append(word)\n",
    "                else:\n",
    "                    q2n.append(vocabulary[word])\n",
    "            questions.append(q2n)\n",
    "        numb_represantations.append(questions)\n",
    "    \n",
    "    return numb_represantations, vocabulary, inverse_vocabulary\n",
    "\n",
    "numb_represantation_train, vocabulary, inverse_vocabulary = text_to_vec(clean_train_df, quora_w2v, vocabulary, inverse_vocabulary)\n",
    "numb_represantation_test, vocabulary, inverse_vocabulary = text_to_vec(clean_test_df, quora_w2v, vocabulary, inverse_vocabulary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eMzjYLsdvfeI"
   },
   "source": [
    "**Care:** In the method below we create and save the **aggregated** (mean) word embeddings for each sentence. This means that we create a numpy array of dimensions: `numb_of_texts, 2, embedding_dim`.\n",
    "\n",
    "Saving the embeddings individually is not possible due to memory constraints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ct-uK8vzvfeJ"
   },
   "outputs": [],
   "source": [
    "# From integer represantation to embedding represantation\n",
    "def int_to_embed(w2v, dims, numb_repr, inverse_vocabulary):\n",
    "    embeddings = []\n",
    "    for numb in numb_repr:\n",
    "        word = inverse_vocabulary[numb]\n",
    "        if word in w2v.vocab:\n",
    "                embeddings.append(w2v.word_vec(word))\n",
    "        else:\n",
    "            embeddings.append(np.random.normal(0, 1, dims))\n",
    "    \n",
    "    return embeddings\n",
    "\n",
    "def create_avg_embedding_matrix(numb_represantation_of_texts, w2v, dims, inverse_vocabulary):\n",
    "    ret_embedding_mat = []\n",
    "    for questions in numb_represantation_of_texts:\n",
    "        both_question_embeddings = []\n",
    "        for q in questions:\n",
    "            q_embeddings = np.mean(np.array(int_to_embed(w2v, dims, q, inverse_vocabulary)), axis=0)\n",
    "            both_question_embeddings.append(q_embeddings)\n",
    "            \n",
    "        ret_embedding_mat.append(both_question_embeddings)\n",
    "    return ret_embedding_mat\n",
    "                \n",
    "\n",
    "train_embedding_mat = create_avg_embedding_matrix(numb_represantation_train, quora_w2v, google_news_emb_dim, inverse_vocabulary)\n",
    "test_embedding_mat = create_avg_embedding_matrix(numb_represantation_test, quora_w2v, google_news_emb_dim, inverse_vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "IcW0qHcCvfeK"
   },
   "outputs": [],
   "source": [
    "# Transforming to numpy arrays and saving in disk\n",
    "train_embedding_arr = np.array(train_embedding_mat)\n",
    "np.save('../storage/datasets/q2b/word_embeddings/train_embedding_avg.npy', train_embedding_arr)\n",
    "\n",
    "test_embedding_arr = np.array(test_embedding_mat)\n",
    "np.save('../storage/datasets/q2b/word_embeddings/test_embedding_avg.npy', test_embedding_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Pp8RitQfvfeN"
   },
   "source": [
    "## Cosine similarity of averaged word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OXMoL1LHvfeO"
   },
   "outputs": [],
   "source": [
    "# We first load the averaged embeddings from disk\n",
    "train_embeddings = np.load('../storage/datasets/q2b/word_embeddings/train_embedding_avg.npy')\n",
    "test_embeddings = np.load('../storage/datasets/q2b/word_embeddings/test_embedding_avg.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ekJut3u3vfeP"
   },
   "outputs": [],
   "source": [
    "# We then calculate the cosine similarities of the two questions of each row\n",
    "def matrix_cosine(x, y):\n",
    "    return np.einsum('ij,ij->i', x, y) / (\n",
    "              np.linalg.norm(x, axis=1) * np.linalg.norm(y, axis=1)\n",
    "    )\n",
    "\n",
    "train_cosine_similarities = matrix_cosine(train_embeddings[:, 0, :], train_embeddings[:, 1, :])\n",
    "test_cosine_similarities = matrix_cosine(test_embeddings[:, 0, :], test_embeddings[:, 1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ivkYqLIuvfeR"
   },
   "outputs": [],
   "source": [
    "# We store them in memory in a `TextFeature` object\n",
    "avg_embeddings_cos_similarities = TextFeature(train_cosine_similarities, test_cosine_similarities, \"AvgEmbeddingsCosine\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SVXTWneuvfeT"
   },
   "source": [
    "## BoW cosine similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZudXzEaZvfeU"
   },
   "source": [
    "We first create a bag of words represantation using the the tf-idf vectorizer on all the questions. Then we calculate their cosine similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aUryQca23ZP4"
   },
   "outputs": [],
   "source": [
    "df_train_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/train_benchmark_clean.csv')\n",
    "df_test_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/test_benchmark_clean.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-IeU9X_bvfeV"
   },
   "outputs": [],
   "source": [
    "concatenated_train_questions = pd.concat([df_train_cleaned.Q1, df_train_cleaned.Q2])\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "\n",
    "# Fit on all the quetions in the train set\n",
    "tfidf_vectorizer.fit(concatenated_train_questions)\n",
    "\n",
    "# Trnaform the train questions separately\n",
    "q1_train_tfidf = tfidf_vectorizer.transform(df_train_cleaned.Q1)\n",
    "q2_train_tfidf = tfidf_vectorizer.transform(df_train_cleaned.Q2)\n",
    "\n",
    "# Trnaform the test questions separately\n",
    "q1_test_tfidf = tfidf_vectorizer.transform(df_test_cleaned.Q1)\n",
    "q2_test_tfidf = tfidf_vectorizer.transform(df_test_cleaned.Q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3vVb4TDuvfeW",
    "outputId": "7945fe86-3571-4de1-e64c-f3e46b1a11ab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-65-37ebad811058>:7: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return (t1 @ t2) / (np.linalg.norm(t1) * np.linalg.norm(t2))\n"
     ]
    }
   ],
   "source": [
    "# Calculate the cosine similarities\n",
    "from scipy.spatial import distance\n",
    "\n",
    "def cosine_of_vectors(t1, t2):\n",
    "    t1 = t1.toarray()[0]\n",
    "    t2 = t2.toarray()[0]\n",
    "    return (t1 @ t2) / (np.linalg.norm(t1) * np.linalg.norm(t2))\n",
    "\n",
    "train_bow_similarities = np.array([cosine_of_vectors(t1, t2) for t1, t2 in zip(q1_train_tfidf, q2_train_tfidf)])\n",
