13_temporal_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''Main'''\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os, time, re\n",
    "import pickle, gzip, datetime\n",
    "from os import listdir, walk\n",
    "from os.path import isfile, join\n",
    "\n",
    "'''Data Viz'''\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.axes_grid1 import Grid\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "'''Data Prep and Model Evaluation'''\n",
    "from sklearn import preprocessing as pp\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.metrics import log_loss, accuracy_score\n",
    "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, mean_squared_error\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.metrics import adjusted_rand_score\n",
    "import random\n",
    "\n",
    "'''Algos'''\n",
    "from kshape.core import kshape, zscore\n",
    "import tslearn\n",
    "from tslearn.utils import to_time_series_dataset\n",
    "from tslearn.clustering import KShape, TimeSeriesScalerMeanVariance\n",
    "from tslearn.clustering import TimeSeriesKMeans\n",
    "import hdbscan\n",
    "\n",
    "'''TensorFlow and Keras'''\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Activation, Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
    "from keras.layers import LeakyReLU, Reshape, UpSampling2D, Conv2DTranspose\n",
    "from keras.layers import BatchNormalization, Input, Lambda\n",
    "from keras.layers import Embedding, Flatten, dot\n",
    "from keras import regularizers\n",
    "from keras.losses import mse, binary_crossentropy\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.optimizers import Adam, RMSprop\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'ucr_time_series_data', ''])\n",
    "data_train = np.loadtxt(current_path+file+\n",
    "                        \"ECGFiveDays/ECGFiveDays_TRAIN\", \n",
    "                        delimiter=\",\")\n",
    "X_train = to_time_series_dataset(data_train[:, 1:])\n",
    "y_train = data_train[:, 0].astype(np.int)\n",
    "\n",
    "data_test = np.loadtxt(current_path+file+\n",
    "                       \"ECGFiveDays/ECGFiveDays_TEST\", \n",
    "                       delimiter=\",\")\n",
    "X_test = to_time_series_dataset(data_test[:, 1:])\n",
    "y_test = data_test[:, 0].astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of time series: 23\n",
      "Number of unique classes: 2\n",
      "Time series length: 136\n"
     ]
    }
   ],
   "source": [
    "# Basic summary statistics\n",
    "print(\"Number of time series:\", len(data_train))\n",
    "print(\"Number of unique classes:\", len(np.unique(data_train[:,0])))\n",
    "print(\"Time series length:\", len(data_train[0,1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of time series in class 1.0: 14\n",
      "Number of time series in class 2.0: 9\n"
     ]
    }
   ],
   "source": [
    "# Number of examples in each class in the training set\n",
    "print(\"Number of time series in class 1.0:\", \n",
    "      len(data_train[data_train[:,0]==1.0]))\n",
    "print(\"Number of time series in class 2.0:\", \n",
    "      len(data_train[data_train[:,0]==2.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  0  Class  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  1  Class  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  4  Class  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  7  Class  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  9  Class  1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examples of Class 1.0\n",
    "for i in range(0,10):\n",
    "    if data_train[i,0]==1.0:\n",
    "        print(\"Plot \",i,\" Class \",data_train[i,0])\n",
    "        plt.plot(data_train[i])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  2  Class  2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  3  Class  2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  5  Class  2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  6  Class  2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot  8  Class  2.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examples of Class 2.0\n",
    "for i in range(0,10):\n",
    "    if data_train[i,0]==2.0:\n",
    "        print(\"Plot \",i,\" Class \",data_train[i,0])\n",
    "        plt.plot(data_train[i])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare the data - Scale\n",
    "X_train = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(X_train)\n",
    "X_test = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# k-Shape Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KShape(max_iter=100, n_clusters=2, n_init=100, random_state=None, tol=1e-06,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train using k-Shape\n",
    "ks = KShape(n_clusters=2, max_iter=100, n_init=100,verbose=0)\n",
    "ks.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index: 0.668041237113402\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on train set and calculate adjusted Rand index\n",
    "preds = ks.predict(X_train)\n",
    "ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "print(\"Adjusted Rand Index:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Test Set: 0.0006332050676187496\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on test set and calculate adjusted Rand index\n",
    "preds_test = ks.predict(X_test)\n",
    "ars = adjusted_rand_score(data_test[:,0],preds_test)\n",
    "print(\"Adjusted Rand Index on Test Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ECG 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'ucr_time_series_data', ''])\n",
    "data_train = np.loadtxt(current_path+file+\n",
    "                        \"ECG5000/ECG5000_TRAIN\", \n",
    "                        delimiter=\",\")\n",
    "\n",
    "data_test = np.loadtxt(current_path+file+\n",
    "                       \"ECG5000/ECG5000_TEST\", \n",
    "                       delimiter=\",\")\n",
    "\n",
    "data_joined = np.concatenate((data_train,data_test),axis=0)\n",
    "data_train, data_test = train_test_split(data_joined, \n",
    "                                    test_size=0.20, random_state=2019)\n",
    "\n",
    "X_train = to_time_series_dataset(data_train[:, 1:])\n",
    "y_train = data_train[:, 0].astype(np.int)\n",
    "X_test = to_time_series_dataset(data_test[:, 1:])\n",
    "y_test = data_test[:, 0].astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of time series: 4000\n",
      "Number of unique classes: 5\n",
      "Time series length: 140\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(\"Number of time series:\", len(data_train))\n",
    "print(\"Number of unique classes:\", len(np.unique(data_train[:,0])))\n",
    "print(\"Time series length:\", len(data_train[0,1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of time series in class 1.0: 2327\n",
      "Number of time series in class 2.0: 1423\n",
      "Number of time series in class 3.0: 75\n",
      "Number of time series in class 4.0: 156\n",
      "Number of time series in class 5.0: 19\n"
     ]
    }
   ],
   "source": [
    "# Calculate number of readings per class\n",
    "print(\"Number of time series in class 1.0:\", \n",
    "      len(data_train[data_train[:,0]==1.0]))\n",
    "print(\"Number of time series in class 2.0:\", \n",
    "      len(data_train[data_train[:,0]==2.0]))\n",
    "print(\"Number of time series in class 3.0:\", \n",
    "      len(data_train[data_train[:,0]==3.0]))\n",
    "print(\"Number of time series in class 4.0:\", \n",
    "      len(data_train[data_train[:,0]==4.0]))\n",
    "print(\"Number of time series in class 5.0:\", \n",
    "      len(data_train[data_train[:,0]==5.0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Class  1.0  Count  2327\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Class  2.0  Count  1423\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Class  3.0  Count  75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Class  4.0  Count  156\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Class  5.0  Count  19\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display readings from each class\n",
    "for j in np.unique(data_train[:,0]):\n",
    "    dataPlot = data_train[data_train[:,0]==j]\n",
    "    cnt = len(dataPlot)\n",
    "    dataPlot = dataPlot[:,1:].mean(axis=0)\n",
    "    print(\" Class \",j,\" Count \",cnt)\n",
    "    plt.plot(dataPlot)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Prepare data - Scale\n",
    "X_train = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(X_train)\n",
    "X_test = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init 1\n",
      "0.024 --> 0.021 --> 0.020 --> 0.019 --> 0.019 --> 0.019 --> 0.019 --> \n",
      "Init 2\n",
      "0.041 --> 0.032 --> 0.030 --> 0.029 --> 0.029 --> 0.028 --> 0.028 --> 0.028 --> 0.027 --> 0.022 --> 0.018 --> 0.018 --> 0.017 --> 0.017 --> 0.018 --> \n",
      "Init 3\n",
      "0.024 --> 0.017 --> 0.017 --> \n",
      "Init 4\n",
      "0.028 --> 0.019 --> 0.018 --> 0.018 --> 0.018 --> 0.018 --> \n",
      "Init 5\n",
      "0.049 --> 0.032 --> 0.032 --> 0.031 --> 0.031 --> 0.030 --> 0.030 --> 0.030 --> \n",
      "Init 6\n",
      "0.044 --> 0.023 --> 0.019 --> 0.017 --> 0.017 --> 0.017 --> \n",
      "Init 7\n",
      "0.039 --> 0.027 --> 0.020 --> 0.019 --> 0.019 --> 0.018 --> 0.018 --> 0.018 --> \n",
      "Init 8\n",
      "0.032 --> 0.018 --> 0.018 --> 0.018 --> \n",
      "Init 9\n",
      "0.023 --> 0.019 --> 0.018 --> 0.019 --> \n",
      "Init 10\n",
      "0.064 --> 0.057 --> 0.055 --> 0.054 --> 0.051 --> 0.049 --> 0.046 --> 0.043 --> 0.041 --> 0.038 --> 0.036 --> 0.034 --> 0.033 --> 0.032 --> 0.031 --> 0.031 --> 0.031 --> 0.031 --> \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KShape(max_iter=100, n_clusters=5, n_init=10, random_state=2019, tol=1e-06,\n",
       "    verbose=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train using k-Shape\n",
    "ks = KShape(n_clusters=5, max_iter=100, n_init=10,verbose=1,random_state=2019)\n",
    "ks.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Training Set: 0.7499312374127193\n"
     ]
    }
   ],
   "source": [
    "# Predict on train set and calculate adjusted Rand index\n",
    "preds = ks.predict(X_train)\n",
    "ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "print(\"Adjusted Rand Index on Training Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Test Set: 0.7172302400677499\n"
     ]
    }
   ],
   "source": [
    "# Predict on test set and calculate adjusted Rand index\n",
    "preds_test = ks.predict(X_test)\n",
    "ars = adjusted_rand_score(data_test[:,0],preds_test)\n",
    "print(\"Adjusted Rand Index on Test Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Cluster  0.0\n",
      "2.0    29\n",
      "4.0     2\n",
      "1.0     2\n",
      "3.0     2\n",
      "5.0     1\n",
      "Name: actual, dtype: int64\n",
      "\n",
      "Predicted Cluster  1.0\n",
      "2.0    270\n",
      "4.0     14\n",
      "3.0      8\n",
      "1.0      2\n",
      "5.0      1\n",
      "Name: actual, dtype: int64\n",
      "\n",
      "Predicted Cluster  2.0\n",
      "1.0    553\n",
      "4.0     16\n",
      "2.0      9\n",
