run_quora.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "0it [00:00, ?it/s]\n",
      "TqdmDeprecationWarning: Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm(...))`.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division\n",
    "from collections import Counter\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "from sklearn.metrics import log_loss\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import math\n",
    "import nltk\n",
    "from nltk import ngrams\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from string import punctuation\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from simhash import Simhash\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from matplotlib import pyplot\n",
    "from sklearn.manifold import TSNE\n",
    "import pickle\n",
    "from tqdm import tqdm, tqdm_pandas\n",
    "tqdm_pandas(tqdm())\n",
    "import spacy\n",
    "import random\n",
    "nlp = spacy.load('en')\n",
    "import h2o\n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator\n",
    "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n",
    "from pyjarowinkler import distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "question_types = [\"what\", \"how\", \"why\", \"is\", \"which\", \"can\", \"i\", \"who\", \"do\", \"where\", \"if\", \"does\", \"are\", \"when\", \"should\", \"will\", \"did\", \"has\", \"would\", \"have\", \"was\", \"could\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def submit(p_test):\n",
    "\n",
    "    sub = pd.DataFrame()\n",
    "\n",
    "    sub['test_id'] = df_test['test_id']\n",
    "    sub['is_duplicate'] = p_test\n",
    "\n",
    "    sub.to_csv('simple_xgb.csv', index=False)   \n",
    "\n",
    "def get_inverse_freq(inverse_freq, count, min_count=2):\n",
    "\n",
    "    if count < min_count:   \n",
    "        return 0\n",
    "    else:\n",
    "        return inverse_freq\n",
    "\n",
    "def get_tf(text):\n",
    "\n",
    "    tf = {}\n",
    "\n",
    "    for word in text:\n",
    "        tf[word] = text.count(word)/len(text)\n",
    "\n",
    "    return tf\n",
    "\n",
    "def tuple_similarity(q1_words, q2_words):\n",
    "\n",
    "    if len(q1_words) == 0 or len(q2_words) == 0:\n",
    "        return 0\n",
    "\n",
    "    common_words = len(set(q1_words).intersection(set(q2_words)))\n",
    "    all_words = len(set(q1_words).union(set(q2_words)))\n",
    "\n",
    "    return common_words/all_words\n",
    "\n",
    "def get_ne_score(row):\n",
    "\n",
    "    q1_words = str(row.question1).lower().split()\n",
    "    q2_words = str(row.question2).lower().split()\n",
    "\n",
    "    # all_words_score = np.sum([weights.get(w, 0) for w in q1_words]) + np.sum([weights.get(w, 0) for w in q2_words])\n",
    "\n",
    "    q1 = nlp(unicode(str(row[\"question1\"]), \"utf-8\"))\n",
    "    q2 = nlp(unicode(str(row[\"question2\"]), \"utf-8\"))\n",
    "\n",
    "    q1_ne = q1.ents\n",
    "    q2_ne = q2.ents\n",
    "\n",
    "    q1_ne = set([str(i) for i in q1_ne])\n",
    "    q2_ne = set([str(i) for i in q2_ne])\n",
    "\n",
    "    if len(q1_ne) == 0:\n",
    "        q1_ne_ratio = 0\n",
    "    else:\n",
    "        q1_ne_ratio = len(q1_ne)/len(q1_words)\n",
    "\n",
    "    if len(q2_ne) == 0:\n",
    "        q2_ne_ratio = 0\n",
    "    else:\n",
    "        q2_ne_ratio = len(q2_ne)/len(q2_words)\n",
    "\n",
    "    common_ne = list(q1_ne.intersection(q2_ne))\n",
    "    # common_ne_weights = np.sum([weights.get(w, 0) for w in common_ne])\n",
    "\n",
    "    if len(q1_ne) + len(q2_ne) == 0:\n",
    "        common_ne_score = 0\n",
    "    else:\n",
    "       common_ne_score = len(common_ne)/(len(q1_words) + len(q2_words) - len(common_ne))\n",
    "\n",
    "    return pd.Series({\n",
    "        \"q1_ne_ratio\": q1_ne_ratio,\n",
    "        \"q2_ne_ratio\": q2_ne_ratio,\n",
    "        \"ne_diff\": abs(q1_ne_ratio - q2_ne_ratio),\n",
    "        \"ne_score\": common_ne_score\n",
    "    })\n",
    "\n",
    "def get_weighted_edge_score(row):\n",
    "    \n",
    "    q1_words = row[\"question1\"].lower().split()\n",
    "    q2_words = row[\"question2\"].lower().split()\n",
    "\n",
    "    #modify this!\n",
    "    if len(q1_words) == 0 or len(q2_words) == 0:\n",
    "        return 0\n",
    "\n",
    "    common_words = list(set(q1_words).intersection(q2_words))\n",
    "    \n",
    "    common_words_score = np.sum([weights.get(w, 0) for w in common_words])\n",
    "    all_words_score = np.sum([weights.get(w, 0) for w in q1_words]) + np.sum([weights.get(w, 0) for w in q2_words]) - common_words_score\n",
    "\n",
    "    return common_words_score/all_words_score\n",
    "\n",
    "def generate_edge_scores(row):\n",
    "    \n",
    "    node1 = row[\"qid1\"]\n",
    "    node2 = row[\"qid2\"]\n",
    "    \n",
    "    if node1 not in edge_weights:\n",
    "        edge_weights[node1] = {}\n",
    "    \n",
    "    if node2 not in edge_weights:\n",
    "        edge_weights[node2] = {}\n",
    "\n",
    "    edge_weight = get_weighted_edge_score(row)\n",
    "\n",
    "    edge_weights[node1][node2] = edge_weight\n",
    "    edge_weights[node2][node1] = edge_weight\n",
    "\n",
    "def pagerank_neighbor_features(row):\n",
    "\n",
    "    q1_neighbors = graph[row[\"question1\"]]\n",
    "    q2_neighbors = graph[row[\"question2\"]]\n",
    "    \n",
    "    common_neighbors = set(q1_neighbors).intersection(q2_neighbors)\n",
    "            \n",
    "    common_neighbors_pr = sum([pr[j] for j in common_neighbors])\n",
    "    q1_neighbors_pr = sum([pr[j] for j in q1_neighbors])\n",
    "    q2_neighbors_pr = sum([pr[j] for j in q2_neighbors])\n",
    "\n",
    "    if q1_neighbors_pr + q2_neighbors_pr - common_neighbors_pr == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return common_neighbors_pr/(q1_neighbors_pr + q2_neighbors_pr - common_neighbors_pr)\n",
    "    \n",
    "    \n",
    "def weighted_neighbor_features(row):\n",
    "\n",
    "    q1_neighbors = qid_graph[row[\"qid1\"]]\n",
    "    q2_neighbors = qid_graph[row[\"qid2\"]]\n",
    "\n",
    "    common_neighbors = set(q1_neighbors).intersection(q2_neighbors)\n",
    "            \n",
    "    common_neighbors_weight = sum([edge_weights[row[\"qid1\"]][j] for j in common_neighbors])\n",
    "    q1_weight = sum([edge_weights[row[\"qid1\"]][j] for j in q1_neighbors])\n",
    "    q2_weight = sum([edge_weights[row[\"qid2\"]][j] for j in q2_neighbors])\n",
    "    \n",
    "    edge_weight = edge_weights[row[\"qid1\"]][row[\"qid2\"]]\n",
    "    \n",
    "    if q1_weight + q2_weight - common_neighbors_weight == 0:\n",
    "        return pd.Series({\n",
    "            \"edge_weight\": edge_weight,\n",
    "            \"edge_weight_ratio\": 0,\n",
    "            \"weighted_neighbor_ratio\": 0\n",
    "        })\n",
    "\n",
    "    edge_weight_ratio = edge_weight/(q1_weight + q2_weight - common_neighbors_weight)\n",
    "    weighted_neighbor_ratio = common_neighbors_weight/(q1_weight + q2_weight - common_neighbors_weight)\n",
    "    \n",
    "    \n",
    "    return pd.Series({\n",
    "        \"edge_weight\": edge_weight,\n",
    "        \"edge_weight_ratio\": edge_weight_ratio,\n",
    "        \"weighted_neighbor_ratio\": weighted_neighbor_ratio\n",
    "    })    \n",
    "\n",
    "def pagerank():\n",
    "        \n",
    "    pr = {i:1/len(graph) for i in graph}\n",
    "    \n",
    "    for iter in range(0, 20):\n",
    "        print iter\n",
    "        for node in graph:    \n",
    "            local_pr = 0\n",
    "            \n",
    "            for neighbor in graph[node]:\n",
    "                local_pr += pr[neighbor]/len(graph[neighbor])\n",
    "            \n",
    "            pr[node] = 0.15/len(pr) + 0.85*local_pr\n",
    "\n",
    "    return pr\n",
    "    \n",
    "def get_pagerank_value(row):\n",
    "\n",
    "    return pd.Series({\n",
    "        \"q1_pr\": pr[row[\"question1\"]],\n",
    "        \"q2_pr\": pr[row[\"question2\"]]\n",
    "    })\n",
    "\n",
    "def basic_nlp(row):\n",
    "\n",
    "    # q1_tf = get_tf(q1_words)\n",
    "    # q2_tf = get_tf(q2_words)\n",