    "test_bow_similarities = np.array([cosine_of_vectors(t1, t2) for t1, t2 in zip(q1_test_tfidf, q2_test_tfidf)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Wdo4HcRFvfeX"
   },
   "outputs": [],
   "source": [
    "# We store them in memory in a `TextFeature` object\n",
    "tfidf_cos_sim = TextFeature(np.nan_to_num(train_bow_similarities), np.nan_to_num(test_bow_similarities), \"TfIdfCosSimilarity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uJcbaDJ-vfeX"
   },
   "source": [
    "## Edit distance (Levenshtein)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xL74iS-AvfeX"
   },
   "outputs": [],
   "source": [
    "df_train_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/train_benchmark_clean.csv')\n",
    "df_test_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/test_benchmark_clean.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SKA9t1JuvfeZ"
   },
   "outputs": [],
   "source": [
    "train_edit_ratio = np.array([fuzz.ratio(row.Q1, row.Q2) for ind, row in df_train_cleaned.iterrows()])\n",
    "test_edit_ratio = np.array([fuzz.ratio(row.Q1, row.Q2) for ind, row in df_test_cleaned.iterrows()])\n",
    "\n",
    "train_edit_partial_ratio = np.array([fuzz.partial_ratio(row.Q1, row.Q2) for ind, row in df_train_cleaned.iterrows()])\n",
    "test_edit_partial_ratio = np.array([fuzz.partial_ratio(row.Q1, row.Q2) for ind, row in df_test_cleaned.iterrows()])\n",
    "\n",
    "train_edit_token_sort_ratio = np.array([fuzz.token_sort_ratio(row.Q1, row.Q2) for ind, row in df_train_cleaned.iterrows()])\n",
    "test_edit_token_sort_ratio = np.array([fuzz.token_sort_ratio(row.Q1, row.Q2) for ind, row in df_test_cleaned.iterrows()])\n",
    "\n",
    "train_edit_token_set_ratio = np.array([fuzz.token_set_ratio(row.Q1, row.Q2) for ind, row in df_train_cleaned.iterrows()])\n",
    "test_edit_token_set_ratio = np.array([fuzz.token_set_ratio(row.Q1, row.Q2) for ind, row in df_test_cleaned.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b4Q2LoGavfeZ"
   },
   "outputs": [],
   "source": [
    "# We store them in memory in `TextFeature` objects\n",
    "edit_ratio = TextFeature(train_edit_ratio, test_edit_ratio, \"edit_ratio\")\n",
    "edit_partial_ratio = TextFeature(train_edit_partial_ratio, test_edit_partial_ratio, \"edit_partial_ratio\")\n",
    "edit_token_sort_ratio = TextFeature(train_edit_token_sort_ratio, test_edit_token_sort_ratio, \"edit_token_sort_ratio\")\n",
    "edit_token_set_ratio = TextFeature(train_edit_token_set_ratio, test_edit_token_set_ratio, \"edit_token_set_ratio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1aR4uV2zvfeZ"
   },
   "source": [
    "## Text Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PND0jcE0vfea"
   },
   "outputs": [],
   "source": [
    "df_train_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/train_benchmark_clean.csv')\n",
    "df_test_cleaned = pd.read_csv('../storage/datasets/q2b/preprocessed/test_benchmark_clean.csv')\n",
    "\n",
    "df_train = pd.read_csv('../storage/datasets/q2b/train.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/test_without_labels.csv')\n",
    "\n",
    "df_train_quora = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test_quora = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B5iZowlSvfea"
   },
   "outputs": [],
   "source": [
    "train_lengths_q1_clean = np.array([len(row.Q1.split()) for ind, row in df_train_cleaned.iterrows()])\n",
    "train_lengths_q2_clean = np.array([len(row.Q2.split()) for ind, row in df_train_cleaned.iterrows()])\n",
    "test_lengths_q1_clean = np.array([len(row.Q1.split()) for ind, row in df_test_cleaned.iterrows()])\n",
    "test_lengths_q2_clean = np.array([len(row.Q2.split()) for ind, row in df_test_cleaned.iterrows()])\n",
    "\n",
    "train_lengths_q1_original = np.array([len(row.Question1.split()) for ind, row in df_train.iterrows()])\n",
    "train_lengths_q2_original = np.array([len(row.Question2.split()) if not isinstance(row.Question2, float) else 0 for ind, row in df_train.iterrows()])\n",
    "test_lengths_q1_original = np.array([len(row.Question1.split()) if not isinstance(row.Question1, float) else 0 for ind, row in df_test.iterrows()])\n",
    "test_lengths_q2_original = np.array([len(row.Question2.split()) if not isinstance(row.Question2, float) else 0 for ind, row in df_test.iterrows()])\n",
    "\n",
    "train_lengths_q1_quora = np.array([len(row.Q1.split()) for ind, row in df_train_quora.iterrows()])\n",
    "train_lengths_q2_quora = np.array([len(row.Q2.split()) for ind, row in df_train_quora.iterrows()])\n",
    "test_lengths_q1_quora = np.array([len(row.Q1.split()) for ind, row in df_test_quora.iterrows()])\n",
    "test_lengths_q2_quora = np.array([len(row.Q2.split()) for ind, row in df_test_quora.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qOuCZJhmvfeb"
   },
   "outputs": [],
   "source": [
    "# Store all of the above into feature objects\n",
    "length_q1_clean = TextFeature(train_lengths_q1_clean, test_lengths_q1_clean, \"length_q1_clean\")\n",
    "length_q2_clean = TextFeature(train_lengths_q2_clean, test_lengths_q2_clean, \"length_q2_clean\")\n",
    "length_q1_original = TextFeature(train_lengths_q1_original, test_lengths_q1_original, \"length_q1_original\")\n",
    "length_q2_original = TextFeature(train_lengths_q2_original, test_lengths_q2_original, \"length_q2_original\")\n",
    "length_q1_quora = TextFeature(train_lengths_q1_quora, test_lengths_q1_quora, \"length_q1_quora\")\n",
    "length_q2_quora = TextFeature(train_lengths_q2_quora, test_lengths_q2_quora, \"length_q2_quora\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uXPlNR38Tvk2"
   },
   "source": [
    "## Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "myN2d5tuTzHH"
   },
   "outputs": [],
   "source": [
    "clean_train_df = pd.read_csv('train_quora_clean.csv')\n",
    "clean_test_df = pd.read_csv('test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q_hSYk_gUMSZ"
   },
   "outputs": [],
   "source": [
    "vecs = [[w if w[0].isupper() else \"\" for w in q[1:]] for q in train_q1]\n",
    "train_q1_names = []\n",
    "train_q1_num_of_names = []\n",
    "for q in vecs:\n",
    "    aver = np.zeros(300)\n",
    "    words = 0\n",
    "    for w in q:\n",
    "        if w==\"\":\n",
    "            continue\n",
    "        try:\n",
    "            aver += np.array(quora_w2v.word_vec(w))\n",
