numer_merge_all_files_in_Dir.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "os.chdir(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/New Outputs/CV/\")\n",
    "i=0\n",
    "# MergedDF1=None\n",
    "MergedDF2=pd.read_csv(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/numerai_training_data.csv\")\n",
    "MergedDF2=MergedDF2[['t_id','target']]\n",
    "for file in os.listdir(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/New Outputs/CV/\"):\n",
    "    if file.endswith(\".csv\"):\n",
    "        i=i+1\n",
    "#         currentDF = pd.read_csv(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/CV/CV_alg_DTree.csv\")\n",
    "        currentDF = pd.read_csv(file)\n",
    "        MergedDF2 = pd.merge(MergedDF2,currentDF, on=['t_id'])\n",
    "#         print MergedDF2\n",
    "#         if i==0:\n",
    "#             MergedDF2 = currentDF.copy()\n",
    "#         else:\n",
    "#             MergedDF2 = pd.merge(MergedDF2,currentDF, on=['t_id'])\n",
    "MergedDF2.to_csv(\"Merged/MERGED.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_id</th>\n",
       "      <th>CV_alg_DTree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
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      ],
      "text/plain": [
       "  t_id  CV_alg_DTree\n",
       "0   A1      0.534462\n",
       "1   A2      0.534462\n",
       "2   A3      0.513412\n",
       "3   A4      0.408747\n",
       "4   A5      0.525126"
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     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "currentDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "os.chdir(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/New Outputs/submission/\")\n",
    "\n",
    "# MergedDF1=None\n",
    "Merged_Test=pd.read_csv(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/New Outputs/numerai_tournament_data.csv\")\n",
    "Merged_Test=Merged_Test[['t_id']]\n",
    "for file in os.listdir(\"/Users/PraveenGupta/Downloads/Kaggle/numerai_datasets/New Outputs/submission/\"):\n",
    "    if file.endswith(\".csv\"):\n",
    "        currentDF = pd.read_csv(file)\n",
    "        currentDF=currentDF.rename(columns={'probability': 'CV_'+file[:-4]})\n",
    "        Merged_Test = pd.merge(Merged_Test,currentDF, on=['t_id'])\n",
    "Merged_Test.to_csv(\"Merged/MERGED.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t_id</th>\n",
       "      <th>target</th>\n",
       "      <th>CV_alg_DTree</th>\n",
       "      <th>CV_alg_DTree_Normalize_L1</th>\n",
       "      <th>CV_alg_DTree_Robust</th>\n",
       "      <th>CV_alg_ET</th>\n",
       "      <th>CV_alg_ET_Normalize_L1</th>\n",
       "      <th>CV_alg_ET_Robust</th>\n",
       "      <th>CV_alg_GBM</th>\n",
       "      <th>CV_alg_GNB</th>\n",
       "      <th>...</th>\n",
       "      <th>CV_alg_KNN</th>\n",
       "      <th>CV_alg_Logistic_AdaB</th>\n",
       "      <th>CV_alg_Logistic_Normalize_L1</th>\n",
       "      <th>CV_alg_Logistic_Robust</th>\n",
       "      <th>CV_alg_RandomForest</th>\n",
       "      <th>CV_alg_RandomForest_Normalize_L1</th>\n",
       "      <th>CV_alg_RandomForest_Robust</th>\n",
       "      <th>CV_alg_SGD</th>\n",
       "      <th>CV_alg_svm</th>\n",
       "      <th>CV_xgBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.534462</td>\n",
       "      <td>0.524238</td>\n",
       "      <td>0.534462</td>\n",
       "      <td>0.529417</td>\n",
       "      <td>0.519693</td>\n",
       "      <td>0.529417</td>\n",
       "      <td>0.528096</td>\n",
       "      <td>0.595730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.500522</td>\n",
       "      <td>0.526132</td>\n",
       "      <td>0.513016</td>\n",
       "      <td>0.529785</td>\n",
       "      <td>0.524143</td>\n",
       "      <td>0.529785</td>\n",
       "      <td>0.529785</td>\n",
       "      <td>0.529785</td>\n",
       "      <td>0.543261</td>\n",
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       "      <th>1</th>\n",
       "      <td>A2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.534462</td>\n",
