sklearn.ipynb

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     "input": [
      "import pandas as pd\n",
      "import sklearn\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.linear_model import LogisticRegression\n",
      "from sklearn import feature_extraction\n",
      "import numpy as np\n",
      "from matplotlib import pyplot\n",
      "from sklearn.metrics import roc_curve, precision_recall_curve, precision_recall_fscore_support, mean_squared_error\n",
      "from sklearn.ensemble import ExtraTreesClassifier\n",
      "%matplotlib inline\n",
      "from matplotlib import rcParams\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Load all the CSV files into data frames\n",
      "'''\n",
      "next_records = pd.read_csv('next_records.csv')\n",
      "# print next_records.columns\n",
      "matched = pd.read_csv('matched.csv')\n",
      "# print matched.columns\n",
      "yelp = pd.read_csv('yelp.csv')\n",
      "# print yelp.head(5)\n",
      "categories = pd.read_csv('categories.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
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     "input": [
      "'''\n",
      "Working on the next_records dataframe: use the matched dataframe to convert four square ids to yelp ids\n",
      "'''\n",
      "#first, create lookup_four_square_id dictionary to convert four square ids to yelp ids\n",
      "lookup_four_square_id = {}\n",
      "for index, row in matched.iterrows():\n",
      "    yelp_id, four_square_id = row['id'], row['four_square_id']\n",
      "    lookup_four_square_id[four_square_id] = yelp_id\n",
      "\n",
      "def do_lookup_four_square_id(four_square_id):\n",
      "    return lookup_four_square_id.get(four_square_id, np.NaN)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_records['from_yelp_id'] = next_records.from_id.apply(do_lookup_four_square_id)\n",
      "next_records['to_yelp_id'] = next_records.to_id.apply(do_lookup_four_square_id)\n",
      "#only keep columns we need\n",
      "next_records_yelp = next_records[['from_yelp_id','to_yelp_id']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#dropping missing values, dups\n",
      "next_records_yelp = next_records_yelp.dropna()\n",
      "next_records_yelp = next_records_yelp.drop_duplicates()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_records_yelp['match'] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create unmatched dataframe by randomly sampling among places that were not matched\n",
      "min_dist = 0.036\n",
      "\n",
      "unmatched_entries = []\n",
      "for i,from_yelp_id in enumerate(next_records_yelp.from_yelp_id):\n",
      "    matched = next_records_yelp[next_records_yelp.from_yelp_id == from_yelp_id].to_yelp_id \n",
      "    unmatched = yelp[~yelp.id.isin(matched)]\n",
      "    origin_record = yelp[yelp.id==from_yelp_id]\n",
      "    origin_lat,origin_long = float(origin_record.latitude), float(origin_record.longitude)\n",
      "    distance = np.sqrt((unmatched.latitude - origin_lat)**2 + (unmatched.longitude - origin_long)**2)\n",
      "    unmatched = unmatched[distance < min_dist]\n",
      "    unmatched_choice = np.random.choice(unmatched.id)\n",
      "    unmatched_entries.append((from_yelp_id, unmatched_choice, 0))\n",
      "    if i % 250 == 0:\n",
      "        print i\n",
      "print len(unmatched_entries)"
     ],
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     ],
     "prompt_number": 7
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_unmatched = pd.DataFrame.from_records(unmatched_entries, columns =['from_yelp_id','to_yelp_id','match'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_records_yelp = next_records_yelp.append(df_unmatched, ignore_index = True)\n",
      "print next_records_yelp.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(46830, 3)\n"
       ]
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     ],
     "prompt_number": 9
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#merging with yelp table to look up 'from' information\n",
      "next_records_yelp = next_records_yelp.merge(yelp[['id','rating','review_count','latitude','longitude']], left_on='from_yelp_id', right_on = 'id')\n",
      "next_records_yelp.rename(columns={'rating': 'from_rating', 'review_count': 'from_review_count', 'latitude': 'from_latitude', 'longitude': 'from_longitude'}, inplace=True)\n",
      "next_records_yelp = next_records_yelp.drop('id',1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#merging with yelp table to look up 'to' information\n",
      "next_records_yelp = next_records_yelp.merge(yelp[['id','rating','review_count','latitude','longitude']], left_on='to_yelp_id', right_on = 'id')\n",
      "next_records_yelp.rename(columns={'rating': 'to_rating', 'review_count': 'to_review_count', 'latitude': 'to_latitude', 'longitude': 'to_longitude'}, inplace=True)\n",
      "next_records_yelp = next_records_yelp.drop('id',1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_records_yelp.to_pickle('for_visualization.pickle')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#compute distance and drop latitude / longitude\n",
      "df = next_records_yelp\n",
      "df['distance'] = np.sqrt((df.from_latitude - df.to_latitude)**2 + (df.from_longitude - df.to_longitude)**2)\n",
      "df = df.drop(['from_latitude','to_latitude','from_longitude','to_longitude'],1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''Trying log transform'''\n",
      "distances = df[df.match == 1].distance\n",
      "stuff = pyplot.hist(np.log10(distances+1e-8), bins = 200)\n",
      "other_distances = np.array(df[df.match == 0].distance)\n",
      "stuff = pyplot.hist(np.log10(other_distances+1e-8), bins = 200, color='r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11f442d50>"
       ]
      }
     ],
     "prompt_number": 428
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>from_yelp_id</th>\n",
        "      <th>to_yelp_id</th>\n",
        "      <th>match</th>\n",
        "      <th>from_rating</th>\n",
        "      <th>from_review_count</th>\n",
        "      <th>to_rating</th>\n",
        "      <th>to_review_count</th>\n",
        "      <th>distance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  starbucks-menlo-park-8</td>\n",
        "      <td> walgreens-menlo-park-2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 21</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.003528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>      una-mas-menlo-park</td>\n",
        "      <td> walgreens-menlo-park-2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 42</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.000089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> le-boulanger-menlo-park</td>\n",
        "      <td> walgreens-menlo-park-2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 81</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.001220</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> tokyo-subway-menlo-park</td>\n",
        "      <td> walgreens-menlo-park-2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3.0</td>\n",
        "      <td> 86</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.001114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  trader-joes-menlo-park</td>\n",
        "      <td> walgreens-menlo-park-2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4.0</td>\n",
