13_temporal_clustering.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"},"colab":{"name":"13_temporal_clustering.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"}},"cells":[{"cell_type":"markdown","metadata":{"id":"1vNtpkDW8Xzr"},"source":["# Author's Notes\n","\n","From [Hands-on Unsupervised Learning using Python](https://amzn.to/2tVABNx) - Chapter 13 - Temporal Clustering\n","\n","Note for Mac Users: The **tslearn** library is not compatible with M1 chips yet. Please use Google Colab to run this notebook if you are using an M1 chip.\n","\n","Data courtesy of the [The UCR Time Series Classification Archive](https://www.cs.ucr.edu/~eamonn/time_series_data/)."]},{"cell_type":"markdown","metadata":{"id":"oGBsqC2l8dBM"},"source":["# Google Colab Setup\n","**Only for users on Google Colab**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ox_8pywc8cac","executionInfo":{"status":"ok","timestamp":1620260142969,"user_tz":240,"elapsed":1931,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"742ad937-a554-4ee7-9604-06b912a0415a"},"source":["# Define functions to connect to Google and change directories\n","def connectDrive():\n","    from google.colab import drive\n","    drive.mount('/content/drive', force_remount=True)\n","\n","def changeDirectory(path):\n","    import os\n","    original_path = os.getcwd()\n","    os.chdir(path)\n","    new_path = os.getcwd()\n","    print(\"Original path: \",original_path)\n","    print(\"New path: \",new_path)\n","\n","# Connect to Google Drive\n","connectDrive()\n","\n","# Change path\n","changeDirectory(\"/content/drive/My Drive/github/handson-unsupervised-learning/\")"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n","Original path:  /content\n","New path:  /content/drive/My Drive/github/handson-unsupervised-learning\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Mrwm-JY18e93","executionInfo":{"status":"ok","timestamp":1620260151149,"user_tz":240,"elapsed":10101,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"ff4c5e52-1429-4a6d-f4c3-7f9b8d05a8ce"},"source":["# Install additional libraries\n","!pip install kshape==1.0.1\n","!pip install tslearn==0.5.0.5\n","!pip install hdbscan==0.8.27"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: kshape==1.0.1 in /usr/local/lib/python3.7/dist-packages (1.0.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from kshape==1.0.1) (1.19.5)\n","Requirement already satisfied: tslearn==0.5.0.5 in /usr/local/lib/python3.7/dist-packages (0.5.0.5)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (1.19.5)\n","Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (0.51.2)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (0.22.2.post1)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (1.4.1)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (1.0.1)\n","Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from tslearn==0.5.0.5) (0.29.22)\n","Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->tslearn==0.5.0.5) (0.34.0)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->tslearn==0.5.0.5) (56.0.0)\n","Requirement already satisfied: hdbscan==0.8.27 in /usr/local/lib/python3.7/dist-packages (0.8.27)\n","Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (1.19.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (1.15.0)\n","Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (1.4.1)\n","Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (0.22.2.post1)\n","Requirement already satisfied: joblib>=1.0 in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (1.0.1)\n","Requirement already satisfied: cython>=0.27 in /usr/local/lib/python3.7/dist-packages (from hdbscan==0.8.27) (0.29.22)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"A1KpU3B98Xz0"},"source":["# Import Libraries"]},{"cell_type":"code","metadata":{"jupyter":{"outputs_hidden":true},"colab":{"base_uri":"https://localhost:8080/"},"id":"f9i319HY8Xz0","executionInfo":{"status":"ok","timestamp":1620260152053,"user_tz":240,"elapsed":10997,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"f07d9641-beee-47d5-e836-11e6df05a528"},"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 matplotlib as mpl\n","from mpl_toolkits.axes_grid1 import Grid\n","%matplotlib inline\n","\n","import seaborn as sns\n","color = sns.color_palette()\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 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.preprocessing import TimeSeriesScalerMeanVariance\n","from tslearn.clustering import KShape, TimeSeriesKMeans\n","import hdbscan"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tslearn/clustering/kmeans.py:17: UserWarning: Scikit-learn <0.24 will be deprecated in a future release of tslearn\n","  \"Scikit-learn <0.24 will be deprecated in a \"\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"8JQArQXL8Xz1"},"source":["# Data Preparation"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"bnx902UA8Xz2","executionInfo":{"status":"ok","timestamp":1620260153465,"user_tz":240,"elapsed":12406,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZsGXjyXo8Xz2","executionInfo":{"status":"ok","timestamp":1620260153466,"user_tz":240,"elapsed":12400,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"dff2c2dc-6d4f-4414-d6ba-118f8a44f46c"},"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:]))"],"execution_count":5,"outputs":[{"output_type":"stream","text":["Number