       "      <td>0.524238</td>\n",
       "      <td>0.534462</td>\n",
       "      <td>0.529704</td>\n",
       "      <td>0.519693</td>\n",
       "      <td>0.529704</td>\n",
       "      <td>0.519974</td>\n",
       "      <td>0.604800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500407</td>\n",
       "      <td>0.515874</td>\n",
       "      <td>0.518912</td>\n",
       "      <td>0.532368</td>\n",
       "      <td>0.522714</td>\n",
       "      <td>0.532368</td>\n",
       "      <td>0.532368</td>\n",
       "      <td>0.532368</td>\n",
       "      <td>0.502714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.513412</td>\n",
       "      <td>0.500187</td>\n",
       "      <td>0.513412</td>\n",
       "      <td>0.514228</td>\n",
       "      <td>0.494142</td>\n",
       "      <td>0.514228</td>\n",
       "      <td>0.530777</td>\n",
       "      <td>0.538378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.500322</td>\n",
       "      <td>0.504078</td>\n",
       "      <td>0.512147</td>\n",
       "      <td>0.509858</td>\n",
       "      <td>0.498313</td>\n",
       "      <td>0.509800</td>\n",
       "      <td>0.509858</td>\n",
       "      <td>0.509858</td>\n",
       "      <td>0.485308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.408747</td>\n",
       "      <td>0.363395</td>\n",
       "      <td>0.408747</td>\n",
       "      <td>0.446607</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.446607</td>\n",
       "      <td>0.400990</td>\n",
       "      <td>0.258875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.498793</td>\n",
       "      <td>0.424148</td>\n",
       "      <td>0.438537</td>\n",
       "      <td>0.425587</td>\n",
       "      <td>0.392463</td>\n",
       "      <td>0.425587</td>\n",
       "      <td>0.425587</td>\n",
       "      <td>0.425587</td>\n",
       "      <td>0.367861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.525126</td>\n",
       "      <td>0.530970</td>\n",
       "      <td>0.525126</td>\n",
       "      <td>0.525140</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.525140</td>\n",
       "      <td>0.475217</td>\n",
       "      <td>0.478912</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.582984</td>\n",
       "      <td>0.504756</td>\n",
       "      <td>0.510398</td>\n",
       "      <td>0.504306</td>\n",
       "      <td>0.510201</td>\n",
       "      <td>0.510398</td>\n",
       "      <td>0.510398</td>\n",
       "      <td>0.496761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  t_id  target  CV_alg_DTree  CV_alg_DTree_Normalize_L1  CV_alg_DTree_Robust  \\\n",
       "0   A1       1      0.534462                   0.524238             0.534462   \n",
       "1   A2       0      0.534462                   0.524238             0.534462   \n",
       "2   A3       0      0.513412                   0.500187             0.513412   \n",
       "3   A4       0      0.408747                   0.363395             0.408747   \n",
       "4   A5       1      0.525126                   0.530970             0.525126   \n",
       "\n",
       "   CV_alg_ET  CV_alg_ET_Normalize_L1  CV_alg_ET_Robust  CV_alg_GBM  \\\n",
       "0   0.529417                0.519693          0.529417    0.528096   \n",
       "1   0.529704                0.519693          0.529704    0.519974   \n",
       "2   0.514228                0.494142          0.514228    0.530777   \n",
       "3   0.446607                0.423077          0.446607    0.400990   \n",
       "4   0.525140                0.583333          0.525140    0.475217   \n",
       "\n",
       "   CV_alg_GNB     ...      CV_alg_KNN  CV_alg_Logistic_AdaB  \\\n",
       "0    0.595730     ...             0.7              0.500522   \n",
       "1    0.604800     ...             0.5              0.500407   \n",
       "2    0.538378     ...             0.6              0.500322   \n",
       "3    0.258875     ...             0.4              0.498793   \n",
       "4    0.478912     ...             0.5              0.499988   \n",
       "\n",
       "   CV_alg_Logistic_Normalize_L1  CV_alg_Logistic_Robust  CV_alg_RandomForest  \\\n",
