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get_score(row):\n+    # TODO (@y, @m): learn a linear model here or find good coeffs by\n+    # brute-force.\n+    return row['MinDistance'] + \\\n+        row['Rank'] + \\\n+        (row['SearchType'] / len(SEARCH_TYPES)) + \\\n+        (row['NameScore'] / len(NAME_SCORES))\n+\n+def normalize_data(data):\n+    data['DistanceToViewport'] = data['DistanceToViewport'].apply(transform_distance)\n+    data['DistanceToPosition'] = data['DistanceToPosition'].apply(transform_distance)\n+    data['Rank'] = data['Rank'].apply(transform_rank)\n+    data['NameScore'] = data['NameScore'].apply(transform_name_score)\n+    data['SearchType'] = data['SearchType'].apply(transform_search_type)\n+    data['Relevance'] = data['Relevance'].apply(transform_relevance)\n+\n+    # Adds some new columns to the data frame.\n+    data['MinDistance'] = pd.Series(np.minimum(data['DistanceToViewport'], data['DistanceToPosition']))\n+    data['Score'] = pd.Series([get_score(data.ix[i]) for i in data.index])\n+\n+def 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