      "3.0      7\n",
      "Name: actual, dtype: int64\n",
      "\n",
      "Predicted Cluster  3.0\n",
      "2.0    35\n",
      "1.0     5\n",
      "4.0     5\n",
      "5.0     3\n",
      "3.0     3\n",
      "Name: actual, dtype: int64\n",
      "\n",
      "Predicted Cluster  4.0\n",
      "1.0    30\n",
      "4.0     1\n",
      "3.0     1\n",
      "2.0     1\n",
      "Name: actual, dtype: int64\n",
      "\n",
      "Count of Non-Primary Points:  83\n"
     ]
    }
   ],
   "source": [
    "# Evaluate goodness of the clusters\n",
    "preds_test = preds_test.reshape(1000,1)\n",
    "preds_test = np.hstack((preds_test,data_test[:,0].reshape(1000,1)))\n",
    "preds_test = pd.DataFrame(data=preds_test)\n",
    "preds_test = preds_test.rename(columns={0: 'prediction', 1: 'actual'})\n",
    "\n",
    "counter = 0\n",
    "for i in np.sort(preds_test.prediction.unique()):\n",
    "    print(\"Predicted Cluster \", i)\n",
    "    print(preds_test.actual[preds_test.prediction==i].value_counts())\n",
    "    print()\n",
    "    cnt = preds_test.actual[preds_test.prediction==i] \\\n",
    "                        .value_counts().iloc[1:].sum()\n",
    "    counter = counter + cnt\n",
    "print(\"Count of Non-Primary Points: \", counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Time Series k-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init 1\n",
      "36.241 --> 27.298 --> 26.273 --> 25.556 --> 25.120 --> 24.911 --> 24.831 --> 24.815 --> 24.813 --> 24.812 --> 24.812 --> 24.812 --> 24.812 --> \n",
      "Init 3\n",
      "37.226 --> 25.264 --> 24.680 --> 24.430 --> 24.296 --> 24.202 --> 24.155 --> 24.136 --> 24.128 --> 24.124 --> 24.121 --> 24.119 --> 24.118 --> 24.117 --> 24.117 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.115 --> 24.115 --> 24.115 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 5\n",
      "33.738 --> 25.341 --> 25.014 --> 24.795 --> 24.664 --> 24.623 --> 24.616 --> 24.615 --> 24.614 --> 24.614 --> 24.614 --> \n",
      "Init 7\n",
      "30.531 --> 24.956 --> 24.740 --> 24.663 --> 24.629 --> 24.615 --> 24.603 --> 24.595 --> 24.585 --> 24.567 --> 24.560 --> 24.559 --> 24.559 --> 24.559 --> 24.559 --> 24.559 --> 24.559 --> \n",
      "Init 9\n",
      "34.030 --> 25.598 --> 25.398 --> 25.344 --> 25.310 --> 25.277 --> 25.236 --> 25.159 --> 25.005 --> 24.834 --> 24.725 --> 24.666 --> 24.636 --> 24.620 --> 24.609 --> 24.600 --> 24.594 --> 24.588 --> 24.583 --> 24.579 --> 24.575 --> 24.572 --> 24.571 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 11\n",
      "35.921 --> 26.806 --> 26.175 --> 25.948 --> 25.847 --> 25.790 --> 25.758 --> 25.737 --> 25.728 --> 25.724 --> 25.720 --> 25.717 --> 25.710 --> 25.706 --> 25.706 --> 25.704 --> 25.704 --> 25.703 --> 25.702 --> 25.699 --> 25.691 --> 25.671 --> 25.637 --> 25.585 --> 25.476 --> 25.350 --> 25.234 --> 25.144 --> 25.060 --> 24.980 --> 24.918 --> 24.878 --> 24.843 --> 24.821 --> 24.807 --> 24.794 --> 24.785 --> 24.778 --> 24.769 --> 24.757 --> 24.746 --> 24.733 --> 24.723 --> 24.711 --> 24.693 --> 24.671 --> 24.648 --> 24.624 --> 24.606 --> 24.594 --> 24.589 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.579 --> 24.577 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 13\n",
      "35.474 --> 25.483 --> 25.102 --> 25.006 --> 24.968 --> 24.947 --> 24.929 --> 24.914 --> 24.899 --> 24.875 --> 24.820 --> 24.691 --> 24.515 --> 24.419 --> 24.334 --> 24.270 --> 24.231 --> 24.205 --> 24.186 --> 24.175 --> 24.165 --> 24.151 --> 24.134 --> 24.121 --> 24.114 --> 24.111 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 15\n",
      "36.109 --> 25.615 --> 24.782 --> 24.420 --> 24.254 --> 24.198 --> 24.163 --> 24.141 --> 24.129 --> 24.124 --> 24.120 --> 24.119 --> 24.118 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.115 --> 24.115 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 17\n",
      "34.295 --> 25.686 --> 25.302 --> 25.149 --> 25.074 --> 25.036 --> 25.014 --> 24.998 --> 24.988 --> 24.972 --> 24.949 --> 24.933 --> 24.921 --> 24.910 --> 24.899 --> 24.886 --> 24.870 --> 24.851 --> 24.821 --> 24.711 --> 24.542 --> 24.436 --> 24.347 --> 24.276 --> 24.236 --> 24.211 --> 24.187 --> 24.176 --> 24.166 --> 24.153 --> 24.137 --> 24.123 --> 24.114 --> 24.111 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 19\n",
      "34.954 --> 25.861 --> 25.397 --> 25.133 --> 24.986 --> 24.876 --> 24.825 --> 24.800 --> 24.786 --> 24.773 --> 24.759 --> 24.747 --> 24.732 --> 24.722 --> 24.710 --> 24.692 --> 24.670 --> 24.647 --> 24.624 --> 24.605 --> 24.594 --> 24.589 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.580 --> 24.578 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 21\n",
      "35.866 --> 26.286 --> 25.437 --> 25.111 --> 24.959 --> 24.889 --> 24.859 --> 24.840 --> 24.830 --> 24.825 --> 24.823 --> 24.820 --> 24.819 --> 24.818 --> 24.816 --> 24.814 --> 24.813 --> 24.812 --> 24.812 --> 24.812 --> 24.812 --> \n",
      "Init 23\n",
      "35.691 --> 25.404 --> 25.179 --> 25.068 --> 24.890 --> 24.641 --> 24.345 --> 24.244 --> 24.204 --> 24.186 --> 24.175 --> 24.168 --> 24.157 --> 24.142 --> 24.127 --> 24.116 --> 24.111 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> \n",
      "Init 25\n",
      "31.996 --> 25.031 --> 24.737 --> 24.662 --> 24.635 --> 24.625 --> 24.623 --> 24.621 --> 24.618 --> 24.616 --> 24.616 --> 24.614 --> 24.612 --> 24.612 --> 24.611 --> 24.611 --> 24.611 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> \n",
      "Init 27\n",
      "36.067 --> 26.033 --> 24.982 --> 24.510 --> 24.320 --> 24.240 --> 24.211 --> 24.194 --> 24.186 --> 24.183 --> 24.180 --> 24.178 --> 24.175 --> 24.171 --> 24.161 --> 24.137 --> 24.093 --> 24.059 --> 24.047 --> 24.040 --> 24.035 --> 24.030 --> 24.025 --> 24.023 --> 24.021 --> 24.020 --> 24.018 --> 24.016 --> 24.015 --> 24.015 --> 24.014 --> 24.013 --> 24.013 --> 24.013 --> 24.013 --> 24.013 --> 24.013 --> 24.013 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n",
      "Init 29\n",
      "35.937 --> 25.837 --> 25.584 --> 25.520 --> 25.430 --> 25.287 --> 25.200 --> 25.122 --> 25.034 --> 24.992 --> 24.975 --> 24.965 --> 24.953 --> 24.943 --> 24.928 --> 24.914 --> 24.903 --> 24.890 --> 24.872 --> 24.854 --> 24.824 --> 24.719 --> 24.547 --> 24.439 --> 24.348 --> 24.276 --> 24.236 --> 24.211 --> 24.187 --> 24.176 --> 24.166 --> 24.153 --> 24.137 --> 24.123 --> 24.114 --> 24.111 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 31\n",
      "33.314 --> 25.105 --> 24.911 --> 24.830 --> 24.770 --> 24.724 --> 24.700 --> 24.683 --> 24.678 --> 24.671 --> 24.663 --> 24.656 --> 24.651 --> 24.645 --> 24.640 --> 24.634 --> 24.628 --> 24.619 --> 24.609 --> 24.597 --> 24.589 --> 24.577 --> 24.561 --> 24.545 --> 24.533 --> 24.525 --> 24.517 --> 24.507 --> 24.496 --> 24.487 --> 24.484 --> 24.481 --> 24.479 --> 24.474 --> 24.471 --> 24.468 --> 24.464 --> 24.461 --> 24.460 --> 24.459 --> 24.459 --> 24.458 --> 24.457 --> 24.457 --> 24.457 --> 24.456 --> 24.456 --> 24.455 --> 24.455 --> 24.455 --> 24.454 --> 24.454 --> 24.454 --> 24.454 --> 24.454 --> 24.454 --> \n",
      "Init 33\n",
      "35.132 --> 25.930 --> 25.168 --> 24.789 --> 24.459 --> 24.245 --> 24.158 --> 24.132 --> 24.123 --> 24.119 --> 24.117 --> 24.115 --> 24.115 --> 24.115 --> 24.115 --> \n",
      "Init 35\n",
      "39.780 --> 24.673 --> 24.143 --> 24.042 --> 24.028 --> 24.023 --> 24.019 --> 24.016 --> 24.014 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> \n",
      "Init 37\n",
      "35.424 --> 25.378 --> 24.952 --> 24.694 --> 24.518 --> 24.404 --> 24.320 --> 24.252 --> 24.186 --> 24.145 --> 24.130 --> 24.122 --> 24.119 --> 24.118 --> 24.117 --> 24.117 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.115 --> 24.115 --> 24.115 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 39\n",
      "34.143 --> 26.742 --> 24.959 --> 24.689 --> 24.644 --> 24.632 --> 24.625 --> 24.620 --> 24.618 --> 24.617 --> 24.616 --> 24.616 --> 24.615 --> 24.614 --> 24.614 --> 24.614 --> \n",
      "Init 41\n",
      "36.437 --> 25.933 --> 25.279 --> 25.046 --> 24.983 --> 24.936 --> 24.893 --> 24.846 --> 24.810 --> 24.780 --> 24.743 --> 24.707 --> 24.680 --> 24.651 --> 24.626 --> 24.609 --> 24.598 --> 24.591 --> 24.587 --> 24.584 --> 24.582 --> 24.580 --> 24.578 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 43\n",
      "34.051 --> 26.401 --> 26.167 --> 26.048 --> 25.956 --> 25.917 --> 25.906 --> 25.901 --> 25.898 --> 25.897 --> 25.894 --> 25.893 --> 25.893 --> 25.893 --> \n",
      "Init 45\n",
      "30.350 --> 24.587 --> 24.329 --> 24.234 --> 24.181 --> 24.153 --> 24.137 --> 24.128 --> 24.122 --> 24.117 --> 24.116 --> 24.115 --> 24.115 --> 24.115 --> 24.114 --> 24.114 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n",
      "Init 47\n",
      "36.365 --> 25.804 --> 24.971 --> 24.693 --> 24.548 --> 24.483 --> 24.440 --> 24.403 --> 24.369 --> 24.331 --> 24.288 --> 24.241 --> 24.184 --> 24.135 --> 24.099 --> 24.082 --> 24.071 --> 24.059 --> 24.047 --> 24.040 --> 24.034 --> 24.027 --> 24.021 --> 24.017 --> 24.015 --> 24.014 --> 24.013 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n",
      "Init 49\n",
      "35.882 --> 25.390 --> 24.900 --> 24.715 --> 24.656 --> 24.635 --> 24.624 --> 24.617 --> 24.612 --> 24.611 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> 24.610 --> \n",
      "Init 51\n",
      "43.128 --> 26.938 --> 26.378 --> 26.109 --> 25.736 --> 25.289 --> 25.034 --> 24.918 --> 24.868 --> 24.847 --> 24.839 --> 24.837 --> 24.837 --> 24.837 --> 24.837 --> \n",
      "Init 53\n",
      "34.117 --> 25.676 --> 25.328 --> 25.086 --> 24.843 --> 24.646 --> 24.495 --> 24.387 --> 24.309 --> 24.244 --> 24.184 --> 24.145 --> 24.129 --> 24.122 --> 24.120 --> 24.118 --> 24.117 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.115 --> 24.115 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 55\n",
      "35.777 --> 25.514 --> 25.233 --> 25.151 --> 25.064 --> 25.006 --> 24.940 --> 24.898 --> 24.869 --> 24.830 --> 24.727 --> 24.552 --> 24.449 --> 24.367 --> 24.285 --> 24.241 --> 24.214 --> 24.192 --> 24.180 --> 24.170 --> 24.159 --> 24.143 --> 24.127 --> 24.116 --> 24.112 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 57\n",
      "37.126 --> 25.976 --> 25.532 --> 25.103 --> 24.633 --> 24.335 --> 24.198 --> 24.122 --> 24.091 --> 24.073 --> 24.061 --> 24.048 --> 24.038 --> 24.031 --> 24.023 --> 24.018 --> 24.014 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n",
      "Init 59\n",
      "35.411 --> 24.754 --> 24.357 --> 24.258 --> 24.193 --> 24.141 --> 24.100 --> 24.082 --> 24.069 --> 24.057 --> 24.045 --> 24.036 --> 24.030 --> 24.024 --> 24.018 --> 24.014 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n",
      "Init 61\n",
      "40.185 --> 26.640 --> 26.455 --> 26.266 --> 25.646 --> 25.111 --> 24.945 --> 24.883 --> 24.854 --> 24.843 --> 24.839 --> 24.838 --> 24.837 --> 24.837 --> 24.837 --> 24.837 --> \n",
      "Init 63\n",
      "32.643 --> 24.651 --> 24.181 --> 24.098 --> 24.072 --> 24.057 --> 24.046 --> 24.035 --> 24.025 --> 24.019 --> 24.015 --> 24.013 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n",
      "Init 65\n",
