Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# The dataset containing samples from all the orders (testplan groups)\n",
    "df = pd.concat([pd.read_csv(f, delimiter=',') for f in glob.glob('/home/lhadhazy/Desktop/Conti/results/yresults_*.txt')],  ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5492, 2363)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIT</th>\n",
       "      <th>C_ER100_SN:ER110_USK:$#0003_VALUE</th>\n",
       "      <th>C_ER100_SN:ER110_USK:$#0004_VALUE</th>\n",
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       "      <th>C_ER100_SN:ER110_USK:$#000C_VALUE</th>\n",
       "      <th>...</th>\n",
       "      <th>C_ER100_FIN_SN:RFT:9_1_VALUE</th>\n",
       "      <th>C_ER100_FIN_SN:RFT:9_2_VALUE</th>\n",
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       "      <th>C_ER100_FIN_SN:RFT:9_8_VALUE</th>\n",
       "      <th>C_ER100_FIN_SN:RFT:9_9_VALUE</th>\n",
       "      <th>class_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>28515736063000061832000328</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>28515703203000061831800478</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 2363 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            UNIT  C_ER100_SN:ER110_USK:$#0003_VALUE  \\\n",
       "37    2851573606300006183190002A                                  1   \n",
       "269   28515736063000061831900112                                  1   \n",
       "1621  28515736063000061832000328                                  1   \n",
       "1855  28515736063000061832000418                                  1   \n",
       "2718  285157360630000618703000B5                                  1   \n",
       "3615  2851570320300006183180011A                                  1   \n",
       "4470  28515703203000061831800476                                  1   \n",
       "4472  28515703203000061831800478                                  1   \n",
       "\n",
       "      C_ER100_SN:ER110_USK:$#0004_VALUE  C_ER100_SN:ER110_USK:$#0005_VALUE  \\\n",
       "37                                    1                                  1   \n",
       "269                                   1                                  1   \n",
       "1621                                  1                                  1   \n",
       "1855                                  1                                  1   \n",
       "2718                                  1                                  1   \n",
       "3615                                  1                                  1   \n",
       "4470                                  1                                  1   \n",
       "4472                                  1                                  1   \n",
       "\n",
       "      C_ER100_SN:ER110_USK:$#0006_VALUE  C_ER100_SN:ER110_USK:$#0007_VALUE  \\\n",
       "37                                    1                                  1   \n",
       "269                                   1                                  1   \n",
       "1621                                  1                                  1   \n",
       "1855                                  1                                  1   \n",
       "2718                                  1                                  1   \n",
       "3615                                  1                                  1   \n",
       "4470                                  1                                  1   \n",
       "4472                                  1                                  1   \n",
       "\n",
       "      C_ER100_SN:ER110_USK:$#0009_VALUE  C_ER100_SN:ER110_USK:$#000A_VALUE  \\\n",
       "37                                    2                                  2   \n",
       "269                                   2                                  2   \n",
       "1621                                  2                                  2   \n",
       "1855                                  2                                  2   \n",
       "2718                                  2                                  2   \n",
       "3615                                  2                                  2   \n",
       "4470                                  2                                  2   \n",
       "4472                                  2                                  2   \n",
       "\n",
       "      C_ER100_SN:ER110_USK:$#000B_VALUE  C_ER100_SN:ER110_USK:$#000C_VALUE  \\\n",
       "37                                    2                                  2   \n",
       "269                                   2                                  2   \n",
       "1621                                  2                                  2   \n",
       "1855                                  2                                  2   \n",
       "2718                                  2                                  2   \n",
       "3615                                  2                                  2   \n",
       "4470                                  2                                  2   \n",
       "4472                                  2                                  2   \n",
       "\n",
       "      ...  C_ER100_FIN_SN:RFT:9_1_VALUE  C_ER100_FIN_SN:RFT:9_2_VALUE  \\\n",
       "37    ...                             1                             1   \n",
       "269   ...                             1                             1   \n",
       "1621  ...                             1                             1   \n",
       "1855  ...                             1                             1   \n",
       "2718  ...                             1                             1   \n",
       "3615  ...                             1                             1   \n",
       "4470  ...                             1                             1   \n",
       "4472  ...                             1                             1   \n",
       "\n",
       "      C_ER100_FIN_SN:RFT:9_3_VALUE  C_ER100_FIN_SN:RFT:9_4_VALUE  \\\n",
       "37                               1                             1   \n",
       "269                              1                             1   \n",
       "1621                             1                             1   \n",
       "1855                             1                             1   \n",
       "2718                             1                             1   \n",
       "3615                             1                             1   \n",
       "4470                             1                             1   \n",
       "4472                             1                             1   \n",
       "\n",
       "      C_ER100_FIN_SN:RFT:9_5_VALUE  C_ER100_FIN_SN:RFT:9_6_VALUE  \\\n",
       "37                               1                             1   \n",
       "269                              1                             1   \n",
