Predictive Maintenance v3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-12T23:38:51.162205Z",
     "start_time": "2018-01-12T23:38:42.083904Z"
    }
   },
   "outputs": [],
   "source": [
    "#%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import fileinput\n",
    "import re\n",
    "\n",
    "from io import StringIO\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import explained_variance_score, r2_score, mean_squared_error, mean_absolute_error\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, IntRangeSlider, Layout, fixed\n",
    "\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation and Features Engineering Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:04.174703Z",
     "start_time": "2018-01-08T10:03:04.169324Z"
    },
    "code_folding": [
     2
    ]
   },
   "outputs": [],
   "source": [
    "train_file_path = \"Data/PM_train.txt\"\n",
    "test_file_path = \"Data/PM_test.txt\"\n",
    "colnames = [\"id\",\"cycle\",\"setting1\",\"setting2\",\"setting3\",\"s1\",\"s2\",\"s3\",\"s4\",\"s5\",\"s6\",\"s7\",\n",
    "           \"s8\",\"s9\",\"s10\",\"s11\",\"s12\",\"s13\",\"s14\",\"s15\",\"s16\",\"s17\",\"s18\",\"s19\",\"s20\",\"s21\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:04.597391Z",
     "start_time": "2018-01-08T10:03:04.588285Z"
    },
    "code_folding": [
     0,
     3,
     6
    ]
   },
   "outputs": [],
   "source": [
    "def move_col_last(df,col_name):\n",
    "  return pd.concat([df.drop(col_name,axis=1),df[col_name]], axis=1)\n",
    "\n",
    "def move_col_first(df,col_name):\n",
    "  return pd.concat([df[col_name],df.drop(col_name,axis=1)], axis=1)\n",
    "\n",
    "def sort_dict(d):\n",
    "  return dict(sorted(d.items(), key=lambda x: x[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:05.546446Z",
     "start_time": "2018-01-08T10:03:04.969663Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def transfer_data(file_path,col_names,features=[],shuffle=False):\n",
    "  # user defined variables to set the windows for classifcation\n",
    "  w1 = 30\n",
    "  w0 = 15\n",
    "  # window size (window_size>=2),  most recent sensor values\n",
    "  window_size = 5\n",
    "  \n",
    "  # read in the file\n",
    "  with open(file_path, 'r') as f:\n",
    "    filedata = f.read()\n",
    "  \n",
    "  # remove white space from the end of line\n",
    "  filedata = re.sub('\\s*$', '',filedata,flags = re.M)\n",
    "  \n",
    "  # filedata is a string that pretends to be a file input\n",
    "  file_as_string = StringIO(filedata)\n",
    "\n",
    "  dataset = pd.read_table(file_as_string,sep=\" \",names=colnames)\n",
    "  \n",
    "  # get the maximum cycle number for each id\n",
    "  d1 = dataset.groupby([\"id\"])[\"cycle\"].max().to_frame()\n",
    "  d1.columns = ['max']\n",
    "\n",
    "  # add column with the name 'id' (just copy the index)\n",
    "  d1['id'] = d1.index\n",
    "  d2 = pd.merge(dataset, d1, on='id')\n",
    "  \n",
    "  # generate the column RUL based on the values of columns \"max\" and \"cycle\"\n",
    "  d2['RUL'] = d2['max'] - d2['cycle']\n",
    "\n",
    "  # exclude column \"max\" from the data frame\n",
    "  d2 = d2.drop('max', 1)\n",
    "\n",
    "  # genearte label1 and label2\n",
    "  dataset = d2\n",
    "  dataset['label1'] = np.where(d2['RUL'] <= w1, 1, 0)\n",
    "  dataset['label2'] = np.where(d2['RUL'] <= w0, 2, (np.where(d2['RUL'] <= w1,1,0)))\n",
    "  \n",
    "  # exclude comuns id, cycle, setting1,setting2,setting3, and last 3 columns\n",
    "  # only the 21 sensor columns are kept in the data frame\n",
    "  n_pre_sensor_columns = 5 # id, cycle, setting1,setting2,setting3\n",
    "  n_after_sensor_columns = 3 #RUL, label1, label2\n",
    "  n_col = len(dataset.columns)\n",
    "\n",
    "  data = dataset[dataset.columns[n_pre_sensor_columns:n_col - n_after_sensor_columns]]\n",
    "  n_sensor=len(data.columns)\n",
    "\n",
    "  ids = dataset.id.unique()\n",
    "  n_id = len(ids) # 100\n",
    "\n",
    "  a = [\"a\" + str(i) for i in range(1,n_sensor+1)] # average\n",
    "  sd = [\"sd\" + str(i) for i in range(1,n_sensor+1)] # standard deviation\n",
    "  diffA = [\"diffA\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  diffB = [\"diffB\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  diffC = [\"diffC\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  \n",
    "  df = None\n",
    "  subsets = []\n",
    "  subset_rolling_mean = pd.DataFrame()\n",
    "  subset_rolling_std = pd.DataFrame()\n",
    "  subset_rolling_diffA = pd.DataFrame()\n",
    "  subset_rolling_diffB = pd.DataFrame()\n",
    "  subset_rolling_diffC = pd.DataFrame()\n",
    "  for i in range(1,n_id+1):\n",
    "    # get the subset of the data that only contains the sensor columns for the id i\n",
    "    if 'avg' in features:\n",
    "      subset_rolling_mean = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).mean()\n",
    "      subset_rolling_mean.columns = a\n",
    "      subsets.append(subset_rolling_mean)\n",
    "    if 'std' in features:\n",
    "      subset_rolling_std = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).std().fillna(0)\n",
    "      subset_rolling_std.columns = sd\n",
    "    if 'diffA' in features:\n",
    "      subset_rolling_diffA = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffA.columns = diffA\n",
    "    if 'diffB' in features:\n",
    "      subset_rolling_diffB = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffB.columns = diffB\n",
    "    if 'diffC' in features:\n",
    "      subset_rolling_diffC = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffC.columns = diffC\n",
    "\n",
    "    subset = pd.concat([subset_rolling_mean,subset_rolling_std,subset_rolling_diffA,subset_rolling_diffB,subset_rolling_diffC], axis=1)\n",
    "    df_tmp = pd.concat([dataset[dataset.id == i],subset], axis=1)\n",
    "    df_tmp = move_col_last(df_tmp,['RUL', 'label1','label2'])\n",
    "\n",
    "    if df is None:\n",
    "      df = df_tmp\n",
    "    else:\n",
    "      df = df.append(df_tmp,ignore_index=True)\n",
    "\n",
    "  \n",
    "  if (shuffle):\n",
    "    df = df.sample(frac=1)\n",
    "                          \n",
    "  # Exclude column names: 'id','RUL','label1','label2'\n",
    "  X = df.drop(['id','RUL','label1','label2'], 1)\n",
    "  y = df['RUL']\n",
    "  \n",
    "  scaler = MinMaxScaler()\n",
    "  #X = pd.DataFrame(scaler.fit_transform(X), index=X.index, columns=X.columns)\n",
    "  X_scaled = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "  X = X_scaled\n",
    "  # Drop all columns that contain only zeros\n",
    "  # X = X.loc[:, (df != 0).any(axis=0)]\n",
    "  # X = X.dropna(axis=1)\n",
    "  \n",
    "  # Drop columns which have same values in all rows\n",