of time series: 23\n","Number of unique classes: 2\n","Time series length: 136\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ec5mszZv8Xz3","executionInfo":{"status":"ok","timestamp":1620260153466,"user_tz":240,"elapsed":12394,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"48d4f4dd-f052-4baa-abb0-f0c9b3387700"},"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]))"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Number of time series in class 1.0: 14\n","Number of time series in class 2.0: 9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"VJt8UUnU8Xz4","executionInfo":{"status":"ok","timestamp":1620260154597,"user_tz":240,"elapsed":13519,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"ca8f1bc8-c52e-4dba-d406-eb7624cccc12"},"source":["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()"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Plot  0  Class  1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  1  Class  1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  4  Class  1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  7  Class  1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  9  Class  1.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1dIO3GEX8Xz4","executionInfo":{"status":"ok","timestamp":1620260155224,"user_tz":240,"elapsed":14144,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"739775ae-5f05-4d8d-fd6e-04a30ce7ef31"},"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()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Plot  2  Class  2.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  3  Class  2.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  5  Class  2.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  6  Class  2.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Plot  8  Class  2.0\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"D7-X-5yM8Xz5","executionInfo":{"status":"ok","timestamp":1620260155226,"user_tz":240,"elapsed":14144,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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)"],"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"sHc_C5yn8Xz5"},"source":["# k-Shape Algorithm"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-R1zzJ-R8Xz5","executionInfo":{"status":"ok","timestamp":1620260168635,"user_tz":240,"elapsed":27552,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"802dde1d-5dcf-4698-d63e-ccdfc8ec03dc"},"source":["# Train using k-Shape\n","ks = KShape(n_clusters=2, max_iter=100, n_init=100,verbose=0)\n","ks.fit(X_train)"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KShape(init='random', max_iter=100, n_clusters=2, n_init=100, random_state=None,\n","       tol=1e-06, verbose=0)"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Dv3e_vSK8Xz6","executionInfo":{"status":"ok","timestamp":1620260168636,"user_tz":240,"elapsed":27551,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"b161f002-7dfa-41dc-bc45-122413b724d2"},"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)"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index: 0.668041237113402\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5ED-7OE08Xz7","executionInfo":{"status":"ok","timestamp":1620260168865,"user_tz":240,"elapsed":27779,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"2b2e4013-064d-4745-b281-16d679d42294"},"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)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index on Test Set: 0.0006332050676187496\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"eT2iTqQl8Xz7"},"source":["# Explore ECG 5000"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"wZK2uB208Xz7","executionInfo":{"status":"ok","timestamp":1620260171252,"user_tz":240,"elapsed":30165,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pQME4u4F8Xz8","executionInfo":{"status":"ok","timestamp":1620260171254,"user_tz":240,"elapsed":30165,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"d361a400-8dac-45d7-a7c2-d166f443e99b"},"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:]))"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Number of time series: 4000\n","Number of unique classes: 5\n","Time series length: 140\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eMUeagA48Xz8","executionInfo":{"status":"ok","timestamp":1620260171254,"user_tz":240,"elapsed":30163,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"030b4c78-dcc1-40cd-ee9f-5b61637eba57"},"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]))"],"execution_count":15,"outputs":[{"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"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"5WnGd_-v8Xz9","executionInfo":{"status":"ok","timestamp":1620260172296,"user_tz":240,"elapsed":31202,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"9d1d383f-f14d-4329-9c0f-91861c3f95c1"},"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()"],"execution_count":16,"outputs":[{"output_type":"stream","text":[" Class  1.0  Count  2327\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[" Class  2.0  Count  1423\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[" Class  3.0  Count  75\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[" Class  4.0  Count  