Weather_Train_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "import numbers\n",
    "\n",
    "import numpy\n",
    "import xgboost\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score,mean_squared_error\n",
    "from sklearn import preprocessing\n",
    "import sklearn.tree\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#графики в svg выглядят более четкими\n",
    "%config InlineBackend.figure_format = 'svg' \n",
    "\n",
    "#увеличим дефолтный размер графиков\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 8, 5\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepareDS(src):\n",
    "    result = pd.DataFrame()\n",
    "\n",
    "    # dataset2['date'] = dataset['date']\n",
    "\n",
    "    result['Level'] = src['Level']\n",
    "    result['NextLevel1'] = src['Level'].shift(-1) - src['Level']\n",
    "    result['NextLevel2'] = src['Level'].shift(-2) - src['Level']\n",
    "\n",
    "    result['P1'] = src['P1']\n",
    "    result['P2'] = src['P2']\n",
    "    result['DP'] = src['P2'] - src['P1']\n",
    "\n",
    "    result['T1'] = src['T1']\n",
    "    result['T2'] = src['T2']\n",
    "\n",
    "    result['S1'] = src['CL1']\n",
    "    result['S2'] = src['CL2']\n",
    "\n",
    "    result['RF1'] = src['Rainfall1'].map(lambda x: 0 if (isinstance(x, numbers.Number) and math.isnan(x)) else 1)\n",
    "    result['RF2'] = src['Rainfall2'].map(lambda x: 0 if (isinstance(x, numbers.Number) and math.isnan(x)) else 1)\n",
    "\n",
    "    result['W_N_1'] = src['W1'].map(\n",
    "        lambda x: 1 if (not isinstance(x, numbers.Number) and x.startswith('С')) else 0)\n",
    "    result['W_N_2'] = src['W2'].map(\n",
    "        lambda x: 1 if (not isinstance(x, numbers.Number) and x.startswith('С')) else 0)\n",
    "    result['W_S_1'] = src['W1'].map(\n",
    "        lambda x: 1 if (not isinstance(x, numbers.Number) and x.startswith('Ю')) else 0)\n",
    "    result['W_S_2'] = src['W2'].map(\n",
    "        lambda x: 1 if (not isinstance(x, numbers.Number) and x.startswith('Ю')) else 0)\n",
    "    result['W_E_1'] = src['W1'].map(lambda x: 1 if (not isinstance(x, numbers.Number) and (\n",
    "            x.startswith('В') or x.startswith('СВ') or x.startswith('ЮВ'))) else 0)\n",
    "    result['W_E_2'] = src['W2'].map(lambda x: 1 if (not isinstance(x, numbers.Number) and (\n",
    "            x.startswith('В') or x.startswith('СВ') or x.startswith('ЮВ'))) else 0)\n",
    "    result['W_W_1'] = src['W1'].map(lambda x: 1 if (not isinstance(x, numbers.Number) and (\n",
    "            x.startswith('З') or x.startswith('СЗ') or x.startswith('ЮЗ'))) else 0)\n",
    "    result['W_W_2'] = src['W2'].map(lambda x: 1 if (not isinstance(x, numbers.Number) and (\n",
    "            x.startswith('З') or x.startswith('СЗ') or x.startswith('ЮЗ'))) else 0)\n",
    "\n",
    "    result['Levelp'] = result['Level'].shift(1)\n",
    "    result['T1p'] = result['T1'].shift(1)\n",
    "    result['T2p'] = result['T2'].shift(1)\n",
    "    result['P1p'] = result['P1'].shift(1)\n",
    "    result['P2p'] = result['P2'].shift(1)\n",
    "    result['S1p'] = result['S1'].shift(1)\n",
    "    result['S2p'] = result['S2'].shift(1)\n",
    "    result['RF1p'] = result['RF1'].shift(1)\n",
    "    result['RF2p'] = result['RF2'].shift(1)\n",
    "\n",
    "    # dataset2.drop(dataset2.tail(2).index,inplace=True)\n",
    "    result = result.dropna()\n",
    "    # dataset2 = dataset2.fillna(-100000)\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset18 = prepareDS(pd.read_csv('khurzuk_18.csv', delimiter=\";\"))\n",
    "dataset19 = prepareDS(pd.read_csv('khurzuk_19.csv', delimiter=\";\"))\n",
    "dataset_all = dataset18.append(dataset19, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'max_depth': range(1,40),'max_features': range(4,18)}\n",
    "#scoring = 'neg_mean_squared_error'\n",
    "scoring = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 546 candidates, totalling 2730 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    1.3s\n",