       "0                      0.526132                0.513016             0.529785   \n",
       "1                      0.515874                0.518912             0.532368   \n",
       "2                      0.504078                0.512147             0.509858   \n",
       "3                      0.424148                0.438537             0.425587   \n",
       "4                      0.582984                0.504756             0.510398   \n",
       "\n",
       "   CV_alg_RandomForest_Normalize_L1  CV_alg_RandomForest_Robust  CV_alg_SGD  \\\n",
       "0                          0.524143                    0.529785    0.529785   \n",
       "1                          0.522714                    0.532368    0.532368   \n",
       "2                          0.498313                    0.509800    0.509858   \n",
       "3                          0.392463                    0.425587    0.425587   \n",
       "4                          0.504306                    0.510201    0.510398   \n",
       "\n",
       "   CV_alg_svm  CV_xgBoost  \n",
       "0    0.529785    0.543261  \n",
       "1    0.532368    0.502714  \n",
       "2    0.509858    0.485308  \n",
       "3    0.425587    0.367861  \n",
       "4    0.510398    0.496761  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MergedDF2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13349, 20)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Merged_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target = 'target'\n",
    "IDCol = ['t_id']\n",
    "predictors=[x for x in MergedDF2.columns if x not in [target]+IDCol+[\"predictions\"]]\n",
    "\n",
    "from sklearn import cross_validation, metrics\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "def modelfit(alg,dtrain,dtest,predictors,target,IDCol,filename):\n",
    "    #fit the algorithm on the data\n",
    "    print datetime.now().time()\n",
    "    alg.fit(dtrain[predictors],dtrain[target])\n",
    "    \n",
    "    #predict training data set\n",
    "    dtrain[\"predictions\"] = alg.predict_proba(dtrain[predictors])[:,1]\n",
    "    \n",
    "    #performs cross validation\n",
    "    cv_score = cross_validation.cross_val_score(alg,dtrain[predictors],dtrain[target],cv=7,scoring='log_loss')\n",
    "    \n",
    "    #Print model report\n",
    "    print \"\\nModel Report :\"\n",
    "    print \"Train Log Loss : %.4g\" % (metrics.log_loss(dtrain[target].values,dtrain[\"predictions\"].values))\n",
    "    print \"CV Score : Mean - %.4g | Std - %.4g | Min - %.4g | Max - %.4g\"%(np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score))\n",
    "    \n",
    "    #Predict on testing data\n",
    "    dtest[target]=alg.predict_proba(dtest[predictors])[:,1]\n",
    "    \n",
    "    #Export submission file\n",
    "    IDCol.append(target)\n",
    "    submission = pd.DataFrame({x : dtest[x] for x in IDCol})\n",
    "    submission = submission.rename(columns={'target': 'probability'})\n",
    "    if not os.path.exists(\"submission\"):\n",
    "        os.makedirs(\"submission\")\n",
    "    submission.to_csv(\"submission/\"+filename,index=False)\n",
    "    \n",
    "    \n",
    "    cv = pd.DataFrame(dtrain[['t_id','predictions']])\n",
    "    cv = cv.rename(columns={'predictions': \"CV_\"+filename[:-4]})\n",
    "    if not os.path.exists(\"CV\"):\n",
    "        os.makedirs(\"CV\")\n",
    "    cv.to_csv(\"CV/\"+\"CV_\"+filename,index=False)\n",
    "    \n",
    "    print datetime.now().time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16:43:07.056000\n",
      "\n",
      "Model Report :\n",
      "Train Log Loss : 0.5778\n",
      "CV Score : Mean - -0.5789 | Std - 0.003483 | Min - -0.5837 | Max - -0.5739\n",
      "16:43:10.748000\n"
     ]
    }
   ],
   "source": [
    "# Stacked using Logisitic, however not a good result\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "alg3=LogisticRegression()\n",
    "modelfit(alg3,MergedDF2,Merged_Test,predictors,target,IDCol,'alg_Logistic_Merged.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16:46:39.491000\n",