    "  X = X.drop(X.std()[(X.std() == 0)].index, axis=1)\n",
    "  \n",
    "  # Azure headers\n",
    "  #   X = X[['cycle', 'a4', 'a11', 'a21', 'a15', 'a20', 'a17', 'a12', 'a7', 'a2', 'a3',\n",
    "  #          's11', 's4', 's12', 's7', 's15', 's21', 's20', 's2', 's17', 'a8', 'a13',\n",
    "  #          's3', 's8', 's13', 'a9', 's9', 'a14', 's14', 'sd6', 'a6', 's6', 'sd14', 'sd9', 'sd13']]\n",
    "  \n",
    "  return X,y.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:13.086950Z",
     "start_time": "2018-01-08T10:03:05.548843Z"
    }
   },
   "outputs": [],
   "source": [
    "X0, y0 = transfer_data(train_file_path,colnames,features=['avg','std'],shuffle=True)\n",
    "Xt0, yt0 = transfer_data(test_file_path,colnames,features=['avg','std'],shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:16.841330Z",
     "start_time": "2018-01-08T10:03:13.090256Z"
    }
   },
   "outputs": [],
   "source": [
    "X1, y1 = transfer_data(train_file_path,colnames,features=[],shuffle=True)\n",
    "Xt1, yt1 = transfer_data(test_file_path,colnames,features=[],shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:28.744044Z",
     "start_time": "2018-01-08T10:03:16.844670Z"
    }
   },
   "outputs": [],
   "source": [
    "X2, y2 = transfer_data(train_file_path,colnames,features=['avg','std','diffA','diffB','diffC'],shuffle=True)\n",
    "Xt2, yt2 = transfer_data(test_file_path,colnames,features=['avg','std','diffA','diffB','diffC'],shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:35.689821Z",
     "start_time": "2018-01-08T10:03:28.746988Z"
    }
   },
   "outputs": [],
   "source": [
    "X3, y3 = transfer_data(train_file_path,colnames,features=['diffA','diffB','diffC'],shuffle=True)\n",
    "Xt3, yt3 = transfer_data(test_file_path,colnames,features=['diffA','diffB','diffC'],shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X0, y0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:36.749695Z",
     "start_time": "2018-01-08T10:03:35.692483Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DecisionTree_regr0 = DecisionTreeRegressor(max_leaf_nodes=2000,max_depth=32)\n",
    "DecisionTree_regr0.fit(X0,y0)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:54.102478Z",
     "start_time": "2018-01-08T10:03:36.754002Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import neural_network\n",
    "NeuralNetwork_regr0 = neural_network.MLPRegressor(alpha=.1,max_iter=400,hidden_layer_sizes=(50,),solver='lbfgs',random_state=1)\n",
    "NeuralNetwork_regr0.fit(X0,y0)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:55.954139Z",
     "start_time": "2018-01-08T10:03:54.117346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.2, loss='ls', max_depth=32, max_features=None,\n",
       "             max_leaf_nodes=20, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=100,\n",
       "             min_samples_split=128, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=10, presort='auto', random_state=None,\n",
       "             subsample=1.0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "GradientBoostingRegressor0 = GradientBoostingRegressor(learning_rate=0.2,\n",
    "                                                       max_leaf_nodes=20,\n",
    "                                                       min_samples_leaf=100,\n",
    "                                                       n_estimators=10,\n",
    "                                                       max_depth=32,\n",
    "                                                       min_samples_split=128,\n",
    "                                                      )\n",
    "GradientBoostingRegressor0.fit(Xz,yz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:57.980947Z",
     "start_time": "2018-01-08T10:03:55.970179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=DecisionTreeRegressor(criterion='mse', max_depth=32, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=0.1,\n",
       "         max_samples=1.0, n_estimators=10, n_jobs=1, oob_score=False,\n",
       "         random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Bagging_regr0 = BaggingRegressor(DecisionTreeRegressor(max_depth=32)\n",
    "                                 ,bootstrap_features=False\n",
    "                                 ,max_features=0.1\n",
    "                                 ,bootstrap=True\n",
    "                                )\n",
    "Bagging_regr0.fit(X0,y0)\n",
    "\n",
    "\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Bagging_regrZ = BaggingRegressor(DecisionTreeRegressor(max_depth=32)\n",
    "                                 ,bootstrap_features=False\n",
    "                                 ,max_features=0.1\n",
    "                                 ,bootstrap=True\n",
    "                                )\n",
    "Bagging_regrZ.fit(Xz,yz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:57.994415Z",
     "start_time": "2018-01-08T10:03:57.985225Z"
    }
   },
   "outputs": [],
   "source": [
    "R=None\n",
    "i = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T11:18:53.124221Z",
     "start_time": "2018-01-08T11:18:52.735739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Dzyf7/zJRfXdW1durage6Ll5PB144xqQ/AxYk2W5g2COBsU7EpR/+yPGm7feBbVm+e4+k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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f321d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113eb4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# if not R:\n",
    "#   R = collections.OrderedDict()\n",
    "# i+=1\n",
    "# R[\"Bagging0 %s\" % (i)] = Bagging_regr0.predict(Xt0)\n",
    "# R[\"BaggingZ %s\" % (i)] = Bagging_regrZ.predict(Xzt)\n",
    "# #R[\"Gradient %s\" % (i)] = GradientBoostingRegressor0.predict(Xzt)\n",
    "# print(len(R))\n",
    "# if len(R) > 7:\n",
    "#   print(len(R))\n",
    "#   R.popitem(last=False)\n",
    "#   print(len(R))\n",
    "\n",
    "#scores = {}\n",
    "# for label,y_pred in R.items():\n",
    "#   #print(\"%s: %s\" % (label,mean_absolute_error(yt0,y_pred)))\n",
    "#   scores[label] = mean_absolute_error(yt0,y_pred)\n",
    "\n",
    "\n",
    "scores[\"BaggingZ\"] = mean_absolute_error(ytz,Bagging_regr0.predict(Xtz))\n",
    "scores[\"Bagging0\"] = mean_absolute_error(yt0,Bagging_regrZ.predict(Xt0))\n",
    "  \n",
    "#print(38.9308997523 - 21.14)\n",
    "bar_chart(sort_dict(scores),\"Mean Absolute Error\\nExplained variance scores (The best value is 0.0)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X1, y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.052442Z",
     "start_time": "2018-01-08T10:03:10.237Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DecisionTree_regr1 = DecisionTreeRegressor(max_leaf_nodes=2000,max_depth=32)\n",