156\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":[" Class  5.0  Count  19\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"s54--hE38Xz-","executionInfo":{"status":"ok","timestamp":1620260172645,"user_tz":240,"elapsed":31549,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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)"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wBXXFO558Xz-"},"source":["# Test Time Series Clustering Algos on Sample of ECG 5000"]},{"cell_type":"markdown","metadata":{"id":"lwivc99D8Xz-"},"source":["## k-Shape "]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"cVfuTbBe8Xz_","executionInfo":{"status":"ok","timestamp":1620260286208,"user_tz":240,"elapsed":145110,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"f75ce2f5-79d3-4c21-909d-396d19d5aa90"},"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)"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Init 1\n","0.015 --> 0.015 --> \n","Init 2\n","0.014 --> 0.014 --> \n","Init 3\n","0.018 --> 0.017 --> 0.018 --> \n","Init 4\n","0.017 --> 0.016 --> 0.018 --> \n","Init 5\n","0.016 --> 0.017 --> \n","Init 6\n","0.012 --> 0.012 --> \n","Init 7\n","0.016 --> 0.014 --> 0.016 --> \n","Init 8\n","0.015 --> 0.014 --> 0.015 --> \n","Init 9\n","0.017 --> 0.017 --> 0.018 --> \n","Init 10\n","0.015 --> 0.016 --> \n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["KShape(init='random', max_iter=100, n_clusters=5, n_init=10, random_state=2019,\n","       tol=1e-06, verbose=1)"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"lstzVTg38Xz_","executionInfo":{"status":"ok","timestamp":1620260288619,"user_tz":240,"elapsed":147519,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"b2703f56-5720-4269-983b-6bb0dc4c2a59"},"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)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index on Training Set: 0.5553639462811412\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y6BoAodc8X0A","executionInfo":{"status":"ok","timestamp":1620260289233,"user_tz":240,"elapsed":148131,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"cb6a2f8d-a896-4f43-fc8e-4fc9ccd71a5d"},"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)"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index on Test Set: 0.5301670526070555\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PA6YIO8H8X0A","executionInfo":{"status":"ok","timestamp":1620260289234,"user_tz":240,"elapsed":148130,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"a15a4aa4-0ba0-43a1-863c-213eda5488fb"},"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)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["Predicted Cluster  0.0\n","2.0    127\n","3.0      9\n","4.0      6\n","5.0      1\n","Name: actual, dtype: int64\n","\n","Predicted Cluster  1.0\n","2.0    197\n","1.0     18\n","4.0      9\n","3.0      3\n","Name: actual, dtype: int64\n","\n","Predicted Cluster  2.0\n","4.0    18\n","2.0    18\n","5.0     3\n","1.0     2\n","3.0     2\n","Name: actual, dtype: int64\n","\n","Predicted Cluster  3.0\n","1.0    130\n","3.0      3\n","4.0      2\n","5.0      1\n","2.0      1\n","Name: actual, dtype: int64\n","\n","Predicted Cluster  4.0\n","1.0    442\n","3.0      4\n","4.0      3\n","2.0      1\n","Name: actual, dtype: int64\n","\n","Count of Non-Primary Points:  86\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"VNeNaIxj8X0A"},"source":["## Time Series k-Means"]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"NTAMItCK8X0B","executionInfo":{"status":"ok","timestamp":1620260653134,"user_tz":240,"elapsed":512027,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"bdd019e4-37ad-421d-935f-756fe1136d36"},"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)"],"execution_count":22,"outputs":[{"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 2\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 3\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 4\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 5\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 6\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 7\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 8\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 9\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 10\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 11\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 12\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 13\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 14\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 15\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 16\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 17\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 18\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 19\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 20\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 21\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 22\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 23\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 24\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 25\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 26\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 27\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 