      "[Parallel(n_jobs=-1)]: Done 1032 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=-1)]: Done 2730 out of 2730 | elapsed:   30.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, gamma=None,\n",
       "                                    gpu_id=None, importance_type='gain',\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, max_delta_step=None,\n",
       "                                    max_depth=None, min_child_weight=None,\n",
       "                                    missing=nan, monotone_constraints=None,\n",
       "                                    n_estima...\n",
       "                                    objective='reg:squarederror',\n",
       "                                    random_state=None, reg_alpha=None,\n",
       "                                    reg_lambda=None, scale_pos_weight=None,\n",
       "                                    subsample=None, tree_method=None,\n",
       "                                    validate_parameters=False, verbosity=None),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'max_depth': range(1, 40),\n",
       "                         'max_features': range(4, 18)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model18 = GridSearchCV(xgboost.XGBRegressor(), params, scoring=scoring, cv=5, n_jobs=-1,verbose=True)\n",
    "model18.fit(dataset18[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values, dataset18['NextLevel1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 546 candidates, totalling 2730 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  60 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1208 tasks      | elapsed:   11.5s\n",
      "[Parallel(n_jobs=-1)]: Done 2730 out of 2730 | elapsed:   29.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, gamma=None,\n",
       "                                    gpu_id=None, importance_type='gain',\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, max_delta_step=None,\n",
       "                                    max_depth=None, min_child_weight=None,\n",
       "                                    missing=nan, monotone_constraints=None,\n",
       "                                    n_estima...\n",
       "                                    objective='reg:squarederror',\n",
       "                                    random_state=None, reg_alpha=None,\n",
       "                                    reg_lambda=None, scale_pos_weight=None,\n",
       "                                    subsample=None, tree_method=None,\n",
       "                                    validate_parameters=False, verbosity=None),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'max_depth': range(1, 40),\n",
       "                         'max_features': range(4, 18)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model19 = GridSearchCV(xgboost.XGBRegressor(), params, scoring=scoring, cv=5, n_jobs=-1,verbose=True)\n",
    "model19.fit(dataset19[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values, dataset19['NextLevel1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 546 candidates, totalling 2730 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=-1)]: Done 696 tasks      | elapsed:    9.9s\n",
      "[Parallel(n_jobs=-1)]: Done 946 tasks      | elapsed:   15.8s\n",
      "[Parallel(n_jobs=-1)]: Done 1296 tasks      | elapsed:   24.7s\n",
      "[Parallel(n_jobs=-1)]: Done 1746 tasks      | elapsed:   37.8s\n",
      "[Parallel(n_jobs=-1)]: Done 2296 tasks      | elapsed:   52.0s\n",
      "[Parallel(n_jobs=-1)]: Done 2730 out of 2730 | elapsed:  1.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, gamma=None,\n",
       "                                    gpu_id=None, importance_type='gain',\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, max_delta_step=None,\n",
       "                                    max_depth=None, min_child_weight=None,\n",
       "                                    missing=nan, monotone_constraints=None,\n",
       "                                    n_estima...\n",
       "                                    objective='reg:squarederror',\n",