    "DecisionTree_regr1.fit(X1,y1)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.054927Z",
     "start_time": "2018-01-08T10:03:10.510Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Bagging_regr1 = BaggingRegressor(DecisionTreeRegressor(max_depth=32,min_samples_split=128),n_estimators=8)\n",
    "Bagging_regr1.fit(X1,y1)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.059127Z",
     "start_time": "2018-01-08T10:03:10.821Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "Lasso_regr1 = linear_model.Lasso(alpha =.1)\n",
    "Lasso_regr1.fit(X1,y1)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.067791Z",
     "start_time": "2018-01-08T10:03:11.093Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import neural_network\n",
    "NeuralNetwork_regr1 = neural_network.MLPRegressor(alpha=.1,max_iter=400,hidden_layer_sizes=(50,),solver='lbfgs',random_state=1)\n",
    "NeuralNetwork_regr1.fit(X1,y1)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X2, y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.073438Z",
     "start_time": "2018-01-08T10:03:11.671Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DecisionTree_regr2 = DecisionTreeRegressor(max_leaf_nodes=2000,max_depth=32)\n",
    "DecisionTree_regr2.fit(X2,y2)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.083240Z",
     "start_time": "2018-01-08T10:03:11.957Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "RandomForest_regr2 = RandomForestRegressor()\n",
    "RandomForest_regr2.fit(X2,y2)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.088265Z",
     "start_time": "2018-01-08T10:03:12.246Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Bagging_regr2 = BaggingRegressor(DecisionTreeRegressor(max_depth=32,min_samples_split=128),n_estimators=8)\n",
    "Bagging_regr2.fit(X2,y2)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.090900Z",
     "start_time": "2018-01-08T10:03:12.551Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "Lasso_regr2 = linear_model.Lasso(alpha =.1)\n",
    "Lasso_regr2.fit(X2,y2)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.105028Z",
     "start_time": "2018-01-08T10:03:12.806Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import neural_network\n",
    "NeuralNetwork_regr2 = neural_network.MLPRegressor(alpha=.09,max_iter=400,hidden_layer_sizes=(50,),solver='lbfgs',random_state=1)\n",
    "NeuralNetwork_regr2.fit(X2,y2)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X3, y3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.118412Z",
     "start_time": "2018-01-08T10:03:13.392Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "DecisionTree_regr3 = DecisionTreeRegressor(max_leaf_nodes=2000,max_depth=32)\n",
    "DecisionTree_regr3.fit(X3,y3)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.120517Z",
     "start_time": "2018-01-08T10:03:13.720Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "Lasso_regr3 = linear_model.Lasso(alpha =.1)\n",
    "Lasso_regr3.fit(X3,y3)\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.122673Z",
     "start_time": "2018-01-08T10:03:13.974Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import neural_network\n",
    "NeuralNetwork_regr3 = neural_network.MLPRegressor(alpha=.09,max_iter=400,hidden_layer_sizes=(50,),solver='lbfgs',random_state=1)\n",
    "NeuralNetwork_regr3.fit(X3,y3)\n",
    "None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charts  Section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:12:14.263457Z",
     "start_time": "2018-01-08T10:12:14.004697Z"
    },
    "code_folding": [
     0,
     15,
     19,
     28,
     40
    ]
   },
   "outputs": [],
   "source": [
    "RangeSlider = widgets.IntRangeSlider(\n",
    "    value=[8311,8391],\n",
    "    min=1,\n",
    "    max=len(Xt0),\n",
    "    step=10,\n",
    "    description='Test:',\n",
    "    disabled=False,\n",
    "    continuous_update=False,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    readout_format='d',\n",
    "    height=3,\n",
    "    layout=Layout(width='90%', height='50px'),\n",
    ")\n",
    "\n",
    "def scaler(arr):\n",
    "  scaler = MinMaxScaler()\n",
    "  return pd.DataFrame(scaler.fit_transform(pd.DataFrame(arr)))\n",
    "\n",
    "def Y_ploter(Range,Y):\n",
    "  start, end = Range\n",
    "  range_len = end-start\n",
    "  \n",
    "  plt.figure(figsize=(16,10))\n",
    "  \n",
    "  # pick colors\n",
    "  colors = cm.rainbow(np.linspace(0, 1, len(Y)))\n",
    "\n",
    "  for y,color in zip(Y.items(),colors):\n",
    "    plt.scatter(np.arange(start, end, 1),y[1][start:end], c=color, s=30/(range_len/80),alpha=0.5, label=y[0])\n",
    "\n",
    "  plt.grid()\n",
    "  plt.xlabel(\"%s data points\\n range [%s:%s]\" % (end-start,start,end))\n",
    "  plt.ylabel(\"GT Cycles and Predicted Cycles\")\n",
    "  plt.title(\" vs \".join([s for s in Y.keys()]))\n",
    "  lgnd = plt.legend(scatterpoints=1, fontsize=14)\n",
    "  for handle in lgnd.legendHandles:\n",
    "    handle.set_sizes([30])\n",
    "  plt.show()\n",
    "\n",
    "def general_data_ploter(Range,Xs,y,scale_y):\n",
    "  start, end = Range\n",
    "  range_len = end-start\n",
    "  \n",
    "  plt.figure(figsize=(13,10))\n",
    "  \n",
    "  # pick colors\n",
    "  colors = cm.rainbow(np.linspace(0, 1, len(Xs)))\n",
    "  \n",
    "  for X,color in zip(Xs,colors):\n",
    "    # Get label\n",
    "    label = pd.DataFrame(X).columns[0]\n",
    "    if(label == 0):\n",
    "      label = 'y'\n",
    "    plt.scatter(np.arange(start, end, 1),X[start:end], c=color, s=30/(range_len/80),alpha=0.5, label=label)\n",
    "\n",
    "  if(scale_y):\n",
    "    y = scaler(y)\n",
    "\n",
    "  plt.grid()\n",
    "  plt.xlabel(\"sample numbers\\n%s data points\\n range [%s:%s]\" % (end-start,start,end))\n",
    "  plt.ylabel(\"Cycles and sensors values (Scalled)\")\n",
    "  plt.title(\"Scalled Data and Cycles\")\n",
    "  lgnd = plt.legend(scatterpoints=1, fontsize=14)\n",
    "  for handle in lgnd.legendHandles:\n",
    "    handle.set_sizes([30])\n",
    "  plt.show()\n",
    "  \n",
    "def bar_chart(scores,title):\n",
    "  plt.figure(figsize=(13,5))\n",
    "  values = np.array(list(scores.values()))\n",
    "  keys = list(scores.keys())\n",
    "  colors = cm.rainbow(np.linspace(0, 1, len(scores)))\n",
    "  plt.bar(range(len(scores)), values, align='center',color=cm.rainbow(np.linspace(0, 1, len(scores))))\n",
    "  plt.xticks(range(len(scores)), keys,rotation=45)\n",
    "  plt.title(title)\n",
    "  plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.139078Z",
     "start_time": "2018-01-08T10:03:15.446Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "scores = {\n",
    "  #\"GT\":1,\n",
    "  #\"Decision Tree avg std\": DecisionTree_regr1.score(Xt1,yt1),\n",