    "            words+=1\n",
    "        except:\n",
    "            pass\n",
    "    aver /= max(words,1)\n",
    "    train_q1_names.append(aver)\n",
    "    train_q1_num_of_names.append(words)\n",
    "\n",
    "vecs = [[w if w[0].isupper() else \"\" for w in q[1:]] for q in train_q2]\n",
    "train_q2_names = []\n",
    "train_q2_num_of_names = []\n",
    "for q in vecs:\n",
    "    aver = np.zeros(300)\n",
    "    words = 0\n",
    "    for w in q:\n",
    "        if w==\"\":\n",
    "            continue\n",
    "        try:\n",
    "            aver += np.array(quora_w2v.word_vec(w))\n",
    "            words+=1\n",
    "        except:\n",
    "            pass\n",
    "    aver /= max(words,1)\n",
    "    train_q2_names.append(aver)\n",
    "    train_q2_num_of_names.append(words)\n",
    "\n",
    "train_names_cosine = []\n",
    "for i in range(len(train_q1_names)):\n",
    "    names1 = train_q1_names[i]\n",
    "    names2 = train_q2_names[i]\n",
    "    train_names_cosine.append(cosine_of_vectors(names1,names2))\n",
    "\n",
    "vecs = [[w if w[0].isupper() else \"\" for w in q[1:]] for q in test_q1]\n",
    "test_q1_names = []\n",
    "test_q1_num_of_names = []\n",
    "for q in vecs:\n",
    "    aver = np.zeros(300)\n",
    "    words = 0\n",
    "    for w in q:\n",
    "        if w==\"\":\n",
    "            continue\n",
    "        try:\n",
    "            aver += np.array(quora_w2v.word_vec(w))\n",
    "            words+=1\n",
    "        except:\n",
    "            pass\n",
    "    aver /= max(words,1)\n",
    "    test_q1_names.append(aver)\n",
    "    test_q1_num_of_names.append(words)\n",
    "\n",
    "vecs = [[w if w[0].isupper() else \"\" for w in q[1:]] for q in test_q2]\n",
    "test_q2_names = []\n",
    "test_q2_num_of_names = []\n",
    "for q in vecs:\n",
    "    aver = np.zeros(300)\n",
    "    words = 0\n",
    "    for w in q:\n",
    "        if w==\"\":\n",
    "            continue\n",
    "        try:\n",
    "            aver += np.array(quora_w2v.word_vec(w))\n",
    "            words+=1\n",
    "        except:\n",
    "            pass\n",
    "    aver /= max(words,1)\n",
    "    test_q2_names.append(aver)\n",
    "    test_q2_num_of_names.append(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KMe3Yg129LfJ"
   },
   "outputs": [],
   "source": [
    "def all_zeros(a):\n",
    "    for i in a:\n",
    "        if i!=0:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "train_names_cosine = []\n",
    "for i in range(len(train_q1_names)):\n",
    "    names1 = train_q1_names[i]\n",
    "    names2 = train_q2_names[i]\n",
    "    if all_zeros(names1) or all_zeros(names2):\n",
    "        cosine=0\n",
    "    else:\n",
    "        cosine = cosine_of_vectors(names1,names2)\n",
    "    train_names_cosine.append(cosine)\n",
    "\n",
    "test_names_cosine = []\n",
    "for i in range(len(test_q1_names)):\n",
    "    names1 = test_q1_names[i]\n",
    "    names2 = test_q2_names[i]\n",
    "    if all_zeros(names1) or all_zeros(names2):\n",
    "        cosine=0\n",
    "    else:\n",
    "        cosine = cosine_of_vectors(names1,names2)\n",
    "    test_names_cosine.append(cosine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "R6TmbmEk-WW5"
   },
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    c = train_names_cosine[i]\n",
    "    print(c, train_q1_num_of_names[i],train_q2_num_of_names[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I7lbxCAmV_41"
   },
   "outputs": [],
   "source": [
    "q1_num_names = TextFeature(train_q1_num_of_names,test_q1_num_of_names, \"q1_num_names\")\n",
    "q2_num_names = TextFeature(train_q2_num_of_names,test_q2_num_of_names, \"q2_num_names\")\n",
    "\n",
    "names_cosine = TextFeature(train_names_cosine,test_names_cosine, \"names_cosine\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wr7eAS4KeciJ"
   },
   "source": [
    "## Number of Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QSeyj4TceciJ"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6-XRr93NeciJ"
   },
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "def stopwords_in_str(txt):\n",
    "    tokens = txt.split()\n",
    "    return sum([1 if word in stop_words else 0 for word in tokens])\n",
    "\n",
    "train_stopwords_q1 = np.array([stopwords_in_str(q) for q in df_train.Q1])\n",
    "train_stopwords_q2 = np.array([stopwords_in_str(q) for q in df_train.Q2])\n",
    "\n",
    "test_stopwords_q1 = np.array([stopwords_in_str(q) for q in df_test.Q1])\n",
    "test_stopwords_q2 = np.array([stopwords_in_str(q) for q in df_test.Q2])\n",
    "\n",
    "train_stopwords_diff = np.abs(train_stopwords_q1 - train_stopwords_q2)\n",
    "test_stopwords_diff = np.abs(test_stopwords_q1 - test_stopwords_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Wi94VM70eciK"
   },
   "outputs": [],
   "source": [
    "stopwords_q1 = TextFeature(train_stopwords_q1, test_stopwords_q1, \"stopwords_q1\")\n",
    "stopwords_q2 = TextFeature(train_stopwords_q2, test_stopwords_q2, \"stopwords_q2\")\n",
    "stopwords_diff = TextFeature(train_stopwords_diff, test_stopwords_diff, \"stopwords_diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AyW3MLExeciK"
   },
   "source": [
    "## Number of Punctuation symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "crUENY7OeciL"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/train.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/test_without_labels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y4da7UjHeciM"
   },
   "outputs": [],
   "source": [
    "punctuations = set(string.punctuation + '–—‘’“”…')\n",
    "def numb_of_punct(txt):\n",
    "    try:\n",
    "        return sum([1 if char in punctuations else 0 for char in txt])\n",
    "    except TypeError:\n",
    "        return 0\n",
    "    \n",
    "train_puncts_q1 = np.array([numb_of_punct(q) for q in df_train.Question1])\n",
    "train_puncts_q2 = np.array([numb_of_punct(q) for q in df_train.Question2])\n",
    "\n",
    "test_puncts_q1 = np.array([numb_of_punct(q) for q in df_test.Question1])\n",
    "test_puncts_q2 = np.array([numb_of_punct(q) for q in df_test.Question2])\n",
    "\n",
    "train_puncts_diff = np.abs(train_puncts_q1 - train_puncts_q2)\n",
    "test_puncts_diff = np.abs(test_puncts_q1 - test_puncts_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0L-dstz4eciM"
   },
   "outputs": [],