      "\n",
      "Model Report :\n",
      "Train Log Loss : 0.676\n",
      "CV Score : Mean - -0.6758 | Std - 0.004832 | Min - -0.6812 | Max - -0.6682\n",
      "16:46:40.585000\n"
     ]
    }
   ],
   "source": [
    "# Stacked using Logisitic, however not a good result\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "alg2R=DecisionTreeClassifier(max_depth=3,min_samples_split=100, min_samples_leaf=50,max_features='sqrt',random_state=111)\n",
    "modelfit(alg2R,MergedDF2,Merged_Test,predictors,target,IDCol,'alg_DTree_Merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20:29:16.607000\n",
      "\n",
      "Model Report :\n",
      "Train Log Loss : 0.8671\n",
      "CV Score : Mean - -0.869 | Std - 0.02927 | Min - -0.9208 | Max - -0.8333\n",
      "20:29:17.434000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "algGNB=GaussianNB()\n",
    "modelfit(algGNB,MergedDF2,Merged_Test,predictors,target,IDCol,'alg_GNB_Merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min LogLoss obtained as of now 0.6897471\n",
      "Min LogLoss obtained as of now 0.689698\n",
      "Min LogLoss obtained as of now 0.6896499\n",
      "Min LogLoss obtained as of now 0.6896027\n",
      "Min LogLoss obtained as of now 0.6895566\n",
      "Min LogLoss obtained as of now 0.6895113\n",
      "Min LogLoss obtained as of now 0.689467\n",
      "Min LogLoss obtained as of now 0.6894237\n",
      "Min LogLoss obtained as of now 0.6893813\n",
      "Min LogLoss obtained as of now 0.6893399\n",
      "Min LogLoss obtained as of now 0.6892994\n",
      "Min LogLoss obtained as of now 0.6892599\n",
      "Min LogLoss obtained as of now 0.6892213\n",
      "Min LogLoss obtained as of now 0.6891837\n",
      "Min LogLoss obtained as of now 0.6891469\n",
      "Min LogLoss obtained as of now 0.6891111\n",
      "Min LogLoss obtained as of now 0.6890763\n",
      "Min LogLoss obtained as of now 0.6890424\n",
      "Min LogLoss obtained as of now 0.6890093\n",
      "Min LogLoss obtained as of now 0.6889773\n",
      "Min LogLoss obtained as of now 0.6889461\n",
      "Min LogLoss obtained as of now 0.6889159\n",
      "Min LogLoss obtained as of now 0.6888866\n",
      "Min LogLoss obtained as of now 0.6888582\n",
      "Min LogLoss obtained as of now 0.6888307\n",
      "Min LogLoss obtained as of now 0.6888041\n",
      "Min LogLoss obtained as of now 0.6887784\n",
      "Min LogLoss obtained as of now 0.6887537\n",
      "Min LogLoss obtained as of now 0.6887298\n",
      "Min LogLoss obtained as of now 0.6887069\n",
      "Min LogLoss obtained as of now 0.6886849\n",
      "Min LogLoss obtained as of now 0.6886638\n",
      "Min LogLoss obtained as of now 0.6886435\n",
      "Min LogLoss obtained as of now 0.6886242\n",
      "Min LogLoss obtained as of now 0.6886058\n",
      "Min LogLoss obtained as of now 0.6885883\n",
      "Min LogLoss obtained as of now 0.6885717\n",
      "Min LogLoss obtained as of now 0.688556\n",
      "Min LogLoss obtained as of now 0.6885412\n",
      "Min LogLoss obtained as of now 0.6885273\n",
      "Min LogLoss obtained as of now 0.6885142\n",
      "Min LogLoss obtained as of now 0.6885021\n",
      "Min LogLoss obtained as of now 0.6884909\n",
      "Min LogLoss obtained as of now 0.6884806\n",
      "Min LogLoss obtained as of now 0.6884711\n",
      "Min LogLoss obtained as of now 0.6884626\n",
      "Min LogLoss obtained as of now 0.6884482\n",
      "Min LogLoss obtained as of now 0.6884424\n",
      "Min LogLoss obtained as of now 0.6884374\n",
      "Min LogLoss obtained as of now 0.6884333\n",
      "Min LogLoss obtained as of now 0.6884301\n",
      "Min LogLoss obtained as of now 0.6884279\n",
      "Min LogLoss obtained as of now 0.6884265\n",
      "Min LogLoss obtained as of now 0.688426\n",
      "Max Blend1 coef = 0.54\n",
      "Max Blend2 coef = 0.46\n",
      "If only series1 = 0.6893823\n",
      "If only series2 = 0.6897471\n"
     ]
    }
   ],
   "source": [
    "# Perform blending\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import roc_auc_score\n",
    "noIter=1\n",
    "multiplier=1/100.0\n",
