├── README.md
├── Spark ML Logistic Regression.ipynb
├── SparkML GBT.ipynb
├── AssignmentML1.ipynb
├── SparkML GBT Grid Search.ipynb
├── SparkML Linear Regression.ipynb
├── AssignmentML2.ipynb
├── AssignmentML3.ipynb
├── AssignmentML4.ipynb
├── ETL Exercise .ipynb
└── SparkML Support Vector Machine.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Advanced-Machine-Learning-and-Signal-Processing-IBM
2 | Advanced Machine Learning and Signal Processing IBM
3 | 


--------------------------------------------------------------------------------
/Spark ML Logistic Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "execution_count": 1, 
  6 |             "cell_type": "code", 
  7 |             "metadata": {}, 
  8 |             "outputs": [
  9 |                 {
 10 |                     "output_type": "stream", 
 11 |                     "name": "stdout", 
 12 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190903091321-0003\nKERNEL_ID = f1ba51b4-1b5b-47c8-abb0-07567c8b35db\n"
 13 |                 }
 14 |             ], 
 15 |             "source": "# The code was removed by Watson Studio for sharing."
 16 |         }, 
 17 |         {
 18 |             "execution_count": 2, 
 19 |             "cell_type": "code", 
 20 |             "metadata": {}, 
 21 |             "outputs": [], 
 22 |             "source": "splits = df.randomSplit([0.8, 0.2])\ndf_train = splits[0]\ndf_test = splits[1]"
 23 |         }, 
 24 |         {
 25 |             "execution_count": 3, 
 26 |             "cell_type": "code", 
 27 |             "metadata": {}, 
 28 |             "outputs": [], 
 29 |             "source": "from pyspark.ml.feature import StringIndexer, OneHotEncoder\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.feature import Normalizer\n\n\nindexer = StringIndexer(inputCol=\"class\", outputCol=\"label\")\nvectorAssembler = VectorAssembler(inputCols=[\"x\",\"y\",\"z\"],outputCol=\"features\")\nnormalizer = Normalizer(inputCol=\"features\", outputCol=\"features_norm\", p=1.0)"
 30 |         }, 
 31 |         {
 32 |             "execution_count": 4, 
 33 |             "cell_type": "code", 
 34 |             "metadata": {}, 
 35 |             "outputs": [], 
 36 |             "source": "from pyspark.ml.classification import LogisticRegression\n\nlr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)"
 37 |         }, 
 38 |         {
 39 |             "execution_count": 5, 
 40 |             "cell_type": "code", 
 41 |             "metadata": {}, 
 42 |             "outputs": [], 
 43 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[indexer, vectorAssembler, normalizer,lr])"
 44 |         }, 
 45 |         {
 46 |             "execution_count": 6, 
 47 |             "cell_type": "code", 
 48 |             "metadata": {}, 
 49 |             "outputs": [], 
 50 |             "source": "model = pipeline.fit(df_train)"
 51 |         }, 
 52 |         {
 53 |             "execution_count": 7, 
 54 |             "cell_type": "code", 
 55 |             "metadata": {}, 
 56 |             "outputs": [], 
 57 |             "source": "prediction = model.transform(df_train)"
 58 |         }, 
 59 |         {
 60 |             "execution_count": 8, 
 61 |             "cell_type": "code", 
 62 |             "metadata": {}, 
 63 |             "outputs": [
 64 |                 {
 65 |                     "output_type": "stream", 
 66 |                     "name": "stdout", 
 67 |                     "text": "root\n |-- x: integer (nullable = true)\n |-- y: integer (nullable = true)\n |-- z: integer (nullable = true)\n |-- source: string (nullable = true)\n |-- class: string (nullable = true)\n |-- label: double (nullable = false)\n |-- features: vector (nullable = true)\n |-- features_norm: vector (nullable = true)\n |-- rawPrediction: vector (nullable = true)\n |-- probability: vector (nullable = true)\n |-- prediction: double (nullable = false)\n\n"
 68 |                 }
 69 |             ], 
 70 |             "source": "prediction.printSchema()"
 71 |         }, 
 72 |         {
 73 |             "execution_count": 9, 
 74 |             "cell_type": "code", 
 75 |             "metadata": {}, 
 76 |             "outputs": [
 77 |                 {
 78 |                     "execution_count": 9, 
 79 |                     "metadata": {}, 
 80 |                     "data": {
 81 |                         "text/plain": "0.1293416049064505"
 82 |                     }, 
 83 |                     "output_type": "execute_result"
 84 |                 }
 85 |             ], 
 86 |             "source": "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\nbinEval = MulticlassClassificationEvaluator().setMetricName(\"accuracy\") .setPredictionCol(\"prediction\").setLabelCol(\"label\")\nbinEval.evaluate(prediction) "
 87 |         }, 
 88 |         {
 89 |             "execution_count": 10, 
 90 |             "cell_type": "code", 
 91 |             "metadata": {}, 
 92 |             "outputs": [], 
 93 |             "source": "prediction = model.transform(df_test)"
 94 |         }, 
 95 |         {
 96 |             "execution_count": 11, 
 97 |             "cell_type": "code", 
 98 |             "metadata": {}, 
 99 |             "outputs": [
100 |                 {
101 |                     "execution_count": 11, 
102 |                     "metadata": {}, 
103 |                     "data": {
104 |                         "text/plain": "0.12903906777752758"
105 |                     }, 
106 |                     "output_type": "execute_result"
107 |                 }
108 |             ], 
109 |             "source": "binEval.evaluate(prediction) "
110 |         }
111 |     ], 
112 |     "metadata": {
113 |         "kernelspec": {
114 |             "display_name": "Python 3.6 with Spark", 
115 |             "name": "python36", 
116 |             "language": "python3"
117 |         }, 
118 |         "language_info": {
119 |             "mimetype": "text/x-python", 
120 |             "nbconvert_exporter": "python", 
121 |             "version": "3.6.8", 
122 |             "name": "python", 
123 |             "file_extension": ".py", 
124 |             "pygments_lexer": "ipython3", 
125 |             "codemirror_mode": {
126 |                 "version": 3, 
127 |                 "name": "ipython"
128 |             }
129 |         }
130 |     }, 
131 |     "nbformat": 4
132 | }


--------------------------------------------------------------------------------
/SparkML GBT.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "source": "import ibmos2spark\n\n# @hidden_cell\ncredentials = {\n    'endpoint': 'https://s3-api.us-geo.objectstorage.service.networklayer.com',\n    'api_key': 'iTfSE_YVE6zqnxjd1oK0E37R2aVYY4dFhXFFCljE1AcJ',\n    'service_id': 'iam-ServiceId-d4b06e46-293a-4417-b76c-2f16076a9353',\n    'iam_service_endpoint': 'https://iam.ng.bluemix.net/oidc/token'}\n\nconfiguration_name = 'os_b0f1407510994fd1b793b85137baafb8_configs'\ncos = ibmos2spark.CloudObjectStorage(sc, credentials, configuration_name, 'bluemix_cos')\n\nfrom pyspark.sql import SparkSession\nspark = SparkSession.builder.getOrCreate()\n# Since JSON data can be semi-structured and contain additional metadata, it is possible that you might face issues with the DataFrame layout.\n# Please read the documentation of 'SparkSession.read()' to learn more about the possibilities to adjust the data loading.\n# PySpark documentation: http://spark.apache.org/docs/2.0.2/api/python/pyspark.sql.html#pyspark.sql.DataFrameReader.json\n\n\ndf = spark.read.parquet(cos.url('hmp.parquet', 'courseraml-donotdelete-pr-qve0ttzezdeodc'))", 
  6 |             "cell_type": "code", 
  7 |             "execution_count": 3, 
  8 |             "outputs": [], 
  9 |             "metadata": {}
 10 |         }, 
 11 |         {
 12 |             "source": "df.createOrReplaceTempView('df')\ndf_two_class = spark.sql(\"select * from df where class in ('Use_telephone','Standup_chair')\")", 
 13 |             "cell_type": "code", 
 14 |             "execution_count": 4, 
 15 |             "outputs": [], 
 16 |             "metadata": {}
 17 |         }, 
 18 |         {
 19 |             "source": "splits = df_two_class.randomSplit([0.8, 0.2])\ndf_train = splits[0]\ndf_test = splits[1]", 
 20 |             "cell_type": "code", 
 21 |             "execution_count": 5, 
 22 |             "outputs": [], 
 23 |             "metadata": {}
 24 |         }, 
 25 |         {
 26 |             "source": "from pyspark.ml.feature import StringIndexer, OneHotEncoder\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.feature import Normalizer\n\n\nindexer = StringIndexer(inputCol=\"class\", outputCol=\"label\")\n\nvectorAssembler = VectorAssembler(inputCols=[\"x\",\"y\",\"z\"],\n                                  outputCol=\"features\")\n\nnormalizer = Normalizer(inputCol=\"features\", outputCol=\"features_norm\", p=1.0)", 
 27 |             "cell_type": "code", 
 28 |             "execution_count": 10, 
 29 |             "outputs": [], 
 30 |             "metadata": {}
 31 |         }, 
 32 |         {
 33 |             "source": "from pyspark.ml.classification import GBTClassifier\n\ngbt = GBTClassifier(labelCol=\"label\", featuresCol=\"features\", maxIter=10)", 
 34 |             "cell_type": "code", 
 35 |             "execution_count": 11, 
 36 |             "outputs": [], 
 37 |             "metadata": {}
 38 |         }, 
 39 |         {
 40 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[indexer, vectorAssembler, normalizer,gbt])", 
 41 |             "cell_type": "code", 
 42 |             "execution_count": 14, 
 43 |             "outputs": [], 
 44 |             "metadata": {}
 45 |         }, 
 46 |         {
 47 |             "source": "model = pipeline.fit(df_train)", 
 48 |             "cell_type": "code", 
 49 |             "execution_count": 13, 
 50 |             "outputs": [], 
 51 |             "metadata": {}
 52 |         }, 
 53 |         {
 54 |             "source": "prediction = model.transform(df_train)", 
 55 |             "cell_type": "code", 
 56 |             "execution_count": 15, 
 57 |             "outputs": [], 
 58 |             "metadata": {}
 59 |         }, 
 60 |         {
 61 |             "source": "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\nbinEval = MulticlassClassificationEvaluator().setMetricName(\"accuracy\") .setPredictionCol(\"prediction\").setLabelCol(\"label\")\n    \nbinEval.evaluate(prediction) ", 
 62 |             "cell_type": "code", 
 63 |             "execution_count": 16, 
 64 |             "outputs": [
 65 |                 {
 66 |                     "output_type": "execute_result", 
 67 |                     "metadata": {}, 
 68 |                     "data": {
 69 |                         "text/plain": "0.912288266091777"
 70 |                     }, 
 71 |                     "execution_count": 16
 72 |                 }
 73 |             ], 
 74 |             "metadata": {}
 75 |         }, 
 76 |         {
 77 |             "source": "prediction = model.transform(df_test)", 
 78 |             "cell_type": "code", 
 79 |             "execution_count": 17, 
 80 |             "outputs": [], 
 81 |             "metadata": {}
 82 |         }, 
 83 |         {
 84 |             "source": "binEval.evaluate(prediction) ", 
 85 |             "cell_type": "code", 
 86 |             "execution_count": 19, 
 87 |             "outputs": [
 88 |                 {
 89 |                     "output_type": "execute_result", 
 90 |                     "metadata": {}, 
 91 |                     "data": {
 92 |                         "text/plain": "0.9024718551150269"
 93 |                     }, 
 94 |                     "execution_count": 19
 95 |                 }
 96 |             ], 
 97 |             "metadata": {}
 98 |         }
 99 |     ], 
100 |     "nbformat": 4, 
101 |     "metadata": {
102 |         "kernelspec": {
103 |             "display_name": "Python 3.6 with Spark", 
104 |             "name": "python36", 
105 |             "language": "python3"
106 |         }, 
107 |         "language_info": {
108 |             "mimetype": "text/x-python", 
109 |             "nbconvert_exporter": "python", 
110 |             "version": "3.6.8", 
111 |             "name": "python", 
112 |             "pygments_lexer": "ipython3", 
113 |             "file_extension": ".py", 
114 |             "codemirror_mode": {
115 |                 "version": 3, 
116 |                 "name": "ipython"
117 |             }
118 |         }
119 |     }
120 | }


--------------------------------------------------------------------------------
/AssignmentML1.ipynb:
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  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "source": "This is the first assignment for the Coursera course \"Advanced Machine Learning and Signal Processing\"\n\nJust execute all cells one after the other and you are done - just note that in the last one you have to update your email address (the one you've used for coursera) and obtain a submission token, you get this from the programming assignment directly on coursera.", 
  6 |             "cell_type": "markdown", 
  7 |             "metadata": {}
  8 |         }, 
  9 |         {
 10 |             "execution_count": 1, 
 11 |             "cell_type": "code", 
 12 |             "metadata": {}, 
 13 |             "outputs": [
 14 |                 {
 15 |                     "output_type": "stream", 
 16 |                     "name": "stdout", 
 17 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190831122534-0002\nKERNEL_ID = c4fa93e1-e6bd-4b47-bf24-52d309b7e5e1\n--2019-08-31 12:25:37--  https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet\nResolving github.com (github.com)... 140.82.113.3\nConnecting to github.com (github.com)|140.82.113.3|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet [following]\n--2019-08-31 12:25:37--  https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 59032 (58K) [application/octet-stream]\nSaving to: 'a2.parquet.2'\n\na2.parquet.2        100%[===================>]  57.65K  --.-KB/s    in 0.002s  \n\n2019-08-31 12:25:37 (30.0 MB/s) - 'a2.parquet.2' saved [59032/59032]\n\n"
 18 |                 }
 19 |             ], 
 20 |             "source": "!wget https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet"
 21 |         }, 
 22 |         {
 23 |             "execution_count": 2, 
 24 |             "cell_type": "code", 
 25 |             "metadata": {}, 
 26 |             "outputs": [
 27 |                 {
 28 |                     "output_type": "stream", 
 29 |                     "name": "stdout", 
 30 |                     "text": "+-----+-----------+-------------------+-------------------+-------------------+\n|CLASS|   SENSORID|                  X|                  Y|                  Z|\n+-----+-----------+-------------------+-------------------+-------------------+\n|    0|         26| 380.66434005495194| -139.3470983812975|-247.93697521077704|\n|    0|         29| 104.74324299209692| -32.27421440203938|-25.105013725863852|\n|    0| 8589934658| 118.11469236129976| 45.916682927433534| -87.97203782706572|\n|    0|34359738398| 246.55394030642543|-0.6122810693132044|-398.18662513951506|\n|    0|17179869241|-190.32584900181487|  234.7849657520335|-206.34483804019288|\n|    0|25769803830| 178.62396382387422| -47.07529438881511|  84.38310769821979|\n|    0|25769803831|  85.03128805189493|-4.3024316644854546|-1.1841857567516714|\n|    0|34359738411| 26.786262674736566| -46.33193951911338| 20.880756008396055|\n|    0| 8589934592|-16.203752396859194| 51.080957032176954| -96.80526656416971|\n|    0|25769803852|   47.2048142440404|  -78.2950899652916| 181.99604091494786|\n|    0|34359738369| 15.608872398939273| -79.90322809181754|  69.62150711098005|\n|    0|         19|-4.8281721129789315| -67.38050508399905| 221.24876396496404|\n|    0|         54| -98.40725712852762|-19.989364074314732|  -302.695196085276|\n|    0|17179869313| 22.835845394816594|   17.1633660118843| 32.877914832011385|\n|    0|34359738454|  84.20178070080324| -32.81572075916947| -48.63517643958031|\n|    0|          0|  56.54732521345129| -7.980106018032676|  95.05162719436447|\n|    0|17179869201|  -57.6008655247749|  5.135393798773895| 236.99158698947267|\n|    0|17179869308| -65.59264738389012| -48.92660057215126| -61.58970715383383|\n|    0|25769803790|  34.82337351291005|  9.483542084393937|  197.6066372962772|\n|    0|25769803825|  39.80573823439121|-0.7955236412785212| -79.66652640650325|\n+-----+-----------+-------------------+-------------------+-------------------+\nonly showing top 20 rows\n\n"
 31 |                 }
 32 |             ], 
 33 |             "source": "df=spark.read.load('a2.parquet')\n\ndf.createOrReplaceTempView(\"df\")\nspark.sql(\"SELECT * from df\").show()\n"
 34 |         }, 
 35 |         {
 36 |             "execution_count": 3, 
 37 |             "cell_type": "code", 
 38 |             "metadata": {}, 
 39 |             "outputs": [], 
 40 |             "source": "!rm -Rf a2_m1.json"
 41 |         }, 
 42 |         {
 43 |             "execution_count": 4, 
 44 |             "cell_type": "code", 
 45 |             "metadata": {}, 
 46 |             "outputs": [], 
 47 |             "source": "df = df.repartition(1)\ndf.write.json('a2_m1.json')"
 48 |         }, 
 49 |         {
 50 |             "execution_count": 5, 
 51 |             "cell_type": "code", 
 52 |             "metadata": {}, 
 53 |             "outputs": [
 54 |                 {
 55 |                     "output_type": "stream", 
 56 |                     "name": "stdout", 
 57 |                     "text": "--2019-08-31 12:25:58--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.48.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.48.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2540 (2.5K) [text/plain]\nSaving to: 'rklib.py'\n\nrklib.py            100%[===================>]   2.48K  --.-KB/s    in 0s      \n\n2019-08-31 12:25:58 (73.7 MB/s) - 'rklib.py' saved [2540/2540]\n\n"
 58 |                 }
 59 |             ], 
 60 |             "source": "!rm -f rklib.py\n!wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py"
 61 |         }, 
 62 |         {
 63 |             "execution_count": 6, 
 64 |             "cell_type": "code", 
 65 |             "metadata": {}, 
 66 |             "outputs": [], 
 67 |             "source": "import zipfile\n\ndef zipdir(path, ziph):\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            ziph.write(os.path.join(root, file))\n\nzipf = zipfile.ZipFile('a2_m1.json.zip', 'w', zipfile.ZIP_DEFLATED)\nzipdir('a2_m1.json', zipf)\nzipf.close()"
 68 |         }, 
 69 |         {
 70 |             "execution_count": 7, 
 71 |             "cell_type": "code", 
 72 |             "metadata": {}, 
 73 |             "outputs": [], 
 74 |             "source": "!base64 a2_m1.json.zip > a2_m1.json.zip.base64"
 75 |         }, 
 76 |         {
 77 |             "execution_count": 9, 
 78 |             "cell_type": "code", 
 79 |             "metadata": {}, 
 80 |             "outputs": [
 81 |                 {
 82 |                     "output_type": "stream", 
 83 |                     "name": "stdout", 
 84 |                     "text": "Submission successful, please check on the coursera grader page for the status\n-------------------------\n{\"elements\":[{\"itemId\":\"O8C69\",\"id\":\"f_F-qCtuEei_fRLwaVDk3g~O8C69~gcz34MvqEemwXg5NLafzvA\",\"courseId\":\"f_F-qCtuEei_fRLwaVDk3g\"}],\"paging\":{},\"linked\":{}}\n-------------------------\n"
 85 |                 }
 86 |             ], 
 87 |             "source": "from rklib import submit\nkey = \"1injH2F0EeiLlRJ3eJKoXA\"\npart = \"wNLDt\"\nemail = \"shriganesh.patel@gmail.com\"\nsecret = \"y501QqIPXG8byF7N\"\n\nwith open('a2_m1.json.zip.base64', 'r') as myfile:\n    data=myfile.read()\nsubmit(email, secret, key, part, [part], data)"
 88 |         }, 
 89 |         {
 90 |             "execution_count": null, 
 91 |             "cell_type": "code", 
 92 |             "metadata": {}, 
 93 |             "outputs": [], 
 94 |             "source": ""
 95 |         }
 96 |     ], 
 97 |     "metadata": {
 98 |         "kernelspec": {
 99 |             "display_name": "Python 3.6 with Spark", 
100 |             "name": "python36", 
101 |             "language": "python3"
102 |         }, 
103 |         "language_info": {
104 |             "mimetype": "text/x-python", 
105 |             "nbconvert_exporter": "python", 
106 |             "version": "3.6.8", 
107 |             "name": "python", 
108 |             "file_extension": ".py", 
109 |             "pygments_lexer": "ipython3", 
110 |             "codemirror_mode": {
111 |                 "version": 3, 
112 |                 "name": "ipython"
113 |             }
114 |         }
115 |     }, 
116 |     "nbformat": 4
117 | }


--------------------------------------------------------------------------------
/SparkML GBT Grid Search.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "execution_count": 1, 
  6 |             "cell_type": "code", 
  7 |             "metadata": {}, 
  8 |             "outputs": [
  9 |                 {
 10 |                     "output_type": "stream", 
 11 |                     "name": "stdout", 
 12 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190906073242-0001\nKERNEL_ID = c41fa2d3-7578-4c35-91e0-ec025ee323e5\n"
 13 |                 }
 14 |             ], 
 15 |             "source": "# The code was removed by Watson Studio for sharing."
 16 |         }, 
 17 |         {
 18 |             "execution_count": 2, 
 19 |             "cell_type": "code", 
 20 |             "metadata": {}, 
 21 |             "outputs": [], 
 22 |             "source": "df.createOrReplaceTempView('df')\ndf_two_class = spark.sql(\"select * from df where class in ('Use_telephone','Standup_chair')\")"
 23 |         }, 
 24 |         {
 25 |             "execution_count": 3, 
 26 |             "cell_type": "code", 
 27 |             "metadata": {}, 
 28 |             "outputs": [], 
 29 |             "source": "splits = df_two_class.randomSplit([0.8, 0.2])\ndf_train = splits[0]\ndf_test = splits[1]"
 30 |         }, 
 31 |         {
 32 |             "execution_count": 4, 
 33 |             "cell_type": "code", 
 34 |             "metadata": {}, 
 35 |             "outputs": [], 
 36 |             "source": "from pyspark.ml.feature import StringIndexer, OneHotEncoder\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.feature import Normalizer\n\n\nindexer = StringIndexer(inputCol=\"class\", outputCol=\"label\")\nvectorAssembler = VectorAssembler(inputCols=[\"x\",\"y\",\"z\"],outputCol=\"features\")\nnormalizer = Normalizer(inputCol=\"features\", outputCol=\"features_norm\", p=1.0)"
 37 |         }, 
 38 |         {
 39 |             "execution_count": 5, 
 40 |             "cell_type": "code", 
 41 |             "metadata": {}, 
 42 |             "outputs": [], 
 43 |             "source": "from pyspark.ml.classification import GBTClassifier\ngbt = GBTClassifier(labelCol=\"label\", featuresCol=\"features\", maxIter=10)"
 44 |         }, 
 45 |         {
 46 |             "execution_count": 6, 
 47 |             "cell_type": "code", 
 48 |             "metadata": {}, 
 49 |             "outputs": [], 
 50 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[indexer, vectorAssembler, normalizer,gbt])"
 51 |         }, 
 52 |         {
 53 |             "execution_count": 8, 
 54 |             "cell_type": "code", 
 55 |             "metadata": {}, 
 56 |             "outputs": [
 57 |                 {
 58 |                     "output_type": "stream", 
 59 |                     "name": "stdout", 
 60 |                     "text": "CPU times: user 108 ms, sys: 52 ms, total: 160 ms\nWall time: 25.3 s\n"
 61 |                 }
 62 |             ], 
 63 |             "source": "%%time\nmodel = pipeline.fit(df_train)"
 64 |         }, 
 65 |         {
 66 |             "execution_count": 9, 
 67 |             "cell_type": "code", 
 68 |             "metadata": {}, 
 69 |             "outputs": [], 
 70 |             "source": "prediction = model.transform(df_train)"
 71 |         }, 
 72 |         {
 73 |             "execution_count": 10, 
 74 |             "cell_type": "code", 
 75 |             "metadata": {}, 
 76 |             "outputs": [
 77 |                 {
 78 |                     "execution_count": 10, 
 79 |                     "metadata": {}, 
 80 |                     "data": {
 81 |                         "text/plain": "0.9107966684212155"
 82 |                     }, 
 83 |                     "output_type": "execute_result"
 84 |                 }
 85 |             ], 
 86 |             "source": "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\nbinEval = MulticlassClassificationEvaluator().setMetricName(\"accuracy\") .setPredictionCol(\"prediction\").setLabelCol(\"label\")\nbinEval.evaluate(prediction) "
 87 |         }, 
 88 |         {
 89 |             "execution_count": 11, 
 90 |             "cell_type": "code", 
 91 |             "metadata": {}, 
 92 |             "outputs": [], 
 93 |             "source": "prediction = model.transform(df_test)"
 94 |         }, 
 95 |         {
 96 |             "execution_count": 12, 
 97 |             "cell_type": "code", 
 98 |             "metadata": {}, 
 99 |             "outputs": [
100 |                 {
101 |                     "execution_count": 12, 
102 |                     "metadata": {}, 
103 |                     "data": {
104 |                         "text/plain": "0.9085680047932894"
105 |                     }, 
106 |                     "output_type": "execute_result"
107 |                 }
108 |             ], 
109 |             "source": "binEval.evaluate(prediction) "
110 |         }, 
111 |         {
112 |             "execution_count": 13, 
113 |             "cell_type": "code", 
114 |             "metadata": {}, 
115 |             "outputs": [], 
116 |             "source": "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n\nparamGrid = ParamGridBuilder() \\\n    .addGrid(normalizer.p, [1.0, 2.0, 10.0]) \\\n    .addGrid(gbt.maxBins, [2,4,8,16]) \\\n    .addGrid(gbt.maxDepth, [2,4,8,16]) \\\n    .build()"
117 |         }, 
118 |         {
119 |             "execution_count": 14, 
120 |             "cell_type": "code", 
121 |             "metadata": {}, 
122 |             "outputs": [], 
123 |             "source": "crossval = CrossValidator(estimator=pipeline,\n                          estimatorParamMaps=paramGrid,\n                          evaluator=MulticlassClassificationEvaluator(),\n                          numFolds=4)  \n"
124 |         }, 
125 |         {
126 |             "execution_count": null, 
127 |             "cell_type": "code", 
128 |             "metadata": {}, 
129 |             "outputs": [], 
130 |             "source": "%%timeit\ncvModel = crossval.fit(df_train)"
131 |         }, 
132 |         {
133 |             "execution_count": 33, 
134 |             "cell_type": "code", 
135 |             "metadata": {}, 
136 |             "outputs": [], 
137 |             "source": "prediction = cvModel.transform(df_test)"
138 |         }, 
139 |         {
140 |             "execution_count": 34, 
141 |             "cell_type": "code", 
142 |             "metadata": {}, 
143 |             "outputs": [
144 |                 {
145 |                     "execution_count": 34, 
146 |                     "metadata": {}, 
147 |                     "data": {
148 |                         "text/plain": "0.9121697059918125"
149 |                     }, 
150 |                     "output_type": "execute_result"
151 |                 }
152 |             ], 
153 |             "source": "binEval.evaluate(prediction) "
154 |         }, 
155 |         {
156 |             "execution_count": 43, 
157 |             "cell_type": "code", 
158 |             "metadata": {}, 
159 |             "outputs": [
160 |                 {
161 |                     "execution_count": 43, 
162 |                     "metadata": {}, 
163 |                     "data": {
164 |                         "text/plain": "\"cacheNodeIds: If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. (default: False)\\ncheckpointInterval: set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext (default: 10)\\nfeatureSubsetStrategy: The number of features to consider for splits at each tree node. Supported options: auto, all, onethird, sqrt, log2, (0.0-1.0], [1-n]. (undefined)\\nfeaturesCol: features column name (default: features, current: features)\\nimpurity: Criterion used for information gain calculation (case-insensitive). Supported options: entropy, gini (undefined)\\nlabelCol: label column name (default: label, current: label)\\nlossType: Loss function which GBT tries to minimize (case-insensitive). Supported options: logistic (default: logistic)\\nmaxBins: Max number of bins for discretizing continuous features.  Must be >=2 and >= number of categories for any categorical feature. (default: 32, current: 16)\\nmaxDepth: Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default: 5, current: 16)\\nmaxIter: maximum number of iterations (>= 0) (default: 20, current: 10)\\nmaxMemoryInMB: Maximum memory in MB allocated to histogram aggregation. (default: 256)\\nminInfoGain: Minimum information gain for a split to be considered at a tree node. (default: 0.0)\\nminInstancesPerNode: Minimum number of instances each child must have after split.  If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1. (default: 1)\\npredictionCol: prediction column name (default: prediction)\\nprobabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities (undefined)\\nrawPredictionCol: raw prediction (a.k.a. confidence) column name (undefined)\\nseed: random seed (default: -6370738711343826501)\\nstepSize: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default: 0.1)\\nsubsamplingRate: Fraction of the training data used for learning each decision tree, in range (0, 1]. (default: 1.0)\\nthresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold (undefined)\""
165 |                     }, 
166 |                     "output_type": "execute_result"
167 |                 }
168 |             ], 
169 |             "source": "cvModel.bestModel.stages[-1].explainParams()"
170 |         }, 
171 |         {
172 |             "execution_count": null, 
173 |             "cell_type": "code", 
174 |             "metadata": {}, 
175 |             "outputs": [], 
176 |             "source": ""
177 |         }
178 |     ], 
179 |     "metadata": {
180 |         "kernelspec": {
181 |             "display_name": "Python 3.6 with Spark", 
182 |             "name": "python36", 
183 |             "language": "python3"
184 |         }, 
185 |         "language_info": {
186 |             "mimetype": "text/x-python", 
187 |             "nbconvert_exporter": "python", 
188 |             "version": "3.6.8", 
189 |             "name": "python", 
190 |             "file_extension": ".py", 
191 |             "pygments_lexer": "ipython3", 
192 |             "codemirror_mode": {
193 |                 "version": 3, 
194 |                 "name": "ipython"
195 |             }
196 |         }
197 |     }, 
198 |     "nbformat": 4
199 | }


--------------------------------------------------------------------------------
/SparkML Linear Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "execution_count": 1, 
  6 |             "cell_type": "code", 
  7 |             "metadata": {}, 
  8 |             "outputs": [
  9 |                 {
 10 |                     "output_type": "stream", 
 11 |                     "name": "stdout", 
 12 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190903070647-0000\nKERNEL_ID = 49492b07-a361-4d7a-ade0-ef8355c2a218\n"
 13 |                 }
 14 |             ], 
 15 |             "source": "# The code was removed by Watson Studio for sharing."