        "      <td> 92</td>\n",
        "      <td> 2.5</td>\n",
        "      <td> 31</td>\n",
        "      <td> 0.001358</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 302,
       "text": [
        "              from_yelp_id              to_yelp_id  match  from_rating  \\\n",
        "0   starbucks-menlo-park-8  walgreens-menlo-park-2      1          3.0   \n",
        "1       una-mas-menlo-park  walgreens-menlo-park-2      1          2.5   \n",
        "2  le-boulanger-menlo-park  walgreens-menlo-park-2      1          3.0   \n",
        "3  tokyo-subway-menlo-park  walgreens-menlo-park-2      1          3.0   \n",
        "4   trader-joes-menlo-park  walgreens-menlo-park-2      1          4.0   \n",
        "\n",
        "   from_review_count  to_rating  to_review_count  distance  \n",
        "0                 21        2.5               31  0.003528  \n",
        "1                 42        2.5               31  0.000089  \n",
        "2                 81        2.5               31  0.001220  \n",
        "3                 86        2.5               31  0.001114  \n",
        "4                 92        2.5               31  0.001358  \n",
        "\n",
        "[5 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 302
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = pyplot.hist(np.array(df[df.match == 1].to_rating - df[df.match == 1].from_rating), bins = 100)\n",
      "ax = pyplot.hist(np.array(df[df.match == 0].to_rating - df[df.match == 0].from_rating), bins = 100, color = 'red')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mXDadxFOyfx67b//8WTipKTBzZuykpgCAK5cjh8ayRvyqHOAacklPIwIALt9pJSdfVUom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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113f30550>"
       ]
      }
     ],
     "prompt_number": 303
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax = pyplot.hist(np.array(df[df.match==1].to_review_count), bins = 100)\n",
      "ax = pyplot.hist(np.array(df[df.match==0].to_review_count), bins = 100, color = 'red')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x119c558d0>"
       ]
      }
     ],
     "prompt_number": 304
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#find top categories\n",
      "cats = categories.groupby('short_category').id.count()\n",
      "cats.sort(ascending=False)\n",
      "top_cats = cats.head(20)\n",
      "print top_cats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "short_category\n",
        "hair                  2856\n",
        "contractors           2162\n",
        "coffee                1831\n",
        "realestateagents      1675\n",
        "othersalons           1548\n",
        "grocery               1546\n",
        "massage               1528\n",
        "drycleaninglaundry    1427\n",
        "skincare              1326\n",
        "sandwiches            1316\n",
        "mexican               1306\n",
        "womenscloth           1294\n",
        "apartments            1147\n",
        "banks                 1075\n",
        "chinese               1060\n",
        "jewelry               1032\n",
        "realestatesvcs         971\n",
        "pizza                  880\n",
        "bakeries               854\n",
        "beer_and_wine          833\n",
        "dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 305
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#creating dummy variables with multiple features\n",
      "#http://stackoverflow.com/questions/18889588/create-dummies-from-column-with-multiple-values-in-pandas\n",
      "grouped = categories.groupby('id').short_category.apply(lambda lst: tuple((k, 1) for k in lst))\n",
      "category_dicts = [dict(tuples) for tuples in grouped]\n",
      "v = feature_extraction.DictVectorizer(sparse=False)\n",
      "v.fit(category_dicts)\n",
      "#restrict to only top categories\n",
      "support = [f in top_cats for f in v.get_feature_names()]\n",
      "v.restrict(support)\n",
      "X = v.transform(category_dicts)\n",
      "merged_cats = pd.DataFrame(X, columns=v.get_feature_names(), index=grouped.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 306
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print merged_cats.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'apartments', u'bakeries', u'banks', u'beer_and_wine', u'chinese', u'coffee', u'contractors', u'drycleaninglaundry', u'grocery', u'hair', u'jewelry', u'massage', u'mexican', u'othersalons', u'pizza', u'realestateagents', u'realestatesvcs', u'sandwiches', u'skincare', u'womenscloth'], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 307
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#merging with categories index\n",
      "df = df.merge(merged_cats, left_on = 'from_yelp_id', right_index = True)\n",
      "df = df.merge(merged_cats, left_on = 'to_yelp_id', right_index = True, suffixes=('_from', '_to'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 308
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'from_yelp_id', u'to_yelp_id', u'match', u'from_rating', u'from_review_count', u'to_rating', u'to_review_count', u'distance', u'apartments_from', u'bakeries_from', u'banks_from', u'beer_and_wine_from', u'chinese_from', u'coffee_from', u'contractors_from', u'drycleaninglaundry_from', u'grocery_from', u'hair_from', u'jewelry_from', u'massage_from', u'mexican_from', u'othersalons_from', u'pizza_from', u'realestateagents_from', u'realestatesvcs_from', u'sandwiches_from', u'skincare_from', u'womenscloth_from', u'apartments_to', u'bakeries_to', u'banks_to', u'beer_and_wine_to', u'chinese_to', u'coffee_to', u'contractors_to', u'drycleaninglaundry_to', u'grocery_to', u'hair_to', u'jewelry_to', u'massage_to', u'mexican_to', u'othersalons_to', u'pizza_to', u'realestateagents_to', u'realestatesvcs_to', u'sandwiches_to', u'skincare_to', u'womenscloth_to'], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 309
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Final cleaning step before learning\n",
      "'''\n",
      "def normalize(column):\n",
      "    mean = df[column].mean()\n",
      "    std = df[column].std()\n",
      "    df[column] = (df[column] - mean) / std\n",
      "    return mean,std\n",
      "\n",
      "from_rating_mean, from_rating_std = normalize('from_rating')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 310
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "to_rating_mean, to_rating_std = normalize('to_rating')\n",
      "print to_rating_mean, to_rating_std"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3.73555413197 0.84975061906\n"
       ]
      }
     ],
     "prompt_number": 311
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#normalize from_review_count\n",
      "from_count_mean,from_count_std = normalize('from_review_count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 312
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#normalize to_review_count\n",
      "to_count_mean, to_count_std = normalize('to_review_count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 313
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#normalize distance\n",
      "distance_mean, disance_std = normalize('distance')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 314
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#drop ids, no longer need them\n",
      "df = df.drop(['from_yelp_id','to_yelp_id'], 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 315