    "    \n",
    "    q1_words = str(row.question1).lower().split()\n",
    "    q2_words = str(row.question2).lower().split()\n",
    "\n",
    "    #modify this!\n",
    "    if len(q1_words) == 0 or len(q2_words) == 0:\n",
    "        return 0\n",
    "\n",
    "    common_words = list(set(q1_words).intersection(q2_words))\n",
    "    \n",
    "    common_words_score = np.sum([weights.get(w, 0) for w in common_words])\n",
    "    all_words_score = np.sum([weights.get(w, 0) for w in q1_words]) + np.sum([weights.get(w, 0) for w in q2_words]) - common_words_score\n",
    "\n",
    "    hamming_score = sum(1 for i in zip(q1_words, q2_words) if i[0]==i[1])/max(len(q1_words), len(q2_words))\n",
    "\n",
    "    jacard_score =  len(common_words)/(len(q1_words) + len(q2_words) - len(common_words))  \n",
    "    cosine_score = len(common_words)/(pow(len(q1_words),0.5)*pow(len(q2_words),0.5))\n",
    "\n",
    "    bigrams_q1 = set(ngrams(q1_words, 2))\n",
    "    bigrams_q2 = set(ngrams(q2_words, 2))\n",
    "    common_bigrams = len(bigrams_q1.intersection(bigrams_q2))\n",
    "    if common_bigrams == 0:\n",
    "        bigram_score = 0\n",
    "    else:\n",
    "        bigram_score = common_bigrams/(len(bigrams_q1.union(bigrams_q2)))    \n",
    "\n",
    "    trigrams_q1 = set(ngrams(q1_words, 3))\n",
    "    trigrams_q2 = set(ngrams(q2_words, 3))\n",
    "    common_trigrams = len(trigrams_q1.intersection(trigrams_q2))\n",
    "    if common_trigrams == 0:\n",
    "        trigram_score = 0\n",
    "    else:\n",
    "        trigram_score = common_trigrams/(len(trigrams_q1.union(trigrams_q2)))    \n",
    "\n",
    "    # sequence1 = get_word_bigrams(q1_words)\n",
    "    # sequence2 = get_word_bigrams(q2_words)\n",
    "\n",
    "    # try:\n",
    "    #     simhash_diff = Simhash(sequence1).distance(Simhash(sequence2))/64\n",
    "    # except:\n",
    "    #     simhash_diff = 0.5\n",
    "\n",
    "    q1 = nlp(unicode(str(row[\"question1\"]), \"utf-8\"))\n",
    "    q2 = nlp(unicode(str(row[\"question2\"]), \"utf-8\"))\n",
    "\n",
    "    q1_ne = q1.ents\n",
    "    q2_ne = q2.ents\n",
    "\n",
    "    q1_ne = set([str(i) for i in q1_ne])\n",
    "    q2_ne = set([str(i) for i in q2_ne])\n",
    "\n",
    "    if len(q1_ne) == 0:\n",
    "        q1_ne_ratio = 0\n",
    "    else:\n",
    "        q1_ne_ratio = len(q1_ne)/len(q1_words)\n",
    "\n",
    "    if len(q2_ne) == 0:\n",
    "        q2_ne_ratio = 0\n",
    "    else:\n",
    "        q2_ne_ratio = len(q2_ne)/len(q2_words)\n",
    "\n",
    "    common_ne = list(q1_ne.intersection(q2_ne))\n",
    "    # common_ne_weights = np.sum([weights.get(w, 0) for w in common_ne])\n",
    "\n",
    "    if len(q1_ne) + len(q2_ne) == 0:\n",
    "        common_ne_score = 0\n",
    "    else:\n",
    "        common_ne_score = len(common_ne)/(len(q1_words) + len(q2_words) - len(common_ne))\n",
    "\n",
    "    pos_hash = {}\n",
    "    common_pos = []\n",
    "\n",
    "    for word in q1:\n",
    "        if word.tag_ not in pos_hash:\n",
    "            pos_hash.update({word.tag_ : [word.text]})\n",
    "        else:\n",
    "            pos_hash[word.tag_].append(word.text)\n",
    "\n",
    "    for word in q2:\n",
    "        if word.tag_ not in pos_hash:\n",
    "            continue\n",
    "        if word.text in pos_hash[word.tag_]:\n",
    "            common_pos.append(word.text)\n",
    "\n",
    "    common_pos_score = np.sum([weights.get(w, 0) for w in common_pos])\n",
    "    all_pos_score = np.sum([weights.get(w, 0) for w in q1_words]) + np.sum([weights.get(w, 0) for w in q2_words]) - common_pos_score\n",
    "\n",
    "    q1_pronouns_count = 0\n",
    "    q2_pronouns_count = 0\n",
    "\n",
    "    for word in q1:\n",
    "        if str(word.tag_) == \"PRP\":\n",
    "            q1_pronouns_count += 1\n",
    "\n",
    "    for word in q2:\n",
    "        if str(word.tag_) == \"PRP\":\n",
    "            q2_pronouns_count += 1\n",
    "\n",
    "    pronouns_diff = abs(q1_pronouns_count - q2_pronouns_count)\n",
    "\n",
    "    q1_nc = q1.noun_chunks\n",
    "    q2_nc = q2.noun_chunks\n",
    "\n",
    "    q1_nc = set([str(i) for i in q1_nc])\n",
    "    q2_nc = set([str(i) for i in q2_nc])\n",
    "\n",
    "    common_nc = len(q1_nc.intersection(q2_nc))\n",
    "\n",
    "    if len(q1_nc) + len(q2_nc) == 0:\n",
    "        common_nc_score = 0\n",
    "    else:\n",
    "        common_nc_score = common_nc/(len(q1_nc) + len(q2_nc) - common_nc)\n",
    "\n",
    "    fw_q1 = q1_words[0]\n",
    "    fw_q2 = q2_words[0]\n",
    "\n",
    "    if fw_q1 == fw_q2 and fw_q1 in question_types:\n",
    "        question_type_same = 1\n",
    "    else:\n",
    "        question_type_same = 0\n",
    "\n",
    "    try:\n",
    "        q1_quotes = len(re.findall(r'\\\"(.+?)\\\"', row[\"question1\"]))\n",
    "    except:\n",
    "        q1_quotes = 0\n",
    "\n",
    "    try:\n",
    "        q2_quotes = len(re.findall(r'\\\"(.+?)\\\"', row[\"question2\"]))\n",
    "    except:\n",
    "        q2_quotes = 0\n",
    "\n",
    "    # if len(q1_ne) == 0:\n",
    "    #     q1_ne_hash_freq = 1\n",
    "    # else:\n",
    "    #     hash_key1 = hash(\"-\".join(set([str(i).lower() for i in q1_ne])))\n",
    "\n",
    "    #     if hash_key1 not in hash_table_ne:\n",
    "    #         q1_ne_hash_freq = 1\n",
    "    #     else:\n",
    "    #         q1_ne_hash_freq = hash_table_ne[hash_key1]\n",
    "\n",
    "    # if len(q2_ne) == 0:\n",
    "    #     q2_ne_hash_freq = 1\n",
    "    # else:\n",
    "    #     hash_key2 = hash(\"-\".join(set([str(i).lower() for i in q2_ne])))\n",
    "\n",
    "    #     if hash_key2 not in hash_table_ne:\n",
    "    #         q2_ne_hash_freq = 1\n",
    "    #     else:\n",
    "    #         q2_ne_hash_freq = hash_table_ne[hash_key2]\n",
    "\n",
    "    try:\n",
    "        q1_sents = len(nltk.tokenize.sent_tokenize(row.question1))\n",
    "    except:\n",
    "        q1_sents = 1\n",
    "    try:\n",
    "        q2_sents = len(nltk.tokenize.sent_tokenize(row.question2))\n",
    "    except:\n",
    "        q2_sents = 1\n",
    "\n",
    "    q1_exclaim = sum([1 for i in str(row.question1) if i == \"!\"])\n",
    "    q2_exclaim = sum([1 for i in str(row.question2) if i == \"!\"])\n",
    "\n",
    "    q1_question = sum([1 for i in str(row.question1) if i == \"?\"])\n",
    "    q2_question = sum([1 for i in str(row.question2) if i == \"?\"])\n",
    "\n",
    "    hash_key1 = hash(str(row[\"question1\"]).lower())\n",
    "    if hash_key1 in hash_table:\n",
    "        q1_hash_freq = hash_table[hash_key1]\n",
    "    else:\n",
    "        q1_hash_freq = 1\n",
    "\n",
    "    hash_key2 = hash(str(row[\"question2\"]).lower())\n",
    "    if hash_key2 in hash_table:\n",
    "        q2_hash_freq = hash_table[hash_key2]\n",
    "    else:\n",
    "        q2_hash_freq = 1\n",
    "\n",
    "    # if hash_key1 in pos_hash_table:\n",
    "    #     q1_dup_ratio = pos_hash_table[hash_key1]/q1_hash_freq\n",
    "    # else:\n",
    "\n",
    "    #     q1_dup_ratio = 0\n",
    "\n",
    "    # if hash_key2 in pos_hash_table:\n",
    "    #     q2_dup_ratio = pos_hash_table[hash_key2]/q2_hash_freq\n",
    "    # else:\n",
    "    #     q2_dup_ratio = 0\n",
    "\n",
    "    spacy_sim = q1.similarity(q2)\n",
    "\n",
    "    return pd.Series({\n",
    "\n",
    "        \"weighted_word_match_ratio\" : common_words_score/all_words_score,\n",
    "        \"weighted_word_match_diff\": all_words_score - common_words_score, \n",
    "        \"weighted_word_match_sum\": common_words_score,\n",
    "        \"jacard_score\": jacard_score,\n",
    "        \"hamming_score\": hamming_score,\n",
    "        \"cosine_score\": cosine_score,\n",
    "        \"bigram_score\": bigram_score,\n",
    "        \"trigram_score\": trigram_score,\n",
    "        \"pos_score\": common_pos_score/all_pos_score,\n",
    "        # \"simhash_diff\": simhash_diff,\n",
    "        \"question_type_same\": question_type_same,\n",
    "        \"q1_stops\": len(set(q1_words).intersection(stops))/len(q1_words),\n",