 16 |         }, 
 17 |         {
 18 |             "execution_count": 2, 
 19 |             "cell_type": "code", 
 20 |             "metadata": {}, 
 21 |             "outputs": [], 
 22 |             "source": "df.createOrReplaceTempView('df')\ndf_energy = spark.sql(\"\"\"\nselect sqrt(sum(x*x)+sum(y*y)+sum(z*z)) as label, class from df group by class\n\"\"\")      \ndf_energy.createOrReplaceTempView('df_energy')          "
 23 |         }, 
 24 |         {
 25 |             "execution_count": 3, 
 26 |             "cell_type": "code", 
 27 |             "metadata": {}, 
 28 |             "outputs": [
 29 |                 {
 30 |                     "output_type": "stream", 
 31 |                     "name": "stdout", 
 32 |                     "text": "+------------------+--------------+\n|             label|         class|\n+------------------+--------------+\n| 8959.680239829991| Use_telephone|\n| 9737.511232342687| Standup_chair|\n| 12542.96539897962|      Eat_meat|\n|13225.945637269193|     Getup_bed|\n|15003.269043778426|   Drink_glass|\n|14454.885091207056|    Pour_water|\n|10616.408809008817|     Comb_hair|\n|11082.626493751379|  Climb_stairs|\n|10261.338314274606| Sitdown_chair|\n|6783.4063714331605|   Liedown_bed|\n| 7173.493500380411|Descend_stairs|\n| 11785.39634462923|   Brush_teeth|\n| 6071.460120926432|      Eat_soup|\n+------------------+--------------+\n\n"
 33 |                 }
 34 |             ], 
 35 |             "source": "df_energy.show()"
 36 |         }, 
 37 |         {
 38 |             "execution_count": 4, 
 39 |             "cell_type": "code", 
 40 |             "metadata": {}, 
 41 |             "outputs": [], 
 42 |             "source": "df_join = spark.sql('select * from df inner join df_energy on df.class=df_energy.class')"
 43 |         }, 
 44 |         {
 45 |             "execution_count": 5, 
 46 |             "cell_type": "code", 
 47 |             "metadata": {}, 
 48 |             "outputs": [
 49 |                 {
 50 |                     "output_type": "stream", 
 51 |                     "name": "stdout", 
 52 |                     "text": "+---+---+---+--------------------+------------+------------------+------------+\n|  x|  y|  z|              source|       class|             label|       class|\n+---+---+---+--------------------+------------+------------------+------------+\n| 30| 36| 52|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 30| 36| 32|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 33| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 30| 37| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 31| 37| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 29| 38| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 39| 48|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 41| 46|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 34| 39| 44|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 34| 39| 41|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 31| 39| 42|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 32| 42| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 31| 43| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 28| 46| 44|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 26| 48| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n| 22| 41| 40|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|\n+---+---+---+--------------------+------------+------------------+------------+\nonly showing top 20 rows\n\n"
 53 |                 }
 54 |             ], 
 55 |             "source": "df_join.show()"
 56 |         }, 
 57 |         {
 58 |             "execution_count": 6, 
 59 |             "cell_type": "code", 
 60 |             "metadata": {
 61 |                 "pixiedust": {
 62 |                     "displayParams": {}
 63 |                 }
 64 |             }, 
 65 |             "outputs": [], 
 66 |             "source": "from pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.feature import Normalizer\n\n\nvectorAssembler = VectorAssembler(inputCols=[\"x\",\"y\",\"z\"], outputCol=\"features\")\nnormalizer = Normalizer(inputCol=\"features\", outputCol=\"features_norm\", p=1.0)"
 67 |         }, 
 68 |         {
 69 |             "execution_count": 7, 
 70 |             "cell_type": "code", 
 71 |             "metadata": {}, 
 72 |             "outputs": [], 
 73 |             "source": "from pyspark.ml.regression import LinearRegression\n\nlr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)"
 74 |         }, 
 75 |         {
 76 |             "execution_count": 8, 
 77 |             "cell_type": "code", 
 78 |             "metadata": {}, 
 79 |             "outputs": [], 
 80 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[vectorAssembler, normalizer,lr])"
 81 |         }, 
 82 |         {
 83 |             "execution_count": 9, 
 84 |             "cell_type": "code", 
 85 |             "metadata": {}, 
 86 |             "outputs": [], 
 87 |             "source": "model = pipeline.fit(df_join)"
 88 |         }, 
 89 |         {
 90 |             "execution_count": 10, 
 91 |             "cell_type": "code", 
 92 |             "metadata": {}, 
 93 |             "outputs": [], 
 94 |             "source": "prediction = model.transform(df_join)"
 95 |         }, 
 96 |         {
 97 |             "execution_count": 11, 
 98 |             "cell_type": "code", 
 99 |             "metadata": {}, 
100 |             "outputs": [
101 |                 {
102 |                     "output_type": "stream", 
103 |                     "name": "stdout", 
104 |                     "text": "+---+---+---+--------------------+------------+------------------+------------+----------------+--------------------+------------------+\n|  x|  y|  z|              source|       class|             label|       class|        features|       features_norm|        prediction|\n+---+---+---+--------------------+------------+------------------+------------+----------------+--------------------+------------------+\n| 30| 36| 52|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[30.0,36.0,52.0]|[0.25423728813559...|12378.337524478058|\n| 30| 36| 32|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[30.0,36.0,32.0]|[0.30612244897959...|10949.223755865942|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,30.0,36.0]|[0.32653061224489...|11232.049623881609|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,30.0,36.0]|[0.32653061224489...|11232.049623881609|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,30.0,36.0]|[0.32653061224489...|11232.049623881609|\n| 32| 33| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,33.0,36.0]|[0.31683168316831...|11251.923475263653|\n| 32| 30| 36|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,30.0,36.0]|[0.32653061224489...|11232.049623881609|\n| 30| 37| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[30.0,37.0,50.0]|[0.25641025641025...|12242.050764744195|\n| 31| 37| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[31.0,37.0,50.0]|[0.26271186440677...| 12260.42617327286|\n| 29| 38| 50|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[29.0,38.0,50.0]|[0.24786324786324...|12230.299973342877|\n| 32| 39| 48|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,39.0,48.0]|[0.26890756302521...|12149.139439195013|\n| 32| 41| 46|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,41.0,46.0]|[0.26890756302521...|12019.477296588495|\n| 34| 39| 44|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[34.0,39.0,44.0]|[0.29059829059829...| 11900.06750252992|\n| 34| 39| 41|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[34.0,39.0,41.0]|[0.29824561403508...|11685.700437238103|\n| 31| 39| 42|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[31.0,39.0,42.0]|[0.27678571428571...| 11702.02990008271|\n| 32| 42| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[32.0,42.0,43.0]|[0.27350427350427...|11811.734848424026|\n| 31| 43| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[31.0,43.0,43.0]|[0.26495726495726...|11799.984057022708|\n| 28| 46| 44|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[28.0,46.0,44.0]|[0.23728813559322...|11836.187371249358|\n| 26| 48| 43|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[26.0,48.0,43.0]|[0.22222222222222...|11741.230100016117|\n| 22| 41| 40|Accelerometer-201...|Climb_stairs|11082.626493751379|Climb_stairs|[22.0,41.0,40.0]|[0.21359223300970...|11406.989080718198|\n+---+---+---+--------------------+------------+------------------+------------+----------------+--------------------+------------------+\nonly showing top 20 rows\n\n"
105 |                 }
106 |             ], 
107 |             "source": "prediction.show()"
108 |         }, 
109 |         {
110 |             "execution_count": 8, 
111 |             "cell_type": "code", 
112 |             "metadata": {}, 
113 |             "outputs": [
114 |                 {
115 |                     "execution_count": 8, 
116 |                     "metadata": {}, 
117 |                     "data": {
118 |                         "text/plain": "0.0912330479847846"
119 |                     }, 
120 |                     "output_type": "execute_result"
121 |                 }
122 |             ], 
123 |             "source": "model.stages[2].summary.r2\n"
124 |         }, 
125 |         {
126 |             "execution_count": null, 
127 |             "cell_type": "code", 
128 |             "metadata": {}, 
129 |             "outputs": [], 
130 |             "source": ""
131 |         }
132 |     ], 
133 |     "metadata": {
134 |         "kernelspec": {
135 |             "display_name": "Python 3.6 with Spark", 
136 |             "name": "python36", 
137 |             "language": "python3"
138 |         }, 
139 |         "language_info": {
140 |             "mimetype": "text/x-python", 
141 |             "nbconvert_exporter": "python", 
142 |             "version": "3.6.8", 
143 |             "name": "python", 
144 |             "file_extension": ".py", 
145 |             "pygments_lexer": "ipython3", 
146 |             "codemirror_mode": {
147 |                 "version": 3, 
148 |                 "name": "ipython"
149 |             }
150 |         }
151 |     }, 
152 |     "nbformat": 4
153 | }


--------------------------------------------------------------------------------
/AssignmentML2.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "source": "This is the second assignment for the Coursera course \"Advanced Machine Learning and Signal Processing\"\n\n\nJust execute all cells one after the other and you are done - just note that in the last one you have to update your email address (the one you've used for coursera) and obtain a submission token, you get this from the programming assignment directly on coursera.\n\nPlease fill in the sections labelled with \"###YOUR_CODE_GOES_HERE###\"", 
  6 |             "cell_type": "markdown", 
  7 |             "metadata": {}
  8 |         }, 
  9 |         {
 10 |             "source": "!wget https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet", 
 11 |             "cell_type": "code", 
 12 |             "execution_count": 1, 
 13 |             "outputs": [
 14 |                 {
 15 |                     "output_type": "stream", 
 16 |                     "name": "stdout", 
 17 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190903110243-0000\nKERNEL_ID = 9d6fc208-4238-49af-b870-3997ce38a0f8\n--2019-09-03 11:02:47--  https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet\nResolving github.com (github.com)... 140.82.114.4\nConnecting to github.com (github.com)|140.82.114.4|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet [following]\n--2019-09-03 11:02:47--  https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 59032 (58K) [application/octet-stream]\nSaving to: 'a2.parquet'\n\na2.parquet          100%[===================>]  57.65K  --.-KB/s    in 0.005s  \n\n2019-09-03 11:02:48 (11.3 MB/s) - 'a2.parquet' saved [59032/59032]\n\n"
 18 |                 }
 19 |             ], 
 20 |             "metadata": {}
 21 |         }, 
 22 |         {
 23 |             "source": "Now it\u2019s time to have a look at the recorded sensor data. You should see data similar to the one exemplified below\u2026.\n", 
 24 |             "cell_type": "markdown", 
 25 |             "metadata": {}
 26 |         }, 
 27 |         {
 28 |             "source": "df=spark.read.load('a2.parquet')\n\ndf.createOrReplaceTempView(\"df\")\nspark.sql(\"SELECT * from df\").show()", 
 29 |             "cell_type": "code", 
 30 |             "execution_count": 2, 
 31 |             "outputs": [
 32 |                 {
 33 |                     "output_type": "stream", 
 34 |                     "name": "stdout", 
 35 |                     "text": "+-----+-----------+-------------------+-------------------+-------------------+\n|CLASS|   SENSORID|                  X|                  Y|                  Z|\n+-----+-----------+-------------------+-------------------+-------------------+\n|    0|         26| 380.66434005495194| -139.3470983812975|-247.93697521077704|\n|    0|         29| 104.74324299209692| -32.27421440203938|-25.105013725863852|\n|    0| 8589934658| 118.11469236129976| 45.916682927433534| -87.97203782706572|\n|    0|34359738398| 246.55394030642543|-0.6122810693132044|-398.18662513951506|\n|    0|17179869241|-190.32584900181487|  234.7849657520335|-206.34483804019288|\n|    0|25769803830| 178.62396382387422| -47.07529438881511|  84.38310769821979|\n|    0|25769803831|  85.03128805189493|-4.3024316644854546|-1.1841857567516714|\n|    0|34359738411| 26.786262674736566| -46.33193951911338| 20.880756008396055|\n|    0| 8589934592|-16.203752396859194| 51.080957032176954| -96.80526656416971|\n|    0|25769803852|   47.2048142440404|  -78.2950899652916| 181.99604091494786|\n|    0|34359738369| 15.608872398939273| -79.90322809181754|  69.62150711098005|\n|    0|         19|-4.8281721129789315| -67.38050508399905| 221.24876396496404|\n|    0|         54| -98.40725712852762|-19.989364074314732|  -302.695196085276|\n|    0|17179869313| 22.835845394816594|   17.1633660118843| 32.877914832011385|\n|    0|34359738454|  84.20178070080324| -32.81572075916947| -48.63517643958031|\n|    0|          0|  56.54732521345129| -7.980106018032676|  95.05162719436447|\n|    0|17179869201|  -57.6008655247749|  5.135393798773895| 236.99158698947267|\n|    0|17179869308| -65.59264738389012| -48.92660057215126| -61.58970715383383|\n|    0|25769803790|  34.82337351291005|  9.483542084393937|  197.6066372962772|\n|    0|25769803825|  39.80573823439121|-0.7955236412785212| -79.66652640650325|\n+-----+-----------+-------------------+-------------------+-------------------+\nonly showing top 20 rows\n\n"
 36 |                 }
 37 |             ], 
 38 |             "metadata": {}
 39 |         }, 
 40 |         {
 41 |             "source": "Please create a VectorAssembler which consumes columns X, Y and Z and produces a column \u201cfeatures\u201d\n", 
 42 |             "cell_type": "markdown", 
 43 |             "metadata": {}
 44 |         }, 
 45 |         {
 46 |             "source": "from pyspark.ml.feature import VectorAssembler\nvectorAssembler = VectorAssembler(inputCols = [\"X\",\"Y\",\"Z\"], outputCol = \"features\")", 
 47 |             "cell_type": "code", 
 48 |             "execution_count": 3, 
 49 |             "outputs": [], 
 50 |             "metadata": {}
 51 |         }, 
 52 |         {
 53 |             "source": "Please instantiate a classifier from the SparkML package and assign it to the classifier variable. Make sure to either\n1.\tRename the \u201cCLASS\u201d column to \u201clabel\u201d or\n2.\tSpecify the label-column correctly to be \u201cCLASS\u201d\n", 
 54 |             "cell_type": "markdown", 
 55 |             "metadata": {}
 56 |         }, 
 57 |         {
 58 |             "source": "from pyspark.ml.classification import GBTClassifier\nclassifier = GBTClassifier(labelCol = \"CLASS\", featuresCol = \"features\", maxIter = 10)", 
 59 |             "cell_type": "code", 
 60 |             "execution_count": 8, 
 61 |             "outputs": [], 
 62 |             "metadata": {}
 63 |         }, 
 64 |         {
 65 |             "source": "Let\u2019s train and evaluate\u2026\n", 
 66 |             "cell_type": "markdown", 
 67 |             "metadata": {}
 68 |         }, 
 69 |         {
 70 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[vectorAssembler, classifier])", 
 71 |             "cell_type": "code", 
 72 |             "execution_count": 9, 
 73 |             "outputs": [], 
 74 |             "metadata": {}
 75 |         }, 
 76 |         {
 77 |             "source": "model = pipeline.fit(df)", 
 78 |             "cell_type": "code", 
 79 |             "execution_count": 10, 
 80 |             "outputs": [], 
 81 |             "metadata": {}
 82 |         }, 
 83 |         {
 84 |             "source": "prediction = model.transform(df)", 
 85 |             "cell_type": "code", 
 86 |             "execution_count": 11, 
 87 |             "outputs": [], 
 88 |             "metadata": {}
 89 |         }, 
 90 |         {
 91 |             "source": "prediction.show()", 
 92 |             "cell_type": "code", 
 93 |             "execution_count": 12, 
 94 |             "outputs": [
 95 |                 {
 96 |                     "output_type": "stream", 
 97 |                     "name": "stdout", 
 98 |                     "text": "+-----+-----------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+----------+\n|CLASS|   SENSORID|                  X|                  Y|                  Z|            features|       rawPrediction|         probability|prediction|\n+-----+-----------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+----------+\n|    0|         26| 380.66434005495194| -139.3470983812975|-247.93697521077704|[380.664340054951...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0|         29| 104.74324299209692| -32.27421440203938|-25.105013725863852|[104.743242992096...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0| 8589934658| 118.11469236129976| 45.916682927433534| -87.97203782706572|[118.114692361299...|[1.32680169324328...|[0.93423273625928...|       0.0|\n|    0|34359738398| 246.55394030642543|-0.6122810693132044|-398.18662513951506|[246.553940306425...|[1.32711813715030...|[0.93427161142354...|       0.0|\n|    0|17179869241|-190.32584900181487|  234.7849657520335|-206.34483804019288|[-190.32584900181...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|25769803830| 178.62396382387422| -47.07529438881511|  84.38310769821979|[178.623963823874...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0|25769803831|  85.03128805189493|-4.3024316644854546|-1.1841857567516714|[85.0312880518949...|[1.32621410603712...|[0.93416049441777...|       0.0|\n|    0|34359738411| 26.786262674736566| -46.33193951911338| 20.880756008396055|[26.7862626747365...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0| 8589934592|-16.203752396859194| 51.080957032176954| -96.80526656416971|[-16.203752396859...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|25769803852|   47.2048142440404|  -78.2950899652916| 181.99604091494786|[47.2048142440404...|[1.32589955669978...|[0.93412179134479...|       0.0|\n|    0|34359738369| 15.608872398939273| -79.90322809181754|  69.62150711098005|[15.6088723989392...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|         19|-4.8281721129789315| -67.38050508399905| 221.24876396496404|[-4.8281721129789...|[1.32396579485090...|[0.93388339065422...|       0.0|\n|    0|         54| -98.40725712852762|-19.989364074314732|  -302.695196085276|[-98.407257128527...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|17179869313| 22.835845394816594|   17.1633660118843| 32.877914832011385|[22.8358453948165...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|34359738454|  84.20178070080324| -32.81572075916947| -48.63517643958031|[84.2017807008032...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0|          0|  56.54732521345129| -7.980106018032676|  95.05162719436447|[56.5473252134512...|[1.32589766213010...|[0.93412155816733...|       0.0|\n|    0|17179869201|  -57.6008655247749|  5.135393798773895| 236.99158698947267|[-57.600865524774...|[1.33122144395126...|[0.93477377205806...|       0.0|\n|    0|17179869308| -65.59264738389012| -48.92660057215126| -61.58970715383383|[-65.592647383890...|[1.32590267922033...|[0.93412217565278...|       0.0|\n|    0|25769803790|  34.82337351291005|  9.483542084393937|  197.6066372962772|[34.8233735129100...|[1.51228073545265...|[0.95367147883022...|       0.0|\n|    0|25769803825|  39.80573823439121|-0.7955236412785212| -79.66652640650325|[39.8057382343912...|[1.32711813715030...|[0.93427161142354...|       0.0|\n+-----+-----------+-------------------+-------------------+-------------------+--------------------+--------------------+--------------------+----------+\nonly showing top 20 rows\n\n"
 99 |                 }
100 |             ], 
101 |             "metadata": {}
102 |         }, 
103 |         {
104 |             "source": "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\nbinEval = MulticlassClassificationEvaluator().setMetricName(\"accuracy\") .setPredictionCol(\"prediction\").setLabelCol(\"CLASS\")\n    \nbinEval.evaluate(prediction) ", 
105 |             "cell_type": "code", 
106 |             "execution_count": 13, 
107 |             "outputs": [
108 |                 {
109 |                     "output_type": "execute_result", 
110 |                     "metadata": {}, 
111 |                     "data": {
112 |                         "text/plain": "0.9986850756081526"
113 |                     }, 
114 |                     "execution_count": 13
115 |                 }
116 |             ], 
117 |             "metadata": {}
118 |         }, 
119 |         {
120 |             "source": "If you are happy with the result (I\u2019m happy with > 0.55) please submit your solution to the grader by executing the following cells, please don\u2019t forget to obtain an assignment submission token (secret) from the Coursera\u2019s graders web page and paste it to the \u201csecret\u201d variable below, including your email address you\u2019ve used for Coursera. (0.55 means that you are performing better than random guesses)\n", 
121 |             "cell_type": "markdown", 
122 |             "metadata": {}
123 |         }, 
124 |         {
125 |             "source": "!rm -Rf a2_m2.json", 
126 |             "cell_type": "code", 
127 |             "execution_count": 14, 
128 |             "outputs": [], 
129 |             "metadata": {}
130 |         }, 
131 |         {
132 |             "source": "prediction = prediction.repartition(1)\nprediction.write.json('a2_m2.json')", 
133 |             "cell_type": "code", 
134 |             "execution_count": 15, 
135 |             "outputs": [], 
136 |             "metadata": {}
137 |         }, 
138 |         {
139 |             "source": "!rm -f rklib.py\n!wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py", 
140 |             "cell_type": "code", 
141 |             "execution_count": 16, 
142 |             "outputs": [
143 |                 {
144 |                     "output_type": "stream", 
145 |                     "name": "stdout", 
146 |                     "text": "--2019-09-03 11:11:42--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2540 (2.5K) [text/plain]\nSaving to: 'rklib.py'\n\nrklib.py            100%[===================>]   2.48K  --.-KB/s    in 0s      \n\n2019-09-03 11:11:42 (16.7 MB/s) - 'rklib.py' saved [2540/2540]\n\n"
147 |                 }
148 |             ], 
149 |             "metadata": {}
150 |         }, 
151 |         {
152 |             "source": "import zipfile\n\ndef zipdir(path, ziph):\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            ziph.write(os.path.join(root, file))\n\nzipf = zipfile.ZipFile('a2_m2.json.zip', 'w', zipfile.ZIP_DEFLATED)\nzipdir('a2_m2.json', zipf)\nzipf.close()", 
153 |             "cell_type": "code", 
154 |             "execution_count": 17, 
155 |             "outputs": [], 
156 |             "metadata": {}
157 |         }, 
158 |         {
159 |             "source": "!base64 a2_m2.json.zip > a2_m2.json.zip.base64", 
160 |             "cell_type": "code", 
161 |             "execution_count": 18, 
162 |             "outputs": [], 
163 |             "metadata": {}
164 |         }, 
165 |         {
166 |             "source": "from rklib import submit\nkey = \"J3sDL2J8EeiaXhILFWw2-g\"\npart = \"G4P6f\"\nemail = \"shriganesh.patel@gmail.com\"\nsecret = \"zBVh4TKfFfgH0SDp\"\n\nwith open('a2_m2.json.zip.base64', 'r') as myfile:\n    data=myfile.read()\nsubmit(email, secret, key, part, [part], data)", 
167 |             "cell_type": "code", 
168 |             "execution_count": 20, 
169 |             "outputs": [
170 |                 {
171 |                     "output_type": "stream", 
172 |                     "name": "stdout", 
173 |                     "text": "Submission successful, please check on the coursera grader page for the status\n-------------------------\n{\"elements\":[{\"itemId\":\"LTL4F\",\"id\":\"f_F-qCtuEei_fRLwaVDk3g~LTL4F~pYY5K847EemAsw6NyXp6ZA\",\"courseId\":\"f_F-qCtuEei_fRLwaVDk3g\"}],\"paging\":{},\"linked\":{}}\n-------------------------\n"
174 |                 }
175 |             ], 
176 |             "metadata": {}
177 |         }
178 |     ], 
179 |     "nbformat": 4, 
180 |     "metadata": {
181 |         "kernelspec": {
182 |             "display_name": "Python 3.6 with Spark", 
183 |             "name": "python36", 
184 |             "language": "python3"
185 |         }, 
186 |         "language_info": {
187 |             "mimetype": "text/x-python", 
188 |             "nbconvert_exporter": "python", 
189 |             "version": "3.6.8", 
190 |             "name": "python", 
191 |             "pygments_lexer": "ipython3", 
192 |             "file_extension": ".py", 
193 |             "codemirror_mode": {
194 |                 "version": 3, 
195 |                 "name": "ipython"
196 |             }
197 |         }
198 |     }
199 | }


--------------------------------------------------------------------------------
/AssignmentML3.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "source": "This is the third assignment for the Coursera course \"Advanced Machine Learning and Signal Processing\"\n\nJust execute all cells one after the other and you are done - just note that in the last one you must update your email address (the one you've used for coursera) and obtain a submission token, you get this from the programming assignment directly on coursera.\n\nPlease fill in the sections labelled with \"###YOUR_CODE_GOES_HERE###\"\n", 