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = df.match"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 316
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_X = df.drop('match', 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 317
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records = df_X.to_records(index = False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 318
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = [list(record) for record in records]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 319
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logreg = LogisticRegression(class_weight = 'auto')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 324
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 325
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print X_train.shape, X_test.shape\n",
      "print y_train.shape, y_test.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(42147, 45) (4683, 45)\n",
        "(42147,) (4683,)\n"
       ]
      }
     ],
     "prompt_number": 326
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "logreg.fit(X_train, y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 327,
       "text": [
        "LogisticRegression(C=1.0, class_weight='auto', dual=False, fit_intercept=True,\n",
        "          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)"
       ]
      }
     ],
     "prompt_number": 327
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print logreg.score(X_test,y_test)\n",
      "probs = logreg.predict_proba(X_test)\n",
      "probs = probs[:,1]\n",
      "champlogreg = logreg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.916720051249\n"
       ]
      }
     ],
     "prompt_number": 328
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_pred = logreg.predict(X_test)\n",
      "y_pred_train = logreg.predict(X_train)\n",
      "precision, recall, fbeta_score, support = precision_recall_fscore_support(y_test, y_pred, average='micro')\n",
      "print 'Precision {}, Recall {}'.format(precision, recall)\n",
      "mse_test = mean_squared_error(y_test, y_pred)\n",
      "mse_train = mean_squared_error(y_train, y_pred_train)\n",
      "print 'MSE Training set {}'.format(mse_train)\n",
      "print 'MSE Test set {}'.format(mse_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Precision 0.899593495935, Recall 0.939303904924\n",
        "MSE Training set 0.0845137257693\n",
        "MSE Test set 0.0832799487508\n"
       ]
      }
     ],
     "prompt_number": 335
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''plot precision and recall curves'''\n",
      "precision, recall, thresholds = precision_recall_curve(y_test, probs)\n",
      "#downsampling both precision and recall\n",
      "sample_factor = 100# sample every 100 data points\n",
      "length = precision.size\n",
      "precision = precision[:length - length % sample_factor]\n",
      "recall = recall[:length - length % sample_factor]\n",
      "precision = precision.reshape(-1, sample_factor).mean(axis=1)\n",
      "recall = recall.reshape(-1, sample_factor).mean(axis=1)\n",
      "thresholds = thresholds[:length - length % sample_factor]\n",
      "thresholds = thresholds.reshape(-1, sample_factor).mean(axis=1)\n",
      "pyplot.plot(recall, precision, '*--', label='Precision-Recall curve')\n",
      "pyplot.xlabel('Recall')\n",
      "pyplot.ylabel('Precision')\n",
      "pyplot.legend(loc=\"lower left\")\n",
      "pyplot.xlim(-.1,1.1)\n",
      "pyplot.ylim(-.1,1.1)\n",
      "# pyplot.plot(recall, thresholds, '*r', label =\"threshold\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 268,
       "text": [
        "(-0.1, 1.1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dLivOGQQvAADQXJz3Xo1eXl7q0aOH9u7d65LCAAAAWpIG1/E6fPiwevfurQED\nBsjX11eSLcmtWLHC5cUBAAB4kgaD12OPPSZJ1brMuKsRAADg3NUZvE6cOKFXXnlFO3fuVJ8+fTRu\n3Dh5e3ubWRsAAIBHqXNy/e9//3u1adNGN9xwg1auXKnw8HA999xzZtcnicn1AACg+TivuxqvvPJK\nffvtt5Jsdzf2799fW7ZscV2V9SB4AQCA5uK87mr08vKq9WsAAACcnzp7vFq3bq327ds7Hp84cUI+\nPj62H7JYVFxcbE6FoscLAAA0H/Xlljq7siorK11WEAAAQEvU4F6NjZGZmamePXsqMjJS8+fPr7Pd\n119/LS8vLy1fvtyV5QAAALiVy4JXZWWlJk+erMzMTG3fvl1LlizR999/X2u7hx9+WAkJCQwnAgAA\nj+ay4JWbm6uIiAiFh4fL29tbiYmJysjIqNHuhRde0O23367OnTu7qhQAAIALgsuCV2FhocLCwhyP\nQ0NDVVhYWKNNRkaGJk2aJIkV8QEAgGdz2ToRzoSoqVOn6oknnnDM/q9vqDElJcXxdWxsrGJjY5ug\nSgAAgMbJzs5Wdna2U23rXE6isTZu3KiUlBRlZmZKkubNm6dWrVrp4YcfdrS5/PLLHWHr4MGDat++\nvRYuXKjbbrutepEsJwEAAJqJ81q5vrEqKirUo0cPZWVlqWvXrhowYICWLFmiqKioWtuPHTtWt956\nq0aMGFGzSIIXAABoJs5rHa/G8vLy0oIFCxQfH6/KykqNHz9eUVFRSk1NlSQlJye76qUBAAAuSC7r\n8WpK9HgBAIDm4rz2agQAAEDTIngBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKC\nFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQhe\nAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUAAGASghcAAIBJCF4AAAAmIXgB\nAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAAgEkIXgAAACYheAEAAJiE4AUA\nAGASghcAAIBJCF4AAAAmIXgBAACYhOAFAABgEoIXAACASQheAAAAJiF4AQAAmITgBQAAYBKCFwAA\ngEkIXgAAACYheAEAAJjE5cErMzNTPXv2VGRkpObPn1/j+TfffFMxMTHq06ePrrvuOm3dutXVJQEA\nALiFxTD6/N/bAAAUgElEQVQMw1Unr6ysVI8ePfTZZ58pJCRE/fv315IlSxQVFeVo8+WXX6pXr17y\n9/dXZmamUlJStHHjxupFWixyYZkAAABNpr7c4tIer9zcXEVERCg8PFze3t5KTExURkZGtTaDBg2S\nv7+/JGngwIEqKChwZUkAAABu49LgVVhYqLCwMMfj0NBQFRYW1tn+tdde07Bhw1xZEgAAgNt4ufLk\nFovF6bZffPGFFi1apPXr17uwIgAAAPdxafAKCQmR1Wp1PLZarQoNDa3RbuvWrZo4caIyMzMVEBBQ\n67lSUlIcX8fGxio2NrapywUAADhn2dnZys7OdqqtSyfXV1RUqEePHsrKylLXrl01YMCAGpPr9+3b\np5tuuknp6em69tpray+SyfUAAKCZqC+3uLTHy8vLSwsWLFB8fLwqKys1fvx4RUVFKTU1VZKUnJys\nP//5zzpy5IgmTZokSfL29lZubq4rywIAAHALl/Z4NRV6vAAAQHPhtuUkAAAAcBrBCwAAwCQELwAA\nAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAkxC8AAAA\nTELwAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAw\nCcELAADAJAQvAAAAkxC8AAAATELwAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsAAMAk\nBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAkxC8AAAATELwAgAAMAnBCwAAwCQELwAAAJMQ\nvAAAAExC8AIAADAJwQsAAMAkBC8AAACTELwAAABMQvACAAAwCcELAADAJAQvAAAAk7g0eGVmZqpn\nz56KjIzU/Pnza20zZcoURUZGKiYmRlu2bHFlOQAAAG7lsuBVWVmpyZMnKzMzU9u3b9eSJUv0/fff\nV2uzcuVK7dy5U/n5+Xr11Vc1adIkV5UDAADgdi4LXrm5uYqIiFB4eLi8vb2VmJiojIyMam1WrFih\nu+++W5I0cOBAFRUV6cCBA64qCQAAwK1cFrwKCwsVFhbmeBwaGqrCwsIG2xQUFLiqJAAAALfyctWJ\nLRaLU+0Mwzivn5OkwMBAHTly5JzqAtwtICBAhw8fdncZAAA3cFnwCgkJkdVqdTy2Wq0KDQ2tt01B\nQYFCQkJqPV9KSorj69jYWMXGxurIkSM1ghtwoTuXf1wAAC582dnZys7OdqqtxXBRcqmoqFCPHj2U\nlZWlrl27asCAAVqyZImioqIcbVauXKkFCxZo5cqV2rhxo6ZOnaqNGzfWLNJiqTVg1fV94ELG7y0A\neLb6/j/vsh4vLy8vLViwQPHx8aqsrNT48eMVFRWl1NRUSVJycrKGDRumlStXKiIiQr6+vkpLS3NV\nOQAAAG7nsh6vpkSPFzwJv7cA4Nnq+/88K9c3Y2+++abi4+MbbDdp0iQ99thjJlRkjvDwcH3++eeS\nbHP/7rzzTjdXBACAcwheLhIeHq727dvLz89PF198scaOHavjx4836WuMHj1aq1atarDdyy+/rNmz\nZzfpa9u1atVKHTp0kJ+fn0JCQjRlyhRVVFS45LXszpyczkR1AEBzQvByEYvFoo8++kglJSXavHmz\nNm3aVGuvk6tDihm2bt2qkpIS5eTkaPny5Xr11VdNe20zhuw84e8IAHBh8NjgZRiGZsx4slEfzE1x\nDknq2rWrEhIStG3bNkm2XqKXXnpJkZGR6tGjhyTpo48+Ut++fRUQEKDrrrtO3377rePnrVarRowY\noYsuukjBwcF64IEHJEmvv/66brjhBketDz74oLp06SJ/f3/16dNH27dvlyTdc889evTRRx3nW7hw\noSIjIxUUFKTf/OY32r9/v+O5Vq1aKTU1VVdccYUCAgI0efJkp99n9+7ddd111zle93zf165du3TT\nTTcpODhYnTt31pgxY3T06FGn6zhTRkaG+vbtK39/f0VERGj16tWSbD2SWVlZjnZnDlnu2bNHrVq1\n0qJFi9StWzfFxcVp2LBhevHFF6udOyYmRh988IEkaceOHRo6dKiCgoLUs2dPvfvuu+dVLwDAs3ls\n8HrvvVV66aX9Wr58tdvOYQ9sVqtVn3zyia666irHcxkZGfr666+1fft2bdmyRePHj9fChQt1+PBh\nJScn67bbblN5ebkqKys1fPhwXXbZZdq7d68KCws1atSoGq+1evVqrV27Vvn5+Tp69KjeffddBQYG\nSrL1vtmH5D7//HPNmjVL7777rvbv369u3bopMTGx2rk+/vhjbdq0SVu3btU777zT4HCm/X3u2LFD\na9eu1YABAyTpnN/XmXU88sgj2r9/v77//ntZrdZq67g5Kzc3V3fffbeefvppHT16VDk5OerWrVuN\na2J/fLacnBzt2LFDq1at0qhRo7RkyRLHc9u3b9e+fft0yy236Pjx4xo6dKjGjBmj//znP1q6dKnu\nu+++GnuTAgAgoxmoq8zavv/KK4uNXr1uMSIjZxlSlREZOcvo1esW45VXFjv9ek1xjm7duhkdOnQw\nOnXqZHTr1s24//77jbKyMsMwDMNisRhffPGFo+29995rPProo9V+vkePHsaaNWuMDRs2GJ07dzYq\nKytrvEZaWppx/fXXG4ZhGFlZWcYVV1xhbNy4sUbbe+65x3H+cePGGQ8//LDjuWPHjhne3t7G3r17\nHbWtX7/e8fzvf/9744knnqjzfVosFqNjx46Gr6+vYbFYjAceeKDR7+ts77//vnHVVVc5HoeHhxtZ\nWVmGYRjGnDlzjDFjxtT6c0lJSca0adNqfe7Mc5x9nt27dxsWi8XYvXu34/ni4mLD19fX2Ldvn2EY\nhjFr1ixj/PjxhmEYxtKlS40bbrihxmvPnTu31tduJv/ZAQDOU33/n/e4Hq+kpNFKSblfZWVVkizK\nz6/S9u2Tde+9o2WxSBaLVFfnSUqK7fl77x2t7dvvV36+7RxlZVWaO3eykpJGO12HxWJRRkaGjhw5\noj179mjBggVq27at4/kz96jcu3evnn76aQUEBDiOgoIC7d+/X1arVd26dVOrVvX/Vd10002aPHmy\n7r//fnXp0kXJyckqKSmp0c7ey2Xn6+uroKCgavtoXnzxxY6v27dv77gpoHfv3vLz85Ofn5/Wr1/v\naLNlyxYdO3ZMb7/9tt544w3t3bu3Ue/rwIEDSkxMVGhoqPz9/XXnnXfq0KFD9b7/2hQUFKh79+7n\n/HN2Z/4d+fn56ZZbbnH0ei1dulSjR9t+H/bu3auvvvqq2vt866232PAdAFCDxwUv+xBSUVGZevWa\nJj+/E1q2zCLDsMgwJMOoP3jZ2lj07rsW+fnZzlFUdKLG0FRT1Gl36aWX6pFHHtGRI0ccx7Fjx/SH\nP/xBYWFh2rdvnyorKxs85wMPPKBNmzZp+/bt+ve//62nnnqqRpuuXbtqz549jsfHjx/XoUOH6tyq\nSTo9lLht2zaVlJSopKRE1113XY12d9xxh4YPH+4YFjzf9zVr1iy1bt1a3333nY4eParFixerqqqq\nwfd/trCwMO3cubPW53x9favdZfrzzz/XaHP237d9uPHLL79UWVmZhgwZ4nifgwcPrvY+S0pKaswJ\nAwDA44KXJOXnW5WWlqDvvntaaWk3Kz/f2vAPueAczpo4caJeeeUV5ebmyjAMHT9+XB9//LGOHTum\ngQMH6pJLLtGMGTNUWlqqsrIybdiwocY5Nm3apK+++krl5eVq37692rVrp9atW0uyBSd7eBo1apTS\n0tKUl5enkydPatasWbr22mt16aWX1lqbcY43FsyYMUNLlixRQUHBeb+vY8eOydfXVx07dlRhYWGt\nAdIZ48ePV1pamj7//HNVVVWpsLBQP/zwgySpb9++Wrp0qSoqKrRp0ya99957DQbrYcOGae/evZoz\nZ061+WjDhw/Xv//9b6Wnp6u8vFzl5eX6+uuvtWPHjvOqGwDguTwyeM2cOVEjR8bLYrFo5Mh4zZgx\nwS3nqMvZH/DXXHONFi5cqMmTJyswMFCRkZF64403JNnuMvzwww+1c+dOXXrppQoLC9M777zjOI/9\nXMXFxUpKSlJgYKDCw8MVHByshx56qEa7uLg4/eUvf9HIkSPVtWtX7d69W0uXLq2ztoZ6+s5+Ljo6\nWjfddJOeeeaZ835fc+bM0ebNm+Xv769bb71VI0eOrLOG+urr37+/0tLS9OCDD6pTp06KjY3Vvn37\nJEl/+ctftGvXLgUEBCglJcUxbFjX+5KkNm3aaMSIEcrKytL//M//OL7foUMHrV69WkuXLlVISIgu\nueQSzZw5U6dOnarzugEAWia2DAJMxu8tAHg2tgwCAAC4ABC8AAAATELwAgAAMAnBCwAAwCQELwAA\nAJMQvAAAAExC8AIAADCJl7sLaIyAgIAm3cYHMENAQIC7SwAAuEmzXkAVAADgQsMCqm6SnZ3t7hJa\nHK65+bjm5uOam49rbj5PveYELxfy1F+aCxnX3Hxcc/Nxzc3HNTefp15zghcAAIBJCF4AAAAmaRaT\n62NjY7VmzRp3lwEAANCgwYMH1zlU2iyCFwAAgCdgqBEAAMAkBC8AAACTELyaQGZmpnr27KnIyEjN\nnz+/1jZTpkxRZGSkYmJitGXLFpMr9DwNXfM333xTMTEx6tOnj6677jpt3brVDVV6Fmd+zyXp66+/\nlpeXl5YvX25idZ7JmWuenZ2tq666StHR0YqNjTW3QA/U0DU/ePCgEhIS1LdvX0VHR+v11183v0gP\nMm7cOHXp0kVXXnllnW087vPTQKNUVFQY3bt3N3bv3m2cOnXKiImJMbZv316tzccff2zcfPPNhmEY\nxsaNG42BAwe6o1SP4cw137Bhg1FUVGQYhmF88sknXPNGcuaa29sNGTLEuOWWW4xly5a5oVLP4cw1\nP3LkiNGrVy/DarUahmEY//nPf9xRqsdw5prPmTPHmDFjhmEYtusdGBholJeXu6Ncj5CTk2Ns3rzZ\niI6OrvV5T/z8pMerkXJzcxUREaHw8HB5e3srMTFRGRkZ1dqsWLFCd999tyRp4MCBKioq0oEDB9xR\nrkdw5poPGjRI/v7+kmzXvKCgwB2legxnrrkkvfDCC7r99tvVuXNnN1TpWZy55m+99ZZGjhyp0NBQ\nSVJwcLA7SvUYzlzzSy65RMXFxZKk4uJiBQUFycurWW977FY33HBDvfvXeuLnJ8GrkQoLCxUWFuZ4\nHBoaqsLCwgbbEATOnzPX/Eyvvfaahg0bZkZpHsvZ3/OMjAxNmjRJktjAvpGcueb5+fk6fPiwhgwZ\non79+mnx4sVml+lRnLnmEydO1LZt29S1a1fFxMToueeeM7vMFsUTPz+J6Y3k7IeLcdaqHXwonb9z\nuXZffPGFFi1apPXr17uwIs/nzDWfOnWqnnjiCcfmsGf/zuPcOHPNy8vLtXnzZmVlZam0tFSDBg3S\ntddeq8jISBMq9DzOXPPHH39cffv2VXZ2tnbt2qWhQ4cqLy9Pfn5+JlTYMnna5yfBq5FCQkJktVod\nj61Wq6Pbv642BQUFCgkJMa1GT+PMNZekrVu3auLEicrMzKy3KxsNc+aaf/PNN0pMTJRkm4D8ySef\nyNvbW7fddpuptXoKZ655WFiYgoOD5ePjIx8fH914443Ky8sjeJ0nZ675hg0b9Mgjj0iSunfvrssu\nu0w//PCD+vXrZ2qtLYVHfn66d4pZ81deXm5cfvnlxu7du42TJ082OLn+yy+/9IjJge7kzDXfu3ev\n0b17d+PLL790U5WexZlrfqZ77rnHeO+990ys0PM4c82///57Iy4uzqioqDCOHz9uREdHG9u2bXNT\nxc2fM9f8wQcfNFJSUgzDMIyff/7ZCAkJMQ4dOuSOcj3G7t27nZpc7ymfn/R4NZKXl5cWLFig+Ph4\nVVZWavz48YqKilJqaqokKTk5WcOGDdPKlSsVEREhX19fpaWlubnq5s2Za/7nP/9ZR44cccw38vb2\nVm5urjvLbtacueZoWs5c8549eyohIUF9+vRRq1atNHHiRPXq1cvNlTdfzlzzWbNmaezYsYqJiVFV\nVZWefPJJBQYGurny5mvUqFFas2aNDh48qLCwMM2dO1fl5eWSPPfzky2DAAAATMJdjQAAACYheAEA\nAJiE4AUAAGASghcAAIBJCF4AAAAmIXgBAACYhOAFoNlr3bq1rrrqKvXp00cjRozQsWPHmvT84eHh\nOnz4sCSpQ4cOTXpuAC0LwQtAs9e+fXtt2bJFW7duVceOHR0LXjaVM/eGa+77xAFwL4IXAI8yaNAg\n7dq1S5K0a9cu3XzzzerXr59uvPFG/fDDD5KkAwcO6He/+5369u2rvn37auPGjZKk3/3ud+rXr5+i\no6O1cOFCt70HAJ6LLYMAeIzKykqtXr1acXFxkqSkpCSlpqYqIiJCX331le677z5lZWVpypQpGjJk\niN5//31VVVU5hiYXLVqkgIAAnThxQgMGDNDtt9/OBusAmhRbBgFo9ry8vHTllVeqsLBQ4eHh2rhx\no0pLS3XRRRepR48ejnanTp3Stm3bdNFFF6mwsFDe3t7VzpOSkqIPPvhAkrRnzx6tXr1aAwYM0GWX\nXaZvvvlGgYGB8vPzU0lJianvD4DnoMcLQLPn4+OjLVu26MSJE4qPj1dGRoZ+9atfqVOnTtqyZUut\nP3P2vzmzs7OVlZWljRs3ql27dhoyZIjKysrMKB9AC8IcLwAew8fHR88//7weeeQRdejQQZdddpmW\nLVsmyRa0tm7dKkmKi4vTyy+/LMk2PFlcXKzi4mIFBASoXbt22rFjh2PeFwA0JYIXgGbvzDsN+/bt\nq4iICL3zzjt688039dprr6lv376Kjo7WihUrJEnPPfecvvjiC/Xp00f9+vXT999/r4SEBFVUVKhX\nr16aOXOmBg0a1OBrAcC5Yo4XAACASejxAgAAMAnBCwAAwCQELwAAAJMQvAAAAExC8AIAADAJwQsA\nAMAkBC8AAACTELwAAABM8v/eCWHPBhALUAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x119e42810>"
       ]
      }
     ],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "with open('logreg.pickle','w') as f:\n",
      "    pickle.dump(champlogreg, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print X_test[0][4]\n",
      "print logreg.predict_proba(X_test[0]), y_test[0]\n",
      "print X_test[1][4]\n",
      "print logreg.predict_proba(X_test[1]), y_test[1]\n",
      "print X_test[2][4]\n",
      "print logreg.predict_proba(X_test[2]), y_test[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-0.829143276759\n",
        "[[ 0.09657281  0.90342719]] 1\n",
        "-0.834795497785\n",
        "[[ 0.33812389  0.66187611]] 1\n",
        "-0.823360227499\n",
        "[[ 0.01838904  0.98161096]] 1\n"
       ]
      }
     ],
     "prompt_number": 269
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_test.sum() / float(y_test.size)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 270,
       "text": [
        "0.50992953235105698"
       ]
      }
     ],
     "prompt_number": 270
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "feature_number = len(X_test[0])\n",
      "print feature_number"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "45\n"
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_X.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "Index([u'from_rating', u'from_review_count', u'to_rating', u'to_review_count', u'distance'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "What are the important features?\n",
      "'''\n",
      "all_scores = []\n",
      "for i in range(feature_number):\n",
      "    logreg = LogisticRegression(class_weight = 'auto')\n",
      "    Xs = [[x[i]] for x in X]\n",
      "    feature = df_X.columns[i]\n",
      "    Xs_train, Xs_test, y_train, y_test = train_test_split(Xs, y, test_size=0.1)\n",
      "    logreg.fit(Xs_train, y_train)\n",
      "    score = logreg.score(Xs_test,y_test)\n",