   "source": [
    "puncts_q1 = TextFeature(train_puncts_q1, test_puncts_q1, \"punctuations_q1\")\n",
    "puncts_q2 = TextFeature(train_puncts_q2, test_puncts_q2, \"punctuations_q2\")\n",
    "puncts_diff = TextFeature(train_puncts_diff, test_puncts_diff, \"punctuations_diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8QP6tbfeciM"
   },
   "source": [
    "## Number of words not in Google News embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zmDpb_hxeciM"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')\n",
    "\n",
    "quora_w2v = gensim.downloader.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cjNmeL2KeciN"
   },
   "outputs": [],
   "source": [
    "def numb_of_words_not_in_embeddings(txt):\n",
    "    tokens = txt.split()\n",
    "    return sum([1 if word not in quora_w2v.vocab else 0 for word in tokens])\n",
    "\n",
    "train_not_in_w2v_q1 = np.array([numb_of_words_not_in_embeddings(q) for q in df_train.Q1])\n",
    "train_not_in_w2v_q2 = np.array([numb_of_words_not_in_embeddings(q) for q in df_train.Q2])\n",
    "\n",
    "test_not_in_w2v_q1 = np.array([numb_of_words_not_in_embeddings(q) for q in df_test.Q1])\n",
    "test_not_in_w2v_q2 = np.array([numb_of_words_not_in_embeddings(q) for q in df_test.Q2])\n",
    "\n",
    "train_not_in_w2v_diff = np.abs(train_not_in_w2v_q1 - train_not_in_w2v_q2)\n",
    "test_not_in_w2v_diff = np.abs(test_not_in_w2v_q1 - test_not_in_w2v_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z-ShUPc0eciN"
   },
   "outputs": [],
   "source": [
    "not_in_w2v_q1 = TextFeature(train_not_in_w2v_q1, test_not_in_w2v_q1, \"not_in_w2v_q1\")\n",
    "not_in_w2v_q2 = TextFeature(train_not_in_w2v_q2, test_not_in_w2v_q2, \"not_in_w2v_q2\")\n",
    "not_in_w2v_diff = TextFeature(train_not_in_w2v_diff, test_not_in_w2v_diff, \"not_in_w2v_diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n9rp443seciN"
   },
   "source": [
    "## Edit distance of words not in Google News embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jUrwuuDpeciN"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')\n",
    "\n",
    "quora_w2v = gensim.downloader.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jXh_L0PIeciO"
   },
   "outputs": [],
   "source": [
    "def dist_of_words_not_in_w2v(q1, q2, distance):\n",
    "    # Get which words are not in google news w2v, and remove any extra whitespace\n",
    "    not_words_q1 = re.sub(' +', ' ', \" \".join([word if word not in quora_w2v.vocab else \"\" for word in q1.split()]))\n",
    "    not_words_q2 = re.sub(' +', ' ', \" \".join([word if word not in quora_w2v.vocab else \"\" for word in q2.split()]))\n",
    "    \n",
    "    return distance(q1, q2)\n",
    "    \n",
    "train_not_in_w2v_ratio_dist = np.array([dist_of_words_not_in_w2v(row.Q1, row.Q2, fuzz.token_sort_ratio) for ind, row in df_train.iterrows()])\n",
    "test_not_in_w2v_ratio_dist = np.array([dist_of_words_not_in_w2v(row.Q1, row.Q2, fuzz.token_sort_ratio) for ind, row in df_test.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nW8I74tjeciP"
   },
   "outputs": [],
   "source": [
    "not_in_w2v_ratio = TextFeature(train_not_in_w2v_ratio_dist, test_not_in_w2v_ratio_dist, \"not_in_w2v_ratio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mWsjyNAAeciP"
   },
   "source": [
    "## Number of digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jJ_Td5yXeciP"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/train.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/test_without_labels.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CHyLUKBPeciP"
   },
   "outputs": [],
   "source": [
    "digits = set(range(10))\n",
    "def numb_of_digits(txt):\n",
    "    try:\n",
    "        return sum([1 if char in digits else 0 for char in txt])\n",
    "    except TypeError:\n",
    "        return 0\n",
    "    \n",
    "train_digits_q1 = np.array([numb_of_digits(q) for q in df_train.Question1])\n",
    "train_digits_q2 = np.array([numb_of_digits(q) for q in df_train.Question2])\n",
    "\n",
    "test_digits_q1 = np.array([numb_of_digits(q) for q in df_test.Question1])\n",
    "test_digits_q2 = np.array([numb_of_digits(q) for q in df_test.Question2])\n",
    "\n",
    "train_digits_diff = np.abs(train_digits_q1 - train_digits_q2)\n",
    "test_digits_diff = np.abs(test_digits_q1 - test_digits_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EIewynMPeciQ"
   },
   "outputs": [],
   "source": [
    "digits_q1 = TextFeature(train_digits_q1, test_digits_q1, \"digits_q1\")\n",
    "digits_q2 = TextFeature(train_digits_q2, test_digits_q2, \"digits_q2\")\n",
    "digits_diff = TextFeature(train_digits_diff, test_digits_diff, \"digits_diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UGlxBMdyeciR"
   },
   "source": [
    "## Number of nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HxTtGAcVeciR"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pv6DuS3FeciR"
   },
   "outputs": [],
   "source": [
    "def numb_of_nouns(txt):\n",
    "    tags = nltk.pos_tag(txt.split())\n",
    "    return sum([1 if tag=='NN' else 0 for word, tag in tags])\n",
    "\n",
    "train_nouns_q1 = np.array([numb_of_nouns(q) for q in df_train.Q1])\n",
    "train_nouns_q2 = np.array([numb_of_nouns(q) for q in df_train.Q2])\n",
    "\n",
    "test_nouns_q1 = np.array([numb_of_nouns(q) for q in df_test.Q1])\n",
    "test_nouns_q2 = np.array([numb_of_nouns(q) for q in df_test.Q2])\n",
    "\n",
    "train_nouns_diff = np.abs(train_nouns_q1 - train_nouns_q2)\n",
    "test_nouns_diff = np.abs(test_nouns_q1 - test_nouns_q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XMJ99ugceciS"
   },
   "outputs": [],
   "source": [
    "nouns_q1 = TextFeature(train_nouns_q1, test_nouns_q1, \"nouns_q1\")\n",
    "nouns_q2 = TextFeature(train_nouns_q2, test_nouns_q2, \"nouns_q2\")\n",
    "nouns_diff = TextFeature(train_nouns_diff, test_nouns_diff, \"nouns_diff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K90TK0AqeciS"
   },
   "source": [
    "## Edit distance of nouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0vIbi47WeciS"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tx8fV5hPeciT"
   },
   "outputs": [],
   "source": [
    "def dist_of_nouns(q1, q2, distance):\n",
    "    tags_q1 = nltk.pos_tag(q1.split())\n",
    "    tags_q2 = nltk.pos_tag(q2.split())\n",
    "\n",
    "    nouns_q1 = re.sub(' +', ' ', \" \".join([word if tag=='NN' else \"\" for word, tag in tags_q1]))\n",