      "36.987 --> 26.258 --> 25.229 --> 24.861 --> 24.660 --> 24.500 --> 24.372 --> 24.294 --> 24.225 --> 24.191 --> 24.169 --> 24.149 --> 24.132 --> 24.123 --> 24.119 --> 24.118 --> 24.117 --> 24.116 --> 24.115 --> 24.115 --> 24.113 --> 24.112 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 67\n",
      "36.448 --> 27.056 --> 26.656 --> 26.235 --> 25.590 --> 25.358 --> 25.274 --> 25.192 --> 25.055 --> 24.915 --> 24.840 --> 24.812 --> 24.796 --> 24.778 --> 24.763 --> 24.748 --> 24.734 --> 24.721 --> 24.704 --> 24.683 --> 24.661 --> 24.636 --> 24.614 --> 24.599 --> 24.592 --> 24.588 --> 24.586 --> 24.584 --> 24.582 --> 24.580 --> 24.578 --> 24.575 --> 24.572 --> 24.571 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 69\n",
      "33.242 --> 26.436 --> 25.798 --> 25.057 --> 24.869 --> 24.821 --> 24.787 --> 24.767 --> 24.749 --> 24.734 --> 24.710 --> 24.687 --> 24.661 --> 24.635 --> 24.613 --> 24.599 --> 24.592 --> 24.588 --> 24.585 --> 24.583 --> 24.581 --> 24.579 --> 24.577 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 71\n",
      "34.636 --> 25.300 --> 24.923 --> 24.655 --> 24.472 --> 24.390 --> 24.316 --> 24.257 --> 24.221 --> 24.197 --> 24.182 --> 24.174 --> 24.165 --> 24.150 --> 24.131 --> 24.119 --> 24.113 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n",
      "Init 73\n",
      "41.475 --> 27.367 --> 26.386 --> 25.404 --> 24.908 --> 24.717 --> 24.594 --> 24.506 --> 24.452 --> 24.393 --> 24.315 --> 24.234 --> 24.176 --> 24.138 --> 24.128 --> 24.124 --> 24.121 --> 24.118 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> \n",
      "Init 75\n",
      "31.835 --> 24.996 --> 24.577 --> 24.454 --> 24.331 --> 24.251 --> 24.206 --> 24.179 --> 24.159 --> 24.147 --> 24.142 --> 24.137 --> 24.131 --> 24.121 --> 24.115 --> 24.113 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n",
      "Init 77\n",
      "37.564 --> 25.992 --> 25.333 --> 24.974 --> 24.868 --> 24.830 --> 24.810 --> 24.798 --> 24.787 --> 24.781 --> 24.774 --> 24.765 --> 24.754 --> 24.742 --> 24.729 --> 24.717 --> 24.701 --> 24.680 --> 24.655 --> 24.631 --> 24.611 --> 24.598 --> 24.591 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.580 --> 24.578 --> 24.575 --> 24.572 --> 24.571 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 79\n",
      "37.942 --> 25.110 --> 24.868 --> 24.830 --> 24.821 --> 24.815 --> 24.813 --> 24.812 --> 24.812 --> 24.812 --> 24.812 --> 24.812 --> \n",
      "Init 81\n",
      "34.761 --> 27.148 --> 26.308 --> 25.888 --> 25.703 --> 25.557 --> 25.431 --> 25.306 --> 25.196 --> 25.058 --> 24.864 --> 24.720 --> 24.643 --> 24.598 --> 24.582 --> 24.573 --> 24.568 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 83\n",
      "34.503 --> 24.416 --> 24.231 --> 24.184 --> 24.164 --> 24.153 --> 24.145 --> 24.139 --> 24.132 --> 24.125 --> 24.120 --> 24.117 --> 24.114 --> 24.111 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n",
      "Init 85\n",
      "36.234 --> 26.059 --> 25.199 --> 24.904 --> 24.753 --> 24.686 --> 24.660 --> 24.647 --> 24.641 --> 24.637 --> 24.632 --> 24.630 --> 24.629 --> 24.628 --> 24.627 --> 24.626 --> 24.624 --> 24.624 --> 24.624 --> 24.624 --> \n",
      "Init 87\n",
      "33.049 --> 24.671 --> 24.418 --> 24.385 --> 24.379 --> 24.377 --> 24.376 --> 24.376 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> \n",
      "Init 89\n",
      "37.180 --> 25.466 --> 24.907 --> 24.832 --> 24.797 --> 24.780 --> 24.769 --> 24.756 --> 24.743 --> 24.731 --> 24.721 --> 24.708 --> 24.690 --> 24.670 --> 24.647 --> 24.623 --> 24.605 --> 24.594 --> 24.589 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.580 --> 24.578 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 91\n",
      "37.233 --> 25.446 --> 25.136 --> 24.957 --> 24.840 --> 24.771 --> 24.723 --> 24.693 --> 24.668 --> 24.643 --> 24.624 --> 24.609 --> 24.600 --> 24.595 --> 24.590 --> 24.583 --> 24.581 --> 24.580 --> 24.579 --> 24.578 --> 24.578 --> 24.578 --> 24.578 --> 24.577 --> 24.577 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> \n",
      "Init 93\n",
      "34.869 --> 26.043 --> 25.584 --> 25.256 --> 25.096 --> 24.999 --> 24.907 --> 24.819 --> 24.745 --> 24.685 --> 24.650 --> 24.621 --> 24.601 --> 24.590 --> 24.579 --> 24.571 --> 24.567 --> 24.564 --> 24.563 --> 24.563 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> \n",
      "Init 95\n",
      "35.529 --> 25.838 --> 25.306 --> 25.095 --> 24.974 --> 24.897 --> 24.846 --> 24.824 --> 24.808 --> 24.796 --> 24.786 --> 24.779 --> 24.771 --> 24.760 --> 24.749 --> 24.735 --> 24.724 --> 24.711 --> 24.693 --> 24.671 --> 24.648 --> 24.624 --> 24.606 --> 24.594 --> 24.589 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.580 --> 24.578 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 97\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.995 --> 26.409 --> 26.223 --> 26.084 --> 25.908 --> 25.709 --> 25.519 --> 25.283 --> 25.122 --> 25.002 --> 24.931 --> 24.881 --> 24.845 --> 24.824 --> 24.810 --> 24.796 --> 24.785 --> 24.778 --> 24.768 --> 24.756 --> 24.745 --> 24.732 --> 24.722 --> 24.710 --> 24.692 --> 24.671 --> 24.648 --> 24.624 --> 24.606 --> 24.594 --> 24.589 --> 24.587 --> 24.585 --> 24.583 --> 24.582 --> 24.579 --> 24.577 --> 24.574 --> 24.572 --> 24.570 --> 24.569 --> 24.568 --> 24.567 --> 24.566 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n",
      "Init 99\n",
      "31.104 --> 25.217 --> 24.696 --> 24.446 --> 24.321 --> 24.254 --> 24.212 --> 24.193 --> 24.183 --> 24.175 --> 24.164 --> 24.150 --> 24.131 --> 24.118 --> 24.113 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TimeSeriesKMeans(dtw_inertia=False, max_iter=100, max_iter_barycenter=100,\n",
       "         metric='euclidean', metric_params=None, n_clusters=5, n_init=100,\n",
       "         random_state=2019, tol=1e-06, verbose=1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train using Time Series k-Means\n",
    "km = TimeSeriesKMeans(n_clusters=5, max_iter=100, n_init=100, \\\n",
    "                      metric=\"euclidean\", verbose=1, random_state=2019)\n",
    "km.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index of Time Series k-Means on Training Set: 0.5063464656715959\n"
     ]
    }
   ],
   "source": [
    "# Predict on training set and evaluate using adjusted Rand index\n",
    "preds = km.predict(X_train)\n",
    "ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "print(\"Adjusted Rand Index of Time Series k-Means on Training Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index of Time Series k-Means on Test Set: 0.4864981997585834\n"
     ]
    }
   ],
   "source": [
    "# Predict on test set and evaluate using adjusted Rand index\n",
    "preds_test = km.predict(X_test)\n",
    "ars = adjusted_rand_score(data_test[:,0],preds_test)\n",
    "print(\"Adjusted Rand Index of Time Series k-Means on Test Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# HDBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Training Set: 0.7689563655060421\n"
     ]
    }
   ],
   "source": [
    "# Train model and evaluate on training set\n",
    "min_cluster_size = 5\n",
    "min_samples = None\n",
    "alpha = 1.0\n",
    "cluster_selection_method = 'eom'\n",
    "prediction_data = True\n",
    "\n",
    "hdb = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size, \\\n",
    "                      min_samples=min_samples, alpha=alpha, \\\n",
    "                      cluster_selection_method=cluster_selection_method, \\\n",
    "                      prediction_data=prediction_data)\n",
    "\n",
    "preds = hdb.fit_predict(X_train.reshape(4000,140))\n",
    "ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "print(\"Adjusted Rand Index on Training Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Test Set: 0.7200816245545564\n"
     ]
    }
   ],
   "source": [
    "# Predict on test set and evaluate\n",
    "preds_test = hdbscan.prediction.approximate_predict( \\\n",
    "                hdb, X_test.reshape(1000,140))\n",
    "ars = adjusted_rand_score(data_test[:,0],preds_test[0])\n",
    "print(\"Adjusted Rand Index on Test Set:\", ars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Experiment to compare time series clustering algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the datasets\n",
    "current_path = os.getcwd()\n",
    "file = os.path.sep.join(['', 'datasets', 'ucr_time_series_data', ''])\n",
    "\n",
    "mypath = current_path + file\n",
    "d = []\n",
    "f = []\n",
    "for (dirpath, dirnames, filenames) in walk(mypath):\n",
    "    for i in dirnames:\n",
    "        newpath = os.path.sep.join([mypath, i, \"\"])\n",
    "        onlyfiles = [f for f in listdir(newpath) if isfile(join(newpath, f))]\n",
    "        f.extend(onlyfiles)\n",
    "    d.extend(dirnames)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# k-Shape Experiment - FULL RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dataframe\n",
    "kShapeDF = pd.DataFrame(data=[],index=[v for v in d], \\\n",
    "                        columns=[\"Train ARS\",\"Test ARS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset  1\n",
      "50words\n",
      "Adjusted Rand Index on Training Set: 0.33141709829469923\n",
      "Adjusted Rand Index on Test Set: 0.28178766416273154\n",
      "Dataset  2\n",
      "Adiac\n",
      "Adjusted Rand Index on Training Set: 0.0056982414737593275\n",
      "Adjusted Rand Index on Test Set: 0.0032752154154245226\n",
      "Dataset  3\n",
      "ArrowHead\n",
      "Adjusted Rand Index on Training Set: 0.1171844628000625\n",
      "Adjusted Rand Index on Test Set: 0.030556785699907458\n",
      "Dataset  4\n",
      "Beef\n",
      "Adjusted Rand Index on Training Set: 0.07754088914498472\n",
      "Adjusted Rand Index on Test Set: -0.08641975308641976\n",
      "Dataset  5\n",
      "BeetleFly\n",
      "Adjusted Rand Index on Training Set: 0.03236819430670938\n",
      "Adjusted Rand Index on Test Set: -0.1487179487179487\n",
      "Dataset  6\n",
      "BirdChicken\n",
      "Adjusted Rand Index on Training Set: -0.007756188019027647\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  7\n",
      "Car\n",
      "Adjusted Rand Index on Training Set: 0.08302004569779552\n",
      "Adjusted Rand Index on Test Set: 0.11675289348765265\n",
      "Dataset  8\n",
      "CBF\n",
      "Adjusted Rand Index on Training Set: 0.4300191679141888\n",
      "Adjusted Rand Index on Test Set: 0.4464515716637103\n",
      "Dataset  9\n",
      "ChlorineConcentration\n",
      "Adjusted Rand Index on Training Set: 0.004417834137649424\n",
      "Adjusted Rand Index on Test Set: 0.0010502228635036084\n",
      "Dataset  10\n",
      "CinC_ECG_torso\n",
      "Adjusted Rand Index on Training Set: 0.07495613778505084\n",
      "Adjusted Rand Index on Test Set: 0.08140683701809101\n",
      "Dataset  11\n",
      "Coffee\n",
      "Adjusted Rand Index on Training Set: -0.010697146691743027\n",
      "Adjusted Rand Index on Test Set: 0.0638297872340425\n",
      "Dataset  12\n",
      "Computers\n",
      "Adjusted Rand Index on Training Set: 0.023161070057011277\n",
      "Adjusted Rand Index on Test Set: 0.13575757575757577\n",
      "Dataset  13\n",
      "Cricket_X\n",
      "Adjusted Rand Index on Training Set: 0.16976146445673967\n",
      "Adjusted Rand Index on Test Set: 0.182969943211159\n",
      "Dataset  14\n",