28\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 29\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 30\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 31\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 32\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 33\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 34\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 35\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 36\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 37\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 38\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 39\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 40\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 41\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 42\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 43\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 44\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 45\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 46\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 47\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 48\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 49\n","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 50\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","Init 51\n","35.292 --> 26.296 --> 25.382 --> 24.928 --> 24.560 --> 24.369 --> 24.268 --> 24.206 --> 24.158 --> 24.126 --> 24.102 --> 24.079 --> 24.058 --> 24.045 --> 24.037 --> 24.030 --> 24.023 --> 24.018 --> 24.015 --> 24.014 --> 24.014 --> 24.014 --> 24.014 --> 24.014 --> 24.014 --> \n","Init 52\n","37.300 --> 25.473 --> 25.103 --> 25.037 --> 25.008 --> 24.986 --> 24.958 --> 24.942 --> 24.928 --> 24.910 --> 24.892 --> 24.860 --> 24.820 --> 24.701 --> 24.533 --> 24.441 --> 24.354 --> 24.278 --> 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 53\n","32.536 --> 25.431 --> 25.017 --> 24.765 --> 24.641 --> 24.603 --> 24.587 --> 24.583 --> 24.581 --> 24.579 --> 24.574 --> 24.564 --> 24.549 --> 24.546 --> 24.545 --> 24.544 --> 24.544 --> 24.544 --> 24.544 --> 24.544 --> 24.544 --> \n","Init 54\n","32.753 --> 25.200 --> 24.792 --> 24.719 --> 24.680 --> 24.669 --> 24.661 --> 24.654 --> 24.649 --> 24.647 --> 24.646 --> 24.645 --> 24.644 --> 24.643 --> 24.642 --> 24.641 --> 24.640 --> 24.639 --> 24.639 --> 24.638 --> 24.636 --> 24.634 --> 24.631 --> 24.630 --> 24.629 --> 24.628 --> 24.627 --> 24.626 --> 24.625 --> 24.622 --> 24.618 --> 24.612 --> 24.610 --> 24.607 --> 24.604 --> 24.601 --> 24.600 --> 24.599 --> 24.597 --> 24.596 --> 24.594 --> 24.593 --> 24.591 --> 24.588 --> 24.583 --> 24.580 --> 24.578 --> 24.577 --> 24.577 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> \n","Init 55\n","33.116 --> 25.545 --> 24.832 --> 24.672 --> 24.636 --> 24.628 --> 24.625 --> 24.624 --> 24.623 --> 24.623 --> 24.622 --> 24.622 --> 24.621 --> 24.621 --> 24.620 --> 24.620 --> 24.620 --> 24.620 --> \n","Init 56\n","33.412 --> 25.128 --> 24.843 --> 24.684 --> 24.641 --> 24.630 --> 24.624 --> 24.620 --> 24.617 --> 24.616 --> 24.616 --> 24.616 --> 24.615 --> 24.615 --> 24.614 --> 24.614 --> 24.614 --> \n","Init 57\n","33.190 --> 25.188 --> 24.783 --> 24.624 --> 24.516 --> 24.484 --> 24.470 --> 24.463 --> 24.460 --> 24.458 --> 24.457 --> 24.456 --> 24.455 --> 24.454 --> 24.454 --> 24.454 --> \n","Init 58\n","33.758 --> 24.329 --> 24.185 --> 24.163 --> 24.127 --> 24.079 --> 24.055 --> 24.046 --> 24.039 --> 24.034 --> 24.029 --> 24.026 --> 24.024 --> 24.022 --> 24.020 --> 24.019 --> 24.017 --> 24.017 --> 24.016 --> 24.015 --> 24.015 --> 24.015 --> \n","Init 59\n","33.880 --> 25.769 --> 25.600 --> 25.439 --> 25.285 --> 25.142 --> 25.036 --> 24.937 --> 24.836 --> 24.750 --> 24.650 --> 24.555 --> 24.490 --> 24.429 --> 24.359 --> 24.267 --> 24.196 --> 24.150 --> 24.131 --> 24.124 --> 24.120 --> 24.117 --> 24.116 --> 24.115 --> 24.115 --> 24.114 --> 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 60\n","39.867 --> 25.609 --> 24.940 --> 24.617 --> 24.574 --> 24.561 --> 24.560 --> 24.560 --> 24.559 --> 24.559 --> 24.559 --> 24.559 --> \n","Init 61\n","33.718 --> 26.054 --> 25.406 --> 25.222 --> 25.138 --> 25.086 --> 25.033 --> 24.962 --> 24.846 --> 24.700 --> 24.627 --> 24.605 --> 24.592 --> 24.584 --> 24.579 --> 24.575 --> 24.570 --> 24.567 --> 24.566 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> 24.565 --> \n","Init 62\n","35.292 --> 25.835 --> 25.079 --> 24.613 --> 24.360 --> 24.258 --> 24.195 --> 24.158 --> 24.126 --> 24.114 --> 24.111 --> 24.110 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> \n","Init 63\n","42.687 --> 25.520 --> 25.255 --> 24.950 --> 24.652 --> 24.562 --> 24.548 --> 24.545 --> 24.544 --> 24.544 --> 24.544 --> 24.544 --> 24.544 --> \n","Init 64\n","39.070 --> 26.860 --> 25.921 --> 25.236 --> 24.774 --> 24.540 --> 24.416 --> 24.382 --> 24.378 --> 24.376 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> \n","Init 65\n","36.582 --> 27.826 --> 27.354 --> 27.114 --> 26.997 --> 26.907 --> 26.849 --> 26.815 --> 26.756 --> 26.653 --> 26.455 --> 26.193 --> 26.045 --> 26.006 --> 25.993 --> 25.982 --> 25.970 --> 25.960 --> 25.951 --> 25.947 --> 25.942 --> 25.936 --> 25.931 --> 25.919 --> 25.907 --> 25.903 --> 25.902 --> 25.900 --> 25.899 --> 25.899 --> 25.899 --> \n","Init 66\n","33.489 --> 25.012 --> 24.633 --> 24.381 --> 24.277 --> 24.233 --> 24.202 --> 24.184 --> 24.174 --> 24.160 --> 24.145 --> 24.129 --> 24.119 --> 24.115 --> 24.112 --> 24.110 --> 24.110 --> 24.109 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n","Init 67\n","35.340 --> 27.074 --> 26.613 --> 26.304 --> 25.979 --> 25.937 --> 25.924 --> 25.914 --> 25.903 --> 25.893 --> 25.881 --> 25.843 --> 25.721 --> 25.314 --> 24.763 --> 24.639 --> 24.599 --> 24.584 --> 24.576 --> 24.571 --> 24.569 --> 24.567 --> 24.565 --> 24.563 --> 24.563 --> 24.563 --> 24.563 --> 24.563 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> \n","Init 68\n","32.867 --> 26.864 --> 26.640 --> 26.263 --> 25.454 --> 25.030 --> 24.913 --> 24.864 --> 24.844 --> 24.838 --> 24.836 --> 24.833 --> 24.828 --> 24.826 --> 24.824 --> 24.821 --> 24.819 --> 24.818 --> 24.818 --> 24.817 --> 24.817 --> 24.817 --> 24.817 --> 24.817 --> 24.817 --> 24.817 --> \n","Init 69\n","34.041 --> 25.139 --> 24.580 --> 24.323 --> 24.202 --> 24.148 --> 24.123 --> 24.116 --> 24.114 --> 24.112 --> 24.111 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> 24.108 --> \n","Init 70\n","32.140 --> 25.318 --> 24.611 --> 24.332 --> 24.212 --> 24.156 --> 24.131 --> 24.122 --> 24.120 --> 24.118 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.115 --> 24.114 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n","Init 71\n","34.087 --> 24.836 --> 24.644 --> 24.592 --> 24.493 --> 24.380 --> 24.268 --> 24.173 --> 24.115 --> 24.081 --> 24.059 --> 24.045 --> 24.038 --> 24.035 --> 24.031 --> 24.027 --> 24.021 --> 24.017 --> 24.015 --> 24.014 --> 24.013 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n","Init 72\n","36.633 --> 25.928 --> 25.068 --> 24.812 --> 24.704 --> 24.657 --> 24.633 --> 24.629 --> 24.627 --> 24.624 --> 24.621 --> 24.615 --> 24.611 --> 24.609 --> 24.606 --> 24.602 --> 24.601 --> 24.600 --> 24.598 --> 24.596 --> 24.595 --> 24.594 --> 24.592 --> 24.589 --> 24.585 --> 24.581 --> 24.579 --> 24.578 --> 24.577 --> 24.577 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> 24.576 --> \n","Init 73\n","34.397 --> 25.910 --> 25.071 --> 24.698 --> 24.534 --> 24.448 --> 24.372 --> 24.282 --> 24.203 --> 24.156 --> 24.133 --> 24.127 --> 24.124 --> 24.121 --> 24.118 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.115 --> 24.115 --> 24.115 --> 24.115 --> 24.115 --> 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 74\n","32.488 --> 25.109 --> 24.814 --> 24.703 --> 24.653 --> 24.627 --> 24.608 --> 24.595 --> 24.589 --> 24.586 --> 24.582 --> 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 75\n","35.182 --> 24.823 --> 24.501 --> 24.423 --> 24.389 --> 24.377 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> 24.375 --> \n","Init 76\n","33.231 --> 24.812 --> 24.285 --> 24.151 --> 24.114 --> 24.087 --> 24.065 --> 24.047 --> 24.033 --> 24.026 --> 24.023 --> 24.020 --> 24.018 --> 24.017 --> 24.016 --> 24.015 --> 24.015 --> 24.015 --> \n","Init 77\n","36.481 --> 26.722 --> 25.798 --> 25.549 --> 25.409 --> 25.329 --> 25.272 --> 25.232 --> 25.201 --> 25.173 --> 25.153 --> 25.142 --> 25.135 --> 25.129 --> 25.124 --> 25.119 --> 25.113 --> 25.108 --> 25.103 --> 25.100 --> 25.099 --> 25.098 --> 25.097 --> 25.095 --> 25.094 --> 25.093 --> 25.092 --> 25.092 --> 25.091 --> 25.091 --> 25.091 --> 25.091 --> 25.091 --> 25.091 --> \n","Init 78\n","33.536 --> 24.791 --> 24.567 --> 24.469 --> 24.383 --> 24.281 --> 24.212 --> 24.169 --> 24.153 --> 24.141 --> 24.131 --> 24.124 --> 24.120 --> 24.118 --> 24.115 --> 24.114 --> 24.114 --> 24.113 --> 24.112 --> 24.110 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n","Init 79\n","34.516 --> 24.444 --> 24.159 --> 24.129 --> 24.122 --> 24.118 --> 24.117 --> 24.117 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> 24.116 --> \n","Init 80\n","35.177 --> 24.635 --> 24.334 --> 24.257 --> 24.211 --> 24.187 --> 24.162 --> 24.144 --> 24.131 --> 24.124 --> 24.121 --> 24.119 --> 24.118 --> 24.117 --> 24.116 --> 24.116 --> 24.115 --> 24.114 --> 24.112 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n","Init 81\n","32.887 --> 24.556 --> 24.300 --> 24.201 --> 24.165 --> 24.140 --> 24.118 --> 24.112 --> 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 82\n","34.790 --> 25.897 --> 25.554 --> 25.261 --> 24.992 --> 24.793 --> 24.715 --> 24.674 --> 24.646 --> 24.635 --> 24.632 --> 24.630 --> 24.630 --> 24.629 --> 24.629 --> 24.629 --> 24.629 --> 24.629 --> \n","Init 83\n","39.367 --> 26.832 --> 25.991 --> 25.694 --> 25.371 --> 24.680 --> 24.131 --> 24.062 --> 24.041 --> 24.030 --> 24.023 --> 24.021 --> 24.019 --> 24.017 --> 24.016 --> 24.015 --> 24.014 --> 24.014 --> 24.014 --> \n","Init 84\n","37.414 --> 24.763 --> 24.369 --> 24.145 --> 24.073 --> 24.054 --> 24.046 --> 24.035 --> 24.026 --> 24.020 --> 24.016 --> 24.015 --> 24.015 --> 24.014 --> 24.013 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> 24.012 --> \n","Init 85\n","32.585 --> 24.954 --> 24.709 --> 24.631 --> 24.601 --> 24.586 --> 24.577 --> 24.571 --> 24.569 --> 24.567 --> 24.564 --> 24.563 --> 24.563 --> 24.563 --> 24.563 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> \n","Init 86\n","34.222 --> 25.434 --> 25.130 --> 25.002 --> 24.934 --> 24.903 --> 24.888 --> 24.870 --> 24.849 --> 24.814 --> 24.691 --> 24.527 --> 24.433 --> 24.344 --> 24.275 --> 24.234 --> 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 87\n","41.254 --> 25.313 --> 24.755 --> 24.554 --> 24.476 --> 24.434 --> 24.404 --> 24.369 --> 24.328 --> 24.283 --> 24.232 --> 24.174 --> 24.124 --> 24.093 --> 24.077 --> 24.066 --> 24.053 --> 24.043 --> 24.035 --> 24.029 --> 24.023 --> 24.018 --> 24.014 --> 24.012 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> 24.011 --> \n","Init 88\n","37.023 --> 25.132 --> 24.968 --> 24.884 --> 24.838 --> 24.804 --> 24.779 --> 24.759 --> 24.738 --> 24.717 --> 24.697 --> 24.675 --> 24.658 --> 24.636 --> 24.619 --> 24.609 --> 24.603 --> 24.595 --> 24.589 --> 24.585 --> 24.580 --> 24.574 --> 24.562 --> 24.554 --> 24.550 --> 24.548 --> 24.546 --> 24.546 --> 24.545 --> 24.545 --> \n","Init 89\n","33.314 --> 25.409 --> 25.187 --> 25.135 --> 25.110 --> 25.094 --> 25.076 --> 25.053 --> 25.022 --> 24.985 --> 24.943 --> 24.866 --> 24.621 --> 24.337 --> 24.200 --> 24.129 --> 24.097 --> 24.078 --> 24.067 --> 24.057 --> 24.045 --> 24.032 --> 24.022 --> 24.018 --> 24.016 --> 24.015 --> 24.015 --> 24.015 --> \n","Init 90\n","35.795 --> 25.128 --> 24.778 --> 24.567 --> 24.427 --> 24.336 --> 24.262 --> 24.197 --> 24.151 --> 24.133 --> 24.123 --> 24.121 --> 24.119 --> 24.118 --> 24.117 --> 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 91\n","38.644 --> 24.842 --> 24.399 --> 24.224 --> 24.176 --> 24.160 --> 24.148 --> 24.137 --> 24.127 --> 24.122 --> 24.119 --> 24.117 --> 24.115 --> 24.114 --> 24.113 --> 24.112 --> 24.111 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n","Init 92\n","47.428 --> 27.610 --> 26.671 --> 26.360 --> 25.903 --> 24.975 --> 24.435 --> 24.254 --> 24.193 --> 24.168 --> 24.155 --> 24.147 --> 24.137 --> 24.131 --> 24.125 --> 24.120 --> 24.118 --> 24.115 --> 24.113 --> 24.112 --> 24.111 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n","Init 93\n","36.397 --> 26.820 --> 26.348 --> 26.096 --> 25.885 --> 25.710 --> 25.446 --> 25.141 --> 24.909 --> 24.694 --> 24.550 --> 24.466 --> 24.389 --> 24.324 --> 24.265 --> 24.203 --> 24.147 --> 24.109 --> 24.087 --> 24.072 --> 24.056 --> 24.044 --> 24.037 --> 24.031 --> 24.024 --> 24.019 --> 24.016 --> 24.015 --> 24.014 --> 24.014 --> 24.014 --> 24.013 --> 24.013 --> 24.013 --> 24.013 --> 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 94\n","34.634 --> 25.925 --> 25.068 --> 24.564 --> 24.319 --> 24.208 --> 24.160 --> 24.135 --> 24.116 --> 24.109 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n","Init 95\n","36.264 --> 25.036 --> 24.568 --> 24.363 --> 24.265 --> 24.219 --> 24.195 --> 24.180 --> 24.160 --> 24.143 --> 24.129 --> 24.122 --> 24.120 --> 24.118 --> 24.117 --> 24.116 --> 24.115 --> 24.113 --> 24.112 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.108 --> \n","Init 96\n","35.494 --> 26.247 --> 25.863 --> 25.487 --> 25.167 --> 25.009 --> 24.905 --> 24.832 --> 24.777 --> 24.727 --> 24.702 --> 24.685 --> 24.679 --> 24.673 --> 24.664 --> 24.656 --> 24.652 --> 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 97\n","30.816 --> 25.332 --> 25.070 --> 24.993 --> 24.913 --> 24.782 --> 24.658 --> 24.617 --> 24.610 --> 24.605 --> 24.604 --> 24.603 --> 24.601 --> 24.598 --> 24.596 --> 24.595 --> 24.594 --> 24.594 --> 24.594 --> \n","Init 98\n","32.081 --> 26.596 --> 26.191 --> 25.739 --> 24.901 --> 24.394 --> 24.284 --> 24.235 --> 24.204 --> 24.176 --> 24.156 --> 24.143 --> 24.136 --> 24.128 --> 24.119 --> 24.114 --> 24.113 --> 24.111 --> 24.110 --> 24.109 --> 24.108 --> 24.108 --> 24.107 --> 24.107 --> 24.107 --> 24.107 --> \n","Init 99\n","37.977 --> 25.812 --> 25.520 --> 25.390 --> 25.286 --> 25.178 --> 25.043 --> 24.866 --> 24.723 --> 24.640 --> 24.602 --> 24.580 --> 24.571 --> 24.567 --> 24.565 --> 24.563 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> 24.562 --> \n","Init 100\n","34.967 --> 24.660 --> 24.275 --> 24.179 --> 24.152 --> 24.135 --> 24.126 --> 24.121 --> 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"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["TimeSeriesKMeans(dtw_inertia=False, init='k-means++', max_iter=100,\n","                 max_iter_barycenter=100, metric='euclidean',\n","                 metric_params=None, n_clusters=5, n_init=100, n_jobs=None,\n","                 random_state=2019, tol=1e-06, verbose=1)"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xQGbcbts8X0B","executionInfo":{"status":"ok","timestamp":1620260653338,"user_tz":240,"elapsed":512229,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"708a2db0-ae3c-4e16-dc9b-8469b745a30f"},"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)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index of Time Series k-Means on Training Set: 0.5063464656715959\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aOWrHO8W8X0C","executionInfo":{"status":"ok","timestamp":1620260653339,"user_tz":240,"elapsed":512229,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"800cbf4f-f205-48ba-fa86-71ec2497a246"},"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)"],"execution_count":24,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index of Time Series k-Means on Test Set: 