  6 |             "cell_type": "markdown", 
  7 |             "metadata": {}
  8 |         }, 
  9 |         {
 10 |             "source": "!wget https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet", 
 11 |             "cell_type": "code", 
 12 |             "execution_count": 1, 
 13 |             "outputs": [
 14 |                 {
 15 |                     "output_type": "stream", 
 16 |                     "name": "stdout", 
 17 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190903114518-0000\nKERNEL_ID = e2c70554-29f2-42f3-9ff9-9db5d900ee12\n--2019-09-03 11:45:21--  https://github.com/IBM/coursera/raw/master/coursera_ml/a2.parquet\nResolving github.com (github.com)... 140.82.114.3\nConnecting to github.com (github.com)|140.82.114.3|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet [following]\n--2019-09-03 11:45:21--  https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/a2.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 59032 (58K) [application/octet-stream]\nSaving to: 'a2.parquet'\n\na2.parquet          100%[===================>]  57.65K  --.-KB/s    in 0.003s  \n\n2019-09-03 11:45:21 (17.6 MB/s) - 'a2.parquet' saved [59032/59032]\n\n"
 18 |                 }
 19 |             ], 
 20 |             "metadata": {}
 21 |         }, 
 22 |         {
 23 |             "source": "Now it\u2019s time to have a look at the recorded sensor data. You should see data similar to the one exemplified below\u2026.\n", 
 24 |             "cell_type": "markdown", 
 25 |             "metadata": {}
 26 |         }, 
 27 |         {
 28 |             "source": "df=spark.read.load('a2.parquet')\ndf.createOrReplaceTempView(\"df\")\nspark.sql(\"SELECT * from df\").show()", 
 29 |             "cell_type": "code", 
 30 |             "execution_count": 2, 
 31 |             "outputs": [
 32 |                 {
 33 |                     "output_type": "stream", 
 34 |                     "name": "stdout", 
 35 |                     "text": "+-----+-----------+-------------------+-------------------+-------------------+\n|CLASS|   SENSORID|                  X|                  Y|                  Z|\n+-----+-----------+-------------------+-------------------+-------------------+\n|    0|         26| 380.66434005495194| -139.3470983812975|-247.93697521077704|\n|    0|         29| 104.74324299209692| -32.27421440203938|-25.105013725863852|\n|    0| 8589934658| 118.11469236129976| 45.916682927433534| -87.97203782706572|\n|    0|34359738398| 246.55394030642543|-0.6122810693132044|-398.18662513951506|\n|    0|17179869241|-190.32584900181487|  234.7849657520335|-206.34483804019288|\n|    0|25769803830| 178.62396382387422| -47.07529438881511|  84.38310769821979|\n|    0|25769803831|  85.03128805189493|-4.3024316644854546|-1.1841857567516714|\n|    0|34359738411| 26.786262674736566| -46.33193951911338| 20.880756008396055|\n|    0| 8589934592|-16.203752396859194| 51.080957032176954| -96.80526656416971|\n|    0|25769803852|   47.2048142440404|  -78.2950899652916| 181.99604091494786|\n|    0|34359738369| 15.608872398939273| -79.90322809181754|  69.62150711098005|\n|    0|         19|-4.8281721129789315| -67.38050508399905| 221.24876396496404|\n|    0|         54| -98.40725712852762|-19.989364074314732|  -302.695196085276|\n|    0|17179869313| 22.835845394816594|   17.1633660118843| 32.877914832011385|\n|    0|34359738454|  84.20178070080324| -32.81572075916947| -48.63517643958031|\n|    0|          0|  56.54732521345129| -7.980106018032676|  95.05162719436447|\n|    0|17179869201|  -57.6008655247749|  5.135393798773895| 236.99158698947267|\n|    0|17179869308| -65.59264738389012| -48.92660057215126| -61.58970715383383|\n|    0|25769803790|  34.82337351291005|  9.483542084393937|  197.6066372962772|\n|    0|25769803825|  39.80573823439121|-0.7955236412785212| -79.66652640650325|\n+-----+-----------+-------------------+-------------------+-------------------+\nonly showing top 20 rows\n\n"
 36 |                 }
 37 |             ], 
 38 |             "metadata": {}
 39 |         }, 
 40 |         {
 41 |             "source": "Let\u2019s check if we have balanced classes \u2013 this means that we have roughly the same number of examples for each class we want to predict. This is important for classification but also helpful for clustering", 
 42 |             "cell_type": "markdown", 
 43 |             "metadata": {}
 44 |         }, 
 45 |         {
 46 |             "source": "spark.sql(\"SELECT count(class), class from df group by class\").show()", 
 47 |             "cell_type": "code", 
 48 |             "execution_count": 3, 
 49 |             "outputs": [
 50 |                 {
 51 |                     "output_type": "stream", 
 52 |                     "name": "stdout", 
 53 |                     "text": "+------------+-----+\n|count(class)|class|\n+------------+-----+\n|        1416|    1|\n|        1626|    0|\n+------------+-----+\n\n"
 54 |                 }
 55 |             ], 
 56 |             "metadata": {}
 57 |         }, 
 58 |         {
 59 |             "source": "Let's create a VectorAssembler which consumes columns X, Y and Z and produces a column \u201cfeatures\u201d\n", 
 60 |             "cell_type": "markdown", 
 61 |             "metadata": {}
 62 |         }, 
 63 |         {
 64 |             "source": "from pyspark.ml.feature import VectorAssembler\nvectorAssembler = VectorAssembler(inputCols=[\"X\",\"Y\",\"Z\"],\n                                  outputCol=\"features\")", 
 65 |             "cell_type": "code", 
 66 |             "execution_count": 4, 
 67 |             "outputs": [], 
 68 |             "metadata": {}
 69 |         }, 
 70 |         {
 71 |             "source": "Please insatiate a clustering algorithm from the SparkML package and assign it to the clust variable. Here we don\u2019t need to take care of the \u201cCLASS\u201d column since we are in unsupervised learning mode \u2013 so let\u2019s pretend to not even have the \u201cCLASS\u201d column for now \u2013 but it will become very handy later in assessing the clustering performance. PLEASE NOTE \u2013 IN REAL-WORLD SCENARIOS THERE IS NO CLASS COLUMN \u2013 THEREFORE YOU CAN\u2019T ASSESS CLASSIFICATION PERFORMANCE USING THIS COLUMN \n\n", 
 72 |             "cell_type": "markdown", 
 73 |             "metadata": {}
 74 |         }, 
 75 |         {
 76 |             "source": "from pyspark.ml.clustering import KMeans\n\nclust = KMeans().setK(3).setSeed(1)", 
 77 |             "cell_type": "code", 
 78 |             "execution_count": 6, 
 79 |             "outputs": [], 
 80 |             "metadata": {}
 81 |         }, 
 82 |         {
 83 |             "source": "Let\u2019s train...\n", 
 84 |             "cell_type": "markdown", 
 85 |             "metadata": {}
 86 |         }, 
 87 |         {
 88 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[vectorAssembler, clust])\nmodel = pipeline.fit(df)", 
 89 |             "cell_type": "code", 
 90 |             "execution_count": 7, 
 91 |             "outputs": [], 
 92 |             "metadata": {}
 93 |         }, 
 94 |         {
 95 |             "source": "...and evaluate...", 
 96 |             "cell_type": "markdown", 
 97 |             "metadata": {}
 98 |         }, 
 99 |         {
100 |             "source": "prediction = model.transform(df)\nprediction.show()", 
101 |             "cell_type": "code", 
102 |             "execution_count": 8, 
103 |             "outputs": [
104 |                 {
105 |                     "output_type": "stream", 
106 |                     "name": "stdout", 
107 |                     "text": "+-----+-----------+-------------------+-------------------+-------------------+--------------------+----------+\n|CLASS|   SENSORID|                  X|                  Y|                  Z|            features|prediction|\n+-----+-----------+-------------------+-------------------+-------------------+--------------------+----------+\n|    0|         26| 380.66434005495194| -139.3470983812975|-247.93697521077704|[380.664340054951...|         2|\n|    0|         29| 104.74324299209692| -32.27421440203938|-25.105013725863852|[104.743242992096...|         0|\n|    0| 8589934658| 118.11469236129976| 45.916682927433534| -87.97203782706572|[118.114692361299...|         2|\n|    0|34359738398| 246.55394030642543|-0.6122810693132044|-398.18662513951506|[246.553940306425...|         2|\n|    0|17179869241|-190.32584900181487|  234.7849657520335|-206.34483804019288|[-190.32584900181...|         1|\n|    0|25769803830| 178.62396382387422| -47.07529438881511|  84.38310769821979|[178.623963823874...|         0|\n|    0|25769803831|  85.03128805189493|-4.3024316644854546|-1.1841857567516714|[85.0312880518949...|         0|\n|    0|34359738411| 26.786262674736566| -46.33193951911338| 20.880756008396055|[26.7862626747365...|         0|\n|    0| 8589934592|-16.203752396859194| 51.080957032176954| -96.80526656416971|[-16.203752396859...|         1|\n|    0|25769803852|   47.2048142440404|  -78.2950899652916| 181.99604091494786|[47.2048142440404...|         0|\n|    0|34359738369| 15.608872398939273| -79.90322809181754|  69.62150711098005|[15.6088723989392...|         0|\n|    0|         19|-4.8281721129789315| -67.38050508399905| 221.24876396496404|[-4.8281721129789...|         0|\n|    0|         54| -98.40725712852762|-19.989364074314732|  -302.695196085276|[-98.407257128527...|         1|\n|    0|17179869313| 22.835845394816594|   17.1633660118843| 32.877914832011385|[22.8358453948165...|         0|\n|    0|34359738454|  84.20178070080324| -32.81572075916947| -48.63517643958031|[84.2017807008032...|         2|\n|    0|          0|  56.54732521345129| -7.980106018032676|  95.05162719436447|[56.5473252134512...|         0|\n|    0|17179869201|  -57.6008655247749|  5.135393798773895| 236.99158698947267|[-57.600865524774...|         0|\n|    0|17179869308| -65.59264738389012| -48.92660057215126| -61.58970715383383|[-65.592647383890...|         0|\n|    0|25769803790|  34.82337351291005|  9.483542084393937|  197.6066372962772|[34.8233735129100...|         0|\n|    0|25769803825|  39.80573823439121|-0.7955236412785212| -79.66652640650325|[39.8057382343912...|         2|\n+-----+-----------+-------------------+-------------------+-------------------+--------------------+----------+\nonly showing top 20 rows\n\n"
108 |                 }
109 |             ], 
110 |             "metadata": {}
111 |         }, 
112 |         {
113 |             "source": "prediction.createOrReplaceTempView('prediction')\nspark.sql('''\nselect max(correct)/max(total) as accuracy from (\n\n    select sum(correct) as correct, count(correct) as total from (\n        select case when class != prediction then 1 else 0 end as correct from prediction \n    ) \n    \n    union\n    \n    select sum(correct) as correct, count(correct) as total from (\n        select case when class = prediction then 1 else 0 end as correct from prediction \n    ) \n)\n''').rdd.map(lambda row: row.accuracy).collect()[0]", 
114 |             "cell_type": "code", 
115 |             "execution_count": 9, 
116 |             "outputs": [
117 |                 {
118 |                     "output_type": "execute_result", 
119 |                     "metadata": {}, 
120 |                     "data": {
121 |                         "text/plain": "0.646285338593031"
122 |                     }, 
123 |                     "execution_count": 9
124 |                 }
125 |             ], 
126 |             "metadata": {}
127 |         }, 
128 |         {
129 |             "source": "If you reached at least 55% of accuracy you are fine to submit your predictions to the grader. Otherwise please experiment with parameters setting to your clustering algorithm, use a different algorithm or just re-record your data and try to obtain. In case you are stuck, please use the Coursera Discussion Forum. Please note again \u2013 in a real-world scenario there is no way in doing this \u2013 since there is no class label in your data. Please have a look at this further reading on clustering performance evaluation https://en.wikipedia.org/wiki/Cluster_analysis#Evaluation_and_assessment\n", 
130 |             "cell_type": "markdown", 
131 |             "metadata": {}
132 |         }, 
133 |         {
134 |             "source": "!rm -f rklib.py\n!wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py", 
135 |             "cell_type": "code", 
136 |             "execution_count": 10, 
137 |             "outputs": [
138 |                 {
139 |                     "output_type": "stream", 
140 |                     "name": "stdout", 
141 |                     "text": "--2019-09-03 11:46:27--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2540 (2.5K) [text/plain]\nSaving to: 'rklib.py'\n\nrklib.py            100%[===================>]   2.48K  --.-KB/s    in 0s      \n\n2019-09-03 11:46:27 (37.9 MB/s) - 'rklib.py' saved [2540/2540]\n\n"
142 |                 }
143 |             ], 
144 |             "metadata": {}
145 |         }, 
146 |         {
147 |             "source": "!rm -Rf a2_m3.json", 
148 |             "cell_type": "code", 
149 |             "execution_count": 11, 
150 |             "outputs": [], 
151 |             "metadata": {}
152 |         }, 
153 |         {
154 |             "source": "prediction= prediction.repartition(1)\nprediction.write.json('a2_m3.json')", 
155 |             "cell_type": "code", 
156 |             "execution_count": 12, 
157 |             "outputs": [], 
158 |             "metadata": {}
159 |         }, 
160 |         {
161 |             "source": "import zipfile\n\ndef zipdir(path, ziph):\n    for root, dirs, files in os.walk(path):\n        for file in files:\n            ziph.write(os.path.join(root, file))\n\nzipf = zipfile.ZipFile('a2_m3.json.zip', 'w', zipfile.ZIP_DEFLATED)\nzipdir('a2_m3.json', zipf)\nzipf.close()", 
162 |             "cell_type": "code", 
163 |             "execution_count": 13, 
164 |             "outputs": [], 
165 |             "metadata": {}
166 |         }, 
167 |         {
168 |             "source": "!base64 a2_m3.json.zip > a2_m3.json.zip.base64", 
169 |             "cell_type": "code", 
170 |             "execution_count": 14, 
171 |             "outputs": [], 
172 |             "metadata": {}
173 |         }, 
174 |         {
175 |             "source": "from rklib import submit\nkey = \"pPfm62VXEeiJOBL0dhxPkA\"\npart = \"EOTMs\"\nemail = \"shriganesh.patel@gmail.com\"\nsecret = \"v3Wp3b3ikEmBlNd9\"\n\n\nwith open('a2_m3.json.zip.base64', 'r') as myfile:\n    data=myfile.read()\nsubmit(email, secret, key, part, [part], data)", 
176 |             "cell_type": "code", 
177 |             "execution_count": 15, 
178 |             "outputs": [
179 |                 {
180 |                     "output_type": "stream", 
181 |                     "name": "stdout", 
182 |                     "text": "Submission successful, please check on the coursera grader page for the status\n-------------------------\n{\"elements\":[{\"itemId\":\"Cu6KW\",\"id\":\"f_F-qCtuEei_fRLwaVDk3g~Cu6KW~hrqE7s5AEemDMQoe-5Knvg\",\"courseId\":\"f_F-qCtuEei_fRLwaVDk3g\"}],\"paging\":{},\"linked\":{}}\n-------------------------\n"
183 |                 }
184 |             ], 
185 |             "metadata": {}
186 |         }
187 |     ], 
188 |     "nbformat": 4, 
189 |     "metadata": {
190 |         "kernelspec": {
191 |             "display_name": "Python 3.6 with Spark", 
192 |             "name": "python36", 
193 |             "language": "python3"
194 |         }, 
195 |         "language_info": {
196 |             "mimetype": "text/x-python", 
197 |             "nbconvert_exporter": "python", 
198 |             "version": "3.6.8", 
199 |             "name": "python", 
200 |             "pygments_lexer": "ipython3", 
201 |             "file_extension": ".py", 
202 |             "codemirror_mode": {
203 |                 "version": 3, 
204 |                 "name": "ipython"
205 |             }
206 |         }
207 |     }
208 | }


--------------------------------------------------------------------------------
/AssignmentML4.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "source": "This is the last assignment for the Coursera course \"Advanced Machine Learning and Signal Processing\"\n\nJust execute all cells one after the other and you are done - just note that in the last one you should update your email address (the one you've used for coursera) and obtain a submission token, you get this from the programming assignment directly on coursera.\n\nPlease fill in the sections labelled with \"###YOUR_CODE_GOES_HERE###\"\n\nThe purpose of this assignment is to learn how feature engineering boosts model performance. You will apply Discrete Fourier Transformation on the accelerometer sensor time series and therefore transforming the dataset from the time to the frequency domain. \n\nAfter that, you\u2019ll use a classification algorithm of your choice to create a model and submit the new predictions to the grader. Done.\n\nPlease make sure you run this notebook from an Apache Spark 2.3 notebook.\n\nSo the first thing we need to ensure is that we are on the latest version of SystemML, which is 1.3.0 (as of 20th March'19) Please use the code block below to check if you are already on 1.3.0 or higher. 1.3 contains a necessary fix, that's we are running against the SNAPSHOT\n", 
  6 |             "cell_type": "markdown", 
  7 |             "metadata": {}
  8 |         }, 
  9 |         {
 10 |             "execution_count": 1, 
 11 |             "cell_type": "code", 
 12 |             "metadata": {}, 
 13 |             "outputs": [
 14 |                 {
 15 |                     "output_type": "stream", 
 16 |                     "name": "stdout", 
 17 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190903121131-0001\nKERNEL_ID = 56e53b6b-9cc4-4cc3-9ded-5a71ef9f3f17\n"
 18 |                 }, 
 19 |                 {
 20 |                     "execution_count": 1, 
 21 |                     "metadata": {}, 
 22 |                     "data": {
 23 |                         "text/plain": "'1.3.0-SNAPSHOT'"
 24 |                     }, 
 25 |                     "output_type": "execute_result"
 26 |                 }
 27 |             ], 
 28 |             "source": "from systemml import MLContext\nml = MLContext(spark)\nml.version()"
 29 |         }, 
 30 |         {
 31 |             "source": "\n\nIf you are blow version 1.3.0, or you got the error message \"No module named 'systemml'\"  please execute the next two code blocks and then\n\n# PLEASE RESTART THE KERNEL !!!\n\nOtherwise your changes won't take effect, just double-check every time you run this notebook if you are on SystemML 1.3\n", 
 32 |             "cell_type": "markdown", 
 33 |             "metadata": {}
 34 |         }, 
 35 |         {
 36 |             "execution_count": 2, 
 37 |             "cell_type": "code", 
 38 |             "metadata": {}, 
 39 |             "outputs": [
 40 |                 {
 41 |                     "output_type": "stream", 
 42 |                     "name": "stdout", 
 43 |                     "text": "Collecting https://github.com/IBM/coursera/blob/master/systemml-1.3.0-SNAPSHOT-python.tar.gz?raw=true\n  Using cached https://github.com/IBM/coursera/blob/master/systemml-1.3.0-SNAPSHOT-python.tar.gz?raw=true\nCollecting numpy>=1.8.2 (from systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/75/92/57179ed45307ec6179e344231c47da7f3f3da9e2eee5c8ab506bd279ce4e/numpy-1.17.1-cp36-cp36m-manylinux1_x86_64.whl\nCollecting scipy>=0.15.1 (from systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl\nCollecting pandas (from systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/73/9b/52e228545d14f14bb2a1622e225f38463c8726645165e1cb7dde95bfe6d4/pandas-0.25.1-cp36-cp36m-manylinux1_x86_64.whl\nCollecting scikit-learn (from systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/a0/c5/d2238762d780dde84a20b8c761f563fe882b88c5a5fb03c056547c442a19/scikit_learn-0.21.3-cp36-cp36m-manylinux1_x86_64.whl\nCollecting Pillow>=2.0.0 (from systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/14/41/db6dec65ddbc176a59b89485e8cc136a433ed9c6397b6bfe2cd38412051e/Pillow-6.1.0-cp36-cp36m-manylinux1_x86_64.whl\nCollecting pytz>=2017.2 (from pandas->systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/87/76/46d697698a143e05f77bec5a526bf4e56a0be61d63425b68f4ba553b51f2/pytz-2019.2-py2.py3-none-any.whl\nCollecting python-dateutil>=2.6.1 (from pandas->systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/41/17/c62faccbfbd163c7f57f3844689e3a78bae1f403648a6afb1d0866d87fbb/python_dateutil-2.8.0-py2.py3-none-any.whl\nCollecting joblib>=0.11 (from scikit-learn->systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/cd/c1/50a758e8247561e58cb87305b1e90b171b8c767b15b12a1734001f41d356/joblib-0.13.2-py2.py3-none-any.whl\nCollecting six>=1.5 (from python-dateutil>=2.6.1->pandas->systemml==1.3.0)\n  Using cached https://files.pythonhosted.org/packages/73/fb/00a976f728d0d1fecfe898238ce23f502a721c0ac0ecfedb80e0d88c64e9/six-1.12.0-py2.py3-none-any.whl\nBuilding wheels for collected packages: systemml\n  Building wheel for systemml (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Stored in directory: /home/spark/shared/.cache/pip/wheels/aa/bf/28/4344dd13abd8b9b6cbd4032baf4b851873d2e2288a65631fd2\nSuccessfully built systemml\n\u001b[31mtensorflow 1.13.1 requires tensorboard<1.14.0,>=1.13.0, which is not installed.\u001b[0m\n\u001b[31mibm-cos-sdk-core 2.4.3 has requirement urllib3<1.25,>=1.20, but you'll have urllib3 1.25.3 which is incompatible.\u001b[0m\n\u001b[31mbotocore 1.12.82 has requirement urllib3<1.25,>=1.20, but you'll have urllib3 1.25.3 which is incompatible.\u001b[0m\nInstalling collected packages: numpy, scipy, pytz, six, python-dateutil, pandas, joblib, scikit-learn, Pillow, systemml\nSuccessfully installed Pillow-6.1.0 joblib-0.13.2 numpy-1.17.1 pandas-0.25.1 python-dateutil-2.8.0 pytz-2019.2 scikit-learn-0.21.3 scipy-1.3.1 six-1.12.0 systemml-1.3.0\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/scikit_learn-0.21.3.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/pytz-2019.2.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/six.py already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/systemml already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/systemml-1.3.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/python_dateutil-2.8.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/pandas-0.25.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/joblib-0.13.2.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/pytz already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/joblib already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/__pycache__ already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/pandas already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/scipy-1.3.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/scipy already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/numpy already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/numpy-1.17.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/sklearn already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/dateutil already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/Pillow-6.1.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/six-1.12.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/PIL already exists. Specify --upgrade to force replacement.\u001b[0m\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/bin already exists. Specify --upgrade to force replacement.\u001b[0m\n"
 44 |                 }
 45 |             ], 
 46 |             "source": "!pip install https://github.com/IBM/coursera/blob/master/systemml-1.3.0-SNAPSHOT-python.tar.gz?raw=true"
 47 |         }, 
 48 |         {
 49 |             "source": "\n\nNow we need to create two sym links that the newest version is picket up - this is a workaround and will be removed as soon as SystemML 1.3 will be pre-installed on Watson Studio once officially released.\n", 
 50 |             "cell_type": "markdown", 
 51 |             "metadata": {}
 52 |         }, 
 53 |         {
 54 |             "execution_count": 3, 
 55 |             "cell_type": "code", 
 56 |             "metadata": {}, 
 57 |             "outputs": [], 
 58 |             "source": "!ln -s -f ~/user-libs/python3/systemml/systemml-java/systemml-1.3.0-SNAPSHOT-extra.jar ~/user-libs/spark2/systemml-1.3.0-SNAPSHOT-extra.jar\n!ln -s -f ~/user-libs/python3/systemml/systemml-java/systemml-1.3.0-SNAPSHOT.jar ~/user-libs/spark2/systemml-1.3.0-SNAPSHOT.jar"
 59 |         }, 
 60 |         {
 61 |             "source": "# Please now restart the kernel and start from the beginning to make sure you've installed SystemML 1.3\n\nLet's download the test data since it's so small we don't use COS (IBM Cloud Object Store) here", 
 62 |             "cell_type": "markdown", 
 63 |             "metadata": {}
 64 |         }, 
 65 |         {
 66 |             "execution_count": 4, 
 67 |             "cell_type": "code", 
 68 |             "metadata": {}, 
 69 |             "outputs": [
 70 |                 {
 71 |                     "output_type": "stream", 
 72 |                     "name": "stdout", 
 73 |                     "text": "--2019-09-03 12:12:02--  https://github.com/IBM/coursera/blob/master/coursera_ml/shake.parquet?raw=true\nResolving github.com (github.com)... 140.82.113.3\nConnecting to github.com (github.com)|140.82.113.3|:443... connected.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://github.com/IBM/coursera/raw/master/coursera_ml/shake.parquet [following]\n--2019-09-03 12:12:08--  https://github.com/IBM/coursera/raw/master/coursera_ml/shake.parquet\nReusing existing connection to github.com:443.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/shake.parquet [following]\n--2019-09-03 12:12:08--  https://raw.githubusercontent.com/IBM/coursera/master/coursera_ml/shake.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 74727 (73K) [application/octet-stream]\nSaving to: 'shake.parquet?raw=true'\n\nshake.parquet?raw=t 100%[===================>]  72.98K  --.-KB/s    in 0.003s  \n\n2019-09-03 12:12:08 (24.8 MB/s) - 'shake.parquet?raw=true' saved [74727/74727]\n\n"