       "1621                             1                             1   \n",
       "1855                             1                             1   \n",
       "2718                             1                             1   \n",
       "3615                             1                             1   \n",
       "4470                             1                             1   \n",
       "4472                             1                             1   \n",
       "\n",
       "      C_ER100_FIN_SN:RFT:9_7_VALUE  C_ER100_FIN_SN:RFT:9_8_VALUE  \\\n",
       "37                               1                             1   \n",
       "269                              1                             1   \n",
       "1621                             1                             1   \n",
       "1855                             1                             1   \n",
       "2718                             1                             1   \n",
       "3615                             1                             1   \n",
       "4470                             1                             1   \n",
       "4472                             1                             1   \n",
       "\n",
       "      C_ER100_FIN_SN:RFT:9_9_VALUE  class_label  \n",
       "37                           -4.64            1  \n",
       "269                          -4.84            1  \n",
       "1621                         -5.14            1  \n",
       "1855                         -4.68            1  \n",
       "2718                         -5.00            1  \n",
       "3615                         -4.86            1  \n",
       "4470                         -4.58            1  \n",
       "4472                         -4.84            1  \n",
       "\n",
       "[8 rows x 2363 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only attributes (columns) that do not have any null values in them\n",
    "dfnan=df.dropna(axis='columns')\n",
    "\n",
    "#check size of dataset\n",
    "print(dfnan.shape)\n",
    "\n",
    "#check that positive samples are all contained in dataset\n",
    "dfnan.loc[df['class_label'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X vector contains all the measurement for a unit\n",
    "X = np.asarray(dfnan.iloc[:, 1:-1])    \n",
    "\n",
    "# y vector contains the class labels for the units\n",
    "y = np.asarray(dfnan.iloc[:, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#Scale the feature vectors\n",
    "scaler = preprocessing.StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#scaler = preprocessing.MinMaxScaler()\n",
    "#X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "#Create the (scaled/unscaled) train and test datasets\n",
    "X_train_scaled, X_test_scaled, y_train, y_test = train_test_split(X_scaled, y, random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "#Number of positive (damaged) units in the test dataset.\n",
    "list(y_test).count(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
      "            splitter='best')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.99757282, 0.99854298])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from EstimatorModel.dtree import CompositeDTEstimator\n",
    "\n",
    "# Instantiate a Composite Decision Tree Estimator object\n",
    "dtestimator = CompositeDTEstimator()\n",
    "\n",
    "# Fit the model\n",
    "dtestimator.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate cross-validation scores\n",
    "cross_val_score(dtestimator.trained_estimator_, X_train, y_train, cv=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(dtestimator.predict([X[4009]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier(random_state=0)\n",
    "cross_val_score(model, X_train, y_train, cv=2)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyOwnTransformer(),NullColumnCleanse()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('myowntransformer', MyOwnTransformer())"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.preprocessing import Binarizer\n",
    "#from TransformerModel.NullColumnCleanse import NullColumnCleanse\n",
    "from sklearn.base import TransformerMixin\n",
    "\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class MyOwnTransformer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X\n",
    "\n",
    "class NullColumnCleanse(TransformerMixin):\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        return X.dropna(axis='columns')\n",
    "\n",
    "mylist= ['MyOwnTransformer(),NullColumnCleanse()']\n",
    "print(*mylist)\n",
    "pipe=make_pipeline( MyOwnTransformer(),NullColumnCleanse(), MultinomialNB()) \n",
    "pipe.steps[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import ClassifierMixin, BaseEstimator\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from TransformerModel.StandardScaler import StandardScaler\n",
    "from TransformerModel.NullColumnCleanse import NullColumnCleanse\n",
    "from TransformerModel.LDATransformer import LDA\n",
    "\n",
    "\n",
    "# Random Forest classifier decorator class\n",
    "class CompositeRFEstimator(BaseEstimator, ClassifierMixin):\n",
    "\n",
    "    def __init__(self, n_estimators=100):\n",
    "        self.decorated_estimator = RandomForestClassifier(\n",
    "            n_estimators=n_estimators, random_state=0)\n",
    "        self.trained_estimator_ = None\n",
    "        self.transform_steps = [\n",
    "            NullColumnCleanse(),\n",
    "            StandardScaler(),\n",
    "            LDA(3)\n",
    "            ]\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        # forging of the data pipeline steps inside the fit method\n",
    "        self.trained_estimator_ = make_pipeline(\n",
    "            # self.transform_steps,\n",
    "            self.decorated_estimator\n",
    "        ).fit(X, y)\n",
    "\n",
    "        print(self.decorated_estimator)\n",
    "        return self\n",
    "\n",
    "    def score(self, X, y=None):\n",
    "        return(sum(self.predict(X)))\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.trained_estimator_.predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = CompositeRFEstimator(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
      "            oob_score=False, random_state=0, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompositeRFEstimator(n_estimators=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cs.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "print(cs.predict([X[4009]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
      "            oob_score=False, random_state=0, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9987861720035648"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cs.fit(X_train, y_train)\n",
    "\n",
    "scores = cross_val_score(cs.trained_estimator_, X_train, y_train, cv=2)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