    "        \"q2_stops\": len(set(q2_words).intersection(stops))/len(q2_words),\n",
    "        \"q1_len\": len(str(row.question1)),\n",
    "        \"q2_len\": len(str(row.question2)),\n",
    "        \"len_diff\": abs(len(str(row.question1)) - len(str(row.question2))),\n",
    "        \"len_avg\": (len(str(row.question1)) + len(str(row.question2)))/2,\n",
    "        \"q1_sents\": q1_sents,\n",
    "        \"q2_sents\": q2_sents,\n",
    "        \"sents_diff\": abs(q1_sents - q2_sents),\n",
    "        \"q1_words\": len(q1_words),\n",
    "        \"q2_words\": len(q2_words),\n",
    "        \"words_diff\": abs(len(q1_words) - len(q2_words)),\n",
    "        \"words_avg\": (len(q1_words) + len(q2_words))/2,\n",
    "        \"q1_caps_count\": sum([1 for i in str(row.question1) if i.isupper()]),\n",
    "        \"q2_caps_count\": sum([1 for i in str(row.question2) if i.isupper()]),\n",
    "        \"q1_exclaim\": q1_exclaim,\n",
    "        \"q2_exclaim\": q2_exclaim,\n",
    "        \"exclaim_diff\": abs(q1_exclaim - q2_exclaim),\n",
    "        \"q1_question\": q1_question,\n",
    "        \"q2_question\": q2_question,\n",
    "        \"question_diff\": abs(q1_question - q2_question),\n",
    "        \"ne_score\": common_ne_score,\n",
    "        \"nc_score\": common_nc_score,\n",
    "        \"q1_ne_ratio\": q1_ne_ratio,\n",
    "        \"q2_ne_ratio\": q2_ne_ratio,\n",
    "        \"ne_diff\": abs(q1_ne_ratio - q2_ne_ratio),\n",
    "        \"q1_quotes\": q1_quotes,\n",
    "        \"q2_quotes\": q2_quotes,\n",
    "        \"quotes_diff\": abs(q1_quotes - q2_quotes),\n",
    "        # \"q1_ne_hash_freq\": q1_ne_hash_freq,\n",
    "        # \"q2_ne_hash_freq\": q2_ne_hash_freq,\n",
    "        # \"chunk_hash_diff\": abs(q1_ne_hash_freq - q2_ne_hash_freq),\n",
    "        \"q1_hash_freq\": q1_hash_freq,\n",
    "        \"q2_hash_freq\": q2_hash_freq,\n",
    "        \"q_freq_avg\": (q1_hash_freq + q2_hash_freq)/2,\n",
    "        \"freq_diff\": abs(q1_hash_freq - q2_hash_freq),\n",
    "        \"spacy_sim\": spacy_sim,\n",
    "        \"q1_pronouns_count\": q1_pronouns_count,\n",
    "        \"q2_pronouns_count\": q2_pronouns_count,\n",
    "        \"pronouns_diff\": pronouns_diff\n",
    "        # \"q1_dup_ratio\": q1_dup_ratio,\n",
    "        # \"q2_dup_ratio\": q2_dup_ratio,\n",
    "        # \"q1_q2_dup_ratio_avg\": (q1_dup_ratio + q2_dup_ratio)/2\n",
    "    })\n",
    "\n",
    "def neighbor_intersection(row):\n",
    "\n",
    "    q1_neighbors = graph[row[\"question1\"]]\n",
    "    q2_neighbors = graph[row[\"question2\"]]\n",
    "\n",
    "    common_neighbors = set(q1_neighbors).intersection(q2_neighbors)\n",
    "    \n",
    "    return len(common_neighbors)    \n",
    "\n",
    "def neighbor_features(row):\n",
    "\n",
    "    q1_neighbors = graph[row[\"question1\"]]\n",
    "    q2_neighbors = graph[row[\"question2\"]]\n",
    "\n",
    "    common_neighbors = set(q1_neighbors).intersection(q2_neighbors)\n",
    "    \n",
    "    neighbor_ratio = len(common_neighbors)/(len(q1_neighbors) + len(q2_neighbors) - len(common_neighbors))\n",
    "    neighbor_dissimilar = len(q1_neighbors) + len(q2_neighbors) - len(common_neighbors)\n",
    "    \n",
    "    return pd.Series({\n",
    "        \"neighbor_ratio\": neighbor_ratio,\n",
    "        \"neighbor_dissimilar\": neighbor_dissimilar\n",
    "    })    \n",
    "\n",
    "def get_q1_second_degree_freq(row):\n",
    "\n",
    "    q1_neighbors = graph[row[\"question1\"]]\n",
    "\n",
    "    q1_second_degree_neighbors = []\n",
    "    for i in q1_neighbors:\n",
    "        q1_second_degree_neighbors += graph[i]\n",
    "\n",
    "    return len(set(q1_second_degree_neighbors))\n",
    "\n",
    "def get_q2_second_degree_freq(row):\n",
    "\n",
    "    q2_neighbors = graph[row[\"question2\"]]\n",
    "\n",
    "    q2_second_degree_neighbors = []\n",
    "    for i in q2_neighbors:\n",
    "        q2_second_degree_neighbors += graph[i]\n",
    "\n",
    "    return len(set(q2_second_degree_neighbors))\n",
    "\n",
    "\n",
    "def second_degree_intersection(row):\n",
    "\n",
    "    q1_neighbors = graph[row[\"question1\"]]\n",
    "    q2_neighbors = graph[row[\"question2\"]]\n",
    "\n",
    "    q1_second_degree_neighbors = []\n",
    "    for i in q1_neighbors:\n",
    "        q1_second_degree_neighbors += graph[i]\n",
    "\n",
    "    q2_second_degree_neighbors = []\n",
    "    for i in q2_neighbors:\n",
    "        q2_second_degree_neighbors += graph[i]\n",
    "    \n",
    "    common_second_degree_neighbors = set(q1_second_degree_neighbors).intersection(set(q2_second_degree_neighbors))\n",
    "\n",
    "    return len(common_second_degree_neighbors)\n",
    "\n",
    "# def pos_neighbor_intersection(row):\n",
    "\n",
    "#     if row[\"question1\"] in pos_graph and row[\"question2\"] in pos_graph:\n",
    "#         q1_neighbors = pos_graph[row[\"question1\"]]\n",
    "#         q2_neighbors = pos_graph[row[\"question2\"]]\n",
    "\n",
    "#         common_neighbors = set(q1_neighbors).intersection(q2_neighbors)\n",
    "\n",
    "#         return len(common_neighbors)/(len(q1_neighbors) + len(q2_neighbors) - len(common_neighbors))\n",
    "\n",
    "#     else:\n",
    "#         return 0\n",
    "\n",
    "def get_word_bigrams(words):\n",
    "\n",
    "    ngrams = []\n",
    "\n",
    "    for i in range(0, len(words)):\n",
    "        if i > 0:\n",
    "            ngrams.append(\"%s %s\"%(words[i-1], words[i]))\n",
    "\n",
    "    return ngrams\n",
    "\n",
    "def generate_hash_freq(row):\n",
    "\n",
    "    hash_key1 = hash(row[\"question1\"].lower())\n",
    "    hash_key2 = hash(row[\"question2\"].lower())\n",
    "\n",
    "    if hash_key1 not in hash_table:\n",
    "        hash_table[hash_key1] = 1\n",
    "    else:\n",
    "        hash_table[hash_key1] += 1\n",
    "\n",
    "    if hash_key2 not in hash_table:\n",
    "        hash_table[hash_key2] = 1\n",
    "    else:\n",
    "        hash_table[hash_key2] += 1\n",
    "\n",
    "def generate_duplicate_freq(row):\n",
    "\n",
    "    hash_key1 = hash(row[\"question1\"].lower())\n",
    "    hash_key2 = hash(row[\"question2\"].lower())\n",
    "\n",
    "    if hash_key1 not in pos_hash_table and row[\"is_duplicate\"] == 1:\n",
    "        pos_hash_table[hash_key1] = 1\n",
    "    elif hash_key1 not in pos_hash_table and row[\"is_duplicate\"] == 0:\n",
    "        pos_hash_table[hash_key1] = 0\n",
    "    elif hash_key1 in pos_hash_table and row[\"is_duplicate\"] == 1:\n",
    "        pos_hash_table[hash_key1] += 1\n",
    "    # elif hash_key1 in pos_hash_table and row[\"is_duplicate\"] == 0:\n",
    "    #     pass\n",
    "\n",
    "    if hash_key2 not in pos_hash_table and row[\"is_duplicate\"] == 1:\n",
    "        pos_hash_table[hash_key2] = 1\n",
    "    elif hash_key2 not in pos_hash_table and row[\"is_duplicate\"] == 0:\n",
    "        pos_hash_table[hash_key2] = 0\n",
    "    elif hash_key2 in pos_hash_table and row[\"is_duplicate\"] == 1:\n",
    "        pos_hash_table[hash_key2] += 1\n",
    "    # elif hash_key1 in pos_hash_table and row[\"is_duplicate\"] == 0:\n",
    "    #     pass\n",
    "\n",
    "def generate_positive_graph(row):\n",
    "\n",
    "    hash_key1 = row[\"question1\"]\n",
    "    hash_key2 = row[\"question2\"]\n",
    "        \n",
    "    if row[\"is_duplicate\"] == 1:\n",
    "        if hash_key1 not in pos_graph:\n",
    "            pos_graph[hash_key1] = [hash_key2]\n",
    "        elif hash_key1 in pos_graph:\n",
    "            pos_graph[hash_key1].append(hash_key2)\n",
    "\n",
    "        if hash_key2 not in pos_graph:\n",
    "            pos_graph[hash_key2] = [hash_key1]\n",
    "        elif hash_key2 in pos_graph:\n",
    "            pos_graph[hash_key2].append(hash_key1)\n",
    "\n",
    "def generate_graph_table(row):\n",
    "\n",
    "    hash_key1 = row[\"question1\"]\n",
    "    hash_key2 = row[\"question2\"]\n",
    "        \n",
    "    if hash_key1 not in graph:\n",
    "        graph[hash_key1] = [hash_key2]\n",
    "    elif hash_key1 in graph:\n",
