NAS_Transaction_PRICE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import random\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import json\n",
    "import csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we define parameters for the four queries\n",
    "db_name = \"nebulas\"\n",
    "batch_size = \"10\"\n",
    "nr_batch_size = \"2\"\n",
    "nr_batch_date = \"20181111\"\n",
    "nr_date_range = \"20180101-20181221\"\n",
    "nr_date_address = \"n1ZKXqnRWSBSdrR5eDr6nA2E7BL2dJCEavr\"\n",
    "nr_week_address = \"n1ZKXqnRWSBSdrR5eDr6nA2E7BL2dJCEavr\"\n",
    "transaction_address = \"n1ZKXqnRWSBSdrR5eDr6nA2E7BL2dJCEavr\"\n",
    "collection = \"nr_week\"\n",
    "start_ts = \"15150648422\" # 1.1.2018\n",
    "end_ts = \"1526257000\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    " #this function query the nr address on the given date\t\n",
    "def nrGivenDate(db, batch, date, address):\n",
    "\tnr_f = csv.writer(open('nr_date1.csv', 'w'))\n",
    "\tnr_f.writerow(['has_more', 'in_outs', 'in_val', 'out_degree', 'weight', 'out_val', '_key', 'in_degree', '_rev', 'median', 'score', 'degrees',\n",
    "\t\t   'address', 'date', '_id', 'id'])\n",
    "\n",
    "\tnr_url = \"http://111.203.228.11:9973/nr?db=\" + db + \"&batch_size=\" + batch + \"&date=\" + date + \"&address=\" + address\n",
    "\tnr_page = requests.get(nr_url)\n",
    "\tnr_json = nr_page.text\n",
    "\tnr_dict = json.loads(nr_json)\n",
    "\t\n",
    "\thas_more = nr_dict['has_more']\n",
    "\tresults = nr_dict['result']\n",
    "\tid = nr_dict['id']\n",
    "\tif id == None:\n",
    "\t\tid = \"None\"\n",
    "\t\t\n",
    "\tinouts = []\n",
    "\tinvals = []\n",
    "\toutdegrees = []\n",
    "\tweights = []\n",
    "\toutvals = []\n",
    "\t_keys = []\n",
    "\tindegrees = []\n",
    "\t_revs = []\n",
    "\tmedians = []\n",
    "\tscores = []\n",
    "\tdegreess = []\n",
    "\taddresses = []\n",
    "\tdates = []\n",
    "\t_ids = []\n",
    "\t\n",
    "\tfor i in range(0, len(results)):\n",
    "\t\tin_outs = nr_dict['result'][i]['in_outs']\n",
    "\t\tinouts.append(in_outs)\n",
    "\t\tin_val = nr_dict['result'][i]['in_val']\n",
    "\t\tinvals.append(in_val)\n",
    "\t\tout_degree = nr_dict['result'][i]['out_degree']\n",
    "\t\toutdegrees.append(out_degree)\n",
    "\t\tweight = nr_dict['result'][i]['weight']\n",
    "\t\tweights.append(weight)\n",
    "\t\tout_val = nr_dict['result'][i]['out_val']\n",
    "\t\toutvals.append(out_val)\n",
    "\t\t_key = nr_dict['result'][i]['_key']\n",
    "\t\t_keys.append(_key)\n",
    "\t\tin_degree = nr_dict['result'][i]['in_degree']\n",
    "\t\tindegrees.append(in_degree)\n",
    "\t\t_rev = nr_dict['result'][i]['_rev']\n",
    "\t\t_revs.append(_rev)\n",
    "\t\tmedian = nr_dict['result'][i]['median']\n",
    "\t\tmedians.append(median)\n",
    "\t\tscore = nr_dict['result'][i]['score']\n",
    "\t\tscores.append(score)\n",
    "\t\tdegrees = nr_dict['result'][i]['degrees']\n",
    "\t\tdegreess.append(degrees)\n",
    "\t\taddress = nr_dict['result'][i]['address']\n",
    "\t\taddresses.append(address)\n",
    "\t\tdate = nr_dict['result'][i]['date']\n",
    "\t\tdates.append(date)\n",
    "\t\t_id = nr_dict['result'][i]['_id']\n",
    "\t\t_ids.append(_id)\n",
    "\t\t\n",
    "\t\tnr_f.writerow([has_more, in_outs, in_val, out_degree, weight, out_val, _key, in_degree, _rev, median, score, degrees, address,\n",
    "\t\t\t\t\tdate, _id, id])\n",
    "\t\t\t\t\t\n",
    "\tnr_date_df = pd.DataFrame({'has_more': has_more,\n",
    "\t\t\t\t\t  'in_outs': inouts,\n",
    "\t\t\t\t\t  'in_val': invals,\n",
    "\t\t\t\t\t  'out_degree': outdegrees,\n",
    "\t\t\t\t\t  'weight': weights,\n",
    "\t\t\t\t\t  'out_val': outvals,\n",
    "\t\t\t\t\t  '_key': _keys,\n",
    "\t\t\t\t\t  'in_degree': indegrees,\n",
    "\t\t\t\t\t  '_rev': _revs,\n",
    "\t\t\t\t\t  'median': medians,\n",
    "\t\t\t\t\t  'score': scores,\n",
    "\t\t\t\t\t  'degrees': degreess,\n",
    "\t\t\t\t\t  'address': addresses,\n",
    "\t\t\t\t\t  'date': dates,\n",
    "\t\t\t\t\t  '_id': _ids,\n",
    "\t\t\t\t\t  'id': id\n",
    "\t})\n",
    "\tprint(nr_date_df)\n",
    "\t"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "nr_date_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [has_more, nonce, status, chainId, from, gas_price, type_from, to, _key, timestamp, _rev, gas_used, type_to, height, tx_id, gas_limit, contract_address, hash, _id, data, tx_value, tx_type, id]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "# QUery Transactions \n",