    "  #\"Decision Tree diff\": DecisionTree_regr2.score(Xt2,yt2),\n",
    "  #\"Bagging avg std\": Bagging_regr1.score(Xt1,yt1),\n",
    "  #\"Lasso avg std\": Lasso_regr1.score(Xt1,yt1),\n",
    "  #\"Lasso diff\": Lasso_regr2.score(Xt2,yt2),\n",
    "  \"NeuralNetwork none\": NeuralNetwork_regr0.score(Xt0,yt0),\n",
    "  \"NeuralNetwork avg std\": NeuralNetwork_regr1.score(Xt1,yt1),\n",
    "  \"NeuralNetwork avg std diff\": NeuralNetwork_regr2.score(Xt2,yt2),\n",
    "  \"NeuralNetwork diff\": NeuralNetwork_regr3.score(Xt3,yt3),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:12:19.305808Z",
     "start_time": "2018-01-08T10:12:18.980634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AwApWFfS7+4ruvm2Sg5PcIcnhq11Bd5/Q3Ud295EbNmzYzjIBAIC1WNNVd7r74iTvSnKn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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155c1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c782b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart(sort_dict(scores),\"Regressors Scores (+1 is the best)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T11:18:29.894135Z",
     "start_time": "2018-01-08T11:18:29.829599Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DecisionTree_regr1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-7247170f11f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m Y = {\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0;34m\"DecisionTree\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mDecisionTree_regr1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt1\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      3\u001b[0m   \u001b[0;31m#\"DecisionTree2\": DecisionTree_regr2.predict(Xt2),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;34m\"Bagging 0\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBagging_regr0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0;34m\"Bagging 1\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBagging_regr1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'DecisionTree_regr1' is not defined"
     ]
    }
   ],
   "source": [
    "Y = {\n",
    "  \"DecisionTree\": DecisionTree_regr1.predict(Xt1),\n",
    "  #\"DecisionTree2\": DecisionTree_regr2.predict(Xt2),\n",
    "  \"Bagging 0\": Bagging_regr0.predict(Xt0),\n",
    "  \"Bagging 1\": Bagging_regr1.predict(Xt1),\n",
    "  #\"Lasso\": Lasso_regr1.predict(Xt),\n",
    "  #\"Lasso2\": Lasso_regr2.predict(Xt2),\n",
    "  #\"NeuralNetwork none\": NeuralNetwork_regr0.predict(Xt0),\n",
    "  #\"NeuralNetwork avg std\": NeuralNetwork_regr1.predict(Xt1),\n",
    "  #\"NeuralNetwork avg std diff\": NeuralNetwork_regr2.predict(Xt2),\n",
    "  \"NeuralNetwork diff\": NeuralNetwork_regr3.predict(Xt3),\n",
    "}\n",
    "\n",
    "scores = {}\n",
    "for label,y_pred in Y.items():\n",
    "  print(\"%s: %s\" % (label,explained_variance_score(yt3,y_pred)))\n",
    "  scores[label] = mean_absolute_error(yt3,y_pred)\n",
    "  \n",
    "bar_chart(sort_dict(scores),\"Mean Absolute Error\\nExplained variance scores (The best value is 0.0)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.161452Z",
     "start_time": "2018-01-08T10:03:16.565Z"
    }
   },
   "outputs": [],
   "source": [
    "Y = {\n",
    "  #\"GT\": yt0,\n",
    "  \"DecisionTree none\": DecisionTree_regr0.predict(Xt0),\n",
    "  \"DecisionTree avg std\": DecisionTree_regr1.predict(Xt1),\n",
    "  \"DecisionTree avg std diff\": DecisionTree_regr2.predict(Xt2),\n",
    "  \"RandomForest avg std diff\": RandomForest_regr2.predict(Xt2),\n",
    "  \"Bagging\": Bagging_regr0.predict(Xt0),\n",
    "  \"Bagging avg std\": Bagging_regr1.predict(Xt1),\n",
    "  \"Bagging avg std diff\": Bagging_regr2.predict(Xt2),\n",
    "#   \"NeuralNetwork none\": NeuralNetwork_regr0.predict(Xt0),\n",
    "#   \"NeuralNetwork avg std\": NeuralNetwork_regr1.predict(Xt1),\n",
    "#   \"NeuralNetwork avg std diff\": NeuralNetwork_regr2.predict(Xt2),\n",
    "   \"NeuralNetwork diff\": NeuralNetwork_regr3.predict(Xt3),\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "scores = {}\n",
    "for label,y_pred in Y.items():\n",
    "  print(\"%s: %s\" % (label,mean_absolute_error(yt3,y_pred)))\n",
    "  scores[label] = mean_absolute_error(yt3,y_pred)\n",
    "print(38.9308997523 - 21.14)\n",
    "bar_chart(sort_dict(scores),\"Mean Absolute Error\\nExplained variance scores (The best value is 0.0)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.164794Z",
     "start_time": "2018-01-08T10:03:16.830Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "interact(Y_ploter,Range=RangeSlider,Y=fixed(Y))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.166521Z",
     "start_time": "2018-01-08T10:03:17.173Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "Xs = [scaler(yt1),\n",
    "      #X['s3'],X['a3'],X['sd3'],\n",
    "      Xt1['s3'],\n",
    "      Xt1['s4'],\n",
    "      Xt1['s7'],\n",
    "      Xt1['s8'],\n",
    "      Xt1['s9'],\n",
    "      Xt1['s11'],\n",
    "      Xt1['s12'],\n",
    "      Xt1['s13'],\n",
    "      Xt1['s14'],\n",
    "      Xt1['s17'],\n",
    "      Xt1['s20'],\n",
    "     ]\n",
    "\n",
    "interact(general_data_ploter,Range=RangeSlider,Xs=fixed(Xs),y=fixed(yt1),scale_y=fixed(True))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.168199Z",
     "start_time": "2018-01-08T10:03:17.463Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "Xs = [scaler(yt1),\n",
    "      #X['s3'],X['a3'],X['sd3'],\n",
    "      X1['s3'],\n",
    "      X1['s4'],\n",
    "      X1['s7'],\n",
    "      X1['s8'],\n",
    "      X1['s9'],\n",
    "      X1['s11'],\n",
    "      X1['s12'],\n",
    "      X1['s13'],\n",
    "      X1['s14'],\n",
    "      X1['s17'],\n",
    "      X1['s20'],\n",
    "     ]\n",
    "\n",
    "interact(general_data_ploter,Range=RangeSlider,Xs=fixed(Xs),y=fixed(yt1),scale_y=fixed(True))\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.169665Z",
     "start_time": "2018-01-08T10:03:17.773Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def cycle_vs_sensors(Range,Xs,y,scale_y):\n",
    "  start, end = Range\n",
    "  range_len = end-start\n",
    "  \n",
    "  plt.figure(figsize=(13,10))\n",
    "  \n",
    "  # pick colors\n",
    "  colors = cm.rainbow(np.linspace(0, 1, len(Xs)))\n",
    "  \n",
    "  for X,color in zip(Xs,colors):\n",
    "    label = pd.DataFrame(X).columns[1]  # Get label\n",
    "    plt.scatter(X.iloc[:, 1][start:end],X.iloc[:, 0][start:end], c=color, s=30/(range_len/80),alpha=0.5, label=label)\n",
    "\n",
    "  if(scale_y):\n",
    "    y = scaler(y)\n",
    "\n",
    "  plt.grid()\n",
    "  plt.ylabel(\"Cycles (Scalled)\")\n",
    "  plt.xlabel(\"Sensors value (Scalled)\")\n",
    "  plt.title(\"%s data points\\n range [%s:%s]\" % (end-start,start,end))\n",