0.4864981997585834\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"ptlZTpK78X0C"},"source":["## HDBSCAN"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DlZeXgst8X0C","executionInfo":{"status":"ok","timestamp":1620260659281,"user_tz":240,"elapsed":518170,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"56d6dc1d-ecb3-437a-f362-3729c86b72a1"},"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)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index on Training Set: 0.7689563655060421\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gVmBio_n8X0D","executionInfo":{"status":"ok","timestamp":1620260659804,"user_tz":240,"elapsed":518691,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"6e6858fd-81de-4a84-95ca-28db89dfe5d5"},"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)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Adjusted Rand Index on Test Set: 0.7200816245545564\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"hqrnJlUA8X0E"},"source":["# Compare Time Series Clustering Algos on Full ECG 5000 Dataset"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"GiwrxLU28X0E","executionInfo":{"status":"ok","timestamp":1620260659806,"user_tz":240,"elapsed":518692,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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"],"execution_count":27,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"Z9p_ucGx8X0J"},"source":["## k-Shape Experiment - FULL RUN"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"ZWEfkl-28X0L","executionInfo":{"status":"ok","timestamp":1620260659806,"user_tz":240,"elapsed":518690,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["# Create dataframe\n","kShapeDF = pd.DataFrame(data=[],index=[v for v in d], \\\n","                        columns=[\"Train ARS\",\"Test ARS\"])"],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"61s8XS8j8X0M","executionInfo":{"status":"ok","timestamp":1620260707208,"user_tz":240,"elapsed":566091,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"85561f22-4190-40e7-fbbb-7e2f76233f5b"},"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()"],"execution_count":29,"outputs":[{"output_type":"stream","text":["Dataset  1\n","ECG5000\n","Adjusted Rand Index on Training Set: 0.4853120877634212\n","Adjusted Rand Index on Test Set: 0.5043510852253306\n","Dataset  2\n","ECGFiveDays\n","Adjusted Rand Index on Training Set: 0.806420975979831\n","Adjusted Rand Index on Test Set: 0.7457652100767738\n","Elapsed: 47.284302949905396 sec \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PtZrDcwc8X0M","executionInfo":{"status":"ok","timestamp":1620260707209,"user_tz":240,"elapsed":566091,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"8a8c0f18-6851-410d-ba34-5d9135e2a496"},"source":["print(\"Time to Run k-Shape Experiment in Minutes:\", kShapeTime/60)"],"execution_count":30,"outputs":[{"output_type":"stream","text":["Time to Run k-Shape Experiment in Minutes: 0.7880724668502808\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"YNV4mQVL8X0M","executionInfo":{"status":"ok","timestamp":1620260707210,"user_tz":240,"elapsed":566091,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["kShapeDF.to_pickle(os.path.sep.join([current_path, 'datasets', 'pickled_data', \\\n","                   \"kShapeDF.pickle\"]))"],"execution_count":31,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"zWr_YWFx8X0N"},"source":["## k-Means Experiment - FULL RUN"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"X47I_z3O8X0N","executionInfo":{"status":"ok","timestamp":1620260707211,"user_tz":240,"elapsed":566090,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["# Create dataframe\n","kMeansDF = pd.DataFrame(data=[],index=[v for v in d], \\\n","                        columns=[\"Train ARS\",\"Test ARS\"])"],"execution_count":32,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"Q3ghokkZ8X0N","executionInfo":{"status":"ok","timestamp":1620260724479,"user_tz":240,"elapsed":583357,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"e22bae59-47a1-4823-ae61-16f8b5e77707"},"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()"],"execution_count":33,"outputs":[{"output_type":"stream","text":["Dataset  1\n","ECG5000\n","Adjusted Rand Index on Training Set: 0.4304250099835039\n","Adjusted Rand Index on Test Set: 0.4356207241975151\n","Dataset  2\n","ECGFiveDays\n","Adjusted Rand Index on Training Set: 0.003670239528847993\n","Adjusted Rand Index on Test Set: -0.004520907838344356\n","Elapsed: 17.14858865737915 sec \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hvf57ZvT8X0O","executionInfo":{"status":"ok","timestamp":1620260724481,"user_tz":240,"elapsed":583356,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"7d9b701a-de20-4e5d-94d1-f86cda9fe7b8"},"source":["print(\"Time to Run k-Means Experiment in Minutes:\", kMeansTime/60)"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Time to Run k-Means Experiment in Minutes: 0.28581069310506185\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"EMMANLaF8X0P","executionInfo":{"status":"ok","timestamp":1620260724482,"user_tz":240,"elapsed":583355,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["kMeansDF.to_pickle(os.path.sep.join([current_path, \"datasets\", \"pickled_data\", \\\n","                                     \"kMeansDF.pickle\"]))"],"execution_count":35,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"FJs4mWm78X0Q"},"source":["## HDBSCAN Experiment - FULL