    "maxAns=100\n",
    "i=0\n",
    "temp=0\n",
    "\n",
    "series1=pd.Series(MergedDF2['CV_alg_DTree_Normalize_L1'])\n",
    "series2=pd.Series(MergedDF2['CV_alg_DTree'])\n",
    "targetseries=pd.Series(MergedDF2['target'])\n",
    "for inc in range(0,100,noIter):\n",
    "    MergedDF2['predBlended']=0\n",
    "    blendDF1=0\n",
    "    blendDF1 = series1*(inc*multiplier)+ series2*(100-inc)*multiplier\n",
    "    temp=log_loss(targetseries.values,blendDF1.values)\n",
    "    if(temp<maxAns):\n",
    "        maxAns=temp\n",
    "        print \"Min LogLoss obtained as of now %.7g\" %(maxAns)\n",
    "        Blend1=inc*multiplier\n",
    "        Blend2=(100-inc)*multiplier\n",
    "print \"Max Blend1 coef = %.7g\" %(Blend1) \n",
    "print \"Max Blend2 coef = %.7g\" %(Blend2) \n",
    "print \"If only series1 = %.7g\" %(log_loss(targetseries.values,series1.values))\n",
    "print \"If only series2 = %.7g\" %(log_loss(targetseries.values,series2.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Prepare submission based on blending coeff\n",
    "# CV_alg_Logistic_AdaB - 0.49 and CV_alg_RandomForest_Normalize_L1 = 0.51 LB = 0.69224 --> Blended_Submission_1\n",
    "# CV_alg_RandomForest_Normalize_L1 - 0.41 and CV_alg_RandomForest = 0.59 LB = 0.69174 --> Blended_Submission_RF_w_wo_N1\n",
    "#### blended without xgboost following coeff LB = 0.69170, CV = 0.69103####\n",
    "# # Merged_Test['CV_alg_svm']*0.253+ Merged_Test['CV_alg_RandomForest']*0.382+Merged_Test['CV_alg_Logistic']*0.168+\\\n",
    "# Merged_Test['CV_alg_KNN']*0.07+Merged_Test['CV_alg_GNB']*0.029+Merged_Test['CV_alg_GBM']*0.098\n",
    "\n",
    "Merged_Test['probability']=Merged_Test['CV_alg_svm']*0.253+ \\\n",
    "                            Merged_Test['CV_alg_RandomForest']*0.382+Merged_Test['CV_alg_Logistic']*0.168+\\\n",
    "                            Merged_Test['CV_alg_KNN']*0.07+Merged_Test['CV_alg_GNB']*0.029+\\\n",
    "                            Merged_Test['CV_alg_GBM']*0.098\n",
    "    \n",
    "submission_Blended = pd.DataFrame({\"t_id\": Merged_Test.t_id, \"probability\": Merged_Test.probability})\n",
    "submission_Blended.to_csv(\"submission/Blended_Submission_except_xgboost.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68938225913694295"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss(MergedDF2['target'].values,MergedDF2['CV_alg_DTree_Normalize_L1'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.65822686907540484"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "MergedDF2['predBlended'] = MergedDF2['CV_alg_KNN']*0.625+ \\\n",
    "                            MergedDF2['CV_xgBoost']*0.374+MergedDF2['CV_alg_DTree']*0.001\n",
    "log_loss(MergedDF2['target'].values,MergedDF2['predBlended'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "5         3.453878e+01\n",
       "6         9.992007e-16\n",
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       "29        9.992007e-16\n",
       "             ...      \n",
       "57741     3.453878e+01\n",
       "57742     9.992007e-16\n",
       "57743     3.453878e+01\n",
       "57744     3.453878e+01\n",
       "57745     3.453878e+01\n",
       "57746     3.453878e+01\n",
       "57747     9.992007e-16\n",
       "57748     3.453878e+01\n",
       "57749     3.453878e+01\n",
       "57750     3.453878e+01\n",
       "57751     9.992007e-16\n",
       "57752     9.992007e-16\n",
       "57753     3.453878e+01\n",
       "57754     9.992007e-16\n",
       "57755     9.992007e-16\n",
       "57756     3.453878e+01\n",
       "57757     9.992007e-16\n",
       "57758     3.453878e+01\n",
       "57759     3.453878e+01\n",
       "57760     3.453878e+01\n",
       "57761     3.453878e+01\n",
       "57762     9.992007e-16\n",
       "57763     9.992007e-16\n",
       "57764     3.453878e+01\n",
       "57765     3.453878e+01\n",
       "57766     9.992007e-16\n",
       "57767     3.453878e+01\n",
       "57768     3.453878e+01\n",
       "57769     3.453878e+01\n",
       "57770     3.453878e+01\n",