    "  lgnd = plt.legend(scatterpoints=1, fontsize=14)\n",
    "  for handle in lgnd.legendHandles:\n",
    "    handle.set_sizes([30])\n",
    "  plt.show()\n",
    "  \n",
    "Xs = [#X1[['cycle','s3']],\n",
    "      #X1[['cycle','s7']],\n",
    "      #X1[['cycle','s8']],\n",
    "      #X1[['cycle','s9']],\n",
    "      #X1[['cycle','s11']],\n",
    "      #X1[['cycle','s12']],\n",
    "      #X1[['cycle','s13']],\n",
    "      X1[['cycle','s14']],\n",
    "      #X1[['cycle','s4']],\n",
    "      X1[['cycle','s17']],\n",
    "      X1[['cycle','s20']],]\n",
    "\n",
    "interact(cycle_vs_sensors,Range=RangeSlider,Xs=fixed(Xs),y=fixed(y1),scale_y=fixed(True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.171209Z",
     "start_time": "2018-01-08T10:03:18.046Z"
    }
   },
   "outputs": [],
   "source": [
    "# todo Find the test set in the case of bagging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:30:10.117893Z",
     "start_time": "2017-12-19T18:30:09.588567Z"
    }
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.173226Z",
     "start_time": "2018-01-08T10:03:18.606Z"
    }
   },
   "outputs": [],
   "source": [
    "Y_azure = azure[\"RUL\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.183131Z",
     "start_time": "2018-01-08T10:03:18.918Z"
    }
   },
   "outputs": [],
   "source": [
    "azure = azure.drop([\"RUL\"],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.190265Z",
     "start_time": "2018-01-08T10:03:19.205Z"
    }
   },
   "outputs": [],
   "source": [
    "#azure[X0.columns.tolist()]\n",
    "X0[azure.columns.tolist()]\n",
    "print(azure.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.199567Z",
     "start_time": "2018-01-08T10:03:19.502Z"
    }
   },
   "outputs": [],
   "source": [
    "#Xt0.merge(yt0)\n",
    "dfy = pd.DataFrame(yt0)\n",
    "dfy.columns = [\"RUL\"]\n",
    "df = pd.concat([Xt0,dfy],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.201629Z",
     "start_time": "2018-01-08T10:03:19.766Z"
    }
   },
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.203134Z",
     "start_time": "2018-01-08T10:03:20.043Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def transfer_data_gt(file_path,col_names,features=[],shuffle=False):\n",
    "  scaler = MinMaxScaler()\n",
    "  \n",
    "  # user defined variables to set the windows for classifcation\n",
    "  w1 = 30\n",
    "  w0 = 15\n",
    "  # window size (window_size>=2),  most recent sensor values\n",
    "  window_size = 5\n",
    "  \n",
    "  # read in the file\n",
    "  with open(file_path, 'r') as f:\n",
    "    filedata = f.read()\n",
    "  \n",
    "  # remove white space from the end of line\n",
    "  filedata = re.sub('\\s*$', '',filedata,flags = re.M)\n",
    "  \n",
    "  # filedata is a string that pretends to be a file input\n",
    "  file_as_string = StringIO(filedata)\n",
    "\n",
    "  dataset = pd.read_table(file_as_string,sep=\" \",names=colnames)\n",
    "  \n",
    "  # Drop columns which have two or less unique values\n",
    "  criteria = (dataset.apply(unique_values_count,axis=0) > 2).values\n",
    "  dataset = dataset[dataset.columns[criteria]]\n",
    "\n",
    "  # get the maximum cycle number for each id\n",
    "  d1 = dataset.groupby([\"id\"])[\"cycle\"].max().to_frame()\n",
    "  d1.columns = ['max']\n",
    "\n",
    "  # add column with the name 'id' (just copy the index)\n",
    "  d1['id'] = d1.index\n",
    "  d2 = pd.merge(dataset, d1, on='id')\n",
    "  \n",
    "  # generate the column RUL based on the values of columns \"max\" and \"cycle\"\n",
    "  d2['RUL'] = d2['max'] - d2['cycle']\n",
    "\n",
    "  # genearte label1 and label2\n",
    "  dataset = d2\n",
    "  dataset['label1'] = np.where(d2['RUL'] <= w1, 1, 0)\n",
    "  dataset['label2'] = np.where(d2['RUL'] <= w0, 2, (np.where(d2['RUL'] <= w1,1,0)))\n",
    "  \n",
    "  # exclude comuns id, cycle, setting1,setting2,setting3, and last 3 columns\n",
    "  # only the 21 sensor columns are kept in the data frame\n",
    "  n_pre_sensor_columns = 5 # id, cycle, setting1,setting2,setting3\n",
    "  n_after_sensor_columns = 3 #RUL, label1, label2\n",
    "  n_col = len(dataset.columns)\n",
    "\n",
    "  data = dataset[dataset.columns[n_pre_sensor_columns:n_col - n_after_sensor_columns]]\n",
    "  n_sensor=len(data.columns)\n",
    "\n",
    "  ids = dataset.id.unique()\n",
    "  n_id = len(ids) # 100\n",
    "\n",
    "  a = [\"a\" + str(i) for i in range(1,n_sensor+1)] # average\n",
    "  sd = [\"sd\" + str(i) for i in range(1,n_sensor+1)] # standard deviation\n",
    "  diffA = [\"diffA\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  diffB = [\"diffB\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  diffC = [\"diffC\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "  \n",
    "  df = None\n",
    "  subsets = []\n",
    "  subset_rolling_mean = pd.DataFrame()\n",
    "  subset_rolling_std = pd.DataFrame()\n",
    "  subset_rolling_diffA = pd.DataFrame()\n",
    "  subset_rolling_diffB = pd.DataFrame()\n",
    "  subset_rolling_diffC = pd.DataFrame()\n",
    "  for i in range(1,n_id+1):\n",
    "    # get the subset of the data that only contains the sensor columns for the id i\n",
    "    if 'avg' in features:\n",
    "      subset_rolling_mean = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).mean()\n",
    "      subset_rolling_mean.columns = a\n",
    "      subsets.append(subset_rolling_mean)\n",
    "    if 'std' in features:\n",
    "      subset_rolling_std = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).std().fillna(0)\n",
    "      subset_rolling_std.columns = sd\n",
    "    if 'diffA' in features:\n",
    "      subset_rolling_diffA = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffA.columns = diffA\n",
    "    if 'diffB' in features:\n",
    "      subset_rolling_diffB = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffB.columns = diffB\n",
    "    if 'diffC' in features:\n",
    "      subset_rolling_diffC = data[dataset.id==i].diff(1).fillna(0)\n",
    "      subset_rolling_diffC.columns = diffC\n",
    "\n",
    "    subset = pd.concat([subset_rolling_mean,subset_rolling_std,subset_rolling_diffA,subset_rolling_diffB,subset_rolling_diffC], axis=1)\n",
    "    df_tmp = pd.concat([dataset[dataset.id == i],subset], axis=1)\n",
    "    df_tmp = move_col_last(df_tmp,['RUL', 'label1','label2'])\n",
    "\n",
    "    if df is None:\n",
    "      df = df_tmp\n",
    "    else:\n",
    "      df = df.append(df_tmp,ignore_index=True)\n",
    "  \n",
    "  # Drop columns which have same values in all rows\n",
    "  dfx = df.drop(df.std()[(df.std() == 0)].index, axis=1)\n",
    "  # Get only the raw of the max RUL for each engine sorted by engine ID\n",