      "    all_scores.append((score, feature, i))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sorted(all_scores, key = lambda x: x[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(0.48494554772581677, 'banks_from', 7), (0.48537262438607731, 'hair_from', 14), (0.4857997010463378, 'skincare_from', 23), (0.48686739269698909, 'from_review_count', 1), (0.48921631432842194, 'chinese_from', 9), (0.48964339098868248, 'womenscloth_to', 44), (0.49028400597907323, 'beer_and_wine_from', 8), (0.49028400597907323, 'realestateagents_from', 20), (0.49241938928037582, 'sandwiches_from', 22), (0.49348708093102711, 'jewelry_from', 15), (0.49391415759128765, 'coffee_from', 10), (0.49519538757206921, 'mexican_from', 17), (0.4962630792227205, 'massage_from', 16), (0.4962630792227205, 'pizza_to', 39), (0.49839846252402309, 'drycleaninglaundry_from', 12), (0.4986120008541533, 'sandwiches_to', 42), (0.49903907751441384, 'pizza_from', 19), (0.49925261584454411, 'mexican_to', 37), (0.49946615417467438, 'contractors_from', 11), (0.50010676916506513, 'skincare_to', 43), (0.50181507580610718, 'apartments_from', 5), (0.50352338244714923, 'chinese_to', 29), (0.50416399743754003, 'realestatesvcs_to', 41), (0.50459107409780057, 'bakeries_from', 6), (0.50480461242793084, 'contractors_to', 31), (0.50480461242793084, 'othersalons_to', 38), (0.50565876574845181, 'realestatesvcs_from', 21), (0.50565876574845181, 'bakeries_to', 26), (0.50587230407858208, 'hair_to', 34), (0.50608584240871235, 'grocery_from', 13), (0.50672645739910316, 'womenscloth_from', 24), (0.5077941490497544, 'othersalons_from', 18), (0.50886184070040574, 'massage_to', 36), (0.50950245569079655, 'banks_to', 27), (0.51035660901131752, 'from_rating', 0), (0.51334614563314118, 'realestateagents_to', 40), (0.51398676062353188, 'beer_and_wine_to', 28), (0.51420029895366215, 'apartments_to', 25), (0.5152679906043135, 'drycleaninglaundry_to', 32), (0.51590860559470419, 'jewelry_to', 35), (0.54815289344437323, 'grocery_to', 33), (0.56865257313687811, 'to_rating', 2), (0.57441810805039506, 'coffee_to', 30), (0.76083707025411063, 'to_review_count', 3), (0.86632500533845824, 'distance', 4)]\n"
       ]
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#checking categories\n",
      "all_results = []\n",
      "for i in range(5,25):\n",
      "    print i\n",
      "    for j in range(25,45):\n",
      "        logreg = LogisticRegression(class_weight = 'auto')\n",
      "        Xs = [[x[i],x[j]] for x in X]\n",
      "        Xs_train, Xs_test, y_train, y_test = train_test_split(Xs, y, test_size=0.1)\n",
      "        # print Xs_train.shape, y_train.shape\n",
      "        logreg.fit(Xs_train, y_train)\n",
      "        score = logreg.score(Xs_test,y_test)\n",
      "        all_results.append((score, df_X.columns[i],df_X.columns[j]))\n",
      "print len(all_results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "7"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "9"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "11"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "12"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "14"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "15"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "16"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "17"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "18"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "19"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "20"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "21"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "22"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "23"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "24"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "400"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sorted(all_results, key = lambda x: x[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(0.48216954943412343, 'coffee_from', 'mexican_to'), (0.48494554772581677, 'drycleaninglaundry_from', 'mexican_to'), (0.48537262438607731, 'realestatesvcs_from', 'mexican_to'), (0.48814862267777065, 'sandwiches_from', 'mexican_to'), (0.48878923766816146, 'womenscloth_from', 'chinese_to'), (0.48985692931881275, 'mexican_from', 'pizza_to'), (0.48985692931881275, 'skincare_from', 'sandwiches_to'), (0.49007046764894296, 'pizza_from', 'jewelry_to'), (0.49092462096946404, 'jewelry_from', 'mexican_to'), (0.49113815929959426, 'massage_from', 'sandwiches_to'), (0.49135169762972453, 'mexican_from', 'mexican_to'), (0.49220585095024555, 'mexican_from', 'realestatesvcs_to'), (0.49241938928037582, 'chinese_from', 'pizza_to'), (0.49263292761050609, 'skincare_from', 'realestatesvcs_to'), (0.49284646594063636, 'bakeries_from', 'jewelry_to'), (0.49284646594063636, 'grocery_from', 'massage_to'), (0.49391415759128765, 'bakeries_from', 'womenscloth_to'), (0.49434123425154813, 'hair_from', 'womenscloth_to'), (0.49434123425154813, 'mexican_from', 'womenscloth_to'), (0.49498184924193894, 'skincare_from', 'womenscloth_to'), (0.49519538757206921, 'beer_and_wine_from', 'mexican_to'), (0.49519538757206921, 'mexican_from', 'jewelry_to'), (0.49519538757206921, 'realestatesvcs_from', 'sandwiches_to'), (0.49519538757206921, 'skincare_from', 'mexican_to'), (0.49540892590219943, 'banks_from', 'pizza_to'), (0.49540892590219943, 'jewelry_from', 'sandwiches_to'), (0.49540892590219943, 'sandwiches_from', 'pizza_to'), (0.4956224642323297, 'chinese_from', 'womenscloth_to'), (0.4956224642323297, 'grocery_from', 'mexican_to'), (0.49583600256245997, 'grocery_from', 'sandwiches_to'), (0.4962630792227205, 'coffee_from', 'realestatesvcs_to'), (0.49647661755285072, 'womenscloth_from', 'realestatesvcs_to'), (0.49690369421311126, 'massage_from', 'chinese_to'), (0.49711723254324153, 'banks_from', 'realestatesvcs_to'), (0.49711723254324153, 'massage_from', 'realestatesvcs_to'), (0.49754430920350201, 'chinese_from', 'jewelry_to'), (0.49754430920350201, 'realestateagents_from', 'sandwiches_to'), (0.49754430920350201, 'skincare_from', 'pizza_to'), (0.49775784753363228, 'beer_and_wine_from', 'womenscloth_to'), (0.49775784753363228, 'mexican_from', 'drycleaninglaundry_to'), (0.49797138586376255, 'apartments_from', 'sandwiches_to'), (0.49797138586376255, 'hair_from', 'banks_to'), (0.49797138586376255, 'massage_from', 'mexican_to'), (0.49818492419389282, 'realestateagents_from', 'mexican_to'), (0.49818492419389282, 'skincare_from', 'jewelry_to'), (0.49839846252402309, 'coffee_from', 'sandwiches_to'), (0.49839846252402309, 'hair_from', 'pizza_to'), (0.49839846252402309, 'pizza_from', 'sandwiches_to'), (0.49882553918428357, 'grocery_from', 'womenscloth_to'), (0.49925261584454411, 'hair_from', 'mexican_to'), (0.49946615417467438, 'beer_and_wine_from', 'jewelry_to'), (0.49989323083493487, 'pizza_from', 'chinese_to'), (0.50010676916506513, 'banks_from', 'chinese_to'), (0.50010676916506513, 'contractors_from', 'pizza_to'), (0.50010676916506513, 'massage_from', 'pizza_to'), (0.50010676916506513, 'womenscloth_from', 'drycleaninglaundry_to'), (0.50010676916506513, 'womenscloth_from', 'mexican_to'), (0.5003203074951954, 'bakeries_from', 'pizza_to'), (0.50053384582532567, 'chinese_from', 'realestatesvcs_to'), (0.50053384582532567, 'drycleaninglaundry_from', 'pizza_to'), (0.50074738415545594, 'chinese_from', 'massage_to'), (0.50074738415545594, 'massage_from', 'banks_to'), (0.50074738415545594, 'womenscloth_from', 'sandwiches_to'), (0.50096092248558621, 'contractors_from', 'mexican_to'), (0.50096092248558621, 'hair_from', 'chinese_to'), (0.50096092248558621, 'othersalons_from', 'realestatesvcs_to'), (0.50117446081571637, 'apartments_from', 'mexican_to'), (0.50117446081571637, 'chinese_from', 'beer_and_wine_to'), (0.50138799914584664, 'coffee_from', 'chinese_to'), (0.50138799914584664, 'drycleaninglaundry_from', 'chinese_to'), (0.50138799914584664, 'drycleaninglaundry_from', 'womenscloth_to'), (0.50138799914584664, 'realestateagents_from', 'pizza_to'), (0.50160153747597691, 'beer_and_wine_from', 'beer_and_wine_to'), (0.50160153747597691, 'chinese_from', 'skincare_to'), (0.50160153747597691, 