 74 |                 }
 75 |             ], 
 76 |             "source": "!wget https://github.com/IBM/coursera/blob/master/coursera_ml/shake.parquet?raw=true\n!mv shake.parquet?raw=true shake.parquet"
 77 |         }, 
 78 |         {
 79 |             "source": "Now it\u2019s time to read the sensor data and create a temporary query table.", 
 80 |             "cell_type": "markdown", 
 81 |             "metadata": {}
 82 |         }, 
 83 |         {
 84 |             "execution_count": 5, 
 85 |             "cell_type": "code", 
 86 |             "metadata": {}, 
 87 |             "outputs": [], 
 88 |             "source": "df=spark.read.parquet('shake.parquet')"
 89 |         }, 
 90 |         {
 91 |             "execution_count": 6, 
 92 |             "cell_type": "code", 
 93 |             "metadata": {}, 
 94 |             "outputs": [
 95 |                 {
 96 |                     "output_type": "stream", 
 97 |                     "name": "stdout", 
 98 |                     "text": "+-----+---------+-----+-----+-----+\n|CLASS| SENSORID|    X|    Y|    Z|\n+-----+---------+-----+-----+-----+\n|    2| qqqqqqqq| 0.12| 0.12| 0.12|\n|    2|aUniqueID| 0.03| 0.03| 0.03|\n|    2| qqqqqqqq|-3.84|-3.84|-3.84|\n|    2| 12345678| -0.1| -0.1| -0.1|\n|    2| 12345678|-0.15|-0.15|-0.15|\n|    2| 12345678| 0.47| 0.47| 0.47|\n|    2| 12345678|-0.06|-0.06|-0.06|\n|    2| 12345678|-0.09|-0.09|-0.09|\n|    2| 12345678| 0.21| 0.21| 0.21|\n|    2| 12345678|-0.08|-0.08|-0.08|\n|    2| 12345678| 0.44| 0.44| 0.44|\n|    2|    gholi| 0.76| 0.76| 0.76|\n|    2|    gholi| 1.62| 1.62| 1.62|\n|    2|    gholi| 5.81| 5.81| 5.81|\n|    2| bcbcbcbc| 0.58| 0.58| 0.58|\n|    2| bcbcbcbc|-8.24|-8.24|-8.24|\n|    2| bcbcbcbc|-0.45|-0.45|-0.45|\n|    2| bcbcbcbc| 1.03| 1.03| 1.03|\n|    2|aUniqueID|-0.05|-0.05|-0.05|\n|    2| qqqqqqqq|-0.44|-0.44|-0.44|\n+-----+---------+-----+-----+-----+\nonly showing top 20 rows\n\n"
 99 |                 }
100 |             ], 
101 |             "source": "df.show()"
102 |         }, 
103 |         {
104 |             "execution_count": 7, 
105 |             "cell_type": "code", 
106 |             "metadata": {}, 
107 |             "outputs": [
108 |                 {
109 |                     "output_type": "stream", 
110 |                     "name": "stdout", 
111 |                     "text": "Collecting pixiedust\nCollecting lxml (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/ec/be/5ab8abdd8663c0386ec2dd595a5bc0e23330a0549b8a91e32f38c20845b6/lxml-4.4.1-cp36-cp36m-manylinux1_x86_64.whl\nCollecting geojson (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/e4/8d/9e28e9af95739e6d2d2f8d4bef0b3432da40b7c3588fbad4298c1be09e48/geojson-2.5.0-py2.py3-none-any.whl\nCollecting mpld3 (from pixiedust)\nCollecting astunparse (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/2e/37/5dd0dd89b87bb5f0f32a7e775458412c52d78f230ab8d0c65df6aabc4479/astunparse-1.6.2-py2.py3-none-any.whl\nCollecting markdown (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/c0/4e/fd492e91abdc2d2fcb70ef453064d980688762079397f779758e055f6575/Markdown-3.1.1-py2.py3-none-any.whl\nCollecting colour (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/74/46/e81907704ab203206769dee1385dc77e1407576ff8f50a0681d0a6b541be/colour-0.1.5-py2.py3-none-any.whl\nCollecting requests (from pixiedust)\n  Using cached https://files.pythonhosted.org/packages/51/bd/23c926cd341ea6b7dd0b2a00aba99ae0f828be89d72b2190f27c11d4b7fb/requests-2.22.0-py2.py3-none-any.whl\nCollecting six<2.0,>=1.6.1 (from astunparse->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/73/fb/00a976f728d0d1fecfe898238ce23f502a721c0ac0ecfedb80e0d88c64e9/six-1.12.0-py2.py3-none-any.whl\nCollecting wheel<1.0,>=0.23.0 (from astunparse->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/00/83/b4a77d044e78ad1a45610eb88f745be2fd2c6d658f9798a15e384b7d57c9/wheel-0.33.6-py2.py3-none-any.whl\nCollecting setuptools>=36 (from markdown->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/b2/86/095d2f7829badc207c893dd4ac767e871f6cd547145df797ea26baea4e2e/setuptools-41.2.0-py2.py3-none-any.whl\nCollecting certifi>=2017.4.17 (from requests->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/69/1b/b853c7a9d4f6a6d00749e94eb6f3a041e342a885b87340b79c1ef73e3a78/certifi-2019.6.16-py2.py3-none-any.whl\nCollecting idna<2.9,>=2.5 (from requests->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/14/2c/cd551d81dbe15200be1cf41cd03869a46fe7226e7450af7a6545bfc474c9/idna-2.8-py2.py3-none-any.whl\nCollecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 (from requests->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/e6/60/247f23a7121ae632d62811ba7f273d0e58972d75e58a94d329d51550a47d/urllib3-1.25.3-py2.py3-none-any.whl\nCollecting chardet<3.1.0,>=3.0.2 (from requests->pixiedust)\n  Using cached https://files.pythonhosted.org/packages/bc/a9/01ffebfb562e4274b6487b4bb1ddec7ca55ec7510b22e4c51f14098443b8/chardet-3.0.4-py2.py3-none-any.whl\n\u001b[31mtensorflow 1.13.1 requires tensorboard<1.14.0,>=1.13.0, which is not installed.\u001b[0m\n\u001b[31mspyder 3.3.3 requires pyqt5<=5.12; python_version >= \"3\", which is not installed.\u001b[0m\n\u001b[31mibm-cos-sdk-core 2.4.3 has requirement urllib3<1.25,>=1.20, but you'll have urllib3 1.25.3 which is incompatible.\u001b[0m\n\u001b[31mbotocore 1.12.82 has requirement urllib3<1.25,>=1.20, but you'll have urllib3 1.25.3 which is incompatible.\u001b[0m\nInstalling collected packages: lxml, geojson, mpld3, six, wheel, astunparse, setuptools, markdown, colour, certifi, idna, urllib3, chardet, requests, pixiedust\nSuccessfully installed astunparse-1.6.2 certifi-2019.6.16 chardet-3.0.4 colour-0.1.5 geojson-2.5.0 idna-2.8 lxml-4.4.1 markdown-3.1.1 mpld3-0.3 pixiedust-1.1.17 requests-2.22.0 setuptools-41.2.0 six-1.12.0 urllib3-1.25.3 wheel-0.33.6\n\u001b[33mTarget directory /home/spark/shared/user-libs/python3/colour.py already exists. 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112 |                 }
113 |             ], 
114 |             "source": "!pip install pixiedust"
115 |         }, 
116 |         {
117 |             "execution_count": null, 
118 |             "cell_type": "code", 
119 |             "metadata": {
120 |                 "pixiedust": {
121 |                     "displayParams": {
122 |                         "handlerId": "tableView"
123 |                     }
124 |                 }
125 |             }, 
126 |             "outputs": [], 
127 |             "source": "import pixiedust\ndisplay(df)"
128 |         }, 
129 |         {
130 |             "execution_count": 9, 
131 |             "cell_type": "code", 
132 |             "metadata": {}, 
133 |             "outputs": [], 
134 |             "source": "df.createOrReplaceTempView(\"df\")"
135 |         }, 
136 |         {
137 |             "source": "We\u2019ll use Apache SystemML to implement Discrete Fourier Transformation. This way all computation continues to happen on the Apache Spark cluster for advanced scalability and performance.", 
138 |             "cell_type": "markdown", 
139 |             "metadata": {}
140 |         }, 
141 |         {
142 |             "execution_count": 10, 
143 |             "cell_type": "code", 
144 |             "metadata": {}, 
145 |             "outputs": [], 
146 |             "source": "from systemml import MLContext, dml\nml = MLContext(spark)"
147 |         }, 
148 |         {
149 |             "source": "As you\u2019ve learned from the lecture, implementing Discrete Fourier Transformation in a linear algebra programming language is simple. Apache SystemML DML is such a language and as you can see the implementation is straightforward and doesn\u2019t differ too much from the mathematical definition (Just note that the sum operator has been swapped with a vector dot product using the %*% syntax borrowed from R\n):\n\n<img style=\"float: left;\" src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/1af0a78dc50bbf118ab6bd4c4dcc3c4ff8502223\">\n\n", 
150 |             "cell_type": "markdown", 
151 |             "metadata": {}
152 |         }, 
153 |         {
154 |             "execution_count": 11, 
155 |             "cell_type": "code", 
156 |             "metadata": {}, 
157 |             "outputs": [], 
158 |             "source": "dml_script = '''\nPI = 3.141592654\nN = nrow(signal)\n\nn = seq(0, N-1, 1)\nk = seq(0, N-1, 1)\n\nM = (n %*% t(k))*(2*PI/N)\n\nXa = cos(M) %*% signal\nXb = sin(M) %*% signal\n\nDFT = cbind(Xa, Xb)\n'''"
159 |         }, 
160 |         {
161 |             "source": "Now it\u2019s time to create a function which takes a single row Apache Spark data frame as argument (the one containing the accelerometer measurement time series for one axis) and returns the Fourier transformation of it. In addition, we are adding an index column for later joining all axis together and renaming the columns to appropriate names. The result of this function is an Apache Spark DataFrame containing the Fourier Transformation of its input in two columns. \n", 
162 |             "cell_type": "markdown", 
163 |             "metadata": {}
164 |         }, 
165 |         {
166 |             "execution_count": 12, 
167 |             "cell_type": "code", 
168 |             "metadata": {}, 
169 |             "outputs": [], 
170 |             "source": "from pyspark.sql.functions import monotonically_increasing_id\n\ndef dft_systemml(signal,name):\n    prog = dml(dml_script).input('signal', signal).output('DFT')\n    \n    return (\n\n    #execute the script inside the SystemML engine running on top of Apache Spark\n    ml.execute(prog) \n     \n         #read result from SystemML execution back as SystemML Matrix\n        .get('DFT') \n     \n         #convert SystemML Matrix to ApacheSpark DataFrame \n        .toDF() \n     \n         #rename default column names\n        .selectExpr('C1 as %sa' % (name), 'C2 as %sb' % (name)) \n     \n         #add unique ID per row for later joining\n        .withColumn(\"id\", monotonically_increasing_id())\n    )\n        \n\n\n"
171 |         }, 
172 |         {
173 |             "source": "Now it\u2019s time to create DataFrames containing for each accelerometer sensor axis and one for each class. This means you\u2019ll get 6 DataFrames. Please implement this using the relational API of DataFrames or SparkSQL. Please use class 1 and 2 and not 0 and 1.\n", 
174 |             "cell_type": "markdown", 
175 |             "metadata": {}
176 |         }, 
177 |         {
178 |             "execution_count": 13, 
179 |             "cell_type": "code", 
180 |             "metadata": {}, 
181 |             "outputs": [], 
182 |             "source": "x0 = spark.sql(\"SELECT X from df where class = 0\")\ny0 = spark.sql(\"SELECT Y from df where class = 0\")\nz0 = spark.sql(\"SELECT Z from df where class = 0\")\nx1 = spark.sql(\"SELECT X from df where class = 1\")\ny1 = spark.sql(\"SELECT Y from df where class = 1\")\nz1 = spark.sql(\"SELECT Z from df where class = 1\")"
183 |         }, 
184 |         {
185 |             "source": "Since we\u2019ve created this cool DFT function before, we can just call it for each of the 6 DataFrames now. And since the result of this function call is a DataFrame again we can use the pyspark best practice in simply calling methods on it sequentially. So what we are doing is the following:\n\n- Calling DFT for each class and accelerometer sensor axis.\n- Joining them together on the ID column. \n- Re-adding a column containing the class index.\n- Stacking both Dataframes for each classes together\n\n", 
186 |             "cell_type": "markdown", 
187 |             "metadata": {}
188 |         }, 
189 |         {
190 |             "execution_count": 14, 
191 |             "cell_type": "code", 
192 |             "metadata": {}, 
193 |             "outputs": [
194 |                 {
195 |                     "output_type": "stream", 
196 |                     "name": "stdout", 
197 |                     "text": "SystemML Statistics:\nTotal execution time:\t\t0.012 sec.\nNumber of executed Spark inst:\t0.\n\n\nSystemML Statistics:\nTotal execution time:\t\t0.000 sec.\nNumber of executed Spark inst:\t0.\n\n\nSystemML Statistics:\nTotal execution time:\t\t0.000 sec.\nNumber of executed Spark inst:\t0.\n\n\nSystemML Statistics:\nTotal execution time:\t\t0.566 sec.\nNumber of executed Spark inst:\t0.\n\n\nSystemML Statistics:\nTotal execution time:\t\t0.417 sec.\nNumber of executed Spark inst:\t0.\n\n\nSystemML Statistics:\nTotal execution time:\t\t0.301 sec.\nNumber of executed Spark inst:\t0.\n\n\n+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+\n|         id|                  xa|                  xb|                  ya|                  yb|                  za|                  zb|class|\n+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+\n| 8589934592|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|          0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n| 8589934593|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|17179869186|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|          1|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|17179869185|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|17179869187|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n| 8589934594|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n| 8589934595|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|25769803776|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|          2|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|25769803777|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|17179869184|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|25769803778|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|\n|         26| 0.05842761397588824|-0.01175613530955...| 0.05842761397588824|-0.01175613530955...| 0.05842761397588824|-0.01175613530955...|    1|\n|17179869191|-0.10895275079055323|-0.04017484023996344|-0.10895275079055323|-0.04017484023996344|-0.10895275079055323|-0.04017484023996344|    1|\n| 8589934592| 0.02589077121123453|0.024145789283483897| 0.02589077121123453|0.024145789283483897| 0.02589077121123453|0.024145789283483897|    1|\n|         19| 0.07600499297235766|-0.04165706301892977| 0.07600499297235766|-0.04165706301892977| 0.07600499297235766|-0.04165706301892977|    1|\n|          0|-0.05120371300320002|0.015844251463831505|-0.05120371300320002|0.015844251463831505|-0.05120371300320002|0.015844251463831505|    1|\n|17179869201| 0.03912775214058598|-0.09049016952668232| 0.03912775214058598|-0.09049016952668232| 0.03912775214058598|-0.09049016952668232|    1|\n+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+\nonly showing top 20 rows\n\n"
198 |                 }
199 |             ], 
200 |             "source": "from pyspark.sql.functions import lit\n\ndf_class_0 = dft_systemml(x0,'x') \\\n    .join(dft_systemml(y0,'y'), on=['id'], how='inner') \\\n    .join(dft_systemml(z0,'z'), on=['id'], how='inner') \\\n    .withColumn('class', lit(0))\n    \ndf_class_1 = dft_systemml(x1,'x') \\\n    .join(dft_systemml(y1,'y'), on=['id'], how='inner') \\\n    .join(dft_systemml(z1,'z'), on=['id'], how='inner') \\\n    .withColumn('class', lit(1))\n\ndf_dft = df_class_0.union(df_class_1)\n\ndf_dft.show()"
201 |         }, 
202 |         {
203 |             "source": "Please create a VectorAssembler which consumes the newly created DFT columns and produces a column \u201cfeatures\u201d\n", 
204 |             "cell_type": "markdown", 
205 |             "metadata": {}
206 |         }, 
207 |         {
208 |             "execution_count": 15, 
209 |             "cell_type": "code", 
210 |             "metadata": {}, 
211 |             "outputs": [], 
212 |             "source": "from pyspark.ml.feature import VectorAssembler"
213 |         }, 
214 |         {
215 |             "execution_count": 17, 
216 |             "cell_type": "code", 
217 |             "metadata": {}, 
218 |             "outputs": [], 
219 |             "source": "vectorAssembler = VectorAssembler(\n    inputCols=[\"xa\", \"xb\", \"ya\", \"yb\", \"za\", \"zb\"],\n    outputCol=\"features\")"
220 |         }, 
221 |         {
222 |             "source": "Please insatiate a classifier from the SparkML package and assign it to the classifier variable. Make sure to set the \u201cclass\u201d column as target.\n", 
223 |             "cell_type": "markdown", 
224 |             "metadata": {}
225 |         }, 
226 |         {
227 |             "execution_count": 19, 
228 |             "cell_type": "code", 
229 |             "metadata": {}, 
230 |             "outputs": [], 
231 |             "source": "from pyspark.ml.classification import RandomForestClassifier\nclassifier = RandomForestClassifier(labelCol=\"class\", featuresCol=\"features\", numTrees=10)"
232 |         }, 
233 |         {
234 |             "source": "Let\u2019s train and evaluate\u2026\n", 
235 |             "cell_type": "markdown", 
236 |             "metadata": {}
237 |         }, 
238 |         {
239 |             "execution_count": 20, 
240 |             "cell_type": "code", 
241 |             "metadata": {}, 
242 |             "outputs": [], 
243 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[vectorAssembler, classifier])"
244 |         }, 
245 |         {
246 |             "execution_count": 21, 
247 |             "cell_type": "code", 
248 |             "metadata": {}, 
249 |             "outputs": [], 
250 |             "source": "model = pipeline.fit(df_dft)"
251 |         }, 
252 |         {
253 |             "execution_count": 22, 
254 |             "cell_type": "code", 
255 |             "metadata": {}, 
256 |             "outputs": [], 
257 |             "source": "prediction = model.transform(df_dft)"
258 |         }, 
259 |         {
260 |             "execution_count": 23, 
261 |             "cell_type": "code", 
262 |             "metadata": {}, 
263 |             "outputs": [
264 |                 {
265 |                     "output_type": "stream", 
266 |                     "name": "stdout", 
267 |                     "text": "+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+--------------------+-------------+-----------+----------+\n|         id|                  xa|                  xb|                  ya|                  yb|                  za|                  zb|class|            features|rawPrediction|probability|prediction|\n+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+--------------------+-------------+-----------+----------+\n| 8589934592|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|          0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n| 8589934593|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|17179869186|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|          1|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|17179869185|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|17179869187|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n| 8589934594|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n| 8589934595|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|25769803776|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|          2|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|25769803777|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|17179869184|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|25769803778|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|                 0.0|    0|           (6,[],[])|   [10.0,0.0]|  [1.0,0.0]|       0.0|\n|         26| 0.05842761397588824|-0.01175613530955...| 0.05842761397588824|-0.01175613530955...| 0.05842761397588824|-0.01175613530955...|    1|[0.05842761397588...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n|17179869191|-0.10895275079055323|-0.04017484023996344|-0.10895275079055323|-0.04017484023996344|-0.10895275079055323|-0.04017484023996344|    1|[-0.1089527507905...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n| 8589934592| 0.02589077121123453|0.024145789283483897| 0.02589077121123453|0.024145789283483897| 0.02589077121123453|0.024145789283483897|    1|[0.02589077121123...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n|         19| 0.07600499297235766|-0.04165706301892977| 0.07600499297235766|-0.04165706301892977| 0.07600499297235766|-0.04165706301892977|    1|[0.07600499297235...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n|          0|-0.05120371300320002|0.015844251463831505|-0.05120371300320002|0.015844251463831505|-0.05120371300320002|0.015844251463831505|    1|[-0.0512037130032...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n|17179869201| 0.03912775214058598|-0.09049016952668232| 0.03912775214058598|-0.09049016952668232| 0.03912775214058598|-0.09049016952668232|    1|[0.03912775214058...|   [0.0,10.0]|  [0.0,1.0]|       1.0|\n+-----------+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+-----+--------------------+-------------+-----------+----------+\nonly showing top 20 rows\n\n"
268 |                 }
269 |             ], 
270 |             "source": "prediction.show()"
271 |         }, 
272 |         {
273 |             "execution_count": 24, 
274 |             "cell_type": "code", 
275 |             "metadata": {}, 
276 |             "outputs": [
277 |                 {
278 |                     "execution_count": 24, 
279 |                     "metadata": {}, 
280 |                     "data": {
281 |                         "text/plain": "1.0"
282 |                     }, 
283 |                     "output_type": "execute_result"
284 |                 }
285 |             ], 
286 |             "source": "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\nbinEval = MulticlassClassificationEvaluator().setMetricName(\"accuracy\") .setPredictionCol(\"prediction\").setLabelCol(\"class\")  \nbinEval.evaluate(prediction) "
287 |         }, 
288 |         {
289 |             "source": "If you are happy with the result (I\u2019m happy with > 0.8) please submit your solution to the grader by executing the following cells, please don\u2019t forget to obtain an assignment submission token (secret) from the Courera\u2019s graders web page and paste it to the \u201csecret\u201d variable below, including your email address you\u2019ve used for Coursera. \n", 
290 |             "cell_type": "markdown", 
291 |             "metadata": {}
292 |         }, 
293 |         {
294 |             "execution_count": 25, 
295 |             "cell_type": "code", 
296 |             "metadata": {}, 
297 |             "outputs": [], 
298 |             "source": "!rm -Rf a2_m4.json"
299 |         }, 
300 |         {
301 |             "execution_count": 26, 
302 |             "cell_type": "code", 
303 |             "metadata": {}, 
304 |             "outputs": [], 
305 |             "source": "prediction = prediction.repartition(1)\nprediction.write.json('a2_m4.json')"
306 |         }, 
307 |         {
308 |             "execution_count": 27, 
309 |             "cell_type": "code", 
310 |             "metadata": {}, 
311 |             "outputs": [
312 |                 {
313 |                     "output_type": "stream", 
314 |                     "name": "stdout", 
315 |                     "text": "--2019-09-03 12:14:10--  http://wget/\nResolving wget (wget)... failed: Name or service not known.\nwget: unable to resolve host address 'wget'\n--2019-09-03 12:14:10--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2540 (2.5K) [text/plain]\nSaving to: 'rklib.py'\n\nrklib.py            100%[===================>]   2.48K  --.-KB/s    in 0s      \n\n2019-09-03 12:14:10 (54.6 MB/s) - 'rklib.py' saved [2540/2540]\n\nFINISHED --2019-09-03 12:14:10--\nTotal wall clock time: 0.6s\nDownloaded: 1 files, 2.5K in 0s (54.6 MB/s)\n"
316 |                 }
317 |             ], 
318 |             "source": "!rm -f rklib.py\n!wget wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py"
319 |         }, 
320 |         {
321 |             "execution_count": 28, 
322 |             "cell_type": "code", 
323 |             "metadata": {}, 
324 |             "outputs": [], 
325 |             "source": "from rklib import zipit\nzipit('a2_m4.json.zip','a2_m4.json')"
326 |         }, 
327 |         {
328 |             "execution_count": 29, 
329 |             "cell_type": "code", 
330 |             "metadata": {}, 
331 |             "outputs": [], 
332 |             "source": "!base64 a2_m4.json.zip > a2_m4.json.zip.base64"
333 |         }, 
334 |         {
335 |             "execution_count": 30, 
336 |             "cell_type": "code", 
337 |             "metadata": {}, 
338 |             "outputs": [
339 |                 {
340 |                     "output_type": "stream", 
341 |                     "name": "stdout", 
342 |                     "text": "Submission successful, please check on the coursera grader page for the status\n-------------------------\n{\"elements\":[{\"itemId\":\"B8wXV\",\"id\":\"f_F-qCtuEei_fRLwaVDk3g~B8wXV~VnH-vc5EEem5hRKzMl4oag\",\"courseId\":\"f_F-qCtuEei_fRLwaVDk3g\"}],\"paging\":{},\"linked\":{}}\n-------------------------\n"
343 |                 }
344 |             ], 
345 |             "source": "from rklib import submit\nkey = \"-fBiYHYDEeiR4QqiFhAvkA\"\npart = \"IjtJk\"\nemail = \"shriganesh.patel@gmail.com\"\nsubmission_token = \"20qskVRSuQktqcMv\"\n\nwith open('a2_m4.json.zip.base64', 'r') as myfile:\n    data=myfile.read()\nsubmit(email, submission_token, key, part, [part], data)"
346 |         }
347 |     ], 
348 |     "metadata": {
349 |         "kernelspec": {
350 |             "display_name": "Python 3.6 with Spark", 
351 |             "name": "python36", 
352 |             "language": "python3"
353 |         }, 
354 |         "language_info": {
355 |             "mimetype": "text/x-python", 
356 |             "nbconvert_exporter": "python", 
357 |             "version": "3.6.8", 
358 |             "name": "python", 
359 |             "file_extension": ".py", 
360 |             "pygments_lexer": "ipython3", 
361 |             "codemirror_mode": {
362 |                 "version": 3, 
363 |                 "name": "ipython"
364 |             }
365 |         }
366 |     }, 
367 |     "nbformat": 4
368 | }


--------------------------------------------------------------------------------
/ETL Exercise .ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |     "nbformat_minor": 1, 
 3 |     "cells": [
 4 |         {
 5 |             "execution_count": 1, 