    "        graph[hash_key1].append(hash_key2)\n",
    "\n",
    "    if hash_key2 not in graph:\n",
    "        graph[hash_key2] = [hash_key1]\n",
    "    elif hash_key2 in graph:\n",
    "        graph[hash_key2].append(hash_key1)\n",
    "\n",
    "def generate_qid_graph_table(row):\n",
    "\n",
    "    hash_key1 = row[\"qid1\"]\n",
    "    hash_key2 = row[\"qid2\"]\n",
    "        \n",
    "    if hash_key1 not in qid_graph:\n",
    "        qid_graph[hash_key1] = [hash_key2]\n",
    "    elif hash_key1 in qid_graph:\n",
    "        qid_graph[hash_key1].append(hash_key2)\n",
    "\n",
    "    if hash_key2 not in qid_graph:\n",
    "        qid_graph[hash_key2] = [hash_key1]\n",
    "    elif hash_key2 in qid_graph:\n",
    "        qid_graph[hash_key2].append(hash_key1)\n",
    "\n",
    "def augment_rows():\n",
    "\n",
    "    new_graph = graph\n",
    "\n",
    "    for q1 in graph:\n",
    "\n",
    "        q2_list = graph[q1]\n",
    "        \n",
    "        for i in q2_list:\n",
    "            for j in q2_list:\n",
    "                if i != j:                    \n",
    "                    if j not in graph[i]:\n",
    "                        new_graph[i].append(j)\n",
    "\n",
    "    # new_df_train = df_train[[\"question1\", \"question2\", \"is_duplicate\"]]\n",
    "\n",
    "    # for i in new_graph:\n",
    "\n",
    "def generate_ne_freq(row):\n",
    "\n",
    "    q1 = nlp(unicode(str(row[\"question1\"]), \"utf-8\"))\n",
    "    q2 = nlp(unicode(str(row[\"question2\"]), \"utf-8\"))\n",
    "\n",
    "    q1_ne = q1.ents\n",
    "    q2_ne = q2.ents\n",
    "\n",
    "    q1_ne = \"-\".join(set([str(i).lower() for i in q1_ne]))\n",
    "    q2_ne = \"-\".join(set([str(i).lower() for i in q2_ne]))\n",
    "    \n",
    "    hash_key1 = hash(q1_ne)\n",
    "    hash_key2 = hash(q2_ne)\n",
    "\n",
    "    if hash_key1 not in hash_table_ne:\n",
    "        hash_table_ne[hash_key1] = 1\n",
    "    else:\n",
    "        hash_table_ne[hash_key1] += 1\n",
    "\n",
    "    if hash_key2 not in hash_table_ne:\n",
    "        hash_table_ne[hash_key2] = 1\n",
    "    else:\n",
    "        hash_table_ne[hash_key2] += 1\n",
    "\n",
    "def oversample(x_train):\n",
    "\n",
    "    neg_train = x_train[x_train.is_duplicate == 0]\n",
    "    pos_train = x_train[x_train.is_duplicate == 1]\n",
    "\n",
    "    #Oversampling negative class\n",
    "#     p = 0.1742\n",
    "    p = 0.165\n",
    "\n",
    "#     scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1 #How much times greater is the train ratio than actual\n",
    "\n",
    "#     while scale > 1:\n",
    "#         neg_train = pd.concat([neg_train, neg_train])\n",
    "#         scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\n",
    "    \n",
    "#     scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\n",
    "    \n",
    "#     while ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1 > 1e-6:\n",
    "#         neg_train = pd.concat([neg_train, neg_train[:int(scale * len(neg_train))]])\n",
    "#         scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\n",
    "    \n",
    "    #Number of neg_trains that should be added -- (((len(pos_train)/p) - len(x_train))/len(neg_train) - 1)*len(neg_train)\n",
    "    neg_train = pd.concat([neg_train, neg_train])\n",
    "#     neg_train = pd.concat([neg_train, neg_train[:197531]]) #When p is 0.1742\n",
    "    neg_train = pd.concat([neg_train, neg_train[:245307]]) #When p is 0.165\n",
    "\n",
    "    \n",
    "    return pd.concat([pos_train, neg_train])\n",
    "\n",
    "#When plotted a histogram of degrees, only -1,1 and 2 are observed. Which means either you're max 2 degree separated or you're separate(with 5 as a cutoff). \n",
    "#Add (number of second degree connections) and its intersection as a feature\n",
    "def bfs(q_node, q_search, separation):\n",
    "\n",
    "    if separation > 5:\n",
    "        return -1\n",
    "\n",
    "    if len(graph[q_node]) > 0:\n",
    "        \n",
    "        shortest_res = 32768\n",
    "\n",
    "        if q_search in graph[q_node]:\n",
    "            return separation\n",
    "        else:\n",
    "\n",
    "            for i,j in enumerate(graph[q_node]):\n",
    "\n",
    "                if i > 5:\n",
    "                    return shortest_res\n",
    "\n",
    "                bfs_res = bfs(j, q_search, separation + 1)\n",
    "\n",
    "                if bfs_res != -1 and bfs_res < shortest_res:\n",
    "                    shortest_res = bfs_res\n",
    "\n",
    "            return shortest_res\n",
    "\n",
    "    else:\n",
    "        return -1\n",
    "\n",
    "def initialize_bfs(row):\n",
    "\n",
    "    q1 = row[\"question1\"]\n",
    "    q2 = row[\"question2\"]\n",
    "\n",
    "    shortest_res = 32768\n",
    "\n",
    "    for i in graph[q1]:\n",
    "        if i != q2:\n",
    "            res = bfs(i, q2, 1)\n",
    "\n",
    "            if res != -1 and res < shortest_res:\n",
    "                shortest_res = res\n",
    "\n",
    "    if shortest_res == 32768:\n",
    "        return -1\n",
    "    else:\n",
    "        return shortest_res\n",
    "\n",
    "def augment_test(row):\n",
    "    global new_df_test\n",
    "\n",
    "    #map q1 with dups of q2\n",
    "    if row[\"question2\"] in pos_graph:\n",
    "        new_rows = pd.DataFrame()\n",
    "        q2_dups = pos_graph[row[\"question2\"]]\n",
    "        new_rows[\"question2\"] = [i for i in q2_dups]\n",
    "        new_rows[\"question1\"] = row[\"question1\"]    \n",
    "        new_rows[\"test_id\"] = row[\"test_id\"]\n",
    "        new_df_test = pd.concat([new_df_test, new_rows])\n",
    "\n",
    "    #map q2 with dups of q1\n",
    "    if row[\"question1\"] in pos_graph:\n",
    "        new_rows = pd.DataFrame()\n",
    "        q1_dups = pos_graph[row[\"question1\"]]\n",
    "        new_rows[\"question1\"] = [i for i in q1_dups]\n",
    "        new_rows[\"question2\"] = row[\"question2\"]    \n",
    "        new_rows[\"test_id\"] = row[\"test_id\"]\n",
    "        new_df_test = pd.concat([new_df_test, new_rows])\n",
    "\n",
    "def get_pr_ratio(row):\n",
    "    \n",
    "    if row[\"q1_pr\"] != 0 and row[\"q2_pr\"] != 0:\n",
    "        return max(row[\"q1_pr\"]/row[\"q2_pr\"], row[\"q2_pr\"]/row[\"q1_pr\"])\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "# def run_xgb(x_train, x_valid, y_train, y_valid):\n",
    "def run_xgb(x_train, x_label):\n",
    "\n",
    "    # x_train = pd.concat([pos_train, neg_train]) #Concat positive and negative\n",
    "    # y_train = (np.zeros(len(pos_train)) + 1).tolist() + np.zeros(len(neg_train)).tolist() #Putting in 1 and 0\n",
    "\n",
    "    # x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=4242)\n",
    "\n",
    "    # Set our parameters for xgboost\n",
    "    params = {}\n",
    "    params['objective'] = 'binary:logistic'\n",
    "    params['eval_metric'] = 'logloss'\n",
    "    params['eta'] = 0.05\n",
    "    params['max_depth'] = 5\n",
    "    params['silent'] = 1\n",
    "    params['min_child_weight'] = 0\n",
    "    params['subsample'] = 0.8\n",
    "    params['colsample_bytree'] = 0.8\n",
    "    params['nthread'] = 13\n",
    "    #params['scale_pos_weight'] = 0.36\n",
    "\n",
    "    d_train = xgb.DMatrix(x_train, label=x_label)\n",
    "\n",
    "    watchlist = [(d_train, 'train')]\n",
    "\n",
    "    bst = xgb.train(params, d_train, 2000, watchlist, early_stopping_rounds=50, verbose_eval=50)\n",
    "    \n",
    "    return bst\n",
    "\n",
    "def run_rf(x_train, x_label):\n",
    "    \n",
    "    h2o.init(max_mem_size = \"2G\")             \n",
    "    h2o.remove_all()                          \n",
    "    \n",
    "    rf = H2ORandomForestEstimator(\n",
    "            model_id=\"rf_model_1\",\n",
    "            ntrees=500,\n",
    "            max_depth=10,\n",
    "            stopping_rounds=2,\n",
    "            stopping_tolerance=0.01,\n",
    "            score_each_iteration=True,\n",
    "            seed=3000000\n",
    "    )\n",
    "\n",
    "    hf_train = h2o.H2OFrame(pd.concat([x_train, x_label], axis = 1))\n",