       "Name: MaxpredBlended, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MergedDF2['MaxpredBlended']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.529785\n",
       "1    0.532368\n",
       "2    0.509800\n",
       "3    0.425587\n",
       "4    0.510201\n",
       "Name: CV_alg_RandomForest_Robust, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MergedDF2['CV_alg_RandomForest_Robust'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.500606</td>\n",
       "      <td>0.500512</td>\n",
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       "      <td>0.500090</td>\n",
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       "<p>5 rows × 101 columns</p>\n",
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       "\n",
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       "\n",
       "         94        95             96        97        98        99  \n",
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       "2  0.500891  0.500796   4.979947e-01  0.500606  0.500512  0.500417  \n",
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       "4  0.500600  0.500498   4.992041e-01  0.500294  0.500192  0.500090  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blendDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A3</td>\n",
       "      <td>0.509800</td>\n",
       "      <td>0.509706</td>\n",
       "      <td>0.509611</td>\n",
       "      <td>0.509516</td>\n",
       "      <td>0.732376</td>\n",
       "      <td>0.025016</td>\n",
       "      <td>0.509232</td>\n",
       "      <td>0.509137</td>\n",
       "      <td>0.751616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501270</td>\n",
       "      <td>0.501175</td>\n",
       "      <td>0.501080</td>\n",
       "      <td>0.500985</td>\n",
       "      <td>0.500891</td>\n",
       "      <td>0.500796</td>\n",
       "      <td>6.947402e-01</td>\n",
       "      <td>0.500606</td>\n",
       "      <td>6.943606e-01</td>\n",
       "      <td>0.500417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A4</td>\n",
       "      <td>0.425587</td>\n",
       "      <td>0.426320</td>\n",
       "      <td>0.427052</td>\n",
       "      <td>0.427784</td>\n",
       "      <td>0.578190</td>\n",
       "      <td>0.024940</td>\n",
       "      <td>0.429980</td>\n",
       "      <td>0.430712</td>\n",
       "      <td>0.603272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.491472</td>\n",
       "      <td>0.492204</td>\n",
       "      <td>0.492937</td>\n",
       "      <td>0.493669</td>\n",
       "      <td>0.494401</td>\n",
       "      <td>0.495133</td>\n",
       "      <td>6.834597e-01</td>\n",
       "      <td>0.496597</td>\n",
       "      <td>6.863639e-01</td>\n",
       "      <td>0.498061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A5</td>\n",
       "      <td>0.510201</td>\n",
       "      <td>0.510099</td>\n",
       "      <td>0.509997</td>\n",
       "      <td>0.509895</td>\n",
       "      <td>0.693550</td>\n",
       "      <td>0.024999</td>\n",
       "      <td>0.509589</td>\n",
       "      <td>0.509486</td>\n",
       "      <td>0.714351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501009</td>\n",
       "      <td>0.500907</td>\n",
       "      <td>0.500805</td>\n",
       "      <td>0.500703</td>\n",
       "      <td>0.500600</td>\n",
       "      <td>0.500498</td>\n",
       "      <td>6.921511e-01</td>\n",
       "      <td>0.500294</td>\n",
       "      <td>6.925594e-01</td>\n",
       "      <td>0.500090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  t_id         0         1         2         3         4         5         6  \\\n",
       "0   A1  0.529785  0.529493  0.529200  0.528907  0.020021  0.025026  0.528030   \n",
       "1   A2  0.532368  0.532048  0.531729  0.531409  0.020016  0.025020  0.530450   \n",
       "2   A3  0.509800  0.509706  0.509611  0.509516  0.732376  0.025016  0.509232   \n",
       "3   A4  0.425587  0.426320  0.427052  0.427784  0.578190  0.024940  0.429980   \n",
       "4   A5  0.510201  0.510099  0.509997  0.509895  0.693550  0.024999  0.509589   \n",
       "\n",
       "          7         8    ...           90        91        92        93  \\\n",