 6 |             "cell_type": "code", 
 7 |             "metadata": {}, 
 8 |             "outputs": [
 9 |                 {
10 |                     "output_type": "stream", 
11 |                     "name": "stdout", 
12 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190831124229-0000\nKERNEL_ID = 09289198-fec6-4e04-a84b-c48131496234\nCloning into 'HMP_Dataset'...\nremote: Enumerating objects: 865, done.\u001b[K\nremote: Total 865 (delta 0), reused 0 (delta 0), pack-reused 865\u001b[K\nReceiving objects: 100% (865/865), 1010.96 KiB | 4.91 MiB/s, done.\nChecking out files: 100% (848/848), done.\n"
13 |                 }
14 |             ], 
15 |             "source": "!rm -Rf HMP_Dataset\n!git clone https://github.com/wchill/HMP_Dataset"
16 |         }, 
17 |         {
18 |             "execution_count": 2, 
19 |             "cell_type": "code", 
20 |             "metadata": {}, 
21 |             "outputs": [
22 |                 {
23 |                     "output_type": "stream", 
24 |                     "name": "stdout", 
25 |                     "text": "Accelerometer-2011-04-11-13-28-18-brush_teeth-f1.txt\r\nAccelerometer-2011-04-11-13-29-54-brush_teeth-f1.txt\r\nAccelerometer-2011-05-30-08-35-11-brush_teeth-f1.txt\r\nAccelerometer-2011-05-30-09-36-50-brush_teeth-f1.txt\r\nAccelerometer-2011-05-30-10-34-16-brush_teeth-m1.txt\r\nAccelerometer-2011-05-30-21-10-57-brush_teeth-f1.txt\r\nAccelerometer-2011-05-30-21-55-04-brush_teeth-m2.txt\r\nAccelerometer-2011-05-31-15-16-47-brush_teeth-f1.txt\r\nAccelerometer-2011-06-02-10-42-22-brush_teeth-f1.txt\r\nAccelerometer-2011-06-02-10-45-50-brush_teeth-f1.txt\r\nAccelerometer-2011-06-06-10-45-27-brush_teeth-f1.txt\r\nAccelerometer-2011-06-06-10-48-05-brush_teeth-f1.txt\r\n"
26 |                 }
27 |             ], 
28 |             "source": "!ls HMP_Dataset/Brush_teeth"
29 |         }, 
30 |         {
31 |             "execution_count": 3, 
32 |             "cell_type": "code", 
33 |             "metadata": {}, 
34 |             "outputs": [], 
35 |             "source": "from pyspark.sql.types import StructType, StructField, IntegerType\n\nschema = StructType([\n    StructField(\"x\", IntegerType(), True),\n    StructField(\"y\", IntegerType(), True),\n    StructField(\"z\", IntegerType(), True)])"
36 |         }, 
37 |         {
38 |             "execution_count": null, 
39 |             "cell_type": "code", 
40 |             "metadata": {}, 
41 |             "outputs": [
42 |                 {
43 |                     "output_type": "stream", 
44 |                     "name": "stdout", 
45 |                     "text": "Accelerometer-2011-04-11-13-28-18-brush_teeth-f1.txt\nAccelerometer-2011-04-11-13-29-54-brush_teeth-f1.txt\nAccelerometer-2011-05-30-08-35-11-brush_teeth-f1.txt\nAccelerometer-2011-05-30-09-36-50-brush_teeth-f1.txt\nAccelerometer-2011-05-30-10-34-16-brush_teeth-m1.txt\nAccelerometer-2011-05-30-21-10-57-brush_teeth-f1.txt\nAccelerometer-2011-05-30-21-55-04-brush_teeth-m2.txt\nAccelerometer-2011-05-31-15-16-47-brush_teeth-f1.txt\nAccelerometer-2011-06-02-10-42-22-brush_teeth-f1.txt\nAccelerometer-2011-06-02-10-45-50-brush_teeth-f1.txt\nAccelerometer-2011-06-06-10-45-27-brush_teeth-f1.txt\nAccelerometer-2011-06-06-10-48-05-brush_teeth-f1.txt\nAccelerometer-2011-03-24-10-24-39-climb_stairs-f1.txt\nAccelerometer-2011-03-24-10-25-44-climb_stairs-f1.txt\nAccelerometer-2011-03-29-09-55-46-climb_stairs-f1.txt\nAccelerometer-2011-04-05-18-21-22-climb_stairs-f1.txt\nAccelerometer-2011-04-05-18-32-29-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-44-35-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-57-50-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-58-30-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-21-38-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-30-37-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-39-09-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-29-58-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-38-52-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-43-37-climb_stairs-f1.txt\nAccelerometer-2011-05-30-10-20-47-climb_stairs-m1.txt\nAccelerometer-2011-05-30-10-32-53-climb_stairs-m1.txt\nAccelerometer-2011-05-30-10-41-10-climb_stairs-m1.txt\nAccelerometer-2011-05-30-20-54-39-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-02-43-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-14-55-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-36-58-climb_stairs-m2.txt\nAccelerometer-2011-05-30-21-46-05-climb_stairs-m2.txt\nAccelerometer-2011-05-30-22-00-32-climb_stairs-m2.txt\nAccelerometer-2011-05-31-14-58-41-climb_stairs-f1.txt\nAccelerometer-2011-05-31-15-06-32-climb_stairs-f1.txt\nAccelerometer-2011-05-31-15-20-50-climb_stairs-f1.txt\nAccelerometer-2011-05-31-16-26-09-climb_stairs-f1.txt\nAccelerometer-2011-05-31-16-32-44-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-37-15-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-37-44-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-39-21-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-40-10-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-57-19-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-57-43-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-08-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-58-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-00-50-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-01-27-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-02-24-climb_stairs-f1.txt\nAccelerometer-2012-05-28-17-56-03-climb_stairs-m1.txt\nAccelerometer-2012-05-28-17-56-40-climb_stairs-m1.txt\nAccelerometer-2012-05-29-16-53-12-climb_stairs-f2.txt\nAccelerometer-2012-05-29-16-54-38-climb_stairs-f2.txt\nAccelerometer-2012-05-29-17-17-05-climb_stairs-m3.txt\nAccelerometer-2012-05-29-17-17-51-climb_stairs-m3.txt\nAccelerometer-2012-05-30-18-31-08-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-31-52-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-32-56-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-33-57-climb_stairs-f3.txt\nAccelerometer-2012-05-30-19-12-19-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-12-57-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-13-50-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-14-51-climb_stairs-m4.txt\nAccelerometer-2012-05-30-22-06-17-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-06-47-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-07-15-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-07-44-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-08-15-climb_stairs-m2.txt\nAccelerometer-2012-06-06-08-53-29-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-53-47-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-54-08-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-54-29-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-59-53-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-00-24-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-00-51-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-01-08-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-05-28-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-03-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-28-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-49-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-32-04-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-32-36-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-32-58-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-33-15-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-37-23-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-37-58-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-38-17-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-38-43-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-42-55-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-43-14-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-43-47-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-44-02-climb_stairs-m6.txt\nAccelerometer-2012-06-06-14-04-18-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-04-51-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-05-05-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-05-18-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-08-44-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-08-57-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-09-12-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-09-24-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-12-35-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-12-49-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-13-05-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-13-20-climb_stairs-m7.txt\nAccelerometer-2012-06-07-10-48-08-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-48-24-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-49-17-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-49-35-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-53-34-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-53-53-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-54-12-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-54-29-climb_stairs-f1.txt\nAccelerometer-2011-03-24-09-44-34-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-26-33-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-57-40-comb_hair-f1.txt\nAccelerometer-2011-03-24-16-10-36-comb_hair-f2.txt\nAccelerometer-2011-03-29-08-57-24-comb_hair-f1.txt\nAccelerometer-2011-04-11-13-31-24-comb_hair-f1.txt\nAccelerometer-2011-05-30-08-32-58-comb_hair-f1.txt\nAccelerometer-2011-05-30-09-39-49-comb_hair-f1.txt\nAccelerometer-2011-05-30-10-36-26-comb_hair-m1.txt\nAccelerometer-2011-05-30-21-08-40-comb_hair-f1.txt\nAccelerometer-2011-05-30-21-57-03-comb_hair-m2.txt\nAccelerometer-2011-05-31-15-14-55-comb_hair-f1.txt\nAccelerometer-2011-06-02-10-41-33-comb_hair-f1.txt\nAccelerometer-2011-06-02-10-44-17-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-12-44-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-14-07-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-18-54-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-20-04-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-48-41-comb_hair-f4.txt\nAccelerometer-2011-06-02-16-49-27-comb_hair-f4.txt\nAccelerometer-2011-06-02-16-56-03-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-02-50-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-03-33-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-18-58-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-32-57-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-39-02-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-39-54-comb_hair-m1.txt\nAccelerometer-2011-06-06-10-03-47-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-05-40-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-08-56-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-10-30-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-24-02-descend_stairs-f1.txt\nAccelerometer-2011-03-24-10-25-11-descend_stairs-f1.txt\nAccelerometer-2011-03-24-11-20-43-descend_stairs-f1.txt\nAccelerometer-2011-03-29-09-15-58-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-16-34-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-17-18-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-17-50-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-18-14-descend_stairs-f1.txt\nAccelerometer-2011-04-05-18-26-06-descend_stairs-f1.txt\nAccelerometer-2011-04-11-11-51-27-descend_stairs-f1.txt\nAccelerometer-2011-04-11-13-32-24-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-20-55-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-24-58-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-34-06-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-29-15-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-31-43-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-40-50-descend_stairs-f1.txt\nAccelerometer-2011-05-30-10-19-56-descend_stairs-m1.txt\nAccelerometer-2011-05-30-10-24-51-descend_stairs-m1.txt\nAccelerometer-2011-05-30-10-37-30-descend_stairs-m1.txt\nAccelerometer-2011-05-30-20-48-10-descend_stairs-f1.txt\nAccelerometer-2011-05-30-20-56-43-descend_stairs-f1.txt\nAccelerometer-2011-05-30-21-09-46-descend_stairs-f1.txt\nAccelerometer-2011-05-30-21-32-18-descend_stairs-m2.txt\nAccelerometer-2011-05-30-21-39-11-descend_stairs-m2.txt\nAccelerometer-2011-05-30-21-53-35-descend_stairs-m2.txt\nAccelerometer-2011-05-31-14-52-31-descend_stairs-f1.txt\nAccelerometer-2011-05-31-15-00-30-descend_stairs-f1.txt\nAccelerometer-2011-05-31-15-15-48-descend_stairs-f1.txt\nAccelerometer-2011-05-31-16-21-49-descend_stairs-f1.txt\nAccelerometer-2011-05-31-16-28-22-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-36-04-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-36-42-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-38-15-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-38-52-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-56-23-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-56-55-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-58-14-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-58-40-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-38-descend_stairs-f1.txt\nAccelerometer-2011-06-06-11-00-26-descend_stairs-f1.txt\nAccelerometer-2011-06-06-11-01-54-descend_stairs-f1.txt\nAccelerometer-2011-03-24-10-07-02-drink_glass-f1.txt\nAccelerometer-2011-03-24-10-16-02-drink_glass-f1.txt\nAccelerometer-2011-03-24-10-46-25-drink_glass-f1.txt\nAccelerometer-2011-03-24-11-14-00-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-09-29-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-17-06-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-31-22-drink_glass-f1.txt\nAccelerometer-2011-03-24-16-08-10-drink_glass-f2.txt\nAccelerometer-2011-03-24-16-08-29-drink_glass-f2.txt\nAccelerometer-2011-04-05-18-55-33-drink_glass-f1.txt\nAccelerometer-2011-04-08-17-33-35-drink_glass-f3.txt\nAccelerometer-2011-04-08-17-35-00-drink_glass-f3.txt\nAccelerometer-2011-04-08-18-10-09-drink_glass-m4.txt\nAccelerometer-2011-04-11-12-55-39-drink_glass-f1.txt\nAccelerometer-2011-04-11-13-17-55-drink_glass-f1.txt\nAccelerometer-2011-04-12-21-43-48-drink_glass-m8.txt\nAccelerometer-2011-04-12-21-44-55-drink_glass-m8.txt\nAccelerometer-2011-05-30-21-06-15-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-06-42-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-07-10-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-51-35-drink_glass-m2.txt\nAccelerometer-2011-05-30-21-51-59-drink_glass-m2.txt\nAccelerometer-2011-05-30-21-52-25-drink_glass-m2.txt\nAccelerometer-2011-05-31-15-12-05-drink_glass-f1.txt\nAccelerometer-2011-05-31-15-12-36-drink_glass-f1.txt\nAccelerometer-2011-05-31-15-13-09-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-35-12-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-35-41-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-37-39-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-13-57-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-14-22-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-14-49-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-15-19-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-15-49-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-20-46-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-21-52-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-23-15-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-24-51-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-28-50-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-30-43-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-33-59-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-04-40-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-09-37-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-14-42-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-25-43-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-44-44-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-45-39-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-47-29-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-49-14-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-53-24-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-57-10-drink_glass-f1.txt\nAccelerometer-2011-06-02-16-51-25-drink_glass-f4.txt\nAccelerometer-2011-06-02-16-51-58-drink_glass-f4.txt\nAccelerometer-2011-06-02-16-52-53-drink_glass-f4.txt\nAccelerometer-2011-06-02-17-19-56-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-20-17-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-20-47-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-23-53-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-24-39-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-29-13-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-29-50-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-30-51-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-32-31-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-40-40-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-52-26-drink_glass-m1.txt\nAccelerometer-2012-03-23-03-47-02-drink_glass-m9.txt\nAccelerometer-2012-03-23-03-47-36-drink_glass-m9.txt\nAccelerometer-2012-03-23-03-54-54-drink_glass-m9.txt\nAccelerometer-2012-03-26-04-53-07-drink_glass-f2.txt\nAccelerometer-2012-03-26-04-56-11-drink_glass-f2.txt\nAccelerometer-2012-03-26-05-02-06-drink_glass-m3.txt\nAccelerometer-2012-03-26-05-03-53-drink_glass-m3.txt\nAccelerometer-2012-05-25-18-30-30-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-31-18-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-32-32-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-33-57-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-36-27-drink_glass-f4.txt\nAccelerometer-2012-05-28-17-42-29-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-44-02-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-44-31-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-47-23-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-47-49-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-50-04-drink_glass-m1.txt\nAccelerometer-2012-05-29-16-42-17-drink_glass-f2.txt\nAccelerometer-2012-05-29-16-44-12-drink_glass-f2.txt\nAccelerometer-2012-05-29-16-48-07-drink_glass-f2.txt\nAccelerometer-2012-05-29-17-11-01-drink_glass-m3.txt\nAccelerometer-2012-05-29-17-20-28-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-51-49-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-53-19-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-53-49-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-54-48-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-57-08-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-58-10-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-58-44-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-59-40-drink_glass-m3.txt\nAccelerometer-2012-05-30-21-48-13-drink_glass-m2.txt\nAccelerometer-2012-05-30-21-48-45-drink_glass-m2.txt\nAccelerometer-2012-05-30-22-10-16-drink_glass-m2.txt\nAccelerometer-2012-05-30-22-10-40-drink_glass-m2.txt\nAccelerometer-2011-03-24-13-06-15-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-10-14-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-12-52-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-17-42-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-21-39-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-33-22-eat_soup-f1.txt\nAccelerometer-2011-03-24-13-44-18-eat_soup-f1.txt\nAccelerometer-2011-03-24-13-56-42-eat_soup-f1.txt\nAccelerometer-2011-03-29-09-21-17-getup_bed-f1.txt\nAccelerometer-2011-03-29-09-24-50-getup_bed-f1.txt\nAccelerometer-2011-03-29-09-49-03-getup_bed-f1.txt\nAccelerometer-2011-04-05-18-28-21-getup_bed-f1.txt\nAccelerometer-2011-04-11-11-53-01-getup_bed-f1.txt\nAccelerometer-2011-04-11-13-34-05-getup_bed-f1.txt\nAccelerometer-2011-05-30-08-26-56-getup_bed-f1.txt\nAccelerometer-2011-05-30-08-38-15-getup_bed-f1.txt\nAccelerometer-2011-05-30-09-33-26-getup_bed-f1.txt\nAccelerometer-2011-05-30-09-42-46-getup_bed-f1.txt\nAccelerometer-2011-05-30-10-27-01-getup_bed-m1.txt\nAccelerometer-2011-05-30-10-39-27-getup_bed-m1.txt\nAccelerometer-2011-05-30-20-53-40-getup_bed-f1.txt\nAccelerometer-2011-05-30-20-59-57-getup_bed-f1.txt\nAccelerometer-2011-05-30-21-13-57-getup_bed-f1.txt\nAccelerometer-2011-05-30-21-36-01-getup_bed-m2.txt\nAccelerometer-2011-05-30-21-42-30-getup_bed-m2.txt\nAccelerometer-2011-05-30-21-59-46-getup_bed-m2.txt\nAccelerometer-2011-05-31-14-57-24-getup_bed-f1.txt\nAccelerometer-2011-05-31-15-02-55-getup_bed-f1.txt\nAccelerometer-2011-05-31-15-19-41-getup_bed-f1.txt\nAccelerometer-2011-05-31-16-25-09-getup_bed-f1.txt\nAccelerometer-2011-05-31-16-30-18-getup_bed-f1.txt\nAccelerometer-2011-06-02-16-57-57-getup_bed-f4.txt\nAccelerometer-2011-06-02-16-58-46-getup_bed-f4.txt\nAccelerometer-2011-06-02-17-00-08-getup_bed-f4.txt\nAccelerometer-2011-06-02-17-22-34-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-52-54-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-53-29-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-54-04-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-54-37-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-55-04-getup_bed-m1.txt\nAccelerometer-2012-05-30-21-51-41-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-52-18-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-53-05-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-53-38-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-54-12-getup_bed-m2.txt\nAccelerometer-2012-06-07-22-15-16-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-16-05-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-16-42-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-17-45-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-19-25-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-20-59-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-23-46-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-24-33-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-26-32-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-28-50-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-30-31-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-31-18-getup_bed-f4.txt\nAccelerometer-2012-06-12-15-28-34-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-29-15-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-30-07-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-31-01-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-31-56-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-32-38-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-34-01-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-34-40-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-35-29-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-36-02-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-50-48-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-51-25-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-52-04-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-52-46-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-53-20-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-54-03-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-54-33-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-56-41-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-57-21-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-58-01-getup_bed-f3.txt\nAccelerometer-2012-06-12-18-39-37-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-40-26-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-41-40-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-42-44-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-43-26-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-44-04-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-44-53-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-48-47-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-49-31-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-50-08-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-50-50-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-51-33-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-52-06-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-52-37-getup_bed-m3.txt\nAccelerometer-2012-06-20-20-27-19-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-28-27-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-29-11-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-29-49-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-30-45-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-31-40-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-32-40-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-33-22-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-34-30-getup_bed-f5.txt\nAccelerometer-2012-06-20-21-52-24-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-53-06-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-53-40-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-55-30-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-56-36-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-57-33-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-58-13-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-59-57-getup_bed-m10.txt\nAccelerometer-2012-06-20-22-00-55-getup_bed-m10.txt\nAccelerometer-2011-03-29-09-19-22-liedown_bed-f1.txt\nAccelerometer-2011-03-29-09-23-22-liedown_bed-f1.txt\nAccelerometer-2011-03-29-09-45-46-liedown_bed-f1.txt\nAccelerometer-2011-04-05-18-27-12-liedown_bed-f1.txt\nAccelerometer-2011-04-11-11-52-20-liedown_bed-f1.txt\nAccelerometer-2011-04-11-13-33-26-liedown_bed-f1.txt\nAccelerometer-2011-05-30-08-25-59-liedown_bed-f1.txt\nAccelerometer-2011-05-30-08-37-27-liedown_bed-f1.txt\nAccelerometer-2011-05-30-09-32-42-liedown_bed-f1.txt\nAccelerometer-2011-05-30-09-41-49-liedown_bed-f1.txt\nAccelerometer-2011-05-30-10-26-14-liedown_bed-m1.txt\nAccelerometer-2011-05-30-10-38-41-liedown_bed-m1.txt\nAccelerometer-2011-05-30-20-52-31-liedown_bed-f1.txt\nAccelerometer-2011-05-30-20-59-04-liedown_bed-f1.txt\nAccelerometer-2011-05-30-21-13-15-liedown_bed-f1.txt\nAccelerometer-2011-05-30-21-35-19-liedown_bed-m2.txt\nAccelerometer-2011-05-30-21-41-32-liedown_bed-m2.txt\nAccelerometer-2011-05-30-21-59-16-liedown_bed-m2.txt\nAccelerometer-2011-05-31-14-56-04-liedown_bed-f1.txt\nAccelerometer-2011-05-31-15-02-19-liedown_bed-f1.txt\nAccelerometer-2011-05-31-15-18-50-liedown_bed-f1.txt\nAccelerometer-2011-05-31-16-24-36-liedown_bed-f1.txt\nAccelerometer-2011-05-31-16-29-43-liedown_bed-f1.txt\nAccelerometer-2011-06-02-16-57-35-liedown_bed-f4.txt\nAccelerometer-2011-06-02-16-58-20-liedown_bed-f4.txt\nAccelerometer-2011-06-02-16-59-13-liedown_bed-f4.txt\nAccelerometer-2011-06-02-17-00-38-liedown_bed-f4.txt\nAccelerometer-2011-06-02-17-21-57-liedown_bed-m1.txt\nAccelerometer-2011-03-24-10-04-32-pour_water-f1.txt\nAccelerometer-2011-03-24-10-51-12-pour_water-f1.txt\nAccelerometer-2011-03-24-13-30-01-pour_water-f1.txt\nAccelerometer-2011-04-05-18-54-14-pour_water-f1.txt\nAccelerometer-2011-04-11-12-55-26-pour_water-f1.txt\nAccelerometer-2011-05-30-21-05-39-pour_water-f1.txt\nAccelerometer-2011-05-30-21-51-05-pour_water-m2.txt\nAccelerometer-2011-05-31-15-11-25-pour_water-f1.txt\nAccelerometer-2011-05-31-16-34-32-pour_water-f1.txt\nAccelerometer-2011-06-01-14-12-53-pour_water-f1.txt\nAccelerometer-2011-06-01-14-13-29-pour_water-f1.txt\nAccelerometer-2011-06-01-14-16-35-pour_water-f1.txt\nAccelerometer-2011-06-01-14-17-02-pour_water-f1.txt\nAccelerometer-2011-06-01-14-18-36-pour_water-f1.txt\nAccelerometer-2011-06-01-14-25-16-pour_water-f1.txt\nAccelerometer-2011-06-01-14-27-40-pour_water-f1.txt\nAccelerometer-2011-06-01-14-32-38-pour_water-f1.txt\nAccelerometer-2011-06-01-16-46-50-pour_water-f1.txt\nAccelerometer-2011-06-02-16-50-40-pour_water-f4.txt\nAccelerometer-2011-06-02-16-52-30-pour_water-f4.txt\nAccelerometer-2011-06-02-17-04-09-pour_water-f4.txt\nAccelerometer-2011-06-02-17-06-15-pour_water-f4.txt\nAccelerometer-2011-06-02-17-06-44-pour_water-f4.txt\nAccelerometer-2011-06-02-17-23-16-pour_water-m1.txt\nAccelerometer-2011-06-02-17-28-27-pour_water-m1.txt\nAccelerometer-2011-06-02-17-33-30-pour_water-m1.txt\nAccelerometer-2011-06-02-17-45-45-pour_water-m1.txt\nAccelerometer-2011-06-06-09-39-30-pour_water-f1.txt\nAccelerometer-2011-06-06-09-40-34-pour_water-f1.txt\nAccelerometer-2011-06-06-09-42-11-pour_water-f1.txt\nAccelerometer-2011-06-06-09-44-12-pour_water-f1.txt\nAccelerometer-2011-06-06-09-45-45-pour_water-f1.txt\nAccelerometer-2011-06-06-09-46-20-pour_water-f1.txt\nAccelerometer-2011-06-06-09-47-48-pour_water-f1.txt\nAccelerometer-2011-06-06-09-48-59-pour_water-f1.txt\nAccelerometer-2011-06-06-09-49-51-pour_water-f1.txt\nAccelerometer-2011-06-06-09-50-17-pour_water-f1.txt\nAccelerometer-2011-06-06-09-51-53-pour_water-f1.txt\nAccelerometer-2011-06-06-09-55-25-pour_water-f1.txt\nAccelerometer-2011-06-06-09-56-28-pour_water-f1.txt\nAccelerometer-2012-03-23-03-46-21-pour_water-m9.txt\nAccelerometer-2012-03-23-03-54-26-pour_water-m9.txt\nAccelerometer-2012-03-23-03-55-46-pour_water-m9.txt\nAccelerometer-2012-03-26-04-53-59-pour_water-f2.txt\nAccelerometer-2012-03-26-04-55-40-pour_water-f2.txt\nAccelerometer-2012-03-26-05-01-39-pour_water-m3.txt\nAccelerometer-2012-03-26-05-03-27-pour_water-m3.txt\nAccelerometer-2012-05-25-18-29-48-pour_water-f4.txt\nAccelerometer-2012-05-25-18-31-58-pour_water-f4.txt\nAccelerometer-2012-05-25-18-34-46-pour_water-f4.txt\nAccelerometer-2012-05-25-18-37-06-pour_water-f4.txt\nAccelerometer-2012-05-28-17-41-55-pour_water-m1.txt\nAccelerometer-2012-05-28-17-43-20-pour_water-m1.txt\nAccelerometer-2012-05-28-17-46-43-pour_water-m1.txt\nAccelerometer-2012-05-28-17-49-34-pour_water-m1.txt\nAccelerometer-2012-05-29-16-43-41-pour_water-f2.txt\nAccelerometer-2012-05-29-16-49-00-pour_water-f2.txt\nAccelerometer-2012-05-29-17-10-30-pour_water-m3.txt\nAccelerometer-2012-05-29-17-12-07-pour_water-m3.txt\nAccelerometer-2012-05-29-17-19-55-pour_water-m3.txt\nAccelerometer-2012-05-30-18-26-02-pour_water-f3.txt\nAccelerometer-2012-05-30-18-26-49-pour_water-f3.txt\nAccelerometer-2012-05-30-18-27-15-pour_water-f3.txt\nAccelerometer-2012-05-30-18-27-41-pour_water-f3.txt\nAccelerometer-2012-05-30-19-07-14-pour_water-m4.txt\nAccelerometer-2012-05-30-19-07-43-pour_water-m4.txt\nAccelerometer-2012-05-30-19-08-20-pour_water-m4.txt\nAccelerometer-2012-05-30-19-08-50-pour_water-m4.txt\nAccelerometer-2012-05-30-19-50-42-pour_water-f2.txt\nAccelerometer-2012-05-30-19-52-29-pour_water-f2.txt\nAccelerometer-2012-05-30-19-54-24-pour_water-f2.txt\nAccelerometer-2012-05-30-19-56-40-pour_water-m3.txt\nAccelerometer-2012-05-30-19-57-40-pour_water-m3.txt\nAccelerometer-2012-05-30-19-59-17-pour_water-m3.txt\nAccelerometer-2012-06-07-21-27-46-pour_water-f4.txt\nAccelerometer-2012-06-07-21-28-23-pour_water-f4.txt\nAccelerometer-2012-06-07-21-28-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-29-09-pour_water-f4.txt\nAccelerometer-2012-06-07-21-29-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-30-16-pour_water-f4.txt\nAccelerometer-2012-06-07-21-30-37-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-05-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-24-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-32-12-pour_water-f4.txt\nAccelerometer-2012-06-07-21-32-37-pour_water-f4.txt\nAccelerometer-2012-06-07-21-33-21-pour_water-f4.txt\nAccelerometer-2012-06-07-21-33-47-pour_water-f4.txt\nAccelerometer-2012-06-07-21-34-22-pour_water-f4.txt\nAccelerometer-2012-06-07-21-34-47-pour_water-f4.txt\nAccelerometer-2012-06-07-21-35-28-pour_water-f4.txt\nAccelerometer-2012-06-07-21-35-50-pour_water-f4.txt\nAccelerometer-2012-06-09-22-54-40-pour_water-m11.txt\nAccelerometer-2012-06-09-22-55-08-pour_water-m11.txt\nAccelerometer-2012-06-09-22-55-42-pour_water-m11.txt\nAccelerometer-2012-06-09-22-56-17-pour_water-m11.txt\nAccelerometer-2012-06-09-22-56-53-pour_water-m11.txt\nAccelerometer-2012-06-09-22-57-12-pour_water-m11.txt\nAccelerometer-2012-06-09-22-58-08-pour_water-m11.txt\nAccelerometer-2012-06-09-22-58-28-pour_water-m11.txt\nAccelerometer-2011-03-24-09-50-16-sitdown_chair-f1.txt\nAccelerometer-2011-03-24-10-27-33-sitdown_chair-f1.txt\nAccelerometer-2011-03-24-16-09-29-sitdown_chair-f2.txt\nAccelerometer-2011-03-29-09-06-48-sitdown_chair-f1.txt\nAccelerometer-2011-03-29-09-12-27-sitdown_chair-f1.txt\nAccelerometer-2011-04-05-18-22-14-sitdown_chair-f1.txt\nAccelerometer-2011-04-05-18-47-38-sitdown_chair-f1.txt\nAccelerometer-2011-04-08-17-33-10-sitdown_chair-f3.txt\nAccelerometer-2011-04-08-17-34-35-sitdown_chair-f3.txt\nAccelerometer-2011-04-08-18-09-40-sitdown_chair-m4.txt\nAccelerometer-2011-04-11-11-45-28-sitdown_chair-f1.txt\nAccelerometer-2011-04-12-21-42-24-sitdown_chair-m8.txt\nAccelerometer-2011-04-12-21-43-15-sitdown_chair-m8.txt\nAccelerometer-2011-05-30-08-22-28-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-08-31-30-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-09-23-12-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-09-24-48-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-10-21-50-sitdown_chair-m1.txt\nAccelerometer-2011-05-30-10-31-12-sitdown_chair-m1.txt\nAccelerometer-2011-05-30-20-48-57-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-20-49-32-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-20-55-28-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-21-04-13-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-21-33-15-sitdown_chair-m2.txt\nAccelerometer-2011-05-30-21-37-52-sitdown_chair-m2.txt\nAccelerometer-2011-05-30-21-48-01-sitdown_chair-m2.txt\nAccelerometer-2011-05-31-14-53-37-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-14-59-27-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-15-07-32-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-22-37-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-27-00-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-33-27-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-37-35-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-38-38-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-39-48-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-40-31-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-41-42-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-43-55-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-45-42-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-46-43-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-50-18-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-51-28-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-15-55-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-17-10-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-21-01-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-21-36-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-43-56-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-44-37-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-45-19-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-46-07-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-46-43-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-53-58-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-54-34-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-55-35-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-04-59-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-05-36-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-25-34-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-26-23-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-27-18-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-32-07-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-41-43-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-42-45-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-44-20-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-45-12-sitdown_chair-m1.txt\nAccelerometer-2011-06-06-09-34-12-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-35-11-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-35-43-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-36-06-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-39-34-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-44-08-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-46-34-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-48-48-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-50-54-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-53-32-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-58-23-sitdown_chair-f1.txt\nAccelerometer-2011-12-11-08-11-11-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-12-14-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-13-16-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-14-09-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-15-21-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-16-20-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-19-32-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-20-24-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-21-20-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-22-36-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-23-42-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-24-32-sitdown_chair-m2.txt\nAccelerometer-2012-03-23-03-45-26-sitdown_chair-m9.txt\nAccelerometer-2012-03-23-03-48-40-sitdown_chair-m9.txt\nAccelerometer-2012-03-26-04-54-57-sitdown_chair-f2.txt\nAccelerometer-2012-03-26-04-58-33-sitdown_chair-f2.txt\nAccelerometer-2012-03-26-05-02-58-sitdown_chair-m3.txt\nAccelerometer-2012-03-26-05-04-48-sitdown_chair-m3.txt\nAccelerometer-2012-05-25-18-27-57-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-28-46-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-33-29-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-36-06-sitdown_chair-f4.txt\nAccelerometer-2012-05-28-17-40-02-sitdown_chair-m1.txt\nAccelerometer-2012-05-28-17-41-12-sitdown_chair-m1.txt\nAccelerometer-2012-05-28-17-46-13-sitdown_chair-m1.txt\nAccelerometer-2011-03-23-10-39-18-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-42-01-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-45-51-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-47-59-standup_chair-f1.txt\nAccelerometer-2011-03-24-09-49-36-standup_chair-f1.txt\nAccelerometer-2011-03-24-10-38-17-standup_chair-f1.txt\nAccelerometer-2011-03-24-11-19-29-standup_chair-f1.txt\nAccelerometer-2011-03-24-16-09-19-standup_chair-f2.txt\nAccelerometer-2011-03-29-09-04-40-standup_chair-f1.txt\nAccelerometer-2011-03-29-09-10-24-standup_chair-f1.txt\nAccelerometer-2011-04-05-18-25-12-standup_chair-f1.txt\nAccelerometer-2011-04-08-17-32-45-standup_chair-f3.txt\nAccelerometer-2011-04-08-17-34-10-standup_chair-f3.txt\nAccelerometer-2011-04-08-18-09-06-standup_chair-m4.txt\nAccelerometer-2011-04-11-11-48-09-standup_chair-f1.txt\nAccelerometer-2011-04-11-13-24-10-standup_chair-f1.txt\nAccelerometer-2011-04-12-21-42-53-standup_chair-m8.txt\nAccelerometer-2011-04-12-21-45-21-standup_chair-m8.txt\nAccelerometer-2011-05-30-08-24-19-standup_chair-f1.txt\nAccelerometer-2011-05-30-08-32-03-standup_chair-f1.txt\nAccelerometer-2011-05-30-09-24-04-standup_chair-f1.txt\nAccelerometer-2011-05-30-09-25-14-standup_chair-f1.txt\nAccelerometer-2011-05-30-10-23-55-standup_chair-m1.txt\nAccelerometer-2011-05-30-10-31-41-standup_chair-m1.txt\nAccelerometer-2011-05-30-20-50-04-standup_chair-f1.txt\nAccelerometer-2011-05-30-20-50-30-standup_chair-f1.txt\nAccelerometer-2011-05-30-20-56-05-standup_chair-f1.txt\nAccelerometer-2011-05-30-21-07-51-standup_chair-f1.txt\nAccelerometer-2011-05-30-21-33-37-standup_chair-m2.txt\nAccelerometer-2011-05-30-21-38-19-standup_chair-m2.txt\nAccelerometer-2011-05-30-21-52-55-standup_chair-m2.txt\nAccelerometer-2011-05-31-14-54-09-standup_chair-f1.txt\nAccelerometer-2011-05-31-14-59-53-standup_chair-f1.txt\nAccelerometer-2011-05-31-15-13-49-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-23-03-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-27-45-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-38-19-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-38-03-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-39-38-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-40-19-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-41-32-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-43-42-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-45-33-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-46-35-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-50-10-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-51-21-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-52-02-standup_chair-f1.txt\nAccelerometer-2011-06-02-16-43-22-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-44-18-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-44-59-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-45-48-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-46-25-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-53-39-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-54-15-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-55-12-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-04-40-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-05-17-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-25-13-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-26-02-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-26-55-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-31-39-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-41-11-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-42-24-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-43-41-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-44-52-standup_chair-m1.txt\nAccelerometer-2011-12-05-09-38-00-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-40-57-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-45-47-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-48-17-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-50-23-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-52-58-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-55-36-standup_chair-f1.txt\nAccelerometer-2011-12-11-08-09-59-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-11-36-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-12-46-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-13-41-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-14-55-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-15-55-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-19-06-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-19-58-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-20-56-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-22-05-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-23-15-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-24-09-standup_chair-m2.txt\nAccelerometer-2012-03-23-03-44-39-standup_chair-m9.txt\nAccelerometer-2012-03-23-03-48-20-standup_chair-m9.txt\nAccelerometer-2012-03-26-04-54-30-standup_chair-f2.txt\nAccelerometer-2012-03-26-04-58-13-standup_chair-f2.txt\nAccelerometer-2012-03-26-05-02-40-standup_chair-m3.txt\nAccelerometer-2012-03-26-05-04-33-standup_chair-m3.txt\nAccelerometer-2012-05-25-18-27-29-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-28-24-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-33-08-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-35-48-standup_chair-f4.txt\nAccelerometer-2012-05-28-17-39-31-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-40-45-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-45-43-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-50-40-standup_chair-m1.txt\nAccelerometer-2012-05-29-16-43-05-standup_chair-f2.txt\nAccelerometer-2012-05-29-16-47-10-standup_chair-f2.txt\nAccelerometer-2012-05-29-17-12-51-standup_chair-m3.txt\nAccelerometer-2012-05-29-17-14-27-standup_chair-m3.txt\nAccelerometer-2011-03-29-09-30-56-use_telephone-f1.txt\nAccelerometer-2011-03-29-09-32-59-use_telephone-f1.txt\nAccelerometer-2011-03-29-09-52-41-use_telephone-f1.txt\nAccelerometer-2011-04-05-18-29-19-use_telephone-f1.txt\nAccelerometer-2011-04-11-11-53-34-use_telephone-f1.txt\nAccelerometer-2011-04-11-11-54-10-use_telephone-f1.txt\nAccelerometer-2011-05-30-08-27-44-use_telephone-f1.txt\nAccelerometer-2011-05-30-09-34-12-use_telephone-f1.txt\nAccelerometer-2011-05-30-10-28-13-use_telephone-m1.txt\nAccelerometer-2011-05-30-21-00-40-use_telephone-f1.txt\nAccelerometer-2011-05-30-21-43-16-use_telephone-m2.txt\nAccelerometer-2011-05-31-15-03-53-use_telephone-f1.txt\nAccelerometer-2011-05-31-16-31-11-use_telephone-f1.txt\nAccelerometer-2011-03-24-09-51-07-walk-f1.txt\nAccelerometer-2011-03-24-09-52-11-walk-f1.txt\nAccelerometer-2011-03-24-10-58-34-walk-f1.txt\nAccelerometer-2011-03-24-11-20-24-walk-f1.txt\nAccelerometer-2011-03-24-11-21-13-walk-f1.txt\nAccelerometer-2011-03-29-16-16-01-walk-f1.txt\nAccelerometer-2011-04-05-18-30-57-walk-f1.txt\nAccelerometer-2011-04-11-11-55-57-walk-f1.txt\nAccelerometer-2011-05-30-08-29-07-walk-f1.txt\nAccelerometer-2011-05-30-09-35-03-walk-f1.txt\nAccelerometer-2011-05-30-10-29-28-walk-m1.txt\nAccelerometer-2011-05-30-20-51-03-walk-f1.txt\nAccelerometer-2011-05-30-20-57-19-walk-f1.txt\nAccelerometer-2011-05-30-21-01-39-walk-f1.txt\nAccelerometer-2011-05-30-21-34-15-walk-m2.txt\nAccelerometer-2011-05-30-21-39-55-walk-m2.txt\nAccelerometer-2011-05-30-21-44-35-walk-m2.txt\nAccelerometer-2011-05-31-14-54-41-walk-f1.txt\nAccelerometer-2011-05-31-15-01-05-walk-f1.txt\nAccelerometer-2011-05-31-15-05-44-walk-f1.txt\nAccelerometer-2011-05-31-16-23-32-walk-f1.txt\nAccelerometer-2011-05-31-16-28-55-walk-f1.txt\nAccelerometer-2011-05-31-16-32-12-walk-f1.txt\nAccelerometer-2012-05-29-16-45-30-walk-f2.txt\nAccelerometer-2012-05-29-16-46-02-walk-f2.txt\nAccelerometer-2012-05-29-16-51-03-walk-f2.txt\nAccelerometer-2012-05-29-16-51-40-walk-f2.txt\nAccelerometer-2012-05-29-17-13-45-walk-m3.txt\nAccelerometer-2012-05-29-17-15-15-walk-m3.txt\nAccelerometer-2012-05-29-17-16-24-walk-m3.txt\nAccelerometer-2012-05-29-17-18-55-walk-m3.txt\nAccelerometer-2012-05-30-18-28-32-walk-f3.txt\nAccelerometer-2012-05-30-18-29-02-walk-f3.txt\nAccelerometer-2012-05-30-18-29-35-walk-f3.txt\nAccelerometer-2012-05-30-18-30-08-walk-f3.txt\nAccelerometer-2012-05-30-19-09-54-walk-m4.txt\nAccelerometer-2012-05-30-19-10-27-walk-m4.txt\nAccelerometer-2012-05-30-19-10-56-walk-m4.txt\nAccelerometer-2012-05-30-19-11-27-walk-m4.txt\nAccelerometer-2012-05-30-22-02-42-walk-m2.txt\nAccelerometer-2012-05-30-22-03-03-walk-m2.txt\nAccelerometer-2012-05-30-22-03-27-walk-m2.txt\nAccelerometer-2012-05-30-22-03-52-walk-m2.txt\nAccelerometer-2012-05-30-22-04-20-walk-m2.txt\nAccelerometer-2012-06-06-08-56-33-walk-m5.txt\nAccelerometer-2012-06-06-08-57-26-walk-m5.txt\nAccelerometer-2012-06-06-08-58-20-walk-m5.txt\nAccelerometer-2012-06-06-08-59-00-walk-m5.txt\nAccelerometer-2012-06-06-09-02-06-walk-m5.txt\nAccelerometer-2012-06-06-09-02-54-walk-m5.txt\nAccelerometer-2012-06-06-09-03-57-walk-m5.txt\nAccelerometer-2012-06-06-09-04-41-walk-m5.txt\nAccelerometer-2012-06-06-09-07-30-walk-m5.txt\nAccelerometer-2012-06-06-09-08-19-walk-m5.txt\nAccelerometer-2012-06-06-09-09-21-walk-m5.txt\nAccelerometer-2012-06-06-09-10-02-walk-m5.txt\nAccelerometer-2012-06-06-09-34-20-walk-m6.txt\nAccelerometer-2012-06-06-09-35-07-walk-m6.txt\nAccelerometer-2012-06-06-09-36-06-walk-m6.txt\nAccelerometer-2012-06-06-09-36-38-walk-m6.txt\nAccelerometer-2012-06-06-09-39-47-walk-m6.txt\nAccelerometer-2012-06-06-09-40-33-walk-m6.txt\nAccelerometer-2012-06-06-09-41-32-walk-m6.txt\nAccelerometer-2012-06-06-09-42-05-walk-m6.txt\nAccelerometer-2012-06-06-09-45-03-walk-m6.txt\nAccelerometer-2012-06-06-09-45-38-walk-m6.txt\nAccelerometer-2012-06-06-09-46-31-walk-m6.txt\nAccelerometer-2012-06-06-09-47-07-walk-m6.txt\nAccelerometer-2012-06-06-14-06-10-walk-m7.txt\nAccelerometer-2012-06-06-14-06-43-walk-m7.txt\nAccelerometer-2012-06-06-14-07-41-walk-m7.txt\nAccelerometer-2012-06-06-14-08-12-walk-m7.txt\nAccelerometer-2012-06-06-14-10-02-walk-m7.txt\nAccelerometer-2012-06-06-14-10-33-walk-m7.txt\nAccelerometer-2012-06-06-14-11-30-walk-m7.txt\nAccelerometer-2012-06-06-14-12-00-walk-m7.txt\nAccelerometer-2012-06-06-14-13-56-walk-m7.txt\nAccelerometer-2012-06-06-14-14-27-walk-m7.txt\nAccelerometer-2012-06-06-14-15-25-walk-m7.txt\nAccelerometer-2012-06-06-14-15-57-walk-m7.txt\nAccelerometer-2012-06-07-10-50-09-walk-f1.txt\nAccelerometer-2012-06-07-10-50-42-walk-f1.txt\nAccelerometer-2012-06-07-10-52-14-walk-f1.txt\n"
46 |                 }
47 |             ], 
48 |             "source": "import os\nimport fnmatch\n\nd = 'HMP_Dataset/'\n\n# filter list for all folders containing data (folders that don't start with .)\nfile_list_filtered = [s for s in os.listdir(d) if os.path.isdir(os.path.join(d,s)) & ~fnmatch.fnmatch(s, '.*')]\n\nfrom pyspark.sql.functions import lit\n\n#create pandas data frame for all the data\n\ndf = None\n\nfor category in file_list_filtered:\n    data_files = os.listdir('HMP_Dataset/'+category)\n    \n    #create a temporary pandas data frame for each data file\n    for data_file in data_files:\n        print(data_file)\n        temp_df = spark.read.option(\"header\", \"true\").option(\"header\", \"false\").option(\"delimiter\", \" \").csv('HMP_Dataset/'+category+'/'+data_file,schema=schema)\n        \n        #create a column called \"source\" storing the current CSV file\n        temp_df = temp_df.withColumn(\"source\", lit(data_file))\n        \n        #create a column called \"class\" storing the current data folder\n        temp_df = temp_df.withColumn(\"class\", lit(category))\n        \n        #append to existing data frame list\n        #data_frames = data_frames + [temp_df]\n                                                                                                             \n        if df is None:\n            df = temp_df\n        else:\n            df = df.union(temp_df)\n        \n"
49 |         }, 
50 |         {
51 |             "execution_count": null, 
52 |             "cell_type": "code", 
53 |             "metadata": {}, 
54 |             "outputs": [], 
55 |             "source": "df.write.parquet('hmp.parquet')\n"
56 |         }
57 |     ], 
58 |     "metadata": {
59 |         "kernelspec": {
60 |             "display_name": "Python 3.6 with Spark", 
61 |             "name": "python36", 
62 |             "language": "python3"
63 |         }, 
64 |         "language_info": {
65 |             "mimetype": "text/x-python", 
66 |             "nbconvert_exporter": "python", 
67 |             "version": "3.6.8", 
68 |             "name": "python", 
69 |             "file_extension": ".py", 
70 |             "pygments_lexer": "ipython3", 
71 |             "codemirror_mode": {
72 |                 "version": 3, 
73 |                 "name": "ipython"
74 |             }
75 |         }
76 |     }, 
77 |     "nbformat": 4
78 | }


--------------------------------------------------------------------------------
/SparkML Support Vector Machine.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |     "nbformat_minor": 1, 
  3 |     "cells": [
  4 |         {
  5 |             "execution_count": 1, 
  6 |             "cell_type": "code", 
  7 |             "metadata": {}, 
  8 |             "outputs": [
  9 |                 {
 10 |                     "output_type": "stream", 
 11 |                     "name": "stdout", 
 12 |                     "text": "Waiting for a Spark session to start...\nSpark Initialization Done! ApplicationId = app-20190904102622-0002\nKERNEL_ID = 6ee9079e-9381-4230-9d5e-cd7e3ab02cf9\nCloning into 'HMP_Dataset'...\nremote: Enumerating objects: 865, done.\u001b[K\nremote: Total 865 (delta 0), reused 0 (delta 0), pack-reused 865\u001b[K\nReceiving objects: 100% (865/865), 1010.96 KiB | 4.09 MiB/s, done.\nChecking out files: 100% (848/848), done.\n"
 13 |                 }
 14 |             ], 
 15 |             "source": "!rm -Rf HMP_Dataset\n!git clone https://github.com/wchill/HMP_Dataset"
 16 |         }, 
 17 |         {
 18 |             "execution_count": 2, 
 19 |             "cell_type": "code", 
 20 |             "metadata": {}, 
 21 |             "outputs": [], 
 22 |             "source": "from pyspark.sql.types import StructType, StructField, IntegerType\n\nschema = StructType([\n    StructField(\"x\", IntegerType(), True),\n    StructField(\"y\", IntegerType(), True),\n    StructField(\"z\", IntegerType(), True)])\n\n#df = spark.read.csv(\"user_click_seq.csv\",header=False,schema=schema)\n"
 23 |         }, 
 24 |         {
 25 |             "execution_count": 3, 
 26 |             "cell_type": "code", 
 27 |             "metadata": {}, 
 28 |             "outputs": [
 29 |                 {
 30 |                     "output_type": "stream", 
 31 |                     "name": "stdout", 