RUN"]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"2wRsgl0X8X0Q","executionInfo":{"status":"ok","timestamp":1620260724483,"user_tz":240,"elapsed":583354,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["# Create dataframe\n","hdbscanDF = pd.DataFrame(data=[],index=[v for v in d], \\\n","                         columns=[\"Train ARS\",\"Test ARS\"])"],"execution_count":36,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QKjALhVN8X0Q","executionInfo":{"status":"ok","timestamp":1620260732107,"user_tz":240,"elapsed":590977,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"0f773474-785b-4910-a4e6-b6c6f8690832"},"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()"],"execution_count":37,"outputs":[{"output_type":"stream","text":["Dataset  1\n","ECG5000\n","Adjusted Rand Index on Training Set: 0.7556104485290581\n","Adjusted Rand Index on Test Set: 0.7561234640907506\n","Dataset  2\n","ECGFiveDays\n","Adjusted Rand Index on Training Set: 0.16072052207840315\n","Adjusted Rand Index on Test Set: 0.17272704427554567\n","Elapsed: 7.622999906539917 sec \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s5Vgid0B8X0R","executionInfo":{"status":"ok","timestamp":1620260732107,"user_tz":240,"elapsed":590976,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"a72bd491-4456-481d-f681-4b06817611c1"},"source":["print(\"Time to Run HDBSCAN Experiment in Minutes:\", hdbscanTime/60)"],"execution_count":38,"outputs":[{"output_type":"stream","text":["Time to Run HDBSCAN Experiment in Minutes: 0.12705084880193074\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"tR_7Sn5A8X0R","executionInfo":{"status":"ok","timestamp":1620260732108,"user_tz":240,"elapsed":590976,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"source":["hdbscanDF.to_pickle(os.path.sep.join([current_path, \"datasets\", \"pickled_data\", \\\n","                    \"hdbscanDF.pickle\"]))"],"execution_count":39,"outputs":[]},{"cell_type":"markdown","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"JIz2UBDK8X0R"},"source":["# Compare All Three Experiments"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m404Rqvt8X0S","executionInfo":{"status":"ok","timestamp":1620260732108,"user_tz":240,"elapsed":590975,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"75daaf90-b0b7-4831-ae63-56692e8dcbaa"},"source":["print(\"k-Shape Results\")\n","kShapeDF.mean()"],"execution_count":40,"outputs":[{"output_type":"stream","text":["k-Shape Results\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Train ARS    0.645867\n","Test ARS     0.625058\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"plXzUy268X0S","executionInfo":{"status":"ok","timestamp":1620260732108,"user_tz":240,"elapsed":590973,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"0d2c6b3b-200f-4e68-913c-4fe31cdefc29"},"source":["print(\"k-Means Results\")\n","kMeansDF.mean()"],"execution_count":41,"outputs":[{"output_type":"stream","text":["k-Means Results\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Train ARS    0.217048\n","Test ARS     0.215550\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kwl5xWoY8X0S","executionInfo":{"status":"ok","timestamp":1620260732109,"user_tz":240,"elapsed":590973,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"e090d284-b1a2-4368-8d05-9b92b0e0644f"},"source":["print(\"HDBSCAN Results\")\n","hdbscanDF.mean()"],"execution_count":42,"outputs":[{"output_type":"stream","text":["HDBSCAN Results\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Train ARS    0.458165\n","Test ARS     0.464425\n","dtype: float64"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"iDb7UKTf8X0S","executionInfo":{"status":"ok","timestamp":1620260732326,"user_tz":240,"elapsed":591189,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}}},"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"],"execution_count":43,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":144},"id":"WqYOf4MA8X0S","executionInfo":{"status":"ok","timestamp":1620260732327,"user_tz":240,"elapsed":591188,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"e3a289b3-abbd-434a-cc9d-b9bd6b85585c"},"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"],"execution_count":44,"outputs":[{"output_type":"execute_result","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>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>secondPlace</th>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>thirdPlace</th>\n","      <td>0.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             kShape  kMeans  hdbscan\n","firstPlace      1.0     0.0      1.0\n","secondPlace     1.0     0.0      1.0\n","thirdPlace      0.0     2.0      0.0"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"PEugT_2Q8X0S"},"source":["# Conclusion"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fcymuoluEkWK","executionInfo":{"status":"ok","timestamp":1620260834587,"user_tz":240,"elapsed":481,"user":{"displayName":"Ankur Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuyT9p2uQcITYiB6Ja4gdHeMPvvVZ_RNUvS_DFuY0=s64","userId":"08322043083056308084"}},"outputId":"abc76a66-f7a2-41da-8c33-f6992888c260"},"source":["import datetime as dt\n","print(\"Completed: \", dt.datetime.now())"],"execution_count":45,"outputs":[{"output_type":"stream","text":["Completed:  2021-05-06 00:27:14.295021\n"],"name":"stdout"}]}]}