    "    \n",
    "    rf.train(hf_train.col_names[:-1], hf_train.col_names[-1], training_frame = hf_train)\n",
    "    \n",
    "    return rf\n",
    "\n",
    "def run_gbm(x_train, x_label):\n",
    "\n",
    "    h2o.remove_all()                          \n",
    "    h2o.init(max_mem_size = \"2G\")             \n",
    "    \n",
    "    gbm = H2OGradientBoostingEstimator(\n",
    "            ntrees=1000,\n",
    "            learn_rate=0.3,\n",
    "            max_depth=10,\n",
    "            sample_rate=0.7,\n",
    "            col_sample_rate=0.7,\n",
    "            stopping_rounds=2,\n",
    "            stopping_tolerance=0.001, #10-fold increase in threshold as defined in rf_v1\n",
    "            score_each_iteration=True,\n",
    "            model_id=\"gbm_covType_v3\",\n",
    "            seed=2000000\n",
    "        )\n",
    "\n",
    "    hf_train = h2o.H2OFrame(pd.concat([x_train, x_label], axis = 1))\n",
    "    \n",
    "    gbm.train(hf_train.col_names[:-1], hf_train.col_names[-1], training_frame = hf_train)\n",
    "    \n",
    "    return gbm\n",
    "\n",
    "\n",
    "def run_tsne(pos_train, neg_train, x_test_feat):\n",
    "\n",
    "    x_train = pd.concat([pos_train, neg_train]) #Concat positive and negative\n",
    "    y_train = (np.zeros(len(pos_train)) + 1).tolist() + np.zeros(len(neg_train)).tolist() #Putting in 1 and 0\n",
    "\n",
    "    x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=4242)\n",
    "\n",
    "    df_subsampled = x_train[0:3000]\n",
    "    X = MinMaxScaler().fit_transform(df_subsampled[['z_len1', 'z_len2', 'z_words1', 'z_words2', 'word_match']])\n",
    "    # y = y_train['is_duplicate'].values\n",
    "\n",
    "    tsne = TSNE(\n",
    "        n_components=3,\n",
    "        init='random', # pca\n",
    "        random_state=101,\n",
    "        method='barnes_hut',\n",
    "        n_iter=200,\n",
    "        verbose=2,\n",
    "        angle=0.5\n",
    "    ).fit_transform(X)\n",
    "\n",
    "    trace1 = go.Scatter3d(\n",
    "        x=tsne[:,0],\n",
    "        y=tsne[:,1],\n",
    "        z=tsne[:,2],\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            sizemode='diameter',\n",
    "            color = y_train,\n",
    "            colorscale = 'Portland',\n",
    "            colorbar = dict(title = 'duplicate'),\n",
    "            line=dict(color='rgb(255, 255, 255)'),\n",
    "            opacity=0.75\n",
    "        )\n",
    "    )\n",
    "\n",
    "    data=[trace1]\n",
    "    layout=dict(height=800, width=800, title='3d embedding with engineered features')\n",
    "    fig=dict(data=data, layout=layout)\n",
    "    py.plot(data, filename='3d_bubble')\n",
    "\n",
    "def validate(training):\n",
    "\n",
    "    training_res = training.pop(\"is_duplicate\")\n",
    "    x_train, x_valid, y_train, y_valid = train_test_split(training, training_res, test_size=0.2, random_state=4242, stratify = training_res)\n",
    "\n",
    "    return(x_train, x_valid, y_train, y_valid)\n",
    "\n",
    "def get_jarowinkler(row):\n",
    "    \n",
    "    try:\n",
    "        return distance.get_jaro_distance(row.question1, row.question2, winkler=True, scaling=0.1)\n",
    "    except:\n",
    "        return 0\n",
    "    \n",
    "def real_testing(gen_filename, df_with_qs = None, res_file = None):\n",
    "\n",
    "    # Required for initial setup!\n",
    "    # row = df_with_qs.progress_apply(basic_nlp, axis = 1)\n",
    "    old_filename = './old/' + gen_filename \n",
    "    # row.to_csv(old_filename, index = False)\n",
    "    \n",
    "#REPLACE ROW WITH DF_MODIFIED\n",
    "#     if gen_filename == \"x_train.csv\":\n",
    "#         kcore_features = get_train_max_kcore()\n",
    "# # #         s2v_features = pd.read_csv('train_s2v_features.csv')\n",
    "# # #         df_with_qid = pd.read_csv('df_train_with_max_qid.csv') #max_qid overfits\n",
    "#     else:\n",
    "#         kcore_features = get_test_max_kcore()\n",
    "# #         s2v_features = pd.read_csv('test_s2v_features.csv')\n",
    "# #         df_with_qid = pd.read_csv('df_test_with_max_qid.csv') #max_qid overfits\n",
    "\n",
    "# #     row = pd.concat([dataframe, s2v_features], axis = 1)\n",
    "# #     row = pd.concat([dataframe, df_with_qid.max_qid], axis = 1)\n",
    "\n",
    "#     row = pd.concat([dataframe, kcore_features], axis = 1)\n",
    "\n",
    "#     row[\"neighbor_intersection\"] = df_with_qs.apply(neighbor_intersection, axis = 1)\n",
    "\n",
    "#     neighbor_feats = df_with_qs.apply(neighbor_features, axis = 1)\n",
    "#     row = pd.concat([dataframe, neighbor_feats], axis = 1)\n",
    "        \n",
    "#     dataframe_modified = dataframe.drop(\"neighbor_dissimilar\", axis = 1)\n",
    "#     dataframe_modified = dataframe_modified.drop(\"neighbor_ratio\", axis = 1)\n",
    "    \n",
    "#     q1_second_degree_freq = df_with_qs.apply(get_q1_second_degree_freq, axis = 1)\n",
    "#     q2_second_degree_freq = df_with_qs.apply(get_q2_second_degree_freq, axis = 1)\n",
    "#     row[\"second_degree_avg\"] = (q1_second_degree_freq + q2_second_degree_freq)/2\n",
    "#     row[\"second_degree_diff\"] = abs(q1_second_degree_freq - q2_second_degree_freq)\n",
    "#     row[\"second_degree_intersection\"] = df_with_qs.apply(second_degree_intersection, axis = 1)\n",
    "#     row[\"separation\"] = df_with_qs.progress_apply(initialize_bfs, axis = 1)\n",
    "\n",
    "#     weighted_neighbor_feats = df_with_qs.progress_apply(weighted_neighbor_features, axis = 1)    \n",
    "#     weighted_neighbor_feats.index = dataframe.index\n",
    "#     row = pd.concat([dataframe, weighted_neighbor_feats], axis = 1)\n",
    "    \n",
    "#     dataframe_modified[\"pr_diff\"] = abs(dataframe_modified[\"q1_pr\"] - dataframe_modified[\"q2_pr\"])\n",
    "#     dataframe_modified[\"pr_avg\"] = (dataframe_modified[\"q1_pr\"] + dataframe_modified[\"q2_pr\"])/2\n",
    "    \n",
    "#     dataframe_modified[\"pr_ratio\"] = dataframe_modified.apply(get_pr_ratio, axis = 1)\n",
    "\n",
    "#     dataframe_modified = pd.read_csv(old_filename).fillna(\"\")\n",
    "#     dataframe_modified[\"pr_neighbor_ratio\"] = df_with_qs.progress_apply(pagerank_neighbor_features, axis = 1)\n",
    "        \n",
    "    dataframe_modified = pd.read_csv(old_filename).fillna(\"\")\n",
    "    dataframe_modified[\"jarowinkler\"] = df_with_qs.progress_apply(get_jarowinkler, axis = 1)\n",
    "    \n",
    "    if res_file is None:\n",
    "        new_filename = \"./new/\" + gen_filename\n",
    "    else:\n",
    "        new_filename = \"./new/\" + res_file\n",
    "        \n",
    "    dataframe_modified.to_csv(new_filename, index=False)\n",
    "    # %reset_selective dataframe_modified \n",
    "\n",
    "def pred_n_submit(x_test, res_filename):\n",
    "\n",
    "#     XGB \n",
    "    d_test = xgb.DMatrix(x_test)\n",
    "    p_test = bst.predict(d_test)\n",
    "    sub = pd.DataFrame()\n",
    "    sub['is_duplicate'] = p_test\n",
    "    sub.to_csv(res_filename + \"_xgb.csv\", index=False)   \n",
    "\n",
    "#     Keras Dense Neural net\n",
    "#     res_keras = model.predict(np.array(x_test))\n",
    "#     np.savetxt(res_filename + \"_keras.csv\", res_keras, delimiter=\",\", header = \"is_duplicate\")\n",
    "\n",
    "#     H2O Random Forest\n",
    "#     hf_test = h2o.H2OFrame(x_test)\n",
    "#     rf_pred = rf.predict(hf_test)\n",
    "#     h2o.export_file(rf_pred, res_filename + \"_rf.csv\")   \n",
    "\n",
    "#   H2O GBM\n",
    "#     hf_test = h2o.H2OFrame(x_test)\n",
    "#     gbm_pred = gbm.predict(hf_test)\n",
    "#     h2o.export_file(gbm_pred, res_filename + \"_gbm.csv\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "def make_nx_graph(x_train, x_test):\n",
    "    \n",
    "    df = pd.concat([df_train, df_test])\n",
    "    \n",
    "    g = nx.Graph()\n",
    "\n",
    "    g.add_nodes_from(df.qid1)\n",
    "\n",
    "    edges = list(df[['qid1', 'qid2']].to_records(index=False))\n",
    "\n",
    "    g.add_edges_from(edges)\n",
    "\n",
    "    g.remove_edges_from(g.selfloop_edges())\n",