 32 |                     "text": "Accelerometer-2011-04-11-13-28-18-brush_teeth-f1.txt\nAccelerometer-2011-04-11-13-29-54-brush_teeth-f1.txt\nAccelerometer-2011-05-30-08-35-11-brush_teeth-f1.txt\nAccelerometer-2011-05-30-09-36-50-brush_teeth-f1.txt\nAccelerometer-2011-05-30-10-34-16-brush_teeth-m1.txt\nAccelerometer-2011-05-30-21-10-57-brush_teeth-f1.txt\nAccelerometer-2011-05-30-21-55-04-brush_teeth-m2.txt\nAccelerometer-2011-05-31-15-16-47-brush_teeth-f1.txt\nAccelerometer-2011-06-02-10-42-22-brush_teeth-f1.txt\nAccelerometer-2011-06-02-10-45-50-brush_teeth-f1.txt\nAccelerometer-2011-06-06-10-45-27-brush_teeth-f1.txt\nAccelerometer-2011-06-06-10-48-05-brush_teeth-f1.txt\nAccelerometer-2011-03-24-10-24-39-climb_stairs-f1.txt\nAccelerometer-2011-03-24-10-25-44-climb_stairs-f1.txt\nAccelerometer-2011-03-29-09-55-46-climb_stairs-f1.txt\nAccelerometer-2011-04-05-18-21-22-climb_stairs-f1.txt\nAccelerometer-2011-04-05-18-32-29-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-44-35-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-57-50-climb_stairs-f1.txt\nAccelerometer-2011-04-11-11-58-30-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-21-38-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-30-37-climb_stairs-f1.txt\nAccelerometer-2011-05-30-08-39-09-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-29-58-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-38-52-climb_stairs-f1.txt\nAccelerometer-2011-05-30-09-43-37-climb_stairs-f1.txt\nAccelerometer-2011-05-30-10-20-47-climb_stairs-m1.txt\nAccelerometer-2011-05-30-10-32-53-climb_stairs-m1.txt\nAccelerometer-2011-05-30-10-41-10-climb_stairs-m1.txt\nAccelerometer-2011-05-30-20-54-39-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-02-43-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-14-55-climb_stairs-f1.txt\nAccelerometer-2011-05-30-21-36-58-climb_stairs-m2.txt\nAccelerometer-2011-05-30-21-46-05-climb_stairs-m2.txt\nAccelerometer-2011-05-30-22-00-32-climb_stairs-m2.txt\nAccelerometer-2011-05-31-14-58-41-climb_stairs-f1.txt\nAccelerometer-2011-05-31-15-06-32-climb_stairs-f1.txt\nAccelerometer-2011-05-31-15-20-50-climb_stairs-f1.txt\nAccelerometer-2011-05-31-16-26-09-climb_stairs-f1.txt\nAccelerometer-2011-05-31-16-32-44-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-37-15-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-37-44-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-39-21-climb_stairs-f1.txt\nAccelerometer-2011-06-02-10-40-10-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-57-19-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-57-43-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-08-climb_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-58-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-00-50-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-01-27-climb_stairs-f1.txt\nAccelerometer-2011-06-06-11-02-24-climb_stairs-f1.txt\nAccelerometer-2012-05-28-17-56-03-climb_stairs-m1.txt\nAccelerometer-2012-05-28-17-56-40-climb_stairs-m1.txt\nAccelerometer-2012-05-29-16-53-12-climb_stairs-f2.txt\nAccelerometer-2012-05-29-16-54-38-climb_stairs-f2.txt\nAccelerometer-2012-05-29-17-17-05-climb_stairs-m3.txt\nAccelerometer-2012-05-29-17-17-51-climb_stairs-m3.txt\nAccelerometer-2012-05-30-18-31-08-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-31-52-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-32-56-climb_stairs-f3.txt\nAccelerometer-2012-05-30-18-33-57-climb_stairs-f3.txt\nAccelerometer-2012-05-30-19-12-19-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-12-57-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-13-50-climb_stairs-m4.txt\nAccelerometer-2012-05-30-19-14-51-climb_stairs-m4.txt\nAccelerometer-2012-05-30-22-06-17-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-06-47-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-07-15-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-07-44-climb_stairs-m2.txt\nAccelerometer-2012-05-30-22-08-15-climb_stairs-m2.txt\nAccelerometer-2012-06-06-08-53-29-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-53-47-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-54-08-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-54-29-climb_stairs-m5.txt\nAccelerometer-2012-06-06-08-59-53-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-00-24-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-00-51-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-01-08-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-05-28-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-03-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-28-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-06-49-climb_stairs-m5.txt\nAccelerometer-2012-06-06-09-32-04-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-32-36-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-32-58-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-33-15-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-37-23-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-37-58-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-38-17-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-38-43-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-42-55-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-43-14-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-43-47-climb_stairs-m6.txt\nAccelerometer-2012-06-06-09-44-02-climb_stairs-m6.txt\nAccelerometer-2012-06-06-14-04-18-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-04-51-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-05-05-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-05-18-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-08-44-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-08-57-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-09-12-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-09-24-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-12-35-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-12-49-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-13-05-climb_stairs-m7.txt\nAccelerometer-2012-06-06-14-13-20-climb_stairs-m7.txt\nAccelerometer-2012-06-07-10-48-08-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-48-24-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-49-17-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-49-35-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-53-34-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-53-53-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-54-12-climb_stairs-f1.txt\nAccelerometer-2012-06-07-10-54-29-climb_stairs-f1.txt\nAccelerometer-2011-03-24-09-44-34-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-26-33-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-57-40-comb_hair-f1.txt\nAccelerometer-2011-03-24-16-10-36-comb_hair-f2.txt\nAccelerometer-2011-03-29-08-57-24-comb_hair-f1.txt\nAccelerometer-2011-04-11-13-31-24-comb_hair-f1.txt\nAccelerometer-2011-05-30-08-32-58-comb_hair-f1.txt\nAccelerometer-2011-05-30-09-39-49-comb_hair-f1.txt\nAccelerometer-2011-05-30-10-36-26-comb_hair-m1.txt\nAccelerometer-2011-05-30-21-08-40-comb_hair-f1.txt\nAccelerometer-2011-05-30-21-57-03-comb_hair-m2.txt\nAccelerometer-2011-05-31-15-14-55-comb_hair-f1.txt\nAccelerometer-2011-06-02-10-41-33-comb_hair-f1.txt\nAccelerometer-2011-06-02-10-44-17-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-12-44-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-14-07-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-18-54-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-20-04-comb_hair-f1.txt\nAccelerometer-2011-06-02-16-48-41-comb_hair-f4.txt\nAccelerometer-2011-06-02-16-49-27-comb_hair-f4.txt\nAccelerometer-2011-06-02-16-56-03-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-02-50-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-03-33-comb_hair-f4.txt\nAccelerometer-2011-06-02-17-18-58-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-32-57-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-39-02-comb_hair-m1.txt\nAccelerometer-2011-06-02-17-39-54-comb_hair-m1.txt\nAccelerometer-2011-06-06-10-03-47-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-05-40-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-08-56-comb_hair-f1.txt\nAccelerometer-2011-06-06-10-10-30-comb_hair-f1.txt\nAccelerometer-2011-03-24-10-24-02-descend_stairs-f1.txt\nAccelerometer-2011-03-24-10-25-11-descend_stairs-f1.txt\nAccelerometer-2011-03-24-11-20-43-descend_stairs-f1.txt\nAccelerometer-2011-03-29-09-15-58-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-16-34-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-17-18-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-17-50-descend_stairs-f1.txt\nAccelerometer-2011-03-29-16-18-14-descend_stairs-f1.txt\nAccelerometer-2011-04-05-18-26-06-descend_stairs-f1.txt\nAccelerometer-2011-04-11-11-51-27-descend_stairs-f1.txt\nAccelerometer-2011-04-11-13-32-24-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-20-55-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-24-58-descend_stairs-f1.txt\nAccelerometer-2011-05-30-08-34-06-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-29-15-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-31-43-descend_stairs-f1.txt\nAccelerometer-2011-05-30-09-40-50-descend_stairs-f1.txt\nAccelerometer-2011-05-30-10-19-56-descend_stairs-m1.txt\nAccelerometer-2011-05-30-10-24-51-descend_stairs-m1.txt\nAccelerometer-2011-05-30-10-37-30-descend_stairs-m1.txt\nAccelerometer-2011-05-30-20-48-10-descend_stairs-f1.txt\nAccelerometer-2011-05-30-20-56-43-descend_stairs-f1.txt\nAccelerometer-2011-05-30-21-09-46-descend_stairs-f1.txt\nAccelerometer-2011-05-30-21-32-18-descend_stairs-m2.txt\nAccelerometer-2011-05-30-21-39-11-descend_stairs-m2.txt\nAccelerometer-2011-05-30-21-53-35-descend_stairs-m2.txt\nAccelerometer-2011-05-31-14-52-31-descend_stairs-f1.txt\nAccelerometer-2011-05-31-15-00-30-descend_stairs-f1.txt\nAccelerometer-2011-05-31-15-15-48-descend_stairs-f1.txt\nAccelerometer-2011-05-31-16-21-49-descend_stairs-f1.txt\nAccelerometer-2011-05-31-16-28-22-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-36-04-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-36-42-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-38-15-descend_stairs-f1.txt\nAccelerometer-2011-06-02-10-38-52-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-56-23-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-56-55-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-58-14-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-58-40-descend_stairs-f1.txt\nAccelerometer-2011-06-06-10-59-38-descend_stairs-f1.txt\nAccelerometer-2011-06-06-11-00-26-descend_stairs-f1.txt\nAccelerometer-2011-06-06-11-01-54-descend_stairs-f1.txt\nAccelerometer-2011-03-24-10-07-02-drink_glass-f1.txt\nAccelerometer-2011-03-24-10-16-02-drink_glass-f1.txt\nAccelerometer-2011-03-24-10-46-25-drink_glass-f1.txt\nAccelerometer-2011-03-24-11-14-00-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-09-29-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-17-06-drink_glass-f1.txt\nAccelerometer-2011-03-24-13-31-22-drink_glass-f1.txt\nAccelerometer-2011-03-24-16-08-10-drink_glass-f2.txt\nAccelerometer-2011-03-24-16-08-29-drink_glass-f2.txt\nAccelerometer-2011-04-05-18-55-33-drink_glass-f1.txt\nAccelerometer-2011-04-08-17-33-35-drink_glass-f3.txt\nAccelerometer-2011-04-08-17-35-00-drink_glass-f3.txt\nAccelerometer-2011-04-08-18-10-09-drink_glass-m4.txt\nAccelerometer-2011-04-11-12-55-39-drink_glass-f1.txt\nAccelerometer-2011-04-11-13-17-55-drink_glass-f1.txt\nAccelerometer-2011-04-12-21-43-48-drink_glass-m8.txt\nAccelerometer-2011-04-12-21-44-55-drink_glass-m8.txt\nAccelerometer-2011-05-30-21-06-15-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-06-42-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-07-10-drink_glass-f1.txt\nAccelerometer-2011-05-30-21-51-35-drink_glass-m2.txt\nAccelerometer-2011-05-30-21-51-59-drink_glass-m2.txt\nAccelerometer-2011-05-30-21-52-25-drink_glass-m2.txt\nAccelerometer-2011-05-31-15-12-05-drink_glass-f1.txt\nAccelerometer-2011-05-31-15-12-36-drink_glass-f1.txt\nAccelerometer-2011-05-31-15-13-09-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-35-12-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-35-41-drink_glass-f1.txt\nAccelerometer-2011-05-31-16-37-39-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-13-57-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-14-22-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-14-49-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-15-19-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-15-49-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-20-46-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-21-52-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-23-15-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-24-51-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-28-50-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-30-43-drink_glass-f1.txt\nAccelerometer-2011-06-01-14-33-59-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-04-40-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-09-37-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-14-42-drink_glass-f1.txt\nAccelerometer-2011-06-01-15-25-43-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-44-44-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-45-39-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-47-29-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-49-14-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-53-24-drink_glass-f1.txt\nAccelerometer-2011-06-01-16-57-10-drink_glass-f1.txt\nAccelerometer-2011-06-02-16-51-25-drink_glass-f4.txt\nAccelerometer-2011-06-02-16-51-58-drink_glass-f4.txt\nAccelerometer-2011-06-02-16-52-53-drink_glass-f4.txt\nAccelerometer-2011-06-02-17-19-56-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-20-17-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-20-47-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-23-53-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-24-39-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-29-13-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-29-50-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-30-51-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-32-31-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-40-40-drink_glass-m1.txt\nAccelerometer-2011-06-02-17-52-26-drink_glass-m1.txt\nAccelerometer-2012-03-23-03-47-02-drink_glass-m9.txt\nAccelerometer-2012-03-23-03-47-36-drink_glass-m9.txt\nAccelerometer-2012-03-23-03-54-54-drink_glass-m9.txt\nAccelerometer-2012-03-26-04-53-07-drink_glass-f2.txt\nAccelerometer-2012-03-26-04-56-11-drink_glass-f2.txt\nAccelerometer-2012-03-26-05-02-06-drink_glass-m3.txt\nAccelerometer-2012-03-26-05-03-53-drink_glass-m3.txt\nAccelerometer-2012-05-25-18-30-30-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-31-18-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-32-32-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-33-57-drink_glass-f4.txt\nAccelerometer-2012-05-25-18-36-27-drink_glass-f4.txt\nAccelerometer-2012-05-28-17-42-29-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-44-02-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-44-31-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-47-23-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-47-49-drink_glass-m1.txt\nAccelerometer-2012-05-28-17-50-04-drink_glass-m1.txt\nAccelerometer-2012-05-29-16-42-17-drink_glass-f2.txt\nAccelerometer-2012-05-29-16-44-12-drink_glass-f2.txt\nAccelerometer-2012-05-29-16-48-07-drink_glass-f2.txt\nAccelerometer-2012-05-29-17-11-01-drink_glass-m3.txt\nAccelerometer-2012-05-29-17-20-28-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-51-49-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-53-19-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-53-49-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-54-48-drink_glass-f2.txt\nAccelerometer-2012-05-30-19-57-08-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-58-10-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-58-44-drink_glass-m3.txt\nAccelerometer-2012-05-30-19-59-40-drink_glass-m3.txt\nAccelerometer-2012-05-30-21-48-13-drink_glass-m2.txt\nAccelerometer-2012-05-30-21-48-45-drink_glass-m2.txt\nAccelerometer-2012-05-30-22-10-16-drink_glass-m2.txt\nAccelerometer-2012-05-30-22-10-40-drink_glass-m2.txt\nAccelerometer-2011-03-24-13-06-15-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-10-14-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-12-52-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-17-42-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-21-39-eat_meat-f1.txt\nAccelerometer-2011-03-24-13-33-22-eat_soup-f1.txt\nAccelerometer-2011-03-24-13-44-18-eat_soup-f1.txt\nAccelerometer-2011-03-24-13-56-42-eat_soup-f1.txt\nAccelerometer-2011-03-29-09-21-17-getup_bed-f1.txt\nAccelerometer-2011-03-29-09-24-50-getup_bed-f1.txt\nAccelerometer-2011-03-29-09-49-03-getup_bed-f1.txt\nAccelerometer-2011-04-05-18-28-21-getup_bed-f1.txt\nAccelerometer-2011-04-11-11-53-01-getup_bed-f1.txt\nAccelerometer-2011-04-11-13-34-05-getup_bed-f1.txt\nAccelerometer-2011-05-30-08-26-56-getup_bed-f1.txt\nAccelerometer-2011-05-30-08-38-15-getup_bed-f1.txt\nAccelerometer-2011-05-30-09-33-26-getup_bed-f1.txt\nAccelerometer-2011-05-30-09-42-46-getup_bed-f1.txt\nAccelerometer-2011-05-30-10-27-01-getup_bed-m1.txt\nAccelerometer-2011-05-30-10-39-27-getup_bed-m1.txt\nAccelerometer-2011-05-30-20-53-40-getup_bed-f1.txt\nAccelerometer-2011-05-30-20-59-57-getup_bed-f1.txt\nAccelerometer-2011-05-30-21-13-57-getup_bed-f1.txt\nAccelerometer-2011-05-30-21-36-01-getup_bed-m2.txt\nAccelerometer-2011-05-30-21-42-30-getup_bed-m2.txt\nAccelerometer-2011-05-30-21-59-46-getup_bed-m2.txt\nAccelerometer-2011-05-31-14-57-24-getup_bed-f1.txt\nAccelerometer-2011-05-31-15-02-55-getup_bed-f1.txt\nAccelerometer-2011-05-31-15-19-41-getup_bed-f1.txt\nAccelerometer-2011-05-31-16-25-09-getup_bed-f1.txt\nAccelerometer-2011-05-31-16-30-18-getup_bed-f1.txt\nAccelerometer-2011-06-02-16-57-57-getup_bed-f4.txt\nAccelerometer-2011-06-02-16-58-46-getup_bed-f4.txt\nAccelerometer-2011-06-02-17-00-08-getup_bed-f4.txt\nAccelerometer-2011-06-02-17-22-34-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-52-54-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-53-29-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-54-04-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-54-37-getup_bed-m1.txt\nAccelerometer-2012-05-28-17-55-04-getup_bed-m1.txt\nAccelerometer-2012-05-30-21-51-41-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-52-18-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-53-05-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-53-38-getup_bed-m2.txt\nAccelerometer-2012-05-30-21-54-12-getup_bed-m2.txt\nAccelerometer-2012-06-07-22-15-16-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-16-05-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-16-42-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-17-45-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-19-25-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-20-59-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-23-46-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-24-33-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-26-32-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-28-50-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-30-31-getup_bed-f4.txt\nAccelerometer-2012-06-07-22-31-18-getup_bed-f4.txt\nAccelerometer-2012-06-12-15-28-34-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-29-15-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-30-07-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-31-01-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-31-56-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-32-38-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-34-01-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-34-40-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-35-29-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-36-02-getup_bed-m4.txt\nAccelerometer-2012-06-12-15-50-48-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-51-25-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-52-04-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-52-46-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-53-20-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-54-03-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-54-33-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-56-41-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-57-21-getup_bed-f3.txt\nAccelerometer-2012-06-12-15-58-01-getup_bed-f3.txt\nAccelerometer-2012-06-12-18-39-37-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-40-26-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-41-40-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-42-44-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-43-26-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-44-04-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-44-53-getup_bed-f2.txt\nAccelerometer-2012-06-12-18-48-47-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-49-31-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-50-08-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-50-50-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-51-33-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-52-06-getup_bed-m3.txt\nAccelerometer-2012-06-12-18-52-37-getup_bed-m3.txt\nAccelerometer-2012-06-20-20-27-19-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-28-27-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-29-11-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-29-49-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-30-45-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-31-40-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-32-40-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-33-22-getup_bed-f5.txt\nAccelerometer-2012-06-20-20-34-30-getup_bed-f5.txt\nAccelerometer-2012-06-20-21-52-24-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-53-06-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-53-40-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-55-30-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-56-36-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-57-33-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-58-13-getup_bed-m10.txt\nAccelerometer-2012-06-20-21-59-57-getup_bed-m10.txt\nAccelerometer-2012-06-20-22-00-55-getup_bed-m10.txt\nAccelerometer-2011-03-29-09-19-22-liedown_bed-f1.txt\nAccelerometer-2011-03-29-09-23-22-liedown_bed-f1.txt\nAccelerometer-2011-03-29-09-45-46-liedown_bed-f1.txt\nAccelerometer-2011-04-05-18-27-12-liedown_bed-f1.txt\nAccelerometer-2011-04-11-11-52-20-liedown_bed-f1.txt\nAccelerometer-2011-04-11-13-33-26-liedown_bed-f1.txt\nAccelerometer-2011-05-30-08-25-59-liedown_bed-f1.txt\nAccelerometer-2011-05-30-08-37-27-liedown_bed-f1.txt\nAccelerometer-2011-05-30-09-32-42-liedown_bed-f1.txt\nAccelerometer-2011-05-30-09-41-49-liedown_bed-f1.txt\nAccelerometer-2011-05-30-10-26-14-liedown_bed-m1.txt\nAccelerometer-2011-05-30-10-38-41-liedown_bed-m1.txt\nAccelerometer-2011-05-30-20-52-31-liedown_bed-f1.txt\nAccelerometer-2011-05-30-20-59-04-liedown_bed-f1.txt\nAccelerometer-2011-05-30-21-13-15-liedown_bed-f1.txt\nAccelerometer-2011-05-30-21-35-19-liedown_bed-m2.txt\nAccelerometer-2011-05-30-21-41-32-liedown_bed-m2.txt\nAccelerometer-2011-05-30-21-59-16-liedown_bed-m2.txt\nAccelerometer-2011-05-31-14-56-04-liedown_bed-f1.txt\nAccelerometer-2011-05-31-15-02-19-liedown_bed-f1.txt\nAccelerometer-2011-05-31-15-18-50-liedown_bed-f1.txt\nAccelerometer-2011-05-31-16-24-36-liedown_bed-f1.txt\nAccelerometer-2011-05-31-16-29-43-liedown_bed-f1.txt\nAccelerometer-2011-06-02-16-57-35-liedown_bed-f4.txt\nAccelerometer-2011-06-02-16-58-20-liedown_bed-f4.txt\nAccelerometer-2011-06-02-16-59-13-liedown_bed-f4.txt\nAccelerometer-2011-06-02-17-00-38-liedown_bed-f4.txt\nAccelerometer-2011-06-02-17-21-57-liedown_bed-m1.txt\nAccelerometer-2011-03-24-10-04-32-pour_water-f1.txt\nAccelerometer-2011-03-24-10-51-12-pour_water-f1.txt\nAccelerometer-2011-03-24-13-30-01-pour_water-f1.txt\nAccelerometer-2011-04-05-18-54-14-pour_water-f1.txt\nAccelerometer-2011-04-11-12-55-26-pour_water-f1.txt\nAccelerometer-2011-05-30-21-05-39-pour_water-f1.txt\nAccelerometer-2011-05-30-21-51-05-pour_water-m2.txt\nAccelerometer-2011-05-31-15-11-25-pour_water-f1.txt\nAccelerometer-2011-05-31-16-34-32-pour_water-f1.txt\nAccelerometer-2011-06-01-14-12-53-pour_water-f1.txt\nAccelerometer-2011-06-01-14-13-29-pour_water-f1.txt\nAccelerometer-2011-06-01-14-16-35-pour_water-f1.txt\nAccelerometer-2011-06-01-14-17-02-pour_water-f1.txt\nAccelerometer-2011-06-01-14-18-36-pour_water-f1.txt\nAccelerometer-2011-06-01-14-25-16-pour_water-f1.txt\nAccelerometer-2011-06-01-14-27-40-pour_water-f1.txt\nAccelerometer-2011-06-01-14-32-38-pour_water-f1.txt\nAccelerometer-2011-06-01-16-46-50-pour_water-f1.txt\nAccelerometer-2011-06-02-16-50-40-pour_water-f4.txt\nAccelerometer-2011-06-02-16-52-30-pour_water-f4.txt\nAccelerometer-2011-06-02-17-04-09-pour_water-f4.txt\nAccelerometer-2011-06-02-17-06-15-pour_water-f4.txt\nAccelerometer-2011-06-02-17-06-44-pour_water-f4.txt\nAccelerometer-2011-06-02-17-23-16-pour_water-m1.txt\nAccelerometer-2011-06-02-17-28-27-pour_water-m1.txt\nAccelerometer-2011-06-02-17-33-30-pour_water-m1.txt\nAccelerometer-2011-06-02-17-45-45-pour_water-m1.txt\nAccelerometer-2011-06-06-09-39-30-pour_water-f1.txt\nAccelerometer-2011-06-06-09-40-34-pour_water-f1.txt\nAccelerometer-2011-06-06-09-42-11-pour_water-f1.txt\nAccelerometer-2011-06-06-09-44-12-pour_water-f1.txt\nAccelerometer-2011-06-06-09-45-45-pour_water-f1.txt\nAccelerometer-2011-06-06-09-46-20-pour_water-f1.txt\nAccelerometer-2011-06-06-09-47-48-pour_water-f1.txt\nAccelerometer-2011-06-06-09-48-59-pour_water-f1.txt\nAccelerometer-2011-06-06-09-49-51-pour_water-f1.txt\nAccelerometer-2011-06-06-09-50-17-pour_water-f1.txt\nAccelerometer-2011-06-06-09-51-53-pour_water-f1.txt\nAccelerometer-2011-06-06-09-55-25-pour_water-f1.txt\nAccelerometer-2011-06-06-09-56-28-pour_water-f1.txt\nAccelerometer-2012-03-23-03-46-21-pour_water-m9.txt\nAccelerometer-2012-03-23-03-54-26-pour_water-m9.txt\nAccelerometer-2012-03-23-03-55-46-pour_water-m9.txt\nAccelerometer-2012-03-26-04-53-59-pour_water-f2.txt\nAccelerometer-2012-03-26-04-55-40-pour_water-f2.txt\nAccelerometer-2012-03-26-05-01-39-pour_water-m3.txt\nAccelerometer-2012-03-26-05-03-27-pour_water-m3.txt\nAccelerometer-2012-05-25-18-29-48-pour_water-f4.txt\nAccelerometer-2012-05-25-18-31-58-pour_water-f4.txt\nAccelerometer-2012-05-25-18-34-46-pour_water-f4.txt\nAccelerometer-2012-05-25-18-37-06-pour_water-f4.txt\nAccelerometer-2012-