      "Cricket_Y\n",
      "Adjusted Rand Index on Training Set: 0.22286240401119306\n",
      "Adjusted Rand Index on Test Set: 0.23224160887267567\n",
      "Dataset  15\n",
      "Cricket_Z\n",
      "Adjusted Rand Index on Training Set: 0.16526403018829078\n",
      "Adjusted Rand Index on Test Set: 0.15001695403251467\n",
      "Dataset  16\n",
      "DiatomSizeReduction\n",
      "Adjusted Rand Index on Training Set: 0.015823817916421807\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  17\n",
      "DistalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.42341995617548944\n",
      "Adjusted Rand Index on Test Set: 0.32791136957547806\n",
      "Dataset  18\n",
      "DistalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.01160268699565799\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  19\n",
      "DistalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.002928701689761342\n",
      "Adjusted Rand Index on Test Set: 3.607080699415818e-05\n",
      "Dataset  20\n",
      "Earthquakes\n",
      "Adjusted Rand Index on Training Set: 0.0037961924568304505\n",
      "Adjusted Rand Index on Test Set: -0.002443897563716349\n",
      "Dataset  21\n",
      "ECG200\n",
      "Adjusted Rand Index on Training Set: 0.29496493777244304\n",
      "Adjusted Rand Index on Test Set: 0.10138248847926272\n",
      "Dataset  22\n",
      "ECG5000\n",
      "Adjusted Rand Index on Training Set: 0.5463682297549879\n",
      "Adjusted Rand Index on Test Set: 0.5506884913514337\n",
      "Dataset  23\n",
      "ECGFiveDays\n",
      "Adjusted Rand Index on Training Set: 0.7367521607772766\n",
      "Adjusted Rand Index on Test Set: 0.6879858106122982\n",
      "Dataset  24\n",
      "ElectricDevices\n",
      "Adjusted Rand Index on Training Set: 0.10408716136327975\n",
      "Adjusted Rand Index on Test Set: 0.11337296287421782\n",
      "Dataset  25\n",
      "FaceAll\n",
      "Adjusted Rand Index on Training Set: 0.32492880780839567\n",
      "Adjusted Rand Index on Test Set: 0.35397645785525017\n",
      "Dataset  26\n",
      "FaceFour\n",
      "Adjusted Rand Index on Training Set: 0.4557529657687818\n",
      "Adjusted Rand Index on Test Set: 0.33962549650085117\n",
      "Dataset  27\n",
      "FacesUCR\n",
      "Adjusted Rand Index on Training Set: 0.39001583238124987\n",
      "Adjusted Rand Index on Test Set: 0.34940154851786337\n",
      "Dataset  28\n",
      "FISH\n",
      "Adjusted Rand Index on Training Set: 0.0033148293387528344\n",
      "Adjusted Rand Index on Test Set: -0.004518505675836564\n",
      "Dataset  29\n",
      "FordA\n",
      "Adjusted Rand Index on Training Set: 0.13549231655358876\n",
      "Adjusted Rand Index on Test Set: 0.11615890520504485\n",
      "Dataset  30\n",
      "FordB\n",
      "Adjusted Rand Index on Training Set: 0.02688237531510243\n",
      "Adjusted Rand Index on Test Set: 0.03370268756111131\n",
      "Dataset  31\n",
      "Gun_Point\n",
      "Adjusted Rand Index on Training Set: -0.004912969824990243\n",
      "Adjusted Rand Index on Test Set: -0.003157686994276681\n",
      "Dataset  32\n",
      "Ham\n",
      "Adjusted Rand Index on Training Set: 0.0837497989377934\n",
      "Adjusted Rand Index on Test Set: 0.10028860028860029\n",
      "Dataset  33\n",
      "HandOutlines\n",
      "Adjusted Rand Index on Training Set: 0.01932216693971623\n",
      "Adjusted Rand Index on Test Set: 0.009727348439364365\n",
      "Dataset  34\n",
      "Haptics\n",
      "Adjusted Rand Index on Training Set: 0.00014352853224834877\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  35\n",
      "Herring\n",
      "Adjusted Rand Index on Training Set: 0.010263861988621724\n",
      "Adjusted Rand Index on Test Set: -0.0907783282723348\n",
      "Dataset  36\n",
      "InlineSkate\n",
      "Adjusted Rand Index on Training Set: 0.026595569163195637\n",
      "Adjusted Rand Index on Test Set: 0.03416886789361377\n",
      "Dataset  37\n",
      "InsectWingbeatSound\n",
      "Adjusted Rand Index on Training Set: 0.2503909077282778\n",
      "Adjusted Rand Index on Test Set: 0.223498081090469\n",
      "Dataset  38\n",
      "ItalyPowerDemand\n",
      "Adjusted Rand Index on Training Set: 0.0006759158259042806\n",
      "Adjusted Rand Index on Test Set: -0.0015630592113879673\n",
      "Dataset  39\n",
      "LargeKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.16222135656274403\n",
      "Adjusted Rand Index on Test Set: 0.10254712730235327\n",
      "Dataset  40\n",
      "Lighting2\n",
      "Adjusted Rand Index on Training Set: 0.061208336997625463\n",
      "Adjusted Rand Index on Test Set: 0.0384615384615384\n",
      "Dataset  41\n",
      "Lighting7\n",
      "Adjusted Rand Index on Training Set: 0.31197880656040405\n",
      "Adjusted Rand Index on Test Set: 0.5092794759825328\n",
      "Dataset  42\n",
      "MALLAT\n",
      "Adjusted Rand Index on Training Set: 0.1321934048613034\n",
      "Adjusted Rand Index on Test Set: 0.12902334138718327\n",
      "Dataset  43\n",
      "Meat\n",
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  44\n",
      "MedicalImages\n",
      "Adjusted Rand Index on Training Set: 0.047129541149609794\n",
      "Adjusted Rand Index on Test Set: 0.09816318178745811\n",
      "Dataset  45\n",
      "MiddlePhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.053061395625242105\n",
      "Adjusted Rand Index on Test Set: 0.08829908586059725\n",
      "Dataset  46\n",
      "MiddlePhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: -0.0010721768145707043\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  47\n",
      "MiddlePhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.02789259905128924\n",
      "Adjusted Rand Index on Test Set: 0.011206350001384029\n",
      "Dataset  48\n",
      "MoteStrain\n",
      "Adjusted Rand Index on Training Set: 0.607571338768688\n",
      "Adjusted Rand Index on Test Set: 0.5952522880415954\n",
      "Dataset  49\n",
      "NonInvasiveFatalECG_Thorax1\n",
      "Adjusted Rand Index on Training Set: 0.02261835868266628\n",
      "Adjusted Rand Index on Test Set: 0.022489848009247908\n",
      "Dataset  50\n",
      "NonInvasiveFatalECG_Thorax2\n",
      "Adjusted Rand Index on Training Set: 0.013020100814681966\n",
      "Adjusted Rand Index on Test Set: 0.0140355935600567\n",
      "Dataset  51\n",
      "OliveOil\n",
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  52\n",
      "OSULeaf\n",
      "Adjusted Rand Index on Training Set: 0.2696371023235093\n",
      "Adjusted Rand Index on Test Set: 0.2891733824792923\n",
      "Dataset  53\n",
      "PhalangesOutlinesCorrect\n",
      "Adjusted Rand Index on Training Set: 0.007880445352458241\n",
      "Adjusted Rand Index on Test Set: 0.004509857256984513\n",
      "Dataset  54\n",
      "Phoneme\n",
      "Adjusted Rand Index on Training Set: 0.03766196574544099\n",
      "Adjusted Rand Index on Test Set: 0.04559668943506461\n",
      "Dataset  55\n",
      "Plane\n",
      "Adjusted Rand Index on Training Set: 0.5022319842539696\n",
      "Adjusted Rand Index on Test Set: 0.437205603886871\n",
      "Dataset  56\n",
      "ProximalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.029132901055327596\n",
      "Adjusted Rand Index on Test Set: 0.15292065601630644\n",
      "Dataset  57\n",
      "ProximalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.003189915370497478\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  58\n",
      "ProximalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.11042555894629481\n",
      "Adjusted Rand Index on Test Set: 0.1417941905568511\n",
      "Dataset  59\n",
      "RefrigerationDevices\n",
      "Adjusted Rand Index on Training Set: 0.008796213532723463\n",
      "Adjusted Rand Index on Test Set: -0.006772588135974769\n",
      "Dataset  60\n",
      "ScreenType\n",
      "Adjusted Rand Index on Training Set: 0.014205397273558722\n",
      "Adjusted Rand Index on Test Set: -0.003332502170846456\n",
      "Dataset  61\n",
      "ShapeletSim\n",
      "Adjusted Rand Index on Training Set: 0.5226668101133044\n",
      "Adjusted Rand Index on Test Set: 0.13461538461538458\n",
      "Dataset  62\n",
      "ShapesAll\n",
      "Adjusted Rand Index on Training Set: 0.20179282447778674\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Test Set: 0.17703951455473832\n",
      "Dataset  63\n",
      "SmallKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.023407702303042142\n",
      "Adjusted Rand Index on Test Set: 0.015610823173483574\n",
      "Dataset  64\n",
      "SonyAIBORobotSurface\n",
      "Adjusted Rand Index on Training Set: 0.047769307656884974\n",
      "Adjusted Rand Index on Test Set: 0.10178929994721742\n",
      "Dataset  65\n",
      "SonyAIBORobotSurfaceII\n",
      "Adjusted Rand Index on Training Set: 0.34388079746730893\n",
      "Adjusted Rand Index on Test Set: 0.3415584415584415\n",
      "Dataset  66\n",
      "StarLightCurves\n",
      "Adjusted Rand Index on Training Set: 0.49834088020580236\n",
      "Adjusted Rand Index on Test Set: 0.47949777351601575\n",
      "Dataset  67\n",
      "Strawberry\n",
      "Adjusted Rand Index on Training Set: -0.004303367134402091\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  68\n",
      "SwedishLeaf\n",
      "Adjusted Rand Index on Training Set: 0.11856009198162196\n",
      "Adjusted Rand Index on Test Set: 0.1209977738179147\n",
      "Dataset  69\n",
      "Symbols\n",
      "Adjusted Rand Index on Training Set: 0.7611708849510274\n",
      "Adjusted Rand Index on Test Set: 0.8139995755444789\n",
      "Dataset  70\n",
      "synthetic_control\n",
      "Adjusted Rand Index on Training Set: 0.5800018740031629\n",
      "Adjusted Rand Index on Test Set: 0.5351288659793815\n",
      "Dataset  71\n",
      "ToeSegmentation1\n",
      "Adjusted Rand Index on Training Set: -0.004374586372759009\n",
      "Adjusted Rand Index on Test Set: 0.0035159580546204423\n",
      "Dataset  72\n",
      "ToeSegmentation2\n",
      "Adjusted Rand Index on Training Set: 0.27233885527651797\n",
      "Adjusted Rand Index on Test Set: 0.091762492561095\n",
      "Dataset  73\n",
      "Trace\n",
      "Adjusted Rand Index on Training Set: 0.3614864785448748\n",
      "Adjusted Rand Index on Test Set: 0.3928223114984387\n",
      "Dataset  74\n",
      "TwoLeadECG\n",
      "Adjusted Rand Index on Training Set: 0.10410943422550015\n",
      "Adjusted Rand Index on Test Set: 0.06471155871935959\n",
      "Dataset  75\n",
      "Two_Patterns\n",
      "Adjusted Rand Index on Training Set: 0.24607567135896274\n",
      "Adjusted Rand Index on Test Set: 0.2691930999654907\n",
      "Dataset  76\n",
      "UWaveGestureLibraryAll\n",
      "Adjusted Rand Index on Training Set: 0.612735941277658\n",
      "Adjusted Rand Index on Test Set: 0.6152910803602317\n",
      "Dataset  77\n",
      "uWaveGestureLibrary_X\n",
      "Adjusted Rand Index on Training Set: 0.3464331818971397\n",
      "Adjusted Rand Index on Test Set: 0.34571916120112384\n",
      "Dataset  78\n",
      "uWaveGestureLibrary_Y\n",
      "Adjusted Rand Index on Training Set: 0.2722758749082347\n",
      "Adjusted Rand Index on Test Set: 0.25131476716649814\n",