       "0  0.527737  0.040042    ...     0.503448  0.503156  0.502863  0.502570   \n",
       "1  0.530131  0.040033    ...     0.503603  0.503284  0.502964  0.502645   \n",
       "2  0.509137  0.751616    ...     0.501270  0.501175  0.501080  0.500985   \n",
       "3  0.430712  0.603272    ...     0.491472  0.492204  0.492937  0.493669   \n",
       "4  0.509486  0.714351    ...     0.501009  0.500907  0.500805  0.500703   \n",
       "\n",
       "         94        95             96        97             98        99  \n",
       "0  0.502278  0.501985  2.705938e-315  0.501400  2.705938e-315  0.500815  \n",
       "1  0.502325  0.502005  1.016015e-315  0.501366  1.016015e-315  0.500727  \n",
       "2  0.500891  0.500796   6.947402e-01  0.500606   6.943606e-01  0.500417  \n",
       "3  0.494401  0.495133   6.834597e-01  0.496597   6.863639e-01  0.498061  \n",
       "4  0.500600  0.500498   6.921511e-01  0.500294   6.925594e-01  0.500090  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blendDF1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.495517\n",
       "1    0.495403\n",
       "2    0.502260\n",
       "3    0.500683\n",
       "4    0.501915\n",
       "dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blendDF1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##############ROUGH###############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.py:266: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "c:\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.py:426: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "blendDF10=MergedDF2[['t_id']]\n",
    "# idSeries = MergedDF2[['t_id']]\n",
    "# idSeries=True\n",
    "for i in range(0,100,noIter):\n",
    "    blendDF10.loc[blendDF10['t_id']==MergedDF2['t_id'],'prob'] = series1*(i*multiplier)+ series2*(100-i)*multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "b=series1*(i*multiplier)+ series2*(100-i)*multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_ET = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.06077041\n",
      "Max Blend1 coef = 0.23\n",
      "Max Blend2 coef = 0.77\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree = 0.6897471\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_ET = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Normalize_L1 = 0.6893823\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_ET = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.005070479\n",
      "Max Blend1 coef = 0.02\n",
      "Max Blend2 coef = 0.98\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_DTree_Robust = 0.6897471\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET = 0.6912497\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Normalize_L1 = 0.6919025\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_ET_Robust = 0.6912497\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GBM = 0.6827917\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB = 0.7009638\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_GNB_Normalize_L1 = 0.7181897\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_KNN = 0.6619281\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_AdaB = 0.6930834\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Normalize_L1 = 0.6914383\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_Logistic_Robust = 0.6911484\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest = 0.687968\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Normalize_L1 = 0.6880413\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "If only CV_alg_SGD = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_RandomForest_Robust = 0.6879688\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_SGD = 0.687968\n",
      "If only CV_alg_svm = 0.687968\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_SGD = 0.687968\n",
      "If only CV_xgBoost = 0.6674818\n",
      "************************************************\n",
      "Min LogLoss obtained as of now 0.0005978566\n",
      "Max Blend1 coef = 0.96\n",
      "Max Blend2 coef = 0.04\n",
      "If only CV_alg_svm = 0.687968\n",
      "If only CV_xgBoost = 0.6674818\n"