    "  dfx = dfx.sort_values('cycle', ascending=False).drop_duplicates(['id']).sort_values('id')\n",
    "  dfx = dfx.drop(['id','RUL','label1','label2','max'],axis=1)\n",
    "  dfx = pd.DataFrame(scaler.fit_transform(dfx), columns=dfx.columns)\n",
    "  dfy = pd.read_csv(\"./Data/PM_truth.csv\")['RUL']\n",
    "  \n",
    "  return dfx, dfy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:07:20.779065Z",
     "start_time": "2018-01-08T10:07:20.420652Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n"
     ]
    }
   ],
   "source": [
    "Z_train = pd.read_csv(\"./data/PM_azure_train.csv\")\n",
    "Z_test = pd.read_csv(\"./data/PM_azure_test.csv\")\n",
    "\n",
    "\n",
    "yz = Z_train[\"RUL\"]\n",
    "Xz = Z_train.drop([\"RUL\"],1)\n",
    "\n",
    "ytz = Z_test[\"RUL\"]\n",
    "Xtz = Z_test.drop([\"RUL\"],1)\n",
    "print(len(Xtz.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.215537Z",
     "start_time": "2018-01-08T10:03:20.623Z"
    }
   },
   "outputs": [],
   "source": [
    "X0, y0= transfer_data_gt(train_file_path,colnames,features=['avg','std'],shuffle=True)\n",
    "Xt0, yt0 = transfer_data_gt(test_file_path,colnames,features=['avg','std'],shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:13:01.909918Z",
     "start_time": "2018-01-08T10:12:54.282202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n",
      "Index(['cycle', 'setting1', 'setting2', 's2', 's3', 's4', 's6', 's7', 's8',\n",
      "       's9', 's11', 's12', 's13', 's14', 's15', 's17', 's20', 's21', 'a2',\n",
      "       'a3', 'a4', 'a6', 'a7', 'a8', 'a9', 'a11', 'a12', 'a13', 'a14', 'a15',\n",
      "       'a17', 'a20', 'a21', 'sd2', 'sd3', 'sd4', 'sd6', 'sd7', 'sd8', 'sd9',\n",
      "       'sd11', 'sd12', 'sd13', 'sd14', 'sd15', 'sd17', 'sd20', 'sd21'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'yt0 Xt0': 56.39052280035682}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = {}\n",
    "\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "Bagging_regr = BaggingRegressor(DecisionTreeRegressor(max_depth=32)\n",
    "#                                  ,bootstrap_features=False\n",
    "#                                  ,max_features=0.1\n",
    "#                                  ,bootstrap=True\n",
    "                                )\n",
    "Bagging_regr.fit(X0,y0)\n",
    "scores[\"yt0 Xt0\"] = mean_absolute_error(yt0,Bagging_regr.predict(Xt0))\n",
    "#scores[\"ytz Xtz\"] = mean_absolute_error(ytz,Bagging_regr.predict(Xtz))\n",
    "print(len(Xt0.columns))\n",
    "print(Xt0.columns)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:12:37.383536Z",
     "start_time": "2018-01-08T10:12:37.365310Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['s1' 'a1' 's5' 'a5' 's10' 'a10' 's16' 'a16'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-acc4a7a90c91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mXt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXt0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m's1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'a1'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m's5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'a5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m's10'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'a10'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m's16'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'a16'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mXt0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   2528\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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   2529\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\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[0;32m-> 2530\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   2560\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2561\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2562\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2563\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2564\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   3742\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3743\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3744\u001b[0;31m                                  labels[mask])\n\u001b[0m\u001b[1;32m   3745\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3746\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: labels ['s1' 'a1' 's5' 'a5' 's10' 'a10' 's16' 'a16'] not contained in axis"
     ]
    }
   ],
   "source": [
    "Xt0 = Xt0.drop(['s1','a1','s5','a5','s10','a10','s16','a16'],axis=1)\n",
    "Xt0.columns  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.221846Z",
     "start_time": "2018-01-08T10:03:21.939Z"
    }
   },
   "outputs": [],
   "source": [
    "#Xtz = Xtz.drop(['label1','label2'],axis=1)\n",
    "Xtz.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.223140Z",
     "start_time": "2018-01-08T10:03:22.408Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.DataFrame([Xt0['s2'],Xtz['s2']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.231289Z",
     "start_time": "2018-01-08T10:03:22.669Z"
    }
   },
   "outputs": [],
   "source": [
    "def unique_values_count(x):\n",
    "  return len(pd.unique(x))\n",
    "\n",
    "\n",
    "file_path = test_file_path\n",
    "features=['avg','std']\n",
    "shuffle=True\n",
    "#-------------------\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# user defined variables to set the windows for classifcation\n",
    "w1 = 30\n",
    "w0 = 15\n",
    "# window size (window_size>=2),  most recent sensor values\n",
    "window_size = 5\n",
    "\n",
    "# read in the file\n",
    "with open(file_path, 'r') as f:\n",
    "  filedata = f.read()\n",
    "\n",
    "# remove white space from the end of line\n",
    "filedata = re.sub('\\s*$', '',filedata,flags = re.M)\n",
    "\n",
    "# filedata is a string that pretends to be a file input\n",
    "file_as_string = StringIO(filedata)\n",
    "\n",
    "dataset = pd.read_table(file_as_string,sep=\" \",names=colnames)\n",
    "\n",
    "# Drop columns which have same values in all rows\n",
    "#dataset = dataset.drop(dataset.std()[(dataset.std() == 1)].index, axis=1)\n",
    "\n",
    "# Drop columns which have two or less unique values\n",
    "criteria = (dataset.apply(unique_values_count,axis=0) > 2).values\n",
    "dataset = dataset[dataset.columns[criteria]]\n",
    "\n",
    "# get the maximum cycle number for each id\n",
    "d1 = dataset.groupby([\"id\"])[\"cycle\"].max().to_frame()\n",
    "d1.columns = ['max']\n",
    "\n",
    "# add column with the name 'id' (just copy the index)\n",
    "d1['id'] = d1.index\n",
    "d2 = pd.merge(dataset, d1, on='id')\n",
    "\n",