       "                                    random_state=None, reg_alpha=None,\n",
       "                                    reg_lambda=None, scale_pos_weight=None,\n",
       "                                    subsample=None, tree_method=None,\n",
       "                                    validate_parameters=False, verbosity=None),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'max_depth': range(1, 40),\n",
       "                         'max_features': range(4, 18)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=True)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_all = GridSearchCV(xgboost.XGBRegressor(), params, scoring=scoring, cv=5, n_jobs=-1,verbose=True)\n",
    "model_all.fit(dataset_all[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values, dataset_all['NextLevel1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_m18_d18 = model18.predict(dataset18[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m18_d19 = model18.predict(dataset19[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m18_d_all = model18.predict(dataset_all[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "\n",
    "pred_m19_d18 = model19.predict(dataset18[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m19_d19 = model19.predict(dataset19[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m19_d_all = model19.predict(dataset_all[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "\n",
    "pred_m_all_d18 = model_all.predict(dataset18[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m_all_d19 = model_all.predict(dataset19[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)\n",
    "pred_m_all_d_all = model_all.predict(dataset_all[['Level', 'T1', 'T2p', 'P1', 'P2p', 'S1', 'RF2p']].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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   "source": [
    "## use model_YY (fit on YY year) for data_YY\n",
    "\n",
    "plotDs18 = pd.DataFrame();\n",
    "plotDs18['m18y18'] = pred_m18_d18 - dataset18['NextLevel1']\n",
    "plotDs18['m19y18'] = pred_m19_d18 - dataset18['NextLevel1']\n",
    "plotDs18['m_all_y18'] = pred_m_all_d18 - dataset18['NextLevel1']\n",
    "\n",
    "plotDs19 = pd.DataFrame();\n",
    "plotDs19['m18y19'] = pred_m18_d19 - dataset19['NextLevel1']\n",
    "plotDs19['m19y19'] = pred_m19_d19 - dataset19['NextLevel1']\n",
    "plotDs19['m_all_y19'] = pred_m_all_d19 - dataset19['NextLevel1']\n",
    "\n",
    "plotDsM18 = pd.DataFrame();\n",
    "plotDsM18['m18y18'] = plotDs18['m18y18']\n",
    "plotDsM18['m18y19'] = plotDs19['m18y19']\n",
    "\n",
    "plotDsM19 = pd.DataFrame();\n",
    "plotDsM19['m19y18'] = plotDs18['m19y18']\n",
    "plotDsM19['m19y19'] = plotDs19['m19y19']\n",
    "\n",
    "plotDsAll = pd.DataFrame();\n",
    "plotDsAll['all'] = pred_m_all_d_all - dataset_all['NextLevel1']\n",
    "plotDsAll['m18'] = pred_m18_d_all - dataset_all['NextLevel1']\n",
    "plotDsAll['m19'] = pred_m19_d_all - dataset_all['NextLevel1']\n",
    "\n",
    "\n",
    "plotDsAll.plot()\n",
    "plotDs18.plot()\n",
    "plotDs19.plot()\n",
    "plotDsM18.plot()\n",
    "plotDsM19.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "[\n",
    "    #mean_squared_error(dataset18['NextLevel1'],pd.Series(pred_m18_d18)),\n",
    "    mean_squared_error(dataset18['NextLevel1'],pd.Series(pred_m19_d18)),\n",
    "    mean_squared_error(dataset18['NextLevel1'],pd.Series(pred_m_all_d18)),\n",
    "    \n",
    "    mean_squared_error(dataset19['NextLevel1'],pd.Series(pred_m18_d19)),\n",
    "    #mean_squared_error(dataset19['NextLevel1'],pd.Series(pred_m19_d19)),\n",
    "    mean_squared_error(dataset19['NextLevel1'],pd.Series(pred_m_all_d19)),\n",
    "    \n",
    "    mean_squared_error(dataset_all['NextLevel1'],pd.Series(pred_m18_d_all)),\n",
    "    mean_squared_error(dataset_all['NextLevel1'],pd.Series(pred_m19_d_all)),\n",
    "    mean_squared_error(dataset_all['NextLevel1'],pd.Series(pred_m_all_d_all)),\n",
    "    \n",
    "]"
   ]
  }
 ],
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