'contractors_from', 'beer_and_wine_to'), (0.50181507580610718, 'jewelry_from', 'apartments_to'), (0.50181507580610718, 'realestateagents_from', 'jewelry_to'), (0.50202861413623745, 'apartments_from', 'banks_to'), (0.50202861413623745, 'othersalons_from', 'beer_and_wine_to'), (0.50202861413623745, 'womenscloth_from', 'jewelry_to'), (0.50224215246636772, 'apartments_from', 'massage_to'), (0.50224215246636772, 'banks_from', 'mexican_to'), (0.50224215246636772, 'beer_and_wine_from', 'realestatesvcs_to'), (0.50288276745675853, 'skincare_from', 'chinese_to'), (0.5030963057868888, 'pizza_from', 'massage_to'), (0.50330984411701896, 'drycleaninglaundry_from', 'contractors_to'), (0.50330984411701896, 'massage_from', 'realestateagents_to'), (0.50352338244714923, 'bakeries_from', 'beer_and_wine_to'), (0.50352338244714923, 'bakeries_from', 'realestatesvcs_to'), (0.50352338244714923, 'bakeries_from', 'skincare_to'), (0.50352338244714923, 'pizza_from', 'contractors_to'), (0.50352338244714923, 'skincare_from', 'beer_and_wine_to'), (0.5037369207772795, 'chinese_from', 'apartments_to'), (0.5037369207772795, 'chinese_from', 'banks_to'), (0.5037369207772795, 'coffee_from', 'beer_and_wine_to'), (0.5037369207772795, 'massage_from', 'bakeries_to'), (0.5037369207772795, 'pizza_from', 'skincare_to'), (0.50395045910740977, 'grocery_from', 'realestateagents_to'), (0.50395045910740977, 'hair_from', 'bakeries_to'), (0.50395045910740977, 'jewelry_from', 'pizza_to'), (0.50416399743754003, 'sandwiches_from', 'bakeries_to'), (0.5043775357676703, 'beer_and_wine_from', 'skincare_to'), (0.5043775357676703, 'mexican_from', 'sandwiches_to'), (0.5043775357676703, 'sandwiches_from', 'massage_to'), (0.50459107409780057, 'banks_from', 'womenscloth_to'), (0.50459107409780057, 'grocery_from', 'chinese_to'), (0.50459107409780057, 'pizza_from', 'womenscloth_to'), (0.50459107409780057, 'sandwiches_from', 'beer_and_wine_to'), (0.50480461242793084, 'pizza_from', 'mexican_to'), (0.50480461242793084, 'realestatesvcs_from', 'realestateagents_to'), (0.50501815075806111, 'coffee_from', 'apartments_to'), (0.50501815075806111, 'grocery_from', 'othersalons_to'), (0.50501815075806111, 'mexican_from', 'chinese_to'), (0.50523168908819138, 'beer_and_wine_from', 'chinese_to'), (0.50523168908819138, 'chinese_from', 'bakeries_to'), (0.50523168908819138, 'chinese_from', 'mexican_to'), (0.50523168908819138, 'othersalons_from', 'jewelry_to'), (0.50544522741832154, 'banks_from', 'skincare_to'), (0.50544522741832154, 'othersalons_from', 'sandwiches_to'), (0.50565876574845181, 'jewelry_from', 'jewelry_to'), (0.50565876574845181, 'realestatesvcs_from', 'othersalons_to'), (0.50587230407858208, 'apartments_from', 'contractors_to'), (0.50587230407858208, 'contractors_from', 'banks_to'), (0.50587230407858208, 'contractors_from', 'othersalons_to'), (0.50608584240871235, 'apartments_from', 'beer_and_wine_to'), (0.50608584240871235, 'realestateagents_from', 'drycleaninglaundry_to'), (0.50629938073884262, 'beer_and_wine_from', 'sandwiches_to'), (0.50629938073884262, 'coffee_from', 'pizza_to'), (0.50629938073884262, 'drycleaninglaundry_from', 'othersalons_to'), (0.50651291906897289, 'hair_from', 'sandwiches_to'), (0.50651291906897289, 'othersalons_from', 'realestateagents_to'), (0.50651291906897289, 'realestatesvcs_from', 'banks_to'), (0.50672645739910316, 'bakeries_from', 'sandwiches_to'), (0.50672645739910316, 'realestatesvcs_from', 'chinese_to'), (0.50693999572923343, 'chinese_from', 'chinese_to'), (0.50693999572923343, 'jewelry_from', 'banks_to'), (0.5071535340593637, 'apartments_from', 'apartments_to'), (0.5071535340593637, 'banks_from', 'sandwiches_to'), (0.5071535340593637, 'mexican_from', 'skincare_to'), (0.5071535340593637, 'sandwiches_from', 'apartments_to'), (0.50736707238949397, 'apartments_from', 'bakeries_to'), (0.50736707238949397, 'coffee_from', 'jewelry_to'), (0.50736707238949397, 'othersalons_from', 'womenscloth_to'), (0.50758061071962413, 'bakeries_from', 'mexican_to'), (0.50758061071962413, 'banks_from', 'jewelry_to'), (0.50758061071962413, 'contractors_from', 'chinese_to'), (0.50758061071962413, 'grocery_from', 'pizza_to'), (0.50758061071962413, 'jewelry_from', 'drycleaninglaundry_to'), (0.5077941490497544, 'sandwiches_from', 'sandwiches_to'), (0.5077941490497544, 'skincare_from', 'apartments_to'), (0.50800768737988466, 'contractors_from', 'sandwiches_to'), (0.50800768737988466, 'contractors_from', 'womenscloth_to'), (0.50800768737988466, 'massage_from', 'womenscloth_to'), (0.50800768737988466, 'mexican_from', 'realestateagents_to'), (0.50800768737988466, 'othersalons_from', 'apartments_to'), (0.50800768737988466, 'realestatesvcs_from', 'bakeries_to'), (0.50822122571001493, 'drycleaninglaundry_from', 'beer_and_wine_to'), (0.50822122571001493, 'womenscloth_from', 'banks_to'), (0.5084347640401452, 'beer_and_wine_from', 'apartments_to'), (0.5084347640401452, 'coffee_from', 'drycleaninglaundry_to'), (0.5084347640401452, 'realestatesvcs_from', 'apartments_to'), (0.5084347640401452, 'skincare_from', 'massage_to'), (0.50864830237027547, 'chinese_from', 'drycleaninglaundry_to'), (0.50864830237027547, 'hair_from', 'beer_and_wine_to'), (0.50864830237027547, 'hair_from', 'othersalons_to'), (0.50864830237027547, 'jewelry_from', 'beer_and_wine_to'), (0.50864830237027547, 'sandwiches_from', 'drycleaninglaundry_to'), (0.50886184070040574, 'drycleaninglaundry_from', 'realestatesvcs_to'), (0.50886184070040574, 'grocery_from', 'banks_to'), (0.50907537903053601, 'contractors_from', 'apartments_to'), (0.50907537903053601, 'drycleaninglaundry_from', 'massage_to'), (0.50907537903053601, 'realestateagents_from', 'realestatesvcs_to'), (0.50907537903053601, 'sandwiches_from', 'womenscloth_to'), (0.50907537903053601, 'womenscloth_from', 'womenscloth_to'), (0.50928891736066628, 'beer_and_wine_from', 'massage_to'), (0.50928891736066628, 'massage_from', 'massage_to'), (0.50928891736066628, 'sandwiches_from', 'banks_to'), (0.50928891736066628, 'skincare_from', 'banks_to'), (0.50950245569079655, 'bakeries_from', 'massage_to'), (0.50950245569079655, 'drycleaninglaundry_from', 'sandwiches_to'), (0.50950245569079655, 'jewelry_from', 'womenscloth_to'), (0.50950245569079655, 'massage_from', 'apartments_to'), (0.50950245569079655, 'massage_from', 'contractors_to'), (0.50950245569079655, 'realestatesvcs_from', 'massage_to'), (0.50950245569079655, 'womenscloth_from', 'contractors_to'), (0.50971599402092671, 'contractors_from', 'massage_to'), (0.50971599402092671, 'realestatesvcs_from', 'womenscloth_to'), (0.50971599402092671, 'sandwiches_from', 'othersalons_to'), (0.50992953235105698, 'chinese_from', 'sandwiches_to'), (0.50992953235105698, 'grocery_from', 'skincare_to'), (0.50992953235105698, 'hair_from', 'realestateagents_to'), (0.50992953235105698, 'jewelry_from', 'chinese_to'), (0.51014307068118725, 'apartments_from', 'womenscloth_to'), (0.51014307068118725, 'bakeries_from', 'apartments_to'), (0.51014307068118725, 'jewelry_from', 'realestatesvcs_to'), (0.51057014734144779, 'mexican_from', 'apartments_to'), (0.51057014734144779, 'othersalons_from', 'drycleaninglaundry_to'), (0.51057014734144779, 'sandwiches_from', 'realestatesvcs_to'), (0.51057014734144779, 'skincare_from', 'realestateagents_to'), (0.51078368567157806, 'banks_from', 'drycleaninglaundry_to'), (0.51078368567157806, 'beer_and_wine_from', 'pizza_to'), (0.51078368567157806, 'jewelry_from', 'realestateagents_to'), (0.51078368567157806, 