    "    \n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import PReLU\n",
    "from keras import optimizers\n",
    "\n",
    "def get_keras_model(x_train, x_label):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(71, input_shape = (71,), kernel_initializer='uniform'))\n",
    "    model.add(Activation('relu'))\n",
    "#     model.add(PReLU())\n",
    "    model.add(Dropout(0.05))\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    model.add(Dense(71))\n",
    "    model.add(Activation('relu'))\n",
    "#     model.add(PReLU())\n",
    "    model.add(Dropout(0.05))\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "#     model.add(Dense(71))\n",
    "#     model.add(Activation('relu'))\n",
    "# #     model.add(PReLU())\n",
    "#     model.add(Dropout(0.1))\n",
    "#     model.add(BatchNormalization())\n",
    "\n",
    "    # model.add(LSTM(10)) #LSTM doesnt make sense in numerical data -- lstm requires 3d matrix(which includes how many timeperiods to look back!)\n",
    "    # model.add(Activation('relu'))\n",
    "\n",
    "    model.add(Dense(1))\n",
    "    model.add(Activation('sigmoid'))\n",
    "\n",
    "    # model2 = Sequential()\n",
    "\n",
    "    # model2.add(Dense(32, input_shape = (71,), kernel_initializer='uniform'))\n",
    "    # model2.add(Activation('sigmoid'))\n",
    "    # model2.add(Dropout(0.2))\n",
    "\n",
    "    # model2.add(Dense(32))\n",
    "    # model2.add(Activation('sigmoid'))\n",
    "    # model2.add(Dropout(0.2))\n",
    "\n",
    "    # model2.add(Dense(32))\n",
    "    # model2.add(Activation('sigmoid'))\n",
    "\n",
    "    # model.add(LSTM(10)) #LSTM doesnt make sense in numerical data -- lstm requires 3d matrix(which includes how many timeperiods to look back!)\n",
    "    # model.add(Activation('relu'))\n",
    "\n",
    "    # model2.add(Dense(1))\n",
    "    # model2.add(Activation('sigmoid'))\n",
    "\n",
    "    # merged_model = Sequential()\n",
    "    # merged_model.add(Merge([model, model2], mode='concat'))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(300))\n",
    "    # merged_model.add(PReLU())\n",
    "    # merged_model.add(Dropout(0.2))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(300))\n",
    "    # merged_model.add(PReLU())\n",
    "    # merged_model.add(Dropout(0.2))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(300))\n",
    "    # merged_model.add(PReLU())\n",
    "    # merged_model.add(Dropout(0.2))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(300))\n",
    "    # merged_model.add(PReLU())\n",
    "    # merged_model.add(Dropout(0.2))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(300))\n",
    "    # merged_model.add(PReLU())\n",
    "    # merged_model.add(Dropout(0.2))\n",
    "    # merged_model.add(BatchNormalization())\n",
    "\n",
    "    # merged_model.add(Dense(1))\n",
    "    # merged_model.add(Activation('sigmoid'))\n",
    "\n",
    "#     x_train_1 = x_train[:int(0.8*len(x_train))]\n",
    "#     x_train_2 = x_train[int(0.8*len(x_train)):]\n",
    "#     x_label_1 = x_label[:int(0.8*len(x_label))]\n",
    "#     x_label_2 = x_label[int(0.8*len(x_label)):]\n",
    "\n",
    "    #model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    #Trying optimizers -- sgd bad and slow!\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(lr=0.01, decay = 0.01), metrics=['accuracy'])\n",
    "    #model.compile(loss='binary_crossentropy', optimizer=optimizers.SGD(lr=0.1, momentum=0.9), metrics=['accuracy'])\n",
    "    \n",
    "    model.fit(np.array(x_train), np.array(x_label), batch_size = 500, epochs = 10)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('./train.csv').fillna(\"\")\n",
    "df_test = pd.read_csv('./df_test_with_qid.csv').fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_qs = pd.Series(df_train['question1'].tolist() + df_train['question2'].tolist()).astype(str)\n",
    "test_qs = pd.Series(df_test['question1'].tolist() + df_test['question2'].tolist()).astype(str)\n",
    "qs = pd.concat([train_qs, test_qs])\n",
    "\n",
    "# tfidf = TfidfVectorizer(max_features = 256, stop_words='english', ngram_range=(1, 1))\n",
    "# tfidf.fit_transform(train_qs[0:2500])\n",
    "\n",
    "#Load up the Weights Dictionary\n",
    "# words = (\" \".join(qs)).lower().split()\n",
    "# counts = Counter(words)\n",
    "# weights = {word: get_inverse_freq(1/(10000 + int(count)), count) for word, count in counts.items()}\n",
    "# with open('word_weights.pickle', 'wb') as handle:\n",
    "#     pickle.dump(weights, handle)\n",
    "\n",
    "with open('word_weights.pickle', 'rb') as handle:\n",
    "    weights = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stops = set(stopwords.words(\"english\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load up the hashtable\n",
    "# hash_table = {}\n",
    "# df_train.apply(generate_hash_freq, axis = 1)\n",
    "# df_test.apply(generate_hash_freq, axis = 1)\n",
    "# with open('hash_table.pickle', 'wb') as handle:\n",
    "#     pickle.dump(hash_table, handle)\n",
    "with open('hash_table.pickle', 'rb') as handle:\n",
    "    hash_table = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load up the graph!\n",
    "# graph = {}\n",
    "# df_train.apply(generate_graph_table, axis = 1)\n",
    "# df_test.progress_apply(generate_graph_table, axis = 1)\n",
    "# with open('graph.pickle', 'wb') as handle:\n",
    "#     pickle.dump(graph, handle)\n",
    "with open('graph.pickle', 'rb') as handle:\n",
    "    graph = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#Load up the graph!\n",
    "qid_graph = {}\n",
    "df_train.progress_apply(generate_qid_graph_table, axis = 1)\n",
    "df_test.progress_apply(generate_qid_graph_table, axis = 1)\n",
    "with open('qid_graph.pickle', 'wb') as handle:\n",
    "    pickle.dump(qid_graph, handle)\n",
    "# with open('graph.pickle', 'rb') as handle:\n",
    "#     graph = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pr = pagerank()\n",
    "# with open('pagerank.pickle', 'wb') as handle:\n",
    "#     pickle.dump(pr, handle)\n",
    "with open('pagerank.pickle', 'rb') as handle:\n",
    "    pr = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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    {
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      "\n",
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      "\n",
      "2345796it [05:15, 7427.53it/s]\u001b[A\u001b[A"
     ]
    }
   ],
   "source": [
    "edge_weights = {}\n",
    "\n",
    "df_train.progress_apply(generate_edge_scores, axis = 1)\n",
    "df_test.progress_apply(generate_edge_scores, axis = 1)\n",
    "\n",
    "with open('edge_weights.pickle', 'wb') as handle:\n",
    "        pickle.dump(edge_weights, handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def run_kcore_max():\n",
    "    \n",
    "    df_cores = pd.read_csv(\"question_kcores.csv\", index_col=\"qid\")\n",
    "\n",
    "    df_cores.index.names = [\"qid\"]\n",
    "\n",
    "    df_cores['max_kcore'] = df_cores.apply(lambda row: max(row), axis=1)\n",
    "\n",
    "    df_cores[['max_kcore']].to_csv(\"question_max_kcores.csv\") # with index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = make_nx_graph(df_train, df_test)\n",
    "\n",
    "# nx.write_gpickle(g, \"nx_graph_real.gpickle\")\n",
    "\n",
    "# with open('nx_graph.pickle', 'wb') as handle:\n",
    "#     pickle.dump(g, handle)\n",
    "# with open('nx_graph.pickle', 'rb') as handle:\n",
    "#     g = pickle.load(handle)\n",
    "\n",
    "df_output = pd.DataFrame(data=g.nodes(), columns=[\"qid\"])\n",
    "\n",
    "print(\"df_output.shape:\", df_output.shape)\n",
    "\n",
    "NB_CORES = 15\n",
    "\n",
    "for k in range(2, NB_CORES + 1):\n",
    "    print k\n",
    "    \n",
    "    fieldname = \"kcore{}\".format(k)\n",
    "\n",
    "    ck = nx.k_core(g, k=k).nodes()\n",
    "    df_output[fieldname] = 0\n",
    "\n",
    "    df_output.ix[df_output.qid.isin(ck), fieldname] = k\n",
    "\n",
    "df_output.to_csv(\"question_kcores.csv\", index=None)\n",
    "\n",
    "run_kcore_max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_train_max_kcore():\n",