    "    nouns_q2 = re.sub(' +', ' ', \" \".join([word if tag=='NN' else \"\" for word, tag in tags_q2]))\n",
    "    \n",
    "    return distance(q1, q2)\n",
    "\n",
    "train_nouns_ratio_dist = np.array([dist_of_nouns(row.Q1, row.Q2, fuzz.token_sort_ratio) for ind, row in df_train.iterrows()])\n",
    "test_nouns_ratio_dist = np.array([dist_of_nouns(row.Q1, row.Q2, fuzz.token_sort_ratio) for ind, row in df_test.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OjL_UkmveciU"
   },
   "outputs": [],
   "source": [
    "nouns_ratio = TextFeature(train_nouns_ratio_dist, test_nouns_ratio_dist, \"nouns_ratio\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dy4FhQ7IeciU"
   },
   "source": [
    "## Edit distance of question ending"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "srG_Vxh_eciU"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../storage/datasets/q2b/preprocessed/train_quora_clean.csv')\n",
    "df_test = pd.read_csv('../storage/datasets/q2b/preprocessed/test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "an41IiGSeciU"
   },
   "outputs": [],
   "source": [
    "def distance_of_last_part(q1, q2, divider, distance):\n",
    "    splitted_q1 = q1.split()\n",
    "    q1_divided = \" \".join(splitted_q1[(len(splitted_q1) // divider):])\n",
    "    \n",
    "    splitted_q2 = q2.split()\n",
    "    q2_divided = \" \".join(splitted_q2[(len(splitted_q2) // divider):])\n",
    "    \n",
    "    return distance(q1_divided, q2_divided)\n",
    "\n",
    "train_divided_2_sort_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.token_sort_ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_2_sort_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.token_sort_ratio) for ind, row in df_test.iterrows()])\n",
    "train_divided_4_sort_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.token_sort_ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_4_sort_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.token_sort_ratio) for ind, row in df_test.iterrows()])\n",
    "\n",
    "train_divided_2_simple_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_2_simple_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.ratio) for ind, row in df_test.iterrows()])\n",
    "train_divided_4_simple_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_4_simple_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.ratio) for ind, row in df_test.iterrows()])\n",
    "\n",
    "train_divided_2_partial_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.partial_ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_2_partial_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 2, fuzz.partial_ratio) for ind, row in df_test.iterrows()])\n",
    "train_divided_4_partial_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.partial_ratio) for ind, row in df_train.iterrows()])\n",
    "test_divided_4_partial_ratio_dist = np.array([distance_of_last_part(row.Q1, row.Q2, 4, fuzz.partial_ratio) for ind, row in df_test.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q4-dllhWeciU"
   },
   "outputs": [],
   "source": [
    "divided_2_sort_ratio_dist = TextFeature(train_divided_2_sort_ratio_dist, test_divided_2_sort_ratio_dist, \"divided_2_sort_ratio_dist\")\n",
    "divided_4_sort_ratio_dist = TextFeature(train_divided_4_sort_ratio_dist, test_divided_4_sort_ratio_dist, \"divided_4_sort_ratio_dist\")\n",
    "\n",
    "divided_2_simple_dist = TextFeature(train_divided_2_simple_ratio_dist, test_divided_2_simple_ratio_dist, \"divided_2_simple_dist\")\n",
    "divided_4_simple_dist = TextFeature(train_divided_4_simple_ratio_dist, test_divided_4_simple_ratio_dist, \"divided_4_simple_dist\")\n",
    "\n",
    "divided_2_partial_ratio_dist = TextFeature(train_divided_2_partial_ratio_dist, test_divided_2_partial_ratio_dist, \"divided_2_partial_ratio_dist\")\n",
    "divided_4_partial_ratio_dist = TextFeature(train_divided_4_partial_ratio_dist, test_divided_4_partial_ratio_dist, \"divided_4_partial_ratio_dist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pn6S_NVUolWj"
   },
   "source": [
    "## Edit Distance of Question Beginning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8vlLpVOhooZW"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train_quora_clean.csv')\n",
    "df_test = pd.read_csv('test_quora_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "l8HM-Xfso0lR"
   },
   "outputs": [],
   "source": [
    "def distance_of_first_part(q1, q2, distance):\n",
    "    splitted_q1 = q1.split()\n",
    "    q1_divided = \" \".join(splitted_q1[:4])\n",
    "    \n",
    "    splitted_q2 = q2.split()\n",
    "    q2_divided = \" \".join(splitted_q2[:4])\n",
    "\n",
    "    return distance(q1_divided, q2_divided)\n",
    "\n",
    "train_start_dist = np.array([distance_of_first_part(row.Q1, row.Q2, fuzz.ratio) for ind, row in df_train.iterrows()])\n",
    "test_start_dist = np.array([distance_of_first_part(row.Q1, row.Q2, fuzz.ratio) for ind, row in df_test.iterrows()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Hh3Xl_GZtUGF"
   },
   "outputs": [],
   "source": [
    "start_dist = TextFeature(train_start_dist, test_start_dist, \"start_distances\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tR8Meha79HJF"
   },
   "source": [
    "# Useful functions (CV,BO,...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "nPD44Dul7ZLQ"
   },
   "outputs": [],
   "source": [
    "def metrics_calculating(y_true, y_pred):\n",
    "    return Counter({\n",
    "        'accuracy': accuracy_score(y_true, y_pred),\n",
    "        'recall': recall_score(y_true, y_pred, average='macro'),\n",
    "        'precision': precision_score(y_true, y_pred, average='macro'),\n",
    "        'f1': f1_score(y_true, y_pred, average='macro')\n",
    "    })\n",
    "\n",
    "\n",
    "def pretty_print_grid_search(gs_obj):\n",
    "    print(f\"Best parameters found: {gs_obj.best_params_}, with a score of: {np.round(gs_obj.best_score_, decimals=3)}\")\n",
    "    \n",
    "    df_gs = pd.DataFrame.from_dict(gs_obj.cv_results_)\n",
    "    display(df_gs)\n",
    "    \n",
    "\n",
    "def run_cross_val(model, splits_numb, X, y, lab_encoder=None):\n",
    "    train_metrics = Counter()\n",
    "    validation_metrics = Counter()\n",
    "    conf_mats = []\n",
    "    pred_vals = None\n",
    "    \n",
    "    kf = KFold(n_splits=splits_numb, shuffle=False)\n",