    "# generate the column RUL based on the values of columns \"max\" and \"cycle\"\n",
    "d2['RUL'] = d2['max'] - d2['cycle']\n",
    "\n",
    "# genearte label1 and label2\n",
    "dataset = d2\n",
    "dataset['label1'] = np.where(d2['RUL'] <= w1, 1, 0)\n",
    "dataset['label2'] = np.where(d2['RUL'] <= w0, 2, (np.where(d2['RUL'] <= w1,1,0)))\n",
    "\n",
    "# exclude comuns id, cycle, setting1,setting2,setting3, and last 3 columns\n",
    "# only the 21 sensor columns are kept in the data frame\n",
    "n_pre_sensor_columns = 5 # id, cycle, setting1,setting2,setting3\n",
    "n_after_sensor_columns = 3 #RUL, label1, label2\n",
    "n_col = len(dataset.columns)\n",
    "\n",
    "data = dataset[dataset.columns[n_pre_sensor_columns:n_col - n_after_sensor_columns]]\n",
    "n_sensor=len(data.columns)\n",
    "\n",
    "ids = dataset.id.unique()\n",
    "n_id = len(ids) # 100\n",
    "\n",
    "a = [\"a\" + str(i) for i in range(1,n_sensor+1)] # average\n",
    "sd = [\"sd\" + str(i) for i in range(1,n_sensor+1)] # standard deviation\n",
    "diffA = [\"diffA\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "diffB = [\"diffB\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "diffC = [\"diffC\" + str(i) for i in range(1,n_sensor+1)] # diff\n",
    "\n",
    "df = None\n",
    "subsets = []\n",
    "subset_rolling_mean = pd.DataFrame()\n",
    "subset_rolling_std = pd.DataFrame()\n",
    "subset_rolling_diffA = pd.DataFrame()\n",
    "subset_rolling_diffB = pd.DataFrame()\n",
    "subset_rolling_diffC = pd.DataFrame()\n",
    "for i in range(1,n_id+1):\n",
    "  # get the subset of the data that only contains the sensor columns for the id i\n",
    "  if 'avg' in features:\n",
    "    subset_rolling_mean = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).mean()\n",
    "    subset_rolling_mean.columns = a\n",
    "    subsets.append(subset_rolling_mean)\n",
    "  if 'std' in features:\n",
    "    subset_rolling_std = data[dataset.id==i].rolling(window_size,axis=0,min_periods=1).std().fillna(0)\n",
    "    subset_rolling_std.columns = sd\n",
    "  if 'diffA' in features:\n",
    "    subset_rolling_diffA = data[dataset.id==i].diff(1).fillna(0)\n",
    "    subset_rolling_diffA.columns = diffA\n",
    "  if 'diffB' in features:\n",
    "    subset_rolling_diffB = data[dataset.id==i].diff(1).fillna(0)\n",
    "    subset_rolling_diffB.columns = diffB\n",
    "  if 'diffC' in features:\n",
    "    subset_rolling_diffC = data[dataset.id==i].diff(1).fillna(0)\n",
    "    subset_rolling_diffC.columns = diffC\n",
    "\n",
    "  subset = pd.concat([subset_rolling_mean,subset_rolling_std,subset_rolling_diffA,subset_rolling_diffB,subset_rolling_diffC], axis=1)\n",
    "  df_tmp = pd.concat([dataset[dataset.id == i],subset], axis=1)\n",
    "  df_tmp = move_col_last(df_tmp,['RUL', 'label1','label2'])\n",
    "\n",
    "  if df is None:\n",
    "    df = df_tmp\n",
    "  else:\n",
    "    df = df.append(df_tmp,ignore_index=True)\n",
    "\n",
    "# Drop columns which have same values in all rows\n",
    "dfx = df.drop(df.std()[(df.std() == 0)].index, axis=1)\n",
    "# Get only the raw of the max RUL for each engine sorted by engine ID\n",
    "dfx = dfx.sort_values('cycle', ascending=False).drop_duplicates(['id']).sort_values('id')\n",
    "dfx = dfx.drop(['id','RUL','label1','label2','max'],axis=1)\n",
    "#dfx = pd.DataFrame(scaler.fit_transform(dfx), columns=dfx.columns)\n",
    "dfy = pd.read_csv(\"./Data/PM_truth.csv\")['RUL']\n",
    "\n",
    "#dfx = dfx.loc[:, (dfx != 0).any(axis=0)]\n",
    "\n",
    "\n",
    "print(dfx.columns)\n",
    "dfx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.233578Z",
     "start_time": "2018-01-08T10:03:23.174Z"
    }
   },
   "outputs": [],
   "source": [
    "print(Xzt.columns)\n",
    "Xzt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.235030Z",
     "start_time": "2018-01-08T10:03:23.467Z"
    }
   },
   "outputs": [],
   "source": [
    "dfx['s6'].std() < 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:03:58.237364Z",
     "start_time": "2018-01-08T10:03:23.750Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset['setting2'].std() < 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:15:13.304255Z",
     "start_time": "2018-01-08T10:15:13.276556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "      <th>...</th>\n",
       "      <th>sd11</th>\n",
       "      <th>sd12</th>\n",
       "      <th>sd13</th>\n",
       "      <th>sd14</th>\n",
       "      <th>sd15</th>\n",
       "      <th>sd17</th>\n",
       "      <th>sd20</th>\n",
       "      <th>sd21</th>\n",
       "      <th>label1</th>\n",
       "      <th>label2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.132964</td>\n",
       "      <td>0.603448</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.403614</td>\n",
       "      <td>0.339002</td>\n",
       "      <td>0.482444</td>\n",
       "      <td>1</td>\n",
       "      <td>0.590982</td>\n",
       "      <td>0.30303</td>\n",
       "      <td>0.103383</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397933</td>\n",
       "      <td>0.501384</td>\n",
       "      <td>0.038018</td>\n",
       "      <td>0.138115</td>\n",
       "      <td>0.1738</td>\n",
       "      <td>0.387298</td>\n",
       "      <td>0.214547</td>\n",
       "      <td>0.148524</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cycle  setting1  setting2        s2        s3        s4  s6        s7  \\\n",
       "1  0.132964  0.603448  0.416667  0.403614  0.339002  0.482444   1  0.590982   \n",
       "\n",
       "        s8        s9   ...        sd11      sd12      sd13      sd14    sd15  \\\n",
       "1  0.30303  0.103383   ...    0.397933  0.501384  0.038018  0.138115  0.1738   \n",
       "\n",
       "       sd17      sd20      sd21  label1  label2  \n",
       "1  0.387298  0.214547  0.148524       0       0  \n",
       "\n",
       "[1 rows x 50 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtz[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:15:25.092839Z",
     "start_time": "2018-01-08T10:15:25.066031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>setting1</th>\n",
       "      <th>setting2</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s6</th>\n",
       "      <th>s7</th>\n",
       "      <th>s8</th>\n",
       "      <th>s9</th>\n",
       "      <th>...</th>\n",
       "      <th>sd8</th>\n",
       "      <th>sd9</th>\n",
       "      <th>sd11</th>\n",
       "      <th>sd12</th>\n",
       "      <th>sd13</th>\n",
       "      <th>sd14</th>\n",
       "      <th>sd15</th>\n",
       "      <th>sd17</th>\n",
       "      <th>sd20</th>\n",
       "      <th>sd21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003311</td>\n",