      "            oob_score=False, random_state=0, verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.99854369, 0.99902865])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from EstimatorModel.rforest import CompositeRFEstimator\n",
    "\n",
    "# Instantiate the Random Forest classifier decorator class with two-hundred weak learners\n",
    "rf_estimator = CompositeRFEstimator(n_estimators=200)\n",
    "\n",
    "# Fit the model\n",
    "rf_estimator.fit(X_train, y_train)\n",
    "\n",
    "# Evaluate cross-validation scores\n",
    "cross_val_score(rf_estimator.trained_estimator_, X_train, y_train, cv=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[1516, 1268, 1534]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import heapq\n",
    "\n",
    "plt.rcParams['axes.facecolor'] = 'lightgrey'\n",
    "\n",
    "# obtain the classifier's (last step in pipeline) feature importance list\n",
    "importances =rf_estimator.trained_estimator_.steps[-1][1].feature_importances_\n",
    "\n",
    "# display using matplotlib's bar plotting\n",
    "x = np.arange(len(importances))\n",
    "plt.figure(figsize=(18,5))\n",
    "plt.bar(x[1200:1600],importances[1200:1600], \n",
    "        width=1.2, align='center', color='blue')\n",
    "plt.show()\n",
    "\n",
    "heapq.nlargest(3, range(len(importances)), importances.__getitem__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "       0.00121026])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_estimator.trained_estimator_.steps[-1][1].feature_importances_"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from EstimatorModel.svc import CompositeSVCEstimator\n",
    "\n",
    "svc_estim = CompositeSVCEstimator(class_weight='balanced', cache_size=800, probability=False)\n",
    "param_grid = {'C': [1, 10,],\n",
    "              'gamma': [0.0001, 0.001],\n",
    "              'kernel': ('linear','rbf', 'sigmoid')}\n",
    "grid = GridSearchCV(svc_estim, param_grid,cv=2)\n",
    "\n",
    "#grid.fit(X_train_scaled, y_train)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "grid.best_params_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import ClassifierMixin, BaseEstimator\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from TransformerModel.StandardScaler import StandardScaler\n",
    "from TransformerModel.NullColumnCleanse import NullColumnCleanse\n",
    "from TransformerModel.LDATransformer import LDA\n",
    "\n",
    "\n",
    "# Random Forest classifier decorator class\n",
    "class CompositeSVCEstimator1(BaseEstimator, ClassifierMixin):\n",
    "\n",
    "    def __init__(self, C=1, kernel='rbf', gamma='auto',\n",
    "                 class_weight='balanced', cache_size=800, max_iter=-1, random_state=None, probability=False, **kwargs):\n",
    "        self.decorated_estimator = SVC(C=C, kernel=kernel, gamma=gamma,\n",
    "                                       class_weight=class_weight,\n",
    "                                       cache_size=cache_size, max_iter=max_iter, random_state=random_state,\n",
    "                                       probability=probability, **kwargs)\n",
    "        self.C = C\n",
    "        self.max_iter = max_iter\n",
    "        self.random_state = random_state\n",
    "        self.kernel = kernel\n",
    "        self.class_weight = class_weight\n",
    "        self.gamma = gamma\n",
    "        self.trained_estimator_ = None\n",
    "        self.transform_steps = [\n",
    "            NullColumnCleanse(),\n",
    "            StandardScaler(),\n",
    "            LDA(1)\n",
    "            ]\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        # forging of the data pipeline steps inside the fit method\n",
    "        self.trained_estimator_ = make_pipeline(\n",
    "            # self.transform_steps,\n",
    "            self.decorated_estimator\n",
    "        ).fit(X, y)\n",
    "\n",
    "        print(self.decorated_estimator)\n",
    "        return self\n",
    "\n",
    "    def score(self, X, y=None):\n",
    "        return(sum(self.predict(X)))\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.trained_estimator_.predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.7/site-packages/sklearn/model_selection/_validation.py:542: FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "an integer is required",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-effa871e168f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mfit_parameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'random_seed'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_scaled\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;31m#grid.fit(X_train, y_train)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    720\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    709\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 711\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    823\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    783\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    784\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    543\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    546\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    259\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 261\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    262\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    526\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    529\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-60-8449a508bf94>\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     34\u001b[0m             \u001b[0;31m# self.transform_steps,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorated_estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m         ).fit(X, y)\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorated_estimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    265\u001b[0m         \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[0;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[1;32m    269\u001b[0m                 \u001b[0mcache_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m                 \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m                 max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32msklearn/svm/libsvm.pyx\u001b[0m in \u001b[0;36msklearn.svm.libsvm.fit\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: an integer is required"
     ]
    }
   ],
   "source": [
    "svc_estim = CompositeSVCEstimator1(class_weight='balanced', cache_size=800, probability=False)\n",
    "param_grid = {'C': [1, 10,],\n",
    "              'gamma': [0.0001, 0.001],\n",
    "              'kernel': ('linear','rbf', 'sigmoid')}\n",
    "grid = GridSearchCV(svc_estim, param_grid,cv=2)\n",
    "\n",
    "fit_parameters = {'random_seed': 0}\n",
    "grid.fit(X_train_scaled, y_train)\n",
    "#grid.fit(X_train, y_train)\n",
    "\n",
    "#grid.best_params_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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