05-28-17-41-55-pour_water-m1.txt\nAccelerometer-2012-05-28-17-43-20-pour_water-m1.txt\nAccelerometer-2012-05-28-17-46-43-pour_water-m1.txt\nAccelerometer-2012-05-28-17-49-34-pour_water-m1.txt\nAccelerometer-2012-05-29-16-43-41-pour_water-f2.txt\nAccelerometer-2012-05-29-16-49-00-pour_water-f2.txt\nAccelerometer-2012-05-29-17-10-30-pour_water-m3.txt\nAccelerometer-2012-05-29-17-12-07-pour_water-m3.txt\nAccelerometer-2012-05-29-17-19-55-pour_water-m3.txt\nAccelerometer-2012-05-30-18-26-02-pour_water-f3.txt\nAccelerometer-2012-05-30-18-26-49-pour_water-f3.txt\nAccelerometer-2012-05-30-18-27-15-pour_water-f3.txt\nAccelerometer-2012-05-30-18-27-41-pour_water-f3.txt\nAccelerometer-2012-05-30-19-07-14-pour_water-m4.txt\nAccelerometer-2012-05-30-19-07-43-pour_water-m4.txt\nAccelerometer-2012-05-30-19-08-20-pour_water-m4.txt\nAccelerometer-2012-05-30-19-08-50-pour_water-m4.txt\nAccelerometer-2012-05-30-19-50-42-pour_water-f2.txt\nAccelerometer-2012-05-30-19-52-29-pour_water-f2.txt\nAccelerometer-2012-05-30-19-54-24-pour_water-f2.txt\nAccelerometer-2012-05-30-19-56-40-pour_water-m3.txt\nAccelerometer-2012-05-30-19-57-40-pour_water-m3.txt\nAccelerometer-2012-05-30-19-59-17-pour_water-m3.txt\nAccelerometer-2012-06-07-21-27-46-pour_water-f4.txt\nAccelerometer-2012-06-07-21-28-23-pour_water-f4.txt\nAccelerometer-2012-06-07-21-28-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-29-09-pour_water-f4.txt\nAccelerometer-2012-06-07-21-29-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-30-16-pour_water-f4.txt\nAccelerometer-2012-06-07-21-30-37-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-05-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-24-pour_water-f4.txt\nAccelerometer-2012-06-07-21-31-48-pour_water-f4.txt\nAccelerometer-2012-06-07-21-32-12-pour_water-f4.txt\nAccelerometer-2012-06-07-21-32-37-pour_water-f4.txt\nAccelerometer-2012-06-07-21-33-21-pour_water-f4.txt\nAccelerometer-2012-06-07-21-33-47-pour_water-f4.txt\nAccelerometer-2012-06-07-21-34-22-pour_water-f4.txt\nAccelerometer-2012-06-07-21-34-47-pour_water-f4.txt\nAccelerometer-2012-06-07-21-35-28-pour_water-f4.txt\nAccelerometer-2012-06-07-21-35-50-pour_water-f4.txt\nAccelerometer-2012-06-09-22-54-40-pour_water-m11.txt\nAccelerometer-2012-06-09-22-55-08-pour_water-m11.txt\nAccelerometer-2012-06-09-22-55-42-pour_water-m11.txt\nAccelerometer-2012-06-09-22-56-17-pour_water-m11.txt\nAccelerometer-2012-06-09-22-56-53-pour_water-m11.txt\nAccelerometer-2012-06-09-22-57-12-pour_water-m11.txt\nAccelerometer-2012-06-09-22-58-08-pour_water-m11.txt\nAccelerometer-2012-06-09-22-58-28-pour_water-m11.txt\nAccelerometer-2011-03-24-09-50-16-sitdown_chair-f1.txt\nAccelerometer-2011-03-24-10-27-33-sitdown_chair-f1.txt\nAccelerometer-2011-03-24-16-09-29-sitdown_chair-f2.txt\nAccelerometer-2011-03-29-09-06-48-sitdown_chair-f1.txt\nAccelerometer-2011-03-29-09-12-27-sitdown_chair-f1.txt\nAccelerometer-2011-04-05-18-22-14-sitdown_chair-f1.txt\nAccelerometer-2011-04-05-18-47-38-sitdown_chair-f1.txt\nAccelerometer-2011-04-08-17-33-10-sitdown_chair-f3.txt\nAccelerometer-2011-04-08-17-34-35-sitdown_chair-f3.txt\nAccelerometer-2011-04-08-18-09-40-sitdown_chair-m4.txt\nAccelerometer-2011-04-11-11-45-28-sitdown_chair-f1.txt\nAccelerometer-2011-04-12-21-42-24-sitdown_chair-m8.txt\nAccelerometer-2011-04-12-21-43-15-sitdown_chair-m8.txt\nAccelerometer-2011-05-30-08-22-28-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-08-31-30-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-09-23-12-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-09-24-48-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-10-21-50-sitdown_chair-m1.txt\nAccelerometer-2011-05-30-10-31-12-sitdown_chair-m1.txt\nAccelerometer-2011-05-30-20-48-57-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-20-49-32-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-20-55-28-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-21-04-13-sitdown_chair-f1.txt\nAccelerometer-2011-05-30-21-33-15-sitdown_chair-m2.txt\nAccelerometer-2011-05-30-21-37-52-sitdown_chair-m2.txt\nAccelerometer-2011-05-30-21-48-01-sitdown_chair-m2.txt\nAccelerometer-2011-05-31-14-53-37-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-14-59-27-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-15-07-32-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-22-37-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-27-00-sitdown_chair-f1.txt\nAccelerometer-2011-05-31-16-33-27-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-37-35-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-38-38-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-39-48-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-40-31-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-41-42-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-43-55-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-45-42-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-46-43-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-50-18-sitdown_chair-f1.txt\nAccelerometer-2011-06-01-14-51-28-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-15-55-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-17-10-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-21-01-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-21-36-sitdown_chair-f1.txt\nAccelerometer-2011-06-02-16-43-56-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-44-37-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-45-19-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-46-07-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-46-43-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-53-58-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-54-34-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-16-55-35-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-04-59-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-05-36-sitdown_chair-f4.txt\nAccelerometer-2011-06-02-17-25-34-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-26-23-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-27-18-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-32-07-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-41-43-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-42-45-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-44-20-sitdown_chair-m1.txt\nAccelerometer-2011-06-02-17-45-12-sitdown_chair-m1.txt\nAccelerometer-2011-06-06-09-34-12-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-35-11-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-35-43-sitdown_chair-f1.txt\nAccelerometer-2011-06-06-09-36-06-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-39-34-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-44-08-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-46-34-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-48-48-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-50-54-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-53-32-sitdown_chair-f1.txt\nAccelerometer-2011-12-05-09-58-23-sitdown_chair-f1.txt\nAccelerometer-2011-12-11-08-11-11-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-12-14-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-13-16-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-14-09-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-15-21-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-16-20-sitdown_chair-f4.txt\nAccelerometer-2011-12-11-08-19-32-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-20-24-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-21-20-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-22-36-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-23-42-sitdown_chair-m2.txt\nAccelerometer-2011-12-11-08-24-32-sitdown_chair-m2.txt\nAccelerometer-2012-03-23-03-45-26-sitdown_chair-m9.txt\nAccelerometer-2012-03-23-03-48-40-sitdown_chair-m9.txt\nAccelerometer-2012-03-26-04-54-57-sitdown_chair-f2.txt\nAccelerometer-2012-03-26-04-58-33-sitdown_chair-f2.txt\nAccelerometer-2012-03-26-05-02-58-sitdown_chair-m3.txt\nAccelerometer-2012-03-26-05-04-48-sitdown_chair-m3.txt\nAccelerometer-2012-05-25-18-27-57-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-28-46-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-33-29-sitdown_chair-f4.txt\nAccelerometer-2012-05-25-18-36-06-sitdown_chair-f4.txt\nAccelerometer-2012-05-28-17-40-02-sitdown_chair-m1.txt\nAccelerometer-2012-05-28-17-41-12-sitdown_chair-m1.txt\nAccelerometer-2012-05-28-17-46-13-sitdown_chair-m1.txt\nAccelerometer-2011-03-23-10-39-18-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-42-01-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-45-51-standup_chair-f1.txt\nAccelerometer-2011-03-23-10-47-59-standup_chair-f1.txt\nAccelerometer-2011-03-24-09-49-36-standup_chair-f1.txt\nAccelerometer-2011-03-24-10-38-17-standup_chair-f1.txt\nAccelerometer-2011-03-24-11-19-29-standup_chair-f1.txt\nAccelerometer-2011-03-24-16-09-19-standup_chair-f2.txt\nAccelerometer-2011-03-29-09-04-40-standup_chair-f1.txt\nAccelerometer-2011-03-29-09-10-24-standup_chair-f1.txt\nAccelerometer-2011-04-05-18-25-12-standup_chair-f1.txt\nAccelerometer-2011-04-08-17-32-45-standup_chair-f3.txt\nAccelerometer-2011-04-08-17-34-10-standup_chair-f3.txt\nAccelerometer-2011-04-08-18-09-06-standup_chair-m4.txt\nAccelerometer-2011-04-11-11-48-09-standup_chair-f1.txt\nAccelerometer-2011-04-11-13-24-10-standup_chair-f1.txt\nAccelerometer-2011-04-12-21-42-53-standup_chair-m8.txt\nAccelerometer-2011-04-12-21-45-21-standup_chair-m8.txt\nAccelerometer-2011-05-30-08-24-19-standup_chair-f1.txt\nAccelerometer-2011-05-30-08-32-03-standup_chair-f1.txt\nAccelerometer-2011-05-30-09-24-04-standup_chair-f1.txt\nAccelerometer-2011-05-30-09-25-14-standup_chair-f1.txt\nAccelerometer-2011-05-30-10-23-55-standup_chair-m1.txt\nAccelerometer-2011-05-30-10-31-41-standup_chair-m1.txt\nAccelerometer-2011-05-30-20-50-04-standup_chair-f1.txt\nAccelerometer-2011-05-30-20-50-30-standup_chair-f1.txt\nAccelerometer-2011-05-30-20-56-05-standup_chair-f1.txt\nAccelerometer-2011-05-30-21-07-51-standup_chair-f1.txt\nAccelerometer-2011-05-30-21-33-37-standup_chair-m2.txt\nAccelerometer-2011-05-30-21-38-19-standup_chair-m2.txt\nAccelerometer-2011-05-30-21-52-55-standup_chair-m2.txt\nAccelerometer-2011-05-31-14-54-09-standup_chair-f1.txt\nAccelerometer-2011-05-31-14-59-53-standup_chair-f1.txt\nAccelerometer-2011-05-31-15-13-49-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-23-03-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-27-45-standup_chair-f1.txt\nAccelerometer-2011-05-31-16-38-19-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-38-03-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-39-38-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-40-19-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-41-32-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-43-42-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-45-33-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-46-35-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-50-10-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-51-21-standup_chair-f1.txt\nAccelerometer-2011-06-01-14-52-02-standup_chair-f1.txt\nAccelerometer-2011-06-02-16-43-22-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-44-18-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-44-59-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-45-48-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-46-25-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-53-39-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-54-15-standup_chair-f4.txt\nAccelerometer-2011-06-02-16-55-12-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-04-40-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-05-17-standup_chair-f4.txt\nAccelerometer-2011-06-02-17-25-13-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-26-02-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-26-55-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-31-39-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-41-11-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-42-24-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-43-41-standup_chair-m1.txt\nAccelerometer-2011-06-02-17-44-52-standup_chair-m1.txt\nAccelerometer-2011-12-05-09-38-00-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-40-57-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-45-47-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-48-17-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-50-23-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-52-58-standup_chair-f1.txt\nAccelerometer-2011-12-05-09-55-36-standup_chair-f1.txt\nAccelerometer-2011-12-11-08-09-59-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-11-36-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-12-46-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-13-41-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-14-55-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-15-55-standup_chair-f4.txt\nAccelerometer-2011-12-11-08-19-06-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-19-58-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-20-56-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-22-05-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-23-15-standup_chair-m2.txt\nAccelerometer-2011-12-11-08-24-09-standup_chair-m2.txt\nAccelerometer-2012-03-23-03-44-39-standup_chair-m9.txt\nAccelerometer-2012-03-23-03-48-20-standup_chair-m9.txt\nAccelerometer-2012-03-26-04-54-30-standup_chair-f2.txt\nAccelerometer-2012-03-26-04-58-13-standup_chair-f2.txt\nAccelerometer-2012-03-26-05-02-40-standup_chair-m3.txt\nAccelerometer-2012-03-26-05-04-33-standup_chair-m3.txt\nAccelerometer-2012-05-25-18-27-29-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-28-24-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-33-08-standup_chair-f4.txt\nAccelerometer-2012-05-25-18-35-48-standup_chair-f4.txt\nAccelerometer-2012-05-28-17-39-31-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-40-45-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-45-43-standup_chair-m1.txt\nAccelerometer-2012-05-28-17-50-40-standup_chair-m1.txt\nAccelerometer-2012-05-29-16-43-05-standup_chair-f2.txt\nAccelerometer-2012-05-29-16-47-10-standup_chair-f2.txt\nAccelerometer-2012-05-29-17-12-51-standup_chair-m3.txt\nAccelerometer-2012-05-29-17-14-27-standup_chair-m3.txt\nAccelerometer-2011-03-29-09-30-56-use_telephone-f1.txt\nAccelerometer-2011-03-29-09-32-59-use_telephone-f1.txt\nAccelerometer-2011-03-29-09-52-41-use_telephone-f1.txt\nAccelerometer-2011-04-05-18-29-19-use_telephone-f1.txt\nAccelerometer-2011-04-11-11-53-34-use_telephone-f1.txt\nAccelerometer-2011-04-11-11-54-10-use_telephone-f1.txt\nAccelerometer-2011-05-30-08-27-44-use_telephone-f1.txt\nAccelerometer-2011-05-30-09-34-12-use_telephone-f1.txt\nAccelerometer-2011-05-30-10-28-13-use_telephone-m1.txt\nAccelerometer-2011-05-30-21-00-40-use_telephone-f1.txt\nAccelerometer-2011-05-30-21-43-16-use_telephone-m2.txt\nAccelerometer-2011-05-31-15-03-53-use_telephone-f1.txt\nAccelerometer-2011-05-31-16-31-11-use_telephone-f1.txt\nAccelerometer-2011-03-24-09-51-07-walk-f1.txt\nAccelerometer-2011-03-24-09-52-11-walk-f1.txt\nAccelerometer-2011-03-24-10-58-34-walk-f1.txt\nAccelerometer-2011-03-24-11-20-24-walk-f1.txt\nAccelerometer-2011-03-24-11-21-13-walk-f1.txt\nAccelerometer-2011-03-29-16-16-01-walk-f1.txt\nAccelerometer-2011-04-05-18-30-57-walk-f1.txt\nAccelerometer-2011-04-11-11-55-57-walk-f1.txt\nAccelerometer-2011-05-30-08-29-07-walk-f1.txt\nAccelerometer-2011-05-30-09-35-03-walk-f1.txt\nAccelerometer-2011-05-30-10-29-28-walk-m1.txt\nAccelerometer-2011-05-30-20-51-03-walk-f1.txt\nAccelerometer-2011-05-30-20-57-19-walk-f1.txt\nAccelerometer-2011-05-30-21-01-39-walk-f1.txt\nAccelerometer-2011-05-30-21-34-15-walk-m2.txt\nAccelerometer-2011-05-30-21-39-55-walk-m2.txt\nAccelerometer-2011-05-30-21-44-35-walk-m2.txt\nAccelerometer-2011-05-31-14-54-41-walk-f1.txt\nAccelerometer-2011-05-31-15-01-05-walk-f1.txt\nAccelerometer-2011-05-31-15-05-44-walk-f1.txt\nAccelerometer-2011-05-31-16-23-32-walk-f1.txt\nAccelerometer-2011-05-31-16-28-55-walk-f1.txt\nAccelerometer-2011-05-31-16-32-12-walk-f1.txt\nAccelerometer-2012-05-29-16-45-30-walk-f2.txt\nAccelerometer-2012-05-29-16-46-02-walk-f2.txt\nAccelerometer-2012-05-29-16-51-03-walk-f2.txt\nAccelerometer-2012-05-29-16-51-40-walk-f2.txt\nAccelerometer-2012-05-29-17-13-45-walk-m3.txt\nAccelerometer-2012-05-29-17-15-15-walk-m3.txt\nAccelerometer-2012-05-29-17-16-24-walk-m3.txt\nAccelerometer-2012-05-29-17-18-55-walk-m3.txt\nAccelerometer-2012-05-30-18-28-32-walk-f3.txt\nAccelerometer-2012-05-30-18-29-02-walk-f3.txt\nAccelerometer-2012-05-30-18-29-35-walk-f3.txt\nAccelerometer-2012-05-30-18-30-08-walk-f3.txt\nAccelerometer-2012-05-30-19-09-54-walk-m4.txt\nAccelerometer-2012-05-30-19-10-27-walk-m4.txt\nAccelerometer-2012-05-30-19-10-56-walk-m4.txt\nAccelerometer-2012-05-30-19-11-27-walk-m4.txt\nAccelerometer-2012-05-30-22-02-42-walk-m2.txt\nAccelerometer-2012-05-30-22-03-03-walk-m2.txt\nAccelerometer-2012-05-30-22-03-27-walk-m2.txt\nAccelerometer-2012-05-30-22-03-52-walk-m2.txt\nAccelerometer-2012-05-30-22-04-20-walk-m2.txt\nAccelerometer-2012-06-06-08-56-33-walk-m5.txt\nAccelerometer-2012-06-06-08-57-26-walk-m5.txt\nAccelerometer-2012-06-06-08-58-20-walk-m5.txt\nAccelerometer-2012-06-06-08-59-00-walk-m5.txt\nAccelerometer-2012-06-06-09-02-06-walk-m5.txt\nAccelerometer-2012-06-06-09-02-54-walk-m5.txt\nAccelerometer-2012-06-06-09-03-57-walk-m5.txt\nAccelerometer-2012-06-06-09-04-41-walk-m5.txt\nAccelerometer-2012-06-06-09-07-30-walk-m5.txt\nAccelerometer-2012-06-06-09-08-19-walk-m5.txt\nAccelerometer-2012-06-06-09-09-21-walk-m5.txt\nAccelerometer-2012-06-06-09-10-02-walk-m5.txt\nAccelerometer-2012-06-06-09-34-20-walk-m6.txt\nAccelerometer-2012-06-06-09-35-07-walk-m6.txt\nAccelerometer-2012-06-06-09-36-06-walk-m6.txt\nAccelerometer-2012-06-06-09-36-38-walk-m6.txt\nAccelerometer-2012-06-06-09-39-47-walk-m6.txt\nAccelerometer-2012-06-06-09-40-33-walk-m6.txt\nAccelerometer-2012-06-06-09-41-32-walk-m6.txt\nAccelerometer-2012-06-06-09-42-05-walk-m6.txt\nAccelerometer-2012-06-06-09-45-03-walk-m6.txt\nAccelerometer-2012-06-06-09-45-38-walk-m6.txt\nAccelerometer-2012-06-06-09-46-31-walk-m6.txt\nAccelerometer-2012-06-06-09-47-07-walk-m6.txt\nAccelerometer-2012-06-06-14-06-10-walk-m7.txt\nAccelerometer-2012-06-06-14-06-43-walk-m7.txt\nAccelerometer-2012-06-06-14-07-41-walk-m7.txt\nAccelerometer-2012-06-06-14-08-12-walk-m7.txt\nAccelerometer-2012-06-06-14-10-02-walk-m7.txt\nAccelerometer-2012-06-06-14-10-33-walk-m7.txt\nAccelerometer-2012-06-06-14-11-30-walk-m7.txt\nAccelerometer-2012-06-06-14-12-00-walk-m7.txt\nAccelerometer-2012-06-06-14-13-56-walk-m7.txt\nAccelerometer-2012-06-06-14-14-27-walk-m7.txt\nAccelerometer-2012-06-06-14-15-25-walk-m7.txt\nAccelerometer-2012-06-06-14-15-57-walk-m7.txt\nAccelerometer-2012-06-07-10-50-09-walk-f1.txt\nAccelerometer-2012-06-07-10-50-42-walk-f1.txt\nAccelerometer-2012-06-07-10-52-14-walk-f1.txt\nAccelerometer-2012-06-07-10-52-46-walk-f1.txt\nAccelerometer-2012-06-07-10-55-33-walk-f1.txt\nAccelerometer-2012-06-07-10-57-12-walk-f1.txt\nAccelerometer-2012-06-07-10-57-45-walk-f1.txt\nAccelerometer-2012-06-11-11-23-05-walk-m1.txt\nAccelerometer-2012-06-11-11-30-57-walk-m1.txt\nAccelerometer-2012-06-11-11-31-42-walk-m1.txt\nAccelerometer-2012-06-11-11-32-28-walk-m1.txt\nAccelerometer-2012-06-11-11-33-07-walk-m1.txt\nAccelerometer-2012-06-11-11-33-42-walk-m1.txt\nAccelerometer-2012-06-11-11-34-18-walk-m1.txt\nAccelerometer-2012-06-11-11-36-25-walk-m1.txt\nAccelerometer-2012-06-11-11-37-01-walk-m1.txt\nAccelerometer-2012-06-11-11-37-36-walk-m1.txt\nAccelerometer-2012-06-11-11-38-12-walk-m1.txt\nAccelerometer-2012-06-11-11-38-52-walk-m1.txt\nAccelerometer-2012-06-11-11-39-29-walk-m1.txt\n"
 33 |                 }
 34 |             ], 
 35 |             "source": "import os\nimport fnmatch\n\nd = 'HMP_Dataset/'\n\n# filter list for all folders containing data (folders that don't start with .) \nfile_list_filtered = [s for s in os.listdir(d) if os.path.isdir(os.path.join(d,s)) & ~fnmatch.fnmatch(s, '.*')]\n\nfrom pyspark.sql.functions import lit\n\n#create pandas data frame for all the data\n\ndf = None\n\nfor category in file_list_filtered:\n    data_files = os.listdir('HMP_Dataset/'+category)\n    \n    #create a temporary pandas data frame for each data file\n    for data_file in data_files:\n        print(data_file)\n        temp_df = spark.read.option(\"header\", \"true\").option(\"header\", \"false\").option(\"delimiter\", \" \").csv('HMP_Dataset/'+category+'/'+data_file,schema=schema)\n        \n        #create a column called \"source\" storing the current CSV file\n        temp_df = temp_df.withColumn(\"source\", lit(data_file))\n        \n        #create a column called \"class\" storing the current data folder\n        temp_df = temp_df.withColumn(\"class\", lit(category))\n        \n        #append to existing data frame list\n        #data_frames = data_frames + [temp_df]\n                                                                                                             \n        if df is None:\n            df = temp_df\n        else:\n            df = df.union(temp_df)\n        \n"
 36 |         }, 
 37 |         {
 38 |             "execution_count": 4, 
 39 |             "cell_type": "code", 
 40 |             "metadata": {}, 
 41 |             "outputs": [], 
 42 |             "source": "splits = df.randomSplit([0.8, 0.2])\ndf_train = splits[0]\ndf_test = splits[1]"
 43 |         }, 
 44 |         {
 45 |             "execution_count": 5, 
 46 |             "cell_type": "code", 
 47 |             "metadata": {
 48 |                 "pixiedust": {
 49 |                     "displayParams": {}
 50 |                 }
 51 |             }, 
 52 |             "outputs": [], 
 53 |             "source": "from pyspark.ml.feature import StringIndexer\nfrom pyspark.ml.feature import OneHotEncoder\nfrom pyspark.ml.linalg import Vectors\nfrom pyspark.ml.feature import VectorAssembler\nfrom pyspark.ml.feature import Normalizer\n\nindexer = StringIndexer(inputCol=\"class\", outputCol=\"label\")\nencoder = OneHotEncoder(inputCol=\"label\", outputCol=\"labelVec\")\nvectorAssembler = VectorAssembler(inputCols=[\"x\",\"y\",\"z\"],outputCol=\"features\")\nnormalizer = Normalizer(inputCol=\"features\", outputCol=\"features_norm\", p=1.0)"
 54 |         }, 
 55 |         {
 56 |             "execution_count": 7, 
 57 |             "cell_type": "code", 
 58 |             "metadata": {}, 
 59 |             "outputs": [], 
 60 |             "source": "from pyspark.ml.classification import LinearSVC\n\nlsvc = LinearSVC(maxIter=10, regParam=0.1)"
 61 |         }, 
 62 |         {
 63 |             "execution_count": 8, 
 64 |             "cell_type": "code", 
 65 |             "metadata": {}, 
 66 |             "outputs": [], 
 67 |             "source": "df.createOrReplaceTempView('df')\ndf_two_class = spark.sql(\"select * from df where class in ('Use_telephone','Standup_chair')\")"
 68 |         }, 
 69 |         {
 70 |             "execution_count": 9, 
 71 |             "cell_type": "code", 
 72 |             "metadata": {}, 
 73 |             "outputs": [], 
 74 |             "source": "splits = df_two_class.randomSplit([0.8, 0.2])\ndf_train = splits[0]\ndf_test = splits[1]"
 75 |         }, 
 76 |         {
 77 |             "execution_count": 10, 
 78 |             "cell_type": "code", 
 79 |             "metadata": {}, 
 80 |             "outputs": [], 
 81 |             "source": "from pyspark.ml import Pipeline\npipeline = Pipeline(stages=[indexer, encoder, vectorAssembler, normalizer,lsvc])"
 82 |         }, 
 83 |         {
 84 |             "execution_count": 11, 
 85 |             "cell_type": "code", 
 86 |             "metadata": {}, 
 87 |             "outputs": [], 
 88 |             "source": "model = pipeline.fit(df_train)"
 89 |         }, 
 90 |         {
 91 |             "execution_count": 12, 
 92 |             "cell_type": "code", 
 93 |             "metadata": {}, 
 94 |             "outputs": [], 
 95 |             "source": "prediction = model.transform(df_train)"
 96 |         }, 
 97 |         {
 98 |             "execution_count": 13, 
 99 |             "cell_type": "code", 
100 |             "metadata": {}, 
101 |             "outputs": [
102 |                 {
103 |                     "execution_count": 13, 
104 |                     "metadata": {}, 
105 |                     "data": {
106 |                         "text/plain": "0.9384408611035672"
107 |                     }, 
108 |                     "output_type": "execute_result"
109 |                 }
110 |             ], 
111 |             "source": "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n\n# Evaluate model\nevaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\nevaluator.evaluate(prediction)"
112 |         }, 
113 |         {
114 |             "execution_count": 14, 
115 |             "cell_type": "code", 
116 |             "metadata": {}, 
117 |             "outputs": [], 
118 |             "source": "prediction = model.transform(df_test)"
119 |         }, 
120 |         {
121 |             "execution_count": 15, 
122 |             "cell_type": "code", 
123 |             "metadata": {}, 
124 |             "outputs": [
125 |                 {
126 |                     "execution_count": 15, 
127 |                     "metadata": {}, 
128 |                     "data": {
129 |                         "text/plain": "0.9404706165015819"
130 |                     }, 
131 |                     "output_type": "execute_result"
132 |                 }
133 |             ], 
134 |             "source": "evaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\nevaluator.evaluate(prediction)"
135 |         }
136 |     ], 
137 |     "metadata": {
138 |         "kernelspec": {
139 |             "display_name": "Python 3.6 with Spark", 
140 |             "name": "python36", 
141 |             "language": "python3"
142 |         }, 
143 |         "language_info": {
144 |             "mimetype": "text/x-python", 
145 |             "nbconvert_exporter": "python", 
146 |             "version": "3.6.8", 
147 |             "name": "python", 
148 |             "file_extension": ".py", 
149 |             "pygments_lexer": "ipython3", 
150 |             "codemirror_mode": {
151 |                 "version": 3, 
152 |                 "name": "ipython"
153 |             }
154 |         }
155 |     }, 
156 |     "nbformat": 4
157 | }


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