'othersalons_from', 'mexican_to'), (0.51078368567157806, 'realestateagents_from', 'banks_to'), (0.51099722400170833, 'bakeries_from', 'othersalons_to'), (0.51099722400170833, 'beer_and_wine_from', 'drycleaninglaundry_to'), (0.51099722400170833, 'hair_from', 'realestatesvcs_to'), (0.51099722400170833, 'womenscloth_from', 'apartments_to'), (0.5112107623318386, 'coffee_from', 'realestateagents_to'), (0.5112107623318386, 'drycleaninglaundry_from', 'skincare_to'), (0.5112107623318386, 'jewelry_from', 'massage_to'), (0.5112107623318386, 'realestateagents_from', 'womenscloth_to'), (0.5112107623318386, 'skincare_from', 'skincare_to'), (0.51142430066196887, 'othersalons_from', 'skincare_to'), (0.51142430066196887, 'skincare_from', 'othersalons_to'), (0.51163783899209914, 'apartments_from', 'chinese_to'), (0.51163783899209914, 'contractors_from', 'jewelry_to'), (0.51163783899209914, 'womenscloth_from', 'beer_and_wine_to'), (0.51163783899209914, 'womenscloth_from', 'realestateagents_to'), (0.5118513773222293, 'grocery_from', 'bakeries_to'), (0.51206491565235956, 'apartments_from', 'pizza_to'), (0.51206491565235956, 'coffee_from', 'massage_to'), (0.51227845398248983, 'bakeries_from', 'drycleaninglaundry_to'), (0.51227845398248983, 'realestateagents_from', 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(0.51847106555626732, 'realestateagents_from', 'contractors_to'), (0.51847106555626732, 'realestatesvcs_from', 'hair_to'), (0.51889814221652786, 'banks_from', 'othersalons_to'), (0.51889814221652786, 'beer_and_wine_from', 'contractors_to'), (0.51889814221652786, 'othersalons_from', 'banks_to'), (0.51889814221652786, 'realestateagents_from', 'hair_to'), (0.51911168054665813, 'contractors_from', 'realestateagents_to'), (0.51911168054665813, 'realestateagents_from', 'skincare_to'), (0.5193252188767884, 'banks_from', 'banks_to'), (0.5193252188767884, 'hair_from', 'massage_to'), (0.51953875720691867, 'banks_from', 'apartments_to'), (0.51953875720691867, 'othersalons_from', 'chinese_to'), (0.51975229553704894, 'contractors_from', 'skincare_to'), (0.51975229553704894, 'realestatesvcs_from', 'contractors_to'), (0.51996583386717921, 'beer_and_wine_from', 'realestateagents_to'), (0.51996583386717921, 'drycleaninglaundry_from', 'hair_to'), (0.51996583386717921, 'realestatesvcs_from', 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(0.52252829382874222, 'apartments_from', 'othersalons_to'), (0.52252829382874222, 'hair_from', 'jewelry_to'), (0.52274183215887249, 'drycleaninglaundry_from', 'drycleaninglaundry_to'), (0.52295537048900276, 'womenscloth_from', 'skincare_to'), (0.52316890881913303, 'realestateagents_from', 'bakeries_to'), (0.5233824471492633, 'coffee_from', 'skincare_to'), (0.52359598547939357, 'skincare_from', 'contractors_to'), (0.52402306213965411, 'chinese_from', 'hair_to'), (0.52402306213965411, 'contractors_from', 'realestatesvcs_to'), (0.52402306213965411, 'grocery_from', 'beer_and_wine_to'), (0.52402306213965411, 'grocery_from', 'drycleaninglaundry_to'), (0.52402306213965411, 'mexican_from', 'contractors_to'), (0.5246636771300448, 'massage_from', 'hair_to'), (0.5246636771300448, 'skincare_from', 'drycleaninglaundry_to'), (0.52487721546017507, 'pizza_from', 'hair_to'), (0.52509075379030534, 'beer_and_wine_from', 'banks_to'), (0.52509075379030534, 'sandwiches_from', 'jewelry_to'), (0.52573136878069615, 'othersalons_from', 'massage_to'), (0.52594490711082642, 'mexican_from', 'hair_to'), (0.52594490711082642, 'othersalons_from', 'hair_to'), (0.52658552210121712, 'pizza_from', 'realestateagents_to'), (0.52679906043134739, 'grocery_from', 'jewelry_to'), (0.52765321375186847, 'grocery_from', 'realestatesvcs_to'), (0.52786675208199874, 'grocery_from', 'hair_to'), (0.5287209054025197, 'pizza_from', 'drycleaninglaundry_to'), (0.53106982703395256, 'beer_and_wine_from', 'hair_to'), (0.53128336536408283, 'contractors_from', 'hair_to'), (0.53320521033525514, 'apartments_from', 'hair_to'), (0.53341874866538541, 'jewelry_from', 'contractors_to'), (0.53384582532564595, 'bakeries_from', 'hair_to'), (0.53427290198590649, 'jewelry_from', 'hair_to'), (0.535554131966688, 'womenscloth_from', 'hair_to'), (0.5361947469570788, 'coffee_from', 'hair_to'), (0.5361947469570788, 'grocery_from', 'contractors_to'), (0.54559043348281011, 'beer_and_wine_from', 'coffee_to'), (0.54559043348281011, 'realestatesvcs_from', 'grocery_to'), (0.54601751014307065, 'womenscloth_from', 'grocery_to'), (0.55028827674567582, 'drycleaninglaundry_from', 'grocery_to'), (0.55028827674567582, 'grocery_from', 'grocery_to'), (0.55092889173606663, 'realestateagents_from', 'coffee_to'), (0.55135596839632717, 'bakeries_from', 'grocery_to'), (0.55156950672645744, 'realestateagents_from', 'grocery_to'), (0.55263719837710867, 'chinese_from', 'grocery_to'), (0.55263719837710867, 'mexican_from', 'grocery_to'), (0.55263719837710867, 'othersalons_from', 'grocery_to'), (0.55285073670723894, 'coffee_from', 'grocery_to'), (0.55477258167841126, 'jewelry_from', 'coffee_to'), (0.55477258167841126, 'skincare_from', 'grocery_to'), (0.5551996583386718, 'apartments_from', 'grocery_to'), (0.5551996583386718, 'chinese_from', 'coffee_to'), (0.5551996583386718, 'sandwiches_from', 'grocery_to'), (0.55584027332906261, 'banks_from', 'grocery_to'), (0.55584027332906261, 'pizza_from', 'grocery_to'), (0.55669442664958357, 'contractors_from', 'coffee_to'), (0.55733504163997438, 'beer_and_wine_from', 'grocery_to'), (0.55797565663036519, 'coffee_from', 'coffee_to'), (0.55818919496049546, 'apartments_from', 'coffee_to'), (0.55818919496049546, 'skincare_from', 'coffee_to'), (0.55861627162075589, 'contractors_from', 'grocery_to'), (0.56011103993166778, 'banks_from', 'coffee_to'), (0.56032457826179805, 'othersalons_from', 'coffee_to'), (0.5605381165919282, 'hair_from', 'grocery_to'), (0.56267349989323079, 'massage_from', 'coffee_to'), (0.56267349989323079, 'massage_from', 'grocery_to'), (0.56310057655349133, 'sandwiches_from', 'coffee_to'), (0.5633141148836216, 'pizza_from', 'coffee_to'), (0.56907964979713854, 'mexican_from', 'coffee_to'), (0.56993380311765962, 'jewelry_from', 'grocery_to'), (0.57014734144778989, 'hair_from', 'coffee_to'), (0.57100149476831086, 'bakeries_from', 'coffee_to'), (0.57206918641896221, 'grocery_from', 'coffee_to'), (0.57270980140935301, 'drycleaninglaundry_from', 'coffee_to'), (0.57292333973948328, 'realestatesvcs_from', 'coffee_to'), (0.57804825966260942, 'womenscloth_from', 'coffee_to')]\n"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''Using Tree classifier to find important features'''\n",
      "clf = ExtraTreesClassifier()\n",
      "X_new = clf.fit(X, y).transform(X)\n",
      "df_X.columns[clf.feature_importances_.argsort()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 279,
       "text": [
        "Index([u'realestatesvcs_from', u'contractors_from', u'realestateagents_from', u'apartments_from', u'skincare_from', u'drycleaninglaundry_from', u'hair_from', u'massage_from', u'othersalons_from', u'jewelry_from', u'banks_from', u'beer_and_wine_from', u'pizza_to', u'bakeries_from', u'chinese_from', u'pizza_from', u'mexican_to', u'sandwiches_from', u'beer_and_wine_to', u'womenscloth_from', u'mexican_from', u'realestatesvcs_to', u'womenscloth_to', u'grocery_from', u'sandwiches_to', u'chinese_to', u'bakeries_to', u'coffee_from', u'jewelry_to', u'skincare_to', u'banks_to', u'apartments_to', u'othersalons_to', u'realestateagents_to', u'massage_to', u'contractors_to', u'drycleaninglaundry_to', u'from_rating', u'hair_to', u'grocery_to', u'from_review_count', u'coffee_to', u'to_rating', u'to_review_count', u'distance'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 279