      "Dataset  79\n",
      "uWaveGestureLibrary_Z\n",
      "Adjusted Rand Index on Training Set: 0.40111833372162314\n",
      "Adjusted Rand Index on Test Set: 0.3990591553178942\n",
      "Dataset  80\n",
      "wafer\n",
      "Adjusted Rand Index on Training Set: 0.06896163835273661\n",
      "Adjusted Rand Index on Test Set: 0.08666606446352638\n",
      "Dataset  81\n",
      "Wine\n",
      "Adjusted Rand Index on Training Set: -0.0065497213912263305\n",
      "Adjusted Rand Index on Test Set: -0.028905712319339343\n",
      "Dataset  82\n",
      "WordsSynonyms\n",
      "Adjusted Rand Index on Training Set: 0.21360548498006937\n",
      "Adjusted Rand Index on Test Set: 0.19426099492598015\n",
      "Dataset  83\n",
      "Worms\n",
      "Adjusted Rand Index on Training Set: 0.04981357783082403\n",
      "Adjusted Rand Index on Test Set: 0.053999156861993636\n",
      "Dataset  84\n",
      "WormsTwoClass\n",
      "Adjusted Rand Index on Training Set: 0.004658376142880903\n",
      "Adjusted Rand Index on Test Set: -0.013097576948264524\n",
      "Dataset  85\n",
      "yoga\n",
      "Adjusted Rand Index on Training Set: -0.0017217631275397603\n",
      "Adjusted Rand Index on Test Set: -0.003091207513006575\n",
      "Elapsed: 1.2039239147636625 hr \n"
     ]
    }
   ],
   "source": [
    "# Train and Evaluate k-Shape\n",
    "class ElapsedTimer(object):\n",
    "    def __init__(self):\n",
    "        self.start_time = time.time()\n",
    "    def elapsed(self,sec):\n",
    "        if sec < 60:\n",
    "            return str(sec) + \" sec\"\n",
    "        elif sec < (60 * 60):\n",
    "            return str(sec / 60) + \" min\"\n",
    "        else:\n",
    "            return str(sec / (60 * 60)) + \" hr\"\n",
    "    def elapsed_time(self):\n",
    "        print(\"Elapsed: %s \" % self.elapsed(time.time() - self.start_time))\n",
    "        return (time.time() - self.start_time)\n",
    "\n",
    "timer = ElapsedTimer()\n",
    "cnt = 0\n",
    "for i in d:\n",
    "    cnt += 1\n",
    "    print(\"Dataset \", cnt)\n",
    "    newpath = os.path.sep.join([mypath, i, \"\"])\n",
    "    onlyfiles = [f for f in listdir(newpath) if isfile(join(newpath, f))]\n",
    "    j = onlyfiles[0]\n",
    "    k = onlyfiles[1]\n",
    "    data_train = np.loadtxt(newpath+j, delimiter=\",\")\n",
    "    data_test = np.loadtxt(newpath+k, delimiter=\",\")\n",
    "\n",
    "    data_joined = np.concatenate((data_train,data_test),axis=0)\n",
    "    data_train, data_test = train_test_split(data_joined, \\\n",
    "                                        test_size=0.20, random_state=2019)\n",
    "\n",
    "    X_train = to_time_series_dataset(data_train[:, 1:])\n",
    "    y_train = data_train[:, 0].astype(np.int)\n",
    "    X_test = to_time_series_dataset(data_test[:, 1:])\n",
    "    y_test = data_test[:, 0].astype(np.int)\n",
    "    \n",
    "    X_train = TimeSeriesScalerMeanVariance(mu=0., std=1.) \\\n",
    "                                .fit_transform(X_train)\n",
    "    X_test = TimeSeriesScalerMeanVariance(mu=0., std=1.) \\\n",
    "                                .fit_transform(X_test)\n",
    "    \n",
    "    classes = len(np.unique(data_train[:,0]))\n",
    "    ks = KShape(n_clusters=classes, max_iter=10, n_init=3,verbose=0)\n",
    "    ks.fit(X_train)\n",
    "\n",
    "    print(i)\n",
    "    preds = ks.predict(X_train)\n",
    "    ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "    print(\"Adjusted Rand Index on Training Set:\", ars)\n",
    "    kShapeDF.loc[i,\"Train ARS\"] = ars\n",
    "    \n",
    "    preds_test = ks.predict(X_test)\n",
    "    ars = adjusted_rand_score(data_test[:,0],preds_test)\n",
    "    print(\"Adjusted Rand Index on Test Set:\", ars)\n",
    "    kShapeDF.loc[i,\"Test ARS\"] = ars\n",
    "    \n",
    "kShapeTime = timer.elapsed_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to Run k-Shape Experiment in Minutes: 72.23543488581976\n"
     ]
    }
   ],
   "source": [
    "print(\"Time to Run k-Shape Experiment in Minutes:\", kShapeTime/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kShapeDF.to_pickle(os.path.sep.join([current_path, 'datasets', 'pickled_data', \\\n",
    "                   \"kShapeDF.pickle\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# k-Means Experiment - FULL RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dataframe\n",
    "kMeansDF = pd.DataFrame(data=[],index=[v for v in d], \\\n",
    "                        columns=[\"Train ARS\",\"Test ARS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset  1\n",
      "50words\n",
      "Adjusted Rand Index on Training Set: 0.10619130288518851\n",
      "Adjusted Rand Index on Test Set: 0.08322258807014249\n",
      "Dataset  2\n",
      "Adiac\n",
      "Adjusted Rand Index on Training Set: 0.032298974657873764\n",
      "Adjusted Rand Index on Test Set: 0.03370617406524666\n",
      "Dataset  3\n",
      "ArrowHead\n",
      "Adjusted Rand Index on Training Set: 0.192471764199164\n",
      "Adjusted Rand Index on Test Set: 0.14002053273698203\n",
      "Dataset  4\n",
      "Beef\n",
      "Adjusted Rand Index on Training Set: 0.05051344138284522\n",
      "Adjusted Rand Index on Test Set: 0.09465020576131687\n",
      "Dataset  5\n",
      "BeetleFly\n",
      "Adjusted Rand Index on Training Set: 0.04482941020893944\n",
      "Adjusted Rand Index on Test Set: 0.17204301075268819\n",
      "Dataset  6\n",
      "BirdChicken\n",
      "Adjusted Rand Index on Training Set: 0.08246758609298872\n",
      "Adjusted Rand Index on Test Set: -0.026178010471204227\n",
      "Dataset  7\n",
      "Car\n",
      "Adjusted Rand Index on Training Set: 0.18277640411168794\n",
      "Adjusted Rand Index on Test Set: 0.048341493690090366\n",
      "Dataset  8\n",
      "CBF\n",
      "Adjusted Rand Index on Training Set: 0.2721869804033402\n",
      "Adjusted Rand Index on Test Set: 0.25023291288924226\n",
      "Dataset  9\n",
      "ChlorineConcentration\n",
      "Adjusted Rand Index on Training Set: 0.0023870678451229856\n",
      "Adjusted Rand Index on Test Set: -0.005570128709703299\n",
      "Dataset  10\n",
      "CinC_ECG_torso\n",
      "Adjusted Rand Index on Training Set: 0.21052441502984057\n",
      "Adjusted Rand Index on Test Set: 0.23387652109021426\n",
      "Dataset  11\n",
      "Coffee\n",
      "Adjusted Rand Index on Training Set: 0.49118866073970957\n",
      "Adjusted Rand Index on Test Set: 0.5878962536023055\n",
      "Dataset  12\n",
      "Computers\n",
      "Adjusted Rand Index on Training Set: 0.004051245885803447\n",
      "Adjusted Rand Index on Test Set: -0.0048484848484848485\n",
      "Dataset  13\n",
      "Cricket_X\n",
      "Adjusted Rand Index on Training Set: 0.07313654823204094\n",
      "Adjusted Rand Index on Test Set: 0.061293323068682085\n",
      "Dataset  14\n",
      "Cricket_Y\n",
      "Adjusted Rand Index on Training Set: 0.09023990690504419\n",
      "Adjusted Rand Index on Test Set: 0.09118374824208038\n",
      "Dataset  15\n",
      "Cricket_Z\n",
      "Adjusted Rand Index on Training Set: 0.056647311636158514\n",
      "Adjusted Rand Index on Test Set: 0.036956278087196905\n",
      "Dataset  16\n",
      "DiatomSizeReduction\n",
      "Adjusted Rand Index on Training Set: 0.8189244572859417\n",
      "Adjusted Rand Index on Test Set: 0.8796741947426879\n",
      "Dataset  17\n",
      "DistalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.26282183532854003\n",
      "Adjusted Rand Index on Test Set: 0.22708010226779612\n",
      "Dataset  18\n",
      "DistalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.03869488397176492\n",
      "Adjusted Rand Index on Test Set: 0.0575538429226134\n",
      "Dataset  19\n",
      "DistalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.4243171837449507\n",
      "Adjusted Rand Index on Test Set: 0.3809567397792045\n",
      "Dataset  20\n",
      "Earthquakes\n",
      "Adjusted Rand Index on Training Set: -0.0019431574532234586\n",
      "Adjusted Rand Index on Test Set: 0.006573484664534049\n",
      "Dataset  21\n",
      "ECG200\n",
      "Adjusted Rand Index on Training Set: 0.4100434351901242\n",
      "Adjusted Rand Index on Test Set: 0.2373712604217754\n",
      "Dataset  22\n",
      "ECG5000\n",
      "Adjusted Rand Index on Training Set: 0.42038397904074565\n",
      "Adjusted Rand Index on Test Set: 0.42769378389494733\n",
      "Dataset  23\n",
      "ECGFiveDays\n",
      "Adjusted Rand Index on Training Set: 0.003670239528847993\n",
      "Adjusted Rand Index on Test Set: -0.004520907838344356\n",
      "Dataset  24\n",
      "ElectricDevices\n",
      "Adjusted Rand Index on Training Set: 0.16227808140617175\n",
      "Adjusted Rand Index on Test Set: 0.1648865124154441\n",
      "Dataset  25\n",
      "FaceAll\n",
      "Adjusted Rand Index on Training Set: 0.11297650378263607\n",
      "Adjusted Rand Index on Test Set: 0.095250210051182\n",
      "Dataset  26\n",
      "FaceFour\n",
      "Adjusted Rand Index on Training Set: 0.30439715877912327\n",
      "Adjusted Rand Index on Test Set: 0.3888620492775265\n",
      "Dataset  27\n",
      "FacesUCR\n",
      "Adjusted Rand Index on Training Set: 0.10019642097712311\n",
      "Adjusted Rand Index on Test Set: 0.13491121452889757\n",
      "Dataset  28\n",
      "FISH\n",
      "Adjusted Rand Index on Training Set: 0.15502169885020875\n",
      "Adjusted Rand Index on Test Set: 0.04832940263323387\n",
      "Dataset  29\n",
      "FordA\n",
      "Adjusted Rand Index on Training Set: 0.010129082664755259\n",
      "Adjusted Rand Index on Test Set: 0.009697440285065511\n",
      "Dataset  30\n",
      "FordB\n",
      "Adjusted Rand Index on Training Set: 0.00033380003698735495\n",
      "Adjusted Rand Index on Test Set: 5.265263499026267e-05\n",
      "Dataset  31\n",
      "Gun_Point\n",
      "Adjusted Rand Index on Training Set: 0.09386470740587478\n",
      "Adjusted Rand Index on Test Set: 0.07111882046834347\n",
      "Dataset  32\n",
      "Ham\n",
      "Adjusted Rand Index on Training Set: 0.03508437266757478\n",
      "Adjusted Rand Index on Test Set: 0.007104795737122539\n",
      "Dataset  33\n",
      "HandOutlines\n",
      "Adjusted Rand Index on Training Set: 0.20585079385090616\n",
      "Adjusted Rand Index on Test Set: 0.16010422635928878\n",
      "Dataset  34\n",
      "Haptics\n",
      "Adjusted Rand Index on Training Set: 0.046274123431724094\n",
      "Adjusted Rand Index on Test Set: 0.08090564850130505\n",
      "Dataset  35\n",
      "Herring\n",
      "Adjusted Rand Index on Training Set: 0.0033923029053770496\n",
      "Adjusted Rand Index on Test Set: -0.0317826295159889\n",
      "Dataset  36\n",
      "InlineSkate\n",
      "Adjusted Rand Index on Training Set: 0.01117734227995157\n",
      "Adjusted Rand Index on Test Set: 0.009951768839615037\n",
      "Dataset  37\n",
      "InsectWingbeatSound\n",
      "Adjusted Rand Index on Training Set: 0.32268832409524595\n",
      "Adjusted Rand Index on Test Set: 0.3126926861096694\n",
      "Dataset  38\n",
      "ItalyPowerDemand\n",
      "Adjusted Rand Index on Training Set: 0.5165187047586893\n",