    "\n",
    "    kcore_df = pd.read_csv(\"question_max_kcores.csv\")\n",
    "\n",
    "    df_train = pd.read_csv('./train.csv').fillna(\"\")\n",
    "\n",
    "    q1_kcore = pd.merge(df_train, kcore_df, left_on='qid1', right_on = 'qid', how = \"inner\")\n",
    "    q2_kcore = pd.merge(df_train, kcore_df, left_on='qid2', right_on = 'qid', how = \"inner\")\n",
    "\n",
    "    train_cores = pd.concat([q1_kcore.max_kcore, q2_kcore.max_kcore], axis = 1)\n",
    "    train_cores.columns = [\"q1_kcore\", \"q2_kcore\"]\n",
    "\n",
    "    return train_cores\n",
    "\n",
    "def get_test_max_kcore():\n",
    "    \n",
    "    kcore_df = pd.read_csv(\"question_max_kcores.csv\")\n",
    "    \n",
    "    df_test = pd.read_csv('./df_test_with_qid.csv').fillna(\"\")\n",
    "\n",
    "    q1_kcore = pd.merge(df_test, kcore_df, left_on='qid1', right_on = 'qid', how = \"inner\")\n",
    "    q2_kcore = pd.merge(df_test, kcore_df, left_on='qid2', right_on = 'qid', how = \"inner\")\n",
    "\n",
    "    test_cores = pd.concat([q1_kcore.max_kcore, q2_kcore.max_kcore], axis = 1)\n",
    "    test_cores.columns = [\"q1_kcore\", \"q2_kcore\"]\n",
    "    \n",
    "    return test_cores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "real_testing('x_train.csv', df_train)\n",
    "# real_testing('x_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "390000it [07:35, 855.36it/s] \n",
      "390000it [01:53, 3442.88it/s]        \n",
      "390000it [01:49, 3549.75it/s]        \n",
      "390000it [01:52, 3474.72it/s]        \n",
      "390000it [01:50, 3544.47it/s]        \n",
      "395796it [01:50, 3588.13it/s]        \n"
     ]
    }
   ],
   "source": [
    "real_testing('x_test_1.csv', df_test[:390000])\n",
    "real_testing('x_test_2.csv', df_test[390000:780000])\n",
    "real_testing('x_test_3.csv', df_test[780000:1170000])\n",
    "real_testing('x_test_4.csv', df_test[1170000:1560000])\n",
    "real_testing('x_test_5.csv', df_test[1560000:1950000])\n",
    "real_testing('x_test_6.csv', df_test[1950000:])\n",
    "\n",
    "# real_testing('x_test_1.csv')\n",
    "# real_testing('x_test_2.csv')\n",
    "# real_testing('x_test_3.csv')\n",
    "# real_testing('x_test_4.csv')\n",
    "# real_testing('x_test_5.csv')\n",
    "# real_testing('x_test_6.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "404291it [04:14, 1587.28it/s]                \n"
     ]
    }
   ],
   "source": [
    "#Setting up pagerank features!\n",
    "\n",
    "pagerank_feats_1 = df_test[:390000].progress_apply(get_pagerank_value, axis = 1)\n",
    "pagerank_feats_2 = df_test[390000:780000].progress_apply(get_pagerank_value, axis = 1)\n",
    "pagerank_feats_3 = df_test[780000:1170000].progress_apply(get_pagerank_value, axis = 1)\n",
    "pagerank_feats_4 = df_test[1170000:1560000].progress_apply(get_pagerank_value, axis = 1)\n",
    "pagerank_feats_5 = df_test[1560000:1950000].progress_apply(get_pagerank_value, axis = 1)\n",
    "pagerank_feats_6 = df_test[1950000:].progress_apply(get_pagerank_value, axis = 1)\n",
    "# # pagerank_feats = df_test.progress_apply(get_pagerank_value, axis = 1)\n",
    "\n",
    "# %reset_selective -f df_test\n",
    "\n",
    "dataframe_1 = pd.read_csv(\"./old/x_test_1.csv\").fillna(\"\")\n",
    "pagerank_feats_1.index = dataframe_1.index\n",
    "dataframe_modified_1 = pd.concat([dataframe_1, pagerank_feats_1], axis = 1)\n",
    "%reset_selective -f dataframe_1\n",
    "dataframe_modified_1.to_csv(\"./new/x_test_1.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_1\n",
    "\n",
    "dataframe_2 = pd.read_csv(\"./old/x_test_2.csv\").fillna(\"\")\n",
    "pagerank_feats_2.index = dataframe_2.index\n",
    "dataframe_modified_2 = pd.concat([dataframe_2, pagerank_feats_2], axis = 1)\n",
    "%reset_selective -f dataframe_2\n",
    "dataframe_modified_2.to_csv(\"./new/x_test_2.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_2\n",
    "\n",
    "dataframe_3 = pd.read_csv(\"./old/x_test_3.csv\").fillna(\"\")\n",
    "pagerank_feats_3.index = dataframe_3.index\n",
    "dataframe_modified_3 = pd.concat([dataframe_3, pagerank_feats_3], axis = 1)\n",
    "%reset_selective -f dataframe_3\n",
    "dataframe_modified_3.to_csv(\"./new/x_test_3.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_3\n",
    "\n",
    "dataframe_4 = pd.read_csv(\"./old/x_test_4.csv\").fillna(\"\")\n",
    "pagerank_feats_4.index = dataframe_4.index\n",
    "dataframe_modified_4 = pd.concat([dataframe_4, pagerank_feats_4], axis = 1)\n",
    "%reset_selective -f dataframe_4\n",
    "dataframe_modified_4.to_csv(\"./new/x_test_4.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_4\n",
    "\n",
    "dataframe_5 = pd.read_csv(\"./old/x_test_5.csv\").fillna(\"\")\n",
    "pagerank_feats_5.index = dataframe_5.index\n",
    "dataframe_modified_5 = pd.concat([dataframe_5, pagerank_feats_5], axis = 1)\n",
    "%reset_selective -f dataframe_5\n",
    "dataframe_modified_5.to_csv(\"./new/x_test_5.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_5\n",
    "\n",
    "dataframe_6 = pd.read_csv(\"./old/x_test_6.csv\").fillna(\"\")\n",
    "pagerank_feats_6.index = dataframe_6.index\n",
    "dataframe_modified_6 = pd.concat([dataframe_6, pagerank_feats_6], axis = 1)\n",
    "%reset_selective -f dataframe_6\n",
    "dataframe_modified_6.to_csv(\"./new/x_test_6.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_6\n",
    "\n",
    "#Train pagerank\n",
    "pagerank_feats_train = df_train.progress_apply(get_pagerank_value, axis = 1)\n",
    "dataframe_train = pd.read_csv(\"./old/x_train.csv\").fillna(\"\")\n",
    "pagerank_feats_train.index = dataframe_train.index\n",
    "dataframe_modified_train = pd.concat([dataframe_train, pagerank_feats_train], axis = 1)\n",
    "%reset_selective -f dataframe_train\n",
    "dataframe_modified_train.to_csv(\"./new/x_train.csv\", index=False)\n",
    "%reset_selective -f dataframe_modified_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Finally!\n",
    "x_train = pd.read_csv('./new/x_train.csv').fillna(0)\n",
    "# x_test = pd.read_csv('./new/x_test.csv').fillna(0)\n",
    "\n",
    "x_label = df_train.is_duplicate\n",
    "oversample_label = 1\n",
    "\n",
    "if oversample_label == 1:\n",
    "    x_train[\"is_duplicate\"] = x_label\n",
    "    x_train = oversample(x_train)\n",
    "\n",
    "    x_label = x_train.pop(\"is_duplicate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-logloss:0.6571\n",
      "Will train until train-logloss hasn't improved in 50 rounds.\n",
      "[50]\ttrain-logloss:0.218509\n",
      "[100]\ttrain-logloss:0.191069\n",
      "[150]\ttrain-logloss:0.183678\n",
      "[200]\ttrain-logloss:0.178905\n",
      "[250]\ttrain-logloss:0.175728\n",
      "[300]\ttrain-logloss:0.173028\n",
      "[350]\ttrain-logloss:0.171046\n",
      "[400]\ttrain-logloss:0.169362\n",
      "[450]\ttrain-logloss:0.167635\n",
      "[500]\ttrain-logloss:0.16617\n",
      "[550]\ttrain-logloss:0.164777\n",
      "[600]\ttrain-logloss:0.163492\n",
      "[650]\ttrain-logloss:0.162427\n",
      "[700]\ttrain-logloss:0.161367\n",
      "[750]\ttrain-logloss:0.16042\n",
      "[800]\ttrain-logloss:0.159408\n",
      "[850]\ttrain-logloss:0.158405\n",
      "[900]\ttrain-logloss:0.157514\n",
      "[950]\ttrain-logloss:0.156661\n",
      "[1000]\ttrain-logloss:0.15576\n",
      "[1050]\ttrain-logloss:0.154925\n",
      "[1100]\ttrain-logloss:0.154109\n",
      "[1150]\ttrain-logloss:0.153341\n",
      "[1200]\ttrain-logloss:0.152588\n",
      "[1250]\ttrain-logloss:0.151821\n",
      "[1300]\ttrain-logloss:0.151152\n",
      "[1350]\ttrain-logloss:0.150446\n",
      "[1400]\ttrain-logloss:0.149706\n",
      "[1450]\ttrain-logloss:0.149009\n",
      "[1500]\ttrain-logloss:0.148245\n",
      "[1550]\ttrain-logloss:0.147587\n",
      "[1600]\ttrain-logloss:0.14688\n",
      "[1650]\ttrain-logloss:0.146191\n",
      "[1700]\ttrain-logloss:0.145585\n",
      "[1750]\ttrain-logloss:0.144968\n",
      "[1800]\ttrain-logloss:0.144327\n",
      "[1850]\ttrain-logloss:0.143704\n",
      "[1900]\ttrain-logloss:0.143031\n",
      "[1950]\ttrain-logloss:0.142436\n",
      "[1999]\ttrain-logloss:0.141786\n"
     ]
    }
   ],
