RandomForestRegressor.ipynb

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      "/home/jps/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/jps/anaconda/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
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     "data": {
      "text/plain": [
       "Index([u'rooms', u'bathrooms', u'exterior', u'hasLift', u'size', u'floor',\n",
       "       u'longitude', u'latitude', u'price', u'hasParkingSpace',\n",
       "       u'isParkingSpaceIncludedInPrice', u'chalet', u'duplex', u'flat',\n",
       "       u'penthouse', u'studio', u'good', u'renew'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1,
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     "output_type": "execute_result"
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   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "data = pd.read_csv('version3.csv')\n",
    "\n",
    "data= data.drop(['Unnamed: 0', 'newdevelopment'], axis=1)\n",
    "\n",
    "#preu_park_noinclos\n",
    "#preu_park_noinclos=((data['hasParkingSpace']!=data['isParkingSpaceIncludedInPrice']).astype(float))\n",
    "#data=pd.concat((data,preu_park_noinclos),axis=1)\n",
    "#data.drop(\"isParkingSpaceIncludedInPrice\",axis=1)\n",
    "\n",
    "data.columns"
   ]
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   "source": [
    "y =data['price']\n",
    "X = data.drop(['price'], axis=1)\n",
    "X=np.array(X)\n",
    "y=np.array(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=44)"
   ]
  },
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     "text": [
      "\n",
      "Best parameters set found on training set:\n",
      "\n",
      "{'normalize': False}\n",
      "\n",
      "Mean Absolute Error:\n",
      "\n",
      "482.21202833\n",
      "\n",
      "The model is trained on the training set. The scores are computed on the testing set.\n"
     ]
    }
   ],
   "source": [
    "#Linear regression\n",
    "\n",
    "# Set the parameters by cross-validation\n",
    "#tuned_parameters = [{'bootstrap': [True, False],'criterion': [\"gini\", \"entropy\"],'n_estimators': [10, 50, 100, 200, 500]}]\n",
    "tuned_parameters = [{'normalize' : [True,False]}]\n",
    "#scores = ['precision', 'recall']\n",
    "\n",
    "#scores = ['precision']\n",
    "#scores = ['f1']\n",
    "\n",
    "#print \"Tuning hyper-parameters for %s\" % score\n",
    "print\n",
    "clf = GridSearchCV(LinearRegression(fit_intercept=True), tuned_parameters, cv=10, scoring='neg_mean_absolute_error')\n",
    "clf.fit(X_train, y_train)\n",
    "print \"Best parameters set found on training set:\"\n",
    "print\n",
    "print clf.best_params_ \n",
    "print\n",
    "y_true, y_pred = y_test, clf.predict(X_test)\n",
    "#y_true=y_true.astype('float64')\n",
    "#print \"Confusion matrix:\"\n",
    "#print\n",
    "#print confusion_matrix(y_true, y_pred)\n",
    "#print\n",
    "#print \"Classification report:\"\n",
    "#print classification_report(y_true, y_pred)\n",
    "print \"Mean Absolute Error:\"\n",
    "print\n",
    "print metrics.mean_absolute_error(y_true, y_pred)\n",
    "print\n",
    "print \"The model is trained on the training set. The scores are computed on the testing set.\"\n"
   ]
  },
  {
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   "execution_count": 4,
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     "text": [
      "\n",
      "Best parameters set found on training set:\n",
      "\n",
      "{'max_features': 'sqrt', 'n_estimators': 750, 'bootstrap': False}\n",
      "\n",
      "Mean Absolute Error:\n",
      "\n",
      "303.088681872\n",
      "\n",
      "The model is trained on the training set. The scores are computed on the testing set.\n",
      "--- 604.879652023 seconds ---\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Regressor\n",
    "#n_estimators estimate\n",
    "\n",
    "start_time = time.time()\n",
    "# Set the parameters by cross-validation\n",
    "#tuned_parameters = [{'bootstrap': [True, False],'criterion': [\"gini\", \"entropy\"],'n_estimators': [10, 50, 100, 200, 500]}]\n",
    "tuned_parameters = [{'bootstrap': [True, False],\n",
    "                     'n_estimators': [10, 50, 100, 200, 500, 600, 750], 'max_features': [\"auto\", \"sqrt\", \"log2\"]}]\n",
    "#scores = ['precision', 'recall']\n",
    "\n",
    "#scores = ['precision']\n",
    "#scores = ['f1']\n",
    "\n",
    "#print \"Tuning hyper-parameters for %s\" % score\n",
    "print\n",
    "clf = GridSearchCV(RandomForestRegressor(n_estimators=10, random_state=44, n_jobs=-1), tuned_parameters, cv=10, \n",
    "                   scoring='neg_mean_absolute_error')\n",
    "clf.fit(X_train, y_train)\n",
    "print \"Best parameters set found on training set:\"\n",
    "print\n",
    "print clf.best_params_ \n",
    "print\n",
    "y_true, y_pred = y_test, clf.predict(X_test)\n",
    "#y_true=y_true.astype('float64')\n",