      "Adjusted Rand Index on Test Set: 0.5472545942561559\n",
      "Dataset  39\n",
      "LargeKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.022006383493282933\n",
      "Adjusted Rand Index on Test Set: 0.001392483289899483\n",
      "Dataset  40\n",
      "Lighting2\n",
      "Adjusted Rand Index on Training Set: 0.057948290963353806\n",
      "Adjusted Rand Index on Test Set: 0.10830512887268942\n",
      "Dataset  41\n",
      "Lighting7\n",
      "Adjusted Rand Index on Training Set: 0.19225217328142838\n",
      "Adjusted Rand Index on Test Set: 0.29078204783660305\n",
      "Dataset  42\n",
      "MALLAT\n",
      "Adjusted Rand Index on Training Set: 0.5955949415438153\n",
      "Adjusted Rand Index on Test Set: 0.6034461544951523\n",
      "Dataset  43\n",
      "Meat\n",
      "Adjusted Rand Index on Training Set: 0.6269155567439295\n",
      "Adjusted Rand Index on Test Set: 0.5750969887308331\n",
      "Dataset  44\n",
      "MedicalImages\n",
      "Adjusted Rand Index on Training Set: 0.035097208261349995\n",
      "Adjusted Rand Index on Test Set: 0.04512947961930594\n",
      "Dataset  45\n",
      "MiddlePhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.4420152994673162\n",
      "Adjusted Rand Index on Test Set: 0.3963962522397071\n",
      "Dataset  46\n",
      "MiddlePhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.056194556848172594\n",
      "Adjusted Rand Index on Test Set: 0.04885303694357658\n",
      "Dataset  47\n",
      "MiddlePhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.34302185438060384\n",
      "Adjusted Rand Index on Test Set: 0.3054811520534582\n",
      "Dataset  48\n",
      "MoteStrain\n",
      "Adjusted Rand Index on Training Set: 0.2521483651831544\n",
      "Adjusted Rand Index on Test Set: 0.3305706983183208\n",
      "Dataset  49\n",
      "NonInvasiveFatalECG_Thorax1\n",
      "Adjusted Rand Index on Training Set: 0.11000937739741899\n",
      "Adjusted Rand Index on Test Set: 0.10578346902813682\n",
      "Dataset  50\n",
      "NonInvasiveFatalECG_Thorax2\n",
      "Adjusted Rand Index on Training Set: 0.09718825902410402\n",
      "Adjusted Rand Index on Test Set: 0.09542870434320082\n",
      "Dataset  51\n",
      "OliveOil\n",
      "Adjusted Rand Index on Training Set: 0.714742274086671\n",
      "Adjusted Rand Index on Test Set: 0.6168359941944848\n",
      "Dataset  52\n",
      "OSULeaf\n",
      "Adjusted Rand Index on Training Set: 0.16018905182251295\n",
      "Adjusted Rand Index on Test Set: 0.08675255467210909\n",
      "Dataset  53\n",
      "PhalangesOutlinesCorrect\n",
      "Adjusted Rand Index on Training Set: 0.0038726304522774687\n",
      "Adjusted Rand Index on Test Set: 0.0013160838236778846\n",
      "Dataset  54\n",
      "Phoneme\n",
      "Adjusted Rand Index on Training Set: 0.0023477362638542345\n",
      "Adjusted Rand Index on Test Set: 0.0011087688453910733\n",
      "Dataset  55\n",
      "Plane\n",
      "Adjusted Rand Index on Training Set: 0.5243031747991824\n",
      "Adjusted Rand Index on Test Set: 0.46761929999796154\n",
      "Dataset  56\n",
      "ProximalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.3419463466006671\n",
      "Adjusted Rand Index on Test Set: 0.27396485914616436\n",
      "Dataset  57\n",
      "ProximalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.04243880863466088\n",
      "Adjusted Rand Index on Test Set: 0.0744241809410339\n",
      "Dataset  58\n",
      "ProximalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.405864982306469\n",
      "Adjusted Rand Index on Test Set: 0.4499796413045999\n",
      "Dataset  59\n",
      "RefrigerationDevices\n",
      "Adjusted Rand Index on Training Set: -0.0011304648768439938\n",
      "Adjusted Rand Index on Test Set: -0.0009582164444243453\n",
      "Dataset  60\n",
      "ScreenType\n",
      "Adjusted Rand Index on Training Set: 0.012521915807585025\n",
      "Adjusted Rand Index on Test Set: 0.009001186522740318\n",
      "Dataset  61\n",
      "ShapeletSim\n",
      "Adjusted Rand Index on Training Set: -0.0030513667343154546\n",
      "Adjusted Rand Index on Test Set: 0.0019811788013868533\n",
      "Dataset  62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShapesAll\n",
      "Adjusted Rand Index on Training Set: 0.05231679633940989\n",
      "Adjusted Rand Index on Test Set: 0.04146063996954582\n",
      "Dataset  63\n",
      "SmallKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.0008930092429296139\n",
      "Adjusted Rand Index on Test Set: 0.004989916398183328\n",
      "Dataset  64\n",
      "SonyAIBORobotSurface\n",
      "Adjusted Rand Index on Training Set: 0.24933298488666072\n",
      "Adjusted Rand Index on Test Set: 0.32535294772149903\n",
      "Dataset  65\n",
      "SonyAIBORobotSurfaceII\n",
      "Adjusted Rand Index on Training Set: 0.29260432896668265\n",
      "Adjusted Rand Index on Test Set: 0.21019256694638105\n",
      "Dataset  66\n",
      "StarLightCurves\n",
      "Adjusted Rand Index on Training Set: 0.3682776811025087\n",
      "Adjusted Rand Index on Test Set: 0.34878036775665355\n",
      "Dataset  67\n",
      "Strawberry\n",
      "Adjusted Rand Index on Training Set: 0.008839702094482175\n",
      "Adjusted Rand Index on Test Set: 0.0016505425902195766\n",
      "Dataset  68\n",
      "SwedishLeaf\n",
      "Adjusted Rand Index on Training Set: 0.14152304219571812\n",
      "Adjusted Rand Index on Test Set: 0.13705236145182997\n",
      "Dataset  69\n",
      "Symbols\n",
      "Adjusted Rand Index on Training Set: 0.6701646127914067\n",
      "Adjusted Rand Index on Test Set: 0.7279834416195335\n",
      "Dataset  70\n",
      "synthetic_control\n",
      "Adjusted Rand Index on Training Set: 0.5750325195940434\n",
      "Adjusted Rand Index on Test Set: 0.5580041604928748\n",
      "Dataset  71\n",
      "ToeSegmentation1\n",
      "Adjusted Rand Index on Training Set: -0.0015050692113936254\n",
      "Adjusted Rand Index on Test Set: 0.02905047652665016\n",
      "Dataset  72\n",
      "ToeSegmentation2\n",
      "Adjusted Rand Index on Training Set: 0.014471208742333866\n",
      "Adjusted Rand Index on Test Set: -0.021098314867752904\n",
      "Dataset  73\n",
      "Trace\n",
      "Adjusted Rand Index on Training Set: 0.2928636611551775\n",
      "Adjusted Rand Index on Test Set: 0.33862433862433855\n",
      "Dataset  74\n",
      "TwoLeadECG\n",
      "Adjusted Rand Index on Training Set: 0.004187478885172455\n",
      "Adjusted Rand Index on Test Set: -0.0010030860132344546\n",
      "Dataset  75\n",
      "Two_Patterns\n",
      "Adjusted Rand Index on Training Set: 0.020085428454392873\n",
      "Adjusted Rand Index on Test Set: 0.026208561833555053\n",
      "Dataset  76\n",
      "UWaveGestureLibraryAll\n",
      "Adjusted Rand Index on Training Set: 0.47791036081616656\n",
      "Adjusted Rand Index on Test Set: 0.46283927294029054\n",
      "Dataset  77\n",
      "uWaveGestureLibrary_X\n",
      "Adjusted Rand Index on Training Set: 0.3299648595114634\n",
      "Adjusted Rand Index on Test Set: 0.33622522605612354\n",
      "Dataset  78\n",
      "uWaveGestureLibrary_Y\n",
      "Adjusted Rand Index on Training Set: 0.33675638875880454\n",
      "Adjusted Rand Index on Test Set: 0.3213004461218034\n",
      "Dataset  79\n",
      "uWaveGestureLibrary_Z\n",
      "Adjusted Rand Index on Training Set: 0.31771068800777097\n",
      "Adjusted Rand Index on Test Set: 0.2946489484279623\n",
      "Dataset  80\n",
      "wafer\n",
      "Adjusted Rand Index on Training Set: 0.05569448738524084\n",
      "Adjusted Rand Index on Test Set: 0.058766859149983225\n",
      "Dataset  81\n",
      "Wine\n",
      "Adjusted Rand Index on Training Set: 0.0012341182303352719\n",
      "Adjusted Rand Index on Test Set: -0.030090972708187547\n",
      "Dataset  82\n",
      "WordsSynonyms\n",
      "Adjusted Rand Index on Training Set: 0.10947462940710878\n",
      "Adjusted Rand Index on Test Set: 0.10422118835287301\n",
      "Dataset  83\n",
      "Worms\n",
      "Adjusted Rand Index on Training Set: -0.002845565377816157\n",
      "Adjusted Rand Index on Test Set: 0.0182312308911788\n",
      "Dataset  84\n",
      "WormsTwoClass\n",
      "Adjusted Rand Index on Training Set: 0.00440069789550413\n",
      "Adjusted Rand Index on Test Set: 0.016764655320612016\n",
      "Dataset  85\n",
      "yoga\n",
      "Adjusted Rand Index on Training Set: 0.004227036733026657\n",
      "Adjusted Rand Index on Test Set: 0.004192782410311586\n",
      "Elapsed: 2.9342538197835286 min \n"
     ]
    }
   ],
   "source": [
    "# Train and Evaluate k-Means\n",
    "timer = ElapsedTimer()\n",
    "cnt = 0\n",
    "for i in d:\n",
    "    cnt += 1\n",
    "    print(\"Dataset \", cnt)\n",
    "    newpath = os.path.sep.join([mypath, i, \"\"])\n",
    "    onlyfiles = [f for f in listdir(newpath) if isfile(join(newpath, f))]\n",
    "    j = onlyfiles[0]\n",
    "    k = onlyfiles[1]\n",
    "    data_train = np.loadtxt(newpath+j, delimiter=\",\")\n",
    "    data_test = np.loadtxt(newpath+k, delimiter=\",\")\n",
    "\n",
    "    data_joined = np.concatenate((data_train,data_test),axis=0)\n",
    "    data_train, data_test = train_test_split(data_joined, \\\n",
    "                                        test_size=0.20, random_state=2019)\n",
    "\n",
    "    X_train = to_time_series_dataset(data_train[:, 1:])\n",
    "    y_train = data_train[:, 0].astype(np.int)\n",
    "    X_test = to_time_series_dataset(data_test[:, 1:])\n",
    "    y_test = data_test[:, 0].astype(np.int)\n",
    "    \n",
    "    X_train = TimeSeriesScalerMeanVariance(mu=0., std=1.) \\\n",
    "                                    .fit_transform(X_train)\n",
    "    X_test = TimeSeriesScalerMeanVariance(mu=0., std=1.) \\\n",
    "                                    .fit_transform(X_test)\n",
    "    \n",
    "    classes = len(np.unique(data_train[:,0]))\n",
    "    km = TimeSeriesKMeans(n_clusters=5, max_iter=10, n_init=10, \\\n",
    "                          metric=\"euclidean\", verbose=0, random_state=2019)\n",
    "    km.fit(X_train)\n",
    "\n",
    "    print(i)\n",
    "    preds = km.predict(X_train)\n",
    "    ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "    print(\"Adjusted Rand Index on Training Set:\", ars)\n",
    "    kMeansDF.loc[i,\"Train ARS\"] = ars\n",
    "    \n",
    "    preds_test = km.predict(X_test)\n",
    "    ars = adjusted_rand_score(data_test[:,0],preds_test)\n",
    "    print(\"Adjusted Rand Index on Test Set:\", ars)\n",
    "    kMeansDF.loc[i,\"Test ARS\"] = ars\n",
    "    \n",
    "kMeansTime = timer.elapsed_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to Run k-Means Experiment in Minutes: 2.9342538197835286\n"
     ]
    }
   ],
   "source": [
    "print(\"Time to Run k-Means Experiment in Minutes:\", kMeansTime/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kMeansDF.to_pickle(os.path.sep.join([current_path, \"datasets\", \"pickled_data\"+ \\\n",
    "                                     \"kMeansDF.pickle\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# HDBSCAN Experiment - FULL RUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dataframe\n",