     ]
    }
   ],
   "source": [
    "# For all two combinations of columns in a dataframe\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.metrics import roc_auc_score\n",
    "noIter=1\n",
    "multiplier=1/100.0\n",
    "maxAns=100\n",
    "i=0\n",
    "temp=0\n",
    "\n",
    "ColNum = len(MergedDF2.columns)\n",
    "arrColNames = MergedDF2.columns.values\n",
    "for k in range(0,ColNum,1):\n",
    "    for j in range(k+1,ColNum,1):\n",
    "        if arrColNames[k].startswith('CV_'):\n",
    "            if arrColNames[j].startswith('CV_'):\n",
    "                series1=pd.Series(MergedDF2[arrColNames[k]])\n",
    "                series2=pd.Series(MergedDF2[arrColNames[j]])\n",
    "                targetseries=pd.Series(MergedDF2['target'])\n",
    "                for i in range(0,100,noIter):\n",
    "                    MergedDF2['predBlended']=0\n",
    "                    blendDF1 = series1*(i*multiplier)+ series2*(100-i)*multiplier\n",
    "                    temp=log_loss(targetseries.values,blendDF1.values)\n",
    "                    if(temp<maxAns):\n",
    "                        maxAns=temp\n",
    "#                         print \"Min LogLoss obtained as of now %.7g\" %(maxAns)\n",
    "                        Blend1=i*multiplier\n",
    "                        Blend2=(100-i)*multiplier\n",
    "#                 print '%s-->%s'%(arrColNames[i],arrColNames[j])\n",
    "                print \"************************************************\"\n",
    "                print \"Min LogLoss obtained as of now %.7g\" %(maxAns)\n",
    "                print \"Max Blend1 coef = %.7g\" %(Blend1) \n",
    "                print \"Max Blend2 coef = %.7g\" %(Blend2) \n",
    "                print \"If only %s = %.7g\" %(arrColNames[k],log_loss(targetseries.values,series1.values))\n",
    "                print \"If only %s = %.7g\" %(arrColNames[j],log_loss(targetseries.values,series2.values))\n",
    "#             print '%d-->%d'%(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.523933\n",
       "1        0.525976\n",
       "2        0.507905\n",
       "3        0.440229\n",
       "4        0.508159\n",
       "5        0.483885\n",
       "6        0.524124\n",
       "7        0.526658\n",
       "8        0.517593\n",
       "9        0.529045\n",
       "10       0.504940\n",
       "11       0.528115\n",
       "12       0.496928\n",
       "13       0.504178\n",
       "14       0.508280\n",
       "15       0.474476\n",
       "16       0.491558\n",
       "17       0.514134\n",
       "18       0.506671\n",
       "19       0.442351\n",
       "20       0.513328\n",
       "21       0.536248\n",
       "22       0.495070\n",
       "23       0.496891\n",
       "24       0.498666\n",
       "25       0.484992\n",
       "26       0.515027\n",
       "27       0.519795\n",
       "28       0.492741\n",
       "29       0.474911\n",
       "           ...   \n",
       "57741    0.515281\n",
       "57742    0.527937\n",
       "57743    0.516903\n",
       "57744    0.514509\n",
       "57745    0.525301\n",
       "57746    0.473541\n",
       "57747    0.519531\n",
       "57748    0.528174\n",
       "57749    0.483247\n",
       "57750    0.458239\n",
       "57751    0.489998\n",
       "57752    0.517173\n",
       "57753    0.519594\n",
       "57754    0.493479\n",
       "57755    0.499386\n",
       "57756    0.466165\n",
       "57757    0.519142\n",
       "57758    0.509652\n",
       "57759    0.518326\n",
       "57760    0.512307\n",
       "57761    0.480695\n",
       "57762    0.452941\n",
       "57763    0.450072\n",
       "57764    0.477673\n",
       "57765    0.531378\n",
       "57766    0.509247\n",
       "57767    0.525266\n",
       "57768    0.489219\n",
       "57769    0.505275\n",
       "57770    0.514077\n",
       "dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "series1=pd.Series(MergedDF2['CV_alg_RandomForest_Normalize_L1'])\n",
    "series2=pd.Series(MergedDF2['CV_alg_Logistic_AdaB'])"
   ]
  }
 ],
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