    "#print \"Confusion matrix:\"\n",
    "#print\n",
    "#print confusion_matrix(y_true, y_pred)\n",
    "#print\n",
    "#print \"Classification report:\"\n",
    "#print classification_report(y_true, y_pred)\n",
    "print \"Mean Absolute Error:\"\n",
    "print\n",
    "print metrics.mean_absolute_error(y_true, y_pred)\n",
    "print\n",
    "print \"The model is trained on the training set. The scores are computed on the testing set.\"\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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    "collapsed": false
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     "text": [
      "\n",
      "Best parameters set found on training set:\n",
      "\n",
      "{'max_features': 'sqrt', 'n_estimators': 750, 'bootstrap': False}\n",
      "\n",
      "Mean Absolute Error:\n",
      "\n",
      "303.088681872\n",
      "\n",
      "The model is trained on the training set. The scores are computed on the testing set.\n",
      "--- 1035.2721231 seconds ---\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Regressor\n",
    "#n_estimators estimate\n",
    "\n",
    "start_time = time.time()\n",
    "# Set the parameters by cross-validation\n",
    "#tuned_parameters = [{'bootstrap': [True, False],'criterion': [\"gini\", \"entropy\"],'n_estimators': [10, 50, 100, 200, 500]}]\n",
    "tuned_parameters = [{'bootstrap': [True, False],\n",
    "                     'n_estimators': [650, 700, 750, 800, 850], 'max_features': [\"auto\", \"sqrt\", \"log2\"]}]\n",
    "#scores = ['precision', 'recall']\n",
    "\n",
    "#scores = ['precision']\n",
    "#scores = ['f1']\n",
    "\n",
    "#print \"Tuning hyper-parameters for %s\" % score\n",
    "print\n",
    "clf = GridSearchCV(RandomForestRegressor(n_estimators=10, random_state=44, n_jobs=-1), tuned_parameters, cv=10, \n",
    "                   scoring='neg_mean_absolute_error')\n",
    "clf.fit(X_train, y_train)\n",
    "print \"Best parameters set found on training set:\"\n",
    "print\n",
    "print clf.best_params_ \n",
    "print\n",
    "y_true, y_pred = y_test, clf.predict(X_test)\n",
    "#y_true=y_true.astype('float64')\n",
    "#print \"Confusion matrix:\"\n",
    "#print\n",
    "#print confusion_matrix(y_true, y_pred)\n",
    "#print\n",
    "#print \"Classification report:\"\n",
    "#print classification_report(y_true, y_pred)\n",
    "print \"Mean Absolute Error:\"\n",
    "print\n",
    "print metrics.mean_absolute_error(y_true, y_pred)\n",
    "print\n",
    "print \"The model is trained on the training set. The scores are computed on the testing set.\"\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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     "text": [
      "\n",
      "Best parameters set found on training set:\n",
      "\n",
      "{'max_features': 'sqrt', 'n_estimators': 750, 'bootstrap': False, 'min_samples_leaf': 1}\n",
      "\n",
      "Mean Absolute Error:\n",
      "\n",
      "303.088681872\n",
      "\n",
      "The model is trained on the training set. The scores are computed on the testing set.\n",
      "--- 2905.41767097 seconds ---\n"
     ]
    }
   ],
   "source": [
    "#Random Forest Regressor\n",
    "#n_estimators estimate\n",
    "\n",
    "start_time = time.time()\n",
    "# Set the parameters by cross-validation\n",
    "#tuned_parameters = [{'bootstrap': [True, False],'criterion': [\"gini\", \"entropy\"],'n_estimators': [10, 50, 100, 200, 500]}]\n",
    "tuned_parameters = [{'bootstrap': [True, False],\n",
    "                     'n_estimators': [725, 750, 875], \n",
    "                     'max_features': [\"auto\", \"sqrt\", \"log2\"], \n",
    "                     'min_samples_leaf': [1, 5, 10, 50, 100, 200]}]\n",
    "#scores = ['precision', 'recall']\n",
    "\n",
    "#scores = ['precision']\n",
    "#scores = ['f1']\n",
    "\n",
    "#print \"Tuning hyper-parameters for %s\" % score\n",
    "print\n",
    "clf = GridSearchCV(RandomForestRegressor(n_estimators=10, random_state=44, n_jobs=-1), tuned_parameters, cv=10, \n",
    "                   scoring='neg_mean_absolute_error')\n",
    "clf.fit(X_train, y_train)\n",
    "print \"Best parameters set found on training set:\"\n",
    "print\n",
    "print clf.best_params_ \n",
    "print\n",
    "y_true, y_pred = y_test, clf.predict(X_test)\n",
    "#y_true=y_true.astype('float64')\n",
    "#print \"Confusion matrix:\"\n",
    "#print\n",
    "#print confusion_matrix(y_true, y_pred)\n",
    "#print\n",
    "#print \"Classification report:\"\n",
    "#print classification_report(y_true, y_pred)\n",
    "print \"Mean Absolute Error:\"\n",
    "print\n",
    "print metrics.mean_absolute_error(y_true, y_pred)\n",
    "print\n",
    "print \"The model is trained on the training set. The scores are computed on the testing set.\"\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
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