    "hdbscanDF = pd.DataFrame(data=[],index=[v for v in d], \\\n",
    "                         columns=[\"Train ARS\",\"Test ARS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset  1\n",
      "50words\n",
      "Adjusted Rand Index on Training Set: 0.04712217317560057\n",
      "Adjusted Rand Index on Test Set: 0.01919753314304532\n",
      "Dataset  2\n",
      "Adiac\n",
      "Adjusted Rand Index on Training Set: 0.007247077169102767\n",
      "Adjusted Rand Index on Test Set: 0.005341355551188271\n",
      "Dataset  3\n",
      "ArrowHead\n",
      "Adjusted Rand Index on Training Set: 0.08680758632720399\n",
      "Adjusted Rand Index on Test Set: 0.054936310643678835\n",
      "Dataset  4\n",
      "Beef\n",
      "Adjusted Rand Index on Training Set: 0.07675278820826091\n",
      "Adjusted Rand Index on Test Set: 0.094885100074129\n",
      "Dataset  5\n",
      "BeetleFly\n",
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  6\n",
      "BirdChicken\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\hdbscan\\prediction.py:385: UserWarning: Clusterer does not have any defined clusters, new data will be automatically predicted as noise.\n",
      "  warn('Clusterer does not have any defined clusters, new data'\n",
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\hdbscan\\prediction.py:385: UserWarning: Clusterer does not have any defined clusters, new data will be automatically predicted as noise.\n",
      "  warn('Clusterer does not have any defined clusters, new data'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  7\n",
      "Car\n",
      "Adjusted Rand Index on Training Set: 0.1327460764307008\n",
      "Adjusted Rand Index on Test Set: 0.053611973495204156\n",
      "Dataset  8\n",
      "CBF\n",
      "Adjusted Rand Index on Training Set: 0.23824326045039873\n",
      "Adjusted Rand Index on Test Set: 0.24127819753652197\n",
      "Dataset  9\n",
      "ChlorineConcentration\n",
      "Adjusted Rand Index on Training Set: 0.03471687057268341\n",
      "Adjusted Rand Index on Test Set: 0.03360750032547304\n",
      "Dataset  10\n",
      "CinC_ECG_torso\n",
      "Adjusted Rand Index on Training Set: 0.5942196748524765\n",
      "Adjusted Rand Index on Test Set: 0.5958171238376715\n",
      "Dataset  11\n",
      "Coffee\n",
      "Adjusted Rand Index on Training Set: 0.6425515836066016\n",
      "Adjusted Rand Index on Test Set: 0.5272206303724928\n",
      "Dataset  12\n",
      "Computers\n",
      "Adjusted Rand Index on Training Set: -0.0013452895579769142\n",
      "Adjusted Rand Index on Test Set: -0.006868686868686869\n",
      "Dataset  13\n",
      "Cricket_X\n",
      "Adjusted Rand Index on Training Set: 0.02577712650416776\n",
      "Adjusted Rand Index on Test Set: 0.042728088683931666\n",
      "Dataset  14\n",
      "Cricket_Y\n",
      "Adjusted Rand Index on Training Set: 0.029168876587940432\n",
      "Adjusted Rand Index on Test Set: 0.008648947220127375\n",
      "Dataset  15\n",
      "Cricket_Z\n",
      "Adjusted Rand Index on Training Set: 0.03972087339342956\n",
      "Adjusted Rand Index on Test Set: 0.048097894508440785\n",
      "Dataset  16\n",
      "DiatomSizeReduction\n",
      "Adjusted Rand Index on Training Set: 0.9071439403134605\n",
      "Adjusted Rand Index on Test Set: 0.9644709995712017\n",
      "Dataset  17\n",
      "DistalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.44849852886593217\n",
      "Adjusted Rand Index on Test Set: 0.3667389470954875\n",
      "Dataset  18\n",
      "DistalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.09393532492277866\n",
      "Adjusted Rand Index on Test Set: 0.06354413349309822\n",
      "Dataset  19\n",
      "DistalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.6534997242309845\n",
      "Adjusted Rand Index on Test Set: 0.5698434654306719\n",
      "Dataset  20\n",
      "Earthquakes\n",
      "Adjusted Rand Index on Training Set: 0.010928035357605876\n",
      "Adjusted Rand Index on Test Set: 0.0069634933425928676\n",
      "Dataset  21\n",
      "ECG200\n",
      "Adjusted Rand Index on Training Set: 0.41995430312261994\n",
      "Adjusted Rand Index on Test Set: 0.2675080425637219\n",
      "Dataset  22\n",
      "ECG5000\n",
      "Adjusted Rand Index on Training Set: 0.7556104485290581\n",
      "Adjusted Rand Index on Test Set: 0.7561234640907506\n",
      "Dataset  23\n",
      "ECGFiveDays\n",
      "Adjusted Rand Index on Training Set: 0.16072052207840315\n",
      "Adjusted Rand Index on Test Set: 0.17272704427554567\n",
      "Dataset  24\n",
      "ElectricDevices\n",
      "Adjusted Rand Index on Training Set: 0.003080324382840311\n",
      "Adjusted Rand Index on Test Set: 0.004862356920630733\n",
      "Dataset  25\n",
      "FaceAll\n",
      "Adjusted Rand Index on Training Set: 0.10711222733884268\n",
      "Adjusted Rand Index on Test Set: 0.07375216893457956\n",
      "Dataset  26\n",
      "FaceFour\n",
      "Adjusted Rand Index on Training Set: 0.17037443595419818\n",
      "Adjusted Rand Index on Test Set: 0.13958760991451943\n",
      "Dataset  27\n",
      "FacesUCR\n",
      "Adjusted Rand Index on Training Set: 0.10797422103102844\n",
      "Adjusted Rand Index on Test Set: 0.08636985958018598\n",
      "Dataset  28\n",
      "FISH\n",
      "Adjusted Rand Index on Training Set: 0.1604256783126502\n",
      "Adjusted Rand Index on Test Set: 0.02498014230202374\n",
      "Dataset  29\n",
      "FordA\n",
      "Adjusted Rand Index on Training Set: 0.004283193224377136\n",
      "Adjusted Rand Index on Test Set: 0.00023211062236586075\n",
      "Dataset  30\n",
      "FordB\n",
      "Adjusted Rand Index on Training Set: -0.00074209928219292\n",
      "Adjusted Rand Index on Test Set: 0.00011947224748877147\n",
      "Dataset  31\n",
      "Gun_Point\n",
      "Adjusted Rand Index on Training Set: 0.17534633640824643\n",
      "Adjusted Rand Index on Test Set: 0.19900299102691926\n",
      "Dataset  32\n",
      "Ham\n",
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\hdbscan\\prediction.py:385: UserWarning: Clusterer does not have any defined clusters, new data will be automatically predicted as noise.\n",
      "  warn('Clusterer does not have any defined clusters, new data'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HandOutlines\n",
      "Adjusted Rand Index on Training Set: 0.029797481009491162\n",
      "Adjusted Rand Index on Test Set: 0.019686617807504105\n",
      "Dataset  34\n",
      "Haptics\n",
      "Adjusted Rand Index on Training Set: 0.02733461700411107\n",
      "Adjusted Rand Index on Test Set: 0.06337061391206895\n",
      "Dataset  35\n",
      "Herring\n",
      "Adjusted Rand Index on Training Set: 0.0\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankur_\\Anaconda3\\envs\\unsupervisedLearning\\lib\\site-packages\\hdbscan\\prediction.py:385: UserWarning: Clusterer does not have any defined clusters, new data will be automatically predicted as noise.\n",
      "  warn('Clusterer does not have any defined clusters, new data'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InlineSkate\n",
      "Adjusted Rand Index on Training Set: 0.007028878666084085\n",
      "Adjusted Rand Index on Test Set: 0.018667966408012385\n",
      "Dataset  37\n",
      "InsectWingbeatSound\n",
      "Adjusted Rand Index on Training Set: 0.0968567695614674\n",
      "Adjusted Rand Index on Test Set: 0.0536686991371905\n",
      "Dataset  38\n",
      "ItalyPowerDemand\n",
      "Adjusted Rand Index on Training Set: 0.21466704676509\n",
      "Adjusted Rand Index on Test Set: 0.20486940656857858\n",
      "Dataset  39\n",
      "LargeKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.013751576907123196\n",
      "Adjusted Rand Index on Test Set: 0.008171148245798801\n",
      "Dataset  40\n",
      "Lighting2\n",
      "Adjusted Rand Index on Training Set: 0.05613360146891671\n",
      "Adjusted Rand Index on Test Set: -0.03833865814696479\n",
      "Dataset  41\n",
      "Lighting7\n",
      "Adjusted Rand Index on Training Set: 0.14953599314276508\n",
      "Adjusted Rand Index on Test Set: 0.16885359116022097\n",
      "Dataset  42\n",
      "MALLAT\n",
      "Adjusted Rand Index on Training Set: 0.6188384769726322\n",
      "Adjusted Rand Index on Test Set: 0.5537161824689919\n",
      "Dataset  43\n",
      "Meat\n",
      "Adjusted Rand Index on Training Set: 0.3476487772130403\n",
      "Adjusted Rand Index on Test Set: 0.3015349067502889\n",
      "Dataset  44\n",
      "MedicalImages\n",
      "Adjusted Rand Index on Training Set: -0.150038201550172\n",
      "Adjusted Rand Index on Test Set: -0.11785577580748442\n",
      "Dataset  45\n",
      "MiddlePhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.41765554916607006\n",
      "Adjusted Rand Index on Test Set: 0.39497787362928377\n",
      "Dataset  46\n",
      "MiddlePhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.015681734627540963\n",
      "Adjusted Rand Index on Test Set: 0.011636393161816829\n",
      "Dataset  47\n",
      "MiddlePhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.43682311005267477\n",
      "Adjusted Rand Index on Test Set: 0.41563266126864085\n",
      "Dataset  48\n",
      "MoteStrain\n",
      "Adjusted Rand Index on Training Set: 0.023523425907611924\n",
      "Adjusted Rand Index on Test Set: 0.00913196670794574\n",
      "Dataset  49\n",
      "NonInvasiveFatalECG_Thorax1\n",
      "Adjusted Rand Index on Training Set: 0.07116679511792358\n",
      "Adjusted Rand Index on Test Set: 0.06753522800746767\n",
      "Dataset  50\n",
      "NonInvasiveFatalECG_Thorax2\n",
      "Adjusted Rand Index on Training Set: 0.23704799176948152\n",
      "Adjusted Rand Index on Test Set: 0.20164086261042938\n",
      "Dataset  51\n",
      "OliveOil\n",
      "Adjusted Rand Index on Training Set: 0.5495176515334877\n",
      "Adjusted Rand Index on Test Set: 0.1448763250883392\n",
      "Dataset  52\n",
      "OSULeaf\n",
      "Adjusted Rand Index on Training Set: 0.04001478938901476\n",
      "Adjusted Rand Index on Test Set: 0.02723300352786901\n",
      "Dataset  53\n",
      "PhalangesOutlinesCorrect\n",
      "Adjusted Rand Index on Training Set: 0.005551200182238192\n",
      "Adjusted Rand Index on Test Set: 0.011936637554826941\n",
      "Dataset  54\n",
      "Phoneme\n",
      "Adjusted Rand Index on Training Set: 0.0073787800650459414\n",
      "Adjusted Rand Index on Test Set: 0.006566629090881971\n",
      "Dataset  55\n",
      "Plane\n",
      "Adjusted Rand Index on Training Set: 0.8220409725918153\n",
      "Adjusted Rand Index on Test Set: 0.8338607594936709\n",
      "Dataset  56\n",
      "ProximalPhalanxOutlineAgeGroup\n",
      "Adjusted Rand Index on Training Set: 0.5525206412261244\n",
      "Adjusted Rand Index on Test Set: 0.49069856544677004\n",
      "Dataset  57\n",
      "ProximalPhalanxOutlineCorrect\n",
      "Adjusted Rand Index on Training Set: 0.01767326368681402\n",
      "Adjusted Rand Index on Test Set: 0.05682523773427964\n",
      "Dataset  58\n",
      "ProximalPhalanxTW\n",
      "Adjusted Rand Index on Training Set: 0.5629387103192184\n",
      "Adjusted Rand Index on Test Set: 0.5745613245803115\n",