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'''\n",
      "Finding the most likely next place for every entry\n",
      "'''\n",
      "yelp_filling = pd.read_csv('yelp.csv')[['id','rating','review_count','latitude','longitude']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 307
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yelp_filling = yelp_filling.merge(merged_cats, left_on = 'id', right_index = True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 308
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print yelp_filling.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(59311, 25)\n"
       ]
      }
     ],
     "prompt_number": 309
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#normalzing the arrays\n",
      "df_from = yelp_filling.copy()\n",
      "df_to = yelp_filling.copy()\n",
      "\n",
      "df_from.rating = (df_from.rating - from_rating_mean) / from_rating_std\n",
      "df_from.review_count = (df_from.review_count - from_count_mean) / from_count_std\n",
      "df_to.rating = (df_to.rating - to_rating_mean) / to_rating_std\n",
      "df_to.review_count = (df_to.review_count - to_count_mean) / to_count_std"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 310
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#http://stackoverflow.com/questions/12329853/how-to-rearrange-pandas-column-sequence\n",
      "def set_column_sequence(dataframe, seq):\n",
      "    '''Takes a dataframe and a subsequence of its columns, returns dataframe with seq as first columns'''\n",
      "    cols = seq[:] # copy so we don't mutate seq\n",
      "    for x in dataframe.columns:\n",
      "        if x not in cols:\n",
      "            cols.append(x)\n",
      "    return dataframe[cols]\n",
      "\n",
      "def score_prob(row_number, row1):\n",
      "    left = pd.DataFrame([row1], index = df_to.index)\n",
      "    merged = pd.merge(left, df_to, left_index = True, right_index=True, suffixes=('_from','_to'))\n",
      "    merged['distance'] = np.sqrt((merged.latitude_from - merged.latitude_to)**2 + (merged.longitude_from - merged.longitude_to)**2)\n",
      "    merged['distance'] = (merged['distance'] - distance_mean) / disance_std\n",
      "    merged = merged.drop(['id_from','id_to', 'latitude_from','latitude_to', 'longitude_from','longitude_to'], axis = 1)\n",
      "    #matching columns to the train set\n",
      "    merged.rename(columns={'rating_from': 'from_rating', 'rating_to': 'to_rating','review_count_from':'from_review_count',\n",
      "                           'review_count_to':'to_review_count'}, inplace=True)\n",
      "    merged = set_column_sequence(merged, df_X.columns)\n",
      "    X = merged.values\n",
      "    #hacky way to exclude crazy outlier values\n",
      "    X = np.clip(X, -2, 2)\n",
      "    prob_match = champlogreg.predict_proba(X)[:,1]\n",
      "    #exclude the 'from' place from the list\n",
      "    prob_match[row_number] = 0\n",
      "    best_match, prob = df_to.id.ix[prob_match.argmax()], prob_match[prob_match.argmax()]\n",
      "    return (row1.id, best_match, prob)\n",
      "    \n",
      "all_generated_matches = []\n",
      "for i,row in df_from.iterrows():\n",
      "    from_id, to_id, prob = score_prob(i, row)\n",
      "    all_generated_matches.append((from_id, to_id, prob))\n",
      "    if i % 100 == 0:\n",
      "        print i,from_id, to_id, prob"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 sf-fire-credit-union-san-francisco spruce-san-francisco 0.999525607562\n",
        "100"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1st-united-services-credit-union-oakland-5 berkeley-bowl-berkeley 0.999414356168\n",
        "200"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " chase-san-francisco-92 magnolia-pub-and-brewery-san-francisco 0.99989123348\n",
        "300"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " wells-fargo-bank-south-san-francisco-2 little-lucca-sandwich-shop-and-deli-south-san-francisco 0.999668996378\n",
        "400"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " citibank-west-f-s-b-daly-city-2 kome-japanese-seafood-and-grill-buffet-daly-city 0.999794881429\n",
        "500"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " first-republic-bank-redwood-city-2 old-port-lobster-shack-redwood-city 0.999631322139\n",
        "600"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " bank-of-the-west-oakland-3 mua-oakland-2 0.999893280147\n",
        "700"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1st-united-services-san-leandro zocalo-coffeehouse-san-leandro-2 0.991492450214\n",
        "800"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " wells-fargo-bank-palo-alto-6 sprinkles-cupcakes-palo-alto 0.999838824482\n",
        "900"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " us-bank-sunnyvale-2 dishdash-sunnyvale 0.999701743346\n",
        "1000"
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        " third-avenue-news-san-mateo tpumps-san-mateo 0.999967013196\n",
        "58600"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " patio-classics-san-mateo tpumps-san-mateo 0.999948927567\n",
        "58700"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " chinaville-richmond t-rex-barbeque-restaurant-and-bar-berkeley 0.994487313194\n",
        "58800"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " mod-lang-el-cerrito t-rex-barbeque-restaurant-and-bar-berkeley 0.994994053036\n",
        "58900"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " sears-san-mateo santa-ramen-san-mateo-2 0.999738787508\n",
        "59000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " skyline-vape-berkeley viks-chaat-berkeley 0.999052191628\n",
        "59100"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " bebe-emeryville rudys-cant-fail-caf\u00e9-emeryville-2 0.99984370009\n",
        "59200"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " papy-boez-outlet-berkeley berkeley-bowl-berkeley 0.999641607301\n",
        "59300"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " mancinis-sleepworld-san-rafael sol-food-puerto-rican-cuisine-san-rafael-2 0.998239212385\n"
       ]
      }
     ],
     "prompt_number": 311
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "generated_matches = pd.DataFrame.from_records(all_generated_matches, columns = ['from_id','to_id','prob'])\n",
      "generated_matches.to_csv('generated_matches.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 312
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print generated_matches.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(59311, 3)\n"
       ]
      }
     ],
     "prompt_number": 313
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test = pd.read_csv('generated_matches.csv', index_col=False)[['from_id','to_id','prob']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 325
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print test.to_id[test.from_id == 'citibank-san-carlos']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "169    town-san-carlos\n",
        "Name: to_id, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 330
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}