    "    for train_index, val_index in tqdm(list(kf.split(X)), desc=\"Splits\"):\n",
    "        ### Fit on the input model ###\n",
    "        model.fit(X[train_index], y[train_index])\n",
    "        \n",
    "        ### Predict on the Train set (mainly for debugging) ###\n",
    "        y_pred_train = model.predict(X[train_index])\n",
    "        \n",
    "        ### Predict on the Validation set ###\n",
    "        y_pred_val = model.predict(X[val_index])\n",
    "\n",
    "        try:\n",
    "            proba_preds = model.decision_function(X[val_index])\n",
    "        except:\n",
    "            proba_preds = model.predict_proba(X[val_index])\n",
    "        try:\n",
    "            pred_vals = np.concatenate((pred_vals,proba_preds))\n",
    "        except:\n",
    "            pred_vals = proba_preds\n",
    "        \n",
    "        ### Metrics Bookkeeping ###\n",
    "        train_metrics += metrics_calculating(y[train_index], y_pred_train)\n",
    "        validation_metrics += metrics_calculating(y[val_index], y_pred_val)\n",
    "        \n",
    "        print(train_metrics)\n",
    "        print(validation_metrics)\n",
    "        \n",
    "        ### Confusion Plots Generation ###\n",
    "        if lab_encoder is not None:\n",
    "            y_labels_true = lab_encoder.inverse_transform(y[val_index])\n",
    "            y_labels_pred = lab_encoder.inverse_transform(y_pred_val)\n",
    "            \n",
    "            conf_mats.append(confusion_matrix(y_labels_true, y_labels_pred, labels=list(lab_encoder.classes_)))\n",
    "            \n",
    "\n",
    "    if lab_encoder is not None:\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=np.sum(np.array(conf_mats), axis=0), \n",
    "                                      display_labels=list(lab_encoder.classes_))\n",
    "        fig, ax=plt.subplots(1,1,figsize=(7,7))\n",
    "        disp.plot(ax=ax)\n",
    "        \n",
    "    train_avg_metrics = {k: v / splits_numb for k, v in train_metrics.items()}\n",
    "    validation_avg_metrics = {k: v / splits_numb for k, v in validation_metrics.items()}\n",
    "\n",
    "    try:\n",
    "        LOG['predictions'].append(pred_vals.copy())\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    #print(accuracy_score(pred_vals,y))\n",
    "\n",
    "    return train_avg_metrics, validation_avg_metrics\n",
    "\n",
    "\n",
    "def run_grid_search(X, y, model, params, folds_numb, scoring=\"accuracy\", verbose_res=True):\n",
    "    gs = GridSearchCV(model, params, cv=folds_numb, scoring=scoring,\n",
    "                  n_jobs=10, verbose=3)\n",
    "    \n",
    "    gs.fit(X, y)\n",
    "    \n",
    "    if verbose_res:\n",
    "        pretty_print_grid_search(gs)\n",
    "            \n",
    "    return gs\n",
    "\n",
    "def run_bayesian_optimization(params, obj_function,log=None,iters=8,inits=2):   #log needs to be a dictionary\n",
    "\n",
    "    if log!=None:\n",
    "        if 'predictions' not in log:\n",
    "            log['predictions'] = []\n",
    "\n",
    "    optimizer = BayesianOptimization(obj_function,params,verbose=2)#,random_state=42)\n",
    "\n",
    "    optimizer.maximize(n_iter = iters,init_points=inits) #higher values are recommended, but time.\n",
    "\n",
    "    if log!=None:\n",
    "        if 'hyperparameters' not in log:\n",
    "            log['hyperparameters'] = []\n",
    "        if 'scores' not in log:\n",
    "            log['scores'] = []\n",
    "        for r in optimizer.res:\n",
    "            log['hyperparameters'].append(r['params'])\n",
    "            log['scores'].append(r['target'])\n",
    "\n",
    "    return optimizer.max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gy2dD90l6z60"
   },
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V2CCPw-EpT8s"
   },
   "source": [
    "These need to be defined outside the Bayesian Optimization for scope reasons. Namely, 'stupid'. Try not to change their values anywhere as they are global variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MYBKOJzspRkM"
   },
   "outputs": [],
   "source": [
    "metric = \"accuracy\"\n",
    "folds_numb = 5\n",
    "#X = train_features\n",
    "#y = train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZXYybdwxvfeg"
   },
   "source": [
    "## Feature Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KDE9aus7vfeg"
   },
   "outputs": [],
   "source": [
    "# Before progressing we must first concatenate all of our feature objects into one DataFrame\n",
    "features_added = [\n",
    "    start_dist\n",
    "]\n",
    "\n",
    "# Create the DataFrames\n",
    "#train_features_df = pd.read_csv('../storage/datasets/q2b/train.csv', usecols=['IsDuplicate'])  # The train features will have as their last column the labels\n",
    "#test_features_df = pd.DataFrame()\n",
    "\n",
    "train_features_df = pd.read_csv('train_features.csv') \n",
    "test_features_df = pd.read_csv('test_features.csv')\n",
    "\n",
    "for feature in features_added: #features_used\n",
    "    train_features_df.insert(0, column=feature.name, value=feature.train_arr)\n",
    "    test_features_df.insert(0, column=feature.name, value=feature.test_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4-Br_EMqvfej"
   },
   "outputs": [],
   "source": [
    "# Save the files\n",
    "train_features_df.to_csv(f'train_features.csv', index=False)\n",
    "test_features_df.to_csv(f'test_features.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P5DF8GwwpUmW",
    "outputId": "b83c0602-40be-4875-87e4-9ca1bf7c8ae5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(283004, 40)\n"
     ]
    }
   ],
   "source": [
    "# Read the files\n",
    "train_features_df = pd.read_csv('train_features.csv')\n",
    "test_features_df = pd.read_csv('test_features.csv')\n",
    "\n",
    "print(train_features_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nj4j_o62695R"
   },
   "source": [
    "## Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rv45flqdoBNT"
   },
   "outputs": [],
   "source": [
    "def train_LSVC(C):\n",
    "    #Due to scope issues, the following parameters must be set outside this function:\n",
    "    #X, y, folds_numb, metric, method_name\n",
    "\n",
    "    model = LinearSVC(C=C,dual=False,fit_intercept=True,verbose=0)\n",
    "    train_avg_metrics, validation_avg_metrics = run_cross_val(model, folds_numb, X, y)\n",