       "      <td>0.34375</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.182965</td>\n",
       "      <td>0.504025</td>\n",
       "      <td>0.22524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.800403</td>\n",
       "      <td>0.292683</td>\n",
       "      <td>0.229042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.290698</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.414291</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.934263</td>\n",
       "      <td>0.349719</td>\n",
       "      <td>0.296174</td>\n",
       "      <td>0.301887</td>\n",
       "      <td>0.079888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      cycle  setting1  setting2        s2        s3       s4   s6        s7  \\\n",
       "1  0.003311   0.34375  0.230769  0.182965  0.504025  0.22524  1.0  0.800403   \n",
       "\n",
       "         s8        s9    ...          sd8       sd9  sd11      sd12      sd13  \\\n",
       "1  0.292683  0.229042    ...     0.272727  0.290698  0.75  0.414291  0.230769   \n",
       "\n",
       "       sd14      sd15      sd17      sd20      sd21  \n",
       "1  0.934263  0.349719  0.296174  0.301887  0.079888  \n",
       "\n",
       "[1 rows x 48 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xt0[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:18:35.604398Z",
     "start_time": "2018-01-08T10:18:35.597777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13096"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Xt0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-08T10:18:29.271943Z",
     "start_time": "2018-01-08T10:18:29.264995Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Xtz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-12T23:38:35.826228Z",
     "start_time": "2018-01-12T23:38:35.621289Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Xt0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-93f610b23475>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mXt0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Xt0' is not defined"
     ]
    }
   ],
   "source": [
    "Xt0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
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  "widgets": {
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    "state": {
     "1014b0c4ab4b405db6f6426a53465a77": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ac6b3efb032a4dbfab66d4f45d2f0951",
       "outputs": [
        {
         "data": {
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wZ84cli9fzsMPP0xJ9CjOMBMnTmTt2rXceuut/P73v6e+vp7W1lZuueUW5s2b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          "text/plain": "<matplotlib.figure.Figure at 0x10e226630>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2a321b4ab85943aeae2b1d217908f516": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b6c69732df2d4f52a0b2774395aa2bb4",
       "outputs": [
        {
         "data": {
          "image/png": 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wZ84cli9fzsMPP0xJ9CjOMBMnTmTt2rXceuut/P73v6e+vp7W1lZuueUW5s2b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\nKDRnOk6myDj00s15nLnkeVw/IkmSWpFpS+1gbCwLHMbHs3Sl8fGqF5q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          "text/plain": "<matplotlib.figure.Figure at 0x10db7c828>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "2c9328175efc4795ba9f97f4c63351ba": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
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       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
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       ],
       "layout": "IPY_MODEL_d230a53c694a4b26892c0f0caa140958"
      }
     },
     "2d4e3980217a45d3a1d16054be3fc64a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_44eb60027a63493e82e6cf38f05a18cf",
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       ],
       "layout": "IPY_MODEL_c7c6104d07684409a7a237e155826b94"
      }
     },
     "3e397967637149f2a21dd97c204f9395": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "SliderStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "44eb60027a63493e82e6cf38f05a18cf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "IntRangeSliderModel",
      "state": {
       "_model_name": "IntRangeSliderModel",
       "_view_name": "IntRangeSliderView",
       "continuous_update": false,
       "description": "Test:",
       "layout": "IPY_MODEL_f05ba24043544d0493599af0c38899e1",
       "max": 13096,
       "min": 1,
       "step": 10,
       "style": "IPY_MODEL_3e397967637149f2a21dd97c204f9395",
       "value": [
        8311,
        8391
       ]
      }
     },
     "50af91f9bf5146c8b456d24589c2f2a2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_44eb60027a63493e82e6cf38f05a18cf",
        "IPY_MODEL_f1eef0d0cdc749b6b9b6d68815af1344"
       ],
       "layout": "IPY_MODEL_a728a1bbf8204e738b66d1969dc2c360"
      }
     },
     "9ad83063f1094929af563abb03dfeeec": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "9e781e8724044aa4a858017c05cdecbc": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_ed178f086ba4427c8f705f539c748114",
       "outputs": [
        {
         "data": {
          "image/png": 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pSduPDLGjfYh55Wen6n4rFOWWuUFWzAk6fj0bx8h2GS5pf56Dc99f8OeZq2vphfP0Qhkm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          "text/plain": "<matplotlib.figure.Figure at 0x10dcb4710>"
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     },
     "bb91e9907a9e4aebad7589f764f37cc9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "VBoxModel",
      "state": {
       "_dom_classes": [
        "widget-interact"
       ],
       "children": [
        "IPY_MODEL_44eb60027a63493e82e6cf38f05a18cf",
        "IPY_MODEL_9e781e8724044aa4a858017c05cdecbc"
       ],
       "layout": "IPY_MODEL_b4936dae58af4f189e482e2c306e7485"
      }
     },
     "c7c6104d07684409a7a237e155826b94": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "d230a53c694a4b26892c0f0caa140958": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "ed178f086ba4427c8f705f539c748114": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f05ba24043544d0493599af0c38899e1": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "height": "50px",
       "width": "90%"
      }
     },
     "f1eef0d0cdc749b6b9b6d68815af1344": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_9ad83063f1094929af563abb03dfeeec",
       "outputs": [
        {
         "data": {
          "image/png": 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pSduPDLGjfYh55Wen6n4rFOWWuUFWzAk6fj0bx8h2GS5pf56Dc99f8OeZq2vphfP0Qhkm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          "text/plain": "<matplotlib.figure.Figure at 0x10db7c320>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}