    "\n",
    "f = csv.writer(open('transaction1.csv', 'w'))\n",
    "f.writerow(['has_more', 'nonce', 'status', 'chainId', 'from', 'gas_price', 'type_from', 'to', '_key', 'timestamp', '_rev', 'gas_used',\n",
    "\t\t   'type_to', 'height', 'tx_id', 'gas_limit', 'contract_address', 'hash', '_id', 'data', 'tx_value', 'tx_type', 'id'])\n",
    "\n",
    "url = \"http://111.203.228.11:9973/transaction?db=nebulas&address=n1ZKXqnRWSBSdrR5eDr6nA2E7BL2dJCEavr&batch_size=10&start_ts=1515064842&end_ts=1526257000\"\n",
    "\n",
    "page = requests.get(url)\n",
    "text_json = page.text\n",
    "text_dict = json.loads(text_json)\n",
    "\n",
    "has_more = text_dict['has_more']\n",
    "results = text_dict['result']\n",
    "id = text_dict['id']\n",
    "\n",
    "nonces = []\n",
    "statuses = []\n",
    "chainIds = []\n",
    "froms = []\n",
    "gasprices = []\n",
    "typefroms = []\n",
    "tos = []\n",
    "_keys = []\n",
    "timestamps = []\n",
    "_revs = []\n",
    "gasuseds = []\n",
    "typetos = []\n",
    "heights = []\n",
    "tx_ids = []\n",
    "gas_limits = []\n",
    "contract_addresses = []\n",
    "hashes = []\n",
    "_ids = []\n",
    "datas = []\n",
    "tx_values = []\n",
    "tx_types = []\n",
    "for i in range(0, len(results)):\n",
    "\tnonce = text_dict['result'][i]['nonce']\n",
    "\tnonces.append(nonce)\n",
    "\tstatus = text_dict['result'][i]['status']\n",
    "\tstatuses.append(status)\n",
    "\tchainId = text_dict['result'][i]['chainId']\n",
    "\tchainIds.append(chainId)\n",
    "\tfr_om = text_dict['result'][i]['from']\n",
    "\tfroms.append(fr_om)\n",
    "\tgas_price = text_dict['result'][i]['gas_price']\n",
    "\tgasprices.append(gas_price)\n",
    "\ttype_from = text_dict['result'][i]['type_from']\n",
    "\ttypefroms.append(type_from)\n",
    "\tto = text_dict['result'][i]['to']\n",
    "\ttos.append(to)\n",
    "\t_key = text_dict['result'][i]['_key']\n",
    "\t_keys.append(_key)\n",
    "\ttimestamp = text_dict['result'][i]['timestamp']\n",
    "\ttimestamps.append(timestamp)\n",
    "\t_rev = text_dict['result'][i]['_rev']\n",
    "\t_revs.append(_rev)\n",
    "\tgas_used = text_dict['result'][i]['gas_used']\n",
    "\tgasuseds.append(gas_used)\n",
    "\ttype_to = text_dict['result'][i]['type_to']\n",
    "\ttypetos.append(type_to)\n",
    "\theight = text_dict['result'][i]['height']\n",
    "\theights.append(height)\n",
    "\ttx_id = text_dict['result'][i]['tx_id']\n",
    "\ttx_ids.append(tx_id)\n",
    "\tgas_limit = text_dict['result'][i]['gas_limit']\n",
    "\tgas_limits.append(gas_limit)\n",
    "\tcontract_address = text_dict['result'][i]['contract_address']\n",
    "\tcontract_addresses.append(contract_address)\n",
    "\thash = text_dict['result'][i]['hash']\n",
    "\thashes.append(hash)\n",
    "\t_id = text_dict['result'][i]['_id']\n",
    "\t_ids.append(_id)\n",
    "\tdata = text_dict['result'][i]['data']\n",
    "\tdatas.append(data)\n",
    "\ttx_value = text_dict['result'][i]['tx_value']\n",
    "\ttx_values.append(tx_value)\n",
    "\ttx_type = text_dict['result'][i]['tx_type']\n",
    "\ttx_types.append(tx_type)\n",
    "\t\n",
    "\t\t\n",
    "\tf.writerow([has_more, nonce, status, chainId, fr_om, gas_price, type_from, to, _key, timestamp, _rev, gas_used, type_to, height,\n",
    "\t\t\t   tx_id, gas_limit, contract_address, hash, _id, data, tx_value, tx_type, id])\n",
    "\t\t\n",
    "df = pd.DataFrame({'has_more': has_more,\n",
    "\t\t\t\t  'nonce': nonces,\n",
    "\t\t\t\t  'status': statuses,\n",
    "\t\t\t\t  'chainId': chainIds,\n",
    "\t\t\t\t  'from': froms,\n",
    "\t\t\t\t  'gas_price': gasprices,\n",
    "\t\t\t\t  'type_from': typefroms,\n",
    "\t\t\t\t  'to': tos,\n",
    "\t\t\t\t  '_key': _keys,\n",
    "\t\t\t\t  'timestamp': timestamps,\n",
    "\t\t\t\t  '_rev': _revs,\n",
    "\t\t\t\t  'gas_used': gasuseds,\n",
    "\t\t\t\t  'type_to': typetos,\n",
    "\t\t\t\t  'height': heights,\n",
    "\t\t\t\t  'tx_id': tx_ids,\n",
    "\t\t\t\t  'gas_limit': gas_limits,\n",
    "\t\t\t\t  'contract_address': contract_addresses,\n",
    "\t\t\t\t  'hash': hashes,\n",
    "\t\t\t\t  '_id': _ids,\n",
    "\t\t\t\t  'data': datas,\n",
    "\t\t\t\t  'tx_value': tx_values,\n",
    "\t\t\t\t  'tx_type': tx_types,\n",
    "\t\t\t\t  'id': id\n",
    "})\n",
    "\n",
    "print(df)\n",
    "\n",