    "\n",
    "    return validation_avg_metrics[metric]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sYiyEKlDGbDY"
   },
   "outputs": [],
   "source": [
    "params = {'C': (0.1,10)}\n",
    "\n",
    "best = run_bayesian_optimization(params,train_LSVC) #contains 'target' and 'params'\n",
    "\n",
    "C = best['params']['C']\n",
    "\n",
    "model = LinearSVC(C=C,dual=False,fit_intercept=True,verbose=0)\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wAKJZLsikZwE"
   },
   "source": [
    "# Predictions and Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Pf3rW5egkdu9"
   },
   "outputs": [],
   "source": [
    "train_preds = model.predict(train_features)\n",
    "\n",
    "print(metrics_calculating(train_labels, train_preds))\n",
    "\n",
    "test_preds = model.predict(test_features)\n",
    "\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e877bhGqvfet"
   },
   "source": [
    "## Xgboost - With tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "sHdDiX4Xecih"
   },
   "outputs": [],
   "source": [
    "hall_of_fame_cols = [\n",
    "    #'divided_4_partial_ratio_dist',\n",
    "    #'digits_diff',\n",
    "    'length_q1_quora',\n",
    "    'length_q2_quora',\n",
    "    'length_q1_original',\n",
    "    'length_q2_original',\n",
    "    'length_q1_clean',\n",
    "    'length_q2_clean',\n",
    "    'edit_token_sort_ratio',\n",
    "    'edit_token_set_ratio',\n",
    "    'edit_partial_ratio',\n",
    "    'edit_ratio',\n",
    "    'TfIdfCosSimilarity',\n",
    "    'AvgEmbeddingsCosine',\n",
    "    #'q1_num_names',\n",
    "    #'q2_num_names',\n",
    "    #'names_cosine',\n",
    "    #'stopwords_diff',\n",
    "    #'nouns_diff',\n",
    "    #'start_distances'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "YpydMClDvfet"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train_features.csv', usecols=hall_of_fame_cols + ['IsDuplicate'])\n",
    "df_test = pd.read_csv('test_features.csv', usecols=hall_of_fame_cols)\n",
    "\n",
    "X_train = np.array(df_train)[:, :-1]\n",
    "y_train = np.array(df_train)[:, -1]\n",
    "\n",
    "X_test = np.array(df_test)\n",
    "\n",
    "metric=\"accuracy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "Grjx40YPecih"
   },
   "outputs": [],
   "source": [
    "# Normalize the train and test sets\n",
    "normalizer = preprocessing.Normalizer().fit(X_train)\n",
    "X_train = normalizer.transform(X_train)\n",
    "X_test = normalizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BA1zYkkQkAZe",
    "outputId": "cd82f5b2-a237-4ef5-cd9e-0277f77ebe11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9377779299928577\n",
      "0.9378507492203685\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=9)\n",
    "pca.fit_transform(X_train)\n",
    "print(sum(pca.explained_variance_ratio_))\n",
    "\n",
    "pca.fit_transform(X_test)\n",
    "print(sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zgZ9Dh65vfev",
    "outputId": "1f14db44-10cd-4146-d946-138eeff90253"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, eval_metric='logloss',\n",
       "              gamma=0, learning_rate=0.01, max_delta_step=0, max_depth=10,\n",
       "              min_child_weight=1, missing=None, n_estimators=10000, n_jobs=1,\n",
       "              nthread=None, objective='binary:logistic', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "              silent=None, subsample=1, tree_method='gpu_hist',\n",
       "              use_label_encoder=False, verbosity=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgboost_clf = xgboost.XGBClassifier(max_depth=10, n_estimators=10000, objective='binary:logistic',\n",
    "                                    eval_metric='logloss', learning_rate=0.01,\n",
    "                                    use_label_encoder=False, tree_method='gpu_hist'\n",
    "                                    )\n",
    "xgboost_clf.fit(X_train, y_train)\n",
    "#run_cross_val(xgboost_clf, splits_numb=5, X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "LtaO3pwLzGKo"
   },
   "outputs": [],
   "source": [
    "def trainXGB(learning_rate,max_depth,n_estimators):\n",
    "    xgboost_clf = xgboost.XGBClassifier(learning_rate=learning_rate,max_depth=int(max_depth),n_estimators=int(n_estimators),objective='binary:logistic',tree_method='gpu_hist')\n",
    "\n",
    "    train_avg_metrics, validation_avg_metrics = run_cross_val(xgboost_clf, splits_numb=5, X=X_train, y=y_train)\n",
    "\n",
    "    return validation_avg_metrics[metric]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    },
    "id": "UbFin62-vfex",
    "outputId": "e4748870-9a73-4ed4-ada3-362fed139ed6"
   },
   "outputs": [],
   "source": [
    "param_dict = {#'objective':'binary:logistic', \n",
    "              #'use_label_encoder': False, \n",
    "              'learning_rate': (0.0001,0.1),\n",
    "              'max_depth': (5,15),\n",
    "              #'tree_method': 'gpu_hist',\n",
    "              'n_estimators': (100,1000)}\n",
    "\n",
    "#logs = {}\n",
    "LOG = logs\n",
    "best = run_bayesian_optimization(param_dict,obj_function=trainXGB,log=logs,iters=18,inits=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QZKLBye-dsmp"
   },
   "source": [
    "## Ensembler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XwepqSRYd4a0"
   },
   "source": [
    "We insert a manual callback in our implementation of bayesian optimization and cross-validation. Every time it is called, it records the hyperparameters it received along with the model's validation predictions. Then, our ensembler reads all of these predictions and finds the models whose predictions, when combined, maximize the validation performance.\n",
    "\n",
    "The class may seem daunting, but its interface is like any sklearn model's. Feel free to omit the long cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "-2zNJImwdvho"
   },
   "outputs": [],
   "source": [
    "class ensembler:\n",
    "    \n",
    "    def __init__(self,log,val_labels,max_members,method=\"greedy\"):\n",
    "        #log is a dictionary containing:\n",
    "        #'hyperparameters': a list of hyperparameter combinations.\n",
    "        #'predictions': a list of model predictions on the validation set.\n",
    "        #'scores': a list of accuracy scores on the validation set.\n",
    "\n",
    "        if method==\"greedy\":\n",