      "Dataset  59\n",
      "RefrigerationDevices\n",
      "Adjusted Rand Index on Training Set: 0.001530734090218992\n",
      "Adjusted Rand Index on Test Set: 0.004970605264586349\n",
      "Dataset  60\n",
      "ScreenType\n",
      "Adjusted Rand Index on Training Set: 0.011413727586245542\n",
      "Adjusted Rand Index on Test Set: 0.014201243097304021\n",
      "Dataset  61\n",
      "ShapeletSim\n",
      "Adjusted Rand Index on Training Set: -0.004611646238182005\n",
      "Adjusted Rand Index on Test Set: 0.0\n",
      "Dataset  62\n",
      "ShapesAll\n",
      "Adjusted Rand Index on Training Set: 0.05355914850022222\n",
      "Adjusted Rand Index on Test Set: 0.04112578753877586\n",
      "Dataset  63\n",
      "SmallKitchenAppliances\n",
      "Adjusted Rand Index on Training Set: 0.009721965984657998\n",
      "Adjusted Rand Index on Test Set: 0.02364354241926606\n",
      "Dataset  64\n",
      "SonyAIBORobotSurface\n",
      "Adjusted Rand Index on Training Set: 0.466820018068988\n",
      "Adjusted Rand Index on Test Set: 0.42266703376456677\n",
      "Dataset  65\n",
      "SonyAIBORobotSurfaceII\n",
      "Adjusted Rand Index on Training Set: 0.4161340005017514\n",
      "Adjusted Rand Index on Test Set: 0.3660889446813046\n",
      "Dataset  66\n",
      "StarLightCurves\n",
      "Adjusted Rand Index on Training Set: 0.6043609835986175\n",
      "Adjusted Rand Index on Test Set: 0.5877318883735247\n",
      "Dataset  67\n",
      "Strawberry\n",
      "Adjusted Rand Index on Training Set: -0.03322716392197656\n",
      "Adjusted Rand Index on Test Set: -0.0534154245785985\n",
      "Dataset  68\n",
      "SwedishLeaf\n",
      "Adjusted Rand Index on Training Set: 0.06172433349002788\n",
      "Adjusted Rand Index on Test Set: 0.02933954935424017\n",
      "Dataset  69\n",
      "Symbols\n",
      "Adjusted Rand Index on Training Set: 0.7109545364381192\n",
      "Adjusted Rand Index on Test Set: 0.7291816499721819\n",
      "Dataset  70\n",
      "synthetic_control\n",
      "Adjusted Rand Index on Training Set: 0.6571978927585601\n",
      "Adjusted Rand Index on Test Set: 0.6385919262409767\n",
      "Dataset  71\n",
      "ToeSegmentation1\n",
      "Adjusted Rand Index on Training Set: 0.09365677519451036\n",
      "Adjusted Rand Index on Test Set: -0.013732221677292938\n",
      "Dataset  72\n",
      "ToeSegmentation2\n",
      "Adjusted Rand Index on Training Set: -0.12025489588150488\n",
      "Adjusted Rand Index on Test Set: -0.1408420341613791\n",
      "Dataset  73\n",
      "Trace\n",
      "Adjusted Rand Index on Training Set: 0.4953963339322084\n",
      "Adjusted Rand Index on Test Set: 0.4826344910339311\n",
      "Dataset  74\n",
      "TwoLeadECG\n",
      "Adjusted Rand Index on Training Set: 0.0487611500065211\n",
      "Adjusted Rand Index on Test Set: 0.023504083041584474\n",
      "Dataset  75\n",
      "Two_Patterns\n",
      "Adjusted Rand Index on Training Set: 0.00063356058457108\n",
      "Adjusted Rand Index on Test Set: 0.0014243955328180008\n",
      "Dataset  76\n",
      "UWaveGestureLibraryAll\n",
      "Adjusted Rand Index on Training Set: 0.06190214181417517\n",
      "Adjusted Rand Index on Test Set: 0.05979770969060198\n",
      "Dataset  77\n",
      "uWaveGestureLibrary_X\n",
      "Adjusted Rand Index on Training Set: 0.02653159583979862\n",
      "Adjusted Rand Index on Test Set: 0.03871896821214037\n",
      "Dataset  78\n",
      "uWaveGestureLibrary_Y\n",
      "Adjusted Rand Index on Training Set: 0.0477090649088894\n",
      "Adjusted Rand Index on Test Set: 0.0421662290798025\n",
      "Dataset  79\n",
      "uWaveGestureLibrary_Z\n",
      "Adjusted Rand Index on Training Set: 0.1413590342301642\n",
      "Adjusted Rand Index on Test Set: 0.10947830831693975\n",
      "Dataset  80\n",
      "wafer\n",
      "Adjusted Rand Index on Training Set: 0.008853695526890217\n",
      "Adjusted Rand Index on Test Set: 0.0304939268405748\n",
      "Dataset  81\n",
      "Wine\n",
      "Adjusted Rand Index on Training Set: -0.003866822564008358\n",
      "Adjusted Rand Index on Test Set: -0.009678534393363306\n",
      "Dataset  82\n",
      "WordsSynonyms\n",
      "Adjusted Rand Index on Training Set: 0.08392638087932967\n",
      "Adjusted Rand Index on Test Set: 0.05427600069796473\n",
      "Dataset  83\n",
      "Worms\n",
      "Adjusted Rand Index on Training Set: 0.06311257887340958\n",
      "Adjusted Rand Index on Test Set: 0.06719316749414765\n",
      "Dataset  84\n",
      "WormsTwoClass\n",
      "Adjusted Rand Index on Training Set: -0.02408917899471696\n",
      "Adjusted Rand Index on Test Set: -0.011352371640797927\n",
      "Dataset  85\n",
      "yoga\n",
      "Adjusted Rand Index on Training Set: 0.01188760764450979\n",
      "Adjusted Rand Index on Test Set: 0.013225605272761549\n",
      "Elapsed: 7.0786975185076395 min \n"
     ]
    }
   ],
   "source": [
    "# Train and Evaluate HDBSCAN\n",
    "timer = ElapsedTimer()\n",
    "cnt = 0\n",
    "for i in d:\n",
    "    cnt += 1\n",
    "    print(\"Dataset \", cnt)\n",
    "    newpath = os.path.sep.join([mypath, i, \"\"])\n",
    "    onlyfiles = [f for f in listdir(newpath) if isfile(join(newpath, f))]\n",
    "    j = onlyfiles[0]\n",
    "    k = onlyfiles[1]\n",
    "    data_train = np.loadtxt(newpath+j, delimiter=\",\")\n",
    "    data_test = np.loadtxt(newpath+k, delimiter=\",\")\n",
    "\n",
    "    data_joined = np.concatenate((data_train,data_test),axis=0)\n",
    "    data_train, data_test = train_test_split(data_joined, \\\n",
    "                                    test_size=0.20, random_state=2019)\n",
    "\n",
    "    X_train = data_train[:, 1:]\n",
    "    y_train = data_train[:, 0].astype(np.int)\n",
    "    X_test = data_test[:, 1:]\n",
    "    y_test = data_test[:, 0].astype(np.int)\n",
    "    \n",
    "    X_train = TimeSeriesScalerMeanVariance(mu=0., std=1.) \\\n",
    "                                    .fit_transform(X_train)\n",
    "    X_test = TimeSeriesScalerMeanVariance(mu=0., std=1.)  \\\n",
    "                                    .fit_transform(X_test)\n",
    "    \n",
    "    classes = len(np.unique(data_train[:,0]))\n",
    "    min_cluster_size = 5\n",
    "    min_samples = None\n",
    "    alpha = 1.0\n",
    "    cluster_selection_method = 'eom'\n",
    "    prediction_data = True\n",
    "\n",
    "    hdb = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size, \\\n",
    "                          min_samples=min_samples, alpha=alpha, \\\n",
    "                          cluster_selection_method= \\\n",
    "                              cluster_selection_method, \\\n",
    "                          prediction_data=prediction_data)\n",
    "\n",
    "    print(i)\n",
    "    preds = hdb.fit_predict(X_train.reshape(X_train.shape[0], \\\n",
    "                                            X_train.shape[1]))\n",
    "    ars = adjusted_rand_score(data_train[:,0],preds)\n",
    "    print(\"Adjusted Rand Index on Training Set:\", ars)\n",
    "    hdbscanDF.loc[i,\"Train ARS\"] = ars\n",
    "    \n",
    "    preds_test = hdbscan.prediction.approximate_predict(hdb, \n",
    "                            X_test.reshape(X_test.shape[0], \\\n",
    "                                           X_test.shape[1]))\n",
    "    ars = adjusted_rand_score(data_test[:,0],preds_test[0])\n",
    "    print(\"Adjusted Rand Index on Test Set:\", ars)\n",
    "    hdbscanDF.loc[i,\"Test ARS\"] = ars\n",
    "    \n",
    "hdbscanTime = timer.elapsed_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to Run HDBSCAN Experiment in Minutes: 7.0786975185076395\n"
     ]
    }
   ],
   "source": [
    "print(\"Time to Run HDBSCAN Experiment in Minutes:\", hdbscanTime/60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdbscanDF.to_pickle(os.path.sep.join([current_path, \"datasets\", \"pickled_data\"+ \\\n",
    "                    \"hdbscanDF.pickle\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compare All Three Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-Shape Results\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Train ARS    0.165139\n",
       "Test ARS     0.151103\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"k-Shape Results\")\n",
    "kShapeDF.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-Means Results\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Train ARS    0.184789\n",
       "Test ARS     0.178960\n",
       "dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"k-Means Results\")\n",
    "kMeansDF.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDBSCAN Results\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Train ARS    0.178754\n",
       "Test ARS     0.158238\n",
       "dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"HDBSCAN Results\")\n",
    "hdbscanDF.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Count top place finishes\n",
    "timeSeriesClusteringDF = pd.DataFrame(data=[],index=kShapeDF.index, \\\n",
    "                            columns=[\"kShapeTest\", \\\n",
    "                                    \"kMeansTest\", \\\n",
    "                                    \"hdbscanTest\"])\n",
    "\n",
    "timeSeriesClusteringDF.kShapeTest = kShapeDF[\"Test ARS\"]\n",
    "timeSeriesClusteringDF.kMeansTest = kMeansDF[\"Test ARS\"]\n",
    "timeSeriesClusteringDF.hdbscanTest = hdbscanDF[\"Test ARS\"]\n",
    "\n",
    "tscResults = timeSeriesClusteringDF.copy()\n",
    "\n",
    "for i in range(0,len(tscResults)):\n",
    "    maxValue = tscResults.iloc[i].max()\n",
    "    tscResults.iloc[i][tscResults.iloc[i]==maxValue]=1\n",
    "    minValue = tscResults .iloc[i].min()\n",
    "    tscResults.iloc[i][tscResults.iloc[i]==minValue]=-1\n",
    "    medianValue = tscResults.iloc[i].median()\n",
    "    tscResults.iloc[i][tscResults.iloc[i]==medianValue]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "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>kShape</th>\n",
       "      <th>kMeans</th>\n",
       "      <th>hdbscan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>firstPlace</th>\n",
       "      <td>31.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondPlace</th>\n",
       "      <td>19.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thirdPlace</th>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             kShape  kMeans  hdbscan\n",
       "firstPlace     31.0    24.0     29.0\n",
       "secondPlace    19.0    41.0     26.0\n",
       "thirdPlace     35.0    20.0     30.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show results\n",
    "tscResultsDF = pd.DataFrame(data=np.zeros((3,3)), \\\n",
    "                index=[\"firstPlace\",\"secondPlace\",\"thirdPlace\"], \\\n",
    "                columns=[\"kShape\", \"kMeans\",\"hdbscan\"])\n",
    "tscResultsDF.loc[\"firstPlace\",:] = tscResults[tscResults==1].count().values\n",
    "tscResultsDF.loc[\"secondPlace\",:] = tscResults[tscResults==0].count().values\n",
    "tscResultsDF.loc[\"thirdPlace\",:] = tscResults[tscResults==-1].count().values\n",
    "tscResultsDF"
   ]
  }
 ],
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