    "\n",
    "\t\n",
    "\n",
    "\t\n",
    "\t\n",
    "\t\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        has_more  nonce  status  chainId  from  gas_price  type_from   to  \\\n",
       "count        0.0    0.0     0.0      0.0   0.0        0.0        0.0  0.0   \n",
       "unique       0.0    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "mean         NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "std          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "min          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "25%          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "50%          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "75%          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "max          NaN    NaN     NaN      NaN   NaN        NaN        NaN  NaN   \n",
       "\n",
       "        _key  timestamp ...   height  tx_id  gas_limit  contract_address  \\\n",
       "count    0.0        0.0 ...      0.0    0.0        0.0               0.0   \n",
       "unique   NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "mean     NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "std      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "min      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "25%      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "50%      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "75%      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "max      NaN        NaN ...      NaN    NaN        NaN               NaN   \n",
       "\n",
       "        hash  _id  data  tx_value  tx_type   id  \n",
       "count    0.0  0.0   0.0       0.0      0.0  0.0  \n",
       "unique   NaN  NaN   NaN       NaN      NaN  0.0  \n",
       "mean     NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "std      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "min      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "25%      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "50%      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "75%      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "max      NaN  NaN   NaN       NaN      NaN  NaN  \n",
       "\n",
       "[9 rows x 23 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transaction data \n",
    "#Gas price over time \n",
    "# Number of transaction per day \n",
    "# Top send to address in a bar graph over period of 3 month \n",
    "# See correlation of price and # of transaction per day in a graph \n",
    "# Regression of a lot of activities onchain (transactions, transaction value) = x price=y. attach NAS price to the dat set of price, gas limit, transaction type, transaction value .  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge dataset of NAS price and transaction \n",
    "df_NAS[\"date\"] = df[\"date\"].apply(lambda x: datetime.datetime.strptime(x , \"datetime_format\"))\n",
    "pd.merge(df_NAS, df_Transaction, right_index=True, left_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Regression setup \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import patsy\n",
    "\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegression, RidgeCV, LassoCV, ElasticNetCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_NAS.describe()\n",
    "df_Nas.drop(type_to, height, tx_id, gas_limit, contract_address, hash, _id, has_more, nonce, status, chainId, fr_om, gas_price, type_from, to, _key, timestamp,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_NAS.gas_price.values\n",
    "#everyhting on x except for NAS price with NAS price being the dependent variable\n",
    "X = df_NAS.iloc[:,1:]\n",
    "\n",
    "# Initialize the StandardScaler object\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss = StandardScaler()\n",
    "\n",
    "# use the \"fit_transform\" function to standardize the X design matrix\n",
    "Xs = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso(alpha=optimal_lasso.alpha_)\n",
    "\n",
    "lasso_scores = cross_val_score(lasso, Xs, y, cv=10)\n",
    "\n",
    "print(lasso_scores)\n",
    "print(np.mean(lasso_scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ridge\n",
    "ridge_alphas = np.logspace(0, 5, 200)\n",
    "\n",
    "optimal_ridge = RidgeCV(alphas=ridge_alphas, cv=10)\n",
    "optimal_ridge.fit(Xs, y)\n",
    "\n",
    "print(optimal_ridge.alpha_)"
   ]
  }
 ],
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