    "            self.greedyEnsembling(log,val_labels,max_members)\n",
    "        else:\n",
    "            self.bruteEnsembling(log,val_labels,max_members)\n",
    "\n",
    "    def greedyEnsembling(self,log,val_labels,max_members):\n",
    "        ens_indexes = []\n",
    "        ens_preds = []\n",
    "        self.models = []\n",
    "\n",
    "        #find the best model\n",
    "        best_index = log['scores'].index(max(log['scores']))\n",
    "        ens_indexes.append(best_index)\n",
    "        ens_preds.append(np.array(log['predictions'][best_index]))\n",
    "\n",
    "        #iteratively add the ensemble member from the pool (with repetition)\n",
    "        #that maximizes the validation performance. Continue until you reach max_members\n",
    "        #or until no member is suitable.\n",
    "        valAcc = log['scores'][best_index]\n",
    "        for i in range(1,max_members):\n",
    "            best_p = -1\n",
    "            for p in range(len(log['predictions'])):\n",
    "                possAcc = self.checkAccuracy(ens_preds,log['predictions'][p],val_labels)\n",
    "                if possAcc>valAcc:\n",
    "                    best_p = p\n",
    "                    valAcc = possAcc\n",
    "            if best_p>-1:\n",
    "                ens_preds.append(log['predictions'][best_p])\n",
    "                ens_indexes.append(best_p)\n",
    "            else:\n",
    "                break\n",
    "\n",
    "        for i in ens_indexes:\n",
    "            self.models.append(self.genericModelBuilder(logs['hyperparameters'][i]))\n",
    "        self.valAcc = valAcc\n",
    "\n",
    "    def bruteEnsembling(self,log,val_labels,num_of_members):\n",
    "        #this is painfully slow\n",
    "\n",
    "        ens_indexes = []\n",
    "        ens_preds = []\n",
    "        self.models = []\n",
    "\n",
    "        #find the best model\n",
    "        all_indexes = range(len(log['predictions']))\n",
    "        maxAcc = -1\n",
    "        bestC = None\n",
    "        for c in itertools.combinations(all_indexes,num_of_members):\n",
    "            temp_preds = []\n",
    "            for j in range(len(log['predictions'][0])):\n",
    "                this_res = np.zeros(4)\n",
    "                for i in c:\n",
    "                    this_res+=log['predictions'][i][j]\n",
    "                temp_preds.append(list(this_res).index(max(this_res)))\n",
    "            acc = accuracy_score(temp_preds,val_labels)\n",
    "            if acc>maxAcc:\n",
    "                maxAcc = acc\n",
    "                bestC = c\n",
    "\n",
    "        self.valAcc = maxAcc\n",
    "        for i in bestC:\n",
    "            self.models.append(self.genericModelBuilder(log['hyperparameters'][i]))\n",
    "\n",
    "    def elections(self, preds):\n",
    "        res = []\n",
    "        for j in range(len(preds[0])):\n",
    "            this_res = np.zeros(len(preds[0][1]))\n",
    "            for p in preds:\n",
    "                this_res+=p[j]\n",
    "            res.append(list(this_res).index(max(this_res)))\n",
    "        return res\n",
    "\n",
    "    def checkAccuracy(self, preds,new_preds,labels):\n",
    "        temp_preds = np.concatenate((preds,[new_preds]))\n",
    "        total_preds = self.elections(temp_preds)\n",
    "        return accuracy_score(labels,total_preds)\n",
    "\n",
    "    def genericModelBuilder(self,params):\n",
    "        model = None\n",
    "        if 'C' in params:\n",
    "            model = LinearSVC(C=params['C'],tol=params['tol'],max_iter=int(params['max_iter']),dual=True,fit_intercept=True,verbose=0)\n",
    "        elif 'alpha' in params:\n",
    "            model = SGDClassifier(tol=params['tol'], loss='hinge', max_iter=int(params['max_iter']), alpha=params['alpha'], penalty='elasticnet',early_stopping=True,l1_ratio=params['l1_ratio'])\n",
    "\n",
    "        else:\n",
    "            model = xgboost.XGBClassifier(learning_rate=params['learning_rate'],max_depth=int(params['max_depth']),n_estimators=int(params['n_estimators']),objective='binary:logistic',tree_method='gpu_hist')    \n",
    "        return model\n",
    "\n",
    "    def fit(self,X,y):\n",
    "        for m in self.models:\n",
    "            m.fit(X,y)\n",
    "\n",
    "    def predict(self,X):\n",
    "        preds = []\n",
    "        for m in self.models:\n",
    "            try:\n",
    "                preds.append(m.decision_function(X))\n",
    "            except:\n",
    "                preds.append(m.predict_proba(X))\n",
    "        return self.elections(preds)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "id": "QLstPzZcd-EH"
   },
   "source": [
    "While the class may seem daunting, using it is as simple as can be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "80e5Nv82d-cw"
   },
   "outputs": [],
   "source": [
    "ensemble = ensembler(logs_2,y_train,9,method=\"greedy\")\n",
    "\n",
    "print(ensemble.valAcc)\n",
    "print(len(ensemble.models))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "AkhDfVWN789A"
   },
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   "source": [
    "ensemble.fit(X_train, y_train)\n",
    "#preds = ensemble.predict(X_test)\n",
    "model = ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ID4x7lZ6vfe1"
   },
   "source": [
    "## Create Output File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "hebHDfdpvfe2"
   },
   "outputs": [],
   "source": [
    "# Set the object model and the path that the output will be created\n",
    "file_path = 'final_hope.csv'\n",
    "\n",
    "# Get the Ids from the test file\n",
    "test_ids_df = pd.read_csv('test_without_labels.csv', usecols=['Id'])\n",
    "\n",
    "y_predicted = model.predict(np.array(X_test))\n",
    "y_predicted = np.array([int(x) for x in y_predicted])\n",
    "\n",
    "results = {\n",
    "    \"Id\": list(test_ids_df.Id),\n",
    "    \"Predicted\": y_predicted\n",
    "}\n",
    "    \n",
    "results_df = pd.DataFrame.from_dict(results)\n",
    "\n",
    "results_df.to_csv(file_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HoSjYznzCrkl"
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   "source": [
    "# Goodbye"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dnVGETtPCmoz"
   },
   "source": [
    "*~ That's all, folks! ~*"
   ]
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