   "source": [
    "%reset_selective -f df_train\n",
    "%reset_selective -f df_test\n",
    "\n",
    "# model = get_keras_model(x_train, x_label)\n",
    "\n",
    "bst = run_xgb(x_train, x_label)\n",
    "with open('bst.pickle', 'wb') as handle:\n",
    "    pickle.dump(bst, handle)\n",
    "\n",
    "# rf = run_rf(x_train, x_label)\n",
    "\n",
    "# gbm = run_gbm(x_train, x_label)\n",
    "\n",
    "x_test_1 = pd.read_csv('./new/x_test_1.csv').fillna(0)\n",
    "pred_n_submit(x_test_1, \"./res_files/res_1\")\n",
    "%reset_selective -f x_test_1\n",
    "\n",
    "x_test_2 = pd.read_csv('./new/x_test_2.csv').fillna(0)\n",
    "pred_n_submit(x_test_2, \"./res_files/res_2\")\n",
    "%reset_selective -f x_test_2\n",
    "\n",
    "x_test_3 = pd.read_csv('./new/x_test_3.csv').fillna(0)\n",
    "pred_n_submit(x_test_3, \"./res_files/res_3\")\n",
    "%reset_selective -f x_test_3\n",
    "\n",
    "x_test_4 = pd.read_csv('./new/x_test_4.csv').fillna(0)\n",
    "pred_n_submit(x_test_4, \"./res_files/res_4\")\n",
    "%reset_selective -f x_test_4\n",
    "\n",
    "x_test_5 = pd.read_csv('./new/x_test_5.csv').fillna(0)\n",
    "pred_n_submit(x_test_5, \"./res_files/res_5\")\n",
    "%reset_selective -f x_test_5\n",
    "\n",
    "x_test_6 = pd.read_csv('./new/x_test_6.csv').fillna(0)\n",
    "pred_n_submit(x_test_6, \"./res_files/res_6\")\n",
    "%reset_selective -f x_test_6\n",
    "\n",
    "def collate_res(algo):\n",
    "\n",
    "    res_1 = pd.read_csv('./res_files/res_1_' + algo + '.csv').fillna(\"\")\n",
    "    res_2 = pd.read_csv('./res_files/res_2_' + algo + '.csv').fillna(\"\")\n",
    "    res_3 = pd.read_csv('./res_files/res_3_' + algo + '.csv').fillna(\"\")\n",
    "    res_4 = pd.read_csv('./res_files/res_4_' + algo + '.csv').fillna(\"\")\n",
    "    res_5 = pd.read_csv('./res_files/res_5_' + algo + '.csv').fillna(\"\")\n",
    "    res_6 = pd.read_csv('./res_files/res_6_' + algo + '.csv').fillna(\"\")\n",
    "\n",
    "    res = pd.concat([res_1, res_2, res_3, res_4, res_5, res_6])\n",
    "\n",
    "    sub = pd.DataFrame()\n",
    "\n",
    "    sub['test_id'] = test_ids\n",
    "    sub['is_duplicate'] = res.values\n",
    "\n",
    "    sub.to_csv('./res_files/res_' + algo + '_5.csv', index=False)   \n",
    "\n",
    "collate_res('xgb')\n",
    "# collate_res('keras')\n",
    "# collate_res('rf')\n",
    "# collate_res('gbm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# keras_sub = pd.read_csv(\"./res_files/res_keras_4.csv\")\n",
    "xgb_sub = pd.read_csv(\"./res_files/res_xgb_1.csv\")\n",
    "rf_sub = pd.read_csv(\"./res_files/res_rf_1.csv\")\n",
    "gbm_sub = pd.read_csv(\"./res_files/res_gbm_1.csv\")\n",
    "\n",
    "ensemble_sub = pd.DataFrame()\n",
    "ensemble_sub[\"test_id\"] = test_ids\n",
    "ensemble_sub[\"is_duplicate\"] = (rf_sub.is_duplicate + gbm_sub.is_duplicate + xgb_sub.is_duplicate)/3\n",
    "\n",
    "ensemble_sub.to_csv(\"./res_files/ensemble_sub_3.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_to_qid(row):\n",
    "    \n",
    "    global qid_hash\n",
    "    global max_qid\n",
    "\n",
    "    if row[\"question1\"] not in qid_hash:\n",
    "        qid_hash[row[\"question1\"]] = row[\"qid1\"]    \n",
    "\n",
    "    if row[\"question2\"] not in qid_hash:\n",
    "        qid_hash[row[\"question2\"]] = row[\"qid2\"]  \n",
    "    \n",
    "    max_qid = max(max_qid, row[\"qid1\"], row[\"qid2\"])\n",
    "    \n",
    "#     return max(row[\"qid1\"], row[\"qid2\"])\n",
    "\n",
    "def test_to_qid(row):\n",
    "    \n",
    "    global qid_hash\n",
    "    global max_qid\n",
    "\n",
    "    if row[\"question1\"] not in qid_hash:\n",
    "        max_qid += 1\n",
    "\n",
    "        qid_hash[row[\"question1\"]] = max_qid\n",
    "\n",
    "        q1_qid = max_qid\n",
    "    else:\n",
    "        q1_qid = qid_hash[row[\"question1\"]]\n",
    "\n",
    "    if row[\"question2\"] not in qid_hash:\n",
    "        max_qid += 1\n",
    "\n",
    "        qid_hash[row[\"question2\"]] = max_qid\n",
    "\n",
    "        q2_qid = max_qid\n",
    "    else:\n",
    "        q2_qid = qid_hash[row[\"question2\"]]\n",
    "\n",
    "    return pd.Series({\"qid1\":q1_qid,\"qid2\": q2_qid})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "390001it [10:09, 639.35it/s] \n",
      "390001it [03:11, 2037.02it/s]                \n",
      "390001it [02:56, 2208.72it/s]                \n",
      "390001it [03:12, 2024.59it/s]                \n",
      "390001it [03:21, 1936.36it/s]                \n",
      "395797it [03:01, 2175.14it/s]                \n"
     ]
    }
   ],
   "source": [
    "qid_hash = {}\n",
    "max_qid = 0\n",
    "\n",
    "df_train = pd.read_csv('./train.csv').fillna(\"\")\n",
    "df_train.apply(train_to_qid, axis = 1)\n",
    "%reset_selective -f df_train\n",
    "\n",
    "def part_qid_generate(x_test_part):\n",
    "    test_qid = x_test_part.progress_apply(test_to_qid, axis = 1)\n",
    "    return test_qid\n",
    "    \n",
    "df_test = pd.read_csv('./test.csv').fillna(\"\")\n",
    "\n",
    "test_qid_1 = part_qid_generate(df_test[:390000])\n",
    "test_qid_2 = part_qid_generate(df_test[390000:780000])\n",
    "test_qid_3 = part_qid_generate(df_test[780000:1170000])\n",
    "test_qid_4 = part_qid_generate(df_test[1170000:1560000])\n",
    "test_qid_5 = part_qid_generate(df_test[1560000:1950000])\n",
    "test_qid_6 = part_qid_generate(df_test[1950000:])\n",
    "\n",
    "test_qid = pd.concat([test_qid_1, test_qid_2, test_qid_3, test_qid_4, test_qid_5, test_qid_6])\n",
    "\n",
    "df_test[\"qid1\"] = test_qid.qid1\n",
    "df_test[\"qid2\"] = test_qid.qid2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test.to_csv(\"df_test_with_qid.csv\", index = False)\n",
    "# df_train.to_csv(\"df_train_with_max_qid.csv\", index = False)\n",
    "%reset_selective -f qid_hash\n",
    "%reset_selective -f test_qid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('./train.csv').fillna(\"\")\n",
    "x_label = df_train.is_duplicate\n",
    "%reset_selective -f df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = pd.read_csv('./test.csv').fillna(\"\")\n",
    "test_ids = df_test.test_id\n",
    "%reset_selective -f df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_train = pd.read_csv('./new/x_train.csv').fillna(0)\n",
    "\n",
    "x_test = pd.read_csv('./new/x_test.csv').fillna(0)\n",
    "x_test[\"dupe_rate\"] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from pandas.stats.api import ols\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "\n",
    "x_train[\"is_duplicate\"] = x_label\n",
    "x_train = x_train.sort_values(by=[\"max_qid\"], ascending=True)\n",
    "x_train[\"dupe_rate\"] = x_train.is_duplicate.rolling(window=500, min_periods=500).mean()\n",
    "del x_train[\"is_duplicate\"]\n",
    "\n",
    "x_overall = pd.concat([x_train, x_test])\n",
    "x_overall = x_overall.sort_values(by=[\"max_qid\"], ascending=True)\n",
    "\n",
    "x_overall[\"timeline\"]  = np.arange(x_overall.shape[0]) / float(x_overall.shape[0])\n",
    "\n",
    "res = OLS(x_overall[x_overall.dupe_rate > 0]['dupe_rate'], x_overall[x_overall.dupe_rate > 0][['timeline']]).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = res.predict(x_overall[x_overall.dupe_rate == -1][['timeline']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb463311450>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyplot.plot(x_overall[x_overall.dupe_rate > 0]['timeline'], x_overall[x_overall.dupe_rate > 0]['dupe_rate'], \"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gy1/+g0uYPyEhYTFhHkJLNxOrjlYhsmsnNrlZgwh8AjlxR3LjzqKduJPxBiRA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\njnrwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb46a995750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.961887339082695"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((len(pos_train)/0.165) - len(x_train))/len(neg_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245307.24242424246"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(((len(pos_train)/0.165) - len(x_train))/len(neg_train) - 1)*len(neg_train)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}